call 9422864426 to order : “a complete textbook on phd” along with a cd for your ready...

General Syllabus for PhD Preparation

Chapter

Page

1 Introduction - Meaning, Objectives and Types of research, Research Approach, Research Process, Relevance & scope of research in management.

2

2 Research Design - Features of good Design, Types of Research Design, Basic principles of experimental Design.

30

3 Sampling Design - Steps in sample Design, Characteristics of a good sample Design, Probability & Non Probability sampling.

47

4 Measurement & scaling techniques - Errors in measurement. Test of sound measurement, Scaling and scale construction technique.

68

5 Methods of data collection - Primary data – questionnaire and interviews; Collection of secondary data,

90

6 Collection and Processing data - Survey Errors, Data coding; Editing and Tabulation.

126

7 Analysis of data - Analysis of Variance; Advanced Data Analysis Techniques- Factor Analysis, Cluster Analysis, Discriminant Analysis, Conjoint Analysis, Multi Dimensional Scaling.

146

8 Testing of hypothesis - Procedure for hypothesis testing; Use of statistical techniques for testing of hypothesis.

167

9 Interpretation of data - Techniques of Interpretation, Report writing, Layout of a project report, preparing research reports.

180

10 Research in various Functional Areas 203

Bibliography 207

11 All FAQ on Ph.D , Research Aptitude Test: Examination Pattern 208

12 Most likely asked questions in Ph.D entrance aptitude test 217

13 Most Likely asked Questions for Ph.D Interview 270

14 Enclosed CD contents: Sample Ph.D Thesis, Synopsis , Summary etc

[© 2011-12 : A Complete textbook on PhD (along with CD)] Page 2

Chapter1: Research

Introduction

Research comprises of creative work undertaken on a systematic basis in order to increase the stock of

knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to

devise new applications.

Research can be defined to be search for knowledge or any systematic investigation to establish facts. The

primary purpose for applied research (as opposed to basic research) is discovering, interpreting, and the

development of methods and systems for the advancement of human knowledge on a wide variety of

scientific matters of our world and the universe. Research can use the scientific method, but need not do

so.

Scientific research relies on the application of the scientific method, a harnessing of curiosity. This

research provides scientific information and theories for the explanation of the nature and the properties

of the world around us. It makes practical applications possible. Scientific research is funded by public

authorities, by charitable organisations and by private groups, including many companies. Scientific

research can be subdivided into different classifications according to their academic and application

disciplines.

Research can be defined as a scientific and systematic search for gaining information and knowledge on a

specific topic or phenomena. In management, research is extensively used in various areas. For example,

We all know that, Marketing is the process of Planning & Executing the concepts, pricing, promotion &

distribution of ideas, goods, and services to create exchange that satisfy individual & organizational

objectives. Thus, we can say that, the Marketing Concept requires Customer Satisfaction rather than

Profit Maximization to be the goal of an organization. The organization should be Consumer oriented

and should try to understand consumer‘s requirements & satisfy them quickly and efficiently, in ways

that are beneficial to both the consumer & the organization.

http://en.wikipedia.org/wiki/Applied_research

http://en.wikipedia.org/wiki/Basic_research

http://en.wikipedia.org/wiki/Discovery_(observation)

http://en.wikipedia.org/wiki/Interpretation_(logic)

http://en.wikipedia.org/wiki/Research_and_development

http://en.wikipedia.org/wiki/Knowledge

http://en.wikipedia.org/wiki/Epistemology

http://en.wikipedia.org/wiki/Scientific_method

http://en.wikipedia.org/wiki/Curiosity

http://en.wikipedia.org/wiki/Science

http://en.wikipedia.org/wiki/Nature


This means that any organization should try to obtain information on consumer needs and gather market

intelligence to help satisfy these needs efficiently. This can only be done only by research.

Research in common parlance refers to a search for knowledge. It is an endeavour to discover answers to

problems (of intellectual and practical nature) through the application of scientific methods. Research,

thus, is essentially a systematic inquiry seeking facts (truths) through objective, verifiable methods in

order to discover the relationship among them and to deduce from them broad conclusions. It is thus a

method of critical thinking. It is imperative that any type of organisation in the globalised environment

needs systematic supply of information coupled with tools of analysis for making sound decisions, which

involve minimum risk. In this chapter, we will discuss at length the need and significance of research,

types and methods of research, and the research process.

When research is used for decision-making, it means we are using the methods of science to the art of

management. Every organization operates under some degree of uncertainty. This uncertainty cannot be

eliminated completely, although it can be minimized with the help of research methodology. Research is

particularly important in the decision making process of various business organizations to choose the

best line of action (in the light of growing competition and increasing uncertainty).

The research process usually starts with a broad area of interest, the initial problem that the researcher

wishes to study. For instance, the researcher could be interested in how to use computers to improve the

performance of students in mathematics. However, this initial interest is far too broad to study in any

single research project (it might not even be addressable in a lifetime of research).

The researcher has to narrow the question down to one that can reasonably be studied in a research

project. This might involve formulating a hypothesis or a focus question. For instance, the researcher

might hypothesize that a particular method of computer instruction in math will improve the ability of

elementary school students in a specific district. At the narrowest point of the research hourglass, the

researcher is engaged in direct measurement or observation of the question of interest.

Meaning

Research in common context refers to a search for knowledge. It can also be defined as a scientific and

systematic search for gaining information and knowledge on a specific topic or phenomena. In

management, research is extensively used in various areas. For example, we all know that, Marketing is

the process of Planning & Executing the concepts; pricing, promotion & distribution of ideas, goods, and

services to create exchange that satisfy individual & organizational objectives. Thus, we can say that, the

Marketing Concept requires Customer Satisfaction rather than Profit Maximization to be the goal of an

organization. The organization should be Consumer oriented and should try to understand consumer‘s

requirements & satisfy them quickly and efficiently, in ways that are beneficial to both the consumer &

the organization.


The Random House Dictionary of the English language defines the term ‗Research‘ as a meticulous and

systematic inquiry or investigation into a subject in order to discover or revise facts, theories,

applications, etc. This definition explains that research involves acquisition of knowledge. Research

means search for truth. Truth means the quality of being in agreement with reality or facts. It also means

an established or verified fact. To do research is to get nearer to truth, to understand the reality. Research

is the pursuit of truth with the help of study, observation, comparison and experimentation. In other

words, the search for knowledge through objective and systematic method of finding solution to a

problem/answer to a question is research. There is no guarantee that the researcher will always come out

with a solution or answer. Even then, to put it in Karl Pearson‘s words ―there is no short cut to truth… no

way to gain knowledge of the universe except through the gate way of scientific method‖. Let us see

some definitions of Research:

L.V. Redman and A.V.H. Mory in their book on ―The Romance of Research‖ defined research as ―a

systematized effort to gain new knowledge‖

―Research is a scientific and systematic search for pertinent information on a specific topic‖ (C.R. Kothari,

Research Methodology - Methods and Techniques)

―A careful investigation or inquiry specially through search for new facts in any branch of knowledge‖

(Advanced learners Dictionary of current English) Research refers to a process of enunciating the

problem, formulating a hypothesis, collecting the facts or data, analyzing the same, and reaching certain

conclusions either in the form of solution to the problem enunciated or in certain generalizations for some

theoretical formulation.

D. Slesinger and M. Stephenson in the Encyclopedia of Social Sciences defined research as:

―Manipulation of things, concepts or symbols for the purpose of generalizing and to extend, correct or

verify knowledge, whether that knowledge aids in the construction of a theory or in the practice of an

art‖.

To understand the term „research‟ clearly and comprehensively let us analyze the above definition.

i) Research is manipulation of things, concepts or symbols

manipulation means purposeful handling,

things means objects like balls, rats, vaccine,

concepts mean the terms designating the things and their perceptions about

which science tries to make sense. Examples: velocity, acceleration, wealth, income.

Symbols may be signs indicating +, –, ÷, ×, x , s, S, etc.

Manipulation of a ball or vaccine means when the ball is kept on different degrees of incline how and at

what speed does it move? When the vaccine is used, not used, used with different gaps, used in different

quantities (doses) what are the effects?


ii) Manipulation is for the purpose of generalizing

The purpose of research is to arrive at generalization i.e., to arrive at statements of generality, so that

prediction becomes easy. Generalization or conclusion of an enquiry tells us to expect something in a

class of things under a class of conditions. Examples: Debt repayment capacity of farmers will be

decreased during drought years. When price increases demand falls. Advertisement has a favourable

impact on sales.

iii) The purpose of research (or generalization) is to extend, correct or verify knowledge

Generalization has in turn certain effects on the established corpus or body of knowledge. It may extend

or enlarge the boundaries of existing knowledge by removing inconsistencies if any. It may correct the

existing knowledge by pointing out errors if any. It may invalidate or discard the existing knowledge

which is also no small achievement. It may verify and confirm the existing knowledge which also gives

added strength to the existing knowledge. It may also point out the gaps in the existing corpus of

knowledge requiring attempts to bridge these gaps.

iv) This knowledge may be used for construction of a theory or practice of an art

The extended, corrected or verified knowledge has two possible uses to which persons may put it.

a) may be used for theory building so as to form a more abstract conceptual system. E.g. Theory of

relativity, theory of full employment, theory of wage.

b) may be used for some practical or utilitarian goal. E.g. ‗Salesmanship and advertisement increase sales‘

is the generalization. From this, if sales have to be increased, use salesmanship and advertisement for

increasing sales. Theory and practice are not two independent things. They are interdependent. Theory

gives quality and effectiveness to practice. Practice in turn may enlarge or correct or confirm or even

reject theory.

Some other definitions of Research are:

1. Redman and Mory define research as a ―systematized effort to gain new knowledge‖.

2. Some people consider research as a movement, a movement from known to unknown. It is actually a

voyage to discovery.

3. According to Clifford Woody

“Research comprises of defining and redefining problems, formulating hypothesis or suggested

solutions; making deductions and reaching conclusions; and at last carefully testing the conclusions to

determine whether they fit the formulating hypothesis”.

On evaluating these definitions we can conclude that Research refers to the systematic method consisting

of

Enunciating the problem,

Formulating a hypothesis,

Collecting the fact or data,

Analyzing the facts and


Reaching certain conclusions either in the form of solutions towards the concerned problem or in

certain generals for some theoretical formulation.

Research covers the search for and retrieval of information for a specific purpose. Research has many

categories, from medical research to literary research.

Research is essentially a fact-finding process, which influences decision-making. It is a careful search or

inquiry into any subject or subject matter, which is an endeavour to discover or find out valuable facts,

which would be useful for further application or utilization. Research can be a basic research or applied

research. Basic research is studies conducted toward long-range questions or advancing scientific

knowledge.

Characteristics of Research

a. Systematic Approach

Each step must of your investigation be so planned that it leads to the next step. Planning and

organization are part of this approach. A planned and organized research saves your time and money.

b. Objectivity

It implies that True Research should attempt to find an unbiased answer to the decision-making problem.

c. Reproducible

A reproducible research procedure is one, which an equally competent researcher could duplicate, and

from it deduces approximately the same results. Precise information regarding samples-methods,

collection etc., should be specified.

d. Relevancy

It furnishes three important tasks:

It avoids collection of irrelevant information and saves time and money

It compares the information to be collected with researcher‘s criteria for action

It enables to see whether the research is proceeding in the right direction

e. Control:

Research is not only affected by the factors, which one is investigating but some other extraneous factors

also. It is impossible to control all the factors. All the factors that we think may affect the study have to be

controlled and accounted for.

http://en.wikipedia.org/wiki/Information


For Example

Suppose we are studying the relationship between incomes and shopping behaviour, without controlling

for education and age, it will be a height of folly, since our findings may reflect the effect of education

and age rather than income.

Objectives of research

Following are the key objectives of research:

1. Exploration- an understanding of an area of concern in very general terms. Example: I want to know

how to go about doing more effective research on school violence.

2. Description - an understanding of what is going on. Example: I want to know the attitudes of potential

clients toward Air-Conditioner use.

3. Explanation - an understanding of how things happen. Involves an understanding of cause and effect

relationships between events. Example: I want to know if a group of people who have gone through a

certain program have higher self-esteem than a control group.

4. Prediction - an understanding of what is likely to happen in the future. If I can explain, I may be able to

predict. Example: If one group had higher self-esteem, is it likely to happen with another group?

5. Intelligent intervention - an understanding of what or how in order to help more effectively.

6. Awareness - an understanding of the world, often gained by a failure to describe or explain.

Types of research

Research may be classified into different types for the sake of better understanding of the concept.

Several bases can be adopted for the classification such as nature of data, branch of knowledge, extent of

coverage, place of investigation, method employed, time frame and so on. Depending upon the BASIS

adopted for the classification, research may be classified into a class or type. It is possible that a piece of

research work can be classified under more than one type, hence there will be overlapping. It must be

remembered that good research uses a number of types, methods, & techniques. Hence, rigid

classification is impossible. The following is only an attempt to classify research into different types.

i) According to the Branch of Knowledge

Different Branches of knowledge may broadly be divided into two:

a) Life and physical sciences such as Botany, Zoology, Physics and Chemistry.

b) Social Sciences such as Political Science, Public Administration, Economics, Sociology, Commerce and

Management.


Research in these fields is also broadly referred to as life and physical science research and social science

research. Business education covers both Commerce and Management, which are part of Social sciences.

Research is a broad term which covers many areas.

The research carried out, in these areas, is called management research, production research, personnel

research, financial management research, accounting research, Marketing research etc.

a. Management research includes various functions of management such as planning, organizing,

staffing, communicating, coordinating, motivating, controlling. Various motivational theories are the

result of research.

b. Production (also called manufacturing) research focuses more on materials and equipment rather

than on human aspects. It covers various aspects such as new and better ways of producing goods,

inventing new technologies, reducing costs, improving product quality.

c. Research in personnel management may range from very simple problems to highly complex

problems of all types. It is primarily concerned with the human aspects of the business such as personnel

policies, job requirements, job evaluation, recruitment, selection, placement, training and development,

promotion and transfer, morale and attitudes, wage and salary administration, industrial relations. Basic

research in this field would be valuable as human behaviour affects organizational behaviour and

productivity.

d. Research in Financial Management includes financial institutions, financing instruments (egs. shares,

debentures), financial markets (capital market, money market, primary market, secondary market),

financial services (egs. merchant banking, discounting, factoring), financial analysis (e.g. investment

analysis, ratio analysis, funds flow / cash flow analysis) etc.,

e. Accounting research though narrow in its scope, but is a highly significant area of business

management. Accounting information is used as a basis for reports to the management, shareholders,

investors, tax authorities, regulatory bodies and other interested parties. Areas for accounting research

include inventory valuation, depreciation accounting, generally accepted accounting principles,

accounting standards, corporate reporting etc.

f. Marketing research deals with product development and distribution problems, marketing

institutions, marketing policies and practices, consumer behaviour, advertising and sales promotion,

sales management and after sales service etc. Marketing research is one of the very popular areas and

also a well established one. Marketing research includes market potentials, sales forecasting, product

testing, sales analysis, market surveys, test marketing, consumer behaviour studies, marketing

information system etc.

g. Business policy research is basically the research with policy implications. The results of such studies

are used as indices for policy formulation and implementation.

h. Business history research is concerned with the past. For example, how was trade and commerce

during the Moghul regime.


ii) According to the Nature of Data

A simple dichotomous classification of research is Quantitative research and Qualitative research / non-

quantitative.

a. Quantitative research is variables based where as qualitative research is attributes based. Quantitative

research is based on measurement / quantification of the phenomenon under study. In other words, it is

data based and hence more objective and more popular.

b. Qualitative research is based on the subjective assessment of attributes, motives, opinions, desires,

preferences, behaviour etc. Research in such a situation is a function of researcher‘s insights and

impressions.

iii) According to the Coverage

According to the number of units covered it can be Macro study or Micro study. Macro study is a study

of the whole where as Micro study is a study of the part. For example, working capital management in

State Road Transport Corporations in India is a macro study where as Working Capital Management in

Andhra Pradesh State Road Transport Corporation is a micro study.

iv) According to Utility or Application

Depending upon the use of research results i.e., whether it is contributing to the theory building or

problem solving, research can be Basic or Applied.

a. Basic research is called pure / theoretical / fundamental research. Basic research includes original

investigations for the advancement of knowledge that does not have specific objectives to answer

problems of sponsoring agencies.

b. Applied research also called Action research, constitutes research activities on problems posed by

sponsoring agencies for the purpose of contributing to the solution of these problems.

v) According to the place where it is carried out

Depending upon the place where the research is carried out (according to the data generating source),

research can be classified into:

a) Field Studies or field experiments

b) Laboratory studies or Laboratory experiments

c) Library studies or documentary research

vi) According to the Research Methods used

Depending upon the research method used for the investigation, it can be classified as:

a) Survey research, b) Observation research, c) Case research, d) Experimental research, e) Historical

research, f) Comparative research.


vii) According to the Time Frame

Depending upon the time period adopted for the study, it can be

a) One time or single time period research - e.g. One year or a point of time. Most of the sample studies,

diagnostic studies are of this type.

b) Longitudinal research - e.g. several years or several time periods ( a time series analysis) e.g. industrial

development during the five year plans in India.

viii) According to the purpose of the Study

What is the purpose/aim/objective of the study? Is it to describe or analyze or evaluate or explore?

Accordingly the studies are known as.

a) Descriptive Study: The major purpose of descriptive research is the description of a person, situation,

institution or an event as it exists. Generally fact finding studies are of this type.

b) Analytical Study: The researcher uses facts or information already available and analyses them to

make a critical examination of the material. These are generally Ex-post facto studies or post-mortem

studies.

c) Evaluation Study: This type of study is generally conducted to examine /evaluate the impact of a

particular event, e.g. Impact of a particular decision or a project or an investment.

d) Exploratory Study: The information known on a particular subject matter is little. Hence, a study is

conducted to know more about it so as to formulate the problem and procedures of the study. Such a

study is called exploratory/ formulative study.

Research Approaches

The researcher has to provide answers at the end, to the research questions raised in the beginning of the

study. For this purpose he has investigated and gathered the relevant data and information as a basis or

evidence. The procedures adopted for obtaining the same are described in the literature as methods of

research or approaches to research. In fact, they are the broad methods used to collect the data. These

methods are as follows:

1) Survey Method

2) Observation Method

3) Case Method

4) Experimental Method

5) Historical Method

6) Comparative Method


It is now proposed to explain briefly, each of the above mentioned approaches.

1. Survey Method

The dictionary meaning of ‗Survey‘ is to oversee, to look over, to study, to systematically investigate.

Survey research is used to study large and small populations (or universes). It is a fact finding survey.

Mostly empirical problems are investigated by this approach. It is a critical inspection to gather

information, often a study of an area with respect to a certain condition or its prevalence. For example: a

marketing survey, a household survey, All India Rural Credit Survey.

Survey is a very popular branch of social science research. Survey research has developed as a separate

research activity along with the development and improvement of sampling procedures. Sample surveys

are very popular now a days. As a matter of fact sample survey has become synonymous with survey.

For example, see the following definitions:

Survey research can be defined as “Specification of procedures for gathering information about a large

number of people by collecting information from a few of them”. (Black and Champion). Survey

research is “Studying samples chosen from populations to discover the relative incidence,

distribution, and inter relations of sociological and psychological variables”. (Fred N. Kerlinger) By

surveying data, information may be collected by observation, or personal interview, or mailed

questionnaires, or administering schedules or telephone enquiries.

Features of Survey method

The important features of survey method are as follows:

i) It is a field study, as it is always conducted in a natural setting.

ii) It solicits responses directly from the respondents or people known to have knowledge about the

problem under study.

iii) Generally, it gathers information from a large population.

iv) A survey covers a definite geographical area e.g. A village / city or a district.

v) It has a time frame.

vi) It can be an extensive survey involving a wider sample or it can be an intensive study covering few

samples but is an in-depth and detailed study.

vii) Survey research is best adapted for obtaining personal, socio-economic facts, beliefs, attitudes,

opinions.

Survey research is not a clerical routine of gathering facts and figures. It requires a good deal of research

knowledge and sophistication. The competent survey investigator must know sampling procedures,

questionnaire / schedule / opionionaire construction, techniques of interviewing and other technical

aspects of the survey. Ultimately the quality of the Survey results depends on the imaginative planning,

representative sampling, reliability of data, appropriate analysis and interpretation of the data.


2. Observation Method

Observation means seeing or viewing. It is not a casual but systematic viewing. Observation may

therefore be defined as ―a systematic viewing of a specific phenomenon in its proper setting for the

purpose of gathering information for the specific study‖.

Observation is a method of scientific enquiry. We observe a person or an event or a situation or an

incident. The body of knowledge of various sciences such as biology, physiology, astronomy, sociology,

psychology, anthropology etc., has been built upon centuries of systematic observation.

Observation is also useful in social and business sciences for gathering information and conceptualizing

the same. For example, What is the life style of tribals? How are the marketing activities taking place in

Regulated markets? How will the investment activities be done in Stock Exchange Markets? How are

proceedings taking place in the Indian Parliament or Assemblies? How is a corporate office maintained in

a public sector or a private sector undertaking? What is the behaviour of political leaders? Traffic jams in

Delhi during peak hours?

Observation as a method of data collection has some features:

i) It is not only seeing & viewing but also hearing and perceiving as well. ii) It is both a physical and a

mental activity. The observing eye catches many things which are sighted, but attention is also focused on

data that are relevant to the problem under study.

iii) It captures the natural social context in which the person‘s behaviour occurs.

iv) Observation is selective: The investigator does not observe everything but selects the range of things

to be observed depending upon the nature, scope and objectives of the study.

v) Observation is not casual but with a purpose. It is made for the purpose of noting things relevant to the

study.

vi) The investigator first of all observes the phenomenon and then gathers and accumulates data.

Observation may be classified in different ways. According to the setting it can be (a) observation in a

natural setting, e.g. Observing the live telecast of parliament proceedings or watching from the visitors‘

gallery, Electioneering in India through election meetings or (b) observation in an artificially stimulated

setting, e.g. business games, Tread Mill Test. According to the mode of observation it may be classified as

(a) direct or personal observation, and (b) indirect or mechanical observation. In case of direct

observation, the investigator personally observes the event when it takes place, where as in case of

indirect observation it is done through mechanical devices such as audio recordings, audio visual aids,

still photography, picturization etc. According to the participating role of the observer, it can be classified

as (a) participant observation and (b) non-participant observation. In case of participant observation, the

investigator takes part in the activity, i.e. he acts both as an observer as well as a participant. For example,

studying the customs and life style of tribals by living / staying with them. In case of non-participant

observation, the investigator observes from outside, merely as an on looker. Observation method is

suitable for a variety of research purposes such as a study of human behaviours, behaviour of social


groups, life styles, customs and traditions, inter personal relations, group dynamics, crowd behaviour,

leadership and management styles, dressing habits of different social groups in different seasons,

behaviour of living creatures like birds, animals, lay out of a departmental stores, a factory or a

residential locality, or conduct of an event like a meeting or a conference or Afro- Asian Games.

3. Case Method

Case method of study is borrowed from Medical Science. Just like a patient, the case is intensively studied

so as to diagnose and then prescribe a remedy. A firm, or a unit is to be studied intensively with a view to

finding out problems, differences, specialties so as to suggest remedial measures. It is an in-

depth/intensive study of a unit or problem under study. It is a comprehensive study of a firm or an

industry, or a social group, or an episode, or an incident, or a process, or a programme, or an institution

or any other social unit. According to P.V. Young ―a comprehensive study of a social unit, be that unit a

person, a group, a social institution, a district, or a community, is called a Case Study‖.

Case Study is one of the popular research methods. A case study aims at studying everything about

something rather than something about everything. It examines complex factors involved in a given

situation so as to identify causal factors operating in it. The case study describes a case in terms of its

peculiarities, typical or extreme features. It also helps to secure a fund of information about the unit

under study. It is a most valuable method of study for diagnostic therapeutic purposes.

4. Experimental Method

Experimentation is the basic tool of the physical sciences like Physics, Chemistry for establishing cause

and effect relationship and for verifying inferences. However, it is now also used in social sciences like

Psychology, Sociology. Experimentation is a research process used to observe cause and effect

relationship under controlled conditions. In other words it aims at studying the effect of an independent

variable on a dependent variable, by keeping the other interdependent variables constant through some

type of control. In experimentation, the researcher can manipulate the independent variables and

measure its effect on the dependent variable. The main features of the experimental method are :

i) Isolation of factors or controlled observation.

ii) Replication of the experiment i.e. it can be repeated under similar conditions.

iii) Quantitative measurement of results.

iv) Determination of cause and effect relationship more precisely.

Three broad types of experiments are:

a) The natural or uncontrolled experiment as in case of astronomy made up mostly of observations.

b) The field experiment, the best suited one for social sciences. ―A field experiment is a research study in

a realistic situation in which one or more independent variables are manipulated by the experimenter

under as carefully controlled conditions as the situation will permit‖. ( Fred N. Kerlinger)


c) The laboratory experiment is the exclusive domain of the physical scientist.

―A laboratory experiment is a research study in which the variance of all or nearly all of the possible

influential independent variables, not pertinent to the immediate problem of the investigation, is kept at a

minimum. This is done by isolating the research in a physical situation apart from the routine of ordinary

living and by manipulating one or more independent variables under rigorously specified,

operationalized, and controlled conditions‖. (Fred N. Kerlinger). The contrast between the field

experiment and laboratory experiment is not sharp, the difference is a matter of degree. The laboratory

experiment has a maximum of control, where as the field experiment must operate with less control.

5. Historical Method

When research is conducted on the basis of historical data, the researcher is said to have followed the

historical approach. To some extent, all research is historical in nature, because to a very large extent

research depends on the observations / data recorded in the past. Problems that are based on historical

records, relics, documents, or chronological data can conveniently be investigated by following this

method. Historical research depends on past observations or data and hence is non-repetitive, therefore it

is only a post facto analysis. However, historians, philosophers, social psychiatrists, literary men, as well

as social scientists use the historical approach. Historical research is the critical investigation of events,

developments, experiences of the past, the careful weighing of evidence of the validity of the sources of

information of the past, and the interpretation of the weighed evidence. The historical method, also called

historiography, differs from other methods in its rather elusive subject matter i.e. the past. In historical

research primary and also secondary sources of data can be used. A primary source is the original

repository of a historical datum, like an original record kept of an important occasion, an eye witness

description of an event, the inscriptions on copper plates or stones, the monuments and relics,

photographs, minutes of organization meetings, documents. A secondary source is an account or record

of a historical event or circumstance, one or more steps removed from an original repository. Instead of

the minutes of the meeting of an organization, for example, if one uses a newspaper account of the

meeting, it is a secondary source.

The aim of historical research is to draw explanations and generalizations from the past trends in order to

understand the present and to anticipate the future. It enables us to grasp our relationship with the past

and to plan more intelligently for the future.

For historical data only authentic sources should be depended upon and their authenticity should be

tested by checking and cross checking the data from as many sources as possible. Many a times it is of

considerable interest to use Time Series Data for assessing the progress or for evaluating the impact of

policies and initiatives. This can be meaningfully done with the help of historical data.


6. Comparative Method

The comparative method is also frequently called the evolutionary or Genetic Method. The term

comparative method has come about in this way: Some sciences have long been known as ―Comparative

Sciences‖ - such as comparative philology, comparative anatomy, comparative physiology, comparative

psychology, comparative religion etc. Now the method of these sciences came to be described as the

―Comparative Method‖, an abridged expression for ―the method of the comparative sciences‖. When the

method of most comparative sciences came to be directed more and more to the determination of

evolutionary sequences, it came to be described as the ―Evolutionary Method‖.

The origin and the development of human beings, their customs, their institutions, their innovations and

the stages of their evolution have to be traced and established. The scientific method by which such

developments are traced is known as the Genetic method and also as the Evolutionary method. The

science which appears to have been the first to employ the Evolutionary method is comparative

philology. It is employed to ―compare‖ the different languages in existence, to trace the history of their

evolution in the light of such similarities and differences as the comparisons disclosed. Darwin‘s famous

work ―Origin of Species‖ is the classic application of the Evolutionary method in comparative anatomy.

The whole theory of biological evolution rests on applications of evolutionary method. This method can

be applied not only to plants, to animals, to social customs and social institutions, to the human mind

(comparative psychology), to human ideas and ideals, but also to the evolution of geological strata, to the

differentiation of the chemical elements and to the history of the solar system. The term comparative

method as a method of research is used here in its restricted meaning as synonymous with Evolutionary

method. To say that the comparative method is a ‗method of comparison‘ is not convincing, for

comparison is not a specific method, but something which enters as a factor into every scientific method.

Classification requires careful comparison and every other method of science depends upon a precise

comparison of phenomena and the circumstances of their occurrence. All methods are, therefore,

―comparative‖ in a wider sense.


The Research Process

Having received the research brief, the researcher responds with a research proposal. This is a document

which develops after having given careful consideration to the contents of the research brief. The

research proposal sets out the research design and the

procedures to be followed. The eight steps are set out in figure

below.

Step -I: Problem definition

The point has already been made that the decision-maker should

clearly communicate the purpose of the research to the

researcher but it is often the case that the objectives are not fully

explained to the individual carrying out the study. Decision-

makers seldom work out their objectives fully or, if they have,

they are not willing to fully disclose them. In theory,

responsibility for ensuring that the research proceeds along

clearly defined lines rests with the decision-maker. In many

instances, the researcher has to take the initiative.

In situations, in which the researcher senses that the decision-

maker is either unwilling or unable to fully articulate the

objectives then he/she will have to pursue an indirect line of

questioning. One approach is to take the problem statement supplied by the decision-maker and to break

this down into key components and/or terms and to explore these with the decision-maker. For example,

the decision-maker could be asked what he has in mind when he uses the term market potential. This is a

valid question since the researcher is charged with the responsibility to develop a research design which

will provide the right kind of information. Another approach is to focus the discussions with the person

commissioning the research on the decisions which would be made given alternative findings which the

study might come up with. This process frequently proves of great value to the decision-maker in that it

helps him think through the objectives and perhaps select the most important of the objectives.

Whilst seeking to clarify the objectives of the research it is usually worthwhile having discussions with

other levels of management who have some understanding of the marketing problem and/or the

surrounding issues. Other helpful procedures include brainstorming, reviews of research on related

problems and researching secondary sources of information as well as studying competitive products.

The nature of problems

A decision maker‘s degree of uncertainty influences decisions about the type of research that will be

conducted. A business manager may be completely certain about the situation s/he is facing. Or, at the


other extreme, a manager or researcher may describe a decision-making situation as absolute ambiguity.

The nature of the problem to be solved is unclear. The objectives are vague and the alternatives are

difficult to define. This is by far the most difficult decision situation. Most business decision face

situations falling in-between these two extremes.

The importance of proper problem definition

Business research is conducted to help solve managerial problems. It is extremely important to define the

business problem carefully because such definition will determine the purpose of the research and,

ultimately, the research design.

Formal qualitative research should not begin until the problem has been clearly defined. However, when

a problem or opportunity is discovered, managers may have only vague insights about a complex

situation. If quantitative research is conducted before the researchers understand exactly what is

important, then false conclusions may be drawn from the investigation.

Problem definition indicates a specific business decision area that will be clarified by answering some

research questions.

The process of defining the problem

The process of defining the problem involves several interrelated steps. They are:

1. Ascertain the decision maker‟s objectives.

2. Understand the background of the problem

3. Isolate and identify the problem not the symptoms

4. Determine the unit of analysis

5. Determine the relevant variables

6. State the research questions (Hypotheses) and

7. Research objectives

1) Ascertain the decision maker‟s objectives

The research investigation must attempt to satisfy the decision maker‘s objectives. Sometimes, decision

makers are not able to articulate precise research objectives. Both the research investigator and the

manager requesting the research should attempt to have a clear understanding of the purpose of

undertaking the research. Often, exploratory research—by illuminating the nature of the business

opportunity or problem—helps managers clarify their objectives and decisions.

The iceberg principle

The dangerous part of any business problem, like the submerged part of an iceberg, is neither visible to

nor understood by the business managers. If the submerged portions of the problem are omitted from

the problem definition, and subsequently from the research design, then the decision based on such

research may be less than optimal.


2) Understand the background of the problem.

The background of the problem is vital. A situation analysis is the logical first step in defining the

problem. This analysis involves the informal gathering of background information to familiarize

researchers or managers with the decision area. Exploratory research techniques have been developed to

help formulate clear definitions of the problem (see Chapter 7).

3) Isolate and identify the problem, not the symptoms.

Anticipating the many influences and dimensions of a problem is impossible for any researcher or

executive. Certain occurrences that appear to be the problem may only be symptoms of a deeper

problem. Executive judgment and creativity must be exercised in identifying a problem.

4) What is the unit of analysis?

The researcher must specify the unit of analysis. Will the individual consumer be the source of

information or will it be the parent-child dyad? Industries, organizations, departments, or individuals,

may be the focus for data collection and analysis. Many problems can be investigated at more than one

level of analysis.

5) What are the relevant variables?

One aspect of problem definition is identification of the key variables. A variable is a quality that can

exhibit differences in value, usually magnitude or strength.

In statistical analysis, a variable is identified by a symbol such as X. A category or classificatory variable

has a limited number of distinct variables (e.g., sex—male or female). A continuous variable may

encompass an infinite range of numbers (e.g., sales volume).

Managers and researchers must be careful to include all relevant variables that must be studied in order

to be able to answer the managerial problem. Irrelevant variables should not be included.

In causal research, a dependent variable is a criterion or variable that is expected to be predicted or

explained. An independent variable is a variable that is expected to influence the dependent variable.

6 &7) State the research questions and research objectives

The research question is the researcher‘s translation of the business problem into a specific need for

inquiry.

A. Clarity in Research Questions and Hypotheses

Research questions should be specific, clear, and accompanied by a well-formulated hypothesis.

A hypothesis is an unproven proposition or possible solution to a problem. In its simplest form, a

hypothesis is a guess. Problems and hypotheses are similar; both state relationships, but, whereas

problems are interrogative, hypotheses are declarative and more specifically related to the research

operations and testing. Hypotheses are statements that can be empirically tested.

A formal statement of hypothesis can force researchers to be clear about what they expect to find through

their study. The hypothesis can raise critical questions about the data that will be required in the analysis

stage.


When evaluating a hypothesis, researchers should make sure that the information collected will be useful

in decision making.

B. Decision-oriented research objectives

The research objective is the researcher‘s version of the problem. The research objective is derived from

the problem definition and it explains the purpose of the research in measurable terms, as well as

defining what standards the research should accomplish. Such objectives help ensure that the research

projects will be manageable in size.

In some instances the problems and the project‘s research objectives are identical. The objectives must,

however, specify the information needed to make a decision. Statements about the required precision

may be necessary to clearly communicate exactly what information is required.

It is useful if the research objective is a managerial action standard. That is, if the criterion being

measured turns out to be X, then management will do A; if it is Y, then management will do B. This

leaves no uncertainty concerning the decision to be made once the research is finished.

The number of research objectives should be limited to a manageable number so that each one can be

addressed fully.

How much time should be spent defining the problem?

It is impractical to search for every conceivable cause and minor influence of a problem. The importance

of the recognized problem will usually

dictate what is a reasonable amount of

time and money for determining which

possible explanations are most likely.

The research proposal

The research proposal is a written

statement of the research design—it

explains the purpose of the study,

defines the problem, outlines the

research methodology, details the

procedures to be followed, and states all costs and deadlines.

The proposal should be precise, specific, and concrete. All ambiguities about why and how the research

will be conducted must be "ironed out" before the proposal is complete.

The research proposal can act as a communication tool. It allows managers to evaluate the proposed

research design and determine if alterations are necessary. The proposal should be detailed enough that

managers are clear about exactly how the information will be obtained.

Misstatements and faulty communication may occur if the two parties rely on each other‘s memory of

what occurred at a planning meeting; therefore, it is wise to write down all proposals. Such a written


proposal eliminates many problems that may arise and acts as a record of the researcher‘s obligation. In

the case of an outside consultant, the written proposal serves as a bid to offer a specific service; a

company can then judge the relative quality of alternative research suppliers.

Anticipating outcomes

By anticipating the outcomes of a research study, possibly through the use of a dummy table (a table

filled by the researcher with fictitious data), managers may gain a better understanding of what the

actual outcome is liable to be. These tables help clarify what the findings of the research will be, and if

these findings will meet the needs of the researcher.

Step II: Hypothesis generation

Whilst it is true that the purpose of research is to address some question, nonetheless one does not test

research questions directly. For example, there may be interest in answering the question: "Does a

person's level of education have any bearing upon whether or not he/she adopts new products?" Or,

"Does a person's age bear any relation to brand loyalty behaviour?". Research questions are too broad to

be directly testable. Instead, the question is reduced to one or more hypotheses implied by these

questions.

A hypothesis is a conjectural statement regarding the relation between two or more variables. There are

two key characteristics which all hypotheses must have: they must be statements of the relationship

between variables and they must carry clear implications for testing the stated relations. These

characteristics imply that it is relationships, rather than variables, which are tested; the hypotheses

specify how the variables are related and that these are measurable or potentially measurable. Statements

lacking any or all of these characteristics are not research hypotheses.

For example, consider the following hypothesis:

1. "Red meat consumption increases as real disposable incomes increase."

This is a relation stated between one variable, "red meat consumption", and another variable,

"disposable incomes". Moreover, both variables are potentially measurable. The criteria have been

met. However for the purposes of statistical testing it is more usual to find hypotheses stated in the

so-called null form, e.g.

"There is no relationship between red meat consumption and the level of disposable incomes."

2. Consider a second hypothesis:

"There is no relationship between a farmer's educational level and his degree of innovativeness

with respect to new farming technologies."

Again there is a clear statement of the relationship being investigated but there are question marks

over the measurability with respect to at least one of the variables i.e. "...a farmer's degree of

innovativeness." We may also encounter difficulties in agreeing an appropriate measure of the other

variable, i.e. "level of education". If these problems can be resolved then we may indeed have a

hypothesis.


Hypotheses are central to progress in research. They will direct the researcher's efforts by forcing

him/her to concentrate on gathering the facts, which will enable the hypotheses to be tested. The point

has been made that it is all too easy when conducting research to collect "interesting data" as opposed to

"important data". Data and questions, which enable researchers to test explicit hypotheses, are important.

The rest are merely interesting.

There is a second advantage of stating hypotheses, namely that implicit notions or explanations for events

become explicit and this often leads to modifications of these explanations, even before data is collected.

On occasion a given hypotheses may be too broad to be tested. However, other testable hypotheses may

be deduced from it. A problem really cannot be solved unless it is reduced to hypothesis form, because a

problem is a question, usually of a broad nature, and is not directly testable.

Problem refinement: in most cases a problem statement is refined to a hypothesis: a proposed

hypothetical relationship between two or more variables in terms of cause (independent variable) and

effect (dependent variable). The possible solutions are that the proposed relation is valid or it is invalid.

Examples of possible hypotheses are:

Hypothesis: Gender is related to income

Hypothesis: Crime is related to population size

Hypothesis: Crime is related to social class structure

These are good starting points but much more refinement can be done. For example, as a start:

Hypothesis: females will make lower annual wages then males

Hypothesis: the crime index is related to population density

Hypothesis: the crime index is related to the percentage of the population below the poverty level

In these we have begun to specify our variables, but even more refinement remains. The variable "female"

is indicative of some of the issues involved. We generally think we know what gender -male and female -

is, but after some consideration we realize the Olympic committees have some doubt about masculine

and feminine, and further we realize that there is biological gender, sex roles, and personal sex identity,

this just reinforces the need for clarity and specificity of definitions.

Alternative and Null hypothesis: The alternative hypothesis (H1) is simply the hypothesis statement

of problem as stated above. The null hypothesis (H0) is that there is no relationship between the

variables- crime and population density. It is a statement that there is no relationship between our

independent and dependent variables in the population.

H1, Alternative hypothesis

The CBI Crime Index is related to population density

H0, Null hypothesis

There is no relationship between the CBI Crime Index and the population density.


The goal of research is to reject the null hypothesis and thus support the alternative, theoretical

hypothesis. The theoretical hypothesis is the hypothesis we proposed based on our review of the

literature and theoretical considerations. Incidentally, we can see that this logic will never allow us to

conclusively prove our hypothesis but it does provide support for its acceptance by rejection of the null

hypothesis.

Step III: Decision on type of study

Research can be carried out on one of three levels: a) Exploratory, b) Descriptive research and c)

Experimental research.

Step IV: Decision on data collection method

The next set of decisions concerns the method(s) of data gathering to be employed. The main methods of

data collection are secondary data searches, observation, and the survey, experimentation and consumer

panels.

Under ideal conditions the researcher would select the most appropriate method-field research, survey,

experiment, or secondary

data analysis-for the

research problem.

Realities of available

money, time, access to

information, and own

personal skills often are

decisive factors in design

choice and data

collection. Once the design is firm, follow through the steps in the design and collect the data.

All of us have collected data, not necessarily precisely and carefully in a scientific manner. Frequently we

observe people in a new situation to determine what is expected of us, such as when we first started

college, visited a new city, or started a new job; this is called participant observation, a particular type of

field research. We may ask friends how and why they are going to vote a certain way in an upcoming

election. This is known as interviewing. We may try different types or amounts of spices in a recipe to

find which combination tastes the best. This is called experimenting. Most of us have investigated sources

and data in the library to help us in making a decision about a trip, car, house or major appliance

purchase. This is known as secondary analysis, the analysis of data collected by others.

All of these are research "data collection" techniques, though they lack the rigor, care, and explicitness of

scientific research. Some may approach scientific quality for testing statements, while others would be


considered as primarily acceptable for the generation of hypothesis, but not acceptable for drawing

conclusions.

Research techniques vary in terms of the formal aspects of their structure. Some are more open-ended

and there is less consensus on structure (field studies, content analysis, focus groups, etc.). Most of these

techniques of study are not really lacking in numbers and counting of observations; where they differ

from other techniques is in their more open approach. Additionally, they frequently lack precise agreed

upon data collection techniques and sufficient numbers in their samples to allow using statistics and

generalizing conclusions.

Step V: Development of an analysis plan

Those new to research often intuitively believe that decisions about the techniques of analysis to be used

can be left until after the data has been collected. Such an approach is ill-advised. Before interviews are

conducted the following checklist should be applied:

Is it known how each and every question is to be analysed? (e.g. which univariate or bivariate

descriptive statistics, tests of association, parametric or nonparametric hypotheses tests, or

multivariate methods are to be used?)

Does the researcher have a sufficiently sound grasp of these techniques to apply them with

confidence and to explain them to the decision-maker who commissioned the study?

Does the researcher have the means to perform these calculations? (e.g. access to a computer

which has an analysis program which he/she is familiar with? Or, if the calculations have to be

performed manually, is there sufficient time to complete them and then to check them?)

If a computer program is to be used at the data analysis stage, have the questions been properly

coded?

Have the questions been scaled correctly for the chosen statistical technique? (e.g. a t-test cannot

be used on data which is only ranked)

There is little point in spending time and money on collecting data, which subsequently is not or cannot

be analysed. Therefore consideration has to be given to issues such as these before the fieldwork is

undertaken.

Step VI: Data collection

Research involves the collection of data to obtain insight and knowledge into the needs and wants of

customers and the structure and dynamics of a market. In nearly all cases, it would be very costly and

time-consuming to collect data from the entire population of a market. Accordingly, in market research,

extensive use is made of sampling from which, through careful design and analysis, researchers can

draw information about the market.


i) Sample Design

Sample design covers the method of selection, the sample structure and plans for analysing and

interpreting the results. Sample designs can vary from simple to complex and depend on the type of

information required and the way the sample is selected.

Sample design affects the size of the sample and the way in which analysis is carried out. In simple terms

the more precision the market researcher requires, the more complex will be the design and the larger the

sample size.

The sample design may make use of the characteristics of the overall market population, but it does not

have to be proportionally representative. It may be necessary to draw a larger sample than would be

expected from some parts of the population; for example, to select more from a minority grouping to

ensure that sufficient data is obtained for analysis on such groups.

Many sample designs are built around the concept of random selection. This permits justifiable inference

from the sample to the population, at quantified levels of precision. Random selection also helps guard

against sample bias in a way that selecting by judgement or convenience cannot.

ii) Defining the Population

The first step in good sample design is to ensure that the specification of the target population is as clear

and complete as possible to ensure that all elements within the population are represented. The target

population is sampled using a sampling frame. Often the units in the population can be identified by

existing information; for example, pay-rolls, company lists, government registers etc. A sampling frame

could also be geographical; for example postcodes have become a well-used means of selecting a sample.

iii) Sample Size

For any sample design deciding upon the appropriate sample size will depend on several key factors.

(1) No estimate taken from a sample is expected to be exact: Any assumptions about the overall

population based on the results of a sample will have an attached margin of error.

(2) To lower the margin of error usually requires a larger sample size. The amount of variability in the

population (i.e. the range of values or opinions) will also affect accuracy and therefore the size of sample.

(3) The confidence level is the likelihood that the results obtained from the sample lie within a required

precision. The higher the confidence level that is the more certain you wish to be that the results are not

atypical. Statisticians often use a 95 per cent confidence level to provide strong conclusions.

(4) Population size does not normally affect sample size. In fact the larger the population size the lower

the proportion of that population that needs to be sampled to be representative. It is only when the

proposed sample size is more than 5 per cent of the population that the population size becomes part of

the formulae to calculate the sample size.


iv) Types of Sampling (Discussed in details in the Chapter 3: Sampling)

Step VII: Analysis of data

The word 'analysis' has two component parts, the prefix 'ana' meaning 'above' and the Greek root 'lysis'

meaning 'to break up or dissolve'. Thus data analysis can be described as:

"...a process of resolving data into its constituent components, to reveal its characteristic elements and

structure."

Where the data is quantitative there are three determinants of the appropriate statistical tools for the

purposes of analysis. These are the number of samples to be compared, whether the samples being

compared are independent of one another and the level of data measurement.

Suppose a fruit juice processor wishes to test the acceptability of a new drink based on a novel

combination of tropical fruit juices. There are several alternative research designs which might be

employed, each involving different numbers of samples.

Test A Comparing sales in a test market and the market share of the

product it is targeted to replace.

Number of

samples = 1

Test B Comparing the responses of a sample of regular drinkers of fruit

juices to those of a sample of non-fruit juice drinkers to a trial

formulation.

Number of

samples = 2

Test C Comparing the responses of samples of heavy, moderate and

infrequent fruit juice drinkers to a trial formulation.

Number of

samples = 3

The next consideration is whether the samples being compared are dependent (i.e. related) or

independent of one another (i.e. unrelated). Samples are said to be dependent, or related, when the

measurement taken from one sample in no way affects the measurement taken from another sample.

Take for example the outline of test B above. The measurement of the responses of fruit juice drinkers to

the trial formulation in no way affects or influences the responses of the sample of non-fruit juice

drinkers. Therefore, the samples are independent of one another. Suppose however a sample were given

two formulations of fruit juice to taste. That is, the same individuals are asked first to taste formulation X

and then to taste formulation Y. The researcher would have two sets of sample results, i.e. responses to

product X and responses to product Y. In this case, the samples would be considered dependent or

related to one another. This is because the individual will make a comparison of the two products and

his/her response to one formulation is likely to affect his/her reaction or evaluation of the other product.

The third factor to be considered is the levels of measurement of the data being used. Data can be

nominal, ordinal, interval or ratio scaled. Table summarises the mathematical properties of each of these

levels of measurement.


Once the researcher knows how many samples are to be compared, whether these samples are related or

unrelated to one another and the level of measurement then the selection of the appropriate statistical test

is easily made. To illustrate the importance of understanding these connections consider the following

simple, but common, question in research. In many instances the age of respondents will be of interest.

This question might be asked in either of the two following ways:

Please indicate to which of the following age categories you belong :

(a) 15-21 years ___

22 - 30 years ___

Over 30 years ___

(b) How old are you? ___ Years

Measurement

scale

Measurement Level Examples Mathematical properties

Nominal Frequency counts Producing grading categories Confined to a small number of

tests using the mode and

frequency

Ordinal Ranking of items Placing brands of cooking oil

in order of preference

Wide range of nonparametric

tests which test for order

Interval Relative differences of

magnitude between

items

Scoring products on a 10 point

scale of like/dislike

Wide range of parametric tests

Ratio Absolute differences of

magnitude

Stating how much better one

product is than another in

absolute terms.

All arithmetic operations

Levels of measurement

Choosing format (a) would give rise to nominal (or categorical) data and format (b) would yield ratio

scaled data. These are at opposite ends of the hierarchy of levels of measurement. If by accident or design

format (a) were chosen then the analyst would have only a very small set of statistical tests that could be

applied and these are not very powerful in the sense that they are limited to showing association between

variables and could not be used to establish cause-and-effect. Format (b), on the other hand, since it gives

the analyst ratio data, allows all statistical tests to be used including the more powerful parametric tests

whereby cause-and-effect can be established, where it exists. Thus a simple change in the wording of a

question can have a fundamental effect upon the nature of the data generated.


Selecting statistical tests

The individual responsible for commissioning the research may be unfamiliar with the technicalities of

statistical tests but he/she should at least be aware that the number of samples, their dependence or

independence and the levels of measurement does affect how the data can be analysed. Those who

submit research proposals involving quantitative data should demonstrate an awareness of the factors

that determine the mode of analysis and a capability to undertake such analysis.

Researchers have to plan ahead for the analysis stage. It often happens that data processing begins whilst

the data gathering is still underway. Whether the data is to be analysed manually or through the use of a

computer program, data can be coded, cleaned (i.e. errors removed) and the proposed analytical tests

tried out to ensure that they are effective before all of the data has been collected.

Another important aspect relates to logistics planning. This includes ensuring that once the task of

preparing the data for analysis has begun there is a steady and uninterrupted flow of completed data

forms or questionnaires back from the field interviewers to the data processors. Otherwise, the whole

exercise becomes increasingly inefficient. A second logistical issue concerns any plan to build up a picture

of the pattern of responses as the data comes flowing in. This may require careful planning of the

sequencing of fieldwork. For instance, suppose that research was being undertaken within a particular

agricultural region with a view to establishing the size, number and type of milling enterprises which had

established themselves in rural areas following market liberalisation. It may be that the West of the

district under study mainly wheat is grown whilst in the East it is maize which is the major crop. It would

make sense to coordinate the fieldwork with data analysis so that the interim picture was of either wheat

or maize milling since the two are likely to differ in terms of the type of mill used (e.g. hammer versus

plate mills) as well as screen sizes and end use (e.g. the proportions prepared for animal versus human

food).

http://www.fao.org/docrep/W3241E/w3241e1a.gif


Step VIII: Drawing conclusions and making recommendations

The concluding chapters of this textbook are devoted to the topic of research report writing. However,

it is perhaps worth noting that the end products of research are conclusions and recommendations. With

respect to the marketing planning function, research helps to identify potential threats and opportunities,

generates alternative courses of action, provides information to enable marketing managers to evaluate

those alternatives and advises on the implementation of the alternatives.

Too often research reports chiefly comprise a lengthy series of tables of statistics accompanied by a few

brief comments which verbally describe what is already self-evident from the tables. Without

interpretation, data remains of potential, as opposed to actual use. When conclusions are drawn from raw

data and when recommendations are made then data is converted into information. It is information

which management needs to reduce the inherent risks and uncertainties in management decision making.

Customer oriented researchers will have noted from the outset of the research which topics and issues

are of particular importance to the person(s) who initiated the research and will weight the content of

their reports accordingly. That is, the researcher should determine what the marketing manager's

priorities are with respect to the research study. In particular he/she should distinguish between what

the managers:

1. must know

2. should know

3. could know

This means that there will be information that is essential in order for the manager to make the particular

decision with which he/she is faced (must know), information that would be useful to have if time and

resources within the budget allocation permit (should know) and there will be information that it would

be nice to have but is not at all directly related to the decision at hand (could know). In writing a research

proposal, experienced researchers would be careful to limit the information which they firmly promise to

obtain, in the course of the study, to that which is considered 'must know' information. Moreover, within

their final report, experienced researchers will ensure that the greater part of the report focuses upon

'must know' type information.

Review Question:

1. Define the concept of research and analyze

its characteristics.

2. Write an essay on various types of research.

3. Explain the significance of research in

various functional areas of business.

4. What are the difficulties faced by

researchers of business in India?

5. What is meant by research process? What

are the various stages / aspects involved in

the research process?


6. What do you mean by a method of

research? Briefly explain different methods

of research.

7. Explain the significance of research in

various functional areas of business.

8. What is Survey Research? How is it

different from Observation Research?

9. Write short note on:

a) Case Research

b) Experimental Research

c) Historical Research

d) Comparative Method of research


Chapter 2: Research Design

Introduction

The decisions regarding what, where, when, how much, by what means concerning a research project

constitute a research design. ―A research design is the arrangement of conditions for collection and

analysis of data in a manner that aims to combine relevance to the research purpose with economy in

procedure‖. In fact, the research design is the conceptual structure within which research is conducted; it

constitutes the blueprint for the collection, measurement and analysis of data. As such the design

includes an outline of what the researcher will do from writing the hypothesis and its operational

implications to the final analysis of data. More explicitly, the design decisions happen to be in respect of:

What is the study about?

Why is the study being made?

Where will the study be carried

out?

What type of data is required?

Where can the required data be

found?

What periods of time will the study

include?

What will be the sample design?

What techniques of data collection

will be used?

How will the data be analysed?

In what style will the report be

prepared?

Meaning of Research Design

Research design is also known by different names such as research outline, plan, blue print. In the words

of Fred N. Kerlinger, it is the plan, structure and strategy of investigation conceived so as to obtain

answers to research questions and control variance. The plan includes everything the investigator will do

from writing the hypothesis and their operational implications to the final analysis of data. The structure

is the outline, the scheme, the paradigms of the operation of the variables. The strategy includes the


methods to be used to collect and analyze the data. At the beginning this plan (design) is generally vague

and tentative. It undergoes many modifications and changes as the study progresses and insights into it

deepen. The working out of the plan consists of making a series of decisions with respect to what, why,

where, when, who and how of the research.

According to Pauline V.Young ―a research design is the logical and systematic planning and directing of

a piece of research‖. According to Reger E.Kirk ―research designs are plans that specify how data should

be collected and analyzed‖.

Research design is the plan, structure and strategy of investigation conceived so as to obtain answers

to research questions and to control variance. (Kerlinger)

A research is the specification of methods and procedures for acquiring the information needed. It is

the overall operational pattern or framework of the project that stipulates what information is to be

collected from which sources by what procedures. (Green and Tull).

The research has to be geared to the available time, energy, money and to the availability of data. There is

no such thing as a single or correct design. Research design represents a compromise dictated by many

practical considerations that go into research.

Why Research design is required?

Research design is needed because it facilitates the smooth sailing of the various research operations,

thereby making research as efficient as possible yielding maximal information with minimal expenditure

of effort, time and money.

For example, economical and attractive construction of house we need a blueprint (or what is commonly

called the map of the house) well thought out and prepared by an expert architect, similarly we need a

research design or a plan in advance of data collection and analysis for our research project. Research

design stands for advance planning of the methods to be adopted for collecting the relevant data and the

techniques to be used in their analysis.

Functions of Research Design

Regardless of the type of research design selected by the investigator, all plans perform one or more

functions outlined below.

i) It provides the researcher with a blue print for studying research questions.

ii) It dictates boundaries of research activity and enables the investigator to channel his energies in a

specific direction.

iii) It enables the investigator to anticipate potential problems in the implementation of the study.

iv) The common function of designs is to assist the investigator in providing answers to various kinds of

research questions.


A study design includes a number of component parts which are interdependent and which demand a

series of decisions regarding the definitions, methods, techniques, procedures, time, cost and

administration aspects.

Features of good Design

A research design basically is a plan of action. Once the research problem is selected, then it must be

executed to get the results. Then how to go about it? What is its scope? What are the sources of data?

What is the method of enquiry? What is the time frame? How to record the data? How to analyze the

data? What are the tools and techniques of analysis?

What is the manpower and organization required?

What are the resources required? These and many

such are the subject matter of attacking the research problem demanding decisions in the beginning itself

to have greater clarity about the research study. It is similar to having a building plan before the building

is constructed. Thus, according to P.V. Young the various ―considerations which enter into making

decisions regarding what, where, when, how much, by what means constitute a plan of study or a study

design‖. Usually the features or components of a Research design are as follows:

1) Need for the Study: Explain the need for and importance of this study and its relevance.

2) Review of Previous Studies: Review the previous works done on this topic, understand what they did,

identify gaps and make a case for this study and justify it.

3) Statement of Problem: State the research problem in clear terms and give a title to the study.

4) Objectives of Study: What is the purpose of this study? What are the objectives you want to achieve by

this study? The statement of objectives should not be vague. They must be specific and focused.

5) Formulation of Hypothesis: Conceive possible outcome or answers to the research questions and

formulate into hypothesis tests so that they can be tested.

6) Operational Definitions: If the study is using uncommon concepts or unfamiliar tools or using even

the familiar tools and concepts in a specific sense, they must be specified and defined.

7) Scope of the Study: It is important to define the scope of the study, because the scope decides what is

within its purview and what is outside.

Scope includes Geographical scope, content scope, chronological scope of the study. The territorial area to

be covered by the study should be decided. E.g. only Delhi or northern states or All India. As far as

content scope is concerned according to the problem say for example, industrial relations in so and so

organization, what are aspects to be studied, what are the aspects not coming under this and hence not

studied. Chronological scope i.e., time period selection and its justification is important. Whether the

study is at a point of time or longitudinal say 1991-2003.

8) Sources of Data: This is an important stage in the research design. At this stage, keeping in view the

nature of research, the researcher has to decide the sources of data from which the data are to be


collected. Basically the sources are divided into primary source (field sources) and secondary source

(documentary sources). The data from primary source are called as primary data, and data from

secondary source are called secondary data. Hence, the researcher has to decided whether to collect from

primary source or secondary source or both sources.

9) Method of Collection: After deciding the sources for data collection, the researcher has to determine

the methods to be employed for data collection, primarily, either census method or sampling method.

This decision may depend on the nature, purpose, scope of the research and also time factor and financial

resources.

10) Tools & Techniques: The tools and techniques to be used for collecting data such as observation,

interview, survey, schedule, questionnaire, etc., have to be decided and prepared.

11) Sampling Design: If it is a sample study, the sampling techniques, the size of sample, the way

samples are to be drawn etc., are to be decided.

12) Data Analysis: How are you going to process and analyze the data and information collected? What

simple or advanced statistical techniques are going to be used for analysis and testing of hypothesis, so

that necessary care can be taken at the collection stage.

13) Presentation of the Results of Study: How are you going to present the results of the study? How

many chapters? What is the chapter scheme? The chapters, their purpose, their titles have to be outlined.

It is known as chapterisation.

14) Time Estimates: What is the time available for this study? Is it limited or unlimited time? Generally, it

is a time bound study. The available or permitted time must be apportioned between different activities

and the activities to be carried out within the specified time. For example, preparation of research design

one month, preparation of questionnaire one month, data collection two months, analysis of data two

months, drafting of the report two months etc.,

15) Financial Budget: The design should also take into consideration the various costs involved and the

sources available to meet them. The expenditures like salaries (if any), printing and stationery, postage

and telephone, computer and secretarial assistance etc.

16) Administration of the Enquiry: How is the whole thing to be executed? Who does what and when?

All these activities have to be organized systematically, research personnel have to be identified and

trained. They must be entrusted with the tasks, the various activities are to be coordinated and the whole

project must be completed as per schedule. Research designs provide guidelines for investigative activity

and not necessarily hard and fast rules that must remain unbroken. As the study progresses, new aspects,

new conditions and new connecting links come to light and it is necessary to change the plan / design as

circumstances demand. A universal characteristic of any research plan is its flexibility. Depending upon

the method of research, the designs are also known as survey design, case study design, observation

design and experimental design.


Types of research designs

Various types of research design can be classified under three titles viz :

I) Experimental Research Design

II) Exploratory Research Design

III) Descriptive research Design

I) Experimental Research Design can also be called hypothesis – testing research design. It refers to

that research process in which one or more variable are manipulated under conditions that permit the

collection of data that shows the effect, if any, of such variable in unconfused fashion.

Basic Principles of Experimental Designs

There are three principles of experimental designs:

1. Principle of Replication;

2. Principle of Randomization

3. Principle of Local Control

Now let us discuss each one of these experimental design

1. Principle of Replication

In this design, the experiment should be repeated more than once. Thus, each treatment is applied in

many experimental units instead of one. By doing so the statistical accuracy of the experiments is

increased. For example, suppose we are to examine the effect of two varieties of rice.

For this purpose, we may divide the field into two parts and grow one variety in one part and the other

variety in the other part. We can then compare the yield of the two parts and draw conclusion on that

basis. But if we are to apply the principle of replication to this experiment, then we first divide the field

into several parts, grow one variety in half of these parts and the other variety in the remaining parts. We

can then collect the data of yield of the two varieties and draw conclusion by comparing the same. The

result so obtained will be more reliable in comparison to the conclusion we draw without applying the

principle of replication. The entire experiment can even be repeated several times for better results.

Conceptually replication does not present any difficulty, but computationally it does. For example, if, an

experiment requiring a two-way analysis of variance is replicated, it will then require a three-way

analysis of variance since replication itself may be a source of variation in the data. However, it should be

remembered that replication is introduced in order to increase the precision of a study; that is to say, to

increase the accuracy with which the main effects and interactions can be estimated.


2. Principle of Randomization

This principle indicates that we should design or plan the experiment in such a way that the variations

caused by extraneous factor can all be combined under the general heading of ―chance.‖ For example - if

grow one variety of rice, say, in the first half of the parts of a field and the other variety is grown in the

other half, then it is just possible that the soil fertility may be different in the first half in comparison to

the other half. If this is so our results would not be realistic. In such a situation, we may assign the variety

of rice to be grown in different parts of the field on the basis of some variety ‗sampling technique, i.e., we

may apply randomization principle and random ourselves against the effects of the extraneous factors

(soil fertility processes in the given case.)

3. The Principle of Local Control

It is another important principle of experimental designs. Under it the extraneous factor, the known

source of variability, is made to vary deliberately over as wide a range as necessary and these needs to be

done in such a way that the variability it causes can be measured and hence eliminated from the

experimental error.

This means that we should plan the experiment in a manner that we can perform a two-way analysis of

variance, in which the total variability of the data is divided into three components attributed to

treatments (varieties of rice in our case), the extraneous factor (soil fertility in our case) and experimental

error.

In other words, according to the principle of local control, we first divide the field into several

homogeneous parts, known as blocks, and then each such block is divided into parts equal to the number

of treatments. Then the treatments are randomly assigned to these parts of a block.

Some of the commonly used experimental designs are:

1) After only Design:

2) Before After Design

3) Before After with control Group Design

4) After only with control

1) "After-only" designs

As the name suggests, with after-only experimental designs measures of the independent variable are

only taken after the experimental subjects have

been exposed to the independent variable. This is

a common approach in advertising research where

a sample of target customers are interviewed

following exposure to an advertisement and their

recall of the product, brand, or sales features is


measured. The advertisement could be one appearing on national television and/or radio or may appear

in magazines, newspapers or some other publication. The amount of information recalled by the sample

is taken as an indication of the effectiveness of the advertisement.

The chief problem with after-only designs is that they do not afford any control over extraneous factors

that could have influenced the post-exposure measurements. For example, marketing extension

personnel might have completed a trial campaign to persuade small-scale poultry producers, in a

localised area, to make use of better quality feeds in order to improve the marketability and price of the

end product. The decision to extend the campaign to other districts will depend on the results of this trial.

After-only measures are taken; following the campaign, by checking poultry feed sales with merchants

operating within the area. Suppose a rise in sales of good quality poultry feed mixes occurs four weeks

after the campaign ends. It would be dangerous to assume that this sales increase is wholly due to the

work of the marketing extension officers. A large part of the increase may be due to other factors such as

promotional activity on the part of feed manufacturers and merchants who took advantage of the

campaign, of which they were forewarned, and timed their marketing programme to coincide with the

extension campaign. If the extension service erroneously drew the conclusion that the sales increase was

entirely due to their own promotional activity, then they might be misled into repeating the same

campaign in other areas where there would not necessarily be the same response from feed

manufacturers and merchants.

After-only designs are not true experiments since little or no control is exercised over any of the variables

by the researcher. However its inclusion here serves to underline the need for more complex designs.

2) "Before-after" designs

A before-after design involves the researcher in measuring the dependent variable both before and after

the participants has been exposed to the independent variables.

The before-after design is an improvement

upon the after-only design, in that the effect of

the independent variable, if any, is established

by observing differences between the value of

the dependent variable before and after the

experiment. Nonetheless, before-after designs

still have a number of weaknesses.

Consider the case of the vegetable packer who is thinking about sending his/her produce to the

wholesale market in more expensive, but more protective, plastic crates, instead of cardboard boxes. The

packer is considering doing so in response to complaints from commissioning agents that the present

packaging affords little protection to produce from handling damage. The packer wants to be sure that

the economics of switching to plastic crates makes sense. Therefore, the packer introduces the plastic


crates for a trial period. Before introducing these crates, the packer records the prices received for his/her

top grade produce. Unless prices increase by more than the additional cost of plastic crates then there is

no economic advantage to using the more expensive packaging.

Suppose, for instance, that the packer was receiving ` 15 per crate, when these were of the cardboard

type, but that the price after the introduction of plastic crates had risen to ` 17 per crate. The ` 2 difference

would be attributed to better quality produce reaching the market as a result of the protection afforded

by the plastic crates. However, there are several equally reasonable explanations for the upward drift in

produce prices including a shortfall in supply, a fall in the quality of produce supplied by competitors

who operate in areas suffering adverse weather conditions, random fluctuation in prices, etc.

3) "Before-after with control group" design

This design involves establishing two samples or groups of respondents: an experimental group that

would be exposed to the marketing variable and a control group which would not be subjected to the

marketing variable under study. The two groups would be matched. That is, the two samples would be

identical in all important respects. The idea is that any confounding factors would impact equally on both

groups and therefore any differences in the data drawn from the two groups can be attributed to the

experimental variable.

Study table, which depicts how an experiment involving the measurement of the impact of a sugar beet

seed promotional campaign on brand awareness might be configured with a control group.

An example of a before after with control group design

Experimental Group Control Group

Before1 measure: % recalling Brand X sugarbeet seed 25.5% 25.5%

Exposed to promotional campaign Yes No

'After' measure: % recalling Brand X sugarbeet seed 34.5% 24,5%

First, the two groups would be matched: attributes such as age distribution of group members, spread of

sizes of farms operated, types of farms operated, ratio of dependence on hand tools, animal drawn tools

and tractor mounted equipment, etc. would be matched within each group so that the groups are

interchangeable for the purposes of the test. The initial level of awareness of the sugar beet brand would

be recorded within each group. Only the experimental group would see the test promotional campaign.

After the campaign, a second measure of brand awareness would be taken from each group. Any

difference between the 'after' and 'before' measurements of the control group (C2 - C1) would be due to

uncontrolled variables. Differences between the 'after' and 'before' measurements in the experimental

group (E2 - E1) would be the result of the experimental variable plus the same uncontrolled variables

affecting the control group. Isolating the effect of the experimental variable is simply a matter of

subtracting the difference in the two measurements of the control group from the difference in the two


measures taken from the experimental group. To illustrate the computation consider the following

hypothetical figures.

Awareness of the brand within the experimental group has increased by 9 percent. At the same time, the

awareness level, within the control group, appears to have fallen by 1 percent. This could be due to

random fluctuations or a real lowering of awareness due to some respondents forgetting the brand in the

absence of any supporting advertisements/promotions. Thus the effects of the test campaign would seem

to have been:

Effect of experimental variable = (34.5 - 25.5) - (24.5 - 25.5)

= (9%) - (-1%) = 10%

If a "before and after with control group" experiment is properly designed and executed then the effects

of maturation, pre-testing and measurement variability should be the same for the experimental group as

for the control group. In this case, these factors appear to have had a negative effect on awareness of one

percent. Had it not been for the experimental variable, the experimental group would have shown a

similar fall in awareness over the period of the test. Instead of recording a fall in the level of awareness of

the sugar beet brand, the experimental group actually showed a nine percent increase in brand

awareness. However, the design is not guaranteed to be unflawed. The accurate matching of the two

groups is a difficult, some would say impossible, task. Moreover, over time the rate and extent of

mortality, or drop out, is likely to vary between the groups and create additional problems in maintaining

a close match between groups.

4)"After-only with control group" experimental design

Again, this design involves establishing two matched samples or groups of respondents. There is no

measurement taken from either group before the experimental variable is introduced and the control

group is not subsequently subjected to the experimental variable. Afterwards measures are taken from

both groups and the effect of the experimental variable is established by deducting the control group

measure from the experimental group measure. An illustrative example will help clarify the procedures

followed.

A Sri Lankan food technology research institute was trying to convince small-scale food processors to

adopt solar dryers to produce dried plantain and other dehydrated vegetables. Much of the initial

resistance to the adoption of this technology was due to the belief that the taste characteristics of this

snack food would be altered from those of traditional sun-dried plantain. The research institute was able

to convince the food manufacturers that there would be no perceptible changes in the taste characteristics

by carrying out an "after-only with control group" experiment. Sensory analysis experiments conclusively

showed that almost none of the participants were able to discriminate between plantain dehydrated by

means of the solar powered dryer and that which was sun-dried.


Many product tests are of the "after-only with control group" type. This design escapes the problems of

pre-testing, history and maturation. However, this form of "after-only design" does not facilitate an

analysis of the process of change, whereas a comparable "before-after design" would. The attitudes,

opinions and/or behaviour of individual participants can be recorded both before and afterwards and

changes noted. For instance, the effect of the experimental variable on those participants who held

unfavourable attitudes can be compared with those they held in the "before" measurement. Changes in

those that held favourable attitudes in the "before" measurement can also be assessed after exposure to

the experimental variable.

II) Exploratory research design

Exploratory research: what it is and what it is not

Exploratory research helps ensure that a rigorous and conclusive study will not begin with an inadequate

understanding of the nature of the problem. Most exploratory research designs provide qualitative data

which provides greater understanding of a concept. In contrast, quantitative data provides precise

measurement.

Exploratory research may be a single research investigation or it may be a series of informal studies; both

methods provide background information. Researchers must be creative in the choice of information

sources. They should explore all appropriate inexpensive sources before embarking on expensive

research of their own. However, they should still be systematic and careful at all times.

Why conduct exploratory research?

There are three purposes for conducting exploratory research; all three are interrelated:

A. Diagnosing a situation: Exploratory research helps diagnose the dimensions of problems so that

successive research projects will be on target.

B. Screening alternatives: When several opportunities arise and budgets restrict the use of all possible

options, exploratory research may be utilized to determine the best alternatives. Certain evaluative

information can be obtained through exploratory research. Concept testing is a frequent reason for

conducting exploratory research. Concept testing refers to those research procedures that test some sort

of stimulus as a proxy for a new, revised, or remarketed product or service. Generally, consumers are

presented with an idea and asked if they like it, they would use it, etc. Concept testing is a means of

evaluating ideas by providing a feel for the merits of the idea prior to the commitment of any research

and development, marketing, etc. Concept testing portrays the functions, uses, and possible situations for

the proposed product.

C. Discovering new ideas: Uncovering consumer needs is a great potential source of ideas. Exploratory

research is often used to generate new product ideas, ideas for advertising copy, etc.


Categories of exploratory research

The purpose, rather than the technique, of the research determines whether a study is exploratory,

descriptive, or causal. A manager may choose from three general categories of exploratory research:

A. Experience surveys: Concepts may be discussed with top executives and knowledgeable managers

who have had personal experience in the field being researched. This constitutes an informal experience

survey. Such a study may be conducted by the business manager rather than the research department.

On the other hand, an experience survey may be a small number of interviews with experienced people

who have been carefully selected from outside the organization. The purpose of such a study is to help

formulate the problem and clarify concepts rather than to develop conclusive evidence.

B. Secondary data analysis: A quick and economical source of background information is trade literature

in the public library. Searching through such material is exploratory research with secondary data;

research rarely begins without such an analysis. An informal situation analysis using secondary data and

experience surveys can be conducted by business managers. Should the project need further clarification,

a research specialist can conduct a pilot study.

C. Case study method: The purpose of a case study is to obtain information from one, or a few, situations

similar to the researcher's situation. A case study has no set procedures, but often requires the

cooperation of the party whose history is being studied. However, this freedom to research makes the

success of the case study highly dependent on the ability of the researcher. As with all exploratory

research, the results of a case study should be seen as tentative.

Case study research excels at bringing us to an understanding of a complex issue or object and can extend

experience or add strength to what is already known through previous research. Case studies emphasize

detailed contextual analysis of a limited number of events or conditions and their relationships.

Researchers have used the case study research method for many years across a variety of disciplines.

Social scientists, in particular, have made wide use of this qualitative research method to examine

contemporary real-life situations and provide the basis for the application of ideas and extension of

methods. Researcher Robert K. Yin defines the case study research method as an empirical inquiry that

investigates a contemporary phenomenon within its real-life context; when the boundaries between

phenomenon and context are not clearly evident; and in which multiple sources of evidence are used.

Many well-known case study researchers such as Robert E. Stake, Helen Simons, and Robert K. Yin have

written about case study research and suggested techniques for organizing and conducting the research

successfully. This introduction to case study research draws upon their work and proposes six steps

that should be used:

1. Determine and define the research questions

2. Select the cases and determine data gathering and analysis techniques


3. Prepare to collect the data

4. Collect data in the field

5. Evaluate and analyze the data

6. Prepare the report

D. Pilot studies: The term "pilot studies" is used as a collective to group together a number of diverse

research techniques all of which are conducted on a small scale. Thus, a pilot study is a research project

which generates primary data from consumers, or other subjects of ultimate concern. There are four

major categories of pilot studies:

1. Focus group interviews: These interviews are free-flowing interviews with a small group of people.

They have a flexible format and can discuss anything from brand to a product itself. The group typically

consists of six to ten participants and a moderator. The moderator's role is to introduce a topic and to

encourage the group to discuss it among themselves. There are four primary advantages of the focus

group: (1) it allows people to discuss their true feelings and convictions, (2) it is relatively fast, (3) it is

easy to execute and very flexible, (4) it is inexpensive.

One disadvantage is that a small group of people, no matter how carefully they are selected, will not be

representative.

Specific advantages of focus group interviews have to be categorized as follows:

a) Synergism: the combined effort of the group will produce a wider range of information, insights and

ideas than will the cumulation of separately secured responses.

b) Serendipity: an idea may drop out of the blue, and affords the group the opportunity to develop such

an idea to its full significance.

c) Snowballing: a bandwagon effect occurs. One individual often triggers a chain of responses from the

other participants.

d) Stimulation: respondents want to express their ideas and expose their opinions as the general level of

excitement over the topic increases.

e) Security: the participants are more likely to be candid because they soon realize that the things said are

not being identified with any one individual.

f) Spontaneity: people speak only when they have definite feelings about a subject; not because a

question requires an answer.

g) Specialization: the group interview allows the use of a more highly trained moderator because there

are certain economies of scale when a large number of people are "interviewed" simultaneously.

h) Scientific scrutiny: the group interview can be taped or even videoed for observation. This affords

closer scrutiny and allows the researchers to check for consistency in the interpretations.

i) Structure: the moderator, being one of the groups, can control the topics the group discusses.

j) Speed: a number of interviews are, in effect, being conducted at one time.


The ideal size for a focus group is six to ten relatively homogeneous people. This avoids one or two

members intimidating the others, and yet, is a small enough group that adequate participation is allowed.

Homogeneous groups avoid confusion which might occur if there were too many differing viewpoints.

Researchers who wish to collect information from different groups should conduct several different focus

groups.

The sessions should be as relaxed and natural as possible. The moderator's job is to develop a rapport

with the group and to promote interaction among its members. The discussion may start out general, but

the moderator should be able to focus it on specific topics.

An effective focus group moderator prepares a discussion guide to help ensure that the focus group will

cover all topics of interest. The discussion guide consists of written prefatory remarks to inform the

group about the nature of the focus group and an outline of topics/questions that will be addressed in

the group session.

The focus group technique has two shortcomings:

Without an experienced moderator, a self-appointed leader will dominate the session resulting in

an abnormal "halo effect" on the interview.

There may be sampling problems.

2. Interactive Media and online Focus Group: When a person uses the Internet, he or she interacts with a

computer. It is an interactive media because the user clicks a command and the computer responds. The

use of the Internet for qualitative exploratory research is growing rapidly. The term online focus group

refers to qualitative research where a group of individuals provide unstructured comments by

keyboarding their remarks into a computer connected to the Internet. The group participants either

keyboard their remarks during a chat room format or when they are alone at their computers. Because

respondents enter their comments into the computer, transcripts of verbatim responses are available

immediately afterward the group session. Online groups can be quick and cost efficiency. However,

because there is less interaction between participants, group synergy and snowballing of ideas can suffer.

Research companies often set up a private chat room on their company Web sites for focus group

interviews. Participants in these chat rooms feel their anonymity is very secure. Often they will make

statements or ask questions they would never address under other circumstances. This can be a major

advantage for a company investigating sensitive or embarrassing issues.

Many online focus groups using the chat room format arrange for a sample of participants to be online at

the same time for about typically 60 to 90 minutes. Because participants do not have to be together in the

same room at a research facility, the number of participants in online focus groups can be much larger

than traditional focus groups. A problem with online focus groups is that the moderator cannot see body

language and facial expressions (bewilderment, excitement, interest, etc.) to interpret how people are

reacting. Also, the moderator‘s ability to probe and ask additional questions on the spot is reduced in

online focus groups, especially those in which participants are not simultaneously involved. Research


that requires tactile touch, such as a new easy-opening packaging design, or taste experiences cannot be

performed online.

3. Projective techniques: Individuals may be more likely to give a true answer if the question is

disguised. If respondents are presented with unstructured and ambiguous stimuli and are allowed

considerable freedom to respond, they are more likely to express their true feelings.

A projective technique is an indirect means of questioning that enables respondents to "project their

beliefs onto a third party." Thus, the respondents are allowed to express emotions and opinions that

would normally be hidden from others and even hidden from themselves. Common techniques are as

follows:

a) Word association: The subject is presented with a list of words, one at a time, and asked to respond

with the first word that comes to mind. Both verbal and non-verbal responses are recorded. Word

association should reveal each individual's true feelings about the subject. Interpreting the results is

difficult; the researcher should avoid subjective interpretations and should consider both what the subject

said and did not say (e.g., hesitations).

b) Sentence completion method: This technique is also based on the assumption of free association.

Respondents are required to complete a number of partial sentences with the first word or phrase that

comes to mind. Answers tend to be more complete than in word association, however, the intention of

the study is more apparent.

c) Third-person technique and role playing: Providing a "mask" is the basic idea behind the third-person

technique. Respondents are asked why a third person does what he or she does, or what a third person

thinks of a product. The respondent can transfer his attitudes onto the third person. Role playing is a

dynamic reenactment of the third-person technique in a given situation. This technique requires the

subject to act out someone else's behavior in a particular setting.

d) Thematic apperception test (TAT): This test consists of a series of pictures in which consumers and

products are the center of attention. The investigator asks the subject what is happening in the picture

and what the people might do next. Theses ("thematic") are elicited on the basis of the perceptual-

interpretive ("apperception") use of the pictures. The researcher then analyses the content of the stories

that the subjects relate. The picture should present a familiar, interesting, and well-defined problem, but

the solution should be ambiguous. A cartoon test, or picture frustration version of TAT, uses a cartoon

drawing in which the respondent suggests dialogue that the cartoon characters might say. Construction

techniques request that the consumer draw a picture, construct a collage, or write a short story to express

their perceptions or feelings.

4. Depth interviews: Depth interviews are similar to the client interviews of a clinical psychiatrist. The

researcher asks many questions and probes for additional elaboration after the subject answers; the

subject matter is usually disguised.


Depth interviews have lost their popularity recently because they are time-consuming and expensive as

they require the services of a skilled interviewer.

Limitations

Exploratory research techniques have their limitations. Most of them are qualitative, and the

interpretation of their results is judgmental—thus, they cannot take the place of quantitative,

conclusive research.

Because of certain problems, such as interpreter bias or sample size, exploratory findings should be

treated as preliminary. The major benefit of exploratory research is that it generates insights and

clarifies the problems for testing in future research.

If the findings of exploratory research are very negative, then no further research should probably be

conducted. However, the researcher should proceed with caution because there is a possibility that a

potentially good idea could be rejected because of unfavorable results at the exploratory stage.

In other situations, when everything looks positive in the exploratory stage, there is a temptation to

market the product without further research. In this situation, business managers should determine

the benefits of further information versus the cost of additional research. When a major commitment

of resources is involved, it is often well worth conducting a quantitative study.

III) Descriptive Research design

This is intended to describe certain factors that management is likely to be interested in such as market

conditions, customers‘ feelings or opinions toward a particular company, purchasing behaviour as so

forth. Such research is not intended to allow the researcher to establish causal relationships between

marketing variables and sales or consumer behaviour, or to enable the researcher to predict likely future

conditions. Descriptive research merely examines ‗what is‘. Such research, just like exploratory research,

usually forms part of an ongoing research programme. Once the researcher has established the present

situation in terms of market size, main segments, main competitors, etc., they may then proceed to types

of research of a more predictive and/or conclusive nature. Descriptive research usually makes use of

descriptive statistics to help the user understand the structure of the data and any significant patterns

that may be found in the data. All measures of central tendency such as the mean, median and mode are

often used along with measures of dispersion such as the variance and standard deviation. Descriptive

research results are often presented using pictorial methods such as graphs, ‗pie charts‘, histograms, etc..

When the nature of the initial decision problem is either to describe specific characteristics of existing

market phenomena or to evaluate current marketing mix strategies of a defined target population or

market structure, then a descriptive research design is appropriate.


Or

If the research question(s) is linked to answering specified questions concerning who, what, where,

when, and how about known members or elements of the target population or market structures under

investigation, then the researcher should consider using a descriptive research design to gather the

needed primary data.

Remember, there are two basic ways to gather the primary data needed: observation and asking

questions. When the researcher needs to ask questions, the different approaches used are referred to as

survey methods.

Over time, descriptive research designs have come to be viewed and acknowledged as the different

survey methods available to researchers for collecting quantitative primary data from large groups of

people through the question and answer protocol process.

Examples of questions for descriptive research:

1. Do teachers hold favorable attitudes toward using computers, in schools?

2. What kinds of activities that involve technology occur in 6th-grade classrooms and how frequently do

they occur?

3. Is there a relationship between experience with multimedia computers and problem solving skills?

Descriptive research can be either quantitative or qualitative.

Descriptive research involves gathering data that describe events and then organizes, tabulates, depicts,

and describes the data collection. Descriptive statistics are very important in reducing the data to

manageable form.

The Nature of Descriptive Research

1. The descriptive function of research is heavily dependent on instrumentation for measurement and

observation

2. Once the instruments are developed, they can be used to describe phenomena of interest to the

researchers.

3. The intent of' some descriptive research is to produce statistical information about aspects of education

that interests policymakers and educators.

4. There has been in ongoing debate among researchers about the value of quantitative vs. qualitative

research, with some saying descriptive research is less pure than traditional experimental, quantitative

designs.

Some of the Descriptive Techniques

The descriptive techniques that are commonly used include:

Graphical description

o use graphs to summarize data


o examples: histograms, scatter diagrams, bar charts, pie charts

Tabular description

o use tables to summarize data

o examples: frequency distribution schedule, cross tabs

Parametric description

o estimate the values of certain parameters which summarize the data

measures of location or central tendency

arithmetic mean

median

mode

interquartile mean

measures of statistical dispersion

standard deviation

statistical range

Review Questions:

1. What is a research design? Explain the

functions of a research design.

2. Define a research design and explain its

contents.

3. What are the various features/components of

a research design?

http://en.wikipedia.org/wiki/Central_tendency

http://en.wikipedia.org/wiki/Arithmetic_mean

http://en.wikipedia.org/wiki/Median

http://en.wikipedia.org/wiki/Mode

http://en.wikipedia.org/wiki/Interquartile_mean

http://en.wikipedia.org/wiki/Statistical_dispersion

http://en.wikipedia.org/wiki/Standard_deviation

http://en.wikipedia.org/wiki/Statistical_range


Chapter 3: Sampling Design

What is a Sample?

A sample is a finite part of a statistical population whose properties are studied to gain information about

the whole (Webster, 1985). When dealing with people, it can be defined as a set of respondents (people)

selected from a larger population for the purpose of a survey.

A population is a group of individual‘s persons, objects, or items from which samples are taken for

measurement for example a population of presidents or professors, books or students.

Market research involves the collection of data to obtain insight and knowledge into the needs and

wants of customers and the structure and dynamics of a market. In nearly all cases, it would be very

costly and time-consuming to collect data from the entire population of a market. Accordingly, in market

research, extensive use is made of sampling from which, through careful design and analysis, Marketers

can draw information about the market.

Sampling is the key to survey research. No matter how well a study is done in other ways, if the sample

has not been properly found, the results cannot be regarded as correct. It applies mainly to surveys, but is

also important for planning other types of research.

Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so

that by studying the sample we may fairly generalize our results back to the population from which they

were chosen. Let's begin by covering some of the key terms in sampling like "population" and "sampling

frame." Then, because some types of sampling rely upon quantitative models, we'll talk about some of

the statistical terms used in sampling. Finally, we'll discuss the major distinction between probability and

Non-probability sampling methods and work through the major types in each. Sampling is often used

when conducting a census is impossible or unreasonable.

http://trochim.human.cornell.edu/kb/sampterm.htm

http://trochim.human.cornell.edu/kb/sampstat.htm

http://trochim.human.cornell.edu/kb/sampprob.htm

http://trochim.human.cornell.edu/kb/sampnon.htm


What is sampling?

Sampling is the act, process, or technique of selecting a suitable sample, or a representative part of a

population for the purpose of determining parameters or characteristics of the whole population.

What is the purpose of sampling? To draw conclusions about populations from samples, we must use

inferential statistics which enables us to determine a population‗s characteristics by directly observing

only a portion (or sample) of the population. We obtain a sample rather than a complete enumeration (a

census) of the population for many reasons. Obviously, it is cheaper to observe a part rather than the

whole, but we should prepare ourselves to cope with the dangers of using samples. Some are better than

others but all may yield samples that are inaccurate and unreliable. We will learn how to minimize these

dangers, but some potential error is the price we must pay for the convenience and savings the samples

provide.

Why sampling?

One of the decisions to be made by a researcher in conducting a survey is whether to go for a census or a

sample survey. We obtain a sample rather than a complete enumeration (a census ) of the population for

many reasons. The most important considerations for this are: cost, size of the population, accuracy of

data, accessibility of population, timeliness, and destructive observations.

1) Cost: The cost of conducting surveys through census method would be prohibitive and sampling helps

in substantial cost reduction of surveys. Since most often the financial resources available to conduct a

survey are scarce, it is imperative to go for a sample survey than census.

2) Size of the Population: If the size of the population is very large it is difficult to conduct a census if not

impossible. In such situations sample survey is the only way to analyse the characteristics of a

population.

3) Accuracy of Data: Although reliable information can be obtained through census, sometime the

accuracy of information may be lost because of a large population. Sampling involves a small part of the

population and a few trained people can be involved to collect accurate data. On the other hand, a lot of

people are required to enumerate all the observations. Often it becomes difficult to involve trained

manpower in large numbers to collect the data thereby compromising accuracy of data collected. In such

a situation a sample may be more accurate than a census. A sloppily conducted census can provide less

reliable information than a carefully obtained sample.

4) Accessibility of Population: There are some populations that are so difficult to get access to that only a

sample can be used, e.g., people in prison, birds migrating from one place to another place etc. The

inaccessibility may be economic or time related. In a particular study, population may be so costly to

reach, like the population of planets, that only a sample can be used.

5) Timeliness: Since we are covering a small portion of a large population through sampling, it is

possible to collect the data in far less time than covering the entire population. Not only does it take less


time to collect the data through sampling but the data processing and analysis also takes less time

because fewer observations need to be covered. Suppose a company wants to get a quick feedback from

its consumers on assessing their perceptions about a new improved detergent in comparison to an

existing version of the detergent. Here the time factor is very significant. In such situations it is better to

go for a sample survey rather than census because it reduces a lot of time and product launch decision

can be taken quickly.

6) Destructive Observations: Sometimes the very act of observing the desired characteristics of a unit of

the population destroys it for the intended use. Good examples of this occur in quality control. For

example, to test the quality of a bulb, to determine whether it is defective, it must be destroyed. To obtain

a census of the quality of a lorry load of bulbs, you have to destroy all of them. This is contrary to the

purpose served by quality-control testing. In this case, only a sample should be used to assess the quality

of the bulbs. Another example is blood test of a patient.

The disadvantages of sampling are few but the researcher must be cautious. These are risk, lack of

representativeness and insufficient sample size each of which can cause errors. If researcher don‟t pay

attention to these flaws it may invalidate the results.

1) Risk: Using a sample from a population and drawing inferences about the entire population involves

risk. In other words the risk results from dealing with a part of a population. If the risk is not acceptable

in seeking a solution to a problem then a census must be conducted.

2) Lack of representativeness: Determining the representativeness of the sample is the researcher‘s

greatest problem. By definition, ‗sample‘ means a representative part of an entire population. It is

necessary to obtain a sample that meets the requirement of representativeness otherwise the sample will

be biased. The inferences drawn from non-reprentative samples will be misleading and potentially

dangerous.

3) Insufficient sample size: The other significant problem in sampling is to determine the size of the

sample. The size of the sample for a valid sample depends on several factors such as extent of risk that

the researcher is willing to accept and the characteristics of the population itself.

What is Sampling Design?

Sample design covers the method of selection, the sample structure and plans for analysing and

interpreting the results. Sample designs can vary from simple to complex and depend on the type of

information required and the way the sample is selected.

Sample design affects the size of the sample and the way in which analysis is carried out. In simple terms

the more precision the market researcher requires, the more complex will be the design and the larger the

sample size.


The sample design may make use of the characteristics of the overall market population, but it does not

have to be proportionally representative. It may be necessary to draw a larger sample than would be

expected from some parts of the population; for example, to select more from a minority grouping to

ensure that sufficient data is obtained for analysis on such groups.

Many sample designs are built around the concept of random selection. This permits justifiable inference

from the sample to the population, at quantified levels of precision. Random selection also helps guard

against sample bias in a way that selecting by judgement or convenience cannot.

Characteristics of a good sample Design

It is important that the sampling results must reflect the characteristics of the population. Therefore,

while selecting the sample from the population under investigation it should be ensured that the sample

has the following characteristics:

1) A sample must represent a true picture of the population from which it is drawn.

2) A sample must be unbiased by the sampling procedure.

3) A sample must be taken at random so that every member of the population of data has an equal chance

of selection.

4) A sample must be sufficiently large but as economical as possible.

5) A sample must be accurate and complete. It should not leave any information incomplete and should

include all the respondents, units or items included in the sample.

6) Adequate sample size must be taken considering the degree of precision required in the results of

inquiry.

Sampling and non-sampling errors

The quality of a research project depends on the accuracy of the data collected and its representation to

the population. There are two broad sources of errors. These are sampling errors and non-sampling

errors.

1 Sampling Errors

The principal sources of sampling errors are the sampling

method applied, and the sample size. This is due to the

fact that only a part of the population is covered in the

sample. The magnitude of the sampling error varies from

one sampling method to the other, even for the same

sample size. For example, the sampling error associated

with simple random sampling will be greater than

stratified random sampling if the population is

heterogeneous in nature. Intuitively, we know that the larger the sample the more accurate the research.


In fact, the sampling error varies with samples of different sizes. Increasing the sample size decreases the

sampling error. The following Figure gives an approximate relationship between sample size and

sampling error. Study the following figure carefully.

2 Non-Sampling Errors

The non-sampling errors arise from faulty research design and mistakes in executing research. There are

many sources of non-sampling errors which may be broadly classified as: (a) respondent errors, and (b)

administrative errors.

a) Respondent Errors: If the respondents co-operate and give the correct information the objectives of the

researcher can be easily accomplished. However, in practice, this may not happen. The respondents may

either refuse to provide information or even if he/she provides information it may be biased. If the

respondent fails to provide information, we call it as non-response error. Although this problem is

present in all types of surveys, the problem is more acute in mailed surveys. Non-response also leads to

some extreme situations like those respondents who are willing to provide information are over

represented while those who are indifferent are under-represented in the sample. In order to minimise

the non-response error the researcher often seeks to re-contact with the non-respondents if they were not

available earlier. If the researcher finds that the non-response rate is more in a particular group of

respondents (for example, higher income groups) additional efforts should be made to obtain data from

these under-represented groups of the population. For example, for these people who are not responding

to the mailed questionnaires, personal interviews may be conducted to obtain data. In a mailed

questionnaire the researcher never knows whether the respondent really refused to provide data or was

simply indifferent. There are several techniques which help to encourage respondents to reply. Response

bias occurs when the respondent may not give the correct information and try to mislead the investigator

in a certain direction. The respondents may consciously or unconsciously misrepresent the truth. For

example, if the investigator asks a question on the income of the respondent he/ she may not give the

correct information for obvious reasons. Or the investigator may not be able to put a question that is

sensitive (thus avoiding embarrassment). This may arise from the problems in designing the questionaire

and the content of questions. Respondents who must understand the questions may unconsciously

provide biased information. The response bias may also occur because the interviewer‘s presence

influences respondents to give untrue or modified answers. The respondents/ interviewers tendency is to

please the other person rather than provide/elicit the correct information.

b) Administrative Errors: The errors that have arisen due to improper administration of the research

process are called administrative errors. There are four types of administrative errors. These are as

follows:

i) sample selection error,

ii) investigator error,

iii) investigator cheating, and

iv. data processing error.


i) Sample Selection Error: It is difficult to execute a sampling plan. For example, we may plan to use

systematic sampling plan in a market research study of a new product and decide to interview every 5th

customer coming out of a consumer store. If the day of interview happened to be a working day then we

are excluding all those consumers who are working. This may lead to an error because of the

unrepresentative sample selection.

ii) Investigator Error: When the investigator interviews the respondent, he/ she may fail to record the

information correctly or may fail to cross check the information provided by the respondent. Therefore,

the error may arise due to the way the investigator records the information.

iii) Investigator Cheating: Some times the investigator may try to fake the data even without meeting the

concerned respondents. There should be some mechanism to crosscheck this type of faking by the

investigator.

iv) Data Processing Error: Once the data is collected the next job the researcher does is edit, code and

enter the data into a computer for further processing and analysis. The errors can be minimised by careful

editing, coding and entering the data into a computer.

Control of Errors

In the above two sections we have identified the most significant sources of errors. It is not possible to

eliminate completely the sources of errors. However, the researcher‘s objective and effort should be to

minimise these sources of errors as much as possible. There are ways of reducing the errors. Some of

these are:

(a) designing and executing a good questionnaire; (b) selection of appropriate sampling method; (c)

adequate sample size; (d) employing trained investigators to collect the data; and (e) care in editing,

coding and entering the data into the computer.


What are the Steps involved in sample Design?

The sampling design process consists of five stages:

1. Definition of population of concern

2. Specification of a sampling frame, a set of items or events that it is possible to

measure

3. Specification of sampling method for

selecting items or events from the frame

4. Sampling and data collecting

5. Review of sampling process

1) Populations, (Universe) definition:-

The first concept you need to understand is the difference

between a population and a sample. To make a sample, you

first need a population. In non-technical language,

population means "the number of people living in an area."

This meaning of population is also used in survey research,

but this is only one of many possible definitions of

population. The word universe is sometimes used in survey

research, and means exactly the same in this context as

population.

The unit of population is whatever you are counting: there can be a population of people, a population of

households, a population of events, institutions, transactions, and so forth. Anything you can count can

be a population unit. But if you can't get information from it, and you can't measure it in some way, it's

not a unit of population that is suitable for survey research.

For a survey, various limits (geographical and otherwise) can be placed on a population. Some

populations that could be covered by surveys are...

All people living in India.

All people aged 18 and over.

All households in Nagpur.

All schools in Maharashtra.

All instances of tuning in to FM radio station in the last seven days

...and so on. If you can express it in a phrase beginning "All," and you can count it, it's a population of

some kind. The commonest kind of population used in survey research uses the formula:

All people aged X years and over, who live in area Y.

http://en.wikipedia.org/w/wiki.phtml?title=Sampling_frame&action=edit

http://en.wikipedia.org/wiki/Set

http://en.wikipedia.org/w/wiki.phtml?title=Sampling_method&action=edit


The "X years and over" criterion usually rules out children below a certain age, both because of the

difficulties involved in interviewing them and because many research questions don't apply to them.

Even though some populations can't be questioned directly, they're still populations. For example,

schools can't fill in questionnaires, but somebody can do so on behalf of each school. The distinction is

important when finding the answers to questions like "What proportion of Primary schools have

libraries?" You need only one questionnaire from each school - not one from each teacher, or one from

each student.

Often, the population you end up surveying is not the population you really wanted, because some part

of the population cannot be surveyed. For example, if you want to survey opinions among the whole

population of an area, and choose to do the survey by telephoning people at home, the population you

actually survey will be people with a telephone in their home. If the people with no telephone have

different opinions, you will not discover this.

As long as the surveyed population is a high proportion of the wanted population, the results obtained

should also be true for the larger population. For example, if 90% of homes have a telephone, the 10%

without a phone would have to be very different, for the survey's results not to be true for the whole

population.

2. Sampling frames

A sampling frame can be one of two things: either a list of all members of a population, or a method of

selecting any member of the population. The term general population refers to everybody in a

particular geographical area. Common sampling frames for the general population are electoral rolls,

street directories, telephone directories, and customer lists from utilities which are used by almost all

households: water, electricity, sewerage, and so on.

It is best to use the list that is most accurate, most complete, and most up to date. This differs from

country to country. In some countries, the best lists are of households, in other countries, they are of

people. For most surveys, a list of households is more useful than a list of people. Another commonly

used sampling frame (which is not recommended for sampling people) is a map.

Samples

A sample is a part of the population from which it was drawn. Survey research is based on sampling,

which involves getting information from only some members of the population.

If information is obtained from the whole population, it is not a sample, but a census. Some surveys,

based on very small populations (such as all members of an organization) in fact are censuses and not

sample surveys. When you do a census, the techniques given in this book still apply, but there is no

sampling error - as long as the whole group participates in the census.


Samples can be drawn in several different ways, e.g. probability samples, quota samples, purposive

samples etc.

Sample size

Contrary to popular opinion, sample sizes do not have to be particularly large. Their size is not, as

commonly thought, determined by the size of the population they are to represent. The U.S., for

example, contains more than two and a half million people, yet the General Social Survey, a highly

valued yearly interview survey of the U.S. population, is based on a sample of around 1500 cases.

Political and attitudinal polls, such as the California Poll, typically draw a sample of around 1000, and

some local polls obtain samples of 500 or less. The determiners of sample size are the variability within

the population and the degree of accuracy of population estimates the researcher is willing to accept (pay

for). If you are, for example, interested in the gender distribution of crime victims, the sample could be

relatively small with limited variability of only two possibilities (male and female) compared to the size

of the sample needed to make the same level of accuracy statement about the ethnicity of crime victims

(Germans, Italians, Irish, Poles, Canadians, etc.). To make a statement about the gender makeup of crime

victims that would be within 3% of the population parameter that we would be 95% confident in making

would require a sample of 1200, while a similar statement about the ethnic makeup of victims, would

require a much larger sample due to the variability.

For any sample design deciding upon the appropriate sample size will depend on several key factors:-

(1) No estimate taken from a sample is expected to be exact: Any assumptions about the overall

population based on the results of a sample will have an attached margin of error.

(2) To lower the margin of error usually requires a larger sample size. The amount of variability in the

population (i.e. the range of values or opinions) will also affect accuracy and therefore the size of sample.

(3) The confidence level is the likelihood that the results obtained from the sample lie within a

required precision. The higher the confidence level that is the more certain you wish to be that the results

are not atypical. Statisticians often use a 95 per cent confidence level to provide strong conclusions.

(4) Population size does not normally affect sample size. In fact the larger the population sizes the lower

the proportion of that population that needs to be sampled to be representative. It is only when the

proposed sample size is more than 5 per cent of the population that the population size becomes part of

the formulae to calculate the sample size.

Sampling error is the error in sample estimates of a population. Of course you would like to precisely

know the population characteristics from your sample, but that is not likely. Suppose that you wanted to

know about the students at a school of 1000 students and you choose a random sample of 100. With

much variability at all it is unlikely that your sample of 100 would have exactly the same characteristics

as another sample of 100 from the same 1000 students. This variation in samples is called sampling

error. It is at this point that statistics enters the picture. We know from the logic of statistics that if we

took all possible samples of 100 from our population the distribution of characteristics such as means


and standard deviations of the samples would be "normal," with the mean and standard deviation of the

samples collectively equal to the population mean and standard deviation.

3) Sampling method

The difference between non-probability and probability sampling is that non-probability sampling

does not involve random selection and probability sampling does. Does that mean that non-probability

samples aren't representative of the population? Not necessarily. But it does mean that non-probability

samples cannot depend upon the rationale of probability theory. At least with a probabilistic sample, we

know the odds or probability that we have represented the population well. We are able to estimate

confidence intervals for the statistic. With non-probability samples, we may or may not represent the

population well, and it will often be hard for us to know how well we've done so. In general, researchers

prefer probabilistic or random sampling methods over non-probabilistic ones, and consider them to be

more accurate and rigorous. However, in applied social research there may be circumstances where it is

not feasible, practical or theoretically sensible to do random sampling. Here, we consider a wide range of

non-probabilistic alternatives.

Probability sampling, or random sampling, is a sampling technique in which the probability of getting

any particular sample may be calculated. Nonprobability sampling does not meet this criterion and

should be used with caution. Nonprobability sampling techniques cannot be used to infer from the

sample to the general population. Any generalizations obtained from a nonprobability study must be

filtered through ones knowledge of the topic being studied. Performing nonprobability sampling is

considerably less expense than doing probability sampling.

A) Probability sampling methods

Each subject or unit in the population has a known non-zero probability of being included in the

sample. This allows the application of probability theory to estimate how likely it is that the sample

reflects the target population. In statistical terms, a calculation of sampling error can be made.

Probability sampling method is any method of sampling that utilizes some form of random selection. In

order to have a random selection method, you must set up some process or procedure that assures that

the different units in your population have equal probabilities of being chosen. Humans have long

practiced various forms of random selection, such as picking a name out of a hat, or choosing the short

straw. These days, we tend to use computers as the mechanism for generating random numbers as the

basis for random selection.

- General advantages

A high degree of representativeness is likely

The sampling error can be calculated

- General disadvantages

http://trochim.human.cornell.edu/kb/sampprob.htm


Expensive

Time consuming

Relatively complicated

Definition of basic terms:-

These are:

N = the number of cases in the sampling frame

n = the number of cases in the sample

N C n = the number of combinations (subsets) of n from N

f = n/N = the sampling fraction

In Probability sampling, all items have some chance of selection that can be calculated. Probability

sampling technique ensures that bias is not introduced regarding who is included in the survey.

Five common Probability sampling or random sampling techniques are:

1) Simple random sampling,

2) Systematic sampling,

3) Stratified sampling,

4) Cluster sampling, and

5) Multi-stage sampling

1) Simple random sampling

With simple random sampling, each item in a population has an equal chance of inclusion in the sample.

For example, each name in a telephone book could be numbered sequentially. If the sample size was to

include 2,000 people, then 2,000 numbers could be randomly generated by computer or numbers could be

picked out of a hat. These numbers could then be matched to names in the telephone book, thereby

providing a list of 2,000 people.

Example: - A lotto draw is a good example of simple random sampling. A sample of 6 numbers is

randomly generated from a population of 45, with each number having an equal chance of being selected.

The advantage of simple random sampling is that it is simple and easy to apply when small

populations are involved. However, because every person or item in a population has to be listed

before the corresponding random numbers can be read, this method is very cumbersome to use for

large populations.


2) Systematic sampling

Systematic sampling, sometimes called interval-sampling, means that there is a gap, or interval,

between each selection. This method is often used in industry, where an item is selected for testing

from a production line (say, every fifteen minutes) to ensure that machines and equipment are working

to specification.

Alternatively, the manufacturer might decide to select every 20th item on a production line to test for

defects and quality. This technique requires the first item to be selected at random as a starting point for

testing and, thereafter, every 20th item is chosen.

This technique could also be used when questioning people in a sample survey. A market researcher

might select every 10th person who enters a particular store, after selecting a person at random as a

starting point; or interview occupants of every 5th house in a street, after selecting a house at random as a

starting point.

It may be that a researcher wants to select a fixed size sample. In this case, it is first necessary

to know the whole population size from which the sample is being selected. The appropriate sampling

interval, I, is then calculated by dividing population size, N, by required sample size, n, as follows: I =

N/n

Example:-If a systematic sample of 500 students were to be carried out in a University with an enrolled

population of 10,000, the sampling interval would be: I = N/n = 10,000/500 =20

Note: if I is not a whole number, then it is rounded to the nearest whole number.

All students would be assigned sequential numbers. The starting point would be chosen by selecting a

random number between 1 and 20. If this number was 9, then the 9th student on the list of students

would be selected along with every following 20th student. The sample of students would be those

corresponding to student numbers 9, 29, 49, 69, ........ 9929, 9949, 9969 and 9989.

The advantage of systematic sampling is that it is simpler to select one random number and then every

'Ith' (e.g. 20th) member on the list, than to select as many random numbers as sample size. It also gives a

good spread right across the population. A disadvantage is that you may need a list to start with, if you

wish to know your sample size and calculate your sampling interval.

3) Stratified sampling

A general problem with random sampling is that you could, by chance, miss out a particular group in the

sample. However, if you form the population into groups, and sample from each group, you can make

sure the sample is representative.

In stratified sampling, the population is divided into groups called strata. A sample is then drawn from

within these strata. Some examples of strata commonly used by the research Organisation are States, Age

and Sex. Other strata may be religion, academic ability or marital status.


Example: - The committee of a school of 1,000 students wishes to assess any reaction to the reintroduction

of rural Care into the school timetable. To ensure a representative sample of students from all year levels,

the committee uses the stratified sampling technique.

In this case the strata are the year levels. Within each stratum the committee selects a sample. So, in a

sample of 100 students, all year levels would be included. The students in the sample would be selected

using simple random sampling or systematic sampling within each stratum.

Stratification is most useful when the stratifying variables are simple to work with, easy to observe and

closely related to the topic of the survey.

An important aspect of stratification is that it can be used to select more of one group than another. You

may do this if you feel that responses are more likely to vary in one group than another. So, if you know

everyone in one group has much the same value, you only need a small sample to get information for

that group; whereas in another group, the values may differ widely and a bigger sample is needed.

If you want to combine group level information to get an answer for the whole population, you have to

take account of what proportion you selected from each group

When stratified sampling designs are to be employed, there are 3 key questions which have to be

immediately addressed:

1 The bases of stratification, i.e. what characteristics should be used to subdivide the

universe/population into strata?

2 The number of strata, i.e. how many strata should be constructed and what stratum boundaries

should be used?

3 Sample sizes within strata, i.e. how many observations should be taken in each stratum?

1) Bases of stratification

Intuitively, it seems clear that the best basis would be the frequency distribution of the principal

variable being studied. For example, in a study of coffee consumption we may believe that behavioural

patterns will vary according to whether a particular respondent drinks a lot of coffee, only a moderate

amount of coffee or drinks coffee very occasionally. Thus we may consider that to stratify according to

"heavy users", "moderate users" and "light users" would provide an optimum stratification. However,

two difficulties may arise in attempting to proceed in this way. First, there is usually interest in many

variables, not just one, and stratification on the basis of one may not provide the best stratification for

the others. Secondly, even if one survey variable is of primary importance, current data on its frequency

is unlikely to be available. However, the latter complaint can be attended to since it is possible to

stratify after the data has been completed and before the analysis is undertaken. The only approach is

to create strata on the basis of variables, for which information is, or can be made available, that are

believed to be highly correlated with the principal survey characteristics of interest, e.g. age, socio-

economic group, sex, farm size, firm size, etc.


In general, it is desirable to make up strata in such a way that the sampling units within strata are as

similar as possible. In this way a relatively limited sample within each stratum will provide a generally

precise estimate of the mean of that stratum. Similarly it is important to maximise differences in

stratum means for the key survey variables of interest. This is desirable since stratification has the effect

of removing differences between stratum means from the sampling error.

Total variance within a population has two types of natural variation: between-strata variance and

within-strata variance. Stratification removes the second type of variance from the calculation of the

standard error. Suppose, for example, we stratified students in a particular University by subject

specialty - marketing, engineering, chemistry, computer science, mathematics, history, geography etc.

and questioned them about the distinctions between training and education. The theory goes that

without stratification we would expect variation in the views expressed by students from say within

the marketing specialty and between the views of marketing students, as a whole, and engineering

students as a whole. Stratification ensures that variation between strata does not enter into the standard

error by taking account of this source in drawing the sample.

2) Number of strata

The next question is that of the number of strata and the construction of stratum boundaries. As regards

number of strata, as many as possible should be used. If each stratum could be made as homogeneous

as possible, its mean could be estimated with high reliability and, in turn, the population mean could be

estimated with high precision. However, some practical problems limit the desirability of a large

number of strata:

a) No stratification scheme will completely "explain" the variability among a set of observations. Past a

certain point, the "residual" or "unexplained" variation will dominate, and little improvement will be

effected by creating more strata.

b) Depending on the costs of stratification, a point may be reached quickly where creation of additional

strata is economically unproductive.

If a single overall estimate is to be made (e.g. the average per capita consumption of coffee) we would

normally use no more than about 6 strata. If estimates are required for population subgroups (e.g. by

region and/or age group), then more strata may be justified.


3) Sample sizes within strata

Proportional allocation: Once strata have been established, the question becomes, "How big a sample

must be drawn from each?" Consider a situation where a survey of a two-stratum population is to be

carried out:

Stratum Number of Items in Stratum

A 10,000

B 90,000

If the budget is fixed at ` 3000 and we know the cost per observation is ` 6 in each stratum, so the

available total sample size is 500. The most common approach would be to sample the same proportion

of items in each stratum. This is termed proportional allocation. In this example, the overall sampling

fraction is:

Thus, this method of allocation would result in:

Stratum A (10,000 × 0.5%) = 50

Stratum B (90,000 × 0.5%) = 450

The major practical advantage of proportional allocation is that it leads to estimates which are

computationally simple. Where proportional sampling has been employed we do not need to weight

the means of the individual stratum when calculating the overall mean. So:

sr = W1 1 + W2 2 + W3 3+ - - - Wk k

Optimum allocation: Proportional allocation is advisable when all we know of the strata is their sizes.

In situations where the standard deviations of the strata are known it may be advantageous to make a

disproportionate allocation.

Suppose that, once again, we had stratum A and stratum B, but we know that the individuals assigned

to stratum A were more varied with respect to their opinions than those assigned to stratum B.

Optimum allocation minimises the standard error of the estimated mean by ensuring that more

respondents are assigned to the stratum within which there is greatest variation.

4) Cluster sampling

It is sometimes expensive to spread your sample across the population as a whole. For example, travel

can become expensive if you are using interviewers to travel between people spread all over the country.

To reduce costs you may choose a cluster sampling technique.

Cluster sampling divides the population into groups, or clusters. A number of clusters are selected

randomly to represent the population, and then all units within selected clusters are included in the

sample. No units from non-selected clusters are included in the sample. They are represented by those


from selected clusters. This differs from stratified sampling, where some units are selected from each

group.

Examples of clusters may be factories, schools and geographic areas such as electoral sub-divisions. The

selected clusters are then used to represent the population.

Example:- Suppose an organisation wishes to find out which sports 11 Std students are participating in

across Maharashtra. It would be too costly and take too long to survey every student, or even some

students from every school. Instead, 100 schools are randomly selected from all over Maharashtra.

These schools are considered to be clusters. Then, every 11 Std student in these 100 schools is surveyed.

In effect, students in the sample of 100 schools represent all 11 Std students in Maharashtra.

Cluster sampling has several advantages: reduced costs, simplified fieldwork and administration are

more convenient. Instead of having a sample scattered over the entire coverage area, the sample is more

localised in relatively few centres (clusters).

Cluster sampling's disadvantage is that less accurate results are often obtained due to higher sampling

error than for simple random sampling with the same sample size. In the above example, you might

expect to get more accurate estimates from randomly selecting students across all schools than from

randomly selecting 100 schools and taking every student in those chosen.

5) Multi-stage sampling

Multi-stage sampling is like cluster sampling, but involves selecting a sample within each chosen cluster,

rather than including all units in the cluster. Thus, multi-stage sampling involves selecting a sample in at

least two stages. In the first stage, large groups or clusters are selected. These clusters are designed to

contain more population units than are required for the final sample.

In the second stage, population units are chosen from selected clusters to derive a final sample. If more

than two stages are used, the process of choosing population units within clusters continues until the

final sample is achieved.

Example:- An example of multi-stage sampling is where, firstly, electoral sub-divisions (clusters) are

sampled from a city or state. Secondly, blocks of houses are selected from within the electoral sub-

divisions and, thirdly, individual houses are selected from within the selected blocks of houses.

The advantages of multi-stage sampling are convenience, economy and efficiency. Multi-stage sampling

does not require a complete list of members in the target population, which greatly reduces sample

preparation cost. The list of members is required only for those clusters used in the final stage.

The main disadvantage of multi-stage sampling is the same as for cluster sampling: lower accuracy due

to higher sampling error.


B) Non-probability sampling techniques

The selection of subjects or units is left to the discretion of the researcher and methods are less structured

and less strict. Probability theory cannot be used to estimate sampling error.

Non-probability sampling methods are usually used for qualitative research when the purpose is

exploratory or interpretative.

We can divide non-probability sampling methods into two broad types: accidental or purposive. Most

sampling methods are purposive in nature because we usually approach the sampling problem with a

specific plan in mind. The most important distinctions among these types of sampling methods are the

ones between the different types of purposive sampling approaches.

- General advantages

Typicality of subjects is aimed for

Permits exploration

- General disadvantage

Unrepresentative

Examples of non-probability sampling includes:

1) Accidental, Haphazard or Convenience Sampling

Members of the population are chosen based on their relative ease of access. To sample friends, co-

workers, or shoppers at a single mall, are all examples of Convenience sampling.

Accidental, convenience, available samples are all names for non-purposive non-probability samples. In

these, people in the samples are those who simply agreed to take part, were around and available at the

time. They are quick and cheap but their use is really limited to pilot or exploratory work; or, if one is

used because there re is no alternative form of sampling available, caution must be exercised in the

analysis of the results. Tempting though it may be, you cannot assume the sample is representative.

2) Purposive Sampling

In purposive sampling the people/units/ elements/ in the sample are selected because they are

regarded as having similar characteristics to the people in the designated research population. So, for

example, in research investigating the management skills of owner/managers of small enterprises, the

researcher might select some typical owner managers to take part in the study. They will not be selected

randomly. One advantage of this kind of sample is that it is usually possible to get a targeted sample

together very quickly - and hence cheaply.

All of the methods that follow can be considered subcategories of purposive sampling methods. We

might sample for specific groups or types of people as in modal instance, expert, or quota sampling.

We might sample for diversity as in heterogeneity sampling. Or, we might capitalize on informal

social networks to identify specific respondents who are hard to locate otherwise, as in snowball

sampling. In all of these methods we know what we want -- we are sampling with a purpose.


a) Modal Instance Sampling

In statistics, the mode is the most frequently occurring value in a distribution. In sampling, when we do a

modal instance sample, we are sampling the most frequent case, or the "typical" case. In a lot of informal

public opinion polls, for instance, they interview a "typical" voter. There are a number of problems with

this sampling approach. First, how do we know what the "typical" or "modal" case is? We could say that

the modal voter is a person who is of average age, educational level, and income in the population. But,

it's not clear that using the averages of these is the fairest (consider the skewed distribution of income, for

instance). And, how do you know that those three variables -- age, education, income -- are the only or

event the most relevant for classifying the typical voter? What if religion or ethnicity is an important

discriminator? Clearly, modal instance sampling is only sensible for informal sampling contexts.

b) Expert Sampling

Expert sampling involves the assembling of a sample of persons with known or demonstrable experience

and expertise in some area. Often, we convene such a sample under the auspices of a "panel of experts."

There are actually two reasons you might do expert sampling. First, because it would be the best way to

elicit the views of persons who have specific expertise. In this case, expert sampling is essentially just a

specific sub case of purposive sampling. But the other reason you might use expert sampling is to

provide evidence for the validity of another sampling approach you've chosen. For instance, let's say you

do modal instance sampling and are concerned that the criteria you used for defining the modal instance

are subject to criticism. You might convene an expert panel consisting of persons with acknowledged

experience and insight into that field or topic and ask them to examine your modal definitions and

comment on their appropriateness and validity. The advantage of doing this is that you aren't out on

your own trying to defend your decisions -- you have some acknowledged experts to back you. The

disadvantage is that even the experts can be, and often are, wrong.

c) Quota Sampling

In quota sampling, you select people non-randomly according to some fixed quota. There are two types

of quota sampling: proportional and non proportional.

i) In proportional quota sampling you want to represent the major characteristics of the population by

sampling a proportional amount of each. For instance, if you know the population has 40% women and

60% men, and that you want a total sample size of 100, you will continue sampling until you get those

percentages and then you will stop. So, if you've already got the 40 women for your sample, but not the

sixty men, you will continue to sample men but even if legitimate women respondents come along, you

will not sample them because you have already "met your quota." The problem here (as in much

purposive sampling) is that you have to decide the specific characteristics on which you will base the

quota. Will it be by gender, age, education race, religion, etc.?


ii) Non-proportional quota sampling is a bit less restrictive. In this method, you specify the minimum

number of sampled units you want in each category. Here, you're not concerned with having numbers

that match the proportions in the population. Instead, you simply want to have enough to assure that

you will be able to talk about even small groups in the population. This method is the non-probabilistic

analogue of stratified random sampling in that it is typically used to assure that smaller groups are

adequately represented in your sample.

d) Heterogeneity Sampling

We sample for heterogeneity when we want to include all opinions or views, and we aren't concerned

about representing these views proportionately. Another term for this is sampling for diversity. In many

brainstorming or nominal group processes (including concept mapping), we would use some form of

heterogeneity sampling because our primary interest is in getting broad spectrum of ideas, not

identifying the "average" or "modal instance" ones. In effect, what we would like to be sampling is not

people, but ideas. We imagine that there is a universe of all possible ideas relevant to some topic and that

we want to sample this population, not the population of people who have the ideas. Clearly, in order to

get all of the ideas, and especially the "outlier" or unusual ones, we have to include a broad and diverse

range of participants. Heterogeneity sampling is, in this sense, almost the opposite of modal instance

sampling.

e) Snowball sampling - In snowball sampling, you begin by identifying someone who meets the criteria

for inclusion in your study. You then ask them to recommend others who they may know who also meet

the criteria. Although this method would hardly lead to representative samples, there are times when it

may be the best method available. Snowball sampling is especially useful when you are trying to reach

populations that are inaccessible or hard to find. For instance, if you are studying the homeless, you are

not likely to be able to find good lists of homeless people within a specific geographical area. For

example, we might wish to know whether a new educational program causes subsequent achievement

score gains, whether a special work release program for prisoners causes lower recidivism rates, whether

a novel drug causes a reduction in symptoms, and so on.


The following is the characteristic of most popular sampling techniques:-

Sampling Method Definition Uses Limitations

Cluster Sampling Units in the population can often be

found in certain geographic groups

or "clusters" (e.g. primary school

children in Chandrapur). A random

sample of clusters is taken, then all

units within the cluster are

examined

Quick & easy; does

not require complete

population

information; good for

face-to-face surveys

Expensive if the

clusters are large;

greater risk of

sampling error

Convenience Sampling Uses those who are willing to

volunteer

Readily available;

large amount of

information can be

gathered quickly

Cannot extrapolate

from sample to infer

about the population;

prone to volunteer

bias

Judgement Sampling A deliberate choice of a sample - the

opposite of random

Good for providing

illustrative examples

or case studies

Very prone to bias;

samples often small;

cannot extrapolate

from sample

Quota Sampling Aim is to obtain a sample that is

"representative" of the overall

population; the population is

divided ("stratified") by the most

important variables (e.g. income,.

age, location) and a required quota

sample is drawn from each stratum

Quick & easy way of

obtaining a sample

Not random, so still

some risk of bias; need

to understand the

population to be able

to identify the basis of

stratification

Simply Random

Sampling

Ensures that every member of the

population has an equal chance of

selection

Simply to design and

interpret; can

calculate estimate of

the population and

the sampling error

Need a complete and

accurate population

listing; may not be

practical if the sample

requires lots of small

visits all over the


country

Systematic Sampling After randomly selecting a starting

point from the population, between

1 and "n", every nth unit is selected,

where n equals the population size

divided by the sample size

Easier to extract the

sample than via

simple random;

ensures sample is

spread across the

population

Can be costly and

time-consuming if the

sample is not

conveniently located


Chapter4: Measurement & Scaling Techniques

Introduction

The data consists of quantitative variables like price, income, sales etc., and qualitative variables like

knowledge, performance, character etc. The qualitative information must be converted into numerical

form for further analysis. This is possible through measurement and scaling techniques. A common

feature of survey based research is to have respondent‘s feelings, attitudes, opinions, etc. in some

measurable form. For example, a bank manager may be interested in knowing the opinion of the

customers about the services provided by the bank. Similarly, a fast food company having a network in a

city may be interested in assessing the quality and service provided by them. As a researcher you may be

interested in knowing the attitude of the people towards the government announcement of a metro rail in

Delhi. In this unit we will discuss the issues related to measurement, different levels of measurement

scales, various types of scaling techniques and also selection of an appropriate scaling technique.

Measurement and scaling

Before we proceed further it will be worthwhile to understand the following two terms: (a)

Measurement, and (b) Scaling.

a) Measurement: Measurement is the process of observing and recording the observations that are

collected as part of research. The recording of the observations may be in terms of numbers or other

symbols to characteristics of objects according to certain prescribed rules. The respondent‘s,

characteristics are feelings, attitudes, opinions etc. For example, you may assign ‗1‘ for Male and ‗2‘ for

Female respondents. In response to a question on whether he/she is using the ATM provided by a

particular bank branch, the respondent may say ‗yes‘ or ‗no‘. You may wish to assign the number ‗1‘ for

the response yes and ‗2‘ for the response no. We assign numbers to these characteristics for two reasons.

First, the numbers facilitate further statistical analysis of data obtained. Second, numbers facilitate the


communication of measurement rules and results. The most important aspect of measurement is the

specification of rules for assigning numbers to characteristics. The rules for assigning numbers should be

standardised and applied uniformly. This must not change over time or objects.

b) Scaling: Scaling is the assignment of objects to numbers or semantics according to a rule. In scaling, the

objects are text statements, usually statements of attitude, opinion, or feeling. For example, consider a

scale locating customers of a bank according to the characteristic ―agreement to the satisfactory quality of

service provided by the branch‖. Each customer interviewed may respond with a semantic like ‗strongly

agree‘, or ‗somewhat agree‘, or ‗somewhat disagree‘, or ‗strongly disagree‘. We may even assign each of

the responses a number. For example, we may assign strongly agree as ‗1‘, agree as ‗2‘ disagree as ‗3‘, and

strongly disagree as ‗4‘. Therefore, each of the respondents may assign 1, 2, 3 or 4.

Issues in measurement

When a researcher is interested in measuring the attitudes, feelings or opinions of respondents he/she

should be clear about the following:

a) What is to be measured?

b) Who is to be measured?

c) The choices available in data collection techniques

The first issue that the researcher must consider is ‗what is to be measured‘?

The definition of the problem, based on our judgments or prior research indicates the concept to be

investigated. For example, we may be interested in measuring the performance of a fast food company.

We may require a precise definition of the concept on how it will be measured. Also, there may be more

than one way that we can measure a particular concept. For example, in measuring the performance of a

fast food company we may use a number of measures to indicate the performance of the company. We

may use sales volume in terms of value of sales or number of customers or spread of network of the

company as measures of performance. Further, the measurement of concepts requires assigning numbers

to the attitudes, feelings or opinions. The key question here is that on what basis do we assign the

numbers to the concept. For example, the task is to measure the agreement of customers of a fast food

company on the opinion of whether the food served by the company is tasty, we create five categories: (1)

strongly agree, (2) agree, (3) undecided, (4) disagree, (5) strongly disagree. Then we may measure the

response of respondents. Suppose if a respondent states ‗disagree‘ with the statement that ‗the food is

tasty‘, the measurement is 4.

The second important issue in measurement is that, who is to be measured? That means who are the

people we are interested in. The characteristics of the people such as age, sex, education, income, location,

profession, etc. may have a bearing on the choice of measurement. The measurement procedure must be

designed keeping in mind the characteristics of the respondents under consideration.


Levels of measurement

We know that the level of measurement is a scale by which a variable is measured. For 50 years, with few

detractors, science has used the Stevens (1951) typology of measurement levels (scales). There are three

things, which you need to remember about this typology: Anything that can be measured falls into one of

the four types

The higher the level of measurement, the more precision in measurement and every level up contains all

the properties of the previous level. The four levels of measurement, from lowest to highest, are as

follows:

1. Nominal

2. Ordinal

3. Interval

4. Ratio

1) Nominal scales

This, the crudest of measurement scales, classifies individuals, companies, products, brands or other

entities into categories where no order is implied. Indeed it is often referred to as a categorical scale. It is a

system of classification and does not place the entity along a continuum. It involves a simply count of the

frequency of the cases assigned to the various categories, and if desired numbers can be nominally

assigned to label each category as in the example below:

An example of a nominal scale

Which of the following food items do you tend to buy at least once per month? (Please tick)

Okra Palm Oil Milled Rice

Peppers Prawns Pasteurised milk

The numbers have no arithmetic properties and act only as labels. The only measure of average which

can be used is the mode because this is simply a set of frequency counts. Hypothesis tests can be carried

out on data collected in the nominal form. The most likely would be the Chi-square test. However, it

should be noted that the Chi-square is a test to determine whether two or more variables are associated

and the strength of that relationship. It can tell nothing about the form of that relationship, where it

exists, i.e. it is not capable of establishing cause and effect.

2) Ordinal scales

Ordinal scales involve the ranking of individuals, attitudes or items along the continuum of the

characteristic being scaled. For example, if a researcher asked farmers to rank 5 brands of pesticide in

order of preference he/she might obtain responses like those in table below.


An example of an ordinal scale used to determine farmers' preferences among 5 brands of pesticide.

Order of preference Brand

1 Rambo

2 Harpic

3 DDT

4 Bagyone

5 Rat kill

From such a table the researcher knows the order of preference but nothing about how much more one

brand is preferred to another, which is there is no information about the interval between any two

brands. All of the information a nominal scale would have given is available from an ordinal scale. In

addition, positional statistics such as the median, quartile and percentile can be determined.

It is possible to test for order correlation with ranked data. The two main methods are Spearman's

Ranked Correlation Coefficient and Kendall's Coefficient of Concordance. Using either procedure one

can, for example, ascertain the degree to which two or more survey respondents agree in their ranking of

a set of items. Consider again the ranking of pesticides example in given figure. The researcher might

wish to measure similarities and differences in the rankings of pesticide brands according to whether the

respondents' farm enterprises were classified as "arable" or "mixed" (a combination of crops and

livestock). The resultant coefficient takes a value in the range 0 to 1. A zero would mean that there was no

agreement between the two groups, and 1 would indicate total agreement. It is more likely that an

answer somewhere between these two extremes would be found.

The only other permissible hypothesis testing procedures are the runs test and sign test. The runs test

(also known as the Wald-Wolfowitz). Test is used to determine whether a sequence of binomial data -

meaning it can take only one of two possible values e.g. African/non-African, yes/no, male/female - is

random or contains systematic 'runs' of one or other value. Sign tests are employed when the objective is

to determine whether there is a significant difference between matched pairs of data. The sign test tells

the analyst if the number of positive differences in ranking is approximately equal to the number of

negative rankings, in which case the distribution of rankings is random, i.e. apparent differences are not

significant. The test takes into account only the direction of differences and ignores their magnitude and

hence it is compatible with ordinal data.

3) Interval scales

It is only with an interval scaled data that researchers can justify the use of the arithmetic mean as the

measure of average. The interval or cardinal scale has equal units of measurement, thus making it

possible to interpret not only the order of scale scores but also the distance between them. However, it

must be recognised that the zero point on an interval scale is arbitrary and is not a true zero. This of


course has implications for the type of data manipulation and analysis we can carry out on data collected

in this form. It is possible to add or subtract a constant to all of the scale values without affecting the form

of the scale but one cannot multiply or divide the values. It can be said that two respondents with scale

positions 1 and 2 are as far apart as two respondents with scale positions 4 and 5, but not that a person

with score 10 feels twice as strongly as one with score 5. Temperature is interval scaled, being measured

either in Centigrade or Fahrenheit. We cannot speak of 50°F being twice as hot as 25°F since the

corresponding temperatures on the centigrade scale, 10°C and -3.9°C, are not in the ratio 2:1. Interval

scales may be either numeric or semantic. Study the examples below in figure.

Examples of interval scales in numeric and semantic formats

Please indicate your views on Balkan Olives by scoring them on a scale of 5 down to 1 (i.e. 5 = Excellent; =

Poor) on each of the criteria listed

Balkan Olives are: Circle the appropriate score on each line

Succulence 5 4 3 2 1

Fresh tasting 5 4 3 2 1

Free of skin blemish 5 4 3 2 1

Good value 5 4 3 2 1

Attractively packaged 5 4 3 2 1

(a)

Please indicate your views on Balkan Olives by ticking the appropriate responses below:

Excellent Very Good Good Fair Poor

Succulent

Freshness

Freedom from skin blemish

Value for money

Attractiveness of packaging

(b)

Most of the common statistical methods of analysis require only interval scales in order that they might

be used. These are not recounted here because they are so common and can be found in virtually all basic

texts on statistics.

4) Ratio scales

The highest level of measurement is a ratio scale. This has the properties of an interval scale together with

a fixed origin or zero point. Examples of variables which are ratio scaled include weights, lengths and

times. Ratio scales permit the researcher to compare both differences in scores and the relative magnitude


of scores. For instance the difference between 5 and 10 minutes is the same as that between 10 and 15

minutes, and 10 minutes is twice as long as 5 minutes.

Given that sociological and management research seldom aspires beyond the interval level of

measurement, it is not proposed that particular attention be given to this level of analysis. Suffice it to say

that virtually all statistical operations can be performed on ratio scales.

Errors in measurement In principle, every operation of a survey is a potential source of measurement error. Some examples of

causes of measurement error are non-response, badly designed questionnaires, respondent bias and

processing errors.

Measurement errors can be grouped into two main causes, systematic errors and random errors.

Systematic error (called bias) makes survey results unrepresentative of the target population by

distorting the survey estimates in one direction. For example, if the target population is the entire

population in a country but the sampling frame is just the urban population, then the survey results will

not be representative of the target population due to systematic bias in the sampling frame. On the other

hand, random error can distort the results on any given occasion but tends to balance out on average.

Some of the types of measurement error are outlined below:

1. Failure to identify the target population

Failure to identify the target population can arise from the use of an inadequate sampling frame,

imprecise definition of concepts, and poor coverage rules. Problems can also arise if the target

population and survey population do not match very well. Failure to identify and adequately capture

the target population can be a significant problem for informal sector surveys. While establishment and

population censuses allow for the identification of the target population, it is important to ensure that the

sample is selected as soon as possible after the census is taken so as to improve the coverage of the

survey population.

2. Non-response bias

Non-respondents may differ from respondents in relation to the attributes/variables being measured.

Non-response can be total (where none of the questions were answered) or partial (where some

questions may be unanswered owing to memory problems, inability to answer, etc.). To improve

response rates, care should be taken in training interviewers, assuring the respondent of confidentiality,

motivating him or her to cooperate, and revisiting or calling back if the respondent has been previously

unavailable. 'Call backs' are successful in reducing non-response but can be expensive. It is also

important to ensure that the person who has the information required can be contacted by the

interviewer; that the data required are available and that an adequate follow up strategy is in place


3. Questionnaire design

The content and wording of the questionnaire may be misleading and the layout of the questionnaire

may make it difficult to accurately record responses. Questions should not be misleading or ambiguous,

and should be directly relevant to the objectives of the survey. In order to reduce measurement error

relating to questionnaire design, it is important to ensure that the questionnaire:

can be completed in a reasonable amount of time;

can be properly administered by the interviewer;

uses language that is readily understood by both the interviewer and the respondent; and

can be easily processed.

In designing questionnaires and training interviewers in the case of informal sector survey where there

is a strong potential for inaccurate information being provided by respondents, consideration should be

given to the use of random question sequencing, derived or imputed results, and the use of partial

questionnaires. The random question sequencing approach involves the interviewer asking the survey

respondent a number of questions about the relevant data items (e.g. input costs and quantities, output

prices and output units sold) in a random order. The interviewer would use a deck of questionnaire

cards. The cards would be shuffled and then the interviewer would ask a series of questions out of

sequence, record each answer and then reassemble the questions in the right sequence to get the final

response (e.g. profit or value added information) as a derived result. Another approach-to consider;-

where particular-responding businesses form a reasonably homogeneous group operating with similar

cost structures and market conditions, is aggregating results from sample measures of inputs and

outputs. This approach involves using separate but representative random samples of businesses to

collect information about different data items. The data are then brought together to produce imputed

aggregate level estimates.

4. Interviewer bias

The respondent answers questions can be influenced by the interviewer's behaviour, choice of clothes,

sex, accent and prompting when a respondent does not understand a question. A bias may also be

introduced if interviewers receive poor training as this may have an affect on the way they prompt for,

or record, the answers. The best way to minimise interviewer bias is through effective training and by

ensuring manageable workloads.

Training can be provided in the form of manuals, formal training courses on questionnaire content and

interviewing techniques, and on-the-job training in the field. Topics that should be covered in

interviewer training include - the purpose of the survey; the scope and coverage of the survey; a general

outline of the survey design and sampling approach being used; the questionnaire; interviewing

techniques and recording answers; ways to avoid or reduce non-response; how best to maintain

respondent co-operation; field practice; quality assurance and editing of data; planning workloads; and

administrative arrangements.


5. Respondent bias

Refusals and inability to answer questions, memory biases and inaccurate information will lead to a bias

in the estimates. An increasing level of respondent burden (due to the number of times a person is

included in surveys) can also make it difficult to get the potential respondent to participate in a survey.

When designing a survey it should be remembered that uppermost in the respondent's mind will be

protecting their own personal privacy, integrity and interests. Also, the way the respondent interprets

the questionnaire and the wording of the answer the respondent gives can cause inaccuracies to enter the

survey data. Careful questionnaire design, effective training of interviewers and adequate survey testing

can overcome these problems to some extent.

6. Processing errors

There are four stages in the processing of the data where errors may occur: data grooming, data capture,

editing and estimation. Data grooming involves preliminary checking before entering the data onto the

processing system in the capture stage. Inadequate checking and quality management at this stage can

introduce data loss (where data are not entered into the system) and data duplication (where the same

data are entered into the system more than once). Inappropriate edit checks and inaccurate weights in

the estimation procedure can also introduce errors to the data at the editing and estimation stage. To

minimise these errors, processing staff should be given adequate training and realistic workloads.

Training material for processing staff should cover similar topics to those for interview staff, however,

with greater emphasis on editing techniques and quality assurance practices.

There are five main editing checks that should be considered including structure checks, range edits,

sequencing checks, checks for duplication and omissions, and logic edits. Structure checks are

undertaken to ensure that all the information sought has been provided. This involves checking that all

documents for a record are together and correctly labelled. Range edits are used to ensure that only the

possible codes for each question are used and that no codes outside the valid range has been entered.

Sequencing checks involve the process of ensuring that all those who should have answered the question

(because they gave a particular answer to earlier question) have done so and that respondents who

should not have answered the question did not do so. Duplication and omission checks ensure that the

specific data reported by a respondent has not been recorded more than once or that data reported has

not been omitted. Logic edits involve specifying checks in advance to data collection. An example of a

logic edit would be that males cannot report that they are pregnant.

The key areas that an effective editing strategy should address to reduce processing errors are:

target the editing effort to large contributors and large units within the survey population;

do not over edit the data;

automate the editing process as far as possible; and


feedback information from the data processing stage to refine the conduct of the survey through

changes such as improvements in question wording, questionnaire design, training and

instructions.

7. Misinterpretation of results

This can occur if the researcher is not aware of certain factors that influence the characteristics under

investigation. A researcher or any other user not involved in the data collection process may be unaware

of trends built into the data due to the nature of the collection (e.g. where interviews are always

conducted at a particular time of the weekday could result in only particular types of householders being

interviewed). Researchers should carefully investigate the methodology used in any given survey.

8. Non-response

Non-response results when data are not collected from respondents. The proportion of these non-

respondents in the sample is called the non-response rate. It is important to make all reasonable efforts to

maximise the response rate as non-respondents may have differing characteristics to respondents.

Significant non-response can bias the survey results. When a respondent replies to the survey answering

some but not all questions then it is called partial non-response. Partial non-response can arise due to

memory problems, inadequate information or an inability to answer a particular question. The

respondent may also refuse to answer questions if they find questions particularly sensitive; or have

been asked too many questions (the questionnaire is too long). Total non-response can arise if a

respondent cannot be contacted (the frame contains inaccurate or out-of-date contact information or the

respondent is not at home), is unable to respond (may be due to language difficulties or illness) or

refuses to answer any questions.

Response rates can be improved through good survey design via short, simple questions, good forms

design techniques and by effectively explaining survey purposes and uses. Assurances of confidentiality

are very important as many respondents are unwilling to respond due to privacy concerns. For informal

sector surveys, it is essential to ensure that the survey is directed to the person within the establishment

or household who can provide the data sought. Call backs for those not available and follow-ups can

increase response rates for those who, initially, were unable to reply. Refusals can be minimised through

the use of positive language; contacting the right person who can provide the information required;

explaining how and what the interviewer plans to do to help with completing the questionnaire;

stressing the importance of the survey and the authority under which the survey is being conducted;

explaining the importance of their response as being representative of other units; emphasising the

benefits from the survey results for the individual and/or broader community; giving adequate

assurances of the confidentiality of the responses; and finding out the reasons for their reluctance to

participate and trying to talk through the areas of concern.

Other measures that can improve respondent cooperation and maximise response include public

awareness activities including discussions with key organisations and interest groups, news releases,


media interviews and newspaper articles this is aimed at informing the community about the survey,

identifying issues of concern and addressing them; and where possible, using a primary approach letter,

which gives respondents advance notice and explains the purposes of the survey and how the survey

will be conducted.

In case of a mail survey most of the points above can be stated in an introductory letter or through a

publicity campaign. Other non-response minimisation techniques which could be used in a mail survey

include providing a postage-paid mail-back envelope with the survey form; and reminder letters.

Where non-response is at an unsatisfactory level after all reasonable attempts to follow-up are

undertaken, bias can be reduced by imputation for item non-response (non-response to a particular

question) or imputation for unit non-response (complete non-response for a unit). The main aim of

imputation is to produce consistent data without going back to the respondent for the correct values thus

reducing both respondent burden and costs associated with the survey. Broadly speaking the imputation

methods fall into three groups - the imputed value is derived from other information supplied by the

unit; values by other units can be used to derive a value for the non-respondent (e.g. average); and an

exact value of another unit (called donor) is used as a value for the non-respondent (called recipient).

When deciding on the method for non-response imputation it is desirable to know what effect

imputation will have on the final estimates. If a large amount of imputation is performed the results can

be misleading particularly if the imputation used distorts the distribution of data. If at the planning stage

it is believed that there is likely to be a high non-response rate, then the sample size could be increased to

allow for this. However, the problem may not be overcome by just increasing the sample size,

particularly if the non-responding units have different characteristics to the responding units.

Imputation also fails to totally eliminate non-response bias from the results.

If a low response rate is obtained, estimates are likely to be biased and therefore misleading.

Determining the exact bias in estimates is difficult. However, an indication can be obtained by -

comparing the characteristics of respondents to non-respondents; comparing results with alternative

sources and/or previous estimates; and performing a post-enumeration survey on a sub-sample of the

original sample with intensive follow-up of non-respondents.

Conclusion

In conclusion, while measurement error may be difficult to measure accurately it can be minimised by:

• Careful selection of the time the survey is conducted;

• using an up-to-date, accurate sample framework;

• revisiting or conducting 'call backs' to unavailable respondents;

• Careful questionnaire design;

• Providing thorough training for interviewers and processing staff; and

• being aware of all the factors affecting the topic under investigation.


Test of sound measurement

Scales should be tested for reliability, generalizability, and validity. Generalizability is the ability to

make inferences from a sample to the population, given the scale you have selected. Reliability is the

extent to which a scale will produce consistent results. Test-retest reliability checks how similar the

results are if the research is repeated under similar circumstances. Alternative forms reliability checks

how similar the results are if the research is repeated using different forms of the scale. Internal

consistency reliability checks how well the individual measures included in the scale are converted into a

composite measure.

Scales and indexes have to be validated. Internal validation checks the relation between the individual

measures included in the scale, and the composite scale itself. External validation checks the relation

between the composite scale and other indicators of the variable, indicators not included in the scale.

Content validation (also called face validity) checks how well the scale measures what it is supposed to

measure. Criterion validation checks how meaningful the scale criteria are relative to other possible

criteria. Construct validation checks what underlying construct is being measured. There are three

variants of construct validity. They are convergent validity, discriminant validity, and nomological

validity. The coefficient of reproducibility indicates how well the data from the individual measures

included in the scale can be reconstructed from the composite scale.

Reliability and Validity

For a research study to be accurate, its findings must be both reliable and valid.

Reliability

Research means that the findings would be consistently the same if the study were done over again

Validity

A valid measure is one that provides the information that it was intended to provide. The purpose of a

thermometer, for example, is to provide information on the temperature, and if it works correctly, it is a

valid thermometer.

A study can be reliable but not valid, and it cannot be valid without first being reliable. There are many

different threats to validity as well as reliability but an important early consideration is to ensure you

have internal validity.

Methods of Measuring Reliability

Now, the question arises that how will you measure the reliability of a particular measure? There are

four good methods of measuring reliability:

1. Test-retest

2. Multiple forms

3. Inter-rater

http://en.wikipedia.org/wiki/Reliability_(psychometric)

http://en.wikipedia.org/wiki/Validity_(psychometric)


4. Split-half

1. Test-retest

The Test Retest in the same group technique is to administer your test, instrument, survey, or measure to

the same group of people at different points in time. Most researchers administer what is called a pretest

for this, and to troubleshoot bugs at the same time.

All reliability estimates are usually in the form of a correlation coefficient, so here, all you do is calculate

the correlation coefficient between the two scores of each group and report it as your reliability

coefficient.

2. Multiple Forms

The multiple forms technique has other names, such as parallel forms and disguised test-retest, but it‘s

simply the scrambling or mixing up of questions on your survey, for example, giving it to the same

group twice. It‘s a more rigorous test of reliability.

3. Inter-rater

Inter-rater reliability is most appropriate when you use assistants to do interviewing or content analysis

for you. To calculate this kind of reliability, all you do is report the percentage of agreement on the same

subject between your raters, or assistants.

4.Split-half

Taking half of your test, instrument, or survey, and analyzing that Then, you compare the results of this

analysis with your overall analysis.

Methods of Measuring Validity

Once you find that your measurement of variable under study is reliable, you will want to measure its

validity. There are four good methods of estimating validity:

1. Face

2. Content

3. Criterion

4. Construct

1.Face Validity

Face validity is the least statistical estimate (validity overall is not as easily quantified as reliability) as it‘s

simply an assertion on the researcher‘s part claiming that they‘ve reasonably measured what they

intended to measure. It‘s essentially a ―take my word for it‖ kind of validity. Usually, a researcher asks

a colleague or expert in the field to vouch for the items measuring what they were intended to measure.


2.Content Validity

Content validity goes back to the ideas of conceptualization and operationalization. If the researcher has

focused in too closely on only one type or narrow dimension of a construct or concept, then it‘s

conceivable that other indicators were overlooked. In such a case, the study lacks content validity

Content validity is making sure you‘ve covered all the conceptual space.

There are different ways to estimate it, but one of the most common is a reliability approach where you

correlate scores on one domain or dimension of a concept on your pretest with scores on that domain or

dimension with the actual test.

Another way is to simply look over your inter -item correlations.

3.Criterion Validity

Criterion validity is using some standard or benchmark that is known to be a good indicator. There are

different forms of criterion validity:

Concurrent validity is how well something estimates actual day-by-day behavior;

Predictive validity is how well something estimates some future event or manifestation that hasn‘t

happened yet. It is commonly found in criminology.

4.Construct Validity

Construct validity is the extent to which your items are tapping into the underlying theory or model of

behavior. It‘s how well the items hang together (convergent validity) or distinguish different people on

certain traits or behaviors (discriminant validity). It‘s the most difficult validity to achieve. You have to

either do years and years of research or find a group of people to test that have the exact opposite traits

or behaviors you‘re interested in measuringhalf as if it were the whole thing estimate split-half

reliability.

Scaling techniques

Scaling is the measurement of a variable in such a way that it can be expressed on a continuum.

Rating your preference for a product from 1 to 10 is an example of a scale.

With comparative scaling, the items are directly compared with each other (example: Do you prefer

Pepsi or Coke?). In non-comparative scaling each item is scaled independently of the others (example:

How do you feel about Coke?).

Scale construction decisions

What level of data is involved (nominal, ordinal, interval, or ratio)?

The type of information collected can influence scale construction. Different types of information are

measured in different ways.


1. Some data is measured at the nominal level. That is, any numbers used are mere labels : they

express no mathematical properties. Examples are SKU inventory codes and UPC bar codes.

2. Some data is measured at the ordinal level. Numbers indicate the relative position of items, but

not the magnitude of difference. An example is a preference ranking.

3. Some data is measured at the interval level. Numbers indicate the magnitude of difference

between items, but there is no absolute zero point. Examples are attitude scales and opinion

scales.

4. Some data is measured at the ratio level. Numbers indicate magnitude of difference and there is

a fixed zero point. Ratios can be calculated. Examples include: age, income, price, costs, sales

revenue, sales volume, and market share.

What will the results be used for?

Should you use a scale, index, or typology?

What types of statistical analysis would be useful?

Should you use a comparative scale or a non-comparative scale?

How many scale divisions or categories to use (1 to 10; 1 to 7; -3 to +3)?

Odd or even number of divisions - odd gives neutral center value; even forces respondents to take

a non-neutral position

The nature and descriptiveness of the scale labels?

The physical form or layout of the scale? (Graphic, simple linear, vertical, horizontal)

Forced versus optional response?

Attitude measurement

Many of the questions in a research survey are designed to measure attitudes. Attitudes are a person's

general evaluation of something. Customer attitude is an important factor for the following reasons:

Attitude helps to explain how ready one is to do something.

Attitudes do not change much over time.

Attitudes produce consistency in behavior.

Attitudes can be related to preferences.

Attitudes can be measured using the following procedures:

Self-reporting - subjects are asked directly about their attitudes. Self-reporting is the most

common technique used to measure attitude.

Observation of behaviour - assuming that one's behaviour is a result of one's attitudes, attitudes

can be inferred by observing behaviour. For example, one's attitude about an issue can be

inferred by whether he/she signs a petition related to it.


Indirect techniques - use unstructured stimuli such as word association tests.

Performance of objective tasks - assumes that one's performance depends on attitude. For

example, the subject can be asked to memorize the arguments of both sides of an issue. He/she is

more likely to do a better job on the arguments that favour his/her stance.

Physiological reactions - subject's response to a stimuli is measured using electronic or

mechanical means. While the intensity can be measured, it is difficult to know if the attitude is

positive or negative.

Multiple measures - a mixture of techniques can be used to validate the findings, especially

worthwhile when self-reporting is used.

The attitude-measuring process

There are a remarkable variety of techniques that have been devised to measure attitudes. These

techniques range from direct to indirect, physiological to verbal, etc.

Obtaining verbal statements from respondents generally requires that the respondent perform a task such

as ranking, rating, sorting, or making a choice or a comparison.

• Ranking tasks require that the respondent rank order a small number of objects in overall preference

on the basis of some characteristic or stimulus.

• Rating asks the respondent to estimate the magnitude of a characteristic, or quality, that an object

possesses. The respondent indicates the position on a scale(s) where he or she would rate an object.

• Sorting might present the respondent with several product concepts typed on cards and require that

the respondent arrange the cards into a number of piles or otherwise classify the product concepts.

• Choice between two or more alternatives is another type of attitude measurement—it is assumed that

the chosen object is preferred over the other(s).

Physiological measures of attitudes provide a means of measuring attitudes without verbally questioning

the respondent. For example, galvanic skin responses, measure blood pressure, etc., are physiological

measures.

Scale construction technique

The various types of scales used in research fall into two broad categories: comparative and non

comparative. In comparative scaling, the respondent is asked to compare one brand or product against

another. With non-comparative scaling respondents need only evaluate a single product or brand. Their

evaluation is independent of the other product and/or brands which the researcher is studying.


Non-comparative scaling is frequently referred to as monadic scaling and this is the more widely used

type of scale in commercial research studies.

I) Comparative scales

a) Paired comparison: It is sometimes the case that researchers wish to find out which are the most

important factors in determining the demand for a product. Conversely they may wish to know which

are the most important factors acting to prevent the widespread adoption of a product. Take, for example,

the very poor farmer response to the first design of an animal-drawn mould board plough. A

combination of exploratory research and shrewd observation suggested that the following factors played

a role in the shaping of the attitudes of those farmers who feel negatively towards the design:

Does not ridge

Does not work for inter-cropping

Far too expensive

New technology too risky

Too difficult to carry.

Suppose the organisation responsible wants to know which factors is foremost in the farmer's mind. It

may well be the case that if those factors that are most important to the farmer than the others, being of a

relatively minor nature, will cease to prevent widespread adoption. The alternatives are to abandon the

product's re-development or to completely re-design it which is not only expensive and time-consuming,

but may well be subject to a new set of objections.

The process of rank ordering the objections from most to least important is best approached through the

questioning technique known as 'paired comparison'. Each of the objections is paired by the researcher so

that with 5 factors, as in this example, there are 10 pairs-

In 'paired comparisons' every factor has to be paired with every other factor in turn. However, only one

pair is ever put to the farmer at any one time.

The question might be put as follows:

Which of the following was the more important in making you decide not to buy the plough?

port

In most cases the question, and the alternatives, would be put to the farmer verbally. He/she then

indicates which of the two was the more important and the researcher ticks the box on his questionnaire.

The question is repeated with a second set of factors and the appropriate box ticked again. This process

continues until all possible combinations are exhausted, in this case 10 pairs. It is good practice to mix the

pairs of factors so that there is no systematic bias. The researcher should try to ensure that any particular


factor is sometimes the first of the pair to be mentioned and sometimes the second. The researcher would

never, for example, take the first factor (on this occasion 'Does not ridge') and systematically compare it to

each of the others in succession. That is likely to cause systematic bias.

Below labels have been given to the factors so that the worked example will be easier to understand. The

letters A - E have been allocated as follows:

A = Does not ridge

B = Far too expensive

C = New technology too risky

D = Does not work for inter-cropping

E = Too difficult to carry.

The data is then arranged into a matrix. Assume that 200 farmers have been interviewed and their

responses are arranged in the grid below. Further assume that the matrix is so arranged that we read

from top to side. This means, for example, that 164 out of 200 farmers said the fact that the plough was

too expensive was a greater deterrent than the fact that it was not capable of ridging. Similarly, 174

farmers said that the plough's inability to inter-crop was more important than the inability to ridge when

deciding not to buy the plough.

A preference matrix

A B C D E

A 100 164 120 174 180

B 36 100 160 176 166

C 80 40 100 168 124

D 26 24 32 100 102

E 20 34 76 98 100

If the grid is carefully read, it can be seen that the rank order of the factors is -

Most important E Too difficult to carry

D Does not inter crop

C New technology/high risk

B Too expensive

Least important A Does not ridge.

It can be seen that it is more important for designers to concentrate on improving transportability and, if

possible, to give it an inter-cropping capability rather than focusing on its ridging capabilities (remember

that the example is entirely hypothetical).


One major advantage to this type of questioning is that whilst it is possible to obtain a measure of the

order of importance of five or more factors from the respondent, he is never asked to think about more

than two factors at any one time. This is especially useful when dealing with illiterate farmers. Having

said that, the researcher has to be careful not to present too many pairs of factors to the farmer during the

interview. If he does, he will find that the farmer will quickly get tired and/or bored. It is as well to

remember the formula of n(n - 1)/2. For ten factors, brands or product attributes this would give 45 pairs.

Clearly the farmer should not be asked to subject himself to having the same question put to him 45

times. For practical purposes, six factors is possibly the limit, giving 15 pairs.

It should be clear from the procedures described in these notes that the paired comparison scale gives

ordinal data.

b) Rupee Metric Comparisons: This type of scale is an extension of the paired comparison method in that

it requires respondents to indicate both their preference and how much they are willing to pay for their

preference. This scaling technique gives the researcher an interval - scaled measurement. An example is

given below:-

An example of a Rupee metric scale

Which of the following types of fish do

you prefer?

How much more, in cents, would you be prepared to pay for

your preferred fish?

Fresh Fresh (gutted) ` 0.70

Fresh (gutted) Smoked 0.50

Frozen Smoked 0.60

Frozen Fresh 0.70

Smoked Fresh 0.20

Frozen(gutted) Frozen

From the data above the preferences shown below can be computed as follows:

Fresh fish: 0.70 + 0.70 + 0.20 =1.60

Smoked fish: 0.60 + (-0.20) + (-0.50) =(-1.10)

Fresh fish(gutted): (-0.70) + 0.30 + 0.50 =0.10

Frozen fish: (-0.60) + (-0.70) + (-0.30) =(-1.60)

c) The Unity-sum-gain technique: A common problem with launching new products is one of reaching a

decision as to what options, and how many options one offers. Whilst a company may be anxious to meet

the needs of as many market segments as possible, it has to ensure that the segment is large enough to

enable him to make a profit. It is always easier to add products to the product line but much more


difficult to decide which models should be deleted. One technique for evaluating the options which are

likely to prove successful is the unity-sum-gain approach.

The procedure is to begin with a list of features which might possibly be offered as 'options' on the

product, and alongside each you list its retail cost. A third column is constructed and this forms an index

of the relative prices of each of the items. The table below will help clarify the procedure. For the

purposes of this example the basic reaper is priced at ` 20,000 and some possible 'extras' are listed along

with their prices.

The total value of these hypothetical 'extras' is RS 7,460 but the researcher tells the farmer he has an

equally hypothetical ` 3,950 or similar sum. The important thing is that he should have considerably less

hypothetical money to spend than the total value of the alternative product features. In this way the

farmer is encouraged to reveal his preferences by allowing researchers to observe how he trades one

additional benefit off against another. For example, would he prefer a side rake attachment on a 3 metre

head rather than have a transporters trolley on either a standard or 2.5m wide head? The farmer has to be

told that any unspent money cannot be retained by him so he should seek the best value-for-money he

can get.

In cases where the researcher believes that mentioning specific prices might introduce some form of bias

into the results, then the index can be used instead. This is constructed by taking the price of each item

over the total of ` 7,460 and multiplying by 100. Survey respondents might then be given a maximum of

60 points and then, as before, are asked how they would spend these 60 points. In this crude example the

index numbers are not too easy to work with for most respondents, so one would round them as has been

done in the adjusted column. It is the relative and not the absolute value of the items which is important

so the precision of the rounding need not overly concern us.

The unity-sum-gain technique

Item Additional Cost (` ) Index Adjusted Index

2.5 wide rather than standard 2m 2,000 27 30

Self lubricating chain rather than belt 200 47 50

Side rake attachment 350 5 10

Polymer heads rather than steel 250 3 5

Double rather than single edged cutters 210 2.5 5

Transporter trolley for reaper attachment 650 9 10

Automatic levelling of table 300 4 5

The unity-sum-gain technique is useful for determining which product features are more important to

farmers. The design of the final market version of the product can then reflect the farmers' needs and

preferences. Practitioners treat data gathered by this method as ordinal.


II) Non-comparative scales

a) Continuous rating scales: The respondents are asked to give a rating by placing a mark at the

appropriate position on a continuous

line. The scale can be written on card and

shown to the respondent during the

interview. Two versions of a continuous

rating scale are depicted in figure

When version B is used, the respondent's

score is determined either by dividing

the line into as many categories as

desired and assigning the respondent a

score based on the category into which his/her mark falls, or by measuring the distance, in millimetres or

inches, from either end of the scale.

Whichever of these forms of the continuous scale is used, the results are normally analysed as interval

scaled.

b) Line marking scale: The line marked scale is typically used to measure perceived similarity differences

between products, brands or other objects.

Technically, such a scale is a form of what is termed a

semantic differential scale since each end of the scale

is labelled with a word/phrase (or semantic) that is

opposite in meaning to the other. Following figure

provides an illustrative example of such a scale.

Consider the products below which can be used when

frying food. In the case of each pair, indicate how

similar or different they are in the flavour which they impart to the food.

For some types of respondent, the line scale is an easier format because they do not find discrete numbers

(e.g. 5, 4, 3, 2, 1) best reflect their attitudes/feelings. The line marking scale is a continuous scale.

c) Itemised rating scales: With an itemised

scale, respondents are provided with a

scale having numbers and/or brief

descriptions associated with each category

and are asked to select one of the limited

numbers of categories, ordered in terms of

scale position that best describes the


product, brand, company or product attribute being studied. Examples of the itemised rating scale are

illustrated in figure.

Itemised rating scales can take a variety of innovative forms as demonstrated by the two illustrated in

figure, which are graphic.

Whichever form of itemised scale is

applied, researchers usually treat

the data as interval level.

d) Semantic scales: This type of

scale makes extensive use of words

rather than numbers. Respondents

describe their feelings about the

products or brands on scales with

semantic labels. When bipolar

adjectives are used at the end points of the scales, these are termed semantic differential scales. The

semantic scale and the semantic differential scale are illustrated in figure.

e) Likert scales: A Likert scale is what is termed a summated instrument scale. This means that the items

making up a Liken scale are summed to produce a total score. In fact, a Likert scale is a composite of

itemised scales. Typically, each scale item will have 5 categories, with scale values ranging from -2 to +2

with 0 as neutral response.

This explanation may be clearer from the example in the table below.

Strongly

Agree

Agree Neither Disagree Strongly

Disagree

If the price of raw materials fell firms would reduce

the price of their food products.

-2 -1 0 1 2

Without government regulation the firms would

exploit the consumer.

-2 -1 0 1 2

Most food companies are so concerned about making

profits they do not care about quality.

-2 -1 0 1 2

The food industry spends a great deal of money

making sure that its manufacturing is hygienic.

-2 -1 0 1 2

Food companies should charge the same price for

their products throughout the country

-2 -1 0 1 2

The Likert scale


Likert scales are treated as yielding Interval data by the majority of researchers. The scales which have

been described in this chapter are among the most commonly used in research. Whilst there are a great

many more forms which scales can take, if students are familiar with those described in this chapter they

will be well equipped to deal with most types of survey problem.

Electing a measurement scale: some practical questions

There is no best scale that applies to all research projects. The choice of scale will be a function of the

nature of the attitudinal object to be measured, the manager‘s problem definition, and the backward and

forward linkages to other choices that have already been made (e.g., telephone survey versus mail

survey). There are several issues that will be helpful to consider:

• Is a ranking, sorting, rating, or choice technique best? The answer to this question is largely determined

by the problem definition and especially by the type of statistical analysis that is desired.

• Should a monadic or comparative scale be used? If a scale is other than a ratio scale, the researcher

must make a decision whether to use a standard of comparison. A monadic rating scale uses no such

comparison; it asks a respondent to rate a single concept in isolation. A comparative rating scale asks a

respondent to rate a concept in comparison with a benchmark—in many cases, "the ideal situation"

presents a reference for comparison with the actual situation.

• What type of category labels, if any, will be used for the rating scale? We have discussed verbal labels,

numerical labels, and unlisted choices. The maturity and educational levels of the respondents and the

required statistical analysis will influence this decision.

• How many scale categories or response positions are required to accurately measure an attitude? The

researcher must determine the number of meaningful positions that is best for each specific project.

• Should a balanced or unbalanced rating scale be chosen? The fixed-alternative format may be

balanced—with a neutral or indifferent point at the center of the scale—or unbalanced. Unbalanced scales

may be used when the responses are expected to be distributed at one end of the scale; an unbalanced

scale may eliminate this type of "end piling."

• Should respondents be given a forced-choice scale or a non-forced-choice scale? In many situations, a

respondent has not formed an attitude towards a concept, and simply cannot provide an answer. If many

respondents in the sample are expected to be unaware of the attitudinal object under investigation, this

problem may be eliminated by using a non-forced-choice scale that provides a "no opinion" category. The

argument for forced choice is that people really do have attitudes, even if they are unfamiliar with the

attitudinal object.

• Should a single measure or an index measure be used? The researcher‘s conceptual definition will be

helpful in making this choice. The researcher has many scaling options. The choice is generally

influenced by what is planned for the later stages of the research project.


Chapter 5: Methods of Data Collection

Introduction

A research design is a blue print which directs the plan of action to complete the research work. The

collection of data is an important part in the process of research work.

The quality and credibility of the results derived from the application of

research methodology depends upon the relevant, accurate and

adequate data.

In this unit, we shall study about the various sources of data and

methods of collecting primary and secondary data with their merits and limitations and also the choice of

suitable method for data collection.

Meaning and need for data

Data is required to make a decision in any business situation. The researcher is faced with one of the

most difficult problems of obtaining suitable, accurate and adequate data. Utmost care must be exercised

while collecting data because the quality of the research results depends upon the reliability of the data.

Suppose, you are the Director of your company. Your Board of Directors has asked you to find out why

the profit of the company has decreased since the last two years. Your Board wants you to present facts

and figures. What are you going to do?

The first and foremost task is to collect the relevant information to make an analysis for the above

mentioned problem. It is, therefore, the information collected from various sources, which can be

expressed in quantitative form, for a specific purpose, which is called data. The rational decision maker

seeks to evaluate information in order to select the course of action that maximizes objectives. For

decision making, the input data must be appropriate. This depends on the appropriateness of the method

chosen for data collection. The application of a statistical technique is possible when the questions are

answerable in quantitative nature, for instance; the cost of production, and profit of the company

measured in rupees, age of the workers in the company measured in years. Therefore, the first step in


statistical activities is to gather data. The data may be classified as primary and secondary data. Let us

now discuss these two kinds of data in detail.

Primary and secondary data

The Primary data are original data which are collected for the first time for a specific purpose. Such data

are published by authorities who themselves are responsible for their collection. The Secondary data on

the other hand, are those which have already been collected by some other agency and which have

already been processed. Secondary data may be available in the form of published or unpublished

sources. For instance, population census data collected by the Government in a country is primary data

for that Government.

But the same data becomes secondary for those researchers who use it later. In case you have decided to

collect primary data for your investigation, you have to identify the sources from where you can collect

that data. For example, if you wish to study the problems of the workers of X Company Ltd., then the

workers who are working in that company are the source. On the other hand, if you have decided to use

secondary data, you have to identify the secondary source who have already collected the related data for

their study purpose. With the above discussion, we can understand that the difference between primary

and secondary data is only in terms of degree. That is that the data which is primary in the hands of one

becomes secondary in the hands of another.

Primary data

Primary data can be obtained by communication or by observation. Communication involves questioning

respondents either verbally or in writing. This method is versatile, since one needs only to ask for the

information; however, the response may not be accurate. Communication usually is quicker and cheaper

than observation. Observation involves the recording of actions and is performed by either a person or

some mechanical or electronic device. Observation is less versatile than communication since some

attributes of a person may not be readily observable, such as attitudes, awareness, knowledge, intentions,

and motivation. Observation also might take longer since observers may have to wait for appropriate

events to occur, though observation using scanner data might be quicker and more cost effective.

Observation typically is more accurate than communication.

Some common types of primary data are:

demographic and socioeconomic characteristics

psychological and lifestyle characteristics

attitudes and opinions

awareness and knowledge - for example, brand awareness

Intentions - for example, purchase intentions. While useful, intentions are not a reliable indication

of actual future behaviour


motivation - a person's motives are more stable than his/her behaviour, so motive is a better

predictor of future behaviour than is past behaviour

behaviour

Methods of collecting primary data

If the available secondary data does not meet the requirements of the present study, the researcher has to

collect primary data. As mentioned earlier, the data, which is collected for the first time by the researcher

for his own purpose, is called primary data. There are several methods of collecting primary data, such as

observation, interview through reporters, questionnaires and schedules. Let us study about them in

detail.

1. Observation Method

The Concise Oxford Dictionary defines observation as, ‗accurate watching and noting of phenomena as

they occur in nature with regard to cause and effect or mutual relations‘. Thus observation is not only a

systematic watching but it also involves listening and reading, coupled with consideration of the seen

phenomena. It involves three processes. They are: sensation, attention or concentration and perception.

Under this method, the researcher collects information directly through observation rather than through

the reports of others. It is a process of recording relevant information without asking anyone specific

questions and in some cases, even without the knowledge of the respondents. This method of collection is

highly effective in behavioural surveys. For instance, a study on behaviour of visitors in trade fairs,

observing the attitude of workers on the job, bargaining strategies of customers etc. Observation can be

participant observation or non-participant observation. In Participant Observation Method, the

researcher joins in the daily life of informants or organisations, and observes how they behave. In the

Non-participant Observation Method, the researcher will not join the informants or organisations but will

watch from outside.

Merits

1) This is the most suitable method when the informants are unable or reluctant to provide information.

2) This method provides deeper insights into the problem and generally the data is accurate and quicker

to process. Therefore, this is useful for intensive study rather than extensive study.

Limitations

Despite of the above merits, this method suffers from the following limitations:

1) In many situations, the researcher cannot predict when the events will occur. So when an event occurs

there may not be a ready observer to observe the event.

2) Participants may be aware of the observer and as a result may alter their behaviour.


3) Observer, because of personal biases and lack of training, may not record specifically what he/she

observes.

4) This method cannot be used extensively if the inquiry is large and spread over a wide area.

2. Interview Method

Interview is one of the most powerful tools and most widely used method for primary data collection in

research. In our daily routine, we see interviews on T.V. channels on various topics related to social,

business, sports, budget etc. In the words of C. William Emory, ‗personal interviewing is a two way

purposeful conversation initiated by an interviewer to obtain information that is relevant to some

research purpose‘. Thus an interview is basically, a meeting between two persons to obtain the

information related to the proposed study. The person who is interviewing is named as interviewer and

the person who is being interviewed is named as informant. It is to be noted that, the research

data/information collect through this method is not a simple conversation between the investigator and

the informant, but also the glances, gestures, facial expressions, level of speech etc., are all part of the

process.

Through this method, the researcher can collect varied types of data intensively and extensively.

Interviewes can be classified as direct personal interviews and indirect personal interviews. Under the

techniques of direct personal interview, the investigator meets the informants (who come under the

study) personally, asks them questions pertaining to enquiry and collects the desired information. Thus if

a researcher intends to collect the data on spending habits of Delhi University (DU) students, he/ she

would go to the DU, contact the students, interview them and collect the required information.

Indirect personal interview is another technique of interview method where it is not possible to collect

data directly from the informants who come under the study. Under this method, the investigator

contacts third parties or witnesses, who are closely associated with the persons/situations under study

and are capable of providing necessary information. For example, an investigation regarding a bribery

pattern in an office. In such a case it is inevitable to get the desired information indirectly from other

people who may be knowing them. Similarly, clues about the crimes are gathered by the CBI. Utmost care

must be exercised that these persons who are being questioned are fully aware of the facts of the problem

under study, and are not motivated to give a twist to the facts.

Another technique for data collection through this method can be structured and unstructured

interviewing. In the Structured interview set questions are asked and the responses are recorded in a

standardised form. This is useful in large scale interviews where a number of investigators are assigned

the job of interviewing. The researcher can minimise the bias of the interviewer. This technique is also

named as formal interview. In Un-structured interview, the investigator may not have a set of questions

but have only a number of key points around which to build the interview. Normally, such types of

interviews are conducted in the case of an explorative survey where the researcher is not completely sure


about the type of data he/ she collects. It is also named as informal interview. Generally, this method is

used as a supplementary method of data collection in conducting research in business areas.

Now-a-days, telephone or cellphone interviews are widely used to obtain the desired information for

small surveys. For instance, interviewing credit card holders by banks about the level of services they are

receiving. This technique is used in industrial surveys specially in developed regions.

Merits

The major merits of this method are as follows:

1) People are more willing to supply information if approached directly. Therefore, personal interviews

tend to yield high response rates.

2) This method enables the interviewer to clarify any doubt that the interviewee might have while asking

him/her questions. Therefore, interviews are helpful in getting reliable and valid responses.

3) The informant‘s reactions to questions can be properly studied.

4) The researcher can use the language of communication according to the standard of the information, so

as to obtain personal information of informants which are helpful in interpreting the results.

Limitations

The limitations of this method are as follows:

1) The chance of the subjective factors or the views of the investigator may come in either consciously or

unconsciously.

2) The interviewers must be properly trained, otherwise the entire work may be spoiled.

3) It is a relatively expensive and time-consuming method of data collection especially when the number

of persons to be interviewed is large and they are spread over a wide area.

4) It cannot be used when the field of enquiry is large (large sample).

Precautions : While using this method, the following precautions should be taken:

1. Obtain thorough details of the theoretical aspects of the research problem.

2. Identify who is to be interviewed.

3. The questions should be simple, clear and limited in number.

4. The investigator should be sincere, efficient and polite while collecting data.

5. The investigator should be of the same area (field of study, district, state etc.).

3. Through Local Reporters and Correspondents

Under this method, local investigators/agents or correspondents are appointed in different parts of the

area under investigation. This method is generally adopted by government departments in those cases

where regular information is to be collected. This method is also useful for newspapers, magazines, radio


and TV news channels. This method has been used when regular information is required and a high

degree of accuracy is not of much importance.

Merits

1) This method is cheap and economical for extensive investigations.

2) It gives results easily and promptly.

3) It can cover a wide area under investigation.

Limitations

1) The data obtained may not be reliable.

2) It gives approximate and rough results.

3) It is unsuited where a high degree of accuracy is desired.

4) As the agent/reporter or correspondent uses his own judgement, his personal bias may affect the

accuracy of the information sent.

4. Questionnaire and Schedule Methods

Questionnaire and schedule methods are the popular and common methods for collecting primary data

in research. Both the methods comprise a list of questions arranged in a sequence pertaining to the

investigation. Let us study these methods in detail one after another.

i) Questionnaire Method

Under this method, questionnaires are sent personally or by post to various informants with a request to

answer the questions and return the questionnaire. If the questionnaire is posted to informants, it is called

a Mail Questionnaire. Sometimes questionnaires may also sent through E-mail depending upon the

nature of study and availability of time and resources. After receiving the questionnaires the informants

read the questions and record their responses in the space meant for the purpose on the questionnaire. It

is desirable to send the quetionnaire with self-addressed envelopes for quick and high rate of response.

Merits

1) You can use this method in cases where informants are spread over a vast geographical area.

2) Respondents can take their own time to answer the questions. So the researcher can obtain original

data by this method.

3) This is a cheap method because its mailing cost is less than the cost of personal visits.

4) This method is free from bias of the investigator as the information is given by the respondents

themselves.

5) Large samples can be covered and thus the results can be more reliable and dependable.


Limitations

1) Respondents may not return filled in questionnaires, or they can delay in replying to the

questionnaires.

2) This method is useful only when the respondents are educated and co-operative.

3) Once the questionnaire has been despatched, the investigator cannot modify the questionnaire.

4) It cannot be ensured whether the respondents are truly representative.

ii) Schedule Method

As discussed above, a Schedule is also a list of questions, which is used to collect the data from the field.

This is generally filled in by the researcher or the enumerators. If the scope of the study is wide, then the

researcher appoints people who are called enumerators for the purpose of collecting the data. The

enumerators go to the informants, ask them the questions from the schedule in the order they are listed

and record the responses in the space meant for the answers in the schedule itself. For example, the

population census all over the world is conducted through this method. The difference between

questionnaire and schedule is that the former is filled in by the informants, the latter is filled in by the

researcher or enumerator.

Merits

1) It is a useful method in case the informants are illiterates.

2) The researcher can overcome the problem of non-response as the enumerators go personally to obtain

the information.

3) It is very useful in extensive studies and can obtain more reliable data.

Limitations

1) It is a very expensive and time-consuming method as enumerators are paid persons and also have to

be trained.

2) Since the enumerator is present, the respondents may not respond to some personal questions.

3) Reliability depends upon the sincerity and commitment in data collection.

The success of data collection through the questionnaire method or schedule method depends on how the

questionnaire has been designed.


Primary data collection methods: Some Advantages & Disadvantages

Survey type Advantages Disadvantages

Spoken surveys Effective in all situations, e.g. when

literacy level is low.

Need a lot of organization.

Face to face surveys Usually provides very accurate

results. Any question can be asked.

Can include observation and visual

aids.

Expensive, specially when large areas

are covered.

Face to face surveys at

respondents‟

home/work/etc.

Can cover the entire population. Expensive; much organization needed.

Face to face surveys in

public places

Can do lots of interviews in a short

time.

Samples are usually not representative

of the whole population.

Telephone surveys High accuracy obtainable if most

members of population have

telephones.

No visual aids possible. Only feasible

with high telephone saturation.

Written surveys Cheaper than face-to-face surveys. Hard to tell if questions not correctly

understood. More chance of question

wording causing problems.

Mail surveys Cheap.

Allows anonymity.

Requires high level of literacy and

good postal system. Slow to get

results.

Self-completion,

questionnaires collected

and delivered

Cheap. Gives respondents time to

check documents.

Respondents must be highly literate..

Fax surveys Fast

Cheap.

Questionnaires with more than one

page are often only partially returned.

Email surveys Very cheap

Quick results.

Samples not representative of whole

population. Some respondents lie.

High computer skills needed.

Web surveys More easily processed than email

questionnaires

Many people don‘t have good web

access..

Informal methods Fast

Flexible

Can‘t produce accurate figures.

Experience needed for comparisons.

Subjective. Most suitable for


preliminary studies.

Monitoring Little work required

Cheap.

Often not completely relevant.

Samples often not representative.

Most suitable when assessing

progress.

Observation (can be

combined with surveys)

More accurate than asking people

their behaviour.

Only works in limited situations.

Meters More accurate than asking people

their behaviour.

Very expensive to set up; measures

equipment rather than people. Can‘t

find out reasons for behaviour.

Panels Ability to discover changes in

individuals‘ preferences and

behaviour.

Need to maintain records of previous

contact, etc.

Depth interviews Provide insights not available with

most other methods.

Expensive; need highly skilled

interviewers.

Focus groups Provide insights not available with

most other methods.

Need highly skilled moderator,

trained in psychology etc.

Consensus groups Instant results.

Clear wording.

Cheap

Secretary and/or moderator need

strong verbal skills. Don‘t work well

in some cultures, e.g. Buddhist.

Internet qualitative

research

Easy for a geographically dispersed

group to meet.

Low cost.

Doesn‘t provide the subtlety of

personal interaction. Very new, so few

experts available to help with

problems.


Questionnaire Design

Questionnaires are frequently used in quantitative research. They are a valuable method of collecting a

wide range of information from a large number

of respondents. Good questionnaire

construction is critical to the success of a survey.

Inappropriate questions, incorrect ordering of

questions, incorrect scaling, or bad

questionnaire format can make the survey

valueless. A useful method for checking a

questionnaire for problems is to pretest it. This

usually involves giving it to a small sample of

respondents, then interviewing the respondents

to get their impressions and to confirm that the

questions accurately captured their opinions.

The design of a questionnaire will depend on whether the researcher wishes to collect exploratory

information (i.e. qualitative information for the purposes of better understanding or the generation of

hypotheses on a subject) or quantitative information (to test specific hypotheses that have previously

been generated).

If the data to be collected is qualitative or is not to be statistically evaluated, it may be that no formal

questionnaire is needed. For example, in interviewing the female head of the household to find out how

decisions are made within the family when purchasing breakfast foodstuffs, a formal questionnaire may

restrict the discussion and prevent a full exploration of the woman's views and processes. Instead one

might prepare a brief guide, listing perhaps ten major open-ended questions, with appropriate

probes/prompts listed under each.

If the researcher is looking to test and quantify hypotheses and the data is to be analysed statistically, a

formal standardised questionnaire is designed. Such questionnaires are generally characterised by:

prescribed wording and order of questions, to ensure that each respondent receives the same

stimuli

prescribed definitions or explanations for each question, to ensure interviewers handle

questions consistently and can answer respondents' requests for clarification if they occur

prescribed response format, to enable rapid completion of the questionnaire during the

interviewing process.

Given the same task and the same hypotheses, six different people will probably come up with six

different questionnaires that differ widely in their choice of questions, line of questioning, use of open-

ended questions and length. There are no hard-and-fast rules about how to design a questionnaire, but

there are a number of points that can be borne in mind:

http://en.wikipedia.org/wiki/Quantitative_marketing_research


1. A well-designed questionnaire should meet the research objectives. This may seem obvious, but

many research surveys omit important aspects due to inadequate preparatory work, and do not

adequately probe particular issues due to poor understanding. To a certain degree some of this is

inevitable. Every survey is bound to leave some questions unanswered and provide a need for further

research but the objective of good questionnaire design is to 'minimise' these problems.

2. It should obtain the most complete and accurate information possible. The questionnaire designer

needs to ensure that respondents fully understand the questions and are not likely to refuse to answer, lie

to the interviewer, or try to conceal their attitudes. A good questionnaire is organised and worded to

encourage respondents to provide accurate, unbiased, and complete information.

3. A well-designed questionnaire should make it easy for respondents to give the necessary information

and for the interviewer to record the answer and it should be arranged so that sound analysis and

interpretation are possible.

4. It would keep the interview brief and to the point and be so arranged that the respondent(s) remain

interested throughout the interview.

Even after the exploratory phase, two key steps remain to be completed before the task of designing the

questionnaire should commence. The first of these is to articulate the questions that research is intended

to address. The second step is to determine the hypotheses around which the questionnaire is to be

designed.

It is possible for the piloting exercise to be used to make necessary adjustments to administrative aspects

of the study. This would include, for example, an assessment of the length of time an interview actually

takes, in comparison to the planned length of the interview; or, in the same way, the time needed to

complete questionnaires. Moreover, checks can be made on the appropriateness of the timing of the study

in relation to contemporary events such as avoiding farm visits during busy harvesting periods.

Preliminary decisions in questionnaire design

There are nine steps involved in the development of a questionnaire:

1. Decide the information required.

2. Define the target respondents.

3. Choose the method(s) of reaching your target respondents.

4. Decide on question content.

5. Develop the question wording.

6. Put questions into a meaningful order and format.

7. Check the length of the questionnaire.

8. Pre-test the questionnaire.

9. Administer the questionnaires


1. Deciding on the information required

It should be noted that one does not start by writing questions. The first step is to decide 'what are the

things one needs to know from the respondent in order to meet the survey's objectives?' These, as has

been indicated in the opening chapter of this book, should appear in the research brief and the research

proposal.

One may already have an idea about the kind of information to be collected, but additional help can be

obtained from secondary data, previous rapid rural appraisals and exploratory research. In respect of

secondary data, the researcher should be aware of what work has been done on the same or similar

problems in the past, what factors have not yet been examined, and how the present survey questionnaire

can build on what has already been discovered. Further, a small number of preliminary informal

interviews with target respondents will give a glimpse of reality that may help clarify ideas about what

information is required.

2. Define the target respondents

At the outset, the researcher must define the population about which he/she wishes to generalise from

the sample data to be collected. For example, researchers often have to decide whether they should cover

only existing users of the generic product type or whether to also include non-users. Secondly,

researchers have to draw up a sampling frame. Thirdly, in designing the questionnaire we must take into

account factors such as the age, education, etc. of the target respondents.

3. Choose the method(s) of reaching target respondents

It may seem strange to be suggesting that the method of reaching the intended respondents should

constitute part of the questionnaire design process. However, a moment's reflection is sufficient to

conclude that the method of contact will influence not only the questions the researcher is able to ask but

the phrasing of those questions. The main methods available in survey research are:

Personal interviews

Group or focus interviews

Mailed questionnaires

Telephone interviews.

Within this region the first two mentioned are used much more extensively than the second pair.

However, each has its advantages and disadvantages. A general rule is that the more sensitive or

personal the information, the more personal the form of data collection should be.

4. Decide on question content

Researchers must always be prepared to ask, "Is this question really needed?" The temptation to include

questions without critically evaluating their contribution towards the achievement of the research

objectives, as they are specified in the research proposal, is surprisingly strong. No question should be


included unless the data it gives rise to is directly of use in testing one or more of the hypotheses

established during the research design.

There are only two occasions when seemingly "redundant" questions might be included:

· Opening questions that are easy to answer and which are not perceived as being "threatening", and/or

are perceived as being interesting, can greatly assist in gaining the respondent's involvement in the

survey and help to establish a rapport.

This, however, should not be an approach that should be overly used. It is almost always the case that

questions which are of use in testing hypotheses can also serve the same functions.

· "Dummy" questions can disguise the purpose of the survey and/or the sponsorship of a study. For

example, if a manufacturer wanted to find out whether its distributors were giving the consumers or end-

users of its products a reasonable level of service, the researcher would want to disguise the fact that the

distributors' service level was being investigated. If he/she did not, then rumours would abound that

there was something wrong with the distributor.

5. Develop the question wordings

There are a series of questions that should be posed as the researchers develop the survey questions

themselves:

a) "Is this question sufficient to generate the required information?"

For example, asking the question "Which product do you prefer?" in a taste panel exercise will reveal

nothing about the attribute(s) the product was judged upon. Nor will this question reveal the degree of

preference. In such cases a series of questions would be more appropriate.

b) "Can the respondent answer the question correctly?"

An inability to answer a question arises from three sources:

Having never been exposed to the answer, e.g. "How much does your husband earn?"

Forgetting, e.g. what price did you pay when you last bought maize meal?"

An inability to articulate the answer: e.g. "What improvements would you want to see in food

preparation equipment?"

c) "Are there any external events that might bias response to the question?"

For example, judging the popularity of beef products shortly after a foot and mouth epidemic is likely to

have an effect on the responses.

d) "Do the words have the same meaning to all respondents?"

For example, "How many members are there in your family?"

There is room for ambiguity in such a question since it is open to interpretation as to whether one is

speaking of the immediate or extended family.

e)"Are any of the words or phrases loaded or leading in any way?"

For example," What did you dislike about the product you have just tried?"


The respondent is not given the opportunity to indicate that there was nothing he/she disliked about the

product. A less biased approach would have been to ask a preliminary question along the lines of, "Did

you dislike any aspect of the product you have just tried?", and allow him/her to answer yes or no.

f) "Are there any implied alternatives within the question?"

The presence or absence of an explicitly stated alternative can have dramatic effects on responses. For

example, consider the following two forms of a question asked of a 'Pasta-in-a-Jar' concept test:

a. " Would you buy pasta-in-a-jar if it were locally available?"

b. "If pasta-in-a-jar and the cellophane pack you currently use were both available locally, would

you:

Buy only the cellophane packed pasta?

Buy only the pasta-in-a-jar product?

Buy both products?"

The explicit alternatives provide a context for interpreting the true reactions to the new product idea. If

the first version of the question is used, the researcher is almost certain to obtain a larger number of

positive responses than if the second form is applied.

g)"Will the question be understood by the type of individual to be interviewed?"

It is good practice to keep questions as simple as possible. Researchers must be sensitive to the fact that

some of the people he/she will be interviewing do not have a high level of education. Sometimes he/she

will have no idea how well or badly educated the respondents are until he/she gets into the field. In the

same way, researchers should strive to avoid long questions. The fewer words in a question the better.

Respondents' memories are limited and absorbing the meaning of long sentences can be difficult: in

listening to something they may not have much interest in, the respondents' minds are likely to wander,

they may hear certain words but not others, or they may remember some parts of what is said but not all.

8) "Is there any ambiguity in my questions?"

The careless design of questions can result in the inclusion of two items in one question. For example:

"Do you like the speed and reliability of your tractor?"

The respondent is given the opportunity to answer only 'yes' or 'no', whereas he might like the speed, but

not the reliability, or vice versa. Thus it is difficult for the respondent to answer and equally difficult for

the researcher to interpret the response.

The use of ambiguous words should also be avoided. For example: "Do you regularly service your

tractor?"

The respondents' understanding and interpretation of the term 'regularly' will differ. Some may consider

that regularly means once a week, others may think once a year is regular. The inclusion of such words

again presents interpretation difficulties for the researcher.


h) "Are any words or phrases vague?"

Questions such as 'What is your income?' are vague and one is likely to get many different responses with

different dimensions. Respondents may interpret the question in different terms, for example:

hourly pay?

weekly pay?

yearly pay?

income before tax?

income after tax?

income in kind as well as cash?

income for self or family?

all income or just farm income?

The researcher needs to specify the 'term' within which the respondent is to answer.

i) "Are any questions too personal or of a potentially embarrassing nature?"

The researcher must be clearly aware of the various customs, morals and traditions in the community

being studied. In many communities there can be a great reluctance to discuss certain questions with

interviewers/strangers. Although the degree to which certain topics are taboo varies from area to area,

such subjects as level of education, income and religious issues may be embarrassing and respondents

may refuse to answer.

j) "Do questions rely on feats of memory?"

The respondent should be asked only for such data as he is likely to be able to clearly remember. One has

to bear in mind that not everyone has a good memory, so questions such as 'Four years ago was there a

shortage of labour?' should be avoided.

Putting questions into a meaningful order and format

i. Opening questions: Opening questions should be easy to answer and not in any way threatening

to THE respondents. The first question is crucial because it is the respondent's first exposure to

the interview and sets the tone for the nature of the task to be performed. If they find the first

question difficult to understand, or beyond their knowledge and experience, or embarrassing in

some way, they are likely to break off immediately. If, on the other hand, they find the opening

question easy and pleasant to answer, they are encouraged to continue.

ii. Question flow: Questions should flow in some kind of psychological order, so that one leads

easily and naturally to the next. Questions on one subject, or one particular aspect of a subject,

should be grouped together. Respondents may feel it disconcerting to keep shifting from one

topic to another, or to be asked to return to some subject they thought they gave their opinions

about earlier.

iii. Question variety: Respondents become bored quickly and restless when asked similar questions

for half an hour or so. It usually improves response, therefore, to vary the respondent's task from


time to time. An open-ended question here and there (even if it is not analysed) may provide

much-needed relief from a long series of questions in which respondents have been forced to

limit their replies to pre-coded categories. Questions involving showing cards/pictures to

respondents can help vary the pace and increase interest.

iv. Closing questions: It is natural for a respondent to become increasingly indifferent to the

questionnaire as it nears the end. Because of impatience or fatigue, he may give careless answers

to the later questions. Those questions, therefore, that are of special importance should, if

possible, be included in the earlier part of the questionnaire. Potentially sensitive questions

should be left to the end, to avoid respondents cutting off the interview before important

information is collected.

In developing the questionnaire the researcher should pay particular attention to the

presentation and layout of the interview form itself. The interviewer's task needs to be made as

straight-forward as possible.

Types of Questions

Survey questions can be classified as follows:-

1. Contingency questions - A question that is answered only if the respondent gives a particular

response to a previous question. This avoids asking questions of people that do not apply to them

(for example, asking men if they have ever been pregnant).

2. Matrix questions - Identical response categories are assigned to multiple questions. The questions

are placed one under the other, forming a matrix with response categories along the top and a list of

questions down the side. This is an efficient use of page space and respondents‘ time.

3. Scaled questions - Responses are graded on a continuum (example : rate the appearance of the

product on a scale from 1 to 10, with 10 being the most preferred appearance). Examples of types of

scales include the Likert scale, semantic differential scale, and rank-order scale .

4. Closed ended questions - Respondents‘ answers are limited to a fixed set of responses. Most scales

are closed ended. Other types of closed ended questions include:

* Dichotomous questions - The respondent answers with a “yes” or a “no”.

* Multiple choice - The respondent has several option from which to choose.

Advantage of Closed Format

Closed-that is, forced choice-format

Easy and quick to fill in

Minimise discrimination against the less literate (in self administered questionnaire) or the less

articulate (in interview questionnaire)

Easy to code, record, and analyse results quantitatively

Easy to report results


5. Open ended questions - No options or predefined categories are suggested. The respondent

supplies their own answer without being constrained by a fixed set of possible responses. Examples

of types of open ended questions include:

o Completely unstructured - For example, ―What is your opinion of questionnaires?‖

o Word association - Words are presented and the respondent mentions the first word that comes

to mind.

o Sentence completion - Respondents complete an incomplete sentence. For example, ―The most

important consideration in my decision to buy a new house is . . .‖

o Story completion - Respondents complete an incomplete story.

o Picture completion - Respondents fill in an empty conversation balloon.

o Thematic apperception test - Respondents explain a picture or make up a story about what they

think is happening in the picture

Advantages of open format

Allows exploration of the range of possible themes arising from an issue

Can be used even if a comprehensive range of alternative choices cannot be compiled

There are three commonly used rating scales: graphic, itemized, and comparative.

Graphic - simply a line on which one marks an X anywhere between the extremes with an infinite

number of places where the X can be placed.

Itemized - similar to graphic except there are a limited number of categories that can be marked.

Comparative - the respondent compares one attribute to others. Examples include the Q-sort

technique and the constant sum method, which requires one to divide a fixed number of points

among the alternatives.

Questionnaires typically are administered via a personal or telephone interview or via a mail

questionnaire. Newer methods include e-mail and the Web.

7. Checking and editing

Though completed questionnaires should already have been checked by interviewers and supervisors,

they need to be checked again before (or during) data entry.

What to check

Every questionnaire needs to be thoroughly checked:

All standard items at the beginning or end of a questionnaire should be filled in. They usually include:

the questionnaire serial number

the place where the interview was done (often in coded form)

the interviewer‘s name (or initials, or number)

the date and time of interview.


These are not questions asked of the respondent, but information supplied by the interviewer. If the

interviewer forgot to include something here, the supervisor should have noticed, and made sure it was

added. But sometimes newly trained supervisors don‘t notice these omissions. They sooner these

problems are found, the more easily they can be corrected.

Check that every question which is supposed to have only one answer does not have more.

Check that no question which should have been skipped has an answer entered.

If an answer has been written in because no code applied, perhaps a new code will have to be

created. This will have to be done after looking at all answers to this question, after going

through all the questionnaires.

Physical appearance of the questionnaire

The physical appearance of a questionnaire can have a significant effect upon both the quantity and

quality of marketing data obtained. The quantity of data is a function of the response rate. Ill-designed

questionnaires can give an impression of complexity, medium and too big a time commitment. Data

quality can also be affected by the physical appearance of the questionnaire with unnecessarily confusing

layouts making it more difficult for interviewers, or respondents in the case of self-completion

questionnaires, to complete this task accurately.

Attention to just a few basic details can have a disproportionately advantageous impact on the data

obtained through a questionnaire.

Use of booklets The use of booklets, in the place of loose or stapled sheets of paper, make it easier for

interviewer or respondent to progress through the document. Moreover, fewer pages

tend to get lost.

Simple, clear

formats

The clarity of questionnaire presentation can also help to improve the ease with which

interviewers or respondents are able to complete a questionnaire.

Creative use of

space and

typeface

In their anxiety to reduce the number of pages of a questionnaire these is a tendency to

put too much information on a page. This is counter-productive since it gives the

questionnaire the appearance of being complicated. Questionnaires that make use of

blank space appear easier to use, enjoy higher response rates and contain fewer errors

when completed.

Use of colour

coding

Colour coding can help in the administration of questionnaires. It is often the case that

several types of respondents are included within a single survey (e.g. wholesalers and

retailers). Printing the questionnaires on two different colours of paper can make the

handling easier.

Interviewer

instructions

Interviewer instructions should be placed alongside the questions to which they

pertain. Instructions on where the interviewers should probe for more information or


how replies should be recorded are placed after the question.

In general it is best for a questionnaire to be as short as possible. A long questionnaire leads to a long

interview and this is open to the dangers of boredom on the part of the respondent (and poorly

considered, hurried answers), interruptions by third parties and greater costs in terms of interviewing

time and resources. In a rural situation, an interview should not last longer than 30-45 minutes.

8. Piloting the questionnaires

Even after the researcher has proceeded along the lines suggested, the draft questionnaire is a product

evolved by one or two minds only. Until it has actually been used in interviews and with respondents, it

is impossible to say whether it is going to achieve the desired results. For this reason it is necessary to

pre-test the questionnaire before it is used in a full-scale survey, to identify any mistakes that need

correcting.

The purpose of pre-testing the questionnaire is to determine:

whether the questions as they are worded will achieve the desired results

whether the questions have been placed in the best order

whether the questions are understood by all classes of respondent

whether additional or specifying questions are needed or whether some questions should be

eliminated

whether the instructions to interviewers are adequate.

Usually a small number of respondents are selected for the pre-test. The respondents selected for the pilot

survey should be broadly representative of the type of respondent to be interviewed in the main survey.

If the questionnaire has been subjected to a thorough pilot test, the final form of the questions and

questionnaire will have evolved into its final form. All that remains to be done is the mechanical process

of laying out and setting up the questionnaire in its final form. This will involve grouping and sequencing

questions into an appropriate order, numbering questions, and inserting interviewer instructions.

Recoding frequent "other" answers

It‘s annoying to read a survey report and find that a large proportion of the answers to a question were

"other". The goal should be to make sure the "other" category is the one with the fewest answers -

certainly no more than 5%. Take for example this question:

"Which languages do you understand?"

(Circle all codes that apply)

1 Marathi

2 Hindi

3 Bengali

4 English

5 Other - write in: ......................


If 10% of people gave an "other" answer, the written-in responses will need to be counted. If 4% of people

understood Gujrati, and 3% understood Tamil, two new codes could be created:

6 = Gujrati

7 = Punjabi

For each questionnaire mentioning these languages, the circled 5 should be crossed out (unless a different

"other" language was also mentioned), and 6 and/or 7 written in and circled. This should reduce the

remaining "other" figure to about 3%. Unless at least 2% of respondents give a particular "other" answer,

it‘s usually not worthwhile to create a separate code. Sometimes a number of "other" answers can be

grouped, e.g.

8 = South Indian languages

But when such a code has been made, there is no way to recode the question except by going back to all

the questionnaires with that code. The principle should be not to combine any answers which you might

later want to look at separately.

Coding open-ended questions

With some open-ended questions, you expect to find many answers recurring.

For example: "What is your occupation?" There will be some occupations which are very common, some

less common, and there will probably be a lot of occupations which only one respondent in the sample

mentions. With other open-ended questions (such as "What do you like most about listening to FM

RADIOMIRCHI?") you may find that no two respondents give the same answer.

For both types of question, the standard coding method is the same: you take a sub-sample of answers to

that question - often the first 100 answers to come in. (That may be a lot more than 100 questionnaires, if

not everybody is asked the question.)

Each different answer is written on a slip of paper, and these answers are then sorted into groups with

similar meanings. Usually, there are 10 to 20 groups. If fewer than 2 people in 100 give a particular

answer, it‘s not worthwhile having a separate code for that answer - unless it has a very specific and

different meaning from all the others.

Having defined these 10 to 20 groups, a code number is then assigned to each. Following the example of

what people like about FM RADIOMIRCHI, these codes might be assigned.

01 = like everything about FM RADIOMIRCHI

02 = like nothing about FM RADIOMIRCHI

03 = the announcers in general

04 = the programs in general

05 = the music

06 = news bulletins

07 = talkback


08 = breakfast program

09 = Lunch time programee

10 = other

A practical problem with such a coding scheme is that, the more codes are defined, the more likely some

are to be very similar, and the coders may not be consistent in assigning codes to answers.

When consistency is very important, any codes which are not absolutely clear should be allocated by

several coders working together, or by a single supervisor. As new comments are found, which are not

covered by the original codes, new codes will need to be added.

There are many ways in which an answer can be given a code - what is most useful depends on any

action you might take as a result of the survey. If there are particular types of answer you are looking for,

you could create codes for these. For example, if a station broadcasts programs in a particular language,

that language should be listed as a code. Even if no respondent understands that language, this in itself is

useful information.

For open-ended questions with predefined answers (such as occupations) there may be no need to build a

coding frame by looking at the answers. For example, occupation coding is often done using the 10 major

groups from the International Standard Classification of Occupations.

That‘s one way to code open-ended questions. It works well for questions with a limited number of

answers, but for questions on attitudes, opinions, and so on, counting the coded categories lose much of

the detail in the answers. Another approach is to use the whole wording of the answers - e.g. by entering

the verbatim answers on a computer file. The coding can then be very simple, and summarize the exact

wording. We can use coding schemes such as...

0 = made no comment

1 = made a comment

or...

1 = positive or favourable comment

2 = negative or unfavourable comment

3 = neutral comment, or mixed positive and negative.

These very broad coding schemes are much quicker to apply, and less dependent on a coder‘s opinion.

But the broad codes are not very useful, unless you also report the exact wording of comments.

9. How to administer the questionnaires

There are several ways of administering questionnaires. They may be self administered or read out by

interviewers. Self administered questionnaires may be sent by post, email, or electronically online.

Interview administered questionnaires may be by telephone or face to face.


The exact method of administration also depends on who the respondents are. For example, University

lecturers may be more appropriately surveyed by email; older people by telephone interviews; train

passengers by face-to-face interviews.

Advantages of self-administered questionnaires

Advantages of self-administered questionnaires include:

They are less expensive than interviews.

They do not require a large staff of skilled interviewers.

They can be administered in large numbers all at one place and time.

Anonymity and privacy encourage more candid and honest responses.

Lack of interviewer bias.

Speed of administration and analysis.

Suitable for computer based research methods.

Less pressure on respondents

Advantages of researcher administered interviews

Advantages of researcher administered interviews include:

Fewer misunderstood questions and inappropriate responses.

Fewer incomplete responses.

Higher response rates.

Greater control over the environment that the survey is administered in.

Introduction, personalised letter, and ending

It seems a good idea to have either a personalised covering letter or at least an introduction explaining

briefly the purpose of the survey, the importance of the respondents' participation, who is responsible for

the survey, and a statement guaranteeing confidentiality. A personalised letter can be easily generated

using mail-merge on a word processor. It is also important to thank the respondent at the end of the

questionnaire.

Questionnaire design Issues

The way questions are phrased is important and there are some general rules for constructing good

questions in a questionnaire.

1) Use short and simple sentences

Short, simple sentences are generally less confusing and ambiguous than long, complex ones. As a rule of

thumb, most sentences should contain one or two clauses. Sentences with more than three clauses should

be rephrased.


2) Ask for only one piece of information at a time

For example, "Please rate the lecture in terms of its content and presentation" asks for two pieces of

information at the same time. It should be divided into two parts: "Please rate the lecture in terms of (a)

its content, (b) its presentation."

3) Avoid negatives if possible

Negatives should be used only sparingly. For example, instead of asking students whether they agree

with the statement, "Small group teaching should not be abolished," the statement should be rephrased

as, "Small group teaching should continue." Double negatives should always be avoided.

4) Ask precise questions

Questions may be ambiguous because a word or term may have a different meaning. For example, if we

ask students to rate their interest in "medicine," this term might mean "general medicine" (as opposed to

general surgery) to some, but inclusive of all clinical specialties (as opposed to professions outside

medicine) to others.

Another source of ambiguity is a failure to specify a frame of reference. For example, in the question,

"How often did you borrow books from your library?" the time reference is missing. It might be

rephrased as, "How many books have you borrowed from the library within the past six months

altogether?"

5) Ensure those you ask have the necessary knowledge

For example, in a survey of University lecturers on recent changes in higher education, the question, "Do

you agree with the recommendations in the Dearing report on higher education?" is unsatisfactory for

several reasons. Not only does it ask for several pieces of information at the same time as there are several

recommendations in the report, the question also assumes that all lecturers know about the relevant

recommendations.

6) Level of details

It is important to ask for the exact level of details required. On the one hand, you might not be able to

fulfill the purposes of the survey if you omit to ask essential details. On the other hand, it is important to

avoid unnecessary details. People are less inclined to complete long questionnaires. This is particularly

important for confidential sensitive information, such as personal financial matters or marital relationship

issues.

7) Sensitive issues

It is often difficult to obtain truthful answers to sensitive questions. Clearly, the question, "Have you ever

copied other students' answers in a degree exam?" is likely to produce either no response or negative

responses. Less direct approaches have been suggested. Firstly, the casual approach: "By the way, do you

happen to have copied other students' answers in a degree exam?" may be used as a last part of another

decoy question. Secondly, the numbered card approach: "Please tick one or more of the following items

which correspond to how you have answered degree examination questions in the past." In the list of

items, include "copy from other students" as one of many items. Thirdly, the everybody approach: "As we


all know, most medical students have copied other students' answers in degree exams. Do you happen to

be one of them?" Fourthly, other people approach. This approach was used in the recent medical student

survey. In this survey, students were given the scenario, "Anil copies answers in a degree exam from

Sunil." They were then asked, "Do you feel Anil is wrong, what penalty should be imposed for Anil, and

have you done or would you consider doing the above?"

8) Minimise bias

People tend to answer questions in a way they perceive to be socially desired or expected by the

questioner and they often look for clues in the questions. Many apparently neutral questions can

potentially lead to bias. For example, in the question, "Within the past month, how many lectures have

you missed due to your evening job?" students may perceive the desired responses to be "never" to the

first question. This question could be rephrased as, "Within the past month, how many times did your

evening job commitment clash with lectures? How many times did you give priority to your evening

job?" Take another example. The question, "Please rate how useful the following text-books are. Please

also state whether they are included in your lecturer's recommended reading list?" There is a risk that the

students may perceive that they should rate books recommended by lecturers more favourably than

those not recommended by their lecturers. This risk may be minimised by putting the second question

later on in the questionnaire.


Typical example of: - Questionnaire for consumers

Dear Respondent,

We are conducting a survey of the after-shave lotion market. We would be grateful if you could fill-up

the following questionnaire in this regard.

Madhav B

Researcher

Student of PDIMTR

1. Do you use an after shave lotion?

( ) Yes ( ) No

If you do not use an after shave lotion then go to the Question-12

2. Please name a few after shave lotions you have heard of.

a ……..

b …….

c ………

3. Which of the following brands have you heard of? TICK

a. Park Avenue b Old Spice

c Savage d English Leather

e Patricks f Williams

g Aramis h Givenchy

i Brut j Yardley

4. a. Which after shave lotion are you using at present?.....

b. If you are to select an after shave brand now which brand

will you choose? ......

5. Can you recall the name of the previous brand of after shave lotion you used? Please mention......

6. Can you give reasons for consistency/change in your after shave lotion‘s Consistency

a. Habitual


b. Value for money

c. Don‘t like others

d. Any other, please specify Change

a. Like to try other brands.

b. For a change;

c. All brands are same.

d. Any other, please specify.

7. Why do you use an after shave lotion? TICK

a. For its antiseptic properties

b. As a perfume

c. To feel fresh

d. Girlfriend loves it

e. To get the sting.

f. Any other reason, please mention.

8. When do you use an after shave lotion?

a. Immediate after shaving

b. After a bath

c. Anytime of the day

d. Before going to a party.

e. ............

9. Given an easy availability of Indian and foreign brands of after shave lotion which brand do you

prefer?

( ) Indian ( ) Imported

Why? TICK

a. Perfume is better

b. Quality is better

c. Brand image

d. Price is lower

e. Status

f. Easy availability

g. Any other, please specify.


10. Who buys the after shave lotion for you?

a. Self

b. Family members

c. Normally get it as a gift

d. ............

11. Here we have mentioned a set of factors that you may consider while buying an after shave lotion?

Give your response on a seven point scale ranging from (1) most important to (7) least

important for each of them.

a. Price

b. Brand name

c. Perfume

d. Antiseptic property

e. Type of bottle (with/without atomizer)

12. Personal Information:

Age: ( ) less than 18 years ( )18-25 years

( ) 25-35 years ( ) above 35 years

Family Income:

( ) less than ` 36000 p.a.

( ) ` 36000 to ` 72000 p.a

( ) above ` 72000 p.a.

Profession

Govt. Service/Private Service/Student/Business/ Any Other ......

Thank you for your participation in this survey!


Collection of secondary data

Introduction

Before going through the time and expense of collecting primary data, one should check for secondary

data that previously may have been collected for other purposes but that can be used in the immediate

study. Secondary data may be internal to the firm, such as sales invoices and warranty cards, or may be

external to the firm such as published data or commercially available data. The government census is a

valuable source of secondary data.

Secondary data has the advantage of saving time and reducing data gathering costs. The disadvantages

are that the data may not fit the problem perfectly and that the accuracy may be more difficult to verify

for secondary data than for primary data.

Some secondary data is republished by organizations other than the original source. Because errors can

occur and important explanations may be missing in republished data, one should obtain secondary data

directly from its source. One also should consider who the source is and whether the results may be

biased.

The nature of secondary sources of information

Secondary data is data, which has been collected by individuals or agencies for purposes other than those

of our particular research study. For example, if a government department has conducted a survey of,

say, family food expenditures, and then a food manufacturer might use this data in the organisation's

evaluations of the total potential market for a new product. Similarly, statistics prepared by a ministry on

agricultural production will prove useful to a whole host of people and organisations, including those

marketing agricultural supplies.

No research study should be undertaken without a prior search of secondary sources (also termed desk

research).

There are several grounds for making such a bold statement.

Secondary data may be available which is entirely appropriate and wholly adequate to draw

conclusions and answer the question or solve the problem. Sometimes primary data collection simply

is not necessary.

It is far cheaper to collect secondary data than to obtain primary data. For the same level of research

budget a thorough examination of secondary sources can yield a great deal more information than can

be had through a primary data collection exercise.

The time involved in searching secondary sources is much less than that needed to complete primary

data collection.

Secondary sources of information can yield more accurate data than that obtained through primary

research. This is not always true but where a government or international agency has undertaken a


large scale survey, or even a census, this is likely to yield far more accurate results than custom

designed and executed surveys when these are based on relatively small sample sizes.

It should not be forgotten that secondary data can play a substantial role in the exploratory phase of the

research when the task at hand is to define the research problem and to generate hypotheses. The

assembly and analysis of secondary data almost invariably improves the researcher's understanding of

the marketing problem, the various lines of inquiry that could or should be followed and the

alternative courses of action which might be pursued.

Secondary sources help define the population. Secondary data can be extremely useful both in

defining the population and in structuring the sample to be taken. For instance, government statistics

on a country's agriculture will help decide how to stratify a sample and, once sample estimates have

been calculated, these can be used to project those estimates to the population.

Precaution in Using Secondary Data

With the above discussion, we can understand that there is a lot of published and unpublished sources

where researcher can gets secondary data. However, the researcher must be cautious in using this type of

data. The reason is that such type of data may be full of errors because of bias, inadequate size of the

sample, errors of definitions etc. Bowley expressed that it is never safe to take published or unpublished

statistics at their face value without knowing their meaning and limitations. Hence, before using

secondary data, you must examine the following points.

1. Suitability of Secondary Data

Before using secondary data, you must ensure that the data are suitable for the purpose of your enquiry.

For this, you should compare the objectives, nature and scope of the given enquiry with the original

investigation. For example, if the objective of our enquiry is to study the salary pattern of a firm including

perks and allowances of employees. But, secondary data is available only on basic pay. Such type of data

is not suitable for the purpose of the study.

2. Reliability of Secondary Data

For the reliability of secondary data, these can be tested: i) un-biasedness of the collecting person, ii)

proper check on the accuracy of field work, iii) the editing, tabulating and analysis done carefully, iv) the

reliability of the source of information, v) the methods used for the collection and analysis of the data. If

the data collecting organisations are government, semi-government and international, the secondary data

are more reliable corresponding to data collected by individual and private organisations.

3. Adequacy of Secondary Data

Adequacy of secondary data is to be judged in the light of the objectives of the research. For example, our

objective is to study the growth of industrial production in India. But the published report provide

information on only few states, then the data would not serve the purpose. Adequacy of the data may

also be considered in the light of duration of time for which the data is available. For example, for

studying the trends of per capita income of a country, we need data for the last 10 years, but the


information available for the last 5 years only, which would not serve our objective. Hence, we should

use secondary data if it is reliable, suitable and adequate.

Sources of information

Secondary sources of information may be divided into two categories: internal sources and external

sources.

1) Internal sources of secondary information

a. Sales data: All organisations collect information in the course of their everyday operations. Orders are

received and delivered, costs are

recorded, sales personnel submit

visit reports, invoices are sent

out, and returned goods are

recorded and so on. Much of this

information is of potential use in

marketing research but a

surprising amount of it is

actually used. Organisations

frequently overlook this

valuable resource by not

beginning their search of secondary sources with an internal audit of sales invoices, orders, inquiries

about products not stocked, returns from customers and sales force customer calling sheets. For example,

consider how much information can be obtained from sales orders and invoices:

Sales by territory

Sales by customer type

Prices and discounts

Average size of order by customer, customer type, geographical area

Average sales by sales person and

Sales by pack size and pack type, etc.

This type of data is useful for identifying an organisation's most profitable product and customers. It can

also serve to track trends within the enterprise's existing customer group.

b. Financial data: An organisation has a great deal of data within its files on the cost of producing,

storing, transporting and marketing each of its products and product lines. Such data has many uses in

research including allowing measurement of the efficiency of marketing operations. It can also be used to

estimate the costs attached to new products under consideration, of particular utilisation (in production,

storage and transportation) at which an organisation's unit costs begin to fall.


c. Transport data: Companies that keep good records relating to their transport operations are well

placed to establish which are the most profitable routes, and loads, as well as the most cost effective

routing patterns. Good data on transport operations enables the enterprise to perform trade-off analysis

and thereby establish whether it makes economic sense to own or hire vehicles, or the point at which a

balance of the two gives the best financial outcome.

d. Storage data: The rate of stock turn, stock handling costs, assessing the efficiency of certain marketing

operations and the efficiency of the marketing system as a whole. More sophisticated accounting systems

assign costs to the cubic space occupied by individual products and the time period over which the

product occupies the space. These systems can be further refined so that the profitability per unit, and

rate of sale, are added. In this way, the direct product profitability can be calculated.

2) External sources of secondary information

The researcher who seriously seeks after useful secondary data is more often surprised by its abundance

than by its scarcity. Too often, the researcher has secretly (sometimes subconsciously) concluded from the

outset that his/her topic of study is so unique or specialised that a research of secondary sources is futile.

Consequently, only a specified search is made with no real expectation of sources. Cursory researches

become a self-fulfilling prophecy. Dillon et. al give the following advice:

"You should never begin a half-hearted search with the assumption that what is being sought is so unique

that no one else has ever bothered to collect it and publish it. On the contrary, assume there are scrolling

secondary data that should help provide definition and scope for the primary research effort."

The same authors support their advice by citing the large numbers of organisations that provide

marketing information including national and local government agencies, quasi-government agencies,

trade associations, universities, research institutes, financial institutions, specialist suppliers of secondary

marketing data and professional research enterprises. Dillon et al further advise that searches of printed

sources of secondary data begin with referral texts such as directories, indexes, handbooks and guides.

These sorts of publications rarely provide the data in which the researcher is interested but serve in

helping him/her locate potentially useful data sources.


The main sources of external secondary sources are (1) government (Central, state and local) (2) trade

associations (3) commercial services (4) national and international institutions.

Government

statistics

These may include all or some of the following:

· Population censuses

· Social surveys, family expenditure surveys

· Import/export statistics

· Production statistics

· Agricultural statistics.

Trade associations Trade associations differ widely in the extent of their data collection and information

dissemination activities. However, it is worth checking with them to determine what

they do publish. At the very least one would normally expect that they would

produce a trade directory and, perhaps, a yearbook.

Commercial

services

Published market research reports and other publications are available from a wide

range of organisations which charge for their information. Typically, marketing

people are interested in media statistics and consumer information which has been

obtained from large scale consumer or farmer panels. The commercial organisation

funds the collection of the data, which is wide ranging in its content, and hopes to

make its money from selling this data to interested parties.

National and

international

institutions

Bank economic reviews, University research reports, journals and articles are all

useful sources to contact. International agencies such as World Bank, IMF, UNDP,

ITC, FAO and ILO produce a overabundance of secondary data which can prove

extremely useful to the researcher.

Merits and Limitations of Secondary Data

Merits

1) Secondary data is much more economical and quicker to collect than primary data, as we need not

spend time and money on designing and printing data collection forms (questionnaire/schedule),

appointing enumerators, editing and tabulating data etc.

2) It is impossible to an individual or small institutions to collect primary data with regard to some

subjects such as population census, imports and exports of different countries, national income data etc.

but can obtain from secondary data.

Limitations

1) Secondary data is very risky because it may not be suitable, reliable, adequate and also difficult to find

which exactly fit the need of the present investigation.

2) It is difficult to judge whether the secondary data is sufficiently accurate or not for our investigation.


3) Secondary data may not be available for some investigations. For example, bargaining strategies in live

products marketing, impact of T.V. advertisements on viewers, opinion polls on a specific subject, etc. In

such situations we have to collect primary data.

Collection of secondary Data

As already mentioned, secondary data involves use of published or unpublished data. Published data are

available in:

a) Publications of the central state and local government, b) publication of foreign or of international

bodies c) technical and trade journals d) reports prepared by research scholars, universities in

different fields etc. The sources of unpublished data are many; they may be found in diaries, letter,

biographies, and autobiographies, trade associations etc.

Types of Secondary Published data

Type of

Information

What it Is Why It might be

Useful

Where to Access Examples

Newspapers published daily,

weekly, monthly

written by

journalists,

freelancers, staff who

are usually paid

written for general

public (although some

target specific groups)

provide

immediate news

local news

editorials

can provide

photographs

excellent for

contemporary

reactions

electronic

databases

print

indexes

some

newspapers

have free

websites

Economic Times

Times of India

Employment News

Note: Because newspapers are meant to provide immediate information, some facts might not

be accurate or will change over time.

Popular

Magazines

published weekly,

monthly, etc.

written for a wide,

general (non-

academic) audience

written by

journalists, staff and

freelancers who are

usually paid

usually

provide general

information in

short articles (can

provide analysis)

lots of

graphics,

photographs and

illustrations

electronic

databases

print

indexes

some

magazines

have free

websites and

some exist

India Today

Business World

Sports week


slick appearance,

variety of formats

lots of advertising

which may be tied to

editorial content

can also be a

source for public

opinion.

rarely provide

in-depth

background

information,

overviews of

topics, statistics,

bibliographies,

cited references

solely online

Note: Popular magazines, in general, exist to entertain, sell products, express a particular

point of view, or provide news summaries of current events.

Scholarly

Journals

published

monthly, quarterly,

yearly

written for

scholars, researchers,

students and assumes

scholarly background

written by

scholars/researchers

use language of

specific discipline

generally peer-

reviewed (articles are

evaluated by experts

who make publication

recommendations)

serious appearance

with few images or

graphics

cite sources

and provide

bibliographies

provide in-

depth articles

provide results

of original

research and

experimentation

often a

preliminary step

before publishing

research in book

format

electronic

full-text

databases

print

indexes

some

scholarly

journals have

websites and

some exist

solely online

Journal of Marketing

The Strategist

Western Criminology

Review

Note: Scholarly journals are often published by scholarly societies and organizations or by

publishers of other scholarly information.


Books written by and for

a variety of audiences

generally takes

longer time to be

published

often provides

citations and

bibliographies

can provide

very in-depth

coverage

can be primary

resources

can present

multiple

viewpoints in

compilations and

anthologies

Use a

library

catalogue to

find out what

a library owns

some

published in

electronic

format (e-

Books) and are

accessible

through

library

catalogs

Marketing

Management-Kotlar

Organization

Behaviour- Robbins

Reference

Sources

encyclopaedias

dictionaries

chronologies

thesauri

usually written by

scholars/experts in a

field

provide

general or in-

depth information

provide

background

information and

overview of topics

statistics

bibliographies

facts and

timelines

names,

addresses, and

biographical

information

define terms

Use a

library

catalogue to

find out what

a library owns

some

available

online via

Library

subscriptions

some only

available in

the Library

Encyclopaedia

Britannica

Oxford Dictionary

Monorama

Statistics

(Census)

population

demographics

crime

health care

education

provide a

statistical look at a

particular

population or

topic

Use a

library

catalogue to

find out what

a library owns

Statistical Abstract of

the India

Indian Bureau of the

Census


income

public opinion etc.

some

available via

web

some only

available in

the Library

Websites All kinds of

information:

full-text books

government

documents

online shopping

greeting cards

The web is:

an infinite

array of

information

sources

In a variety of

formats.

Internet

connection

www.goggle.com

www.yahoo.com

Tip: Keep a research notebook or log of databases searched

Review questions

1. What do you mean by data? Why it is

needed for research?

2. Distinguish between primary and

secondary data. Illustrate your answer with

examples.

3. Write names of five web sources of

secondary data which have not been

included in the above table.

4. Explain the merits and limitations of using

secondary data.

5. What precautions must a researcher take

before using the secondary data?

6. In the following situations indicate whether

data from a census should be taken?

i)A TV manufacturer wants to obtain data

on customer preferences with respect to

size of TV.

ii) RTMNU wants to determine the

acceptability of its employees for

subscribing to a new employee insurance

programme.

7. How can data be collected through the

Observation Method?

8. Distinguish between the observation and

the interview method of data collection.

http://www.goggle.com/


Chapter 6: Collection and Processing Data

Introduction

In Chapter 5, we have discussed various methods of collection of data. Once the collection of data is over,

the next step is to organize data so that meaningful conclusions may be drawn. The information content

of the observations has to be reduced to a relatively few concepts and aggregates. The data collected from

the field has to be processed as laid down in the research plan. This is possible only through systematic

processing of data. Data processing involves editing, coding, classification and tabulation of the data

collected so that they are amenable to analysis. This is an intermediary stage between the collection of

data and their analysis and interpretation. In this unit, therefore, we will learn about different stages of

processing of data in detail.

Classification of data

Once the data is collected and edited, the next step towards further processing the data is classification. In

most research studies, voluminous data collected through various methods needs to be reduced into

homogeneous groups for meaningful analysis. This necessitates classification of data, which in simple

terms is the process of dividing data into different groups or classes according to their similarities and

dissimilarities. The groups should be homogeneous within and heterogeneous between themselves.

Classification condenses huge amount of data and helps in understanding the important underlying

features. It enables us to make comparison, draw inferences, locate facts and also helps in bringing out

relationships, so as to draw meaningful conclusions. In fact classification of data provides a basis for

tabulation and analysis of data.

Classification is the process of arranging data under homogeneous groups. “The process of arranging

data in groups or classes according to resemblances and similarities is technically called

classification.”


It is the process of arranging data either actually or notionally in groups or classes according to their

common characteristics classification precedes tabulation and makes tabulation easier.

Objectives of classification:

1) To identify similarity in the data collected.

2) To maintain homogeneity

3) To facilitate comparison

4) To maintain clarity

5) To simplify complex data

6) To achieve effective quantification and

7) To facilitate easy presentation and interpretation of data.

Types of Classification

Data may be classified according to one or more external characteristics or one or more internal

characteristics or both. Let us study these kinds with the help of illustrations.

1. Classification According to External Characteristics

In this classification, data may be classified according to area or region (Geographical) and according to

occurrences (Chronological).

a. Geographical: In this type of classification, data are organized in terms of geographical area or region.

State-wise production of manufactured goods is an example of this type. Data collected from an all India

market survey may be classified geographically. Usually the regions are arranged alphabetically or

according to the size to indicate the importance.

b. Chronological: When data is arranged according to time of occurrence, it is called chronological

classification. Profit of engineering industries over the last few years is an example. We may note that it is

possible to have chronological classification within geographical classification and vice versa. For example,

a large scale all India market survey spread over a number of years.

2. Classification According to Internal Characteristics

Data may be classified according to attributes (Qualitative characteristics which are not capable of being

described numerically) and according to the magnitude of variables (Quantitative characteristics which

are numerically described).

3. Classification according to attributes: In this classification, data are classified by descriptive

characteristic like sex, caste, occupation, place of

residence etc. This is done in two ways – simple

classification and manifold classification. In

simple classification (also called classification


according to dichotomy), data is simply grouped according to presence or absence of a single

characteristics – male or female, employee or unemployee, rural or urban etc.

In manifold classification (also known as multiple classification), data is classified according to more

than one characteristic. First, the data may

be divided into two groups according to

one attribute (employee and unemployee,

say). Then using the remaining attributes,

data is sub-grouped again (male and

female based on sex). This may go on

based on other attributes, like married and

unmarried, rural and urban so on… The

following table is an example of manifold

classification.

4. Classification according to magnitude of the variable: This classification refers to the classification of

data according to some characteristics that can be measured. In this classification, there are two aspects:

one is variables (age, weight, income etc;) another is frequency (number of observations which can be put

into a class). Quantitative variables may be, generally, divided into two groups - discrete and continuous.

A discrete variable is one which can take only isolated (exact) values, it does not carry any fractional

value. The examples are number of children in a household, number of departments in an organization,

number of workers in a factory etc. The variables that take any numerical value within a specified range

are called continuous variables. The examples of continuous variables are the height of a person,

profit/loss of campanies etc. One point may be noted. In practice, even the continuous variables are

measured up to some degree of precision and they also essentially become discrete variables. The

following are two examples of discrete and continuous frequency distribution placed side by side.

Surveys defined

Surveys are quantitative information collection techniques used in marketing, political polling, and

social science research.

All surveys involve questions of some sort. When the questions are administered by a researcher, the

survey is called an interview or a researcher administered survey. When the questions are administered

by the respondent, the survey is referred to as a questionnaire or a self-administered survey.


http://en.wikipedia.org/wiki/Marketing

http://en.wikipedia.org/wiki/Social_science


Advantages of surveys

The advantages of survey techniques include:

It is an efficient way of collecting information from a large number of respondents. Very large

samples are possible. Statistical techniques can be used to determine validity, reliability, and

statistical significance.

Surveys are flexible in the sense that a wide range of information can be collected. They can be used

to study attitudes, values, beliefs, and past behaviours.

Because they are standardized, they are relatively free from several types of errors.

They are relatively easy to administer.

There is an economy in data collection due to the focus provided by standardized questions. Only

questions of interest to the researcher are asked, recorded, codified, and analyzed. Time and money

is not spent on tangential questions.

Disadvantages of surveys

Disadvantages of survey techniques include:

They depend on subjects‘ motivation, honesty, memory, and ability to respond. Subjects may not be

aware of their reasons for any given action. They may have forgotten their reasons. They may not be

motivated to give accurate answers, in fact, they may be motivated to give answers that present

themselves in a favorable light.

Surveys are not appropriate for studying complex social phenomena. The individual is not the best

unit of analysis in these cases. Surveys do not give a full sense of social processes and the analysis

seems superficial.

Structured surveys, particularly those with closed ended questions, may have low validity when

researching affective variables.

Survey Methods

Once the researcher has decided on the size of sample, the next step is to decide on the method of data

collection. Each method has its advantages and disadvantages.

a) Personal Interviews

Interview is one of the most powerful tools and most widely used method for primary data collection in

research. In our daily routine we see interviews on T.V. channels on various topics related to social,

business, sports, budget etc. In the words of C. William Emory, ‗personal interviewing is a two way

purposeful conversation initiated by an interviewer to obtain information that is relevant to some

research purpose‘. Thus an interview is basically, a meeting between two persons to obtain the

information related to the proposed study. The person who is interviewing is named as interviewer and

http://en.wikipedia.org/wiki/Sampling_(statistics)



the person who is being interviewed is named as informant. It is to be noted that, the research

data/information collect through this method is not a simple conversation between the investigator and

the informant, but also the glances, gestures, facial expressions, level of speech etc., are all part of the

process. Through this method, the researcher can collect varied types of data intensively and extensively.

Interviewes can be classified as direct personal interviews and indirect personal interviews. Under the

techniques of direct personal interview, the investigator meets the informants (who come under the

study) personally, asks them questions pertaining to enquiry and collects the desired information. Thus if

a researcher intends to collect the data on spending habits of Delhi University (DU) students, he/ she

would go to the DU, contact the students, interview them and collect the required information.

Indirect personal interview is another technique of interview method where it is not possible to collect

data directly from the informants who come under the study. Under this method, the investigator

contacts third parties or witnesses, who are closely associated with the persons/situations under study

and are capable of providing necessary information. For example, an investigation regarding a bribery

pattern in an office. In such a case it is inevitable to get the desired information indirectly from other

people who may be knowing them. Similarly, clues about the crimes are gathered by the CBI. Utmost care

must be exercised that these persons who are being questioned are fully aware of the facts of the problem

under study, and are not motivated to give a twist to the facts.

Another technique for data collection through this method can be structured and unstructured

interviewing. In the Structured interview set questions are asked and the responses are recorded in a

standardised form. This is useful in large scale interviews where a number of investigators are assigned

the job of interviewing. The researcher can minimise the bias of the interviewer. This technique is also

named as formal interview. In Un-structured interview, the investigator may not have a set of questions

but have only a number of key points around which to build the interview. Normally, such type of

interviews are conducted in the case of an explorative survey where the researcher is not completely sure

about the type of data he/ she collects. It is also named as informal interview. Generally, this method is

used as a supplementary method of data collection in conducting research in business areas.

Now-a-days, telephone or cellphone interviews are widely used to obtain the desired information for

small surveys. For instance, interviewing credit card holders by banks about the level of services they are

receiving. This technique is used in industrial surveys specially in developed regions.

Merits

The major merits of this method are as follows:

1) People are more willing to supply information if approached directly. Therefore, personal interviews

tend to yield high response rates.

2) This method enables the interviewer to clarify any doubt that the interviewee might have while asking

him/her questions. Therefore, interviews are helpful in getting reliable and valid responses.

3) The informant‘s reactions to questions can be properly studied.


4) The researcher can use the language of communication according to the standard of the information, so

as to obtain personal information of informants which are helpful in interpreting the results.

Limitations

The limitations of this method are as follows:

1) The chance of the subjective factors or the views of the investigator may come in either consciously or

unconsciously.

2) The interviewers must be properly trained; otherwise the entire work may be spoiled.

3) It is a relatively expensive and time-consuming method of data collection especially when the number

of persons to be interviewed is large and they are spread over a wide area.

4) It cannot be used when the field of enquiry is large (large sample).

Precautions: While using this method, the following precautions should be taken:

Obtain thorough details of the theoretical aspects of the research problem.

Identify who is to be interviewed.

The questions should be simple, clear and limited in number.

The investigator should be sincere, efficient and polite while collecting data.

The investigator should be of the same area (field of study, district, state etc.).

b) Telephone Surveys

Surveying by telephone is the most popular interviewing method in the most of the country. This is made

possible by nearly universal coverage (Approx. 70 % of homes have a telephone in urban area).

Advantages

1. People can usually be contacted faster over the telephone than with other methods. If the

Interviewers are using CATI (computer-assisted telephone interviewing), the results can be

available minutes after completing the last interview.

2. You can dial random telephone numbers when you do not have the actual telephone numbers of

potential respondents.

3. CATI software, such as The Survey System, makes complex questionnaires practical by offering

many logic options. It can automatically skip questions, perform calculations and modify

questions based on the answers to earlier questions. It can check the logical consistency of

answers and can present questions or answers choices in a random order (the last two are

sometimes important for reasons described later).

4. Skilled interviewers can often elicit longer or more complete answers than people will give on

their own to mail, email surveys (though some people will give longer answers to Web page

surveys). Interviewers can also ask for clarification of unclear responses.

5. Some software, such as The Survey System, can combine survey answers with pre-existing

information you have about the people being interviewed.


Disadvantages

1. Many telemarketers have given legitimate research a bad name by claiming to be doing research

when they start a sales call. Consequently, many people are reluctant to answer phone interviews

and use their answering machines to screen calls.

2. The growing number of working women often means that no one is home during the day. This

limits calling time to a "window" of about 6-9 p.m. (when you can be sure to interrupt dinner or a

favourite TV program).

3. You cannot show or sample products by phone.

c) Mail Surveys

One way of improving response rates to mail surveys is to mail a postcard telling your sample to watch

for a questionnaire in the next week or two. Another is to follow up a questionnaire mailing after a couple

of weeks with a card asking people to return the questionnaire. The downside is that this doubles or

triples your mailing cost. If you have purchased a mailing list from a supplier, you may also have to pay

a second (and third) use fee - you often cannot buy the list once and re-use it.

Another way to increase responses to mail surveys is to use an incentive. One possibility is to send a

dollar bill (or more) along with the survey (or offer to donate the dollar to a charity specified by the

respondent). If you do so, be sure to say that the dollar is a way of saying "thanks," rather than payment

for their time. Many people will consider their time worth more than a dollar. Another possibility is to

include the people who return completed surveys in a drawing for a prize. A third is to offer a copy of the

(non-confidential) result highlights to those who complete the questionnaire. Any of these techniques will

increase the response rates.

Remember that if you want a sample of 1,000 people, and you estimate a 10% response level, you need to

mail 10,000 questionnaires. You may want to check with your local post office about bulk mail rates - you

can save on postage using this mailing method. However, most researchers do not use bulk mail, because

many people associate "bulk" with "junk" and will throw it out without opening the envelope, lowering

your response rate. Also bulk mail moves slowly, increasing the time needed to complete your project.

Advantages

1. Mail surveys are among the least expensive.

2. This is the only kind of survey you can do if you have the names and addresses of the target

population, but not their telephone numbers.

3. The questionnaire can include pictures - something that is not possible over the phone.

4. Mail surveys allow the respondent to answer at their leisure, rather than at the often inconvenient

moment they are contacted for a phone or personal interview. For this reason, they are not

considered as intrusive as other kinds of interviews.


Disadvantages

1. Time! Mail surveys take longer than other kinds. You will need to wait several weeks after

mailing out questionnaires before you can be sure that you have gotten most of the responses.

2. In populations of lower educational and literacy levels, response rates to mail surveys are often

too small to be useful. This, in effect, eliminates many immigrant populations that form

substantial markets in many areas. Even in well-educated populations, response rates vary from

as low as 3% up to 90%. As a rule of thumb, the best response levels are achieved from highly-

educated people and people with a particular interest in the subject (which, depending on your

target population, could lead to a biased sample).

d) Computer Direct Interviews

These are interviews in which the Interviewees enter their own answers directly into a computer. They

can be used at malls, trade shows, offices, and so on. The Survey System's optional Interviewing Module

and Interview Stations can easily create computer-direct interviews. Some researchers set up a Web page

survey for this purpose.

Advantages

1. The virtual elimination of data entry and editing costs.

2. You will get more accurate answers to sensitive questions. Recent studies of potential blood

donors have shown respondents were more likely to reveal HIV-related risk factors to a

computer screen than to either human interviewers or paper questionnaires. The National

Institute of Justice has also found that computer-aided surveys among drug users get better

results than personal interviews. Employees are also more often willing to give more honest

answers to a computer than to a person or paper questionnaire.

3. The elimination of interviewer bias. Different interviewers can ask questions in different ways,

leading to different results. The computer asks the questions the same way every time.

4. Ensuring skip patterns are accurately followed. The Survey System can ensure people are not

asked questions they should skip based on their earlier answers. These automatic skips are more

accurate than relying on an Interviewer reading a paper questionnaire.

5. Response rates are usually higher. Computer-aided interviewing is still novel enough that some

people will answer a computer interview when they would not have completed another kind of

interview.

Disadvantages

1. The Interviewees must have access to a computer or one must be provided for them.

2. As with mail surveys, computer direct interviews may have serious response rate problems in

populations of lower educational and literacy levels. This method may grow in importance as

computer use increases.

http://www.surveysystem.com/#bais


e) Email Surveys

Email surveys are both very economical and very fast. More people have email than have full Internet

access. This makes email a better choice than a Web page survey for some populations. On the other

hand, email surveys are limited to simple questionnaires, whereas Web page surveys can include

complex logic.

Advantages

1. Speed. An email questionnaire can gather several thousand responses within a day or two.

2. There is practically no cost involved once the set up has been completed.

3. You can attach pictures and sound files.

4. The novelty element of an email survey often stimulates higher response levels than ordinary

―snail‖ mail surveys.

Disadvantages

1. You must possess (or purchase) a list of email addresses.

2. Some people will respond several times or pass questionnaires along to friends to answer. Many

programs have no check to eliminate people responding multiple times to bias the results. The

Survey System‘s Email Module will only accept one reply from each address sent the

questionnaire. It eliminates duplicate and pass along questionnaires and checks to ensure that

respondents have not ignored instructions (e.g., giving 2 answers to a question requesting only

one).

3. Many people dislike unsolicited email even more than unsolicited regular mail. You may want to

send email questionnaires only to people who expect to get email from you.

4. You cannot use email surveys to generalize findings to the whole populations. People who have

email are different from those who do not, even when matched on demographic characteristics,

such as age and gender.

5. Email surveys cannot automatically skip questions or randomize question or answer choice order

or use other automatic techniques that can enhance surveys the way Web page surveys can.

Although use of email is growing very rapidly, it is not universal - and is even less so outside the urban

areas. Many ―average‖ citizens still do not possess email facilities, especially older people and those in

lower income and education groups. So email surveys do not reflect the population as a whole. At this

stage they are probably best used in a corporate environment where email is common or when most

members of the target population are known to have email.

f) Internet/Intranet (Web Page) Surveys

Web surveys are rapidly gaining popularity. They have major speed, cost, and flexibility advantages, but

also significant sampling limitations. These limitations make software selection especially important and

restrict the groups you can study using this technique.


Advantages

1. Web page surveys are extremely fast. A questionnaire posted on a popular Web site can gather

several thousand responses within a few hours. Many people who will respond to an email

invitation to take a Web survey will do so the first day, and most will do so within a few days.

2. There is practically no cost involved once the set up has been completed. Large samples do not

cost more than smaller ones (except for any cost to acquire the sample).

3. You can show pictures. Some Web survey software can also show video and play sound.

4. Web page questionnaires can use complex question skipping logic, randomisations and other

features not possible with paper questionnaires or most email surveys. These features can assure

better data.

5. Web page questionnaires can use colours, fonts and other formatting options not possible in most

email surveys.

6. A significant number of people will give more honest answers to questions about sensitive topics,

such as drug use or sex, when giving their answers to a computer, instead of to a person or on

paper.

7. On average, people give longer answers to open-ended questions on Web page questionnaires

than they do on other kinds of self-administered surveys.

8. Some Web survey software, such as The Survey System, can combine the survey answers with

pre-existing information you have about individuals taking a survey.

Disadvantages

1. Current use of the Internet is far from universal. Internet surveys do not reflect the population as

a whole. This is true even if a sample of Internet users is selected to match the general population

in terms of age, gender and other demographics.

2. People can easily quit in the middle of a questionnaire. They are not as likely to complete a long

questionnaire on the Web as they would be if talking with a good interviewer.

3. If your survey pops up on a web page, you often have no control over who replies - anyone from

Antarctica to Zanzibar, cruising that web page may answer.

4. Depending on your software, there is often no control over people responding multiple times to

bias the results.

At this stage we recommend using the Internet for surveys mainly when your target population consists

entirely or almost entirely of Internet users. Business-to-business research and employee attitude

surveys can often meet this requirement. Surveys of the general population usually will not. Another

reason to use a Web page survey is when you want to show video or both sound and graphics. A Web

page survey may be the only practical way to have many people view and react to a video.

In any case, be sure your survey software prevents people from completing more than one questionnaire.

You may also want to restrict access by requiring a password (good software allows this option) or by


putting the survey on a page that can only be accessed directly (i.e., there are no links to it from other

pages).

g) Scanning Questionnaires

Scanning questionnaires is a method of data collection that can be used with paper questionnaires that

have been administered in face-to-face interviews; mail surveys or surveys completed by an Interviewer

over the telephone. The Survey System can produce paper questionnaires that can be scanned using

Remark Office OMR (Optical Mark Reader). Other software can scan questionnaires and produce ASCII

Files that can be read into The Survey System.

Advantages

1. Scanning can be the fastest method of data entry for paper questionnaires.

2. Scanning is more accurate than a person in reading a properly completed questionnaire.

Disadvantages

1. Scanning is best-suited to "check the box" type surveys and bar codes. Scanning programs have

various methods to deal with text responses, but all require additional data entry time.

2. Scanning is less forgiving (accurate) than a person in reading a poorly marked questionnaire.

Requires investment in additional hardware to do the actual scanning.

The choice of survey method will depend on several factors. These include:

Speed Email and Web page surveys are the fastest methods, followed by telephone

interviewing. Mail surveys are the slowest.

Cost Personal interviews are the most expensive followed by telephone and then mail.

Email and Web page surveys are the least expensive for large samples.

Internet Usage Web page and Email surveys offer significant advantages, but you may not be able to

generalize their results to the population as a whole.

Literacy

Levels

Illiterate and less-educated people rarely respond to mail surveys.

Sensitive

Questions

People are more likely to answer sensitive questions when interviewed directly by a

computer in one form or another.

Video, Sound,

Graphics

A need to get reactions to video, music or a picture limits your options. You can play a

video on a Web page, in a computer-direct interview, or in person. You can play music

when using these methods or over a telephone. You can show pictures in those first

methods and in a mail survey.


Survey Errors

A. Random sampling error: Most surveys try to portray a representative cross section of a particular

target population, but even with technically proper random probability samples, statistical errors will

occur because of chance variation. Without increasing sample size, these statistical problems are

unavoidable.

B. Systematic error: Systematic errors result from some imperfect research design or from a mistake in

the execution of the research. These errors are also called non-sampling errors. A sample bias exists

when the results of a sample show a persistent tendency to deviate in one direction from the true value of

the population parameter. The two general categories of systematic error are respondent error and

administrative error.

1. Respondent error: If the respondents do not cooperate or do not give truthful answers then two types

of error may occur.

a) Non-response error: To utilize the results of a survey, the researcher must be sure that those who did

not respond to the questionnaire were representative of those who did not. If only those who responded

are included in the survey then non-response error will occur. Non-respondents are most common in

mail surveys, but may also occur in telephone and personal surveys in the form of no contacts (not-at-

homes) or refusals. The number of no contacts has been increasing because of the proliferation of

answering machines and growing usage of Caller ID to screen telephone calls. Self-selection may also

occur in self-administered questionnaires; in this situation, only those who feel strongly about the subject

matter will respond, causing an over-representation of extreme positions. Comparing demographics of

the sample with the demographics of the target population is one means of inspecting for possible biases.

Additional efforts should be made to obtain data from any underrepresented segments of the population.

For example, call-backs can be made on the not-at-homes.

b) Response bias: Response bias occurs when respondents tend to answer in a certain direction. This bias

may be caused by an intentional or inadvertent falsification or by a misrepresentation of the respondent‘s

answer.

(1) Deliberate falsification: People may misrepresent answers in order to appear intelligent, to avoid

embarrassment, to conceal personal information, to "please" the interviewer, etc. It may be that the

interviewees preferred to be viewed as average and they will alter their responses accordingly.

(2) Unconscious misrepresentation: Response bias can arise from question format, question ambiguity or

content. Time-lapse may lead to best-guess answers.

Types of response bias: There are five specific categories of response bias. These categories overlap and

are by no means mutually exclusive.

(i) Agreement bias: This is a response bias caused by a respondent‘s tendency to concur with a

particular position. For example, "yes Sayers" who accept all statements they are asked about.


(ii) Extremity bias: Some individuals tend to use extremes when responding to questions which

may cause extremity bias.

(iii) Interviewer bias: If an interviewer‘s presence influences respondents to give untrue or

modified answers, the survey will contain interviewer bias. Respondents may wish to appear

wealthy or intelligent, or they may try to give the "right" answer or the socially acceptable answer.

(iv) Patronage bias: The answers to a survey may be deliberately or unintentionally misrepresented

because the respondent is influenced by the organization conducting the survey.

(v) Social desirability bias: This may occur consciously or subconsciously. Answers to questions

that seek factual information or matters of public knowledge are usually quite accurate, but the

interviewer‘s presence may increase a respondent‘s tendency toward an inaccurate response to a

sensitive question in an attempt by the respondent to gain prestige in the interviewer‘s mind.

2. Administrative error: The results of improper administration or execution of the research task are

examples of administrative error. Such errors are inadvertently caused by confusion, neglect, omission, or

some other blunder. There are four types of administrative error:

a) Data processing error: The accuracy of the data processed by computer depends on correct data entry

and programming. Mistakes can be avoided if verification procedures are employed at each processing

stage.

b) Sample selection error: This type of error is a systematic error that results in an unrepresentative

sample because of an error in either the sample design or execution of the sampling procedure.

c) Interviewer error: Interviewers may record an answer incorrectly or selective perception may influence

them to record data supportive of their own attitudes.

d) Interviewer cheating: To avoid possible cheating, it is wise to inform the interviewers that a small

sample of respondents will be back to confirm that the interview actually took place.

Rule-of-thumb estimates for systematic error

Sampling error may be estimated using certain statistical tools, but ways to estimate systematic error are

less precise. Many researchers have found it useful to use some standard of comparison in order to

understand how much error can be expected. For example, one cable TV company knocks down the

number of people saying that they intend to purchase the service by a "ballpark 10 percent" because

previous experience has indicated a 10 percent upward bias on the intention questions.


Data processing

Data are raw facts. When organised and presented properly, they become information. Turning data into

information involves several steps.

These steps are known as data

processing. This section looks at

data processing and the use of

computers to do it easily and

quickly. The diagram below shows a

simplified view of the procedure for

turning data into information. Data, in a range of forms and from various sources, may be entered into a

computer where it can be manipulated to produce useful information (output).

Data processing includes the following steps:

1. Data coding,

2. Data input,

3. Data editing, and

4. Data manipulation.

1) Data coding

Coding is placing data in a usable form. Researcher must make decisions about the level of measurement

needed and assign numbers to variables, including codes for variables where the data is missing or

unusable. This is likely already done if the researcher is using a pre-coded questionnaire, but for other

data collection techniques, such as using public records, this is a step that has to be taken.

Before raw data is entered into a computer it may need to be coded. Coding involves labelling the

responses in a unique and abbreviated way (often by simple numerical codes). The reason raw data are

coded is that it makes data entry and data manipulation easier. Coding can be done by interviewers in

the field or by people in an office.

A closed question implies that only a fixed number of predetermined responses are allowed, and these

responses can have codes affixed on the form. An open question implies that any response is allowed,

making subsequent coding more difficult. One may select a sample of responses, and design a code

structure which captures and categorizes most of these.

Each variable should be carefully examined in terms of research problem. In general the level of

measurement for a variable should be the highest level possible to retain the most information and allow

the most powerful statistics to be used. For example, education could be classified into categories such as

(1) less than 12 years, (2) high school degree, (3) some college, and (4) college degree. This may be


perfectly acceptable for research problem as long as we are examining differences based on degrees.

Frequently, a research hypothesis is modified in the process, but while the original categories worked for

the original hypothesis, the new hypothesis might need more specific data. For example, we may find we

need specific number of years of education and not just degrees, because degrees alone do not seem to be

the relevant categories of education. Thus, it is preferable to code at the highest level of measurement

possible. You can always recode data into simpler categories for testing hypothesis if the original data is

there but you can't create higher-level data from lower level measurement.

Level of measurement: the issue of measurement levels is very complex. Luckily we don't have to

become experts but we do have to know enough to define our variables and later to choose appropriate

statistics. A simple outline of levels of measurement: -

We can demonstrate these levels by defining sex/gender two different ways.

(1) A self-selected choice on a questionnaire

What is your gender, please check the appropriate selection!

(1) Female: - ______

(2) Male: - ______

The first definition of gender is a nominal level measure, a simple classification system with limited

statistics appropriate for analysis: only the mode would be acceptable for measuring central tendency.

Incidentally, while gender is our variable, the choices 1 and 2 are referred to as attributes or values of the

variable gender.

Coding refers to the process by which data are categorized into groups and numerals or other symbols or

both are assigned to each item depending on the class it falls in. Hence, coding involves: (i) deciding the

categories to be used, and (ii) assigning individual codes to them. In general, coding reduces the huge

amount of information collected into a form that is amenable to analysis. A careful study of the answers is

the starting point of coding. Next, a coding frame is to be developed by listing the answers and by

assigning the codes to them. A coding manual is to be prepared with the details of variable names, codes

and instructions. Normally, the coding manual should be prepared before collection of data, but for open-

ended and partially coded questions. These two categories are to be taken care of after the data collection.

The following are the broad general rules for coding:

1) Each respondent should be given a code number (an identification number).

2) Each qualitative question should have codes. Quantitative variables may or may not be coded

depending on the purpose. Monthly income should not be coded if one of the objectives is to compute

average monthly income. But if it is used as a classificatory variable it may be coded to indicate poor,

middle or upper income group.

3) All responses including ―don‘t know‖, ―no opinion‖ ―no response‖ etc., are to be coded.

Sometimes it is not possible to anticipate all the responses and some questions are not coded before

collection of data. Responses of all the questions are to be studied carefully and codes are to be decided

by examining the essence of the answers. In partially coded questions, usually there is an option ―Any


Other (specify)‖. Depending on the purpose, responses to this question may be examined and additional

codes may be assigned.

2) Data input

The keyboard of a computer is one of the more commonly known input, or data entry, devices in current

use. In the past, punched cards or paper tapes have been used.

Other input devices in current use include light pens, trackballs, scanners, mice, optical mark readers and

bar code readers. Some common everyday examples of data input devices are:

Bar code readers used in shops, supermarkets or libraries, and

Scanners used in desktop publishing.

3) Data editing

Before being presented as information, data should be put through a process called editing. This process

checks for accuracy and eliminates problems that can produce disorganised or incorrect information.

Data editing may be performed by clerical staff, computer software, or a combination of both; depending

on the medium in which the data is submitted.

Editing may be broadly defined to be a procedure, which uses available information and assumptions to

substitute inconsistent values in a data set. In other words, editing is the process of examining the data

collected through various methods to detect errors and omissions and correct them for further analysis.

While editing, care has to be taken to see that the data are as accurate and complete as possible, units of

observations and number of decimal places are the same for the same variable.

The following practical guidelines may be handy while editing the data:

1) The editor should have a copy of the instructions given to the interviewers.

2) The editor should not destroy or erase the original entry. Original entry should be crossed out in such a

manner that they are still legible.

3) All answers, which are modified or filled in afresh by the editor, have to be indicated.

4) All completed schedules should have the signature of the editor and the date.

For checking the quality of data collected, it is advisable to take a small sample of the questionnaire and

examine them thoroughly. This helps in understanding the following types of problems: (1) whether all

the questions are answered, (2) whether the answers are properly recorded, (3) whether there is any bias,

(4) whether there is any interviewer dishonesty, (5) whether there are inconsistencies. At times, it may be

worthwhile to group the same set of questionnaires according to the investigators (whether any

particular investigator has specific problems) or according to geographical regions (whether any

particular region has specific problems) or according to the sex or background of the investigators, and

corrective actions may be taken if any problem is observed.


Before tabulation of data it may be good to prepare an operation manual to decide the process for

identifying inconsistencies and errors and also the methods to edit and correct them. The following broad

rules may be helpful.

i. Incorrect answers: It is quite common to get incorrect answers to many of the questions. A person with

a thorough knowledge will be able to notice them. For example, against the question ―Which brand of

biscuits do you purchase?‖ the answer may be ―We purchase biscuits from ABC Stores‖. Now, this

questionnaire can be corrected if ABC Stores stocks only one type of biscuits, otherwise not. Answer to

the question ―How many days did you go for shopping in the last week?‖ would be a number between 0

and 7. A number beyond this range indicates a mistake, and such a mistake cannot be corrected. The

general rule is that changes may be made if one is absolutely sure, otherwise this question should not be

used. Usually a schedule has a number of questions and although answers to a few questions are

incorrect, it is advisable to use the other correct information from the schedule rather than discarding the

schedule entirely.

ii. Inconsistent answers: When there are inconsistencies in the answers or when there are incomplete or

missing answers, the questionnaire should not be used. Suppose that in a survey, per capita expenditure

on various items are reported as follows: Food – ` 700, Clothing – `300, Fuel and Light – ` 200, other

items – ` 550 and Total – ` 1600. The answers are obviously inconsistent as the total of individual items of

expenditure is exceeding the total expenditure.

iii. Modified answers: Sometimes it may be necessary to modify or qualify the answers. They have to be

indicated for reference and checking. Numerical answers to be converted to same units: Against the

question ―What is the plinth area of your house?‖ answers could be either in square feet or in square

metres. It will be convenient to convert all the answers to these questions in the same unit, square metre

for example.

4) Data manipulation

After editing, data may be manipulated by computer to produce the desired output. The software used to

manipulate data will depend on the form of output required.

Software applications such as word processing, desktop publishing, graphics (including graphing and

drawing), databases and spreadsheets are commonly used. Following are some ways that software can

manipulate data:

Spreadsheets are used to create formulas that automatically add columns or rows of figures calculate

means and perform statistical analyses. They can be used to create financial worksheets such as

budgets or expenditure forecasts, balance accounts and analyse costs.

Databases are electronic filing cabinets: systematically storing data for easy access to produce

summaries, stocktakes or reports. A database program should be able to store, retrieve, sort, and

analyse data.


Charts can be created from a table of numbers and displayed in a number of ways, to show the

significance of a selection of data. Bar, line, pie and other types of charts can be generated and

manipulated to advantage.

Processing data provides useful information called output. Computer output may be used in a variety of

ways. It may be saved in storage for later retrieval and use. It may be laser printed on paper as tables or

charts, put on a transparent slide for overhead projector use, saved on floppy disk for portable use in

other computers, or sent as an electronic file via the internet to others. Types of output are limited only by

the available output devices, but their form is usually governed by the need to communicate information

to someone. For whom is output being produced? How will they best understand it? The answers to

these questions help determine one's output type.

Tabulation

Before analysis can be performed, raw data must be transformed into the right format. First, it must be

edited so that errors can be corrected or omitted. The data must then be coded; this procedure converts

the edited raw data into numbers or symbols. A codebook is created to document how the data was

coded. Finally, the data is tabulated to count the number of samples falling into various categories.

Simple tabulations count the occurrences of each variable independently of the other variables. Cross

tabulations, also known as contingency tables or cross tabs, treats two or more variables simultaneously.

However, since the variables are in a two-dimensional table, cross tabbing more than two variables is

difficult to visualize since more than two dimensions would be required. Cross tabulation can be

performed for nominal and ordinal variables.

Cross tabulation is the most commonly utilized data analysis method in research. Many studies take the

analysis no further than cross tabulation. This technique divides the sample into sub-groups to show how

the dependent variable varies from one subgroup to another. A third variable can be introduced to

uncover a relationship that initially was not evident.

Tabulation is an orderly arrangement of data in columns and rows. It is a systematic presentation of

classified data on the basis of the nature of analysis & investigation.

Tabulation refers to the orderly arrangement of data in a table or other summary format. Counting the

number of responses to a question and putting them into a frequency distribution is a simple tabulation,

or marginal tabulation, which provides the most basic form of information for the researcher. Often such

simple tabulation is presented in the form of a frequency table. A frequency table is the arrangement of

statistical data in a row and column format that exhibits the count of responses or observations for each

of the categories or codes assigned to a variable. Large samples generally require computer tabulation of

the data.


Presentation of collected data in the tabular form is one of the techniques of data presentation. The two

other techniques are diagrammatic and graphic presentation. Arranging the data in an orderly manner in

rows and columns is called tabulation of data.

Sometimes data collected by survey or even from publications of official bodies are so numerous that it is

difficult to understand the important features of the data. Therefore it becomes necessary to summarize

data through tabulation to an easily intelligible form. It may be noted that there may be loss of some

minor information in certain cases, but the essential underlying features come out more clearly. Quite

frequently, data presented in tabular form is much easier to read and understand than the data presented

in the text.

In classification, as discussed in the previous section, the data is divided on the basis of similarity and

resemblance, whereas tabulation is the process of recording the classified facts in rows and columns.

Therefore, after classifying the data into various classes, they should be shown in the tabular form.

Tabulation is important because:-

1) It conserves space and reduces explanatory and descriptive statement to the minimum

2) It facilitates the process of comparison

3) It saves time and interpretation, induction, deduction ad conclusion become easier.

Tabulation may be simple or complex. Simple calculation gives information about one or more groups of

independent questions. A complex tabulation gives information or shows the division of data in two or

more categories. A complex table generally results in two way (which give information about two

interrelated characteristics of data), three –way tables or still higher order tables, which supply

information about several interrelated characteristic of data.

Requisites of a Good Statistical Table

After having an understanding of the parts of a statistical table, now let us discuss the features of an ideal

statistical table. Besides the rules relating to part of the table, certain guidelines are very helpful in its

preparation. They are as follows:

1) A good table must present the data in as clear and simple a manner as possible.

2) The title should be brief and self-explanatory. It should represent the description of the contents of the

table.

3) Rows and Columns may be numbered to facilitate easy reference.

4) Table should not be too narrow or too wide. The space of columns and rows should be carefully

planned, so as to avoid unnecessary gaps.

5) Columns and rows which are directly comparable with one another should be placed side by side.

6) Units of measurement should be clearly shown.

7) All the column figures should be properly aligned. Decimal points and plus or minus signs also should

be in perfect alignment.


8) Abbreviations should be avoided in a table. If it is inevitable to use, their meanings must be clearly

explained in footnote.

9) If necessary, the derived data (percentages, indices, ratios, etc.) may also be incorporated in the tables.

10) The sources of the data should be clearly stated so that the reliability of the data could be verified, if

needed.

Review questions

1. What do you mean by Editing of data?

Explain the guidelines to be kept in mind

while editing the statistical data.

2. Explain the meaning of coding? How

would you code your research data?

3. ―Classification of data provides a basis for

tabulation of data. Comment.

4. Discuss the various methods of

classification.

5. What is tabulation? Draw the format of a

statistical table and indicate its various

parts.

6. Describe the requisites of a good statistical

table.

7. Prepare a blank table showing the age, sex

and literacy of the population in a city,

according to five age groups from 0 to 100

years.

8. The following figures relate to the number

of crimes (nearest-hundred) in four

metropolitan cities in India. In 1961,

Bombay recorded the highest number of

crimes i.e. 19,400 followed by Calcutta with

14,200, Delhi 10,000 and Madras 5,700. In

the year 1971, there was an increase of

5,700 in Bombay over its 1961 figure. The

corresponding increase was 6,400 in Delhi

and 1,500 in Madras. However, the number

of these crimes fell to 10,900 in the case of

Calcutta for the corresponding period. In

1981, Bombay recorded a total of 36,300

crimes. In that year, the number of crimes

was 7,000 less in Delhi as compared to

Bombay. In Calcutta the number of crimes

increased by 3,100 in 1981 as compared to

1971. In the case of Madras the increase in

crimes was by 8,500 in 1981 as compared to

1971. Present this data in tabular form.


Chapter7: Analysis of Data

Introduction

Data Analysis is the process of systematically applying statistical and/or logical techniques to describe

and illustrate, condense and recap, and evaluate data. According to Shamoo and Resnik (2003) various

analytic procedures ―provide a way of drawing inductive inferences from data and distinguishing the

signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data‖..

While data analysis in qualitative research can include statistical procedures, many times analysis

becomes an ongoing iterative process where data is continuously collected and analyzed almost

simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire

data collection phase (Savenye, Robinson, 2004).

An essential component of ensuring data integrity is the accurate and appropriate analysis of research

findings. Improper statistical analyses distort scientific findings, mislead casual readers, and may

negatively influence the public perception of research. Integrity issues are just as relevant to analysis of

non-statistical data as well.

Considerations/issues in data analysis

There are a number of issues that researchers should be aware of with respect to data analysis. These

include:

Having the necessary skills to analyze

Concurrently selecting data collection methods and appropriate analysis

Drawing unbiased inference

Inappropriate subgroup analysis

Following acceptable norms for disciplines

Determining statistical significance

Lack of clearly defined and objective outcome measurements

Providing honest and accurate analysis


Manner of presenting data

Environmental/contextual issues

Data recording method

Partitioning „text‟ when analyzing qualitative data

Training of staff conducting analyses

Reliability and Validity

Extent of analysis

Whether statistical or non-statistical methods of analyses are used, researchers should be aware of the

potential for compromising data integrity. While statistical analysis is typically performed on quantitative

data, there are numerous analytic procedures specifically designed for qualitative material including

content, thematic, and ethnographic analysis. Regardless of whether one studies quantitative or

qualitative phenomena, researchers use a variety of tools to analyze data in order to test hypotheses,

discern patterns of behavior, and ultimately answer research questions. Failure to understand or

acknowledge data analysis issues presented can compromise data integrity.

Advanced Data Analysis Techniques

The next step is that of choosing the appropriate statistical test. There are basically two types of statistical

test, parametric and non-parametric. Parametric tests are those, which make assumptions about the

nature of the population from which the scores were drawn (i.e. population values are "parameters", e.g.

means and standard deviations). If we assume, for example, that the distribution of the sample means is

normal, then we require to use a parametric test. Non-parametric tests do not require this type of

assumption and relate mainly to that branch of statistics known as "order statistics". We discard actual

numerical values and focus on the way in which things are ranked or classed. Thereafter, the choice

between alternative types of test is determined by 3 factors:

(1) Whether we are working with dependent or independent samples, (2) whether we have more or less

than two levels of the independent variable, and (3) the mathematical properties of the scale which we

have used, i.e. ratio, interval, ordinal or nominal.

We will reject Ho, our null hypothesis, if a statistical test yields a value whose associated probability of

occurrence is equal to or less than some small probability, known as the critical region (or level).

Common values of this critical level are 0.05 and 0.01.

Bivariate statistical analysis: tests of differences

What is the appropriate test of difference?

One of the most frequently tested hypotheses states that two (or more) groups are different with respect

to some behavior, characteristic, or attitude. Such tests are called tests of differences. For example, a


researcher may be interested to see if male and female consumers purchase a product with equal

frequency. Bivariate statistical analysis is data analysis and hypothesis testing when the investigation

concerns simultaneous investigation of two variables.

1) Tests of Statistical Significance

The chi-square ( Ψ2 ) goodness-of-fit test is used to determine whether a set of proportions have specified

numerical values. It often is used to analyze bivariate cross-tabulated data. Some examples of situations

that are well-suited for this test are:

A manufacturer of packaged products test markets a new product and wants to know if sales of

the new product will be in the same relative proportion of package sizes as sales of existing

products.

A company's sales revenue comes from Product A (50%), Product B (30%), and Product C (20%).

The firm wants to know whether recent fluctuations in these proportions are random or whether

they represent a real shift in sales.

The chi-square test is performed by defining k categories and observing the number of cases falling into

each category. Knowing the expected number of cases falling in each category, one can define chi-squared

as:

Ψ2 i - Ei )2 / Ei

Where,

Oi = the number of observed cases in category i,

Ei = the number of expected cases in category i,

k = the number of categories,

the summation runs from i = 1 to i = k.

Before calculating the chi-square value, one needs to determine the expected frequency for each cell. This

is done by dividing the number of samples by the number of cells in the table.

To use the output of the chi-square function, one uses a chi-square table. To do so, one needs to know the

number of degrees of freedom (df). For chi-square applied to cross-tabulated data, the number of degrees

of freedom is equal to (Number of columns - 1) (Number of rows - 1)

This is equal to the number of categories minus one. The conventional critical level of 0.05 normally is

used. If the calculated output value from the function is greater than the chi-square look-up table value,

the null hypothesis is rejected.

2) The t-test for comparing two means

The t-test may be used to test a hypothesis that the mean scores on some interval- or ratio-scaled variable

will be significantly different for two independent samples or groups. It is used when the number of


observations (sample size) in either group is small (less than 30) and the population standard deviation is

unknown. To use the t-test for difference of means, we assume the two samples are drawn from normal

distributions and the variances of the two populations or groups are equal (homoscedasticity). Further,

we assume interval data.

A pooled estimates of the standard error is a better estimate of the standard error than one based on the

variance from either sample.

In a test of two means, the degrees of freedom are calculated as follows:

d.f. = n - k

An illustration of the t-test would be to test the difference between sociology majors and business majors

on scores on a scale measuring attitudes toward business. The null hypothesis would be that there is no

difference in attitudes toward business (mean score) between the two groups.

Computer programs, such as SPSS, are commonly used to do the calculations in testing the mean

differences of two groups.

3) The z-test for comparing two proportions

When the observed statistic is a proportion, the Z-test for differences of proportions is used to test the

hypothesis that the two proportions will be significantly different for two independent samples or

groups. Again, sample size is the appropriate criterion when selecting either a t-test or a Z-test.

4) Analysis of Variance (ANOVA) test ANOVA (F test)

Introduction:

The analysis of variance is a powerful statistical tool for tests of significance. The term Analysis of

Variance was introduced by Prof. R.A. Fisher to deal with problems in agricultural research. The test of

significance based on t-distribution is an adequate procedure only for testing the significance of the

difference between two sample means. In a situation where we have three or more samples to consider at

a time, an alternative procedure is needed for testing the hypothesis that all the samples are drawn from

the same population, i.e., they have the same mean. For example, five fertilizers are applied to four plots

each of wheat and yield of wheat on each of the plot is given. We may be interested in finding out

whether the effect of these fertilizers on the yields is significantly different or in other words whether the

samples have come from the same normal population. The answer to this problem is provided by the

technique of analysis of variance. Thus basic purpose of the analysis of variance is to test the

homogeneity of several means.

Another test of significance is the Analysis of Variance (ANOVA) test. The primary purpose of ANOVA

is to test for differences between multiple means. Whereas the t-test can be used to compare two means,

ANOVA is needed to compare three or more means. If multiple t-tests were applied, the probability of a

TYPE I error (rejecting a true null hypothesis) increases as the number of comparisons increases.


The term analysis of variance (ANOVA) is used in the field of study called designed experiments. In

this field the goal is to try to maximize the amount of information that is collected when an

experiment (production trial) is performed.

The technique was developed by Sir Ronald Fisher in the 1930's as a way to interpret the results from

agricultural experiments.

The normal way in which things are usually done in experiments is to hold everything constant while

only varying one item at a time. This is a most inefficient way to do things and not very representative of

what happens in the real world.

In designed experimental approaches items are allowed to vary simultaneously and the respective data is

gathered and analyzed. This analysis can not only detect differences in means, but effects of interactions.

As mentioned the area of ANOVA is a whole field of study in itself, and we will only look at one of the

simpler types. One word of caution should be given before ever starting any data collection; the data

gathering should be randomized allowing equal chance of occurrence. This is necessary to prevent any

bias that might result in misinterpretation.

ANOVA is efficient for analyzing data using relatively few observations and can be used with categorical

variables. Note that regression can perform a similar analysis to that of ANOVA.

An example of an ANOVA problem might be to compare women who are working full time outside the

home, working part time outside the home, or working full time inside the home on their willingness to

purchase a microwave oven. Here there is one independent variable— working status—but there are

three groups (levels) and therefore a t-test cannot be used for the testing of statistical significance.

The null hypothesis in such a test is that all the means are equal. The logic of this technique goes as

follows. The variance of the means of the three groups will be large if these women differ from one

another in terms of purchasing intentions. If we calculate this variance within groups and compare it with

the variance of the group means about a grand mean, we can determine if the means are significantly

different.

Variation is inherent in nature. The total variation in any set of numerical data is due to a number of

causes which may be classified as:

(i) Assignable causes and (ii) Chance causes

The variation due to assignable causes can be detected and measured whereas the variation due to chance

causes is beyond the control of human hand and cannot be traced separately.

Definition:

According to R.A. Fisher , Analysis of Variance (ANOVA) is the ― Separation of Variance ascribable to

one group of causes from the variance ascribable to other group‖. By this technique the total variation in

the sample data is expressed as the sum of its nonnegative components where each of these components

is a measure of the variation due to some specific independent source or factor or cause.


Assumptions:

For the validity of the F-test in ANOVA the following assumptions are made.

(i) The observations are independent

(ii) Parent population from which observations are taken is normal and

(iii) Various treatment and environmental effects are additive in nature.

The F-Test

One-way ANOVA examines whether multiple means differ. The test is called an F-test. ANOVA

calculates the ratio of the variation between groups to the variation within groups (the F ratio). While

ANOVA was designed for comparing several means, it also can be used to compare two means. Two-

way ANOVA allows for a second independent variable and addresses interaction.

To run a one-way ANOVA, use the following steps:

1. Identify the independent and dependent variables.

2. Describe the variation by breaking it into three parts - the total variation, the portion that is

within groups, and the portion that is between groups (or among groups for more than two

groups). The total variation (SStotal) is the sum of the squares of the differences between each

value and the grand mean of all the values in all the groups. The in-group variation (SSwithin) is

the sum of the squares of the differences in each element's value and the group mean. The

variation between group means (SSbetween) is the total variation minus the in-group variation

(SStotal - SSwithin).

3. Measure the difference between each group's mean and the grand mean.

4. Perform a significance test on the differences.

5. Interpret the results.

This F-test assumes that the group variances are approximately equal and that the observations are

independent. It also assumes normally distributed data; however, since this is a test on means the Central

Limit Theorem holds as long as the sample size is not too small.

The F-Test is a procedure for comparing one sample variance with another sample variance. The key

question is whether the two sample variances are different from each other or if they are from the same

population.

The F-test utilizes measures of sample variance rather than the sample standard deviation because

summation is allowable with the sample variance.

To test the null hypothesis of no difference between the sample variances, a table of the F-distribution is

necessary.

Identifying and Partitioning the Total Amount of Variation

In the F-test there will be two forms of variation: (1) variation of scores due to random error or within-

group variation due to individual differences (within-group variance) and (2) systematic variation of


scores between the groups as the result of the manipulation of an independent variable or due to

characteristics of the independent variable (between-group variance).

The larger the ratio the greater the value of F. If the F value is large, the results are likely to be statistically

significant.

Calculation of the F Ratio

The calculation of the F ratio requires that we partition the total variance into two parts:

Total sum of squares = Within-group sum of squares + Between-group sum of squares or

SS total = SS within + SS between

The total sum of squares, or SS total, is computed by squaring the deviation of each score from the grand

mean and summing these squares . SS within, the variability that we observe within each group, is

calculated by squaring the deviation of each score from its group mean and summing these scores .

SS between, which is the variability of the group means about a grand mean, is calculated by squaring the

deviation of each mean from the grand mean, multiplying by the number of items in the group, and

summing these scores .

The next calculation requires dividing the various sums of squares by their appropriate degrees of

freedom. The results of these divisions produce the variances, or mean squares.

To obtain the mean square between the groups, SS between is divided by c - 1 degrees of freedom, and to

obtain the mean square within the groups, SS within is divided by cn - c degrees of freedom.

Finally, the F ratio is calculated by taking the ratio of the mean square between groups to the mean

square within groups:


There will be c - 1 degrees of freedom in the numerator and cn - c degrees of freedom in the denominator.

Various types Goal: Design: Scale of

Measurement: Inferential Test:

Establish that a group was drawn

from a population.

Single-Group (sample to

population)

Interval or Ratio

Z-test (Requires that

population mean and

variance are known)

Interval or Ratio T-test: Single Sample

Nominal Chi-square: Goodness of

Fit

Establish a causal relationship

between one level of an

independent variable and a

dependent variable.

Between-Subject: Two

Samples

Interval or Ratio T-test: Independent

Samples

Nominal Chi-square: Test of

Independence

Establish a causal relationship

between multiple levels of an

independent variable and a

dependent variable.

Between-Subject: One

Independent Variable that

Contains Three or More

Groups

Interval or Ratio ANOVA: Fisher's F-test

Various types of inferential test

Advanced Data Analysis Techniques

Some of the Advanced Data Analysis Techniques are as follows:

1) Conjoint analysis

2) Factor analysis

3) Multi dimensional scaling

4) Discriminant analysis

5) Cluster analysis

1) Conjoint analysis

Conjoint analysis, also called multiattribute compositional models, is a statistical technique that

originated in mathematical psychology. Today it is used in many of the social sciences and applied

sciences including marketing, product management, and operations research. The objective of conjoint


http://en.wikipedia.org/wiki/Product_management

http://en.wikipedia.org/wiki/Operations_research


analysis is to determine what combination of a limited number of attributes is most preferred by

respondents. It is used frequently in testing customer acceptance of new product designs and

assessing the appeal of advertisements. It has been used in product positioning, but there are some

problems with this application of the technique.

When asked to do so outright, many consumers are unable to accurately determine the relative

importance that they place on product attributes. For example, when asked which attributes are the more

important ones, the response may be that they all are important. Furthermore, individual attributes in

isolation are perceived differently than in the combinations found in a product. It is difficult for a survey

respondent to take a list of attributes and mentally construct the preferred combinations of them. The task

is easier if the respondent is presented with combinations of attributes that can be visualized as different

product offerings. However, such a survey becomes impractical when there are several attributes that

result in a very large number of possible combinations.

Fortunately, conjoint analysis can facilitate the process. Conjoint analysis is a tool that allows a subset of

the possible combinations of product features to be used to determine the relative importance of each

feature in the purchasing decision. Conjoint analysis is based on the fact that the relative values of

attributes considered jointly can better be measured than when considered in isolation.

In a conjoint analysis, the respondent may be asked to arrange a list of combinations of product attributes

in decreasing order of preference. Once this ranking is obtained, a computer is used to find the utilities of

different values of each attribute that would result in the respondent's order of preference. This method is

efficient in the sense that the survey does not need to be conducted using every possible combination of

attributes. The utilities can be determined using a subset of possible attribute combinations. From these

results one can predict the desirability of the combinations that were not tested.

We can best understand Conjoint analysis with the help of an example:

Example 1

Suppose we have to design a public transport system. We wish to test the relative desirability of three

attributes:

The company aims to provide a service. They wish to test three levels of frequency, and three levels of

prices. Further they want to test the weightage given by consumer to add on features such as AC and

music. The conjoint problem can be presented as follows:

Fare (three levels ` 10, ` 15, ` 20)

Frequency of service (10 minutes, 15 minutes, 20 minutes)

AC vs non AC vs. music (Ac & music, AC, music, nothing)

A sample of 500 respondents are selected and asked to rank their preferences for all possible

combinations and for each level. These are shown below along with one respondent‘s sample rankings.

We can present our trade off information in the form of a table:

http://en.wikipedia.org/wiki/New_product_development

http://en.wikipedia.org/wiki/Advertising

http://en.wikipedia.org/wiki/Positioning_(marketing)


Basically the respondent‘s preference ranking help reveal how desirable a particular feature is to a

respondent. Features respondents are unwilling to give up from one preference ranking to the next are

given a higher utility. Thus in the above example the

respondent gives a high weightage to service followed by AC.

the offer of music is clearly not very important as he ranks it

below AC. However he is not willing to trade off frequency of

service with either AC or music.

Conjoint analysis uses preference rankings to calculate a set of utilities for each respondent where one

utility is calculated for each respondent for each attribute or feature. The calculation of utilities is such

that the sum of utilities for a particular combination shows a good correspondence with that

combination‘s position in the individual‘s original preference rankings. The utilities basically show the

importance of each level of each importance to respondents. We can also identify the more important

attributes by looking at the range of utilities for each of the different levels.

For Example

Frequency of service has a range from 1.6 to .04. The range is therefore equal to =1.2.A high range

implies that the respondent is more sensitive to changes in the level of this attribute.

These utilities are calculated across all respondents for all attributes and for different levels of each

attribute.

At the end of the analysis we would identify 3-4 of the most popular combinations would be identified

for which the relative costs and benefits can be worked out.

Steps in Developing a Conjoint Analysis

1. Choose product attributes, for example, appearance, size, or price.

2. Choose the values or options for each attribute. For example, for the attribute of size, one may

choose the levels of 5", 10", or 20". The higher the number of options used for each attribute, the

more burden that is placed on the respondents.

3. Define products as a combination of attribute options. The set of combinations of attributes that

will be used will be a subset of the possible universe of products.

4. Choose the form in which the combinations of attributes are to be presented to the respondents.

Options include verbal presentation, paragraph description, and pictorial presentation.

5. Decide how responses will be aggregated. There are three choices - use individual responses,

pool all responses into a single utility function, or define segments of respondents who have

similar preferences.

6. Select the technique to be used to analyze the collected data. The part-worth model is one of the

simpler models used to express the utilities of the various attributes. There also are vector (linear)

models and ideal-point (quadratic) models.


The data is processed by statistical software written specifically for conjoint analysis.

Conjoint analysis was first used in the early 1970's and has become an important research tool. It is well-

suited for defining a new product or improving an existing one.

Information collection

Respondents are shown a set of products, prototypes, mock-ups or pictures. Each example is similar

enough that consumers will see them as close substitutes, but dissimilar enough that respondents can

clearly determine a preference. Each example is composed of a unique combination of product features.

Rank-order preferences are obtained. The responses are codified and input into a statistical program like

SPSS or SAS.

Analysis

The computer uses monotonic analysis of variance or linear programming techniques to create utility

functions for each feature. These utility functions indicate the perceived value of the feature and how

sensitive consumer perceptions and preferences are to changes in product features.

Uses of conjoint analysis

It is used in industrial marketing where a product can have many combinations and features and

not all features would be important to all consumers. In industrial marketing the analysis can be

done at the individual level, as each individual is important.

In case of consumer goods the analysis should be done segment wise. To avoid unnecessarily long

questionnaires a preliminary factor analysis should be run to select only testable attributes. Also the

number of attributes should be restricted.

Advantages

estimates psychological tradeoffs that consumers make when evaluating several attributes

together

measures preferences at the individual level

uncovers real or hidden drivers which may not be apparent to the respondent themselves

realistic choice or shopping task

able to use physical objects

if appropriately designed, the ability to model interactions between attributes can be used to

develop needs based segmentation

Disadvantages

designing conjoint studies can be complex

with too many options, respondents resort to simplification strategies

difficult to use for product positioning research because there is no procedure for converting

perceptions about actual features to perceptions about a reduced set of underlying features

http://en.wikipedia.org/w/index.php?title=Needs_based_segmentation&action=edit&redlink=1


respondents are unable to articulate attitudes toward new categories

poorly designed studies may over-value emotional/preference variables and undervalue

concrete variables

does not take into account the number items per purchase so it can give a poor reading of market

share

2) Factor Analysis

Factor analysis is a statistical technique that originated in mathematical psychology. It is used in the

social sciences and in marketing, product management, operations research, and other applied sciences

that deal with large quantities of data. The objective is to discover patterns among variations in the

values of multiple variables. This is done by generating artificial dimensions (called factors) that

correlate highly with the real variables.

Factor analysis is a very popular technique to analyze interdependence. Factor analysis studies the entire

set of interrelationships without defining variables to be dependent or independent. Factor analysis

combines variables to create a smaller set of factors. Mathematically, a factor is a linear combination of

variables. A factor is not directly observable; it is inferred from the variables. The technique identifies

underlying structure among the variables, reducing the number of variables to a more manageable set.

Factor analysis groups variables according to their correlation.

The factor loading can be defined as the correlations between the factors and their underlying variables.

A factor loading matrix is a key output of the factor analysis. An example of matrix is shown below.

Factor 1 Factor 2 Factor 3

Variable 1

Variable 2

Variable 3

Column's Sum of Squares:

Each cell in the matrix represents correlation between the variable and the factor associated with that

cell. The square of this correlation represents the proportion of the variation in the variable explained by

the factor. The sum of the squares of the factor loadings in each column is called an eigenvalue. An

eigenvalue represents the amount of variance in the original variables that is associated with that factor.

The communality is the amount of the variable variance explained by common factors.

A rule of thumb for deciding on the number of factors is that each included factor must explain at least

as much variance as does an average variable. In other words, only factors for which the eigenvalue is

greater than one are used. Other criteria for determining the number of factors include the Scree plot

criteria and the percentage of variance criteria.

http://en.wikipedia.org/w/wiki.phtml?title=Mathematical_psychology&action=edit


http://en.wikipedia.org/wiki/Product_management

http://en.wikipedia.org/wiki/Operations_research


To facilitate interpretation, the axis can be rotated. Rotation of the axis is equivalent to forming linear

combinations of the factors. A commonly used rotation strategy is the varimax rotation. Varimax

attempts to force the column entries to be either close to zero or one.

The basic steps are:

Identify the salient attributes consumers use to evaluate products in this category.

Use quantitative research techniques (such as surveys) to collect data from a sample of potential

customers concerning their ratings of all the product attributes.

Input the data into a statistical program and run the factor analysis procedure. The computer

will yield a set of underlying attributes (or factors).

Use these factors to construct perceptual maps and other product positioning devices.

Typical Problem Studied Using Factor Analysis

Factor analysis is used to study a complex product or service to identify the major characteristics

considered important by consumers.

The two major uses of factor analysis

1. To simplify a set of data by reducing a large number of measures (which in some way may be

interrelated and causing multicollinearity) for a set of respondents to a smaller more manageable set

which are not interrelated and still retain most of the original information .

2. To identify the underlying structure of the data in which a very large number of variables may really

be measuring a small number of basic characteristics or constructs of our sample. For e.g. a survey may

throw up bet 15-20 attributes which a consumer considers when buying a product. However there is a

need to find out what are the key drivers. Factor analysis identifies latent or underlying factors from an

array of seemingly imp variables.

Uses of Factor Analysis

To reduce a large number of variables to a smaller number of factors for modeling purposes, where the

large number of variables precludes modeling all the measures individually. As such, factor analysis is

integrated in structural equation modeling (Sem), helping create the latent variables modeled by Sem.

However, factor analysis can be and is often used on a stand-alone basis for similar purposes.

To select a subset of variables from a larger set, based on which original variables have the highest

correlations with the principal component factors.

To create a set of factors to be treated as uncorrelated variables as one approach to handling

multicollinearity in such procedures as multiple regression

To validate a scale or index by demonstrating that its constituent items load on the same factor, and

to drop proposed scale items which cross-load on more than one factor.

http://en.wikipedia.org/wiki/Product_(business)


http://en.wikipedia.org/wiki/Survey_techniques_(in_marketing)

http://en.wikipedia.org/wiki/Customer

http://en.wikipedia.org/wiki/Perceptual_mapping



To establish that multiple tests measure the same factor, thereby giving justification for

administering fewer tests.

To identify clusters of cases and/or outliers.

To determine network groups by determining which sets of people cluster together (using Q-mode

factor analysis, discussed below)

Information collection

The data collection stage is usually done by research professionals. Survey questions ask the respondent

to rate a product from one to five (or 1 to 7, or 1 to 10) on a range of attributes. Anywhere from five to

twenty attributes are chosen. They could include things like: ease of use, weight, accuracy, durability,

colourfulness, price, or size. The attributes chosen will vary depending on the product being studied. The

same question is asked about all the products in the study. The data for multiple products is codified and

input into a statistical program such as SPSS or SAS.

Analysis

The analysis will isolate the underlying factors that explain the data. Factor analysis is an

interdependence technique. The complete set of interdependent relationships are examined. There is no

specification of either dependent variables, independent variables, or causality. Factor analysis assumes

that all the rating data on different attributes can be reduced down to a few important dimensions. This

reduction is possible because the attributes are related. The rating given to any one attribute is partially

the result of the influence of other attributes. The statistical algorithm deconstructs the rating (called a

raw score) into its various components, and reconstructs the partial scores into underlying factor scores.

The degree of correlation between the initial raw score and the final factor score is called a factor loading.

There are two approaches to factor analysis: "principal component analysis" (the total variance in the data

is considered); and "common factor analysis" (the common variance is considered).

The use of principle components in a semantic space can vary somewhat because the components may

only "predict" but not "map" to the vector space. This produces a statistical principle component use

where the most salient words or themes represent the preferred Basis .

Advantages

1. both objective and subjective attributes can be used

2. it is fairly easy to do, inexpensive, and accurate

3. it is based on direct inputs from customers

4. there is flexibility in naming and using dimensions

Disadvantages

1. usefulness depends on the researchers ability to develop a complete and accurate set of product

attributes - If important attributes are missed the procedure is valueless.

http://en.wikipedia.org/wiki/Principal_component_analysis

http://en.wikipedia.org/wiki/Basis


2. naming of the factors can be difficult - multiple attributes can be highly correlated with no

appearent reason.

3. factor analysis will always produce a pattern between variables, no matter how random.

3) Multidimensional scaling

Multidimensional scaling (MDS) is a statistical technique often used in marketing and the social

sciences. It is a procedure for taking the preferences and perceptions of respondents and representing

them on a visual grid. These grids, called perceptual maps are usually two-dimensional, but they can

represent more than two. Potential customers are asked to compare pairs of products and make

judgements about their similarity. Whereas other techniques (such as factor analysis, discriminant

analysis, and conjoint analysis) obtain underlying dimensions from responses to product attributes

identified by the researcher, MDS obtains the underlying dimensions from respondents‘ judgements

about the similarity of products. This is an important advantage. It does not depend on researchers‘

judgments. It does not require a list of attributes to be shown to the respondents. The underlying

dimensions come from respondents‘ judgements about pairs of products. Because of these advantages,

MDS is the most common technique used in perceptual mapping.

Multidimensional Scaling Procedure

There are several steps in conducting MDS research:

1. Formulating the problem - What brands do you want to compare? How many brands do you want

to compare? More than 20 is cumbersome. Less than 8 (4 pairs) will not give valid results. What

purpose is the study to be used for?

2. Obtaining Input Data - Respondents are asked a series of questions. For each product pair they are

asked to rate similarity (usually on a 7 point Likert scale from very similar to very dissimilar). The

first question could be for Coke/Pepsi for example, the next for Coke/Hires rootbeer, the next for

Pepsi/Dr Pepper, the next for Dr Pepper/Hires rootbeer, etc. The number of questions is a function

of the number of brands and can be calculated as Q = N (N - 1) / 2 where Q is the number of

questions and N is the number of brands. This approach is referred to as the ―Perception data :

direct approach‖. There are two other approaches. There is the ―Perception data : derived approach‖

in which products are decomposed into attributes which are rated on a semantic differential scale.

The other is the ―Preference data approach‖ in which respondents are asked their preference rather

than similarity.

3. Running the MDS statistical program - Software for running the procedure is available in most of

the better statistical applications programs. Often there is a choice between Metric MDS (which

deals with interval or ratio level data), and Nonmetric MDS (which deals with ordinal data). The

researchers must decide on the number of dimensions they want the computer to create. The more

dimensions, the better the statistical fit, but the more difficult it is to interpret the results.


http://en.wikipedia.org/wiki/Social_sciences

http://en.wikipedia.org/wiki/Social_sciences



http://en.wikipedia.org/wiki/Factor_analysis

http://en.wikipedia.org/wiki/Discriminant_analysis_(in_marketing)

http://en.wikipedia.org/wiki/Discriminant_analysis_(in_marketing)

http://en.wikipedia.org/wiki/Conjoint_analysis_(in_marketing)

http://en.wikipedia.org/wiki/Brand


4. Mapping the results and defining the dimensions - The statistical program (or a related module)

will map the results. The map will plot each product (usually in two dimensional space). The

proximity of products to each other indicate either how similar they are or how preferred they are,

depending on which approach was used. The dimensions must be labelled by the researcher. This

requires subjective judgement and is often very challenging. The results must be interpreted ( see

perceptual mapping).

5. Test the results for reliability and Validity - Compute R-squared to determine what proportion of

variance of the scaled data can be accounted for by the MDS procedure. An R-square of .6 is

considered the minimum acceptable level. Other possible tests are Kruskal‘s Stress, split data tests,

data stability tests (i.e.: eliminating one brand), and test-retest reliability.

Perceptual mapping

Perceptual mapping is a graphics technique used

by marketers that attempts to visually display the

perceptions of customers or potential customers.

Typically the position of a product, product line,

brand, or company is displayed relative to their

competition.

Perceptual maps can have any number of

dimensions but the most common is two

dimensions. Any more is a challenge

to draw and confusing to interpret.

The first perceptual map below shows

consumer perceptions of various

automobiles on the two dimensions of

sportiness/conservative and

classy/affordable. This sample of

consumers felt Porsche was the

sportiest and classiest of the cars in the study (top right corner). They felt Plymouth was most practical

and conservative (bottom left corner).

Cars that are positioned close to each other are seen as similar on the relevant dimensions by the

consumer. For example consumers see Buick, Chrysler, and Oldsmobile as similar. They are close

competitors and form a competitive grouping. A company considering the introduction of a new model


http://en.wikipedia.org/wiki/Graphics




http://en.wikipedia.org/wiki/Product_line

http://en.wikipedia.org/wiki/Brand


will look for an area on the map free from competitors. Some perceptual maps use different size circles to

indicate the sales volume or market share of the various competing products.

Displaying consumers‘ perceptions of related products is only half the story. Many perceptual maps also

display consumers‘ ideal points. These points reflect ideal combinations of the two dimensions as seen

by a consumer. The next diagram shows a study of consumers‘ ideal points in the alcohol/spirits

product space. Each dot represents one respondents ideal combination of the two dimensions. Areas

where there is a cluster of ideal points (such as A) indicates a market segment. Areas without ideal

points are sometimes referred to as demand voids.

A company considering introducing a new product will look for areas with a high density of ideal

points. They will also look for areas without competitive rivals. This is best done by placing both the

ideal points and the

competing products on the

same map.

Some maps plot ideal vectors

instead of ideal points. The

map below, displays various

aspirin products as seen on the

dimensions of effectiveness

and gentleness. It also shows

two ideal vectors. The slope of

the ideal vector indicates the

preferred ratio of the two

dimensions by those consumers within that segment. This study indicates there is one segment that is

more concerned with effectiveness than harshness, and another segment that is more interested in

gentleness than strength.

Perceptual maps need not come from a detailed study. There are also intuitive maps (also called

judgmental maps or consensus maps) that are created by marketers based on their understanding of

their industry. Management uses its best judgement. It is questionable how valuable this type of map is.

Often they just give the appearance of credibility to management‘s preconceptions.

When detailed research studies are done methodological problems can arise, but at least the information

is coming directly from the consumer. There is an assortment of statistical procedures that can be used

to convert the raw data collected in a survey into a perceptual map.

http://en.wikipedia.org/wiki/Market_segment


4) Discriminant analysis

Discriminant analysis is a statistical technique used in marketing and the social sciences. It is applicable

when there is only one dependent variable but multiple independent variables (similar to ANOVA and

regression). But unlike ANOVA and regression analysis, the dependent variable must be categorical. It is

similar to factor analysis in that both look for underlying dimensions in responses given to questions

about product attributes. But it differs from factor analysis in that it builds these underlying dimensions

based on differences rather than similarities. Discriminant analysis is also different from factor analysis

in that it is not an interdependence technique: a distinction between independent variables and

dependent variables (also called criterion variables) must be made.

Discriminant analysis works by creating a new variable called the Discriminant function score which is

used to predict to which group a case belongs.

Discriminant function scores are computed similarly to factor scores, i.e. using eigenvalues. The

computations find the coefficients for the independent variables that maximize the measure of distance

between the groups defined by the dependent variable.

The discriminant function is similar to a regression equation in which the independent variables are

multiplied by coefficients and summed to produce a score.

The data structure for DFA is a single grouping variable that is predicted by a series of other variables.

The grouping variable must be nominal, which might also be a reclassification of a continuous variable

into groups. The function is presented thus:

Y‟ = X1W1 + X2W2 + X3W3 + ...XnWn + Constant

This is essentially identical to a multiple regression, but in reality the two techniques are quite different.

Regression is built on a linear combination of variables that maximizes the regression relationship, i.e.,

the least squares regression, between a continuous dependent variable and the regression variate. In

DFA, the dependent variable consists of discrete groups, and what you want to do with the function is to

maximize the distance between those groups, i.e., to come up with a function that has strong

discriminatory power among the groups. Although logit regression does somewhat the same thing when

you have a binary (two group) variable, and the book makes a big thing of the similarities, the reality is

that the way in which they compute the functions is quite different.

Discriminant Analysis Involves:

1. Formulate the problem and gather data - Identify the salient attributes consumers use to evaluate

products in this category - Use quantitative research techniques (such as surveys) to collect data

from a sample of potential customers concerning their ratings of all the product attributes. The data

collection stage is usually done by research professionals. Survey questions ask the respondent to

rate a product from one to five (or 1 to 7, or 1 to 10) on a range of attributes chosen by the

researcher. Anywhere from five to twenty attributes are chosen. They could include things like:


http://en.wikipedia.org/wiki/Social_science

http://en.wikipedia.org/wiki/ANOVA

http://en.wikipedia.org/wiki/Regression




http://en.wikipedia.org/wiki/Survey_techniques_(in_marketing)


ease of use, weight, accuracy, durability, colourfulness, price, or size. The attributes chosen will

vary depending on the product being studied. The same question is asked about all the products in

the study. The data for multiple products is codified and input into a statistical program such as

SPSS or SAS. (This step is the same as in Factor analysis).

2. Estimate the Discriminant Function Coefficients and determine the statistical significance and

validity - Choose the appropriate discimininant analysis method. The direct method involves

estimating the discriminant function so that all the predictors are assessed simultaneously. The

stepwise method enters the predictors sequentially. The two-group method should be used when

the dependant variable has two categories or states. The multiple discriminant method is used

when the dependent variable has three or more categorical states. Use Wilks‘s Lambda to test for

significance in SPSS or F stat in SAS. The most common method used to test validity is to split the

sample into an estimation or analysis sample, and a validation or holdout sample. The estimation

sample is used in constructing the discriminant function. The validation sample is used to

construct a classification matrix which contains the number of correctly classified and incorrectly

classified cases. The percentage of correctly classified cases is called the hit ratio.

3. Plot the results on a two dimensional map, define the dimensions, and interpret the results. The

statistical program (or a related module) will map the results. The map will plot each product

(usually in two dimensional space). The distance of products to each other indicate either how

different they are. The dimensions must be labelled by the researcher. This requires subjective

judgement and is often very challenging.

Applications of Discriminant Function Analysis

General Purpose

Discriminant function analysis is used to determine which variables discriminate between two or more

naturally occurring groups. For example, an educational researcher may want to investigate which

variables discriminate between high school graduates who decide

(1) to go to college, (2) to attend a trade or professional school, or (3) to seek no further training or

education. For that purpose the researcher could collect data on numerous variables prior to students‘

graduation. After graduation, most students will naturally fall into one of the three categories.

Discriminant Analysis could then be used to determine which variable(s) are the best predictors of

students‘ subsequent educational choice. A medical researcher may record different variables relating to

patients‘ backgrounds in order to learn which variables best predict whether a patient is likely to recover

completely (group 1), partially (group 2), or not at all (group 3). A biologist could record different

characteristics of similar types (groups) of flowers, and then perform a discriminant function analysis to

determine the set of characteristics that allows for the best discrimination between the types.


5) Cluster analysis

Cluster analysis is a class of statistical techniques that can be applied to data that exhibits ―natural‖

groupings. Cluster analysis sorts through the raw data and groups them into clusters. A cluster is a

group of relatively homogeneous cases or observations. Objects in a cluster are similar to each other.

They are also dissimilar to objects outside the cluster, particularly objects in other clusters.

The diagram below illustrates the results of a survey that studied drinkers‘ perceptions of spirits

(alcohol). Each point represents the results from one respondent. The research indicates there are four

clusters in this market.

Another example is the vacation travel market. Recent research has identified three clusters or market

segments. They are the: 1) The

demanders - they want exceptional

service and expect to be pampered; 2)

The escapists - they want to get away

and just relax; 3) The educationalist -

they want to see new things, go to

museums, go on a safari, or experience

new cultures.

Cluster analysis, like factor analysis and

multi dimensional scaling, is an

interdependence technique : it makes no

distinction between dependent and independent variables. The entire set of interdependent relationships

is examined. It is similar to multi dimensional scaling in that both examine inter-object similarity by

examining the complete set of interdependent relationships. The difference is that multi dimensional

scaling identifies underlying dimensions, while cluster analysis identifies clusters. Cluster analysis is the

obverse of factor analysis. Whereas factor analysis reduces the number of variables by grouping them

into a smaller set of factors, cluster analysis reduces the number of observations or cases by grouping

them into a smaller set of clusters.

In marketing, cluster analysis is used for:

1. Segmenting the market and determining target markets

2. Product positioning and New Product Development

3. Selecting test markets

The basic procedure is:

1. Formulate the problem - select the variables that you wish to apply the clustering technique to

2. Select a distance measure - various ways of computing distance:

o Squared Euclidean distance - the square root of the sum of the squared differences in value

for each variable

http://en.wikipedia.org/wiki/Statistics




http://en.wikipedia.org/wiki/Multi_dimensional_scaling_(in_marketing)

http://en.wikipedia.org/wiki/Target_market


http://en.wikipedia.org/wiki/New_Product_Development


o Manhattan distance - the sum of the absolute differences in value for any variable

o Chebychev distance - the maximum absolute difference in values for any variable

3. Select a clustering procedure (see below)

4. Decide on the number of clusters

5. Map and interpret clusters - draw conclusions - illustrative techniques like perceptual maps,

icicle plots, and dendrograms are useful

6. Assess reliability and validity - various methods:

o repeat analysis but use different distance measure

o repeat analysis but use different clustering technique

o split the data randomly into two halves and analyze each part separately

o repeat analysis several times, deleting one variable each time

o repeat analysis several times, using a different order each time





Chapter 8: Testing of Hypothesis

Introduction

Many a time, we strongly believe some results to be true. But after taking a sample, we notice that one

sample data does not wholly support the result. The difference is due to (i) the original belief being

wrong, or (ii) the sample being slightly one sided.

Tests are, therefore, needed to distinguish between the two possibilities. These tests tell about the likely

possibilities and reveal whether or not the difference can be due to only chance elements. If the difference

is not due to chance elements, it is significant and, therefore, these tests are called tests of significance. The

whole procedure is known as Testing of Hypothesis.

Setting up and testing hypotheses is an essential part of statistical inference. In order to formulate such a

test, usually some theory has been put forward, either because it is believed to be true or because it is to

be used as a basis for argument, but has not been proved. For example, the hypothesis may be the claim

that a new drug is better than the current drug for treatment of a disease, diagnosed through a set of

symptoms.

In each problem considered, the question of interest is simplified into two competing claims/hypotheses

between which we have a choice; the null hypothesis, denoted by H0, against the alternative hypothesis, denoted

by H1. These two competing claims / hypotheses are not however treated on an equal basis; special

consideration is given to the null hypothesis. We have two common situations :

(i) The experiment has been carried out in an attempt to disprove or reject a particular

hypothesis, the null hypothesis; thus we give that one priority so it cannot be rejected unless

the evidence against it is sufficiently strong. For example, null hypothesis H0: there is no

difference in taste between coke and diet coke, against the alternate hypothesis H1: there is a

difference in the tastes.

(ii) If one of the two hypotheses is ‗simpler‘, we give it priority so that a more ‗complicated‘

theory is not adopted unless there is sufficient evidence against the simpler one. For


example, it is ‗simpler‘ to claim that there is no difference in flavour between coke and diet

coke than it is to say that there is a difference.

The hypotheses are often statements about population parameters like expected value and variance. For

example, H0, might be the statement that the expected value of the height of ten year old boys in the

Indian population, is not different from that of ten year old girls. A hypothesis might also be a statement

about the distributional form of a characteristic of interest; for example, that the height of ten years old

boys is normally distributed within the Indian population.

What is a Hypothesis?

A hypothesis is the assumption that we make about the population parameter. This can be any

assumption about a population parameter not necessarily based on statistical data. For example it can

also be based on the gut feel of a manager. Managerial hypotheses are based on intuition; the market

place decides whether the manager‘s intuitions were in fact correct.

In fact managers propose and test hypotheses all the time. For example:

If a manager says ‗if we drop the price of this car model by ` 15000, we‘ll increase sales by 25000

units‘ is a hypothesis. To test it in reality we have to wait to the end of the year to and count sales.

A manager estimates that sales per territory will grow on average by 30% in the next quarter is also

an assumption or hypotheses.

To understand the meaning of a hypothesis, let us see some definitions:

―A hypothesis is a tentative generalization, the validity of which remains to be tested. In its most

elementary stage the hypothesis may be any guess, hunch, imaginative idea, which becomes the basis for

action or investigation‖. (G.A.Lundberg)

―It is a proposition which can be put to test to determine validity‖. (Goode and Hatt).

―A hypothesis is a question put in such a way that an answer of some kind can be forth coming‖ -

(Rummel and Ballaine).

These definitions lead us to conclude that a hypothesis is a tentative solution or explanation or a guess or

assumption or a proposition or a statement to the problem facing the researcher, adopted on a cursory

observation of known and available data, as a basis of investigation, whose validity is to be tested or

verified.

How would the manager go about testing this assumption?

Suppose he has 70 territories under him.

One option for him is to audit the results of all 70 territories and determine whether the average is

growth is greater than or less than 30%. This is a time consuming and expensive procedure.

Another way is to take a sample of territories and audit sales results for them. Once we have our sales

growth figure, it is likely that it will differ somewhat from our assumed rate. For example we may get

a sample rate of 27%. The manager is then faced with the problem of determining whether his


assumption or hypothesized rate of growth of sales is correct or the sample rate of growth is more

representative. To test the validity of our assumption about the population we collect sample data

and determine the sample value of the statistic.

We then determine whether the sample data supports our hypotheses assumption regarding the average

sales growth.

How is this Done?

If the difference between our hypothesized value and the sample value is small, then it is more likely that

our hypothesized value of the mean is correct. The larger the difference the smaller the probability that

the hypothesized value is correct. In practice however very rarely is the difference between the sample

mean and the hypothesized population value larger enough or small enough for us to be able to accept or

reject the hypothesis prima-facie. We cannot accept or reject a hypothesis about a parameter simply on

intuition; instead we need to use objective criteria based on sampling theory to accept or reject the

hypothesis. Hypotheses testing is the process of making inferences about a population based on a sample.

The key question therefore in hypotheses testing is: how likely is it that a population such as one we have

hypothesized to produce a sample such as the one we are looking at.

Types of Hypothesis

Hypotheses can be classified in a variety of ways into different types or kinds. The following are some of

the types of hypotheses:

i) Explanatory Hypothesis: The purpose of this hypothesis is to explain a certain fact. All hypotheses are

in a way explanatory for a hypothesis is advanced only when we try to explain the observed fact. A large

number of hypotheses are advanced to explain the individual facts in life. A theft, a murder, an accident

are examples.

ii) Descriptive Hypothesis: Sometimes a researcher comes across a complex phenomenon. He/ she does

not understand the relations among the observed facts. But how to account for these facts? The answer is

a descriptive hypothesis. A hypothesis is descriptive when it is based upon the points of resemblance of

something. It describes the cause and effect relationship of a phenomenon e.g., the current

unemployment rate of a state exceeds 25% of the work force. Similarly, the consumers of local made

products constitute a significant market segment.

iii) Analogical Hypothesis: When we formulate a hypothesis on the basis of similarities (analogy), it is

called an analogical hypothesis e.g., families with higher earnings invest more surplus income on long

term investments.

iv) Working hypothesis: Sometimes certain facts cannot be explained adequately by existing hypotheses,

and no new hypothesis comes up. Thus, the investigation is held up. In this situation, a researcher

formulates a hypothesis which enables to continue investigation. Such a hypothesis, though inadequate


and formulated for the purpose of further investigation only, is called a working hypothesis. It is simply

accepted as a starting point in the process of investigation.

v) Null Hypothesis: It is an important concept that is used widely in the sampling theory. It forms the

basis of many tests of significance. Under this type, the hypothesis is stated negatively. It is null because it

may be nullified, if the evidence of a random sample is unfavourable to the hypothesis. It is a hypothesis

being tested (H0). If the calculated value of the test is less than the permissible value, Null hypothesis is

accepted, otherwise it is rejected. The rejection of a null hypothesis implies that the difference could not

have arisen due to chance or sampling fluctuations.

vi) Statistical Hypothesis: Statistical hypotheses are the statements derived from a sample. These are

quantitative in nature and are numerically measurable. For example, the market share of product X is

70%, the average life of a tube light is 2000 hours etc.

Criteria for Workable Hypothesis

A hypothesis controls and directs the research study. When a problem is felt, we require the hypothesis

to explain it. Generally, there is more than one hypothesis which aims at explaining the same fact. But all

of them cannot be equally good. Therefore, how can we judge a hypothesis to be true or false, good or

bad? Agreement with facts is the sole and sufficient test of a true hypothesis. Therefore, certain conditions

can be laid down for distinguishing a good hypothesis from bad ones. The formal conditions laid down

by thinkers provide the criteria for judging a hypothesis as good or valid. These conditions are as follows:

i) A hypothesis should be empirically verifiable: The most important condition for a valid hypothesis is

that it should be empirically verifiable. A hypothesis is said to be verifiable, if it can be shown to be either

true or false by comparing with the facts of experience directly or indirectly. A hypothesis is true if it

conforms to facts and it is false if it does not. Empirical verification is the characteristic of the scientific

method.

ii) A hypothesis should be relevant: The purpose of formulating a hypothesis is always to explain some

facts. It must provide an answer to the problem which initiated the enquiry. A hypothesis is called

relevant if it can explain the facts of enquiry.

iii) A hypothesis must have predictive and explanatory power: Explanatory power means that a good

hypothesis, over and above the facts it proposes to explain, must also explain some other facts which are

beyond its original scope. We must be able to deduce a wide range of observable facts which can be

deduced from a hypothesis. The wider the range, the greater is its explanatory power.

iv) A hypothesis must furnish a base for deductive inference on consequences: In the process of

investigation, we always pass from the known to the unknown. It is impossible to infer anything from the

absolutely unknown. We can only infer what would happen under supposed conditions by applying the

knowledge of nature we possess. Hence, our hypothesis must be in accordance with our previous

knowledge.


v) A hypothesis does not go against the traditionally established knowledge: As far as possible, a new

hypothesis should not go against any previously established law or knowledge. The new hypothesis is

expected to be consistent with the established knowledge.

vi) A hypothesis should be simple: A simple hypothesis is preferable to a complex one. It sometimes

happens that there are two or more hypotheses which explain a given fact equally well. Both of them are

verified by observable facts. Both of them have a predictive power and both are consistent with

established knowledge. All the important conditions of hypothesis are thus satisfied by them. In such

cases the simpler one is to be accepted in preference to the complex one.

vii) A hypothesis must be clear, definite and certain: It is desirable that the hypothesis must be simple

and specific to the point. It must be clearly defined in a manner commonly accepted. It should not be

vague or ambiguous.

(viii) A Hypothesis should be related to available techniques: If tools and techniques are not available

we cannot test the hypothesis. Therefore, the hypothesis should be formulated only after due thought is

given to the methods and techniques that can be used to measure the concepts and variables related to

the hypothesis.

Stages in Hypothesis

There are four stages. The first stage is feeling of a problem. The observation and analysis of the

researcher reveals certain facts. These facts pose a problem. The second stage is formulation of a

hypothesis or hypotheses. A tentative supposition/ guess is made to explain the facts which call for an

explanation. At this stage some past experience is necessary to pick up the significant aspects of the

observed facts. Without previous knowledge, the investigation becomes difficult, if not impossible. The

third stage is deductive development of hypothesis using deductive reasoning. The researcher uses the

hypothesis as a premise and draws a conclusion from it. And the last stage is the verification or testing of

hypothesis. This consists in finding whether the conclusion drawn at the third stage is really true.

Verification consists in finding whether the hypothesis agrees with the facts. If the hypothesis stands the

test of verification, it is accepted as an explanation of the problem. But if the hypothesis does not stand

the test of verification, the researcher has to search for further solutions.

To explain the above stages let us consider a simple example. Suppose, you have started from your home

for college on your scooter. A little while later the engine of your scooter suddenly stops. What can be the

reason? Why has it stopped? From your past experience, you start guessing that such problems generally

arise due to either petrol or spark plug. Then start deducing that the cause could be: (i) that the petrol

knob is not on. (ii) that there is no petrol in the tank. (iii) that the spark plug has to be cleaned. Then start

verifying them one after another to solve the problem. First see whether the petrol knob is on. If it is not,

switch it on and start the scooter. If it is already on, then see whether there is petrol or not by opening the

lid of the petrol tank. If the tank is empty, go to the near by petrol bunk to fill the tank with petrol. If there

is petrol in the tank, this is not the reason, then you verify the spark plug. You clean the plug and fit it.


The scooter starts. That means the problem is with the spark plug. You have identified it. So you got the

answer. That means your problem is solved.

Reliability and validity

Research should be tested for reliability, generalizability, and validity.

Generalizability is the ability to make inferences from a sample to the population.

Reliability is the extent to which a measure will produce consistent results. Test-retest reliability

checks how similar the results are if the research is repeated under similar circumstances.

Stability over repeated measures is assessed with the Pearson coefficient. Alternative forms

reliability checks how similar the results are if the research is repeated using different forms.

Internal consistency reliability checks how well the individual measures included in the research

are converted into a composite measure. Internal consistency may be assessed by correlating

performance on two halves of a test (split-half reliability).

Validity asks whether the research measured what it intended to. Content validation (also called

face validity) checks how well the content of the research are related to the variables to be

studied. Are the research questions representative of the variables being researched. It is a

demonstration that the items of a test are drawn from the domain being measured. Criterion

validation checks how meaningful the research criteria are relative to other possible criteria.

When the criterion is collected later the goal is to establish predictive validity. Construct

validation checks what underlying construct is being measured. There are three variants of

construct validity. They are convergent validity (how well the research relates to other measures

of the same construct), discriminant validity (how poorly the research relates to measures of

opposing constructs), and nomological validity (how well the research relates to other variables

as required by theory) .

Internal validation, used primarily in experimental research designs, checks the relation

between the dependent and independent variables. Did the experimental manipulation of the

independent variable actually cause the observed results? External validation checks whether

the experimental results can be generalized.

Validity implies reliability : a valid measure must be reliable. But reliability does not necessarily imply

validity :a reliable measure need not be valid.

Testing of Hypothesis

When the hypothesis has been framed in the research study, it must be verified as true or false.

Verifiability is one of the important conditions of a good hypothesis. Verification of hypothesis means

testing of the truth of the hypothesis in the light of facts. If the hypothesis agrees with the facts, it is said

to be true and may be accepted as the explanation of the facts. But if it does not agree it is said to be false.




Such a false hypothesis is either totally rejected or modified. Verification is of two types viz., Direct

verification and Indirect verification.

1. Direct verification may be either by observation or by experiments. When direct observation shows

that the supposed cause exists where it was thought to exist, we have a direct verification. When a

hypothesis is verified by an experiment in a laboratory it is called direct verification by experiment. When

the hypothesis is not amenable for direct verification, we have to depend on indirect verification.

2. Indirect verification is a process in which certain possible consequences are deduced from the

hypothesis and they are then verified directly. Two steps are involved in indirect verification. (i)

Deductive development of hypothesis: By deductive development certain consequences are predicted

and (ii) finding whether the predicted consequences follow. If the predicted consequences come true, the

hypothesis is said to be indirectly verified. Verification may be done directly or indirectly or through

logical methods.

Testing of a hypothesis is done by using statistical methods. Testing is used to accept or reject an

assumption or hypothesis about a random variable using a sample from the distribution. The assumption

is the null hypothesis (H0), and it is tested against some alternative hypothesis (H1). Statistical tests of

hypothesis are applied to sample data. The procedure involved in testing a hypothesis is A) select a

sample and collect the data. B) Convert the variables or attributes into statistical form such as mean,

proportion. C) formulate hypotheses. D) select an appropriate test for the data such as t-test, Z-test. E)

perform computations. F) finally draw the inference of accepting or rejecting the null hypothesis.

Procedure for hypothesis testing

Hypothesis testing involves the following steps:

1. Formulate the null and alternative hypotheses.

2. Choose the appropriate test.

3. Choose a level of significance (alpha) - determine the rejection region.

4. Gather the data and calculate the test statistic.

5. Determine the probability of the observed value of the test statistic under the null hypothesis

given the sampling distribution that applies to the chosen test.

6. Compare the value of the test statistic to the rejection threshold.

7. Based on the comparison, reject or do not reject the null hypothesis.

8. Make the research conclusion.

In order to analyze whether research results are statistically significant or simply by chance, a test of

statistical significance can be run.

How do we use Sampling to accept or Reject Hypothesis?

Again we go back to the normal sampling distribution. We use the result that there is a certain fixed

probability associated with intervals from the mean defined in terms of number of standard deviations


from the mean. Therefore our problem of testing a hypothesis reduces to determining the probability that

a sample statistic such as the one we have obtained could have arisen from a population with a

hypothesized mean m. In the hypothesis tests we need two numbers to make our decision whether to

accept or reject the null hypothesis:

an observed value or computed from the sample

a critical value defining the boundary between the acceptance and rejection region

.

Instead of measuring the variables in original units we calculate a standardized z variable for a standard

normal distribution with mean x =0.The z statistic tells us how many how many standard deviations

above or below the mean standardized mean (z,<0, z>0) our observation falls. We can convert our

observed data into the standardized scale using the transformation:

The z statistic measures the number of standard deviations away from the hypothesized mean the sample

mean lies. From the standard normal tables we can calculate the probability of the sample mean differing

from the true population mean by a specified number of standard deviations.

For example:

o we can find the probability that the sample mean differs from the population mean by two or more

standard deviations.

It is this probability value that will tell us how likely it is that a given sample mean can be obtained

from a population with a hypothesized mean m. .

o If the probability is low for example less than 5% , perhaps it can be reasonably concluded that the

difference between the sample mean and hypothesized population mean is too large and the chance

that the population would produce such a random sample is too low.

What probability constitutes too low or acceptable level is a judgment for decision makers to make.

Certain situations demand that decision makers be very sure about the characteristics of the items being

tested and even a 2% probability that the population produces such a sample is too high. In other

situations there is greater latitude and a decision maker may be willing to accept a hypothesis with a 5%

probability of chance variation.

In each situation what needs to be determined are the costs resulting from an incorrect decision and the

exact level of risk we are willing to assume. Our minimum standard for an acceptable probability, say,

5%, is also the risk we run of rejecting a hypothesis that is true.

Hypothesis errors:

type I error (also called alpha error)

o the study results lead to the rejection of the null hypothesis even though it is actually true

http://en.wikipedia.org/wiki/Type_I_error


type II error (also called beta error)

o the study results lead to the acceptance (non-rejection) of the null hypothesis even though it is

actually false

The choice of significance level affects the ratio of correct and incorrect conclusions which will be drawn.

Given a significance level there are four alternatives to consider:

Type I and type II errors

Correct Conclusion Incorrect Conclusion

Accept a correct hypothesis

Reject an incorrect hypothesis

Reject a correct hypothesis

Accept an incorrect hypothesis

Consider the following example. In a straightforward test of two products, we may decide to market

product A if, and only if, 60% of the population prefer the product. Clearly we can set a sample size, so as

to reject the null hypothesis of A = B = 50% at, say, a 5% significance level. If we get a sample which

yields 62% (and there will be 5 chances in a 100 that we get a figure greater than 60%) and the null

hypothesis is in fact true, then we make what is known as a Type I error.

If however, the real population is A = 62%, then we shall accept the null hypothesis A = 50% on nearly

half the occasions as shown in the diagram overleaf. In this situation we shall be saying "do not market

A" when in fact there is a market for A. This is the type II error. We can of course increase the chance of

making a type I error which will automatically decrease the chance of making a type II error.

Obviously some sort of compromise is required. This depends on the relative importance of the two types

of error. If it is more important to avoid rejecting a true hypothesis (type I error) a high confidence

coefficient (low value of x) will be used. If it is more important to avoid accepting a false hypothesis, a

low confidence coefficient may be used. An analogy with the legal profession may help to clarify the

matter. Under our system of law, a man is presumed innocent of murder until proved otherwise. Now, if

a jury convicts a man when he is, in fact, innocent, a type I error will have been made: the jury has

rejected the null hypothesis of innocence although it is actually true. If the jury absolves the man, when

he is, in fact, guilty, a type II error will have been made: the jury has accepted the null hypothesis of

innocence when the man is really guilty. Most people will agree that in this case, a type I error, convicting

an innocent man, is the more serious.

In practice, of course, researchers rarely base their decisions on a single significance test. Significance tests

may be applied to the answers to every question in a survey but the results will be only convincing, if

consistent patterns emerge. For example, we may conduct a product test to find out consumers

preferences. We do not usually base our conclusions on the results of one particular question, but we ask

several, make statistical tests on the key questions and look for consistent significances. We must

remember that when one makes a series of tests, some of the correct hypotheses will be rejected by

chance. For example, if 20 questions were asked in our "before" and "after" survey and we test each

http://en.wikipedia.org/wiki/Type_II_error


question at the 5% level, then one of the differences is likely to give significant results, even if there is no

real difference in the population.

No mention is made in these notes of considerations of costs of incorrect decisions. Statistical significance

is not always the only criterion for basing action. Economic considerations of alternative actions are often

just as important.

These, therefore, are the basic steps in the statistical testing procedure. The majority of tests are likely to

be parametric tests where researchers assume some underlying distribution like the normal or binomial

distribution. Researchers will obtain a result, say a difference between two means, calculate the standard

error of the difference and then ask "How far away from the zero difference hypothesis is the difference

we have found from our samples?"

To enable researchers to answer this question, they convert their actual difference into "standard errors"

by dividing it by its standard deviation, then refer to a chart to ascertain the probability of such a

difference occurring.

Uses of Hypothesis

If a clear scientific hypothesis has been formulated, half of the research work is already done. The

advantages/utility of having a hypothesis are summarized here underneath:

i) It is a starting point for many a research work.

ii) It helps in deciding the direction in which to proceed.

iii) It helps in selecting and collecting pertinent facts.

iv) It is an aid to explanation.

v) It helps in drawing specific conclusions.

vi) It helps in testing theories.

vii) It works as a basis for future knowledge.

Use of statistical techniques for testing of hypothesis

A hypothesis test is a statistical method that uses sample data to evaluate a hypothesis about a

population parameter.

The hypothesis testing is standard and it follows a specific order;

(i) first state a hypothesis about a population (a population parameter, e.g. mean µ),

(ii) obtain a random sample from the population and also find its mean x , and

(iii) compare the sample data with the hypothesis on the scale (standard z or normal distribution).

A hypothesis test is typically used in the context of a research study, i.e. a researcher completes one

round of a field investigation and then uses a hypothesis test to evaluate the results. Depending on the

type of research and the type of data, the details will differ from one research situation to another.


The following are some of the statistical techniques for testing of hypothesis

1. Z-Score Statistics

Z-Score is called a test statistics. The purpose of a test statistics is to determine whether the result of a

research study (the obtained difference) is more than what would be expected by the chance alone.

chancetodueDifference

differenceObtainedz

Now suppose a manufacturer, produces some type of articles of good quality. A purchaser by chance

selects a sample randomly. It so happens that the sample contains many defective articles and it leads the

purchaser to reject the whole product. Now, the manufacturer suffers a loss even though he has produced

a good article of quality. Therefore, this Type-I error is called ―producers risk‖.

On the other hand, if we accept the entire lot on the basis of a sample and the lot is not really good, the

consumers are put in loss. Therefore, this Type-II error is called the ―consumers risk‖.

In practical situations, still other aspects are considered while accepting or rejecting a lot. The risks

involved for both producer and consumer are compared. Then Type-I and Type-II errors are fixed; and a

decision is reached.

2. Student‟s t-distribution

This concept was introduced by W. S. Gosset (1876 - 1937). He adopted the pen name ―student‖.

Therefore, the distribution is known as ‗student‘s t-distribution‘.

It is used to establish confidence limits and test the hypothesis when the population variance is not

known and sample size is small (< 30).

If a random sample x1, x2, . . . , xn of n values be drawn from a normal population with mean and

standard deviation then the mean of sample

n

xx i

3. Chi-square test

Tests like z-score and t are based on the assumption that the samples were drawn from normally

distributed populations or more accurately that the sample means were normally distributed. As these

tests require assumptions about the type of population or parameters, these tests are known as ‗parametric

tests‘.

There are many situations in which it is impossible to make any rigid assumption about the distribution

of the population from which samples are drawn. This limitation led to search for non-parametric tests.

Chi-square (Read as Ki-square) test of independence and goodness of fit is a prominent example of a non-

parametric test. The chi-square (2) test can be used to evaluate a relationship between two nominal or

ordinal variables.


2 (chi-square) is measure of actual divergence of the observed and expected frequencies. In sampling

studies, we never expect that there will be a perfect coincidence between actual and observed frequencies

and the question that we have to tackle is about the degree to which the difference between actual and

observed frequencies can be ignored as arising due to fluctuations of sampling. If there is no difference

between actual and observed frequencies then 2 = 0. If there is a difference, then 2 would be more than

0. But the difference may also be due to sample fluctuation and thus the value of 2 should be ignored in

drawing the inference. Such values of 2 under different conditions are given in the form of tables and if

the actual value is greater than the table value, it indicates that the difference is not solely due to sample

fluctuation and that there is some other reason.

On the other hand, if the calculated 2 is less than the table value, it indicates that the difference may have

arisen due to chance fluctuations and can be ignored. Thus 2-test enables us to find out the divergence

between theory and fact or between expected and actual frequencies.

If the calculated value of 2 is very small, compared to table value, then expected frequencies are very

little and the fit is good.

If the calculated value of 2 is very large as compared to table value then divergence between the

expected and the observed frequencies is very big and the fit is poor.

We know that the degree of freedom r (df) is the number of independent constraints in a set of data.

Suppose there is a two 2 association table and actual frequencies of the various classes are as follows :

B

A a

AB aB

22 38 60

b

Ab ab

8 32 40

30 70 100

Now the formula for calculating expected frequency of any class (cell)

columcellthecontainingrowfortotalRow =nsobservatioofnumbertotalThe

cellthecontainingcolumnforTotal

In notations : Expected frequency N

CR

For example, if we have two attributes A and B that are independent then the expected frequency of the

class (cell) AB would be 18100

6030

.

Once the expected frequency of cell (AB) is decided the expected frequencies of remaining three classes

are automatically fixed.


Thus, for class (aB) it would be 60 – 18 = 42

for class (Ab) it would be 30 – 18 = 12

for class (ab) it would be 70 – 42 = 28

This means that so far as two 2 association (contingency) table is concerned, there is

1 degree of freedom.

In such tables, the degrees of freedom are given by a formula n = (c – 1) (r – 1),

where c = Number of columns and r = Number of rows.

Thus in 2 2 table df = (2 – 1) (2 – 1) = 1

3 3 table df = (3 – 1) (3 – 1) = 4

4 4 table df = (4 – 1) (4 – 1) = 9 etc.

If the data is not in the form of contingency tables but as a series of individual observations or discrete or

continuous series then it is calculated by n = n – 1 where n is the number of frequencies or values of

number of independent individuals.

frequencyExpected

)frequencyExpectedfrequencyObserved( 22

E

EO 22 )(

where O = Observed frequency and E = Expected frequency.

Review questions

1. Distinguish between Estimation and testing

of hypothesis.

2. Explain the procedure for testing a statistical

hypothesis.

3. Discuss the role of normal distribution in

interval estimation and also in testing

hypothesis.

4. Discuss how far the sample proportion

satisfies the desirable properties of a good

estimator.

5. How do you proceed to set confidence limits

to population mean ?

6. Describe how you could set confidence

limits to population proportion on the basis

of a large sample.

7. Explain how you would test for population

mean.

8. Describe the different steps for testing the

significance of population proportion.

9. Describe a situation where you can apply t-

distribution.

10. How would you distinguish between a t-test

for independent sample and a paired t-test?

11. Distinguish between large samples and

small samples.


Chapter9: Interpretation of Data

Introduction

Statistics are not an end in themselves but they are a means to an end, the end being to draw certain

conclusions from them. This has to be done very carefully, otherwise misleading conclusions may be

drawn and the whole purpose of doing research may get vitiated. A researcher/statistician besides the

collection and analysis of data, has to draw inferences and explain their significance. Through

interpretation the meanings and implications of the study become clear. Analysis is not complete without

interpretation, and interpretation cannot proceed without analysis. Both are, thus, inter-dependent. In

this unit, therefore, we will discuss the interpretation of analysed data, summarizing the interpretation

and statistical fallacies.

Meaning of interpretation

The following definitions can explain the meaning of interpretation.

―The task of drawing conclusions or inferences and of explaining their significance after a careful

analysis of selected data is known as interpretation‖.

―It is an inductive process, in which you make generalizations based

on the connections and common aspects among the categories and

patterns‖.

―Scientific interpretation seeks relationship between the data of a

study and between the study findings and other scientific

knowledge‖.

―Interpretation in a simple way means the translation of a statistical

result into an intelligible description‖.

Thus, analysis and interpretation are central steps in the research

process. The purpose of analysis is to summarize the collected data,


where as interpretation is the search for the broader meaning of research findings. In interpretation, the

researcher goes beyond the descriptive data to extract meaning and insights from the data.

Why interpretation?

A researcher/ statistician is expected not only to collect and analyse the data but also to interpret the

results of his/ her findings. Interpretation is essential for the simple reason that the usefulness and utility

of research findings lie in proper interpretation. It is only through interpretation that the researcher can

expose relations and patterns that underlie his findings. In case of hypothesis testing studies the

researcher may arrive at generalizations. In case the researcher had no hypothesis to start with, he would

try to explain his findings on the basis of some theory. It is only through interpretation that the researcher

can appreciate why his findings are what they are, and can make others understand the real significance

of his research findings.

Interpretation is not a mechanical process. It calls for a critical examination of the results of one‘s analysis

in the light of all the limitations of data gathering. For drawing conclusions you need a basis. Some of the

common and important bases of interpretation are: relationships, ratios, rates and percentages, averages

and other measures of comparison.

Essentials for interpretation

Certain points should be kept in mind before proceeding to draw conclusions from statistics. It is

essential that:

a) The data are homogeneous: It is necessary to ascertain that the data are strictly comparable. We must

be careful to compare the like with the like and not with the unlike.

b) The data are adequate: Sometimes it happens that the data are incomplete or insufficient and it is

neither possible to analyze them scientifically nor is it possible to draw any inference from them. Such

data must be completed first.

c) The data are suitable: Before considering the data for interpretation, the researcher must confirm the

required degree of suitability of the data. Inappropriate data are like no data. Hence, no conclusion is

possible with unsuitable data.

d) The data are properly classified and tabulated: Every care is to be taken as a pre-requisite, to base all

types of interpretations on systematically classified and properly tabulated data and information.

e) The data are scientifically analyzed: Before drawing conclusions, it is necessary to analyze the data by

applying scientific methods. Wrong analysis can play havoc with even the most carefully collected data.

If interpretation is based on uniform, accurate, adequate, suitable and scientifically analyzed data, there is

every possibility of attaining a better and representative result. Thus, from the above considerations we

may conclude that it is essential to have all the pre-requisites/pre-conditions of interpretation satisfied to

arrive at better conclusions.


Precautions in interpretation

It is important to recognize that errors can be made in interpretation if proper precautions are not taken.

The interpretation of data is a very difficult task and requires a high degree of skill, care, judgement and

objectivity. In the absence of these, there is every likelihood of data being misused to prove things that

are not true. The following precautions are required before interpreting the data.

1) The interpreter must be objective.

2) The interpreter must understand the problem in its proper perspective.

3) He / she must appreciate the relevance of various elements of the problem.

4) See that all relevant, adequate and accurate data are collected.

5) See that the data are properly classified and analyzed.

6) Find out whether the data are subject to limitations? If so what are they?

7) Guard against the sources of errors.

8) Do not make interpretations that go beyond the information / data.

9) Factual interpretation and personal interpretation should not be confused. They should be kept apart.

If these precautions are taken at the time of interpretation, reasonably good conclusions can be arrived at.

Techniques of Interpretation

There are many different of tnterpretation techniques like graph or chart, but most are not used in

business research.

Those used most often include:

1. pie charts

2. vertical bar charts (histograms)

3. horizontal bar charts (also histograms)

4. pictograms

5. line charts

6. area charts

Some other types of charts, well suited to audience research, but less often used, include

perceptual maps ( Discussed in data analysis techniques)

Though many different kinds of graph are possible, if a report includes too many types, it‘s often

confusing for readers, who must work out how to interpret each new type of graph, and why it is

different from an earlier one. It is recommended using as few types of graph as are necessary.

If you have a spreadsheet or graphics program, such as Excel or Deltagraph, it‘s very easy to produce

graphs. You simply enter the numbers and labels in a table, click a symbol to show which type of graph

you want, and it appears before your eyes. These graphs are usually not very clear when first produced,


but the software has many options for changing headings, scales, and graph layout. You can waste a lot

of time perfecting these graphs. Excel (actually, Microsoft Graph, which Excel uses) has dozens of

options, and it takes a lot of clicking of the right-hand mouse button to discover them all. If you don‘t

have a recent and powerful computer, Excel can be a very slow and frustrating program to use.

The main types of graph include pie charts, bar charts (histograms), line charts, area charts, and several

others.

1) Pie chart

A round graph, cut (like a pie) into slices of varying size, all adding to 100%. Because a pie chart is round,

it‘s useful for communicating data which takes a

"round" form: for example, the answers to "How

many minutes in each hour would you like FM

RADIOMIRCHI to spend on each of the following

types of program...?" In this case, the pie

corresponds to a clock face, and the slices can be

interpreted as fractions of an hour.

Pie charts are easily understood when the slices are

similar in size, but if several slices are less than 5%, or lots of different colours are used, it can be quite

difficult to read a pie chart. In that case the chart has to be very big, taking perhaps half a page to convey

one set of numbers. Not a very efficient way to display information.

2) Vertical bar chart

Also known as a histogram. A very common type of graph, easily understood. But when one of these

charts has more than about 6 vertical bars, there‘s very little space below each bar to explain what it‘s

measuring.

3) Horizontal bar chart

Exactly like a vertical bar chart, but turned

sideways. The big advantage of the

horizontal bar chart is that you can easily

read a description with more than one word.

Unfortunately, most graphics software displays the bars upside down — you‘re expected to read from

the bottom, upwards to the top. A standard bar chart looks like this. (Like the two above charts, this was

created with Excel.)


You don‘t need graphics software to produce a

horizontal bar chart: you can do it easily with a

word processing program. One of the easiest

ways to do this is to use the | symbol to

produce the bars. This symbol is usually found

on the \ key; it is not a lower-case L or upper-

case I or number 1. It stands out best in bold

type. This is what we call a blobbogram.

For example:

Q14. SEX OF RESPONDENT

Male 47.4% |||||||||||||||||||||||||

Female 52.6% |||||||||||||||||||||||||||

Total 100.0% = 325 cases

If each symbol represents 2% of the sample, you can usually fit the graph on a single line. Round each

figure to the nearest 2% to work out how many times to press the symbol key. In the above example,

47.4% is closer to 48% than to 46%, so I pressed the | key 24 times to graph the percentage of men. This is

a very clear layout, and quick to produce, so it is well suited to a preliminary report.

A more elaborate looking graph can be made by using special symbols. For example, if you have the font

Zapf Dingbats or Wingdings, you can use the shaded-

This is wider than the | symbol, and no more than about 20 will fit on a normal-width line, if half the line

Q14. SEX OF RESPONDENT

Male 47.4%

Female 52.6%

Total 100.0% = 325 cases


4) Pictograms

Like a bar chart, a pictogram can be either vertical or horizontal, but instead of showing a solid bar, a

pictogram shows a number of symbols - e.g. small diagrams of people. In fact, the above bar chart with

pictograms show partial symbols. If one little man

means 10%, and the number to be graphed is 45%,

you see four and a half little men...

5) Line chart

This is used when the variable you are graphing is a numeric one. In audience research, most variables

are nominal, not numeric, so line charts aren‘t needed much. But to plot the answers to a question such as

"How many people live in your household?" you could produce a graph like this:

It‘s normal to show the measurement (e.g. percentage) upwards, and the scale (e.g. hours per week) on

the horizontal scale. Unlike a bar chart, it will confuse people if the scales are exchanged. You‘ll find that

almost every line chart has a peak in the middle, and falls off to each side, reflecting what‘s known as the

"normal curve."

A line chart is really another form of a vertical bar chart. You could turn a vertical bar chart into a line

chart by drawing a line connecting the top of each bar, then deleting the bars.

A line chart can have more than one line. For example, you could have a line chart comparing the number

of hours per week that men and women watch TV. There‘d be two lines, one for each sex. Each line needs

to be shown with a different style, or a different colour. With more than 3 or 4 lines, a line chart becomes

very confusing, specially when the lines cross each other.

6) Area chart

In a line chart with several lines — such as the above example, with two sexes — each line starts from the

bottom of the table. That way, you can compare the height of the lines at any point. An area chart is a

little different, in that each line starts

from the line below it. So you don‘t

compare the height of the lines, but the

areas between them. These areas always

add to 100% high. You can think of an

area chart as a lot of pie charts, flattened

out and laid end-to-end.


A common use of area charts in audience research is to show how people‘s behaviour changes across the

24 hours of the day. The horizontal scale runs from midnight to midnight, and the vertical scale from 0 to

100%. This area chart, taken from a survey in Vietnam, shows how people divide their day into sleep,

work, watching TV, listening to radio, and work and everything else.

An area chart needs to be studied closely: the results aren‘t obvious at a glance. However, area charts

provide a lot of information in a small space.

Which type of graph is best?

There are dozens of other chart types not mentioned above, and also dozens of variations on the above

types - specially bar charts. However the above graph types cover most situations. It becomes confusing

to readers of reports if many different types of graph are presented, so it is recommended that any report

should include no more different graph types than necessary.

The most appropriate type of graph to present depends on the number of variables being displayed, and

whether these are nominal variables (with a limited number of separate values) or metric variables

(whose value can be any number). It is suggested to use a horizontal bar chart whenever possible. In a

normal audience survey, less than a third of the graphs are unsuited to being shown as horizontal bar

charts.

Variables Recommended chart type

number type

1 nominal bar chart, pictogram, or pie chart

1 metric line graph, or box and whisker plot

2 both nominal multiple bar chart, or domino chart

2 both metric bubble chart, or scattergram

2 1 metric, 1 nominal box and whisker plot, or area chart

3-D charts can look very impressive, but It is strongly suggested to avoid using them — it‘s just too easy

to misread them. The simpler a graph is, the more effective it is at communicating

Statistical fallacies

Interpretation of data, as we stated earlier, is a very difficult task and requires a high degree of care,

objectivity, skill and judgement. In the absence of these things, it is likely that the data may be misused.

In fact, experience shows that the largest number of mistakes are committed knowingly or unknowingly

while interpreting statistical data which may lead to misinterpretation of data by most of the readers.

Statistical fallacies may arise at any stage – in the collection, presentation, analysis and interpretation of

data. The following are some of the (i) specific examples illustrating how statistics can be misinterpreted,

(ii) Sources of errors leading to false generalizations, (iii) examples how fallacies arise in using statistical

data and statistical methods.


1. Bias: Bias, whether it is conscious or unconscious, is very common in statistical work and it leads to

false generalizations. It is found that wrong interpretations are made want only to prove their point.

Sometimes deliberately statistical information is twisted as to grind one‘s own axe. For example, a

business man may use statistics to prove the superiority of their firm over others by saying that our firm

earned a profit of ` 1,00,000 where as firm ‗X‘ earned only ` 80,000 this year. On the face of it, it appears

that firm ‗X‘ has not performed well. But a little thinking reveals that many other variables have to be

considered before drawing such a conclusion, such as what is the capital employed? If the capital

employed is same, then the quality of product and so on. Unconscious bias is even more insidious.

Perhaps, all statistical reports contain some unconscious bias, since the statistical results are interpreted

by human beings after all. Each may look at things in terms of his own experience and his attitude

towards the problem under study. People suffer from several inhibitions, prejudices, ideologies and

hardened attitudes. They cannot help reflecting these in their interpretation of results. For example: A

pessimist will see the future as being dark, where as an optimist may see it as being bright.

2. Inconsistency in Definitions: Sometimes false conclusions are drawn because of failure to define

properly the object being studied and hold that definition in mid for making comparisons. When the

working capital of two firms is compared, net working capital of one must be compared with only net

working capital of the other and not with gross working capital. Even within the organization, for

facilitating comparison over a period of time it is necessary to keep the definition constant.

3. Inappropriate Comparisons: Comparisons between two things cannot be made unless they are really

alike. Unfortunately, this point is generally forgotten and comparisons are made between two dissimilar

things, thereby, leading to fallacious conclusions. For example, the cost of living index of Bangalore is 150

(with base year 1999) and that of Hyderabad is 155 (with base 1995). Therefore, Hyderabad is a costlier

city than Bangalore city. This conclusion is misleading as the base years of the Indices are different.

4. Faulty Generalizations: Many a time people jump to conclusions or generalizations on the basis of

either too small a sample or a sample that is not representative of the population. For example, if a

foreigner came to Delhi and his purse was stolen by a pick pocket and he comments that there is no safety

and security for foreigners in India. This is not true as thousands of foreigners come to India. They are

safe and secure. Sometimes the sample size may be adequate but not representative.

5. Drawing Wrong Inferences: Sometimes wrong inferences may be drawn from the data. For example,

the population of a town has doubled in 10 years. From this it is interpreted that the birth rate in the town

has doubled. Obviously, this is a wrong inference, as the population of the town can double in many

ways (example: exodus from villages, migration from other places etc.) than doubling of birth rate only.

6. Misuse of Statistical Tools: The various tools of analysis such as measures of central tendency,

measures of variation, measures of correlation, ratios, percentages etc., are very often misused to present

information in such a manner as to convince the public or to camoaflage things. In a company there are

1,00,000 shares and 1,000 share holders. The company claims that their shares are well distributed as the

average share holding is 100. But a close scrutiny reveals that 10 persons hold 90,000 shares where as 990


persons hold 10,000 shares, average being about 10. Similarly, range can be misused to exaggerate

disparities. For example, in a factory the wages may range between ` 1,000 to ` 1,500 a month and the

Manager gets ` 20,000 a month. It is reported that the earnings of their employees range from ` 1,000 to `

20,000.

7. Failure to Comprehend the Data: Very often figures are interpreted without comprehending the total

background of the data and it may lead to wrong conclusions. For example, see the following

interpretations:

o The death rate in the army is 9 per thousand, where as in the city of Delhi it is 15 per thousand.

Therefore, it is safer to be in the army than in the city.

o Most of the patients who were admitted in the intensive care (IC) ward of a hospital died.

Therefore, it is unsafe to be admitted to intensive care ward in that hospital.

Concluding remarks on interpretation

The task of interpretation is not an easy job. It requires skill and dexterity on the part of the researcher.

Interpretation is an art that one learns through practice and experience. The researcher may seek the

guidance of experts for accomplishing the task of interpretation.

The element of comparison is fundamental to all research interpretations. Comparison of one‘s findings

with a criterion, or with results of other comparable investigations or with normal (ideal) conditions, or

with existing theories or with the opinions of a panel of judges / experts forms an important aspect of

interpretation.

The researcher must accomplish the task of interpretation only after considering all relevant factors

affecting the problem to avoid false generalizations. He/she should not conclude without evidence.

He/she should not draw hasty conclusions. He/she should take all possible precautions for proper

interpretation of the data.


Report writing

The last and final phase of the journey in research is writing of the report. After the collected data has

been analyzed and interpreted and generalizations have been drawn the report has to be prepared. The

task of research is incomplete till the report is presented.

Writing of a report is the last step in a research study and requires a set of skills somewhat different from

those called for in respect of the earlier stages of research. This task should be accomplished by the

researcher with utmost care.

Purpose of a report

The report may be meant for the people in general, when the investigation has not been carried out at the

instance of any third party. Research is essentially a cooperative venture and it is essential that every

investigator should know what others have found about the phenomena under study. The purpose of a

report is thus the dissipation of knowledge, broadcasting of generalizations so as to ensure their widest

use.

A report of research has only one function, ―it must inform‖. It has to propagate knowledge. Thus, the

purpose of a report is to convey to

the interested persons the results

and findings of the study in

sufficient detail, and so arranged

as to enable each reader to

comprehend the data, and to

determine for himself the validity

of conclusions. Research results

must invariably enter the general

store of knowledge. A research report is always an addition to knowledge. All this explains the

significance of writing a report. In a broader sense, report writing is common to both academics and

organizations. However, the purpose may be different. In academics, reports are used for comprehensive

and application-oriented learning. Whereas in organizations, reports form the basis for decision making.

Meaning

Reporting simply means communicating or informing through reports. The researcher has collected some

facts and figures, analyzed the same and arrived at certain conclusions. He has to inform or report the

same to the parties interested. Therefore ―reporting is communicating the facts, data and information

through reports to the persons for whom such facts and data are collected and compiled‖.

A report is not a complete description of what has been done during the period of survey/research. It is

only a statement of the most significant facts that are necessary for understanding the conclusions drawn


by the investigator. Thus, ― a report by definition, is simply an account‖. The report thus is an account

describing the procedure adopted, the findings arrived at and the conclusions drawn by the investigator

of a problem.

Types of reports

Broadly speaking reporting can be done in two ways:

a) Oral or Verbal Report: reporting verbally in person, for example; Presenting the findings in a

conference or seminar or reporting orally to the superiors.

b) Written Report: Written reports are more formal, authentic and popular.

Written reports can be presented in different ways as follows.

i) Sentence form reports: Communicating in sentence form

ii) Tabular reports: Communicating through figures in tables

iii) Graphic reports: Communicating through graphs and diagrams

iv) Combined reports: Communicating using all the three of the above. Generally, this is the most

popular

Research reports vary greatly in length and type. In each individual case, both the length and the form

are largely dictated by the purpose of the study and problems at hand. For example, business

organizations generally prefer reports in letter form, that too short in length. Banks, insurance and other

financial institutions generally prefer figure form in tables. The reports prepared by government bureaus,

enquiry commissions etc., are generally very comprehensive on the issues involved. Similarly, research

theses/dissertations usually prepared by students for Ph.D. degree are also elaborate and methodical.

It is, thus, clear that the results of a research enquiry can be presented in a number of ways. They may be

termed as a technical report, a popular report, an article, or a monograph.

1) Technical Report: A technical report is used whenever a full written report (ex: Ph.D. thesis) of the

study is required either for evaluation or for record keeping or for public dissemination. The main

emphasis in a technical report is on :

a) the methodology employed.

b) the objectives of the study.

c) the assumptions made / hypotheses formulated in the course of the study.

d) how and from what sources the data are collected and how have the data been analyzed.

e) the detailed presentation of the findings with evidence, and their limitations.

2) Popular Report: A popular report is one which gives emphasis on simplicity and attractiveness. Its aim

is to make the general public understand the findings and implications. Generally, it is simple. Simplicity

is sought to be achieved through clear language and minimization of technical details. Attention of the


readers is sought to be achieved through attractive layout, liberal use of graphs, charts, diagrams and

pictures. In a popular report emphasis is given on practical aspects and policy implications.

3) Research Article: Sometimes the findings of a research study can be published in the form of a short

paper called an article. This is one form of dissemination. The research papers are generally prepared

either to present in seminars and conferences or to publish in research journals. Since one of the

objectives of doing research is to make a positive contribution to knowledge, in the field, publication

(publicity) of the work serves the purpose.

4) Monograph: A monograph is a treatise or a long essay on a single subject. For the sake of convenience,

reports may also be classified either on the basis of approach or on the basis of the nature of presentation

such as:

i) Journalistic Report

ii) Business Report

iii) Project Report

iv) Dissertation

v) Enquiry Report (Commission Report), and

vi) Thesis

Reports prepared by journalists for publication in the media may be journalistic reports. These reports

have news and information value. A business report may be defined as report for business

communication from one departmental head to another, one functional area to another, or even from top

to bottom in the organizational structure on any specific aspect of business activity. These are

observational reports which facilitate business decisions. A project report is the report on a project

undertaken by an individual or a group of individuals relating to any functional area or any segment of a

functional area or any aspect of business, industry or society. A dissertation, on the other hand, is a

detailed discourse or report on the subject of study. Dissertations are generally used as documents to be

submitted for the acquisition of higher research degrees from a University or an academic institution.

The thesis is an example in point.

An enquiry report or a commission of enquiry report is a detailed report prepared by a commission

appointed for the specific purpose of conducting a detailed study of any matter of dispute or of a subject

requiring greater insight. These reports facilitate action, since they contain expert opinions.

Preparing research report

Research reports are the product of slow and painstaking and accurate work. Therefore, the preparation

of the report may be viewed in the following major stages.

1) The logical understanding and analysis of the subject matter.

2) Planning/designing the final outline of the report.

3) Write up/preparation of rough draft.


4) Polishing/finalization of the Report.

Logical Understanding of the Subject Matter: It is the first stage which is primarily concerned with the

development of a subject. There are two ways to develop a subject viz. a. logically and b. chronologically.

The logical development is done on the basis of mental connections and associations between one aspect

and another by means of logical analysis. Logical treatment often consists of developing material from the

simple to the most complex. Chronological development is based on a connection or sequence in time or

happening of the events. The directions for doing something usually follow the chronological order.

Designing the Final Outline of the Report: It is the second stage in writing the report. Having

understood the subject matter, the next stage is structuring the report and ordering the parts and

sketching them. This stage can also be called as planning and organization stage. Ideas may pass through

the author‘s mind. Unless he first makes his plan/sketch/design he will be unable to achieve a

harmonious succession and will not even know where to begin and how to end. Better communication of

research results is partly a matter of language but mostly a matter of planning and organizing the report.

Preparation of the Rough Draft: The third stage is the write up/drafting of the report. This is the most

crucial stage to the researcher, as he/she now sits to write down what he/she has done in his/her

research study and what and how he/she wants to communicate the same. Here the clarity in

communicating/reporting is influenced by some factors such as who the readers are, how technical the

problem is, the researcher‘s hold over the facts and techniques, the researcher‘s command over language

(his communication skills), the data and completeness of his notes and documentation and the

availability of analyzed results. Depending on the above factors some authors may be able to write the

report with one or two drafts. Some people who have less command over language, no clarity about the

problem and subject matter may take more time for drafting the report and have to prepare more drafts

(first draft, second draft, third draft, fourth draft etc.,)

Finalization of the Report: This is the last stage, perhaps the most difficult stage of all formal writing. It

is easy to build the structure, but it takes more time for polishing and giving finishing touches. Take for

example the construction of a house. Up to roofing (structure) stage the work is very quick but by the

time the building is ready, it takes up a lot of time. The rough draft (whether it is second draft or ‗n‘ th

draft ) has to be rewritten, polished in terms of requirements. The careful revision of the rough draft

makes the difference between a mediocre and a good piece of writing. While polishing and finalizing one

should check the report for its weaknesses in logical development of the subject and presentation

cohesion. He/she should also check the mechanics of writing — language, usage, grammar, spelling and

punctuation.


Characteristics of a good report

Research report is a channel of communicating the research findings to the readers of the report. A good

report is one which does this task efficiently and effectively. As such it should have the following

characteristics/qualities.

i) It must be clear in informing the what, why, who, whom, when, where and how of the research study.

ii) It should be neither too short nor too long. One should keep in mind the fact that it should be long

enough to cover the subject matter but short enough to sustain the reader‘s interest.

iii) It should be written in an objective style and simple language, correctness, precision and clarity

should be the watchwords of the scholar. Wordiness, indirection and pompous language are barriers to

communication.

iv) A good report must combine clear thinking, logical organization and sound interpretation.

v) It should not be dull. It should be such as to sustain the reader‘s interest.

vi) It must be accurate. Accuracy is one of the requirements of a report. It should be factual with objective

presentation. Exaggerations and superlatives should be avoided.

vii) Clarity is another requirement of presentation. It is achieved by using familiar words and

unambiguous statements, explicitly defining new concepts and unusual terms.

viii) Coherence is an essential part of clarity. There should be logical flow of ideas (i.e. continuity of

thought), sequence of sentences. Each sentence must be so linked with other sentences so as to move the

thoughts smoothly.

ix) Readability is an important requirement of good communication. Even a technical report should be

easily understandable. Technicalities should be translated into language understandable by the readers.

x) A research report should be prepared according to the best composition practices. Ensure readability

through proper paragraphing, short sentences, illustrations, examples, section headings, use of charts,

graphs and diagrams.

xi) Draw sound inferences/conclusions from the statistical tables. But don‘t repeat the tables in text

(verbal) form.

xii) Footnote references should be in proper form. The bibliography should be reasonably complete and

in proper form.

xiii) The report must be attractive in appearance, neat and clean whether typed or printed.

xiv) The report should be free from mistakes of all types viz. language mistakes, factual mistakes, spelling

mistakes, calculation mistakes etc.,

The researcher should try to achieve these qualities in his report as far as possible.


Layout of a Report

Under this head, the format/outline/sketch of a comprehensive technical report or research report is

discussed below. A research report has a number of clearly defined sections. The headings of the sections

and their order may differ from one situation to another. The contents of a report can broadly be divided

into following parts as :

A) Front Matters

1. Title Page

2. Certificate

3. Declaration

4. Acknowledgments

5. Executive Summary

6. Table of Contents

7. List of Illustrations and List of Tables

8. List of abbreviations used

B) Main Text

1. Introduction

2. Research methodology

3. · Background to the research problem

· Objectives

· Hypotheses

4. Data collection

5. · Sample and sampling method

· Statistical or qualitative methods used for data analysis

· Sample description

6. Tabulation and Analysis of Data

7. Finding of study

8. Conclusions

9. Recommendations of study

C) Reference Matters

1. Bibliography

2. Appendices (optional)

3. Glossary (optional)

4. References (optional)

http://www.wpi.edu/Academics/Projects/Report/front.html#acknowledge

http://www.wpi.edu/Academics/Projects/Report/front.html#contents

http://www.wpi.edu/Academics/Projects/Report/ref.html

http://www.wpi.edu/Academics/Projects/Report/ref.html#appendices

http://www.wpi.edu/Academics/Projects/Report/ref.html#glossary


A) Front Pages

1) Title Page

The cover page should display full name of researcher, guide along with qualification, and the title of

report.

2) Certificate

Format for same given in sample page at the last of book

3) Declaration

Format for same given in sample page at the last of book

4) Acknowledgments

The researcher may wish to acknowledge people who helped in preparation of report. For example, you

may wish to thank someone you interviewed, or someone who provided you with some special

information.

5) Table of Contents and List of Figures

Report should have a Table of Contents that lists the report's sections and page numbers. If figures

include in report (charts, tables, diagrams), one must also include a list of figures, indicating titles and

page numbers. Figures should be numbered, titled, and mentioned in the text preceding them.

6) List of tables and illustrations used

7) Executive Summary

One of the most important components of the report is the Executive Summary. It answers the

question, "What does the report contain?" and should be written after the rest of the report is

complete. The Executive Summary should be complete in itself and may be consulted by readers who

wish to determine whether they need to read the whole report.

Limit the Executive Summary to two-three pages and discuss:

Purpose and extent of the report

Major points contained in the body of the report

Highlights of key conclusions

Highlights of key recommendations

B) Main Text

1) Introduction:-The Introduction should establish the purpose of the report and should convey what is

in the body of the report. One should provide the reader with the following information:

Necessary background information

major points that will be covered in the report

the situation or problem that will be analyzed

what your aims are in compiling the report Analysis

Why does a problem exist?


How does the problem affect the environment?

What efforts may solve this problem?

What aspects of the problem have been measured and improved? How?

What problems does the potential solution not solve? Why not?

What could be improved?

2) Research Methodology: -

Goals of the study, specific objectives, and purpose of the study.

Statistical design:- Universe of study, sampling method, sample size and unit , secondary data

sources ,and Limitations of study.

Tools of Data collection, and the response rate

3) Tabulation and Analysis: -

Analysis is the most important part of report because it contains "workings out" - how one reaches the

conclusions. Analysis should contain the thoughts, reasons, judgments based on the facts and figures and

data t collected. In analysis one makes INFERENCES, conclusions that are drawn from the research.

4) Finding, Conclusions and Recommendations of study: -

The conclusions are the final results of analysis. They should be brief and should contain no new

information. They should not make direct reference to sources, figures, or tables. The conclusions should

be listed and numbered, with brief explanation for each. Each conclusion should follow logically from the

facts and arguments presented in the main text (body). RECOMMENDATIONS are suggestions, based on

the conclusions reached from the research. These should brief and should follow logically from the

conclusions.

C) Reference Matter

I) BIBLIOGRAPHY

A bibliography is an alphabetical list of all materials consulted in the preparation of research.

II) APPENDICES CONTAINING COPIES OF THE QUESTIONNAIRES, ETC.

Why do a bibliography?

Some reasons:

1. To acknowledge and give credit to sources of words, ideas, diagrams, illustrations, and quotations

borrowed, or any materials summarized or paraphrased.

2. To show that you are respectfully borrowing other people‘s ideas, not stealing them, i.e. to prove that

you are not plagiarizing (Copying).

3. To offer additional information to readers who may wish to further pursue the topic.

4. To give readers an opportunity to check out the sources for accuracy. An honest bibliography inspires

reader confidence in writing.


What must be included in a bibliography?

1. Author

2. Title

3. Place of publication

4. Publisher

5. Date of publication

6. Page number(s) (for articles from magazines, journals, periodicals, newspapers,

encyclopaedias etc.)

1. Author

Ignore any titles, designations or degrees, etc. which appear before or after the name, e.g., The

Honourable, Dr., Mr., Mrs., Ms., Rev., S.J., Esq., Ph.D., M.D., Q.C., etc. Exceptions are Jr. and Sr. Do

include Jr. and Sr. as John Smith, Jr. and John Smith, Sr. are two different individuals. Include also I, II,

III, etc. for the same reason.

Examples:

a) Last name, first name:

Kotlar, Philip.

Christensen, Asger.

Wilson-Smith, Anthony.

b) Last name, first and middle names:

Wyse, Cassandra Ann Lee.

c) Last name, first name and middle initial:

Schwab, Charles R.

d) Last name, initial and middle name:

Holmes, A. William.

e) Last name, initials:

Meister, F.A.

f) Last name, first and middle names, Jr. or Sr. designation:

Davis, Benjamin Oliver, Jr.

g) Last name, first name, I, II, III, etc.:

Stilwell, William E., IV.

2. Title and subtitle

a) If the title on the front cover or spine of the book differs from the title on the title page, use the title on

the title page for your citation.

http://www.aresearchguide.com/#title

http://www.aresearchguide.com/#place

http://www.aresearchguide.com/#publisher

http://www.aresearchguide.com/#date

http://www.aresearchguide.com/#page


b) UNDERLINE the title and subtitle of a book, magazine, journal, periodical, newspaper, or

encyclopaedia, e.g., What to Do When Things Go Wrong, Sports Illustrated, New York Times,

Encyclopaedia Britannica.

c) If the title of a newspaper does not indicate the place of publication, add the name of the city or town

after the title in square brackets, e.g. National Post [Toronto].

Freeze, Colin. "Illinois Puts the Death Penalty Itself on Trial." Globe and Mail [Toronto]

29 Oct. 2002: A3.

Furuta, Aya. "Japan Races to Stay Ahead in Rice-Genome Research." Nikkei Weekly [Tokyo]

5 June 2000: 1+.

d) DO NOT UNDERLINE the title and subtitle of an article in a magazine, journal, periodical, newspaper,

or encyclopedia; put the title and subtitle between quotation marks:

Baker, Peter, and Susan B. Glasser. "No Deals with Terrorists: Putin." Toronto Star

29 Oct. 2002: A1+.

Fisher, Dennis. "Safe Data: At What Price?" eWeek 21 Oct. 2002: 26.

Penny, Nicholas B. "Sculpture, The History of Western." New Encyclopaedia Britannica.

1998 ed.

e) CAPITALIZE the first word of the title, the first word of the subtitle, as well as all important words

except for articles, prepositions, and conjunctions, e.g., Flash and XML: A Developer's Guide, or The Red

Count: The Life and Times of Harry Kessler.

f) Use LOWER CASE letters for conjunctions such as and, because, but, and however; for prepositions

such as in, on, of, for, and to; as well as for articles: a, an, and the, unless they occur at the beginning of a

title or subtitle, or are being used emphatically, e.g., "And Now for Something Completely Different: A

Hedgehog Hospital," "Court OKs Drug Tests for People on Welfare," or "Why Winston Churchill Was The

Man of The Hour."

g) Separate the title from its subtitle with a COLON (:), e.g. "Belfast: A Warm Welcome Awaits."

3. Place of publication - for books only

a) DO NOT use the name of a country, state, province, or country as a Place of Publication, e.g. do not list

India, Australia, Canada, United Kingdom, Great Britain, United States of America, California, or

Maharashtra as a place of publication.

b) Use only the name of a city or a town.

c) Choose the first city or town listed if more than one Place of Publication is indicated in the book.

d) It is not necessary to indicate the Place of Publication when citing articles from major encyclopaedias,

magazines, journals, or newspapers.

e) If the city is well known, it is not necessary to add the State or Province after it, e.g.:

New Delhi:

Mumbai:


London:

New York:

f) If the city or town is not well known, or if there is a chance that the name of the city or town may create

confusion, add the abbreviated letters for State, Province, or Territory after it for clarification. Example:

Amravati, MS

Hyderabad, AP

Austin, TX:

g) Use "n.p." to indicate that no place of publication is given.

4. Publisher - for books only

a) Be sure to write down the Publisher, NOT the Printer.

b) If a book has more than one publisher, not one publisher with multiple places of publication, list the

publishers in the order given each with its corresponding year of publication, e.g.: Conrad, Joseph. Lord

Jim. 1920. New York: Doubleday; New York: Signet, 1981.

c) Shorten the Publisher's name, e.g. use Macmillan, not Macmillan Publishing Co., Inc.

d) No need to indicate Publisher for encyclopaedias, magazines, journals, and newspapers.

e) If you cannot find the name of the publisher anywhere in the book, use "n.p." to indicate there is no

publisher listed.

5. Date of publication

a) For a book, use the copyright year as the date of publication, e.g.: 2003, not ©2003 or Copyright 2003,

i.e. do not draw the symbol © for copyright or add the word Copyright in front of the year.

b) For a monthly or quarterly publication use month and year, or season and year. For the months May,

June, and July, spell out the months, for all other months with five or more letters, use abbreviations: Jan.,

Feb., Mar., Apr., Aug., Sept., Oct., Nov., and Dec. Note that there is no period after the month. For

instance, the period after Jan. is for the abbreviation of January only. See Abbreviations of Months of the

Year, Days of the Week, and Other Time Abbreviations. If no months are stated, use Spring, Summer,

Fall, Winter, etc. as given, e.g.:

Alternatives Journal Spring 2004.

Classroom Connect Dec. 2003/Jan. 2004.

Discover July 2003.

Scientific American Apr. 2004.

c) For a weekly or daily publication use date, month, and year, e.g.:

Newsweek 11 Aug. 2003.

d) Use the most recent Copyright year if two or more years are listed, e.g., ©1988, 1990, 2004. Use 2004.

e) Do not confuse Date of Publication with Date of Printing, e.g., 7th Printing 2004, or Reprinted in 2004.

These are not publication dates.

http://www.aresearchguide.com/monthdayabb.html

http://www.aresearchguide.com/monthdayabb.html


f) If you cannot find a publication date anywhere in the book, use "n.d." to indicate there is "No Date"

listed for this publication.

g) If there is no publication date, but you are able to find out from reliable sources the approximate date

of publication, use [c. 2004] for circa 2004, or use [2003?]. Always use square brackets [ ] to indicate

information that is not given but is supplied by you.

6. Page number(s)

a) Page numbers are not needed for a book, unless the citation comes from an article or essay in an

anthology, i.e. a collection of works by different authors.

Example of a work in an anthology (page numbers are for the entire essay or piece of work):

Fish, Barry, and Les Kotzer. "Legals for Life." Death and Taxes: Beating One of the Two Certainties in

Life. Ed. Jerry White. Toronto: Warwick, 1998. 32-56.

b) If there is no page number given, use "n. pag."

(Works Cited example)

Schulz, Charles M. The Meditations of Linus. N.p.: Hallmark, 1967.

(Footnote or Endnote example)

1 Charles M. Schulz, The Meditations of Linus (N.p.: Hallmark, 1967) n. pag.

c) To cite a source with no author, no editor, no place of publication or publisher stated, no year of

publication, but you know where the book was published, follow this example:

Full View of Temples of Taiwan - Tracks of Pilgrims. [Taipei]: n.p., n.d.

d) Frequently, page numbers are not printed on some pages in magazines and journals. Where page

numbers may be counted or guessed accurately, count the pages and indicate the page number or

numbers.

WORKING LIST OF BOOKS (BIBLIOGRAPHY)


WORKING LIST OF JOURNALS, MAGAZINE, NEWSPAPERS (BIBLIOGRAPHY)

Presentation of research report

The research report should be typed following the requirements detailed below:

1. Use Executive bond A-4 size paper, type on one side of the paper only.

2. Use 1.5 spacing

3. Include margins: left-hand 3.8 cm (1&1/2 inches)

Right-hand 2.5 cm (1 inch)

4. Paragraphs should not be indented.

5. Pages should be numbered.

6. Tables should be numbered

7. Figures (e.g. diagrams and graphs) should be treated in a similar way to tables but should be

numbered "Figure 2" etc

8. Headings: Section Heading : upper case (e.g. INTRODUCTION), Subsection Heading: lower case

underlined, numbered 1.1, 1.2 etc indented to start of lettering on main heading

Example:

1. INTRODUCTION: Technological advances have opened many doors in education.....

1.1 The model presented: In the final year the occupational therapy course is being

developed.....

1.2 The task: A tutorial workbook.....

1.2.1 Using the programs: The programs designed are very varied....

9. Length of project: The project should be approximately 15,000 - 22,000 words (For project at Post –

graduate level)

10. Submitted copies of the project should be hard-bound volume only.

11. If you wish to acknowledge any individual's contribution to the project, this should be stated on a

separate acknowledgement page.


12. Your project should contain a list of contents which states the page number of each section of the

project.

13. Appendices should not be considered part of the project report (for example, raw data could be

included in this way). Appendices should be placed at the very end of the project and referred to in the

contents section.

Review questions

1. What are the preconditions for drawing

better conclusions?

2. State any five precautionary steps to be

taken before interpretation.

3. What is meant by interpretation of statistical

data? What precautions should be taken

while interpreting the data?

4. What do you understand by interpretation

of data? Illustrate the types of mistakes

which frequently occur in interpretation.

5. Explain the need, meaning and essentials of

interpretation.

6. What is reporting? What are the different

stages in the preparation of a report?

7. What is a report? What are the

characteristics/qualities of a good report?

8. Briefly describe the structure of a report.

9. What are the various aspects that have to be

checked before going to final typing?

10. What are the points to be kept in mind in

revising the draft report?

11. Give a brief note on the prefatory items.

12. What are the various items that will find a

place in the text / body of the report?

13. Describe briefly how a research report

should be presented.

14. Describe the considerations and steps

involved in planning a report writing work.

15. Write short notes on:

a) Characteristics of a good report.

b) Research article

c) Sources of data

d) Chapter plan


Chapter 10: Research in various Functional Areas

Introduction

Through research, an executive can quickly get a synopsis of the current scenario, which improves his

information base for making sound decisions affecting future operations of the enterprise. The following

are the major areas in which research plays a key role in making effective decisions.

There are many topics that benefit from research. Some major topics are: general business, economic,

and corporate research; financial and accounting research; management and organizational research;

sales and marketing research; information systems research; and corporate responsibility research.

Few of the above important areas are covered in detail below:

1. Marketing

Marketing research is undertaken to assist the marketing function. Marketing research stimulates the

flow of marketing data from the consumer and his environment to marketing information system of the

enterprise. Market research involves the process of

Systematic collection

Compilation

Analysis

Interpretation of relevant data for marketing decisions

This information goes to the executive in the form of data. On the basis of this data the executive develop

plans and programmers. Advertising research, packaging research, performance evaluation research,

sales analysis, distribution channel, etc., may also be considered in management research. Research tools

are applied effectively for studies involving:

1. Demand forecasting

2. Consumer buying behaviour

3. Measuring advertising effectiveness


4. Media selection for advertising

5. Test marketing

6. Product positioning

7. Product potential

Marketing Research

i. Product Research: Assessment of suitability of goods with respect to design and price.

ii. Market Characteristics Research (Qualitative): Who uses the product? Relationship between buyer

and user, buying motive, how a product is used, analysis of consumption rates, units in which product is

purchased, customs and habits affecting the use of a product, consumer attitudes, shopping habits of

consumers, brand loyalty, research of special consumer groups, survey of local markets, basic economic

analysis of the consumer market, etc.

iii. Size of Market (Quantitative): Market potential, total sales quota, territorial sales quota, quota for

individuals, concentration of sales and advertising efforts; appraisal of efficiency, etc.

iv. Competitive position and Trends Research

v. Sales Research: Analysis of sales records.

vi. Distribution Research: Channels of distribution, distribution costs.

vii. Advertising and Promotion Research: Testing and evaluating, advertising and promotion

viii. New product launching and Product Positioning.

2. Production

Research helps you in an enterprise to decide in the field of production on:

What to produce

How much to produce

When to produce

For whom to produce

Some of the areas you can apply research are:

Product development

Cost reduction

Work simplification

Profitability improvement

Inventory control


Materials

The materials department uses research to frame suitable policies regarding:

Where to buy

How much to buy

When to buy

At what prices to buy?

3. Human Resource Development

You must be Aware that The Human Resource Development department uses research to study wage

rates, incentive schemes, cost of living, employee turnover rates, employment trends, and performance

appraisal. It also uses research effectively for its most important activity namely manpower planning.

4. Solving Various Operational and Planning Problems of Business and

Industry

Various types of researches, e.g., market research, operations research and motivational research, when

combined together, help in solving various complex problems of business and industry in a number of

ways. These techniques help in replacing intuitive business decisions by more logical and scientific

decisions

i. Government and Economic System

Research helps a decision maker in a number of ways, e.g., it can help in examining the consequences of

each alternative and help in bringing out the effect on economic conditions. Various examples can be

quoted such as‘ problems of big and small industries due to various factors–up gradation of technology

and its impact on lab our and supervisory deployment, effect of government‘s liberal policy, WTO and its

new guidance, ISO 9000/14000 standards and their impact on our exports allocation of national resources

on national priority basis, etc. Research lays the foundation for all Government Policies in our economic

system.

We all are aware of the fact that research is applied for bringing out union finance budget and railway

budget every year. Government also uses research for economic planning and optimum utilization of

resources for the development of the country. For systematic collection of information on the economic

and social structure of the country, you need Research. Such types of information indicate what is

happening to the national economy and what changes are taking place.

ii. Social Relationships

Research in social sciences is concerned with both-knowledge for self and knowledge for helping in

solving immediate problems of human relations. It is a sort of formal training, which helps an individual

in a better way, e.g.


It helps professionals to earn their livelihood

It helps students to know how to write and report various findings.

It helps philosophers and thinkers in their new thin kings and ideas.

It helps in developing new styles for creative work.

It may help researchers, in general, to generalize new theories.


Bibliography and Suggested readings

Aaker D A, Kumar V & Day G S - Marketing

Research (John Wiley &Sons Inc, 6th ed.)

Agresti A., Categorical Data Analysis. New

York: John Wiley & Sons 1990.

Backstrom H. and Hursh-Cesar G.: Survey

Research, 2nd edition. Wiley, 1981.

Boehm W., Brown J. R., Kaspar H., Liplow

M., Macleod G. J., and Merrit M. J. ,

C.R.Kothari, Research Methodology

(Methods and Techniques), New

AgeInternational Pvt. Ltd. New Delhi

Cauvery – Research Methodology – (S.

Chand & Co.)

Characteristics of Software Quality.

Amsterdam: North-Holland, 1978.

Dillard, J., Hunter, J., & Burgoon, M. (1984).

Sequential request persuasive strategies:

Meta-analysis of foot-in-the-door and door-

in-the-face. Human Communication

Research, 10, 461-488.

Dwivedi – Research Methods in Behavioral

Science, ( Macmillan)

Flower , Floyed J. Jr. : Survey methods, Sage

Publication 1993

Fred N. Kerlinger. Foundations of

Behavioural Research, Surjeet

Publications,Delhi

Golde, Biddle, Koren : Composing

Qualitative Research, Sage Publication

Gupta S.P. : Statistical Methods, Sultan

Chand, New Delhi 2001

Gy, P (1992) Sampling of Heterogeneous and

Dynamic Material Systems: Theories of

Heterogeneity, Sampling and Homogenizing

http://www.unipune.ac.in/

J.F.Rummel & W.C.Ballaine. Research

Methodology in Business, Harper &Row,

Publishers, Newyork

Kothari C R – Quantitative Techniques (Vikas

Publishing House 3rd Ed.)

Nowak, R. (1994). Problems in clinical trials

go far beyond misconduct. Science. 264(5165):

1538-41.

P.Saravanavel. Research Methodology, Kitab

Mahal, Allahabad.

P.V.Young. Scientific Social Surveys and

Research, Prentice-Hall of India,New Delhi

Resnik, D. (2000). Statistics, ethics, and

research: an agenda for educations and

reform. Accountability in Research. 8: 163-88

Spiegel, M R, 1992. Statistics, Schaum‘s

Outline Series, Mc Graw Hill, Singapore.

Sue ZayacAcademic Information Systems

March, 2003

T.S. Wilkinson & P.L.Bhanarkar.

Methodology and Techniques of

SocialResearch, Himalaya Publishing House,

Mumbai

www.indiabix.com

www.nagpuruniversity.org/pdf/Ordinance

www.sgbau.ac.in/


http://www.sgbau.ac.in/


Frequently asked questions(FAQs) on Ph.D

[ University directions and rules on PdD made easy ]

1. Do I need to clear the Research

aptitude Test/Entrance Test for enrolling

myself as Ph.D student?

Yes, UGC has made it mandatory to clear the

entrance test for Ph.D enrolment. The format

and the pattern of test may vary from one

University to other University.

Check the website of the respective

University for more details.

e.g.

http://www.nagpuruniversity.org/news


2. Who shall be exempted from Ph.D.

entrance test ?

The candidates fulfilling one of the following

conditions shall be exempted from PET.

(i) Qualified in GATE/SET/NET/JRF

examination of the apex bodies such as IIT/

CSIR / UGC /ICAR/CMR/DBT etc.

(ii) Candidates holding M.Phil. degree in the

concerned subject from any Statutory

University.

(iii) Full time teacher of any statutory University

or full time approved teacher in an affiliated

college of any statutory University with

minimum 7 years of teaching experience.

(iv) Scientists/ Officers working in Government

organizations, National laboratories and

research institutions having 7 years research/

professional experience,

The Ph.D. registration form shall be submitted

by the candidates exempted from PET with

relevant supporting documents, to the Head,

Place of Research.

3. How do I register for the online

entrance test?

You can register for online entrance test by

logging on to the website of the University and

after filling the form, submitting the hardcopy of

the same to the university along with relevant

documents [ e.g. Draft for entrance fees specified

by the University, Mark sheet & degree

certificate of you PG , Caste certificate ( if

applicable ) etc.]

4. What will be the pattern of the

Entrance test?

Two Solved Model question Papers along with

explanation are given from page no 216 in this

text book for your ready reference and two

additional Solved Model question Papers are

also written in the CD enclosed.


5. Who are eligible for Ph.D. programme ?

a) Persons should have valid score in Ph.D.

Entrance Test (PET) as prescribed in the rules.

(Candidates who score 50% and more shall be

declared as successful) and

b) Persons having passed Post Graduate Degree

(Master‘s Degree) Examination with at least 50%

marks or equivalent Grade Point Average (GPA)

of this university or any other examination

recognized as equivalent thereto.

Provided that, relaxation of 5% marks shall be

provided in case of (a) & (b) above for the

candidates belonging to reserved category in the

State of Maharashtra.

OR

c) Persons working in National Laboratories/

Institutes /Government / Private organizations

nominated/sponsored by the respective

employers. Such persons should have a Post

Graduate Degree and should be holding rank of

Assistant Director or above. The candidates who

have obtained Master‘s degree of any statutory

Indian University but working outside India

shall be included in this category,

OR

d) Persons with exceptional research abilities/

contribution to be judged by Research and

Recognition Committee who have passed

Graduate Degree Examination with 50% of

marks and with 15 years experience after

graduation in related fields.

OR

e) The fellow members of the Institute of

Chartered Accountants and/ or Institute of Cost

and Works Accountants and/ or having

qualification of C.S. shall be held eligible for

registration for Ph.D. in the subject in the

concerned Board of Studies in the faculty of

Commerce provided that they possess a

Bachelor‘s Degree of any statutory University.

Such candidate should have at least 5 years of

professional experience.

or

f) A Graduate in any faculty who has developed

important new techniques (new for the country)

or designed and fabricated special instruments

or apparatus which are deemed by competent

judge to be a valuable contribution to

Engineering/Pharmacy field may be permitted

by the Research and Recognition Committee of

concerned faculty. Such a candidate must have

at least five years of experience after obtaining

Bachelor‘s degree in the concerned faculty.

6. Is there any age bar for taking Ph.D

entrance examination?

There is no maximum or minimum age bar for

doing Ph.D. The basic eligibility criteria is a

TWO Master Degrees with at least 50% marks or

equivalent Grade Point Average (GPA) of this

University or any other examination recognized

as equivalent thereto in respective faculty.

There shall be relaxation of 5% for reserved

category candidates in the state of Maharashtra.


7. How many marks I need to score to

clear the Ph.D entrance exam?

You need to score 50% marks to clear the exam.

There shall be relaxation of 5% for reserved

category candidates in the state of Maharashtra.

8. What if I have Post graduated from

other University?

If you have post graduated from other

University then you need to get an Eligibility

Certificate from RTMNU Nagpur University

and Migration Certificate from Home

University before you are finally enrolled for

Ph.D.

9. Once I clear my entrance , what shall I

do?

The eligible candidate who is declared to be

successful in the PET or the candidate who is

exempted from PET shall approach the Place of

Research where he/she intends to do the

research work. On the basis of number of seats

available with the approved Ph.D. Guides, the

available specialization among the Ph.D. Guides

and the research interest of the candidate, the

guide shall be allotted by Head of the Place of

Research to the candidate in consultation with

the guide and student in formal way.

While granting admission to candidates for

Ph.D. programme, due attention shall be paid to

the State Reservation Policy.

10. How long will my PhD Entrance exam

result will be valid?

The result of PET shall be valid for a period of

12 months from the date of holding of entrance

examination. The candidate who has been

decided to be successful shall be eligible to

submit application(s) for registration within the

period of 12 months. However, after expiry of

period of 12 months , the candidate shall be

required to appear for PET afresh if he fails to

submit application or if the application for

registration is not approved by Research and

Recognition Committee.

11. Will I get a suitable supervisor of my

Choice?

Normally a candidate shall be required to

complete his/her doctoral research under the

supervision of allotted (original) approved

guide. However, the Research & Recognition

Committee concerned may allow change of

guide on the production of a ―No Objection

Certificate‖ from the original guide and an

acceptance letter from the new guide. In case of

such a change, the candidate shall work for a

minimum period of one calendar year under the

new guide before he/she submits the thesis. The

requirement of ―No Objection Certificate‖ shall

not be necessary if the candidate justifies the

non-availability of his original guide. The

justification will have to be endorsed by the

Head, place of research.

Provided further that in specific cases Co-

guide/ second Supervisor shall also be

permitted for justified reasons. However, Guide


and Co-guide shall not be from the same

specialization.

12. What after I Get my supervisor ?

(1) Every registered candidate shall submit to

the Controller of Examinations of the University

through the Head, place of research and the

guide the progress report of his/her research

after every six months. If a candidate fails to

submit three reports consecutively, his/her

registration may be cancelled by Research and

Recognition Committee on recommendation of

guide and of Head of place of Research.

(2) The Head, Place of Research after the

completion of the given period (one and a half

years) shall send to the University office within

15 days a report on the noncompliance of the

condition.

13. What is synopsis and how do I

prepare that? Are there any Specific guide

lines for that ?

No, there are no specific guidelines for the same

. Refer enclosed CD for general format.

14. What are the numbers of synopsis

copies to be submitted to the University?

The applicant shall submit along with the

application synopsis of the proposed research

work in eight copies(8) to the University.

15. Do I need to register myself with a

Research Institute before submission of

my synopsis?

Yes, you are required to register yourself with a

Research Institute before submission of your

synopsis.

16. Is it compulsory for me to secure

admission at a Research Institute for

carrying out my research?

A big ‗Yes‘, without the endorsement of the

head of the Research Institute you cannot carry

out your research.

17. What is the last date for securing

admission in research cell?

The entrance examination is usually conducted

twice a year, tentatively on 15th July & 15th

January. So the admissions can be secured after

you clear the entrance test.

18. What facilities are available in the

Research Institute to conduct research?

The Institute provides well stocked library with

latest books, journals & other secondary data for

the use the scholar. The Research Institute

usually has well equipped computer laboratory

with broadband internet connection for the use

of research scholars.


19. What about COURSE WORK for

Ph.D?

(a) The course work is compulsory and it is

treated as pre Ph.D. preparation. The course

work must include topics on research

methodology , quantitative methods of

computer application , seminars, review of

published research work in the relevant field.

(b) The evaluation of course work is done by the

concerned Guide. Completion report of the

Course work shall be submitted by Guide to

Head of the Place of Research in duplicate. Copy

of completion report shall be thereafter

forwarded by Head of the Place of Research to

the University Ph.D. Cell.

(c) If found necessary by guide with consent of

Head of the Place of Research, course work may

be carried out by the candidates in sister

departments/institutes either within or outside

the University In such case, completion report of

the course work shall be submitted by the Head

of the concerned sister department/institute to

the guide who shall forward it to the Head of

the Place of Research. Copy of the completion

report shall be thereafter forwarded by the Head

of Place of Research to University Ph.D. Cell.

20. How do I choose my research topic?

PhD students can choose research topic of their

area of interest under the supervision and

guidance of a suitable supervisor of their

faculty. The research cell helps the students to

narrow down on these areas of interest and

formulate a well designed research topic.

21. What is the procedure for taking

admission in the research cell of the

Institute?

The student should either be a registered scholar

with RTM Nagpur University or should have

passed the Ph.D entrance examination

conducted by the University or has cleared the

test. The student has to then buy the prospectus

from the Institute & pay the required fees for

enrolment & secure admission.(Kindly refer to

question no 5)

22. What is the Progress Report and when

it is to be submitted?

It is a report wherein the researcher has to show

the progress of his research work and it has to

be submitted every 6 months along with the

retention fees and in prescribed format. (Format

enclosed in CD)

23. What are some of the common

mistakes/ errors committed by the

researcher?

The following are few of the most common

mistakes/errors committed by the researcher.

Faulty/wrong problem definition.

Objectives of the study without starting

with ―To‖ e.g. To find, To know, To analyze etc.

Improper Hypothesis formulation.

24. What is the right time for submission

of Ph.D thesis?

The researcher has to mandatorily carry out

his/her research work for minimum period of

TWO years. After that at any point of time after

the thesis and summary is ready you can submit


the same to Ph.D cell. There is no fixed date for

submission of Ph.D thesis.

The summary and thesis shall be processed for

final viva-voce/open defense examination by

the University through a RRC (Research &

Recognition Committee) meetings.

25. What shall be my tenure of

registration and When do I submit my

thesis?

The registration of the candidate shall be valid

and shall remain in force for a period of 5 years

from the date of registration and shall stand

cancelled automatically on expiry of 5 years.

Provided that extension upto maximum period

of 12 months shall be permissible in those cases

which are recommended by the Guide and

Head of the Place of research and the decision

for extension shall be taken by Research and

Recognition Committee . The application for

extension is required to be submitted at least 3

months prior to the date of expiry of

registration. After expiry of extended period of

registration the candidate shall be required to

apply for registration a fresh following the

denovo procedure.

And

(1) The submission of summary of the thesis

may be permitted only after completion of

twenty two months from the date of

registration. The summary should contain

introduction, chapter wise brief account of the

work done and overall conclusions. Ph.D.

candidate has to publish one research paper in a

standard refereed journal/ monograph before

the submission of the thesis for adjudication,

and produce evidence for the same in the form

of acceptance letter or the reprint. The list of the

reputed journals in the subject shall be prepared

and maintained by the respective Research and

Recognition Committee.

(2) The thesis can be submitted after two months

from the date of submission of summary. At

least three months before the date of submission

of the thesis each candidate shall give a pre-

submission seminar to be arranged by the Head

of the place of research on the request of the

candidate duly endorsed by the guide. The

relevant suggestions if any given by other

research scholars, other research guides and the

Head Place of Research or his/her nominee

present for such a seminar may be considered

while preparing the final draft of the thesis.

(3) On the basis of discussions and suggestions

made in the pre submission seminar the

candidate shall submit to the Controller of

Examinations ten copies of the summary of

his/her thesis through his/her guide within one

month from the date of seminar. (The guide may

suggest list of referees to the Research and

Recognition Committee.)

(4) The candidate shall be allowed to submit

his/her thesis after the completion of a period of

two months and before six months from the date

of submission of the summary, failing which the

candidate will have to pay the prescribed fine to

be decided by the University from to time for

late submission. Late submission of thesis shall

be allowed up to the completion of one year

from the date of submission of the summary or

till the expiry of the registration period,

whichever is earlier.


26. What should be the colour of the

cover page?

The colour of cover page of thesis should be

black in Hardbound volume.

27. What are the specifications for

submission of final thesis?

The final thesis shall be presented in accordance

with the following specifications:

(a) The paper used for printing shall be A4

size paper.

(b) Printing shall be in a standardized form on

one side of the paper and with minimum

of one and half spacing.

(c) A margin of one and a half inches shall be

on the left hand side.

(d) The title of the thesis, name of the

candidate, degree, name of the guide,

place of research, the month and year of

submission shall be printed on the title

page and front cover.

(e) Side cover (Spin) should mention Ph.D

thesis on the top , name of the candidate

and month and year.

(f) There is on binding on the use of executive

bond paper.

(g) All the pages should be properly

numbered.

28. What is the ideal font size and font

type for the contents of Ph.D?

As such there is no specified guidelines given,

but generally Times New Roman with font size

of ‗12‘ is followed with spacing between the line

as 1.5 or Book Antique with font size 11 is

followed with spacing between the line as 1.5.

29. Is there any thumb rule on the

number of pages of Ph.D thesis?

No, but the coverage of the topic should be

adequate, self-explanatory and must justify your

research for the award of the said degree.

30. Is the Certificate and Declaration

format specified by the University?

Yes, RTMNU has prescribed format for the

same. Soft copy of the same is available in the

CD enclosed.

31. How many copies of summary the

researcher has to submit for final

submission?

TEN(10) copies of summary shall be submitted

to the Controller of Examination.


32. What are the documents that are

needed at the time of final submission of

thesis?

(a) Ten Copies(10) of Summary & Five Copies

(5)of thesis along with one soft copy in CD.

(b) Receipt of submission fees

(c) Photocopy of receipts of retention fees.

(d) Photocopy of progress reports.

(e) Photocopy of Ph.D. Registration Certificate.

(f) NOC of University library.

(g) Photocopy of PG mark sheet and degree

certificate.

33. What is the fee for thesis submission

and how many copies has to be submitted

during final submission?

The fee for thesis submission may change one

University to other and five copies(5) of the

thesis have to be submitted during final

submission. Along with a soft copy (CD)

through the research guide and Head of Place of

Research.

34. After submission of the summary and

thesis, when will be the PhD awarded to

me?

On successfully passing the open-defense

examination, the University usually in a

fortnight issues notification regarding the same.

You will get your PhD degree at the time of

Convocation function organized by the

University.

35. As a approved teacher for UG and PG

by profession , what benefits shall I get

once I am awarded PhD?

As per 6th Pay Commission you are entitled to

get three increments in your salary.

N.B:- All the answers provided to questions in

FAQ on Ph.D are generalized in nature and are

not for any specific University and based on the

directions issued by the various University from

time to time. This directions may change based

on the norms and act set by UGC and other

Govt. Agencies.

Candidates are advised to refer to the respective

University website for latest directions and

norms. The authors do not take any legal or

other responsibility for change in the same.

For more detail about RTMNU PET ( Ph.D

Entrance Test) and Direction regarding the

same refer to the Soft copy enclosed in the CD.


Research Aptitude Test: Examination Pattern

In most of the University the Research Aptitude Examination usual consists of :

Paper-I :- Research Aptitude Test and Paper-II : - Subject specific Test

Some University (e.g. RTMNU & SGBAU) do follows only Research Aptitude Test for qualifying

Ph.D entrance test. This is because of the fact that they offer large number of Ph.D options in

different faculties compared to various other University.

Paper-I :- Research Aptitude Test

Paper-I consists of 4 Parts : Time : 90 minutes Total Marks : 100

Part-1 :- Analytical Reasoning Part-2:- Numerical Ability and

Part-3:- Language Competency/ Computer/ Environment/ Logical Reasoning

Part-4:- Data Interpretation

Maximum marks required to clear Paper-I : a) OPEN Category: 50% b) Reserved Category: 45%

Paper-II : - Subject specific Test Total Marks : 100

Paper-II consists of 2 Parts :

a) Multiple Choice Question : 20 Marks b) Theoretical/Descriptive Questions : 80 Marks


Online Ph.D Entrance Test (PET) : Sample Test Paper – I(Solved with explanation)

Time: 90 minutes] [Max Marks: 100

(a) N.B:- a) There are in all 100 multiple Choice Questions

(b) Each correct answer carries 1 Mark.

(c) There is „No negative‟ marking system.

(d) Click online the correct option for each question.

(e) Use of Electronic / Scientific calculator is not allowed

(f) Multiple Choice Questions are divided into four parts

1. Analytical Reasoning,

2. Numerical Ability

3. Language Competency/ Computer/ Environment/ Logical Reasoning

4. and Data Interpretation

Part-1 :- Analytical Reasoning

Logical Sequence of Words 1.In each of the following questions, arrange the given words in a meaningful sequence and thus find the correct answer from alternatives. 1. Arrange the words given below in a meaningful sequence. 1. Key 2. Door 3. Lock 4. Room 5. Switch on A.5, 1, 2, 4, 3 B. 4, 2, 1, 5, 3 C. 1, 3, 2, 4, 5 D. 1, 2, 3, 5, 4 Answer & Explanation Answer: Option C Explanation: The correct order is : Key Lock Door Room Switch on 1 3 2 4 5

2. Arrange the words given below in a meaningful sequence. 1. Word 2. Paragraph 3. Sentence 4. Letters 5. Phrase A.4, 1, 5, 2, 3 B. 4, 1, 3, 5, 2 C. 4, 2, 5, 1, 3 D. 4, 1, 5, 3, 2 Answer & Explanation Answer: Option D

http://www.indiabix.com/verbal-reasoning/logical-sequence-of-words/


Explanation: The correct order is : Letters Word Phrase Sentence Paragraph 4 1 5 3 2 3. Arrange the words given below in a meaningful sequence. 1. Police 2. Punishment 3. Crime 4. Judge 5. Judgement

A.3, 1, 2, 4, 5 B. 1, 2, 4, 3, 5 C. 5, 4, 3, 2, 1 D. 3, 1, 4, 5, 2 Answer & Explanation Answer: Option D Explanation: The correct order is : Crime Police Judge Judgement Punishment 3 1 4 5 2 4. Arrange the words given below in a meaningful sequence. 1. Family 2. Community 3. Member 4. Locality 5. Country

A.3, 1, 2, 4, 5 B. 3, 1, 2, 5, 4 C. 3, 1, 4, 2, 5 D. 3, 1, 4, 5, 2

Answer & Explanation Answer: Option A Explanation:

The correct order is : Member Family Community Locality Country 3 1 2 4 5 5. Arrange the words given below in a meaningful sequence. 1. Poverty 2. Population 3. Death 4. Unemployment 5. Disease

A.2, 3, 4, 5, 1 B. 3, 4, 2, 5, 1 C. 2, 4, 1, 5, 3 D. 1, 2, 3, 4, 5 Answer & Explanation Answer: Option C Explanation: The correct order is : Population Unemployment Poverty Disease Death 2 4 1 5 3

Seating Arrangement 6. A, P, R, X, S and Z are sitting in a row. S and Z are in the centre. A and P are at the ends. R is sitting to the left of A. Who is to the right of P ?

A.A B. X C. S D. Z Answer & Explanation Answer: Option B


Explanation: The seating arrangement is as follows:

Therefore, right of P is X. 7. There are 8 houses in a line and in each house only one boy lives with the conditions as given below: 1. Jack is not the neighbour Siman. 2. Harry is just next to the left of Larry. 3. There is at least one to the left of Larry. 4. Paul lives in one of the two houses in the middle. 5. Mike lives in between Paul and Larry. If at least one lives to the right of Robert and Harry is not between Taud and Larry, then which one of the following statement is not correct ?

A.Robert is not at the left end. B. Robert is in between Simon and Taud. C. Taud is in between Paul and Jack. D. There are three persons to the right of Paul. Answer: Option C 8. A, B, C, D and E are sitting on a bench. A is sitting next to B, C is sitting next to D, D is not sitting with E who is on the left end of the bench. C is on the second position from the right. A is to the right of B and E. A and C are sitting together. In which position A is sitting ? A.Between B and D B. Between B and C C. Between E and D D. Between C and E

Answer: Option B Explanation:

Therefore, A is sitting in between B and C. 9. Six friends P, Q, R, S, T and U are sitting around the hexagonal table each at one corner and are facing the centre of the hexagonal. P is second to the left of U. Q is neighbour of R and S. T is second to the left of S. 1. Which one is sitting opposite to P ? A.R B. Q C. T D. S Answer & Explanation Answer: Option D Explanation:

S is sitting opposite to P.


10.Who is the fourth person to the left of Q ? A.P B. U C. R D. Data inadequate Answer & Explanation Answer: Option A Explanation:

P is the fourth person to the left of Q.

Verification of Truth 11. A train always has A.Rails B. Driver C. Guard D. Engine Answer: Option D 12. Which one of the following is always found in 'Bravery'? A.Experience B. Power C. Courage D. Knowledge Answer: Option C 13. A song always has A.Word B. Chorus C. Musician D. Tymbal Answer: Option A 14. Yesterday I saw a ice cube which had already melted due to heat of a nearby furnace. A.Always B. Never C. Often D.Sometimes Answer & Explanation Answer: Option B Explanation: Since the ice cube had already melted due to the heat of a nearby furnace so after this ice cannot remain as ice cube. 15. What is found necessarily in milk? A.Cream B. Curd C. Water D. Whiteness

Answer: Option D


Cause and Effect Below in each of the questions are given two statements I and II. These statements may be either independent causes or may be effects of independent causes or a common cause. One of these statements may be the effect of the other statements. Read both the statements and decide which of the following answer choice correctly depicts the relationship between these two statements. Mark answer (A) If statement I is the cause and statement II is its effect. (B) If statement II is the cause and statement I is its effect. (C) If both the statements I and II are independent causes. (D) If both the statements I and II are effects of independent causes. (E) If both the statements I and II are effects of some common cause.

16. Statements: 1. Standard of living among the middle class society is constantly going up since part of few years. 2. Indian Economy is observing remarkable growth. A.Statement I is the cause and statement II is its effect. B. Statement II is the cause and statement I is its effect. C. Both the statements I and II are independent causes. D.Both the statements I and II are effects of independent causes. E. Both the statements I and II are effects of some common cause. Answer & Explanation Answer: Option A Explanation: Since the standard of living among the middle class society is constantly going up so Indian Economy is observing remarkable growth. 17. Statements: 1. The meteorological Department has issued a statement mentioning deficient rainfall during monsoon in many parts of the country. 2. The Government has lowered the revised estimated GDP growth from the level of earlier estimates. A.Statement I is the cause and statement II is its effect. B. Statement II is the cause and statement I is its effect. C. Both the statements I and II are independent causes. D. Both the statements I and II are effects of independent causes. E. Both the statements I and II are effects of some common cause. Answer & Explanation

Answer: Option D Explanation: Both the statements I and II are effects of independent causes. 18. Statements: 1. The staff of Airport Authorities called off the strike they were observing in protest against privatization. 2. The staff of Airport Authorities went on strike anticipating a threat to their jobs. A.Statement I is the cause and statement II is its effect. B. Statement II is the cause and statement I is its effect. C. Both the statements I and II are independent causes. D. Both the statements I and II are effects of independent causes. E. Both the statements I and II are effects of some common cause. Answer & Explanation

Answer: Option D Explanation: Both the statements I and II are effects of independent causes.


19. Statements: 1. A huge truck overturned on the middle of the road last night. 2. The police had cordoned of entire area in the locality this morning for half of the day. A.Statement I is the cause and statement II is its effect. B. Statement II is the cause and statement I is its effect. C. Both the statements I and II are independent causes. D.Both the statements I and II are effects of independent causes. E. Both the statements I and II are effects of some common cause. Answer & Explanation Answer: Option A Explanation: Since a huge truck overturned on the middle of the road last night, so, the police had cordoned off the entire area in the locality last morning for half of the day. 20. Statements: 1. Importance of Yoga and exercise is being realized by all sections of the society. 2. There is an increasing awareness about health in the society particularly among middle ages group

of people. A.Statement I is the cause and statement II is its effect. B. Statement II is the cause and statement I is its effect. C. Both the statements I and II are independent causes. D. Both the statements I and II are effects of independent causes. E. Both the statements I and II are effects of some common cause. Answer & Explanation

Answer: Option B Explanation: As the awareness about health in the society is increasing particularly among middle-aged group of people, the importance of Yoga and exercise is being realized by all sections of the society.

Data Sufficiency In each of the questions below consists of a question and two statements numbered I and II given below it. You have to decide whether the data provided in the statements are sufficient to answer the question. Read both the statements and Give answer (A) If the data in statement I alone are sufficient to answer the question, while the data in statement II alone are not sufficient to answer the question (B) If the data in statement II alone are sufficient to answer the question, while the data in statement I alone are not sufficient to answer the question (C) If the data either in statement I alone or in statement II alone are sufficient to answer the question (D) If the data given in both statements I and II together are not sufficient to answer the question and (E) If the data in both statements I and II together are necessary to answer the question. 21. Question: In which year was Rahul born ? Statements: 1. Rahul at present is 25 years younger to his mother. 2. Rahul's brother, who was born in 1964, is 35 years younger to his mother. A.I alone is sufficient while II alone is not sufficient B. II alone is sufficient while I alone is not sufficient C. Either I or II is sufficient D. Neither I nor II is sufficient E. Both I and II are sufficient Answer & Explanation Answer: Option E


Explanation: From both I and II, we find that Rahul is (35 - 25) = 10 years older than his brother, who was born in 1964. So, Rahul was born in 1954. 22. Question: What will be the total weight of 10 poles, each of the same weight ? Statements: 1. One-fourth of the weight of each pole is 5 kg. 2. The total weight of three poles is 20 kilograms more than the total weight of two poles. A.I alone is sufficient while II alone is not sufficient B. II alone is sufficient while I alone is not sufficient C. Either I or II is sufficient D.Neither I nor II is sufficient E. Both I and II are sufficient Answer & Explanation

Answer: Option C Explanation: From I, we conclude that weight of each pole = (4x5) kg = 20 kg. So, total weight of 10 poles = (20 x 10) kg = 200 kg. From II, we conclude that: Weight of each pole = (weight of 3 poles) - (weight of 2 poles) = 20 kg. So, total weight of 10 pojes = (20 x 10) kg = 200 kg. 23. Question: How much was the total sale of the company ? Statements: 1. The company sold 8000 units of product A each costing ` 25. 2. This company has no other product line. A.I alone is sufficient while II alone is not sufficient B. II alone is sufficient while I alone is not sufficient C. Either I or II is sufficient D.Neither I nor II is sufficient E. Both I and II are sufficient Answer & Explanation

Answer: Option E Explanation: From I, total sale of product A = ` (8000 x 25) = ` 200000. From II, we know that the company deals only in product A. This implies that sale of product A is the total sale of the company, which is ` 200000. 24. Question: The last Sunday of March, 2006 fell on which date ? Statements: 1. The first Sunday of that month fell on 5th. 2. The last day of that month was Friday. A.I alone is sufficient while II alone is not sufficient B. II alone is sufficient while I alone is not sufficient C. Either I or II is sufficient D.Neither I nor II is sufficient E. Both I and II are sufficient Answer & Explanation Answer: Option C Explanation: From I, we conclude that 5th, 12th, 19th and 26th of March, 2006 were Sundays. So, the last Sunday fell on 26th. From II, we conclude that 31st March, 2006 was Friday. Thus, 26th March, 2006 was the last Sunday of the month.


25. One morning Udai and Vishal were talking to each other face to face at a crossing. If Vishal's shadow was exactly to the left of Udai, which direction was Udai facing? A. East B. West C. North D. South

Answer & Explanation Answer: Option C Explanation:

Part-2:- Numerical Ability Problems on Ages

1. Father is aged three times more than his son Ronit. After 8 years, he would be two and a half times of Ronit's age. After further 8 years, how many times would he be of Ronit's age?

A. 2 times B. 2 1

times 2

C. 2 3 times

4

D. 3 times

Answer & Explanation Answer: Option A Explanation: Let Ronit's present age be x years. Then, father's present age =(x + 3x) years = 4x years.

(4x + 8) = 5 (x + 8)

2 8x + 16 = 5x + 40 3x = 24 x = 8.

Hence, required ratio = (4x + 16)

= 48

= 2. (x + 16) 24

2. The sum of ages of 5 children born at the intervals of 3 years each is 50 years. What is the age of the

youngest child?

A. 4 years B. 8 years

C. 10 years D. None of these

Answer & Explanation Answer: Option A Explanation: Let the ages of children be x, (x + 3), (x + 6), (x + 9) and (x + 12) years. Then, x + (x + 3) + (x + 6) + (x + 9) + (x + 12) = 50

5x = 20 x = 4. Age of the youngest child = x = 4 years.


3. A father said to his son, "I was as old as you are at the present at the time of your birth". If the

father's age is 38 years now, the son's age five years back was: A. 14 years B. 19 years

C. 33 years D. 38 years

Answer & Explanation Answer: Option A Explanation: Let the son's present age be x years. Then, (38 - x) = x

2x = 38 x = 19 Son's age 5 years back (19 - 5) = 14 years.

4. A is two years older than B who is twice as old as C. If the total of the ages of A, B and C be 27, the

how old is B? A. 7 B. 8 C. 9 D. 10 E. 11

Answer & Explanation Answer: Option D Explanation: Let C's age be x years. Then, B's age = 2x years. A's age = (2x + 2) years.

(2x + 2) + 2x + x = 27 5x = 25 x = 5.

Hence, B's age = 2x = 10 years.

5. Present ages of Sameer and Anand are in the ratio of 5 : 4 respectively. Three years hence, the ratio of their ages will become 11 : 9 respectively. What is Anand's present age in years?

A. 24 B. 27

C. 40 D. Cannot be determined

E. None of these

Answer & Explanation Answer: Option A Explanation: Let the present ages of Sameer and Anand be 5x years and 4x years respectively.

Then, 5x + 3

= 11

4x + 3 9 9(5x + 3) = 11(4x + 3) 45x + 27 = 44x + 33 45x - 44x = 33 - 27 x = 6. Anand's present age = 4x = 24 years.

Percentage 6. A batsman scored 110 runs which included 3 boundaries and 8 sixes. What percent of his total score

did he make by running between the wickets?

A. 45% B. 45 5

% 11

C. 54 6

% 11

D. 55%

Answer & Explanation Answer: Option B


Explanation: Number of runs made by running = 110 - (3 x 4 + 8 x 6) = 110 - (60) = 50.

Required percentage =

50 x 100

% = 45

5 %

110 11

7. Two students appeared at an examination. One of them secured 9 marks more than the other and his

marks was 56% of the sum of their marks. The marks obtained by them are: A. 39, 30 B. 41, 32

C. 42, 33 D. 43, 34

Answer & Explanation Answer: Option C Explanation: Let their marks be (x + 9) and x.

Then, x + 9 = 56

(x + 9 + x) 100

25(x + 9) = 14(2x + 9) 3x = 99 x = 33

So, their marks are 42 and 33.

8. A fruit seller had some apples. He sells 40% apples and still has 420 apples. Originally, he had:

A. 588 apples B. 600 apples C. 672 apples D. 700 apples Answer & Explanation Answer: Option D Explanation: Suppose originally he had x apples. Then, (100 - 40)% of x = 420.

60 x x = 420

100

x =

420 x 100

= 700. 60

9. What percentage of numbers from 1 to 70 have 1 or 9 in the unit's digit?

A. 1 B. 14

C. 20 D. 21

Answer & Explanation Answer: Option C Explanation: Clearly, the numbers which have 1 or 9 in the unit's digit, have squares that end in the digit 1. Such numbers from 1 to 70 are 1, 9, 11, 19, 21, 29, 31, 39, 41, 49, 51, 59, 61, 69. Number of such number =14


14 x 100

% = 20%. 70

10. If A = x% of y and B = y% of x, then which of the following is true?

A. A is smaller than B. B. A is greater than B

C.

Relationship between A and B cannot be determined.

D. If x is smaller than y, then A is greater than B.

E. None of these


Answer & Explanation Answer: Option E Explanation:

x% of y =

x x y

=

y x x

= y% of x 100 100

A = B.

Clock 11. An accurate clock shows 8 o'clock in the morning. Through how may degrees will the hour hand

rotate when the clock shows 2 o'clock in the afternoon?

A. 144º B. 150º

C. 168º D. 180º

Answer & Explanation Answer: Option D Explanation:

Angle traced by the hour hand in 6 hours =

360 x 6

º = 180º. 12

12. The reflex angle between the hands of a clock at 10.25 is:

A. 180º B. 192 1

º 2

C. 195º D. 197 1

º 2


Angle traced by hour hand in 125

hrs =

360 x

125

º = 312 1 º .

12 12 12 2

Angle traced by minute hand in 25 min =

360 x 25

º = 150º. 60

Reflex angle = 360º -

312 1

- 150

º = 360º - 162 1

º = 197 1

. 2 2 2

13. A clock is started at noon. By 10 minutes past 5, the hour hand has turned through:

A. 145º B. 150º C. 155º D. 160º

Answer & Explanation Answer: Option C Explanation: Angle traced by hour hand in 12 hrs = 360º.

Angle traced by hour hand in 5 hrs 10 min. i.e., 31

hrs =

360 x

31

º = 155º. 6 12 6

14. A watch which gains 5 seconds in 3 minutes was set right at 7 a.m. In the afternoon of the same

day, when the watch indicated quarter past 4 o'clock, the true time is:

A. 59 7

min. past 3 12

B. 4 p.m.

C. 58 7

min. past 3 11

D. 2 3

min. past 4 11

Answer & Explanation Answer: Option B Explanation:


Time from 7 a.m. to 4.15 p.m. = 9 hrs 15 min. = 37

hrs. 4

3 min. 5 sec. of this clock = 3 min. of the correct clock.

37 hrs of this clock =

1 hrs of the correct clock.

720 20

37 hrs of this clock =

1 x

720 x

37

hrs of the correct clock. 4 20 37 4

= 9 hrs of the correct clock. The correct time is 9 hrs after 7 a.m. i.e., 4 p.m.

15. How much does a watch lose per day, if its hands coincide ever 64 minutes?

A. 32 8

min. 11

B. 36 5

min. 11

C. 90 min. D. 96 min. Answer & Explanation Answer: Option A Explanation: 55 min. spaces are covered in 60 min.

60 min. spaces are covered in

60 x 60

min. = 65

5 min.

55 11

Loss in 64 min. =

65 5

- 64

= 16

min. 11 11

Loss in 24 hrs =

16 x

1 x 24 x 60

min. = 32

8 min.

11 64 11

Calendar

16. It was Sunday on Jan 1, 2006. What was the day of the week Jan 1, 2010? A. Sunday B. Saturday C. Friday D. Wednesday

Answer & Explanation Answer: Option C Explanation: On 31st December, 2005 it was Saturday. Number of odd days from the year 2006 to the year 2009 = (1 + 1 + 2 + 1) = 5 days.

On 31st December 2009, it was Thursday. Thus, on 1st Jan, 2010 it is Friday.

17. What was the day of the week on 28th May, 2006?

A. Thursday B. Friday

C. Saturday D. Sunday

Answer & Explanation Answer: Option D Explanation: 28 May, 2006 = (2005 years + Period from 1.1.2006 to 28.5.2006) Odd days in 1600 years = 0 Odd days in 400 years = 0 5 years = (4 ordinary years + 1 leap year) = (4 x 1 + 1 x 2) 6 odd days Jan. Feb. March April May (31 + 28 + 31 + 30 + 28 ) = 148 days

148 days = (21 weeks + 1 day) 1 odd day.


Total number of odd days = (0 + 0 + 6 + 1) = 7 0 odd day. Given day is Sunday.

18. What was the day of the week on 17th June, 1998?

A. Monday B. Tuesday C. Wednesday D. Thursday

Answer & Explanation Answer: Option C Explanation: 17th June, 1998 = (1997 years + Period from 1.1.1998 to 17.6.1998) Odd days in 1600 years = 0 Odd days in 300 years = (5 x 3) 1 97 years has 24 leap years + 73 ordinary years. Number of odd days in 97 years ( 24 x 2 + 73) = 121 = 2 odd days. Jan. Feb. March April May June (31 + 28 + 31 + 30 + 31 + 17) = 168 days

168 days = 24 weeks = 0 odd day. Total number of odd days = (0 + 1 + 2 + 0) = 3. Given day is Wednesday.

19. What will be the day of the week 15th August, 2010?

A. Sunday B. Monday C. Tuesday D. Friday Answer & Explanation Answer: Option A Explanation: 15th August, 2010 = (2009 years + Period 1.1.2010 to 15.8.2010) Odd days in 1600 years = 0 Odd days in 400 years = 0 9 years = (2 leap years + 7 ordinary years) = (2 x 2 + 7 x 1) = 11 odd days 4 odd days. Jan. Feb. March April May June July Aug. (31 + 28 + 31 + 30 + 31 + 30 + 31 + 15) = 227 days

227 days = (32 weeks + 3 days) 3 odd days. Total number of odd days = (0 + 0 + 4 + 3) = 7 0 odd days. Given day is Sunday.

20. Today is Monday. After 61 days, it will be:

A. Wednesday B. Saturday C. Tuesday D. Thursday Answer & Explanation Answer: Option B Explanation: Each day of the week is repeated after 7 days. So, after 63 days, it will be Monday.

After 61 days, it will be Saturday.


Odd Man Out and Series 21.Find the odd man out. 1. 3, 5, 11, 14, 17, 21

A. 21 B. 17 C. 14 D. 3 Answer & Explanation Answer: Option C Explanation: Each of the numbers except 14 is an odd number. The number '14' is the only EVEN number.

22. 8, 27, 64, 100, 125, 216, 343

A. 27 B. 100 C. 125 D. 343

Answer & Explanation Answer: Option B Explanation: The pattern is 23, 33, 43, 53, 63, 73. But, 100 is not a perfect cube.

23. 10, 25, 45, 54, 60, 75, 80

A. 10 B. 45 C. 54 D. 75 Answer & Explanation Answer: Option C Explanation: Each of the numbers except 54 is multiple of 5.

24. 396, 462, 572, 396, 427, 671, 264

A. 396 B. 427 C. 671 D. 264

Answer & Explanation Answer: Option B Explanation: In each number except 427, the middle digit is the sum of other two.

25. 6, 9, 15, 21, 24, 28, 30

A. 28 B. 21 C. 24 D. 30

Answer & Explanation Answer: Option A Explanation: Each of the numbers except 28, is a multiple of 3.


Part-3:- (a) Language Competency

Spotting Errors Read the each sentence to find out whether there is any grammatical error in it. The error, if any will be in one part of the sentence. The letter of that part is the answer. If there is no error, the answer is 'D'. (Ignore the errors of punctuation, if any). 1. (solve as per the direction given above) A.We discussed about the problem so thoroughly B. on the eve of the examination C. that I found it very easy to work it out. D. No error. Answer & Explanation Answer: Option A Explanation: We discussed the problem so thoroughly 2. (solve as per the direction given above) A.An Indian ship B. laden with merchandise C. got drowned in the Pacific Ocean. D. No error.

Answer & Explanation Answer: Option C Explanation: sank in the Pacific Ocean 3. (solve as per the direction given above) A.I could not put up in a hotel B. because the boarding and lodging charges C. were exorbitant D. No error.

Answer & Explanation Answer: Option A Explanation: 'I could not put up at a hotel' 4. (solve as per the direction given above) A.The Prime Minister has said that India would not have spent so much on defence B. if some of the neighbouring countries C. adopted the policy of restricting defence expenditure D. No error.

Answer & Explanation Answer: Option A Explanation: The Prime Minister has said that India would not have had to spend so much on defence 5. (solve as per the direction given above) A.The Indian radio B. which was previously controlled by the British rulers C. is free now from the narrow vested interests. D. No error.


Answer & Explanation Answer: Option C Explanation: is now free from the narrow vested interests.

Selecting Words Pick out the most effective word(s) from the given words to fill in the blank to make the sentence meaningfully complete. 6. Fate smiles ...... those who untiringly grapple with stark realities of life. A.with B. over C. on D. round

Answer: Option C 7. The miser gazed ...... at the pile of gold coins in front of him. A.avidly B. admiringly C. thoughtfully D. earnestly

Answer: Option A 8. Catching the earlier train will give us the ...... to do some shopping. A.chance B. luck C. possibility D. occasion

Answer: Option A 9. I saw a ...... of cows in the field. A.group B. herd C. swarm D. flock

Answer: Option B 10. The grapes are now ...... enough to be picked. A.ready B. mature C. ripe D. advanced Answer: Option C

(b) Computer Competency 11) The various cards in a PC requires _______ voltage to function. A) AC B) DC

Answer: Option B 12) Which has more storage capacity CD or DVD A) DVD B) CD Answer: Option A 13) LCD monitor is also known as___________ a) TFT b) CRT

Answer: Option A


14) Acronym of HDD? A) Hard Disk Drive B) Hard Drive Disk Answer: Option A 15) What type of memory is a PEN drive...? A) FLASH Memory B) Catch Memory Answer: Option A

(C)Environment Competency

16. Branch of Biology which is concerned with the inter-relationship between plants and animals is called : (A) Physiology (B) Ecology (C) Anatomy (D) Morphology Answer: Option B 17. The largest unit of living organisms on earth is : (A) Ecosystem (B) biome (C) Biosphere (D) Population Answer: Option C 18. The two components of an ecosystem are : (A) Plants and animals (B) Biotic and abiotic (C) Plants and light (D) Weeds and micro-organisms

Answer: Option B 19. The green plants are called : (A) Producers (B) Consumers (C) Decomposers (D) None of these Answer: Option A 20. Total organic matter present in an ecosystem is called : (A) Biome (B) Biomass (C) Biotic community (D) Litter

Answer: Option B


(d) Logical Reasoning Competency

Each problem consists of three statements. Based on the first two statements, the third statement may be true, false, or uncertain.

1. Tanya is older than Eric. Cliff is older than Tanya. Eric is older than Cliff. If the first two statements are true, the third statement is A.true B. false C. uncertain

Answer & Explanation Answer: Option B Explanation: Because the first two statements are true, Eric is the youngest of the three, so the third statement must be false. 2. Blueberries cost more than strawberries. Blueberries cost less than raspberries. Raspberries cost more than both strawberries and blueberries. If the first two statements are true, the third statement is A.true B. false C. uncertain Answer & Explanation Answer: Option A Explanation: Because the first two statements are true, raspberries are the most expensive of the three. 3. All the trees in the park are flowering trees. Some of the trees in the park are dogwoods. All dogwoods in the park are flowering trees. If the first two statements are true, the third statement is A.true B. false C. uncertain Answer & Explanation Answer: Option A Explanation: All of the trees in the park are flowering trees, So all dogwoods in the park are flowering trees. 4. Mara runs faster than Gail. Lily runs faster than Mara. Gail runs faster than Lily. If the first two statements are true, the third statement is A.true B. false C. uncertain

Answer & Explanation Answer: Option B Explanation: We know from the first two statements that Lily runs fastest. Therefore, the third statement must be false.


5. Apartments in the Riverdale Manor cost less than apartments in The Gaslight Commons. Apartments in the Livingston Gate cost more than apartments in the The Gaslight Commons. Of the three apartment buildings, the Livingston Gate costs the most. If the first two statements are true, the third statement is A.true B. false C. uncertain Answer & Explanation Answer: Option A Explanation: Since the Gaslight Commons costs more than the Riverdale Manor and the Livingston Gate costs more than the Gaslight Commons, it is true that the Livingston Gate costs the most.


Pie Charts The following pie-chart shows the percentage distribution of the expenditure incurred in publishing a book. Study the pie-chart and the answer the questions based on it.

Various Expenditures (in percentage) Incurred in Publishing a Book

1. If for a certain quantity of books, the publisher has to pay ` 30,600 as printing cost, then what will

be amount of royalty to be paid for these books? A. ̀ 19,450 B. ` 21,200 C. ` 22,950 D. ` 26,150 Answer & Explanation Answer: Option C Explanation: Let the amount of Royalty to be paid for these books be ` r.

Then, 20 : 15 = 30600 : r r = `

30600 x 15

= ` 22,950. 20

2. What is the central angle of the sector corresponding to the expenditure incurred on Royalty?

A. 15º B. 24º C. 54º D. 48º

Answer & Explanation Answer: Option C Explanation: Central angle corresponding to Royalty = (15% of 360)º

=

15 x 360

º 100

= 54º.


3. The price of the book is marked 20% above the C.P. If the marked price of the book is ` 180, then

what is the cost of the paper used in a single copy of the book? A. ̀ 36 B. ` 37.50 C. ` 42 D. ` 44.25

Answer & Explanation Answer: Option B Explanation: Clearly, marked price of the book = 120% of C.P. Also, cost of paper = 25% of C.P Let the cost of paper for a single book be ` n.

Then, 120 : 25 = 180 : n n = `

25 x 180

= ` 37.50 . 120

4. If 5500 copies are published and the transportation cost on them amounts to ` 82500, then what

should be the selling price of the book so that the publisher can earn a profit of 25%? A. ̀ 187.50 B. ` 191.50 C. ` 175 D. ` 180 Answer & Explanation Answer: Option A Explanation: For the publisher to earn a profit of 25%, S.P. = 125% of C.P. Also Transportation Cost = 10% of C.P. Let the S.P. of 5500 books be ` x.

Then, 10 : 125 = 82500 : x x = `

125 x 82500

= ` 1031250. 10

S.P. of one book = `

1031250

= ` 187.50 . 5500

5. Royalty on the book is less than the printing cost by:

A. 5% B. 33 1

% 5

C. 20% D. 25% Answer & Explanation Answer: Option D Explanation: Printing Cost of book = 20% of C.P. Royalty on book = 15% of C.P. Difference = (20% of C.P.) - (15% of C.P) = 5% of C.P.

Percentage difference =

Difference x 100

% Printing Cost

=

5% of C.P. x 100

% = 25%. Printing Cost

6. If the difference between the two expenditures are represented by 18º in the pie-chart, then these expenditures possibly are A. Binding Cost and Promotion Cost B. Paper Cost and Royalty C. Binding Cost and Printing Cost D. Paper Cost and Printing Cost

Answer & Explanation


Answer: Option D Explanation:

Central angle of 18º =

18 x 100

% of the total expenditure 360

= 5% of the total expenditure. From the given chart it is clear that: Out of the given combinations, only in combination (d) the difference is 5% i.e. Paper Cost - Printing Cost = (25% - 20%) of the total expenditure

= 5% of the total expenditure.

7. For an edition of 12,500 copies, the amount of Royalty paid by the publisher is ` 2,81,250. What

should be the selling price of the book if the publisher desires a profit of 5%? A. ̀ 152.50 B. ` 157.50 C. ` 162.50 D. ` 167.50

Answer & Explanation Answer: Option B Explanation: Clearly, S.P. of the book = 105% of C.P. Let the selling price of this edition (of 12500 books) be ` x.

Then, 15 : 105 = 281250 : x x = `

105 x 281250

= ` 1968750. 15


1968750

= ` 157.50 . 12500

8. If for an edition of the book, the cost of paper is ` 56250, then find the promotion cost for this

edition. A. ̀ 20,000 B. ` 22,500 C. ` 25,500 D. ` 28,125 Answer & Explanation Answer: Option B Explanation: Let the Promotion Cost for this edition be ` p.

Then, 25 : 10 = 56250 : p p = `

56250 x 10

= ` 22,500. 25

9. Which two expenditures together have central angle of 108º?

A. Biding Cost and Transportation Cost

B. Printing Cost and Paper Cost

C. Royalty and Promotion Cost

D. Binding Cost and Paper Cost


Central angle of 108º =

108 x 100

% of the total expenditure 360

= 30% of the total expenditure. From the pie chart it is clear that: Binding Cost + Transportation Cost = (20% + 10%) of the total expenditure

= 30% of the total expenditure.

Binding Cost and Transportation Cost together have a central angle of 108º.


The pie chart shows the distribution of New York market share by value of different computer companies in 2005.

The pie chart shows the distribution of New York market share by volume of different computer companies in 2005. Number of units sold in 2005 in New York = 1,500 Value of units sold in 2005 in New York = US $1,650,000.

1. For the year 2005, which company has realised the lowest average unit sales price for a PC ?

A. Commodore B. IBM C. Tandy D. Cannot be determined

Answer & Explanation Answer: Option D Explanation: Although it seems to be Commodore, the answer cannot be determined due to the fact that we are unaware of the break-up of the sales value and volume of companies compromising the other categories.

2. Over the period 2005-2006, if sales (value-wise) of IBM PC's increased by 50% and of Apple by 15%

assuming that PC sales of all other computer companies remained the same, by what percentage (approximately) would the PC sales in New York (value-wise) increase over the same period ? A. 16.1 % B. 18 % C. 14 % D. None of these Answer & Explanation Answer: Option A Explanation: If we assume the total sales to be 100 in the first year, IBM's sales would go up by 50% (from 28 to 42) contributing an increase of 14 to the total sales value.


Similarly, Apple's increase of 15% would contribute an increase of 2.1 to the total sales value. The net change would be 14 + 2.1 on 100. (i.e., 16.1%)

3. In 2005, the average unit sale price of an IBM PC was approximately (in US$)

A. 3180 B. 2800 C. 393 D. 3080

Answer & Explanation Answer: Option D Explanation: IBM accounts for 28% of the share by value and 10% of the share by volume. 28% of 1650000 = 28 x 1650000/100 = 462000 10% of 1500 = 10 x 1500/100 = 150 Therefore, average unit sale price = 462000/150 = 3080.

Bar Charts

Study the following bar charts and answer the questions. Foreign Trade (Imports and Exports) by countries for the year (1993 - 1994)

1. The ratio of the maximum exports to the minimum imports was closest to ?

A. 64 B. 69 C. 74 D. 79


Answer & Explanation Answer: Option B Explanation: The value of maximum exports = 6045. The value of minimum imports = 87. Therefore, the required ratio (6045/87) = 69.48 = 69 (approximately).

2. How many countries exhibited a trade surplus ?

A. 5 B. 4 C. 3 D. 6

Answer & Explanation Answer: Option B Explanation: Out of a total of 12 countries, 8 showed a deficit while 4 showed a surplus.

3. The total trade deficit/surplus for all the countries put together was ?

A. 11286 surplus B. 11286 deficit C. 10286 deficit D. None of these Answer & Explanation Answer: Option B Explanation: Sum of exports - Sum of imports = deficit(11286).

4. The highest trade deficit was shown by which country ?

A. C B. G C. H D. L Answer & Explanation Answer: Option D Explanation: Visually it‘s clear that L has the highest trade deficit.

5. The ratio of Exports to Imports was highest for which country ?

A. A B. I C. J D. K

Answer & Explanation Answer: Option B Explanation: I has a ratio of 4002/2744 = 1.45, which is the highest.


The following bar chart shows the composition of the GDP two countries (India and Pakistan). Composition of GDP of Two Countries

1. If the total GDP of Pakistan is ` 10,000 crore, then a GDP accounted for by Manufacturing is ?

A. ̀ 200 crore B. `600 crore C. `2,000 crore D. `6,000 crore


Answer: Option C Explanation: 20% of 10000 = 2000

2. What fraction of India's GDP is accounted for by Services ? A. (6/33)th B. (1/5)th C. (2/3)rd D. None of these Answer & Explanation Answer: Option B Explanation: Service accounts for 20%, i.e., (1/5)th of the GDP of India.

3. If the total GDP of India is `30,000 crores, then the GDP accounted for by Agriculture, Services and

Miscellaneous is ? A. ̀ 18,500 crore B. ` 18,000 crore C. ` 21,000 crore D. ` 15,000 crore Answer & Explanation Answer: Option C Explanation: (40 + 20 + 10)% of 30,000 = ` 21,000 crore.

4. Which country accounts for higher earning out of Services and Miscellaneous together ?

A. India B. Pakistan C. Both spend equal amounts D. Cannot be determined

Answer & Explanation Answer: Option D Explanation: Although the percentage on Services and Miscellaneous put together is equal for both the countries, we cannot comment on this since we have no data about the respective GDP's.


5. If the total GDP is the same for both the countries, then what percentage is Pakistan's income through agriculture over India's income through Services ? A. 100 % B. 200 % C. 133.33 % D. None of these

Answer & Explanation Answer: Option A Explanation: Since the GDP is same, the answer will be got by (40 - 20)/20 = 100%.

Table Charts

The following table shows the number of new employees added to different categories of employees in a company and also the number of employees from these categories who left the company every year since the foundation of the Company in 1995.

Year Managers Technicians Operators Accountants Peons

New Left New Left New Left New Left New Left

1995 760 - 1200 - 880 - 1160 - 820 -

1996 280 120 272 120 256 104 200 100 184 96

1997 179 92 240 128 240 120 224 104 152 88

1998 148 88 236 96 208 100 248 96 196 80

1999 160 72 256 100 192 112 272 88 224 120

2000 193 96 288 112 248 144 260 92 200 104

1. What is the difference between the total number of Technicians added to the Company and the total number of Accountants added to the Company during the years 1996 to 2000?

Answer & Explanation Answer: Option D Explanation: Required difference = (272 + 240 + 236 + 256 + 288) - (200 + 224 + 248 + 272 + 260) = 88. 2. What was the total number of Peons working in the Company in the year 1999?

A. 1312 B. 1192 C. 1088 D. 968

Answer & Explanation Answer: Option B Explanation: Total number of Peons working in the Company in 1999 = (820 + 184 + 152 + 196 + 224) - (96 + 88 + 80 + 120) = 1192.


Online Ph.D Entrance Test (PET) : Sample Test Paper – I(Solved with explanation)

Time: 90 minutes] [Max Marks: 100

(a) N.B:- a) There are in all 100 multiple Choice Questions

(b) Each correct answer carries 1 Mark.

(c) There is „No negative‟ marking system.

(d) Click online the correct option for each question.

(e) Use of Electronic / Scientific calculator is not allowed

(f) Multiple Choice Questions are divided into four parts

1. Analytical Reasoning,

2. Numerical Ability

3. Language Competency/ Computer/ Environment/ Logical Reasoning

4. and Data Interpretation

Part-1 :- Analytical Reasoning

Series Completion Choose the correct alternative that will continue the same pattern and replace the question mark in the given series. 1. 120, 99, 80, 63, 48, ? A. 35 B. 38 C. 39 D. 40 Answer & Explanation Answer: Option A Explanation: The pattern is - 21, - 19, - 17, - 15,..... So, missing term = 48 - 13 = 35. 2. 589654237, 89654237, 8965423, 965423, ? A. 58965 B. 65423 C. 89654 D. 96542

Answer & Explanation Answer: Option D Explanation: The digits are removed one by one from the beginning and the end in order alternately so as to obtain the subsequent terms of the series. 3. 3, 10, 101,? A.10101 B. 10201 C. 10202 D.11012



Explanation: Clearly, 2 x 3 = 6, 6 x 3 = 18, 18 x 3 = 54,..... So, the series is a G.P. in which a = 2, r = 3. Therefore 8th term = ar8-1 = ar7 = 2 x 37 = (2 x 2187) = 4374. 4. In the series 2, 6, 18, 54, ...... what will be the 8th term ? A.4370 B. 4374 C. 7443 D.7434 Answer & Explanation Answer: Option B Explanation: Clearly, 2 x 3 = 6, 6 x 3 = 18, 18 x 3 = 54,..... So, the series is a G.P. in which a = 2, r = 3. Therefore 8th term = ar8-1 = ar7 = 2 x 37 = (2 x 2187) = 4374. 5. 125,80,45,20,? A.5 B. 8 C. 10 D.12 Answer & Explanation Answer: Option A Explanation: The pattern is - 45, - 35, - 25, ..... So, missing term = 20 - 15 = 5.

Classification In each of the following questions, five words have been given out of which four are alike in some manner, while the fifth one is different. Choose the word which is different from the rest. 6. Choose the word which is different from the rest. A.Chicken B. Snake C. Swan D. Crocodile E. Frog Answer & Explanation Answer: Option A Explanation: All except Chicken can live in water. 7. Choose the word which is different from the rest. A.Cap B. Turban C. Helmet D.Veil E. Hat Answer & Explanation Answer: Option D Explanation: All except Veil cover the head, while veil covers the face. 8. Choose the word which is different from the rest. A.Kiwi B. Eagle C. Emu D.Ostrich Answer & Explanation

Answer: Option B Explanation: AH except Eagle are flightless birds.


9. Choose the word which is different from the rest. A.Rigveda B. Yajurveda C. Atharvaveda D.Ayurveda E. Samveda Answer & Explanation Answer: Option D Explanation: All except Ayurveda are names of holy scriptures, the four Vedas. Ayurveda is a branch of medicine. 10. Choose the word which is different from the rest. A.Curd B. Butter C. Oil D. Cheese E. Cream Answer & Explanation Answer: Option C Explanation: All except Oil are products obtained from milk.

Blood Relation Test

11. Pointing to a photograph of a boy Suresh said, "He is the son of the only son of my mother." How is Suresh related to that boy? A.Brother B. Uncle C. Cousin D. Father Answer & Explanation Answer: Option D Explanation: The boy in the photograph is the only son of the son of Suresh's mother i.e., the son of Suresh. Hence, Suresh is the father of boy. 12. If A + B means A is the mother of B; A - B means A is the brother B; A % B means A is the father of B and A x B means A is the sister of B, which of the following shows that P is the maternal uncle of Q? A.Q - N + M x P B. P + S x N - Q C. P - M + N x Q D. Q - S % P Answer & Explanation Answer: Option C Explanation: P - M → P is the brother of M M + N → M is the mother of N N x Q → N is the sister of Q Therefore, P is the maternal uncle of Q. 13. If A is the brother of B; B is the sister of C; and C is the father of D, how D is related to A? A.Brother B. Sister C. Nephew D.Cannot be determined Answer & Explanation Answer: Option D Explanation: If D is Male, the answer is Nephew. If D is Female, the answer is Niece. As the sex of D is not known, hence, the relation between D and A cannot be determined. Note: Niece - A daughter of one's brother or sister, or of one's brother-in-law or sister-in-law. Nephew - A son of one's brother or sister, or of one's brother-in-law or sister-in-law.


Character Puzzles 14. Which one will replace the question mark ?

A.L10 B. K15 C. I15 D.K8 Answer & Explanation Answer: Option D Explanation:

How the number is obtained? 2 + 4 = 6 5 + 9 = 14 Similarly, 3 + 5 = 8 Therefore, the answer is K8. 15. Which one will replace the question mark ?

A.1 B. 4 C. 3 D. 6 Answer & Explanation Answer: Option D Explanation: (5 + 4 + 7)/2 = 8 (6 + 9 + 5)/2 = 10 (3 + 7 + 2)/2 = 6.


16. Which one will replace the question mark ?

A.18 B. 12 C. 9 D. 6 Answer & Explanation Answer: Option C Explanation: (12 + 18 + 30)/10 = 6 (16 + 24 + 40)/10 = 8 Similarly, (45 + 18 + 27)/10 = 9. 17. Which one will replace the question mark ?

A.25 B. 37 C. 41 D. 47 Answer & Explanation Answer: Option C Explanation: (5 x 3) + 4 = 19 and (6 x 4) + 5 = 29 Therefore, (7 x 5) + 6 = 41 18. Which one will replace the question mark ?

A.45 B. 41 C. 32 D. 40 Answer & Explanation Answer: Option A Explanation: (15 x 2 - 3) = 27, (31 x 2 - 6) = 56 and (45 x 2 - 9) = 81 19. Y is in the East of X which is in the North of Z. If P is in the South of Z, then in which direction of Y, is P? A.North B. South C. South-East D. None of these

Answer & Explanation Answer: Option D


Explanation:

P is in South-West of Y. 20. If South-East becomes North, North-East becomes West and so on. What will West become? A.North-East B. North-West C. South-East D.South-West


It is clear from the diagrams that new name of West will become South-East. 21. A man walks 5 km toward south and then turns to the right. After walking 3 km he turns to the left and walks 5 km. Now in which direction is he from the starting place? A.West B. South C. North-East D. South-West Answer & Explanation Answer: Option D Explanation:

Hence required direction is South-West. 22. Rahul put his timepiece on the table in such a way that at 6 P.M. hour hand points to North. In which direction the minute hand will point at 9.15 P.M. ? A.South-East B. South C. North D.West Answer & Explanation Answer: Option D


Explanation:

At 9.15 P.M., the minute hand will point towards west.

23. Rasik walked 20 m towards north. Then he turned right and walks 30 m. Then he turns right and walks 35 m. Then he turns left and walks 15 m. Finally he turns left and walks 15 m. In which direction and how many metres is he from the starting position? A.15 m West B. 30 m East C. 30 m West D. 45 m East Answer & Explanation Answer: Option D Explanation:

24. Two cars start from the opposite places of a main road, 150 km apart. First car runs for 25 km and takes a right turn and then runs 15 km. It then turns left and then runs for another 25 km and then takes the direction back to reach the main road. In the mean time, due to minor break down the other car has run only 35 km along the main road. What would be the distance between two cars at this point? A.65 km B. 75 km C. 80 km D.85 km Answer & Explanation Answer: Option A


Explanation:

25. Starting from the point X, Jayant walked 15 m towards west. He turned left and walked 20 m. He then turned left and walked 15 m. After this he turned to his right and walked 12 m. How far and in which directions is now Jayant from X? A.32 m, South B. 47 m, East C. 42 m, North D. 27 m, South


Part-2:- Numerical Ability

Numbers

1. Which one of the following is not a prime number? A.31 B. 61 C. 71 D. 91 Answer & Explanation Answer: Option D Explanation: 91 is divisible by 7. So, it is not a prime number. 2. (112 x 54) = ? A.67000 B. 70000 C. 76500 D.77200



(112 x 54) = 112 x

10

4 =

112 x 104 =

1120000 = 70000

2 24 16

3. It is being given that (232 + 1) is completely divisible by a whole number. Which of the following numbers is completely divisible by this number? A.(216 + 1) B. (216 - 1) C. (7 x 223) D. (296 + 1) Answer & Explanation Answer: Option D Explanation: Let 232 = x. Then, (232 + 1) = (x + 1). Let (x + 1) be completely divisible by the natural number N. Then, (296 + 1) = [(232)3] = (x3 + 1) = (x + 1)(x2 - x + 1), which is completely divisible by N, since (x + 1) is divisible by N. 4. What least number must be added to 1056, so that the sum is completely divisible by 23 ? A.2 B. 3 C. 18 D. 21 E. None of these Answer & Explanation Answer: Option A Explanation: 23) 1056 (45 92 --- 136 115 --- 21 --- Required number = (23 - 21) = 2 5. 1397 x 1397 = ? A.1951609 B. 1981709 C. 18362619 D. 2031719 E. None of these Answer & Explanation Answer: Option A Explanation: 1397 x 1397= (1397)2 = (1400 - 3)2 = (1400)2 + (3)2 - (2 x 1400 x 3) = 1960000 + 9 - 8400 = 1960009 - 8400 = 1951609. 6. How many of the following numbers are divisible by 132 ? 264, 396, 462, 792, 968, 2178, 5184, 6336

A.4 B. 5 C. 6 D. 7


Answer & Explanation Answer: Option A Explanation: 132 = 4 x 3 x 11 So, if the number divisible by all the three number 4, 3 and 11, then the number is divisible by 132 also. 264 11,3,4 (/) 396 11,3,4 (/) 462 11,3 (X) 792 11,3,4 (/) 968 11,4 (X) 2178 11,3 (X) 5184 3,4 (X) 6336 11,3,4 (/) Therefore the following numbers are divisible by 132 : 264, 396, 792 and 6336. Required number of number = 4. 7. (935421 x 625) = ?

A. 575648125 B. 584638125 C. 584649125 D. 585628125

Answer & Explanation Answer: Option B Explanation:

935421 x 625 = 935421 x 54 = 935421 x

10

4

2

= 935421 x 104

= 9354210000

24 16 = 584638125

8. The largest 4 digit number exactly divisible by 88 is: A.9944 B. 9768 C. 9988 D.8888 E. None of these Answer & Explanation Answer: Option A Explanation: Largest 4-digit number = 9999 88) 9999 (113 88 ---- 119 88 ---- 319 264 --- 55 --- Required number = (9999 - 55) = 9944.


Problems on Trains 1. A train running at the speed of 60 km/hr crosses a pole in 9 seconds. What is the length of the train?

A. 120 metres B. 180 metres C. 324 metres D. 150 metres Answer & Explanation Answer: Option D Explanation:

Speed=

60 x 5

m/sec =

50

m/sec. 18 3

Length of the train = (Speed x Time) =

50 x 9

m = 150 m. 3

2. A train 125 m long passes a man, running at 5 km/hr in the same direction in which the train is

going, in 10 seconds. The speed of the train is: A. 45 km/hr B. 50 km/hr C. 54 km/hr D. 55 km/hr Answer & Explanation Answer: Option B Explanation:

Speed of the train relative to man =

125

m/sec 10

=

25

m/sec. 2

=

25 x

18

km/hr 2 5

= 45 km/hr. Let the speed of the train be x km/hr. Then, relative speed = (x - 5) km/hr.

x - 5 = 45 x = 50 km/hr.

3. The length of the bridge, which a train 130 metres long and travelling at 45 km/hr can cross in 30

seconds, is: A. 200 m B. 225 m C. 245 m D. 250 m


Speed =

45 x 5

m/sec =

25

m/sec. 18 2

Time = 30 sec. Let the length of bridge be x metres.

Then, 130 + x

= 25

30 2 2(130 + x) = 750 x = 245 m.

4. Two trains running in opposite directions cross a man standing on the platform in 27 seconds and 17 seconds respectively and they cross each other in 23 seconds. The ratio of their speeds is: A.1 : 3 B. 3 : 2 C. 3 : 4 D. None of these


Answer & Explanation Answer: Option B Explanation: Let the speeds of the two trains be x m/sec and y m/sec respectively. Then, length of the first train = 27x metres, and length of the second train = 17y metres.

27x + 17y = 23 x+ y

27x + 17y = 23x + 23y 4x = 6y x = 3 .

y 2 5. A train passes a station platform in 36 seconds and a man standing on the platform in 20 seconds. If

the speed of the train is 54 km/hr, what is the length of the platform? A. 120 m B. 240 m C. 300 m D. None of these Answer & Explanation Answer: Option B Explanation:

Speed =

54 x 5

m/sec = 15 m/sec. 18

Length of the train = (15 x 20)m = 300 m. Let the length of the platform be x metres.

Then, x + 300

= 15 36

x + 300 = 540 x = 240 m.

Probability 1. Tickets numbered 1 to 20 are mixed up and then a ticket is drawn at random. What is the probability

that the ticket drawn has a number which is a multiple of 3 or 5?

A.

1 2

B.

2 5

C.

8 15

D.

9 20

Answer & Explanation Answer: Option D Explanation: Here, S = {1, 2, 3, 4, ...., 19, 20}. Let E = event of getting a multiple of 3 or 5 = {3, 6 , 9, 12, 15, 18, 5, 10, 20}.

P(E) = n(E)

= 9

. n(S) 20

2. A bag contains 2 red, 3 green and 2 blue balls. Two balls are drawn at random. What is the

probability that none of the balls drawn is blue?

A.

10 21

B.

11 21

C.

2 7

D.

5 7



Total number of balls = (2 + 3 + 2) = 7. Let S be the sample space. Then, n(S) = Number of ways of drawing 2 balls out of 7

= 7C2 `

=

(7 x 6) (2 x 1)

= 21.

Let E = Event of drawing 2 balls, none of which is blue. n(E) = Number of ways of drawing 2 balls out of (2 + 3) balls.

= 5C2

=

(5 x 4) (2 x 1)

= 10.

P(E) = n(E)

= 10

. n(S) 21

3. In a box, there are 8 red, 7 blue and 6 green balls. One ball is picked up randomly. What is the

probability that it is neither red nor green?

A.

1 3

B.

3 4

C.

7 19

D.

8 21

E.

9 21

Answer & Explanation Answer: Option A Explanation: Total number of balls = (8 + 7 + 6) = 21. Let E = event that the ball drawn is neither red nor green

= event that the ball drawn is blue.

n(E) = 7.

P(E) = n(E)

= 7

= 1 .

n(S) 21 3

4. What is the probability of getting a sum 9 from two throws of a dice?

A.

1 6

B.

1 8

C.

1 9

D.

1 12

Answer & Explanation Answer: Option C Explanation: In two throws of a die, n(S) = (6 x 6) = 36. Let E = event of getting a sum ={(3, 6), (4, 5), (5, 4), (6, 3)}.

P(E) = n(E)

= 4

= 1 .

n(S) 36 9

5. Three unbiased coins are tossed. What is the probability of getting at most two heads?

A.

3 4

B.

1 4

C.

3 8

D.

7 8



Here S = {TTT, TTH, THT, HTT, THH, HTH, HHT, HHH} Let E = event of getting at most two heads. Then E = {TTT, TTH, THT, HTT, THH, HTH, HHT}.

P(E) = n(E)

= 7 .

n(S) 8

Simplification 1. A man has ` 480 in the denominations of one-rupee notes, five-rupee notes and ten-rupee notes. The number of notes of each denomination is equal. What is the total number of notes that he has ? A.45 B. 60 C. 75 D. 90

Answer & Explanation Answer: Option D Explanation: Let number of notes of each denomination be x. Then x + 5x + 10x = 480

16x = 480 x = 30.

Hence, total number of notes = 3x = 90. 2. There are two examinations rooms A and B. If 10 students are sent from A to B, then the number of students in each room is the same. If 20 candidates are sent from B to A, then the number of students in A is double the number of students in B. The number of students in room A is: A.20 B. 80 C. 100 D. 200

Answer & Explanation Answer: Option C Explanation: Let the number of students in rooms A and B be x and y respectively. Then, x - 10 = y + 10 x - y = 20 .... (i) and x + 20 = 2(y - 20) x - 2y = -60 .... (ii) Solving (i) and (ii) we get: x = 100 , y = 80.

The required answer A = 100. 3. The price of 10 chairs is equal to that of 4 tables. The price of 15 chairs and 2 tables together is `

4000. The total price of 12 chairs and 3 tables is: A. ̀ 3500 B. ` 3750 C. ` 3840 D. ` 3900 Answer & Explanation Answer: Option D Explanation: Let the cost of a chair and that of a table be ` x and ` y respectively.

Then, 10x = 4y or y = 5

x. 2

15x + 2y = 4000

15x + 2 x 5 x = 4000

2 20x = 4000 x = 200.

So, y =

5 x 200

= 500. 2

Hence, the cost of 12 chairs and 3 tables = 12x + 3y


= ` (2400 + 1500) = ` 3900.

4. If a - b = 3 and a2 + b2 = 29, find the value of ab. A. 10 B. 12 C. 15 D. 18 Answer & Explanation Answer: Option A Explanation: 2ab = (a2 + b2) - (a - b)2 = 29 - 9 = 20 ab = 10.

5. The price of 2 sarees and 4 shirts is ` 1600. With the same money one can buy 1 saree and 6 shirts. If

one wants to buy 12 shirts, how much shall he have to pay ? A. ̀ 1200 B. ` 2400 C. ` 4800 D. Cannot be determined

E. None of these

Answer & Explanation Answer: Option B Explanation: Let the price of a saree and a shirt be ` x and ` y respectively. Then, 2x + 4y = 1600 .... (i) and x + 6y = 1600 .... (ii) Divide equation (i) by 2, we get the below equation. => x + 2y = 800. --- (iii) Now subtract (iii) from (ii) x + 6y = 1600 (-) x + 2y = 800 ---------------- 4y = 800 ---------------- Therefore, y = 200. Now apply value of y in (iii) => x + 2 x 200 = 800 => x + 400 = 800 Therefore x = 400 Solving (i) and (ii) we get x = 400, y = 200.

Cost of 12 shirts = ` (12 x 200) = ` 2400.


Part-3:- Language Competency

Ordering of Words

In each question below, there is a sentence of which some parts have been jumbled up. Rearranage these parts which are labelled P, Q, R and S to produce the correct sentence. Choose the proper sequence. 1. When he P : did not know Q : he was nervous and R : heard the hue and cry at midnight S : what to do The Proper sequence should be: A.RQPS B. QSPR C. SQPR D. PQRS

Answer: Option A 2. It has been established that P : Einstein was Q : although a great scientist R : weak in arithmetic S : right from his school days The Proper sequence should be: A.SRPQ B. QPRS C. QPSR D. RQPS Answer: Option B 3. Then P : it struck me Q : of course R : suitable it was S : how eminently The Proper sequence should be: A.SPQR B. QSRP C. PSRQ D. QPSR

Answer: Option C 4. I read an advertisement that said P : posh, air-conditioned Q : gentleman of taste R : are available for S : fully furnished rooms The Proper sequence should be: A.PQRS B. PSRQ C. PSQR D. SRPQ

Answer: Option B


5. Since the beginning of history P : have managed to catch Q : the Eskimos and Red Indians R : by a very difficulty method S : a few specimens of this aquatic animal The Proper sequence should be: A.QRPS B. SQPR C. SQRP D. QPSR

Answer: Option D

Completing Statements In each question, an incomplete statement (Stem) followed by fillers is given. Pick out the best one which can complete incomplete stem correctly and meaningfully. 6. Despite his best efforts to conceal his anger ...... A.we could detect that he was very happy B. he failed to give us an impression of his agony C. he succeeded in camouflaging his emotions D.he could succeed in doing it easily E. people came to know that he was annoyed Answer: Option E 7. Even if it rains I shall come means ......

A.if I come it will not rain B. if it rains I shall not come C. I will certainly come whether it rains or not D.whenever there is rain I shall come E. I am less likely to come if it rains Answer: Option C 8. His appearance is unsmiling but ......

A.his heart is full of compassion for others B. he looks very serious on most occasions C. people are afraid of him D. he is uncompromising on matters of task performance E. he is full of jealousy towards his colleagues Answer: Option A 9. She never visits any zoo because she is strong opponent of the idea of ......

A.setting the animals free into forest B. feeding the animals while others are watching C. watching the animals in their natural abode D. going out of the house on a holiday E. holding the animals in captivity for our joy Answer: Option E


10. I felt somewhat more relaxed ...... A.but tense as compared to earlier B. and tense as compared to earlier C. as there was already no tension at all D. and tension-free as compared to earlier E. because the worry had already captured by mind Answer: Option D

Computer Competency 11) What is the name of the printed circuit board? A) Ram B) Mother Board

Answer: Option A 12) TO write, erase, rewrite data on a CD RAM what type of CD ROM you should use? A) CD-RW B) CD R Answer: Option A 13) A byte is equivalent to...? A) 8 bits B) 10 bits Answer: Option A 14) Which of the following retains the information it's storing when the power to the system is turned off? a) CPU b) ROM c) DRAM d) DIMM

Answer: Option B 15) Hard Disk, DVD, CD-ROM are the examples of what type of Memory? a) Primary b) Secondary

Answer: Option B

Environment Competency

16. Plants are killed at low temperature because :

(A) Desiccation takes place owing to the withdrawal of water from vacuolated protoplasm (B) Precipitation of cell proteins (C) Cells rupture due to the mechanical pressure of ice (D) All the above three are correct Answer: Option D 17. Which one of the chemicals is responsible for the reduction of ozone content of the atmosphere?

(A) SO2 (B) Chlorofluoro carbon (C) HCl (D) Photochemical smog

Answer: Option B 18. Acid rains occur when atmosphere is heavily polluted with :

(A) CO, CO2 (B) Smoke particles (C) Ozone (D) SO2 and NO2

Answer: Option D 19. Spraying of DDT on crops causes pollution of: (A) Soil and Water (B) Air and Soil (C) Crops and Air (D) Air and Water


Answer: Option A 20. Soil erosion can be prevented by : (A) Increasing bird population(B) Afforestation(C) Removal of vegetation (D) Over grazing Answer: Option B

Logical Reasoning Competency

In these series, you will be looking at both the letter pattern and the number pattern. Fill the blank in the middle of the series or end of the series. 1. SCD, TEF, UGH, ____, WKL

A. CMN B. UJI C. VIJ D. IJT

Answer & Explanation Answer: Option C Explanation: There are two alphabetical series here. The first series is with the first letters only: STUVW. The second series involves the remaining letters: CD, EF, GH, IJ, KL.

2. B2CD, _____, BCD4, B5CD, BC6D A. B2C2D B. BC3D C. B2C3D D. BCD7 Answer & Explanation Answer: Option B Explanation: Because the letters are the same, concentrate on the number series, which is a simple 2, 3, 4, 5, 6 series, and follows each letter in order.

3. FAG, GAF, HAI, IAH, ____ A. JAK B. HAL C. HAK D. JAI Answer & Explanation Answer: Option A Explanation: The middle letters are static, so concentrate on the first and third letters. The series involves an alphabetical order with a reversal of the letters. The first letters are in alphabetical order: F, G, H, I , J. The second and fourth segments are reversals of the first and third segments. The missing segment begins with a new letter.

4. ELFA, GLHA, ILJA, _____, MLNA A. OLPA B. KLMA C. LLMA D. KLLA

Answer & Explanation Answer: Option D Explanation: The second and forth letters in the series, L and A, are static. The first and third letters consist of an alphabetical order beginning with the letter E.

5. CMM, EOO, GQQ, _____, KUU A. GRR B. GSS C. ISS D. ITT Answer & Explanation Answer: Option C


Explanation: The first letters are in alphabetical order with a letter skipped in between each segment: C, E, G, I, K. The second and third letters are repeated; they are also in order with a skipped letter: M, O, Q, S, U.


Bar Charts

The bar graph given below shows the sales of books (in thousand number) from six branches of a publishing company during two consecutive years 2000 and 2001. Sales of Books (in thousand numbers) from Six Branches - B1, B2, B3, B4, B5 and B6 of a publishing Company in 2000 and 2001.

1. What is the ratio of the total sales of branch B2 for both years to the total sales of branch B4 for both years? A.2:3 B. 3:5 C. 4:5 D.7:9

Answer & Explanation Answer: Option D Explanation: Required ratio =(75 + 65)=140 = 7 . (85 + 95) 180 9

2. Total sales of branch B6 for both the years is what percent of the total sales of branches B3 for both

the years?

A. 68.54% B. 71.11%

C. 73.17% D. 75.55%


Answer: Option C

Explanation:


(70 + 80) x 100

% (95 + 110)

=

150 x 100

% 205


= 73.17%.

3. What percent of the average sales of branches B1, B2 and B3 in 2001 is the average sales of branches

B1, B3 and B6 in 2000?

A. 75% B. 77.5%

C. 82.5% D. 87.5%


Answer: Option D

Explanation:

Average sales (in thousand number) of branches B1, B3 and B6 in 2000

= 1 x (80 + 70 + 95) =

245

. 3 3

Average sales (in thousand number) of branches B1, B2 and B3 in 2001

= 1 x (105 + 65 + 110) =

280

. 3 3


245/3 x 100

% =

245 x 100

% = 87.5%. 280/3 280

4. What is the average sales of all the branches (in thousand numbers) for the year 2000?

A.73 B. 80

C. 83 D. 88


Answer: Option B

Explanation:

Average sales of all the six branches (in thousand numbers) for the year 2000

=1 x [80 + 75 + 95 + 85 + 75 + 70]

6

= 80.

5. Total sales of branches B1, B3 and B5 together for both the years (in thousand numbers) is?

A. 250 B. 310

C. 435 D. 560


Answer: Option D

Explanation:

Total sales of branches B1, b2 and B5 for both the years (in thousand numbers)

= (80 + 105) + (95 + 110) + (75 + 95)

= 560.


Pie Charts

The following pie-chart shows the percentage distribution of the expenditure incurred in publishing a

book. Study the pie-chart and the answer the questions based on it.

Various Expenditures (in percentage) Incurred in Publishing a Book

6. If for a certain quantity of books, the publisher has to pay ` 30,600 as printing cost, then what will

be amount of royalty to be paid for these books?

A.` 19,450 B. ̀ 21,200

C. ̀ 22,950 D. ` 26,150


Answer: Option C

Explanation:

Let the amount of Royalty to be paid for these books be ` r.

Then, 20 : 15 = 30600 : r r = `

30600 x 15

= ` 22,950. 20

7. What is the central angle of the sector corresponding to the expenditure incurred on Royalty?

A. 15º B. 24º

C. 54º D. 48º


Answer: Option C

Explanation:

Central angle corresponding to Royalty = (15% of 360)º

=

15 x 360

º 100

= 54º.


8. The price of the book is marked 20% above the C.P. If the marked price of the book is ` 180, then what

is the cost of the paper used in a single copy of the book?

A. ̀ 36 B. ` 37.50

C. ` 42 D. ̀ 44.25


Answer: Option B

Explanation:

Clearly, marked price of the book = 120% of C.P.

Also, cost of paper = 25% of C.P

Let the cost of paper for a single book be ` n.

Then, 120 : 25 = 180 : n n = `

25 x 180

= ` 37.50 . 120

9.

If 5500 copies are published and the transportation cost on them amounts to ` 82500, then what

should be the selling price of the book so that the publisher can earn a profit of 25%?

A. ̀ . 187.50 B. ` 191.50

C. ` 175 D. ̀ 180


Answer: Option A

Explanation:

For the publisher to earn a profit of 25%, S.P. = 125% of C.P.

Also Transportation Cost = 10% of C.P.

Let the S.P. of 5500 books be ` x.

Then, 10 : 125 = 82500 : x x = `

125 x 82500

= ` 1031250. 10


1031250

= ` 187.50 . 5500

10. Royalty on the book is less than the printing cost by:

A. 5% B. 33 1

% 5

C. 20% D. 25%


Answer: Option D

Explanation:

Printing Cost of book = 20% of C.P.


Royalty on book = 15% of C.P.

Difference = (20% of C.P.) - (15% of C.P) = 5% of C.P.

Percentage difference =

Difference x 100

% Printing Cost

=

5% of C.P. x 100

% = 25%. Printing Cost

Line Charts The following line graph gives the percentage of the number of candidates who qualified an examination out of the total number of candidates who appeared for the examination over a period of seven years from 1994 to 2000.

Percentage of Candidates Qualified to Appeared in an Examination Over the Years

11. The difference between the percentage of candidates qualified to appeared was maximum in which

of the following pairs of years? A. 1994 and 1995 B. 1997 and 1998 C. 1998 and 1999 D. 1999 and 2000

Answer & Explanation Answer: Option B Explanation: The differences between the percentages of candidates qualified to appeared for the give pairs of years are: For 1994 and 1995 = 50 - 30 = 20. For 1998 and 1999 = 80 - 80 = 0. For 1994 and 1997 = 50 - 30 = 20. For 1997 and 1998 = 80 - 50 = 30. For 1999 and 2000 = 80 - 60 = 20. Thus, the maximum difference is between the years 1997 and 1998.


12. In which pair of years was the number of candidates qualified, the same?

A. 1995 and 1997 B. 1995 and 2000 C. 1998 and 1999 D. Data inadequate Answer & Explanation Answer: Option D Explanation: The graph gives the data for the percentage of candidates qualified to appeared and unless the absolute values of number of candidates qualified or candidates appeared is know we cannot compare the absolute values for any two years. Hence, the data is inadequate to solve this question.

13. If the number of candidates qualified in 1998 was 21200, what was the number of candidates appeared in 1998? A. 32000 B. 28500 C. 26500 D. 25000

Answer & Explanation Answer: Option C Explanation: The number of candidates appeared in 1998 be x.

Then, 80% of x = 21200 x = 21200 x 100

= 26500 (required number). 80

14. If the total number of candidates appeared in 1996 and 1997 together was 47400, then the total

number of candidates qualified in these two years together was? A. 34700 B. 32100 C. 31500 D. Data inadequate Answer & Explanation Answer: Option D Explanation: The total number of candidates qualified in 1996 and 1997 together, cannot be determined until we know at least, the number of candidates appeared in any one of the two years 1996 or 1997 or the percentage of candidates qualified to appeared in 1996 and 1997 together. Hence, the data is inadequate.

15. The total number of candidates qualified in 1999 and 2000 together was 33500 and the number of

candidates appeared in 1999 was 26500. What was the number of candidates in 2000? A. 24500 B. 22000 C. 20500 D. 19000 Answer & Explanation Answer: Option C Explanation: The number of candidates qualified in 1999 = (80% of 26500) = 21200.

Number of candidates qualified in 2000 = (33500 - 21200) = 12300. Let the number of candidates appeared in 2000 be x.

Then, 60% of x = 12300 x =

12300 x 100

= 20500. 60


Table Charts

Study the following table and answer the questions based on it. Expenditures of a Company (in Lakh Rupees) per Annum Over the given Years.

Year Item of Expenditure

Salary Fuel and Transport Bonus Interest on Loans Taxes

1998 288 98 3.00 23.4 83

1999 342 112 2.52 32.5 108

2000 324 101 3.84 41.6 74

2001 336 133 3.68 36.4 88

2002 420 142 3.96 49.4 98

16. What is the average amount of interest per year which the company had to pay during this period? A. ̀ 32.43 lakhs B. ` 33.72 lakhs C. ` 34.18 lakhs D. ̀ 36.66 lakhs Answer & Explanation Answer: Option D Explanation: Average amount of interest paid by the Company during the given period

= `

23.4 + 32.5 + 41.6 + 36.4 + 49.4

lakhs 5

= `

183.3

lakhs 5

= ` 36.66 lakhs.

17. The total amount of bonus paid by the company during the given period is approximately what

percent of the total amount of salary paid during this period? A. 0.1% B. 0.5% C. 1% D. 1.25% Answer & Explanation Answer: Option C Explanation:


(3.00 + 2.52 + 3.84 + 3.68 + 3.96) x 100

% (288 + 342 + 324 + 336 + 420)

=

17 x 100

% 1710

= 1%.


18. Total expenditure on all these items in 1998 was approximately what percent of the total

expenditure in 2002?

A. 62% B. 66% C. 69% D. 71%



(288 + 98 + 3.00 + 23.4 + 83) x 100

% (420 + 142 + 3.96 + 49.4 + 98)

=

495.4 x 100

% 713.36

= 69.45%.

19. The total expenditure of the company over these items during the year 2000 is?

A. ̀ 544.44 lakhs B. ` 501.11 lakhs C. ` 446.46 lakhs D. ̀ 478.87 lakhs Answer & Explanation Answer: Option A Explanation: Total expenditure of the Company during 2000 = ` (324 + 101 + 3.84 + 41.6 + 74) lakhs = ` 544.44 lakhs.

20. The ratio between the total expenditure on Taxes for all the years and the total expenditure on Fuel

and Transport for all the years respectively is approximately? A. 4:7 B. 10:13 C. 15:18 D. 5:8 Answer & Explanation Answer: Option B Explanation:

Required ratio =

(83 + 108 + 74 + 88 + 98)

(98 + 112 + 101 + 133 + 142)

=

451

586

=

1 1.3

=

10 .

13

[More Solved MS-Word PET/PAT Question Papers can be found in the enclosed CD ]


Most Likely asked Questions for PhD Entrance Interview(at the time of synopsis submission) and final Open Defense viva –voce examination.

Few Breaking the Ice Questions

1. Tell me about yourself.

2. What are your strengths and

weaknesses?

3. What is the difference between

confidence and over confidence?

4. What is the difference between hard

work and smart work?

5. What are your goals? What motivates

you to do a good job?

6. Give me an example of your creativity.

7. Who has inspired you in your life and

why?

8. What was the toughest decision you ever

had to make?

Few Research appetite Questions

9. Why do you want to do Ph.D? What will

be your Ph.D topic? Why you have

chosen this topic?

10. What are going to be the steps of your

research work?

11. What is your aim behind doing this

research?

12. What are the objectives behind your

study? What is the importance of this

study?

13. What is the scope and limitations of your

study?

14. What is going to be your research area?

15. What benefits the masses are going to

derive from your study?

16. Have you chosen any specific area or

sector to conduct your research work?

17. Have you chosen any particular

organization or institution for conducting

the research work?

18. Whether this organization will give you

the permission to conduct this study?

19. Why you want to do your research work

in this particular sector?

20. If you are not in the education sector then

why you want to do Ph.D?

21. Do you have a work experience? If you

have a work experiences then how this

will help in your research work?

22. What do you understand by the term

Research ‗? Which are the various stages

in the development of a research?

23. Explain the stages in the research process

with the help of a flow chart of research

process.

24. Define the term 'hypothesis'. What will

be your hypothesis?

25. Define a Hypothesis Discuss the

importance of hypothesis in research and

the process of a formation of hypothesis.

26. What are your primary and secondary

sources for data collection?

http://www.indiabix.com/hr-interview/tell-me-about-yourself/

http://www.indiabix.com/hr-interview/what-are-your-strengths-and-weaknesses/

http://www.indiabix.com/hr-interview/what-are-your-strengths-and-weaknesses/

http://www.indiabix.com/hr-interview/what-is-the-difference-between-confidence-and-over-confidence/

http://www.indiabix.com/hr-interview/what-is-the-difference-between-confidence-and-over-confidence/

http://www.indiabix.com/hr-interview/what-is-the-difference-between-hard-work-and-smart-work/

http://www.indiabix.com/hr-interview/what-is-the-difference-between-hard-work-and-smart-work/

http://www.indiabix.com/hr-interview/what-are-your-goals/

http://www.indiabix.com/hr-interview/what-motivates-you-to-do-good-job/

http://www.indiabix.com/hr-interview/what-motivates-you-to-do-good-job/

http://www.indiabix.com/hr-interview/give-me-an-example-of-your-creativity/

http://www.indiabix.com/hr-interview/who-has-inspired-you-in-your-life-and-why/

http://www.indiabix.com/hr-interview/who-has-inspired-you-in-your-life-and-why/

http://www.indiabix.com/hr-interview/what-was-the-toughest-decision-you-ever-had-to-make/

http://www.indiabix.com/hr-interview/what-was-the-toughest-decision-you-ever-had-to-make/


27. What do you think how much time will

be required to complete the research

work?

28. What is going to be the sample size?

Have you decided upon the

demographics of the sample size?

29. What is going to be the research

methodology of your study?

30. How you will do the analysis of the data

collected during the research work?

31. Explain few sources of primary and

secondary data collection methods.

32. How the analysis of your study is going

to help or guide future researchers.

33. The textbook says that one does not start

by writing questions. How should the

researcher begin?

34. Define sampling state briefly various

methods of sampling.

35. A researcher is interested in knowing the

answer to a why question, but does not

know what sort of answer will be

satisfying. Is this exploratory,

descriptive, or casual research? Explain.

36. What are the major characteristics in

sampling? State the type of sampling

with suitable illustrations.

37. What is the task of problem definition?

The city police wishes to understand its

image from the public‘s point of view.

Define the business problem.

38. How do you recognize a research

problem? Describe the criteria of a good

research problem‘

39. With the help of examples, classify

survey research methods.

40. Discuss the use of self – administered

questionnaires along with their

classifications.

41. Design a complete questionnaire to

evaluate job satisfaction of entry level

marketing executives.

42. Define the interviewing and the

questionnaire techniques of data

collection.

43. Define and classify secondary data.

Discuss the process of evaluating

secondary data.

44. Discuss various contents required in the

layout of Internet questionnaire.

45. Discuss various factors that influence the

validity of experimental studies in

research.

46. What type of research should be

conducted? Give reasons to support your

answer.

47. Design the research process in detail.

Support your answer with flow diagram.

48. Explain the significance of statistical tools

in the interpretation of data. What its

limitations?

49. Discuss briefly the various methods of

data collection. What steps will you

follow while writing a Research Report?

50. Define Research Report. Explain the

characteristics of a good research report?

call 9422864426 to order : “a complete textbook on phd” along with a cd for your ready...

Documents

basic research

methods of research

ah research

applied research

research reports

lifetime of research

research aptitude test

researchappro research