mk 0004 marketing research contents
TRANSCRIPT
MK 0004Marketing Research
Contents
Unit 1
An Overview of Marketing Research 1
Unit 2
Research Process and Design 13
Unit 3
Sources of Research Data 40
Unit 4
Measurement Techniques in Marketing Research 77
Unit 5
Sampling and Data Analysis 104
Unit 6
Hypothesis Formation and Testing 124
Unit 7
Techniques of Quantitative Data Analysis 140
Unit 8
Writing a Research Report 147
Unit 9
Ethical Issues in Marketing Research 162
Unit 10
Contemporary Issues in Marketing Research 170
Edition: Fall 2007
BKID – B0798 8th Nov. 2007
Brig. (Dr). R. S. Grewal VSM (Retd.)Pro Vice ChancellorSikkim Manipal University of Health, Medical & Technological Sciences
Board of StudiesMr. Rajen PadukoneMember – Academic Senate, Sikkim Manipal UniversityMs. Vimala Parthasarathy Prof. K. V. VaramballyHOD DirectorConvener Manipal Institute of ManagementDepartment of Management & Commerce ManipalDirectorate of Distance EducationSikkim Manipal UniversityProf. Raj Dorai Mr. JagadeeshIndustry Consultant and Assistant ProfessorVisiting Faculty, IBA, IFIM and BIM, Department of Management &Bangalore Commerce, Directorate of Distance
Education, Sikkim Manipal UniversityMr. Umesh Maiya Mr. R. Ravindra RaoAssistant Professor Senior FacultyDepartment of Management & Commerce Manipal Institute of Management Directorate of Distance Education ManipalSikkim Manipal University
Content Preparation TeamContent Writing and CompilationProf. Xavier V. K.Christ CollegeBangaloreFormat Editing Language EditingMs. Shulagna Sarkar Mr. Radhakrishna RaoLecturer Lecturer in EnglishDept. of Management & Commerce UPMC, UdupiSikkim Manipal University ofHealth, Medical & Technological Sciences (SMU)Manipal – 576 104
Edition: Fall 2007
This book is a distance education module comprising of collection of learning material for our students.
All rights reserved. No part of this work may be reproduced in any form by any means without permission in writing from Sikkim Manipal University of Health, Medical and Technological Sciences, Gangtok, Sikkim.
Printed and Published on behalf of Sikkim Manipal University of Health, Medical and Technological Sciences, Gangtok, Sikkim by Mr. Rajkumar Mascreen, GM, Manipal Universal Learning Pvt. Ltd., Manipal – 576 104. Printed at Manipal Press Limited, Manipal.
SUBJECT INTRODUCTION
Marketing research is a critical part of such a marketing intelligence system;
it helps to improve management decision making by providing relevant,
accurate, and timely (RAT) information. Every decision poses unique needs
for information, and relevant strategies can be developed based on the
information gathered through marketing research.
In practice, marketing research department’s goal can be grouped into three
major categories: programmatic, selective or evaluative. Programmatic
research is performed to develop marketing options through market
segmentation, market opportunity analysis, or consumer attitude and
product usage studies. Selective research is done to test different decision
alternatives such as new product concept testing, advertising copy testing,
pretest marketing, and test marketing. Evaluative research is carried out to
evaluate performance of programs, including tracking advertising recall,
corporate and brand image studies, and measuring customer satisfaction
with the quality of the product ad service.
The research process consists of stages or steps that guide the project form
its conception through the final analysis, recommendation, and ultimate
action. The research process provides a systematic, planned approach to
the research project and ensures that all aspects of the research project are
consistence with each other. It is especially important that the research
design and implementation be consistent with the research purpose and
objectives. Otherwise, results will not help the client.
Unit 1:
An overview of marketing research
Meaning, Definition, and terminologies
The function of marketing research
Information and decision making
Marketing information systems
Marketing decision support systems
Types of research, areas of research etc
Unit 2:
The research process and design
Steps in research design process
Potential errors affecting the research design
Unit 3:
Sources of research data
Primary data and its types
Secondary data and its types
Commercial surveys, audits and panels
Survey research
Experimentation and experiment environment
Unit 4:
Measurement techniques in marketing research
The concept of measurement
Scales of measurement
Components of measurement
Measurement accuracy
Measurement development
Questionnaire design
Direct response attitude scales
Derived attitude scales
Observation and psychological measures
Unit 5:
Sampling and data analysis
The sampling process
Sample size determination
Unit 6:
Hypothesis and its test
Unit 7:
Issues and techniques of quantitative data analysis
Cross tabulation
Multiple Regression Analysis
Perceptual mapping
Conjoint analysis
Unit 8:
Writing a research report
Preparing the research report and approaches to research report
Preparing oral presentation
Reading research reports
Unit 9:
Ethical issues in marketing research
Corporate Espionage
Unit 10:
Contemporary issues in marketing research
Role of marketing research in industry
Ethical issues- code of conduct, data-protection act
Role of agency and client
Role of in-house researcher
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Unit 1 An Overview of Marketing Research
Structure
1. 1 Introduction: Meaning, Definition, and Terminologies
Objectives
1. 2 The Function of Marketing Research
1.2.1 Situation Analysis
1.2.2 Strategy Development
1.2.3 Market Program Development
1.2.4 Implementation
1. 3 Information and Decision Making
1. 4 Marketing Information Systems
1. 5 Marketing Decision Support Systems
1. 6 Types of research, areas of research
Self Assessment Questions
1. 7 Summary
1. 8 Terminal Question
1. 9 Answers to SAQs and TQs
1.1 Introduction: Meaning Definition and Terminologies
Marketing is the process of planning and executing the conception, pricing,
promotion, and distribution of ideas, goods, and services to create
exchanges that satisfy individual and organizational objectives. The
marketing concept requires that customer satisfaction rather than profit
maximization, be the goal of an organisation. In other words, the
organisation should be consumer oriented and should try to understand
consumer’s requirements and satisfy them quickly and efficiently, in ways
that are beneficial to both the consumer and the organisation. This means
that any research organisation should try to obtain information on consumer
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needs and gather marketing intelligence to help satisfy these needs
efficiently.
Marketing research is a critical part of such a marketing intelligence system;
it helps to improve management decision making by providing relevant,
accurate, and timely (RAT) information. Every decision poses unique needs
for information, and relevant strategies can be developed based on the
information gathered through marketing research.
Objectives:
After studying this unit, the student should be able to:
understand the Function of Marketing Research
discuss the information and Decision Making
explain the Situation Analysis
understand Strategy Development
explain Market Program Development
discuss Marketing Information Systems
explain Marketing Decision Support Systems
discuss types of research, areas of research
Definition of Marketing Research
American Marketing Association officially defines marketing research as
follows: Marketing research is the function that links the consumer,
customer, and public to the marketer through information - information used
to identify and define marketing opportunities and problems; generate,
refine, and evaluate marketing actions; monitor marketing performance; and
improve understanding of marketing as a process. Marketing research
specifies the information required to address these issues, designs the
method for collecting information, manages and implements the data
collection process, analyses, and communicates the findings and their
implications.
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This definition highlights the role of marketing research as an id to decision
making. An important feature is the inclusion of the specification ad
interpretation of needed information. Too often, marketing research is
considered narrowly as gathering and analyzing of data for someone to use.
Firms can achieve and sustain a competitive advantage through the creative
use of marketing information. Hence, marketing research is defined as
information input to decisions, not simply the evaluation of decisions that
have been made. Market research alone, however, does not guarantee
success; the intelligent use of market research is likely for business
achievement. A competitive edge is more the result of how information is
used than of who does or does not nave the information.
1.2 The Function of Marketing Research
Marketing decisions involve issues that range from fundamental shifts in the
positioning of a business or the decision to enter a new market to narrow
tactical questions of how best to stock a grocery shelf. The context for these
decisions is the market planning process, which proceeds sequentially
through four stages:
Situation analysis
Strategy development
Marketing program development
Implementation
This is a never-ending process, so the evaluation of past strategic decisions
serves as an input to the situation assessment.
1. 2.1 Situation Analysis
Effective marketing strategies are built on an in-depth understanding of the
market environment of the business, and the specific characteristics of the
market. The micro-environment includes political and regulatory trends,
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economic and social trends, and technological trends. Marketing
researchers tend to focus on those trends that affect the demand for
products and services.
1.2.2 Strategy Development
During the strategy development stage the management team of the
business decides on answers to three critical questions. Marketing research
provides significant help in finding the answers to these questions.
What business should we be in? Specifically, what products or services
should we offer? What technologies will we utilize? Which market segments
should we emphasize? What channels should we use to reach the market?
These questions are compelling in markets that are mature and saturated,
including not only most packaged goods but also household appliances,
automobiles, and services such as banking and air travel. Research
supports this search for niches with large-scale quantitative market studies
that describe buying behaviors, consumer beliefs and attitudes, and
exposure to communication media. Large samples are needed to delineate
the segments, indicate their size, and determine what the people in each
segment are seeking in a product.
What will we compete? The management team has to decide why the
business is better than the competition in serving the needs of the target
segment, and what has to be done to keep it in front. Competitive superiority
is revealed in the market as either differentiation along attributes that are
important to target customers, or the lowest delivered cost position.
Marketing research is essential for getting answers to three key questions
about differentiation: what are the attributes of the product or services that
create value for the customer? Which attributes are most important? How do
we compare to the competition? An understanding of competitive advantage
also requires detailed knowledge of the capabilities, strategies, and
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intentions of present and prospective competitors. Marketing research
contributes here in two ways: identifying the competitive set, and collecting
detailed information about each competitor.
What are the objectives for the business? An objective is a desired
performance result that can be quantified and monitored. There are usually
objectives of revenue growth, market share, and profitability. Increasingly,
firms are adopting objectives for service levels, and customer satisfaction.
Marketing research is needed to establish both the market share and the
level of customer satisfaction.
1.2.3 Marketing Program Development
Programs embrace specific tasks, such as developing a new product or
launching a new advertising campaign. An action program usually focuses
on a single objective in support of one element of the overall business
strategy. This is where the bulk of ongoing marketing research is directed.
Developing the marketing program – representative decisions that draw on
marketing research:
i) Segmentation decisions
Which segment should be the target?
What benefits are most important for each segment?
When geographic area should be entered?
ii) Product decisions
What product features should be included?
How should the price be positioned?
What type of package is preferred by the customers?
iii) Distribution decisions
What type of retailer should be used?
What should be the mark up policy?
Should a few outlets be employed or many?
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iv) Advertising and promotion decisions
What appeals should be used in the advertising?
In which vehicles should the advertising be placed?
What should the budget be?
What sales promotion should be used, and when should it be
scheduled?
v) Personal selling decisions
What customer types have the most potential?
How many sales people are needed?
vi) Price decisions
What price level should be changed?
What sales should e offered during the year?
What response should be made to a competitor’s price?
vii) Branding decisions
What should be the name, symbol, logo and slogan that will be
associated with the product?
What is the position that the brand should adopt vis-à-vis the
competition?
How can brand loyalty be increased?
viii)Customer satisfaction decisions
How should customer satisfaction be measured?
How often should it be measured?
How should customer complaints be handled?
1.2.4 Implementation
The beginning of the implementation phase is signalled by a decision to
proceed with a new program or strategy and by the related commitments to
objectives, budgets, and timetables. At this point, the focus of marketing
research shifts to such questions as:
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i) Did the elements of the marketing program achieve their objectives?
How id sales compare with objectives?
In what areas were sales disappointing? Why?
Were the advertising objectives met?
Did the product achieve its distribution objectives?
Are any supermarkets discounting the product?
ii) Should the marketing program be continued, revised, or expanded?
Are customers satisfied with the product?
Should the product be changed? More features added?
Should the advertising budget be changed?
Is the price appropriate?
For research to be effective at this stage, it is important that specific
measurable objectives be set for all elements of the marketing program.
There should be sales goals by geographic area; distribution goals, perhaps
in terms of the number of stores carrying the product; and advertising goals,
such as achieving certain levels of awareness. The role of marketing
research is to provide measures against these objectives and to provide
more focused studies to determine why results are below or above
expectations.
1.3 Information and Decision Making
The decision whether to conduct marketing research depends on the type
and nature of the information sought. If the information required for decision
making already exists within the organisation in the form of results of a study
conducted for a different problem or in the form of managerial experience
and talents, marketing research is not called for. Under these
circumstances, further research would be redundant and a waste of money.
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1.4 Marketing Information system
An information system is a continuing and interacting structure of people,
equipment and procedures designed to gather, sort, analyze, evaluate and
distribute pertinent, timely and accurate information to decision makers.
While marketing research is concerned mainly with the actual content of the
information and how it is to be generated, the information system is
concerned with managing the flow of data from many different projects and
secondary sources to the managers who will use it. The required databases
organize and store the information and the decision support systems
(DSS) to retrieve the data, transform it into usable information and
disseminate it to users.
Information systems contain three types of information. The first is recurring
day-to-day information, for example, the market and accounting data that
flow into the organisation as a result of market analysis research ad
accounting activities. Automobiles firms use government sources for
monthly data on new cars sales by brand and geographic area. In addition,
surveys are conducted yearly to determine the ages and types of
automobiles currently being driven, the lifestyles of the drivers, their media
habits, and their intentions to replace their cars. The accounting department
submits sales and inventory data for each of its dealers on a continuing
basis to update and supplement the information system.
A second type of information is intelligence relevant to the future strategy of
the business. Automobile firms collect reports about new sources of fuel to
power automobiles. This information might come from scientific meetings,
trade organizations, or perhaps form government reports. It also includes
information from salespersons or dealers about new product tests being
conducted by competitive firms. Intelligence is difficult to develop, because it
usually involves diverse and changing sets of topic and information sources
and is rarely collected systematically.
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A third input to the information system is research studies that are not of a
recurring nature. The potential usefulness of a marketing research study can
be multiplied manifold if the information is accessible instead of filed and
forgotten.
1.5 Marketing Decision Support Systems
Databases have no value if the insights they contain cannot be retrieved. A
decision support system not only allows the manager to interact directly with
the database to retrieve what is wanted, it also provides a modelling function
to help make sense of what has been retrieved.
A common example of a DSS in action is that used by many industrial sales
people – especially those selling products that require significant
customization. The salesperson frequently will be asked whether or not the
price and delivery time of a unique product configuration will meet or exceed
a competitor’s promises. Without leaving the customer’s office, the
salesperson can plug a laptop computer into a phone jack and begin
communicating with a database stored in the company’s main computer
memory. The salesperson types in the product configuration and desired
delivery data, and these requirements are compared to the costs, inventory,
and assembly time contained in the data bank. In a matter of minutes, the
sales person can propose a price and delivery date – and perhaps close the
sales.
Each firm has to develop or adapt a model to support its own decision
problems.
A sales force turnover model, which revealed that the most significant
variable influencing the turnover rate was the level of the appointment fee
that representatives pay for initial materials.
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An order model is that which explains the components of the average order
and isolates the actionable variables such as the size and timing of the
catalogue and the gift incentives.
A procurement model is that which helps determine how much of a new
product to buy, when to purchase it and the risks involved.
1.6 Types of Research, Areas of Research
In practice, marketing research department’s goal can be grouped into three
major categories: programmatic, selective or evaluative.
Programmatic research is performed to develop marketing options through
market segmentation, market opportunity analysis, or consumer attitude and
product usage studies.
Selective research is done to test different decision alternatives such as
new product concept testing, advertising copy testing, pre-test marketing,
and test marketing.
Evaluative research is carried out to evaluate performance of programs,
including tracking advertising recall, corporate and brand image studies, and
measuring customer satisfaction with the quality of the product and service.
Self Assessment Questions I
State whether the following statements are true or false:
1. An information system is a continuing and interacting structure of
people, equipment and procedures designed to gather, sort, analyze,
evaluate and distribute pertinent, timely and accurate information to
decision makers.
2. A decision support system allows the manager to interact directly with
the database to retrieve what is wanted, and provides a modelling
function to help make sense of what has been retrieved.
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3. Marketing research can be grouped into three major categories:
programmatic, selective or evaluative
1.7 Summary
Marketing research is the function that links the consumer, customer, and
public to the marketer through information- information used to identify and
define marketing opportunities and problems; generate refine, and evaluate
marketing actions; monitor marketing performance; and improve
understanding of marketing as a process. Marketing research specifies the
information required to address these issues, designs the method for
collecting information, manages and implements the data collection process,
analyses, and communicates the findings and their implications. The market
planning process, which proceeds sequentially through four stages:
Situation analysis
Strategy development
Marketing program development
Implementation
An information system is a continuing and interacting structure of people,
equipment and procedures designed to gather, sort, analyze, evaluate and
distribute pertinent, timely and accurate information to decision makers. A
decision support system allows the manager to interact directly with the
database to retrieve what is wanted, and provides a modeling function to
help make sense of what has been retrieved. Marketing research can be
grouped into three major categories: programmatic, selective or evaluative
1.8 Terminal Questions
1. Define market research.
2. What are decision support systems?
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3. What are marketing program development?
4. What is strategy development?
5. What is situation analysis?
6. What are the marketing information systems in market research?
7. Explain the functions of marketing research.
8. What are the major questions to be addressed in conducting a marketing
research?
1.9 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
TQs
1. Section 1.2.
2. Section 1.6
3. Section 1.3.3
4. Section 1.3.2
5. Section 1.3.1
6. Section 1.5
7. Section 1.3
8. Section 1.3.3
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Unit 10 Contemporary Issues in
Marketing Research
Structure
10.1 Introduction: Role of Marketing Research in Industry
Objectives
10.2 Assessing Competitive Advantage
10.3 Market Based Assessment
10.3.1 Market Share
10.3.2 Recall Share
10.3.3 Advertisement Share
10.3.4 R&D Share
10.4 Process Based Assessment
10.4.1 Market Skills Audit
10.4.2 Comparison Relative to Cost
10.4.3 Comparison of Winners vs. Losing Competitors
10.4.4 Brand Equity
10.5 Customer Satisfaction
10.6 Total Quality Management
10.7 Identifying High Leverage Phenomena
10.8 Ethical Issues- Code of Conduct, Data-Protection Act
10.9 Role of Agency and Client
10.10 Role of In-House Researcher
Self Assessment Questions
10.11 Summary
10.12 Terminal Questions
10.13 Answers to SAQs and TQs
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10.1 Introduction: Role of Marketing Research in Industry
To survive in twentieth century, firms must not only provide goods and
services to the customer efficiency but should also possess sustainable
competitive advantage. Hence, there has been a shift of focus in marketing,
from delivering goods and services to consumers (satisfying their needs) to
achieve a competitive advantage. Companies are embracing new tools,
techniques and strategies in order to remain competitive
A strategy that companies have begun to adopt is total quality management
(TQM). To decide on and implement this strategy, mangers require
dramatically different information than they need for making marketing-mix
decisions. Hence, marketing research has to rise to the challenge and
provide managers with requisite information. Moreover, tremendous
advances in the field of statistics and computational capabilities have led
market researchers to adopt more and more sophisticated techniques.
Objectives:
After studying this unit, the students should be able to understand various
researches related to:
Assessing competitive advantage
Market based assessment
Market share
Recall share
Advertisement share
R&D share
Process based assessment
Market skills audit
Comparison relative to cost
Comparison of winners vs. losing competitors
Brand equity
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Customer satisfaction
Total Quality Management
Identifying high leverage phenomena
Ethical issues- code of conduct, data-protection act
Role of agency and client
Role of in-house researcher
10.2 Assessing Competitive Advantage
Assessing competitive advantage can be done in different ways. The
methods can be broadly classified as market based assessment and
process based assessment. Market based assessment is direct comparison
with a few target competitors; whereas, process based assessment is
comparison of the methods employed by the competitors in achieving their
distinctive advantage.
10.3 Market-based Assessment
10.3.1 Market Share
Market share is measured as a percentage of total industry sales over a
specified period. Clearly, there are problems in assessing competitive
advantage using market share. A company’s market share can change
dramatically depending on whether the market is defined as global, a
particular export market, domestic market, regional market ,a city, a
segment of users, or is based on product usage.
The change in market share over time is a vital indicator of competitive
dynamics, particular during the growth stage of a product or market. It
indicates whether the firm is ahead, abreast, or behind the market’s total
growth rate.
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10.3.2 Recall Share
Recall share is the percentage of customers who name the brand when they
are asked to name the first brand that comes to mind when they consider
buying a particular type of product. This indicates the consumer’s top-of-
mind brand awareness and preferences and gives a measure of advantage
to that brand over others in the market.
10.3.3 Advertising Share
Advertising share is the percentage of media space or time a brand has of
the total media share for that industry, often measured simply as dollars
spent on advertising. This is likely to lead to a change in recall share.
Advertising share is another measure of the current competition that a firm
faces
10.3.4 R&D Share
R&D share is a company’s research and development expenditure as a
percentage of total industry R&D expenditures. This is a long-term predictor
of new-product development, improvements in quality, cost reductions, and
hence market share. It is very important measure of future competitiveness
in many high-technology markets. All of these shares can be obtained from
either survey data or secondary data.
10.4 Process-based Assessment
10.4.1 Marketing Skills Audit
Skills are the most distinctive encapsulation of the organization’s way of
doing business. One vehicle for assessing skills is the marketing audit. This
is a comprehensive, systematic, independent, and periodic examination of a
business unit’s marketing environment, objectives, strategies and activities.
The audit should be based on customer orientation or focus on customer
satisfaction as its overriding theme. The audit is simply a marketing
research project whose objective is to critically evaluate the way the firm
performs in its environment.
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10.4.2 Comparison of relative to Cost
Another measure of advantage is a comparison of the firm’s costs versus
those of competitors. The company gains a cost advantage when its
cumulative costs are lower than its competitor’s. Competitors’ costs can be
estimated from public data or interviews with suppliers and distributors.
Secondary data can be used to obtain such a data.
10.4.3 Comparison of Winning vs. Losing Competitors
Key success factors can be inferred by analyzing differences in performance
among competitors. For this approach to yield useful insights, three difficult
questions must be answered. First, which competitors should be included in
the comparison set? Second, which criteria should be used to distinguish
the winners form the losers (e.g. profitability, growth, market share, creation
of markets)? Third, what are the reasons for the differences in performance?
10.4.4 Brand Equity
Brand equity is defined as a set of assets and liabilities linked to a brand
that add to or subtract form the value of a product or service to a company
and/or its customers. The assets or liabilities that underlie brand equity must
be linked to the name and symbol of the brand. They can be grouped into
five categories:
Brand loyalty
Name awareness
Perceived quality
Brand associations in addition to perceived quality
Other proprietary brand assets: patents, trademarks, channel
relationships etc.
10.5 Customer Satisfaction
In recent years, American business has become increasingly committed to
the idea of customer satisfaction and product serve quality. The
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measurement of customer satisfaction and its link to product/service
attributes is the vehicle for developing a market driven approach. Customer
satisfaction research has been around for a long time, but it has become a
fixture at most large corporation only in recent years.
10.6 Total Quality Management
Recent years have witnessed a renewed emphasis on delivering superior
quality products and services to customers. With foreign competition
steadily eating away the profitability and the market shares of Indian
companies, more and more of them are adopting total quality management
(TQM) to become more competitive. TQM is a process of managing
complex changes in the organisation with the aim of improving quality.
10.7 Identifying High-Leverage Phenomena
Ideally, these are causal relationships that describe how controllable
variables such as plant scale, production-run length, and sales costs per
unit.
10.8 Ethical Issues- Code of Conduct, Data-Protection Act
The Code of Ethics of Marketing Research Association
a. Ethics refers to moral principles or values that generally govern the
conduct of an individual or group. Researchers have responsibilities to
their profession, clients, and respondents and must adhere to high
ethical standards to ensure that both the function and information are
not brought into disrepute. The marketing Research association, Inc
(Chicago, Illinois) has instituted a code of ethics of professional Ethics
and Practices for marketing ethical decisions.
b. The code of Professional Ethics and Practices
c. To maintain high standard of competence and integrity in marketing and
survey research.
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d. To maintain the highest level of business and professional conduct and
to comply with Federal State and local laws, regulations and ordinances
applicable to my business practice and those of my company.
e. To exercise all reasonable care to observe the best standards of
objectivity and accuracy in the development, collection, processing and
reporting of marketing and survey research information.
f. To thoroughly instruct and supervise all persons for whose work I am
responsible in accordance with study specifications and general
research techniques.
g. To observe the rights of ownership of all materials received from and /or
developed for clients, and to keep in confidence all research techniques,
data and other information considered confidential by their owners.
h. To make available to clients such details on the research methods and
techniques of an assignment as may be reasonably required for proper
interpretation of the data, providing this reporting does not violate the
confidence of respondents of clients.
i. To promote the trust of the public for marketing and survey research
activities and to avoid any procedure which, misrepresents the activities
of a respondents, the rewards of cooperation or the uses of data.
j. To refrain from referring to membership in this organisation as proof of
competence, since the organisation does not so certify any person or
organisation.
k. To encourage the observance of principles of this code among all
people engaged in marketing and survey research.
10.9 Role of Agency and Client
A respondent who participate in a marketing research project has the ethical
obligation to provide the supplier, and hence the client, with honest and
truthful answers. The respondent could refrain from answering a sensitive
question, but falsifying the answer is ethically improper.
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Any respondent who participates in a research project has the following
rights:
The right to privacy
The right to safety
The right to know the true purpose of the research
The right to the research results
The right to decide which questions to answer
10.10 Role of In-House Researcher
The marketing manager’s essential task is to develop a marketing strategy
that involves combining the marketing mix elements in such a way that they
complement each other and positively influence customer’s value
perceptions and behaviours. This task should be much simpler if all
elements that affect customer’s perceptions of value were under the
manager’s control and if customer reaction to any contemplated change
could be predicted with certainty. However, a number of factors affecting the
success of the marketing effort, including economic, political and legal,
social, natural, technological and competitive environments are beyond the
marketing manager’s control and the behaviour of individual customers is
largely unpredictable.
Self Assessment Questions
State whether the following statements are true or false:
1. Market share is measured as a percentage of total industry sales over a
specified time period.
2. Recall share is the percentage of customers who name the brand when
they are asked to name the first brand that comes to mind when they
consider buying a particular type of product.
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3. Advertising share is the percentage of media space or time a brand has
of the total media share for that industry, often measured simply as
dollars spent on advertising.
4. R&D share is a company’s research and development expenditure as a
percentage of total industry R&D expenditures
10.11 Summary
To survive in twentieth century, firms must not only provide goods and
services to the customer efficiency but should also possess sustainable
competitive advantage. Companies are embracing new tools, techniques
and strategies in order to remain competitive. A strategy that companies
have begun to adopt is total quality management (TQM). Assessing
competitive advantage can be done in different ways. The methods can be
broadly classified as market based assessment and process based
assessment. Market based assessment is direct comparison with a few
target competitors; whereas, process based assessment is comparison of
the methods employed by the competitors in achieving their distinctive
advantage. Market share is measured as a percentage of total industry
sales over a specified time period. The change in market share over time is
a vital indicator of competitive dynamics, particular during the growth stage
of a product or market. It indicates whether the firm is ahead, abreast, or
behind the market’s total growth rate. Recall share is the percentage of
customers who name the brand when they are asked to name the first brand
that comes to mind when they consider buying a particular type of product.
Advertising share is the percentage of media space or time a rand has of
the total media share for that industry, often measured simply as dollars
spent on advertising. R&D share is a company’s research and development
expenditure as a percentage of total industry R&D expenditures. Skills are
the most distinctive encapsulation of the organization’s way of doing
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business. One vehicle for assessing skills is the marketing audit. Another
measure of advantage is a comparison of the firm’s costs versus those of
competitors. Key success factors can be inferred by analyzing differences in
performance among competitors. Brand equity is defined as a set of assets
and liabilities linked to a brand that add to or subtract form the value of a
product or service to a company and/or its customers. They can be grouped
into five categories:
Brand loyalty
Name awareness
Perceived quality
Brand associations in addition to perceived quality
Other proprietary brand assets: patents, trademarks, channel relationships
etc. In recent years, American business has become increasingly committed
to the idea of customer satisfaction and product serve quality. Recent years
have witnessed a renewed emphasis on delivering superior quality products
and services to customers. With foreign competition steadily eating away
the profitability and the market shares of Indian companies, more and more
of them are adopting total quality management (TQM) to become more
competitive.
Ethics refers to moral principles or values that generally govern the conduct
of an individual or group. Researchers have responsibilities to their
profession, clients, and respondents and must adhere to high ethical
standards to ensure that both the function and information are not brought
into disrepute. A respondent who participate in a marketing research project
has the ethical obligation to provide the supplier, and hence the client, with
honest and truthful answers. The marketing manager’s essential task if to
develop a marketing strategy that involves combining the marketing mix
elements in such a way that they complement each other and positively
influence customer’s value perceptions and behaviours.
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10.12 Terminal Questions
1. Explain the Role of Marketing Research in Industry
2. How Competitive Advantage is assessed?
3. What is Market-based Assessment research?
4. How do we find out Brand Equity?
5. What is Customer Satisfaction?
6. What is Total Quality Management?
7. Explain The Code of Ethics of Marketing Research Association
8. What is the role of Agency and Client with respect to marketing research
ethics?
9. What is the role of in-house researcher?
10.13 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
4. True
TQs
1. Section 10. 1
2. Section 10.3
3. Section 10.3
4. Section 10.4.3
5. Section 10. 5.
6. Section 10.6
7. Section 10.8.
8. Section 10.9.
9. Section 10.10
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References:
1. Gilbert A. Churchill, Dawn Iacobucci, Marketing Research –
Methodological Foundations, Thomson, United States, 2004.
2. John R. Sparks and Shelly D. Hunt, Marketing Researcher Ethical
Sensitivity; Conceptualisation, Measurement and Exploratory Investigation,
Journal of Marketing, pp. 92-109.
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Unit 2 Research Process and Design
Structure
2.1 Introduction: Nature of Research Process
Objectives
2.2 Steps in research design process
2.2.1 Step 1. Research Purpose
2.2.2 Step 2. Research Objectives
2.2.3 Step 3.Estimating the value of information
2.3 Research Design
2.3.1 Needs of research Design
2.3.2 Characteristics of good research design
2.3.3 Important concepts relating to research design
2.3.4 Types of research design
2.3.5 Principles of research design
2.3.6 Important experimental designs
2.3.7 Formal experimental design
2.4 Potential errors affecting the research design
2.4.1 Sampling error
2.4.2 Non-sampling error
2.4.3 Design error
2.4.4 Selection error
2.4.5 Population specific error
2.4.6 Sampling frame error
2.4.7 Surrogate information error
2.4.8 Measurement error
2.4.9 Experimental error
2.4.10 Data analysis error
2.4.11 Administration error
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2.4.12 Questioning error
2.4.13 Recording error
2.4.14 Interference error
2.4.15 Response error
2.4.16 Non-response error
Self assessment Questions
2.5 Summary
2.6 Terminal Question
2.7 Answers to SAQs and TQs
2.1 Introduction: Nature of Research Process
The research process consists of stages or steps that guide the project form
its conception through the final analysis, recommendation, and ultimate
action. The research process provides a systematic, planned approach to
the research project and ensures that all aspects of the research project are
consistent with each other. It is especially important that the research design
and implementation be consistent with the research purpose and objectives.
Otherwise, results will not help the client.
Research studies evolve through a series of steps, each representing the
answer to a key question.
1. Why should we do research? This establishes the research purpose as
seen by the management team that will be using the results. This step
requires the understanding the decisions to be made and the problems
or opportunities to be diagnosed.
2. What research should be done? Here the management purpose is
translated into objectives that tell the researchers exactly what questions
need to be answered by the research study or project.
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3. Is it worth doing research? The decision has to be made here about
whether the value of the information that is likely to be obtained is going
to be greater than the cost of collecting it.
4. How should the research be designed to achieve the research
objectives? Design issues include the choice of research approach –
reliance on secondary data versus conducting a survey or experiment-
and the specifics of how to collect the data.
5. What will we do with the research? Once the data have been collected,
how will it be analysed, interpreted, and used to make recommendations
for action.
Objectives:
After studying this unit, the student should be able to:
explain the Steps in research design process
discuss the Research Design
understand the needs of research Design
explain the characteristics of good research design
discuss the important concepts relating to research design
explain the types of research design
discuss the principles of research design
understand the important experimental designs
understand the formal experimental design
identify the potential errors affecting the research design
2.2 Steps in Marketing Research Design Process
2. 2.1 Step 1- Research Purpose
It is in the best interest of both the researcher and managers paying for the
research to be sure that the research purpose is fully understood. One of
the hallmarks of a competent researcher is the ability to get the heart of the
management problem.
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The research purpose comprises a shared understanding between the
manager and the researcher of:
1. Problems or opportunities to be studied
Which problems or opportunities are anticipated?
What is the scope of the problems and the possible reasons?
2. Decision alternatives to be evaluated
What are the alternatives being studied?
What are the criteria for choosing among the alternatives?
What is the timing or importance of the decision?
3. Users of the research results
Who are the decision makers?
Are there any covet purpose?
Problem or Opportunity Analysis: In analyzing problems or opportunities
to be studied, constant contact with customers to monitor trends is very
important. Research is often motivated by problem or opportunity. The fact
that people are consuming fewer sweets might be a problem or a potential
opportunity for a candy company. Increased leisure time might be viewed as
an opportunity by a recreation oriented organisation. In such cases the
research purpose should specify the problem or opportunity to be explored.
The manager should make sure that real problem is being addressed.
Decision Alternatives: For research to be effective, it must be associated
with a decision. Marketing research is committed to the principle of utility. In
general, if the research is not going to have an effect on decisions, it is an
exercise in futility. The researcher should be always sensitive to the
possibility that either there are no decision alternatives – and therefore no
decision – or that the research findings will not affect the decision, usually
because of resource or organizational constraints. In such circumstances,
the research will have no practical value and probably should not be
conducted.
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Criteria for Choosing among Alternatives: It is essential for the
researcher to know how the decision maker will choose among the available
alternatives. Suppose the product manager is considering three possible
packages redesigning for a health-care product with declining sales. The
following criteria to choose the best of the three alternative packages:
1. Long run sales
2. Trial purchases by users f competing brands
3. Amount of shelf space assigned to the brand
4. Differentiation from competitive packages
5. Brand-name recognition.
Research Users: When the research results will be used to guide internal
problem solving, the researcher must know the objectives and expectations
of the actual decision makers. The bigger the problem, the more difficult this
becomes, for not only are a large number of people likely to be involved, but
the contact person may simply be acting as a liaison whose interpretation of
the problem and the need for research may be second-hand. The major
benefit from making an effort to reach all the decision makers is that the
research purpose is likely to be specified more adequately.
2.2.2 Step 2 – Research Objective
The research objective is a statement, in as precise terminology as possible,
of what information is needed. The research objective should be framed so
that obtaining the information will ensure that the research purpose is
satisfied.
Research objectives have three components. The first is the research
question. It specifies the information the decision maker needs. The second
and the third elements help the researcher make the research question as
specific and precise as possible. The second is the development of
hypotheses that are basically alternative answers to the research questions.
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The research determines which of these alternative answers is correct. It is
not always possible to develop hypothesis, but an effort should be made.
The third is the scope or boundaries of the research.
2.2.3 Step 3 – Estimating the Value of Information
Before a research approach can be selected, it is necessary to have an
estimate of the value of information – that is, the value of obtaining answers
to the research questions. Such an estimate will help determine how much,
if anything, should be spent on the research.
The value will depend on the importance of the decision as noted in the
research purpose, the uncertainty that surrounds it, and the influence of the
research information on the decision. If the decision is highly significant in
terms of the investment required or in terms of its effect on the long- run
success of the organisation, then information may have a high value.
However, uncertainty that is meaningful to the decision also must exist if the
information is to have value. If the outcomes are already known with
certainty, or if the decision will not be affected by the research information,
the information will have no value.
2.3 Research Design
A research design is a logical and systematic plan prepared for directing a
research study. It specifies the objectives of the study, the methodology and
techniques to be adopted for achieving the objectives. It constitutes the blue
print for the collection, measurement and analysis of data. It is the plan,
structure and strategy of investigation conceived so as to obtain answers to
research questions. The plan is the overall scheme or program of research.
A research design is the program that guides the investigator in the process
of collecting, analyzing and interpreting observations. It provides a
systematic plan of procedure for the researcher to follow.
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Selltiz, Jahoda and Destsch and Cook describe, “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 of with economy in
procedure.”
2.3.1 Needs of Research Design
1) In many a research inquiry, the researcher has no idea as to how
accurate the results of his study ought to be in order to be useful. Where
such is the case, the researcher has to determine how much inaccuracy
may be tolerated. In a quite few cases he may be in a position to know
how much inaccuracy his method of research will produce. In either
case he should design his research if he wants to assure himself of
useful results.
2) In many research projects, the time consumed in trying to ascertain what
the data mean after they have been collected is much greater than the
time taken to design a research which yields data whose meaning is
known as they are collected.
3) The idealized design is concerned with specifying the optimum research
procedure that could be followed where there are no practical
restrictions.
2.3.2 Characteristics of a Good Research Design
1) It is a series of guide posts to keep one going in the right direction.
2) It reduces wastage of time and cost.
3) It encourages coordination and effective organization.
4) It is a tentative plan which undergoes modifications, as
circumstances demand, when the study progresses, new aspects,
new conditions and new relationships come to light and insight into
the study deepens.
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5) It has to be geared to the availability of data and the cooperation of
the informants.
6) It has also to be kept within the manageable limits
2.3.3 Important Concepts relating to Research Design
It is important to be familiar with the important concepts relating to research
design. They are:
1. Dependent and Independent variables: A magnitude that varies is
known as a variable. The concept may assume different quantitative
values, like height, weight, income, etc. Qualitative variables are not
quantifiable in the strictest sense of objectivity. However, the
qualitative phenomena may also be quantified in terms of the presence
or absence of the attribute considered. Phenomena that assume
different values quantitatively even in decimal points are known as
‘continuous variables’. But, all variables need not be continuous.
Values that can be expressed only in integer values are called ‘non-
continuous variables’. In statistical term, they are also known as
‘discrete variable’. For example, age is a continuous variable; where
as the number of children is a non-continuous variable. When changes
in one variable depends upon the changes in one or more other
variables, it is known as a dependent or endogenous variable, and the
variables that cause the changes in the dependent variable are known
as the independent or explanatory or exogenous variables. For
example, if demand depends upon price, then demand is a dependent
variable, while price is the independent variable. And if, more variables
determine demand, like income and prices of substitute commodity,
then demand also depends upon them in addition to the own price.
Then, demand is a dependent variable which is determined by the
independent variables like own price, income and price of substitute.
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2. Extraneous variable: The independent variables which are not
directly related to the purpose of the study but affect the dependent
variable are known as extraneous variables. For instance, assume that
a researcher wants to test the hypothesis that there is relationship
between children’s school performance and their self-concepts, in
which case the latter is an independent variable and the former the
dependent variable. In this context, intelligence may also influence the
school performance. However, since it is not directly related to the
purpose of the study undertaken by the researcher, it would be known
as an extraneous variable. The influence caused by the extraneous
variable on the dependent variable is technically called as an
‘experimental error’. Therefore, a research study should always be
framed in such a manner that the dependent variable that completely
influence the change in the independent variable and any other
extraneous variable or variables.
3. Control: One of the most important features of a good research
design is to minimize the effect of extraneous variable. Technically, the
term control is used when a researcher designs the study in such a
manner that it minimizes the effects of extraneous independent
variables. The term control is used in experimental research to reflect
the restrain in experimental conditions.
4. Confounded relationship: The relationship between dependent and
independent variables is said to be confounded by an extraneous
variable, when the dependent variable is not free from its effects.
5. Research hypotheses: When a prediction or a hypothesized
relationship is tested by adopting scientific methods, it is known as
research hypothesis. The research hypothesis is a predictive
statement which relates a dependent variable and an independent
variable. Generally, a research hypothesis must consist of at least one
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dependent variable and one dependent variable. Whereas, the
relationships that are assumed but not be tested are predictive
statements that are not to be objectively verified are not classified as
research hypotheses.
6. Experimental and non-experimental hypothesis testing research:
when the objective of a research is to test a research hypothesis, it is
known as a hypothesis-testing research. Such research may be in the
nature of experimental design or non-experimental design. A research
in which the independent variable is manipulated is known as
‘experimental hypothesis-testing research’, where as a research in
which the independent variable is no manipulated is termed as ‘non-
experimental hypothesis-testing research’. E.g., assume that a
researcher wants to examine whether family income influences the
social attendance of a group of students, by calculating the coefficient
of correlation between the two variables. Such an example is known
as a non-experimental hypothesis-testing research, because the
independent variable family income is not manipulated. Again assume
that the researcher randomly selects 150 students from a group of
students who pay their school fees regularly and them classifies them
into two sub-groups by randomly including 75 n Group A, whose
parents have regular earning, and 75 in group B, whose parents do not
have regular earning. And that at the end of the study, the researcher
conducts a test on each group in order to examine the effects of
regular earnings of the parents on the school attendance of the
student. Such a study is an example of experimental hypothesis-
testing research, because in this particular study the independent
variable regular earnings of the parents have been manipulated.
7. Experimental and control groups: When a group is exposed to
usual conditions in an experimental hypothesis-testing research, it is
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known as ‘control group’. On the other hand, when the group is
exposed to certain new or special condition, it is known as an
‘experimental group’. In the afore-mentioned example, the Group A
can be called a control group and the Group B an experimental group.
If both the groups A and B are exposed to some special feature, then
both the groups may be called as ‘experimental groups’. A research
design may include only the experimental group or the both
experimental and control groups together.
8. Treatments: Treatments are referred to the different conditions to
which the experimental and control groups are subject to. In the
example considered, the two treatments are the parents with regular
earnings and those with no regular earnings. Likewise, if a research
study attempts to examine through an experiment the comparative
impacts of three different types of fertilizers on the yield of rice crop,
then the three types of fertilizers would be treated as the three
treatments.
9. Experiment: An experiment refers to the process of verifying the truth
of a statistical hypothesis relating to a given research problem. For
instance, experiment may be conducted to examine the yield of a
certain new variety of rice crop developed. Further, Experiments may
be categorized into two types namely, absolute experiment and
comparative experiment. If a researcher wishes to determine the
impact of a chemical fertilizer on the yield of a particular variety of rice
crop, then it is known as absolute experiment. Meanwhile, if the
researcher wishes to determine the impact of chemical fertilizer as
compared to the impact of bio-fertilizer, then the experiment is known
as a comparative experiment.
10. Experiment unit: Experimental units refer to the predetermined plots,
characteristics or the blocks, to which the different treatments are
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applied. It is worth mentioning here that such experimental units must
be selected with great caution.
2.3.4 Types of Research Designs
There are a number of crucial research choices and various writers advance
different classification schemes, some of which are:
1. Experimental, historical and inferential designs (American Marketing
Association).
2. Exploratory, descriptive and causal designs (Selltiz, Jahoda, Deutsch
and Cook).
3. Experimental, and expost facto (Kerlinger)
4. Historical method, and case and clinical studies (Goode and scates)
5. Sample surveys, field studies, experiments in field settings, and
laboratory experiments (Festinger and Katz)
6. Exploratory, descriptive and experimental studies (Body and Westfall)
7. Exploratory, descriptive and casual (Green and Tull)
8. Experimental, ‘quasi-experimental designs’ (Nachmias and Nachmias)
9. True experimental, quasi-experimental and non-experimental designs
(Smith).
10. Experimental, pre-experimental, quasi-experimental designs and
Survey Research (Kidder and Judd).
These different categorizations exist, because ‘research design’ is a
complex concept. In fact, there are different perspectives from which any
given study can be viewed. They are:
1) The degree of formulation of the problem (the study may be
exploratory or formalized)
2) The topical scope-breadth and depth-of the study (a case or a
statistical study)
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3) The research environment: field setting or laboratory (survey,
laboratory experiment)
4) The time dimension (one-time or longitudinal)
5) The mode of data collection (observational or survey)
6) The manipulation of the variables under study (experimental or expost
facto)
7) The nature of the relationship among variables (descriptive or causal)
1. Research design in case of exploratory research studies:
Exploratory research studies are also termed as formulative research
studies. The main purpose of such studies is that of formulating a
problem for more precise investigation or of developing the working
hypotheses from an operational point of view. The major emphasis in
such studies is on the discovery of ideas and insights. As such, the
research design appropriate for such studies must be flexible enough to
provide opportunity for considering different aspects of a problem under
study. Inbuilt flexibility in research design is needed because the
research problem, broadly defined initially, is transformed into one with
more precise meaning in exploratory studies, which in fact may
necessitate changes in the research procedure for gathering relevant
data. Generally, the following three methods in the context of research
design for such studies are talked about:
a. The survey of concerning literature happens to be the most
simple and fruitful method of formulating precisely the research
problem or developing hypotheses. Hypotheses stated by earlier
workers may be reviewed and their usefulness be evaluated as a
basis for further research. It may also be considered whether the
already stated hypotheses suggest new hypotheses. In this way, the
researcher should review and build upon the work already done by
others, but in cases where hypotheses have not yet been
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formulated, his task is to review the available material for deriving
the relevant hypotheses from it.
b. Besides, the bibliographical survey of studies, already made in
one’s area of interest may as well as made by the researcher for
precisely formulating the problem. He should also make an attempt
to apply concepts and theories developed in different research
contexts to the area in which he himself is working. Sometimes, the
works of creative writers also provide a fertile ground for hypotheses
formulation as such may be looked into by the researcher.
c. Experience survey means the survey of people who have had
practical experience with the problem to be studied. The object of
such a survey is to obtain insight into the relationships between
variables and new ideas relating to the research problem. For such a
survey, people who are competent and can contribute new ideas
may be carefully selected as respondents to ensure a representation
of different types of experience. The respondents so selected may
then be interviewed by the investigator. The researcher must
prepare an interview schedule for the systematic questioning of
informants. But, the interview must ensure flexibility in the sense that
the respondents should be allowed to raise issues and questions
which the investigator has not previously considered. Generally, the
experience collecting interview is likely to be long and may last for
few hours. Hence, it is often considered desirable to send a copy of
the questions to be discussed to the respondents well in advance.
This will also give an opportunity to the respondents for doing some
advance thinking over the various issues involved, so that, at the
time of interview, they may be able to contribute effectively. Thus, an
experience survey may enable the researcher to define the problem
more concisely and help in the formulation of the research
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hypotheses. This survey may as well provide information about the
practical possibilities for doing different types of research.
d. Analysis of ‘insight-stimulating’ examples is also a fruitful method
for suggesting hypotheses for research. It is particularly suitable in
areas where there is little experience to serve as a guide. This
method consists of the intensive study of selected instance of the
phenomenon in which one is interested. For this purpose the existing
records may be examined, the unstructured interviewing may take
place, or some other approach may be adopted. Attitude of the
investigator, the intensity of the study and the ability of the
researcher to draw together diverse information into a unified
interpretation are the main features which make this method an
appropriate procedure for evoking insights.
Now, what sorts of examples are to be selected and studied? There
is no clear cut answer to it. Experience indicates that for particular
problems certain types of instances are more appropriate than
others. One can mention few examples of ‘insight-stimulating’ cases
such as the reactions of strangers, the reactions of marginal
individuals, the study of individuals who are in transition from one
stage to another, the reactions of individuals from different social
strata and the like. In general, cases that provide sharp contrasts or
have striking features are considered relatively more useful while
adopting this method of hypotheses formulation.
Thus, in an exploratory or formulative research study, which merely
leads to insights or hypotheses, whatever method or research design
outlined above is adopted, the only thing essential is that it must
continue to remain flexible so that many different facets of a problem
may be considered as and when they arise and come to the notice of
the researcher.
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2. Research design in case of descriptive and diagnostic research
studies: Descriptive research studies are those studies which are
concerned with describing the characteristics of a particular individual, or
of a group, where as diagnostic research studies determine the
frequency with which something occurs or its association with something
else. The studies concerning whether certain variables are associated
are the example of diagnostic research studies. As against this, studies
concerned with specific predictions, with narration of facts and
characteristics concerning individual, group of situation are all examples
of descriptive research studies. Most of the social research comes under
this category. From the point of view of the research design, the
descriptive as well as diagnostic studies share common requirements
and as such we may group together these two types of research studies.
In descriptive as well as in diagnostic studies, the researcher must be
able to define clearly, what he wants to measure and must find adequate
methods for measuring it along with a clear cut definition of population
he wants to study. Since the aim is to obtain complete and accurate
information in the said studies, the procedure to be used must be
carefully planned. The research design must make enough provision for
protection against bias and must maximize reliability. With due concern
for the economical completion of the research study. The design in such
studies must be rigid and not flexible and must focus attention on the
following:
Formulating the objective of the study
Designing the methods of data collection
Selecting the sample
Collecting the data
Processing and analyzing the data
Reporting the findings.
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In a descriptive/diagnostic study, the first step is to specify the objectives
with sufficient precision to ensure that the data collected are relevant. If
this is not done carefully, the study may not provide the desired
information. Then crops up the question of selecting the methods by
which the data are to be obtained. While designing data-collection
procedure, adequate safeguards against bias and unreliability must be
ensured. Which ever method is selected, questions must be well
examined and be made unambiguous; interviewers must be instructed
not to express their own opinion; observers must be trained so that they
uniformly record a given item of behaviour.
More often than not, sample has to be designed. Usually, one or more
forms of probability sampling or what is often described as random
sampling are used. To obtain data free from errors introduced by those
responsible for collecting them, it is necessary to supervise closely the
staff of field workers as they collect and record information. Checks may
be set up to ensure that the data collecting staffs perform their duty
honestly and without prejudice. The data collected must be processed
and analyzed. This includes steps like coding the interview replies,
observations, etc., tabulating the data; and performing several statistical
computations.
Last of all is the question of reporting the findings. This is the task of
communicating the findings to others and the researcher must do it in an
efficient manner.
3. Research design in case of hypothesis-testing research studies:
Hypotheses-testing research studies (generally known as experimental
studies) are those where the researcher tests the hypotheses of causal
relationships between variables. Such studies require procedures that
will not only reduce bias and increase reliability, but will permit drawing
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inferences about causality. Usually experiments meet this requirement.
Hence, when we talk of research design in such studies, we often mean
the design of experiments.
2.3.5 Principles of Experimental Designs
Professor Fisher has enumerated three principles of experimental designs:
1) The principle of replication: The experiment should be repeated. 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
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
collect the data 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. Consequently replication does not present any
difficulty, but computationally it does. 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) The principle of randomization: It provides protection, when we
conduct an experiment, against the effect of extraneous factors by
randomization. In other words, this principle indicates that we should
design or plan the experiment in such a way that the variations caused
by extraneous factors can all be combined under the general heading
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of “chance”. For instance, if we grow one variety of rice, say, in the first
half of the 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 random
sampling technique i.e., we may apply randomization principle and
protect ourselves against the effects of extraneous factors. As such,
through the application of the principle of randomization, we can have
a better estimate of the experimental error.
3) Principle of local control: It is another important principle of
experimental designs. Under it, the extraneous factors, the known
source of variability, is made to vary deliberately over as wide a range
as necessary and this 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, the extraneous factor 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. In general, blocks are the levels at which we hold an
extraneous factors fixed, so that we can measure its contribution to the
variability of the data by means of a two-way analysis of variance. In
brief, through the principle of local control we can eliminate the
variability due to extraneous factors from the experimental error.
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2.3.6 Important Experimental Designs:
Experimental design refers to the framework or structure of an experiment
and as such there are several experimental designs. We can classify
experimental designs into two broad categories, viz., informal experimental
designs and formal experimental designs. Informal experimental designs are
those designs that normally use a less sophisticated form of analysis based
on differences in magnitudes, whereas formal experimental designs offer
relatively more control and use precise statistical procedures for analysis.
Informal experimental designs:
1. Before and after without control design: In such a design, single test
group or area is selected and the dependent variable is measured
before the introduction of the treatment. The treatment is then
introduced and the dependent variable is measured again after the
treatment has been introduced. The effect of the treatment would be
equal to the level of the phenomenon after the treatment minus the level
of the phenomenon before the treatment.
2. After only with control design: In this design two groups or areas (test
and control area) are selected and the treatment is introduced into the
test area only. The dependent variable is then measured in both the
areas at the same time. Treatment impact is assessed by subtracting the
value of the dependent variable in the control area from its value in the
test area.
3. Before and after with control design: In this design two areas are
selected and the dependent variable is measured in both the area for an
identical time-period before the treatment. The treatment is then
introduced into the test area only, and the dependent variable is
measured in both for an identical time-period after the introduction of the
treatment. The treatment effect is determined by subtracting the change
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in the dependent variable in the control area from the change in the
dependent variable in test area.
2.3.7 Formal Experimental Designs
Completely randomized design (CR design): It involves only two
principles viz., the principle of replication and randomization. It is
generally used when experimental areas happen to be homogenous.
Technically, when all the variations due to uncontrolled extraneous
factors are included under the heading of chance variation, we refer to
the design of experiment as CR Design.
Randomized block design (RB design): It is an improvement over the CR
design. In the RB design the principle of local control can be applied
along with the other two principles.
Latin square design (LS design): It is used in agricultural research. The
treatments in a LS design are so allocated among the plots that no
treatment occurs more than once in any one row or column.
Factorial design: It is used in experiments where the effects of varying
more than one factor are to be determined. They are especially
important in several economic and social phenomena where usually a
large number of factors affect a particular problem.
2.4 Potential Errors affecting research design
The usefulness of a research project depends on the overall quality of the
research design and on the data collected and analysed based on the
design. Several potential sources of error can affect the quality of a research
process. The errors can influence the various stages of the research
process and result in inaccurate or useless research findings.
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2.4.1 Sampling Error
Sampling error is the difference between a measure obtained from a sample
representing the population and the true measure that can be obtained only
from the entire population. This error occurs because no sample is a perfect
representation of a given population, unless the sample size equals the
population.
2.4.2 Non-Sampling Error
Non-sampling error includes all other errors associated with a research
project. There may be several different reasons for these errors, which can
be broadly classified into four groups: i) Design errors ii) Administering
errors iii) Response errors and iv) Non-response errors.
2.4.3 Design Error
Design errors, also called researcher-induced errors, are mainly due to
flaws in the research design. There are several different types of design
errors.
2.4.4 Selection Errors
Selection error occurs when a sample obtained through a non-probability
sampling method is not representative of population. For example, if a mail
interviewer interested in shopping habits of the visitors to the mall avoids
interviewing people with children, he or she is inducing a selection error into
the research study.
2.4.5 Population Specific Errors
Population specific error occurs when an inappropriate population is chosen
to obtain data for the research study. For example, if the objective of the
study is to determine what brands of dog food people buy for their pets, and
research draws a sample from a population that consists predominantly of
cat owners, a population specification error is induced into the study.
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2.4.6 Sampling Frame Errors
A sampling frame is directory of population members from which a sample is
selected. A sampling frame error occurs when the sample is drawn from an
inaccurate sampling frame.
2.4.7 Surrogate Information Errors
Surrogate information error is the difference or variation between the
information required for a marketing research study and the information
being sought by the researcher.
2.4.8 Measurement Errors
Measurement error is the difference between the information sought by a
researcher for a study and the information generated by a particular
measurement procedure employed by the researcher. Measurement error
can occur at any state of the measurement process, from the development
of an instrument to the data analysis and interpretation stage.
2.4.9 Experimental Errors
An experiment is designed to determine the existence of any causal
relationship between two variables. Any error caused by the improper
design of the experiment induces an experimental error into the study.
2.4.10 Data Analysis Errors
Data analysis error can occur when data from the questionnaire are coded,
edited, analyzed or interpreted. For example, incorrect coding of data or a
wrong use of a statistical analysis procedure can induce a data analysis
error into the study.
2.4.11 Administering Errors
All errors that occur during administration of a survey instrument to the
respondents are classified as administering errors. They are caused by
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mistakes committed by the person administering the questionnaire. They
may be caused by various factors.
2.4.12 Questioning Errors
This error arises while addressing questions to the respondents. If the
interviewer does not word the question exactly as designed by the
researcher, a questioning error is induced.
2.4.13 Recording Error
This error arises from improperly recording the respondent’s answer. If the
interviewer misinterprets the response or hears it inaccurately, this induces
a recording error into the study.
2.4.14 Interference Error
This error occurs when an interviewer interferes with or fails to follow the
exact procedure while collecting data. For example, if the interviewer
fabricates the responses to a survey, it induces an inference error.
2.4.15 Response Errors
Response errors also called data errors which occur when the respondent –
intentionally or unintentionally – provides inaccurate answers to the survey
questions. This might be due top the respondent’s failing to comprehend the
questions or it may be due to fatigue, boredom, or misinterpretation of the
question.
2.4.16 Non-Response Errors
Non-response errors occur if (i) some members of a sample were not
contacted, and hence their responses were not included in the study; or ii)
some of the members contacted provide an incomplete or no response to
the survey instrument. The primary reason for this error occurring include
the unwillingness of respondents to participate in the study and the inability
of the interviewer to contact the respondents.
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Self Assessment Questions
State whether the following statements are true or false:
1. The research process consists of stages or steps that guide the project
form its conception through the final analysis, recommendation, and
ultimate action.
2. Research studies evolve through a series of steps, each representing
the answer to a key question.
3. Selection error occurs when a sample obtained through a non-
probability sampling method is not representative of population.
4. Population specific error occurs when an inappropriate population is
chosen from which to obtain data for the research study.
5. A sampling frame is directory of population members from which a
sample is selected.
2.5 Summary
The research process consists of stages or steps that guide the project from
its conception through the final analysis, recommendation, and ultimate
action. Research studies evolve through a series of steps, each
representing the answer to a key question. The research objective is a
statement, in as precise terminology as possible, of what information is
needed. The research objective should be framed so that, obtaining the
information will ensure that the research purpose is satisfied. Before a
research approach can be selected, it is necessary to have an estimate of
the value of information that is, the value of obtaining answers to the
research questions. Such an estimate will help determine how much, if
anything, should be spent on the research.
A research design is a logical and systematic plan prepared for directing a
research study.
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In many a research inquiry, the researcher has no idea as to how accurate
the results of his study ought to be in order to be useful. It is important to be
familiar with the important concepts relating to research design. There are
number of crucial research choices, various writers advance different
classification schemes.
Experimental design refers to the framework or structure of an experiment
and as such there are several experimental designs. The usefulness of a
research project depends on the overall quality of the research design and
on the data collected and analysed based on the design.
Sampling error is the difference between a measure obtained from a sample
representing the population and the true measure that can be obtained only
from the entire population. Non-sampling error includes all other errors
associated with a research project. There may be several deferent reasons
for these errors, which can be broadly classified into four groups: i) Design
errors ii) Administering errors iii) Response errors and iv) Non-response
errors.
2.6 Terminal Questions
1. Explain the nature of Research Process
2. What are the steps in Marketing Research Design Process?
3. What is meant by Research Design?
4. What is the need of Research Design?
5. What the potential errors affecting research design?
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2.7 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
4. True
5. True
TQs
1. Section 2.1
2. Section 2.2.
3. Section 2. 5
4. Section 2.2.2
5. Section 2.6
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Unit 3 Sources of Research Data
Structure
3.1 Introduction: Meaning of Data
Objectives
3.2 Types of Data
3.3 Importance of Data
3.4 Sources of Data
3.4.1 Primary Sources of Data
3.4.2 Secondary Sources of Data
3.4.3 Features of Secondary Sources
3.5 Uses of Secondary Data
3.6 Advantages of Secondary Data
3.7 Disadvantages of Secondary Data
3.8 Evaluation of Secondary Data
3.8.1 Data Pertinence
3.8.2 Data Quality
3.9 Commercial Surveys, Audits and Panels
3.9.1 Personal Interview
3.9.2 Advantages of Personal Interview
3.9.3 Limitations of Personal Interview
3.9.4 Characteristics of Personal Interview
3.9.5 Telephone Interviewing
3.9.6 Advantages of Telephone Interviews
3.9.7 Disadvantages of Telephone Interviews
3.9.8 Group Interview
3.9.9 Advantages of Group Interview
3.9.10 Disadvantages of Group Interview
3.9.11 Mail Survey
3.9.12 Procedures of Mail Survey
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3.9.13 Alternative Mode of Sending Questionnaire
3.9.14 Improving Response Rate in Mail Survey
3.9.15 Advantages of Mail Survey
3.9.16 Disadvantages of Mail Survey
3.9.17 Panel Method
3.9.18 Characteristics of Panel Method
3.9.19 Needs of Panel
3.9.20 Types of Panel
3.9.21 Advantages of Panel
3.9.22 Disadvantages of Panel Study
3.10 Survey Research
3.10.1 Steps Involved in Survey
3.11 Experimentation and Experiment Environment
3.11.1 Planning and Conducting Experiments
3.11.2 Advantages of Experiments
3.11.3 Disadvantages of Experiments
3.11.4 Laboratory Experiments
3.11.5 Field Experiments
3.11.6 Advantages of Field Experiments
3.11.7 Limitations of Field Experiments
Self Assessment Questions
3.12 Summary
3.13 Terminal Questions
3.14 Answers to SAQs and TQs
3.1 Introduction: Meaning of Data
The search for answers to research questions is called collection of data.
Data are facts, and other relevant materials, past and present, serving as
bases for study and analyses. Some examples of data are:
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The types of loans secured by borrowers (for a credit survey)
The items if raw materials required for a product line (Materials
management)
The quantity of each material required for a unit of output.
The sex, age, social class, religion, income level of respondents in a
consumer behaviour study.
The opinions of eligible couples on birth control devices (Family
Planning survey)
The capital expenditure proposals considered by a firm during a year
(Financial Management)
The marks obtained by students of a class in a test on a particulars
subject (performances of students)
The opinions of people on voting in a general election (Opinion poll)
The types of news read by newspaper readers (Readership survey)
The aspirations of management trainees (The emerging Managers in
Indian Enterprises)
The types and frequency of breakdowns occurred in particular brand of
scooter (post purchases Behaviour survey) and so on.
Objectives:
After studying this unit, the students should be able to:
understand the Types of data
discuss the importance of Data
explain the sources of data
explain Commercial surveys, audits and panels
explain Personal Interview, telephone interview, group interview etc
explain Advantages and limitations of the different types of interviews
discuss the Mail survey and procedure of mail survey
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explain Panel method, its characteristics, types, advantages and
disadvantages of panel method
explain Survey research, steps involved in survey
discuss experimentation and experiment environment, planning and
conducting experiments, advantages and disadvantages of experiments
discuss the Laboratory experiments, Field experiments, advantages and
disadvantages of the same
3.2 Types of Data
The data needed for a social science research may be broadly classified
into
a) Data pertaining to human beings,
b) Data relating to organization and
c) Data pertaining to territorial areas.
Personal data or data related to human beings consists of:
1. Demographic and socio-economic characteristics of individuals: Age,
sex, race, social class, religion, martial status, education, occupation
income, family size, location of the household life style etc.
2. Behavioural variables: Attitudes, opinions, awareness, knowledge,
practice, intentions, etc
3. Organizational data consist of data relating to an organizations origin,
ownership, objectives, resources, functions, performance and growth.
4. Territorial data are related to geophysical characteristics, resource
endowment, population, occupational pattern infrastructure degree of
development, etc. of spatial divisions like villages, cities, talluks,
districts, state and the nation.
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3.3 Importance of Data
The data serve as the bases or raw materials for analysis. Without an
analysis of factual data, no specific inferences can be drawn on the
questions under study. Inferences based on imagination or guess work
cannot provide correct answers to research questions. The relevance,
adequacy and reliability of data determine the quality of the findings of a
study.
Data form the basis for testing the hypotheses formulated in a study. Data
also provide the facts and figures required for constructing measurement
scales and tables which are analyzed with statistical techniques? Inferences
on the results of statistical analysis and tests of significance provide the
answers to research questions. Thus, the scientific process of
measurements, analysis, testing and inferences depends on the availability
of relevant data and their accuracy. Hence, the importance of data for any
research studies.
3.4 Sources of Data
The sources of data may be classified into (a) primary sources and (b)
secondary sources.
3.4.1 Primary Sources of Data
Primary sources are original sources from which the researcher directly
collects data that have not been previously collected e.g.., collection of data
directly by the researcher on brand awareness, brand preference, brand
loyalty and other aspects of consumer behaviour from sample of consumers
by interviewing them. Primary data are first hand information collected
through various methods such as observation, interviewing, mailing etc.
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3.4.2 Secondary Sources of Data
These are sources containing data which have been collected and compiled
for another purpose. The secondary sources consists of already compiled
statistical statements and reports whose data may be used by researches
for their studies e.g., Census reports , Annual reports and Financial
statements of companies, Statistical statement, Reports of Government
Departments, Annual reports of currency and finance published by the
Reserve Bank of India, Statistical statements relating to Co-operatives and
Regional Banks published by the NABARD, Reports of the National sample
survey Organization, Reports of trade associations, Publications of
international organizations such as UNO, IMF, World Bank, ILO, WHO, etc.,
Trade and Financial journals newspapers etc.,
Secondary sources consist of not only published records and reports, but
also unpublished records. The latter category includes various records and
registers maintained by the firms and organizations, e.g., accounting and
financial records, personnel records, register of members, minutes of
meetings, inventory records etc.
3.4.3 Features of Secondary Sources
Though secondary sources are diverse and consist of all sorts of materials,
they have certain common characteristics.
First, they are readymade and readily available, and do not require the
trouble of constructing tools and administering them.
Second, they consist of data over which a researcher has no original
control over collection and classification. Both the form and the content
of secondary sources are shaped by others. Clearly, this is a feature
which can limit the research value of secondary sources.
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Finally, secondary sources are not limited in time and space. That is, the
researcher using them need not be present when and where they were
gathered.
3.5 Use of Secondary Data
The secondary data may be used in three ways by a researcher.
First, some specific information from secondary sources may be used for
reference purpose. For example, the general statistical information in the
number of co-operative credit societies in the country, their coverage of
villages, their capital structure, volume of business, etc. may be taken
from published reports and quoted as background information in a study
on the evaluation of performance of cooperative credit societies in a
selected district/state.
Second, secondary data may be used as bench marks against which the
findings of research may be tested, e.g., the findings of a local or
regional survey may be compared with the national averages; the
performance indicators of a particular bank may be tested against the
corresponding indicators of the banking industry as a whole; and so on.
Finally, secondary data may be used as the sole source of information
for a research project studies such as securities Market Behaviour,
Financial Analysis of companies, Trade in credit allocation in commercial
banks, Sociological studies on crimes, Historical studies etc. Year
books, statistical reports of government departments, report of public
organizations of Bureau of Public Enterprises, Censes Reports etc,
serve as major data sources for such research studies.
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3.6 Advantages of Secondary Data
Secondary sources have some advantages:
1. Secondary data, if available, can be secured quickly and cheaply. Once
their source of documents and reports are located, collection of data is
just matter of desk work. Even the tediousness of copying the data from
the source can now be avoided, thanks to Xeroxing facilities.
2. Wider geographical area and longer reference period may be covered
without much cost. Thus, the use of secondary data extends the
researcher’s space and time reach.
3. The use of secondary data broadens the data base from which scientific
generalizations can be made. This is especially so when data from
several Environmental and cultural settings are required for the study.
4. The use of secondary data enables a researcher to verify the findings
based on primary data. It readily meets the need for additional empirical
support. The researcher need not wait for the time when additional
primary data can be collected.
3.7 Disadvantages of Secondary Data
The use of a secondary data has its own limitations.
1. The most important limitation is that the available data may not meet our
specific needs. The definitions adopted by those who collected those
data may be different; units of measure may not match; and time
periods may also be different.
2. The available data may not be as accurate as desired. To assess their
accuracy, we need to know how the data were collected
3. The secondary data are not up-to-date and become obsolete when they
appear in print, because of the time lag in producing them. For example,
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population census data are published two or three years later after
compilation and no new figures will be available for another ten years.
4. Finally, information about the whereabouts of sources may not be
available to all social scientists. Even if the location of the source is
known, the accessibility depends primarily on proximity. For example,
most of the unpublished official records and compilations are located in
the capital city, and they are not within the easy reach of researchers
based in far off places.
3.8 Evaluation of Secondary Data
When a researcher wants to use secondary data for his research, he should
evaluate them before deciding to use them.
3.8.1 Data Pertinence
The first consideration in evaluation is to examine the pertinence of the
available secondary data to the research problem under study. The
following questions should be considered.
1. What are the definitions and classifications employed? Are they
consistent with our own?
2. What are the measurements of variables used? What is the degree to
which they conform to the requirements of our research?
3. What is the coverage of the secondary data in terms of topic and time?
Does this coverage fit the needs of our research?
On the basis of above consideration, the pertinence of the secondary data
to the research on hand should be determined. A researcher who is
imaginative and flexible may be able to redefine his research problem so as
to make use of otherwise unusable available data.
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3.8.2 Data Quality
If the researcher is convinced about the available secondary data for his
needs, the next step is to examine the quality of the data. The quality of data
refers to their accuracy, reliability and completeness.
The assurance and reliability of the available secondary data depends on
the organization which collected them and the purpose for which they were
collected. What is the authority and prestige of the organization? Is it well
recognized? Is it noted for reliability? It is capable of collecting reliable data?
Does it use trained and well qualified investigators? The answers to these
questions determine the degree of confidence we can have in the data and
their accuracy.
It is important to go to the original source of the secondary data rather than
to use an immediate source which has quoted from the original. Then only,
the researcher can review the cautionary and other comments that were
made in the original source.
The completeness refers to the actual coverage of the published data. This
depends on the methodology and sampling design adopted by the original
organization. Is the methodology sound? Is the sample size small or large?
Is the sampling method appropriate? Answers to these questions may
indicate the appropriateness and adequacy of the data for the problem
under study.
The question of possible bias should also be examined. Whether the
purpose for which the original organization collected the data had a
particular orientation? Has the study been made to promote the
organization’s own interest? How the study was conducted? These are
important clues.
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The researcher must be on guard when the source does not report the
methodology and sampling design. Then it is not possible to determine the
adequacy of the secondary data for the researcher’s study.
3. 9 Commercial Surveys, Audits and Panels
Primary data are directly collected by the researcher from their original
sources. In this case, the researcher can collect the required data precisely
according to his research needs, he can collect them when he wants them
and in the form he needs them. But the collection of primary data is costly
and time consuming. Yet, for several types of social science research,
required data are not available from secondary sources and they have to be
directly gathered from the primary sources.
Primary data have to be gathered in cases where the available data are
inappropriate, inadequate or obsolete. They include: socio economic
surveys, social anthropological studies of rural communities and tribal
communities, sociological studies of social problems and social institutions,
marketing research, leadership studies, opinion polls, attitudinal surveys,
radio listening and T.V. viewing surveys, knowledge-awareness practice
(KAP) studies, farm managements studies, business management studies
etc.,
There are various methods of data collection. There are four basic survey
methods which include:
Personal interview
Telephone interview
Mail survey and
Fax survey
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3.9.1 Personal interview
Personal interviewing is one of the prominent methods of data collection. It
may be defined as a two way systematic conversation between an
investigator and an informant, initiated for obtaining information relevant to a
specific study. It involves not only conversation, but also learning from the
respondent’s gesture, facial expressions and pauses, and his environment.
Interviewing may be used either as a main method or as a supplementary
one in studies of persons. Interviewing is the only suitable method for
gathering information from illiterate or less educated respondents. It is
useful for collecting a wide range of data from factual demographic data to
highly personal and intimate information relating to a person’s opinions,
attitudes, values, beliefs, experiences and future intentions. Interviewing is
appropriate when qualitative information is required or probing is necessary
to draw out fully. Where the area covered for the survey has a compact or
when a sufficient number of qualified interviewers are available, personal
interview is feasible.
Interview is often superior to other data-gathering methods. People are
usually more willing to talk than to write. Once report is established, even
confidential information may be obtained. It permits probing into the context
and reasons for answers to questions.
Interview can add flesh to statistical information. It enables the investigator
to grasp the behavioural context of the data furnished by the respondents. It
permits the investigator to seek clarifications and brings to the forefront
those questions which for some reason or the others the respondents do not
want to answer.
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3.9.2 Advantages of Personal Interview
There are several real advantages to personal interviewing.
First, the greatest value of this method is the depth and detail of
information that can be secured. When used with well conceived
schedules, an interview can obtain a great deal of information. It far
exceeds mail survey in amount and quality of data that can be secured.
Second, the interviewer can do more to improve the percentage of
responses and the quality of information received than other method. He
can note the conditions of the interview situation, and adopt appropriate
approaches to overcome such problems as the respondent’s
unwillingness, incorrect understanding of question, suspicion, etc.
Third, the interviewer can gather other supplemental information like
economic level, living conditions etc through observation of the
respondent’s environment.
Fourth, the interviewer can use special scoring devices, visual materials
and like in order to improve the quality of interviewing.
Fifth, the accuracy and dependability of the answers given by the
respondent can be checked by observation and probing.
Last, interview is flexible and adaptable to individual situations. Even
more control can be exercised over the interview situation.
3.9.3 Limitations of Personal Interview
Interviewing is not free of limitations.
Its greatest drawback is that it is costly both in terms of money and time.
Second, the interview results are often adversely affected by
interviewer’s mode of asking questions and interactions, incorrect
recording and also by the respondent’s faulty perception, faulty memory,
inability to articulate etc.
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Third, certain types of personal and financial information may be refused
in face-to face interviews. Such information might be supplied more
willingly on mail questionnaires, especially if they are to be unsigned.
Fourth, interview poses the problem of recording information obtained
from the respondents. No full proof system is available. Note taking is
invariably distracting to both the respondent and the interviewer and
affects the thread of the conversation.
Last, interview calls for highly competent interviewers. The availability of
such persons is limited and the training of interviewers is often a long
and costly process.
3.9.4 Characteristics of Personal Interview
Interviewing as a method of data collection has certain characteristics. They
are:
1. The participants – the interviewer and the respondent – are strangers;
hence, the investigator has to get him introduced to the respondent in
an appropriate manner.
2. The relationship between the participants is a transitory one. It has a
fixed beginning and termination points. The interview proper is a
fleeting, momentary experience for them.
3. Interview is not a mere casual conversational exchange, but a
conversation with a specific purpose, viz., obtaining information
relevant to a study.
4. Interview is a mode of obtaining verbal answers to questions put
verbally.
5. The interaction between the interviewer and the respondent need not
necessarily be on a face-to-face basis, because interview can be
conducted over the telephone also.
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6. Although interview is usually a conversation between two persons, it
need not be limited to a single respondent. It can also be conducted
with a group of persons, such as family members, or a group of
children or a group of customers, depending on the requirements of
the study.
7. Interview is an interacting process. The interaction between the
interviewer and the respondent depends upon how they perceive each
other.
8. The respondent reacts to the interviewer’s appearance, behaviour,
gestures, facial expression and intonation. His perception of the thrust
of the questions and his own personal needs. As far as possible, the
interviewer should try to be closer to the social-economic level of the
respondents.
9. Information furnished by the respondent in the interview is recorded by
the investigator. This poses a problem of seeing that recording does
not interfere with the tempo of conversation.
10. Interviewing is not a standardized process like that of a chemical
technician; it is rather a flexible psychological process.
3.9.5 Telephone Interviewing
Telephone interviewing is a non-personal method of data collection. It may
be used as a major method or supplementary method. It will be useful in the
following situations:
1. When the universe is composed of those persons whose names are
listed in telephone directories, e.g. business houses, business
executives, doctors and other professionals.
2. When the study required responses to five or six simple questions. E.g.
Radio or Television programme survey.
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3. When the survey must be conducted in a very short period of time,
provided the units of study are listed in telephone directory.
4. When the subject is interesting or important to respondents, e.g. a
survey relating to trade conducted by a trade association or a chamber
of commerce, a survey relating to a profession conducted by the
concerned professional association.
5. When the respondents are widely scattered and when there are may
call backs to make.
3.9.6 Advantages of Telephone Interviewing
The advantages of telephone interview are:
1. The survey can be completed at very low cost, because telephone
survey does not involve travel time and cost and all calls can be made
from a single location.
2. Information can be collected in a short period of time. 5 to 10 interviews
can be conducted per hours.
3. Quality of response is good, because interviewer bias is reduced as
there is no face-to-face contact between the interviewer and the
respondent.
4. This method of interviewing is less demanding upon the interviewer.
5. It does not involve fieldwork.
6. Individuals who could not be reached or who might not care to be
interviewed personally can be contacted easily.
3.9.7 Disadvantages of Telephone Interviewing
Telephone interview has several limitations:
1. It is limited to persons with listed telephones. The sample will be
distorted if the universe includes persons not on phone. In several
counties like India only a few persons have phone facility and that too in
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urban areas only. Telephone facility is very rare in rural areas. Hence,
the method is not useful for studying the general population.
2. There is a limit to the length of interview. Usually, a call cannot last over
five minutes. Only five or six simple questions can be asked. Hence,
telephone cannot be used for a longer questionnaire.
3. The type of information to be collected is limited to what can be given in
simple, short answers or a few words. Hence telephone is not suitable
for complex surveys, and there is no possibility of obtaining detailed
information.
4. If the questions cover personal matters, most respondents will not
cooperate with the interviewer.
5. The respondent’s characteristics and environment cannot be observed.
6. It is not possible to use visual aids like charts, maps, illustrations or
complex scales.
7. It is rather difficult to establish rapport between the respondent and the
interviewer.
8. There is no possibility to ensure the identity of the interviewer and to
overcome suspicion.
3.9.8 Group Interviews
A group interview may be defined as a method of collecting primary data in
which a number of individuals with a common interest interact with each
other. In a personal interview, the flow of information is multi dimensional.
The group may consist of about six to eight individuals with a common
interest. The interviewer acts as the discussion leader. Free discussion is
encouraged on some aspect of the subject under study. The discussion
leader stimulates the group members to interact with each other.
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The desired information may be obtained through self-administered
questionnaire or interview, with the discussion serving as a guide to ensure
consideration of the areas of concern. In particular, the interviewers look for
evidence of common elements of attitudes, beliefs, intentions and opinions
among individuals in the group. At the same time, he must be aware that a
single comment by a member can provide important insight.
Samples for group interview can be obtained through schools, clubs and
other organized groups.
3.9.9 Advantages of Group Interview
The advantages of this technique are:
1. The respondents comment freely and in detail.
2. The method is highly flexible. The flexibility helps the research work with
new concepts or topics which have not been previously investigated.
3. Visual aids can be used.
4. A group can be interviewed in the time required for one personal
interview.
5. The client can watch the interview unobserved.
6. Respondents are more articulated in a group than in the individual
interviews.
7. The technique eliminates the physical limitations inherent in individual
interviews.
3.9.10 Disadvantages of Group Interview
This method is not free from drawbacks.
1. It is difficult to get a representative sample.
2. There is the possibility of the group being dominated by one individual.
3. The respondents may answer to please the interviewer or the other
members in the group.
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3.9.11 Mail Survey
The mail survey is another method of collecting primary data. This method
involves sending questionnaires to the respondents with a request to
complete them and return them by post. This can be used in the case of
educated respondents only. The mail questionnaires should be simple so
that the respondents can easily understand the questions and answer them.
It should preferably contain mostly closed-end and multiple choice questions
so that it could be completed within a few minutes.
The distinctive feature of the mail survey is that the questionnaire is self-
administered by the respondents themselves and the responses are
recorded by them, and not by the investigator as in the case of personal
interview method. It does not involve face-to-face conversation between the
investigator and the respondent. Communication is carried out only in
writing and this requires more cooperation from the respondents than verbal
communication.
3.9.12 Procedures of Mail Survey
The researcher should prepare a mailing list of the selected respondents by
collecting the addresses from the telephone directory of the association or
organization to which they belong.
A covering letter should accompany a copy of the questionnaire. It must
explain to the respondent the purpose of the study and the importance
of his cooperation to the success of the project.
Anonymity may be assured.
The sponsor’s identity may be revealed. However, when such
information may bias the result, it is not desirable to reveal it. In the later
case, a disguised organization name may be used.
A self-addressed stamped envelope should be enclosed in the covering
letter.
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After a few days from the date of mailing the questionnaires to the
respondents, the researcher can expect the return of completed ones
from them. The progress in return may be watched and at the
appropriate stage follow-up efforts can be made.
3.9.13 Alternative Modes of Sending Questionnaires
There are some alternative methods of distributing questionnaires to the
respondents. They are: (1) personal delivery, (2) attaching questionnaire to
a product (3) advertising questionnaire in a newspaper of magazine, and (4)
news-stand insets.
Personal delivery: The researcher or his assistant may deliver the
questionnaires to the potential respondents with a request to complete them
at their convenience. After a day or two, he can collect the completed
questionnaires from them. Often referred to as the self-administered
questionnaire method, it combines the advantages of the personal interview
and the mail survey. Alternatively, the questionnaires may be delivered in
person and the completed questionnaires may be returned through mail by
the respondents.
Attaching questionnaire to a product: A firm test marketing a product
may attach a questionnaire to a product and request the buyer to complete it
and mail it back to the firm. The respondent usually is rewarded by a gift or
a discount coupon.
Advertising the questionnaire: The questionnaire with the instructions for
completion may be advertised on a page of magazine or in section of
newspapers. The potential respondent completes it, tears it out and mails it
to the advertiser. For example, the committee of Banks customer services
used this method of Management Studies for collecting information from the
customers of commercial banks in India. This method may be useful for
large-scale on topics of common interest.
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News-stand inserts: This method involves inserting the covering letter,
questionnaire and self addressed reply-paid envelope into a random sample
of news-stand copies of a newspaper or magazine.
3.9.14 Improving the Response Rate in a Mail Survey
The response rate in mail surveys is generally very low in developing
countries like India. Certain techniques have to be adopted to increase the
response rate. They are:
1. Quality printing: The questionnaire may be neatly printed in quality
light coloured paper, so as to attract the attention of the respondent.
2. Covering letter: The covering letter should be couched in a pleasant
style so as to attract and hold the interest of the respondent. It must
anticipate objections and answer them briefly. It is desirable to address
the respondent by name.
3. Advance information: Advance information can be provided to
potential respondents by a telephone calls or advance notice in the
newsletter of the concerned organization or by a letter. Such preliminary
contact with potential respondents is more successful than follow-up
efforts.
4. Incentives: Money, stamps for collection and other incentives are also
used to induce respondents to complete and return mail questionnaire.
5. Follow-up-contacts: In the case of respondents belonging to an
organization, they may be approached through some one in that
organization known as the researcher.
6. Larger sample size: A larger sample may be drawn than the estimated
sample size. For example, if the required sample size is 1000, a sample
of 1500 may be drawn. This may help the researcher to secure an
effective sample size closer to the required size.
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3.9.15 Advantages of Mail Survey
They are less costly than personal interviews, as cost of mailing is the
same throughout the country irrespective of distances.
They can cover extensive geographical areas.
Mailing is useful in contacting persons such as senior business
executives who are difficult to reach in any other way.
The respondents can complete the questionnaires at their
conveniences.
Mail surveys, being more impersonal, provide more anonymity than
personal interviews.
Mail surveys are totally free from the interviewer’s bias, as there is no
personal contact between the respondents and the investigator.
Certain personal and economic data may be given accurately in an
unsigned mail questionnaire.
3.9.16. Disadvantages of Mail Survey
The scope for mail surveys is very limited in a country like India where
the percentage of literacy is very low.
The response rate of mail surveys is low. Hence, the resulting sample
will not be a representative one.
It is also difficult to determine the degree of representativeness of a
sample obtained by mail.
The causes for inadequate and non-responses cannot be known and no
probing is possible.
Information on the personal characteristics of the respondent and his
environment cannot be secured.
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Respondents may not be cooperative if the mail questionnaire is long or
complex.
Several returned questionnaire might contain unanswered questions and
incomplete responses.
3.9.17 Panel Method
The panel method is a method of data collection, by which data is collected
from the same sample respondents at intervals either by mail or by personal
interview. This is used for longitudinal studies on economic conditions,
expenditure pattern, consumer behaviour, recreational pattern, effectiveness
of advertising, voting behaviour, and so on. The period over which the panel
members are contacted for information may spread over several months or
years. The time interval at which they are contacted repeatedly may be 10
or 15 days or one or two months depending on the nature of the study and
the memory span of the respondents.
3.9.18 Characteristics of Panel Method
A basic characteristic of the panel method is successive collection of
data on the same items from the same persons over a period of time.
The type of information to be collected should be such facts that can be
accurately and completely furnished by the respondent without any
reservation.
The number of item should be as few as possible so that they could be
furnished within a few minutes, especially when mail survey is adopted.
The average amount of time that a panel method has to spend each
time for reporting can be determined in a pilot study.
The panel method requires carefully selected and well-trained field
workers and effective supervision over their work.
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3.9.19 Need for Panel Study
When sample respondents – particularly household or individuals are
contacted only once, they may not be able to recall exactly what have
occurred during a long period, say a year, because of the short memory
span. Therefore, they will not be able to give correct information on such
facts as cash inflow, source wise income, expenditure pattern, purchases,
savings, and recreation pattern and so on. But when they are contacted
successively at short intervals of 10 or 15 days, they would to able to give
accurate information on happenings between two successive contacts, as
the time span is short and well within the memory span. In this way, data are
collected repeatedly at intervals, and computations for the required period
like a year can be easily made and the data so collected will be more
reliable.
3.9.20 Tpes of Panel
The panel may be static or dynamic. A static or continuous panel is one in
which the membership remains the same throughout the life of panel,
except for members who drop out. The dropouts are not replaced. The static
panel is appropriate when the purpose is to study changes in a given group
of persons over a period of time. The main drawbacks of the static panel
are:
Due to dropouts, it ceases to be representative of the original universe
after a time.
The size of the reporting panel may become too small for satisfactory
results.
In a dynamic panel, to keep the panel representative of the current
population in the area covered by the universe, replacements are found for
the members who drop out. A dynamic panel may also be a rotating one, in
which members are systematically replaced at specified time intervals.
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The dynamic panel is most useful when it is important to keep the panel
representative of the population at all times. The main problem connected
with its use is the cost and difficultly of recruiting new members to replace
the dropouts.
3.9.21 Advantages of Panel Method
The advantages of panel method are:
This method facilities collection of more accurate data for longitudinal
studies than any other method, because under this method event or
action is reported soon after its occurrence.
This method makes it possible to have before and after designs are
made for field based studies. For example, the effect of public relations
or advertising campaigns or welfare measures can be measured by
collecting data before, during and after the campaign.
The panel method offers a good way of studying trends in events,
behaviour or attitudes. For example, a panel enables a market
researcher to study how brand preferences change from month to
month; it enables an economics researcher to study how employment,
income and expenditure of agricultural labourers change from month to
month; a political scientist can study the shifts in inclinations of voters
and the causative influential factors during an election, it is possible to
find out how the constituency of the various economy and social strata
of society changes through time; and so on.
A panel study also provides evidence on the casual relationship
between variables. For example, a cross sectional study of employees
may show an association between their attitude to their jobs and their
positions in the organization, but it does not indicate as to which comes
first-favourable attitude or promotion. A panel study can provide data for
finding an answer to this question.
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It facilities depth interviewing, because panel members become well
acquainted with the field workers and will be willing to allow probing
interviews.
3.9.22 Disadvantages of Panel Method
The major limitations or problems of the panel method are:
This method is very expensive. The selection of panel members, the
payment of premiums, periodic of investigators and supervisors, and the
costs involved in replacing drop-outs, all add to the expenditure.
It is often difficult to set up a representative’s panel and to keep it
representative. Many persons may be unwilling to participate in a panel
study. In the course of the study, there may be frequent drop-outs.
Persons with similar characteristics may replace the drop-outs. But there
is no guarantee that the emerging panel would be a representative.
A real danger with the panel method is “panel conditioning” i.e., the risk
that repeated interviews may sensitize the panel members and they
become untypical as a result of being on the panel. For example, the
members of a panel study of political opinions may try to appear
consistent in the views they express on consecutive occasions. In such
cases the panel becomes untypical of the population it was selected to
represent. One possible safeguard to panel conditioning is to give
members of a panel only a limited panel life and then to replace them
with persons taken randomly from a reserve list.
The quality of reporting may tend to decline due to decreasing interest
after a panel has been in operations for some time. Cheating by panel
members or investigators may be a problem in some cases.
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3.10 Survey Research
Survey is a fact finding study. It is method of research involving collection of
data directly from a population or a sample at a particular time. Data might
be collected by observation or interviewing or mailing questionnaires.
Survey has certain characteristics:
It is always conducted in a natural setting. It is a field study.
It seeks responses directly form the respondents.
It can cover a very large population.
A survey may include extensive study or intensive study.
A survey covers a definite geographical area.
3.10.1 Steps Involved in a Survey
Selection of a problem and its formulation
Preparation of the research design
Operation concepts and construction of measuring indexes and scales
Sampling
Construction of tools for data collection
Field work and collection of data
Processing of data and tabulation
Analysis of data
Reporting
3.11 Experimentation and Experiment Environment
Experimentation is a research process used to study the causal
relationships between variables. 1t aims at studying the effect of an
independent variable on a dependent variable by keeping the other
independent variable constant through some type of control. For example, a
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social scientist may use experimentation for studying the effect of a method
of family planning publicity on people’s awareness of family planning
techniques. Various kinds of designs such as “after-only-design” etc, are
used for experimentation
Experimentation required special effort. It is often extremely difficult to
design, and it is also a time consuming process. Why should then one take
such trouble? Why not simply observe/survey the phenomenon? The
fundamental weakness of any non-experimental study is its inability to
specify causes and effect. It can show only correlations between variables,
but correlations alone never prove causation. The experiment is the only
method which can show the effect of an independent variable on dependent
variable. In experimentation, the researcher can manipulate the independent
variable and measure its effect on the dependent variable. For example, the
effect of various types of promotional strategies on the sale of a given
product can be studied by using different advertising media such as TV’s,
radio and Newspaper.
Moreover, experiment provides “the opportunity to vary the treatment
(experimental variable) in a systematic manner, thus allowing for the
isolation and precise specification of important difference”.
3.11.1 Planning and Conducting Experiments
It is easy to conceive ideas, but difficult to translate the ideas into a
workable, credible, meaningful set of experimental operations. This to a
great extent depends upon the researcher’s knowledge, imagination and
intelligence. Yet the general procedure in experimentation may be outlined
First, determine the hypothesis to be tested and the independent and
dependent variables involved in it.
Second, operate the variables by identifying their measurable
dimensions.
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Third, select the type of experimental plan. The type of experimental
design based on types of control may be classified into: (i) one group
plan, using the same group as experimental and control group and
measuring it before and after experimental treatment; (ii) matched
groups plan, consisting of two identical groups, one to be used as
control group and another as experimental group, with (a) Post-test only
measurement or (b) pre-test post test measurements.
Fourth, choose the setting. The setting may be field or laboratory (See
subsequent paragraphs for detailed discussion on these two types of
experiments)
Fifth, make the experimental conditions as nearly the same as the
expected read life conditions.
Sixth, make a record of pre-experimental conditions.
Seventh, introduce appropriate methods for controlling extraneous
variables that are not manipulated in the experiment. These methods
are:
1. Removing the Variable: An extraneous variable may be eliminated
completely. For example, observer distraction may be removed by
separating the observer by a one-way glass partition. Some
variables may be eliminated by selecting cases with uniform
characteristics, e.g., using only male subjects removing sex as a
variable.
2. Matching Cases: Selecting control and experimental groups with
identical characteristics. But all the variables are kept constant.
3. Balancing Cases: This means assigning subjects to experimental
and control groups in such as way that the means and the variances
of the groups are as nearly equal as possible.
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4. Randomization: This involves pure chance selection and
assignment of subjects to experimental and control groups. This
method eliminates systematic bias and minimizes the effect of
extraneous variable.
5. Analysis of co-variance: This is a statistical method of eliminating
initial difference on several variables between the experimental and
control groups. Pre-test means scores are used as co-variants.
Eighth, apply the experimental treatment and record
observations and measurements using appropriate measurement
devices. If feasible, repeat the tests several times in order to
unsure the accuracy of results.
Ninth, analyse the results, using appropriate statistical devices.
Last, interpret the results, giving consideration to all possible
extraneous conditions. No possible cause should be overlooked,
as unforeseen conditions might influence the results.
3. 11.2. Advantages of Experimentation
Experimentation has some specific advantages and disadvantages:
1. Its power to determine causal relationships between variables
surpasses that of all other methods. The influence of extraneous
variables can be more effectively controlled in this method.
2. The element of human errors is reduced to the minimum.
3. In this method better conditions for conducting experiments may be
created, than is possible in other methods.
4. Experimentation yields generally exact measurements and can be
repeated for verifying results.
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3. 11.3 The Disadvantages of Experimentation
It is difficult to establish comparable control and experimental groups.
Second, the scope for experimentation with human beings is extremely
limited.
Thirdly, experiment is often difficult to design, tends to be expensive and
time-consuming.
Fourthly, it is artificial to some extent and may lack realism.
Fifthly, experimentation can be used only in studies of the present but
not in studies relating to past or future.
Sixthly, it is of no use in determining opinions, motives and intentions of
persons.
Finally, the results observed may not be the true effects of the
experimental treatment, but may be artificial, e.g, effects of some
extraneous variable. For instance, an experimenter may unwillingly
influence his subjects to behave in ways that confirm the hypothesis,
particularly if the subjects want to please the experimenter. Laboratory
experiments are especially vulnerable to such artificialities when
subjects know they are being studied and want to create a good
impression.
3.11.4 Laboratory Experiment
An experiment can be conducted either in a laboratory or in a real life
setting.
A laboratory experiment is an investigation conducted in a setting created
specifically for the purpose. The researcher manipulated an independent
variable and studies its effect on a dependent variable keeping other
variables constant.
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Purpose: The purpose of laboratory experiments are (1) to discover causal
relations under uncontaminated conditions, reduce the discovered relations
to functional from y= f(x) and make predictions on the basis of the functions;
(2) to test the predictions derived from theory and other researches; and (3)
to refine theories.
Applications or Uses: This method is useful not only in physical sciences
research, but also in social sciences research. Even though experimentation
on a social science problem has special difficulties, it can be used for a
variety of studies such as voting behaviour, leadership style, learning
process, effectiveness of advertising media, and effect of package on sales,
influence of incentive schemes on labour productivity and so on.
Advantages
This method has several merits, they are:
The chief merit of experiment method is the possibility of securing
relatively complete control over extraneous factors.
The experimenter can manipulate one or more variables and study its or
their impact on dependent variables.
The experimenter is most cases can achieve a high degree of specificity
on operating the variables.
An experiment yields precise results and it is replicable.
Demerits:
The experimental method is not free from drawbacks:
The greatest weakness of this method is probably the lack of strength of
independent variables. As the laboratory situation is artificial, the effects
of experimental manipulations are usually weak.
This method is highly structured, thus limiting flexibility.
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The result cannot be simply generalized to real life situations, because
in the latter, various extraneous forces operate. Thus, an experimental
method lacks external validity
Nevertheless, the contributions of experimental method to theory
building are so great that this method is one of man’s greatest
achievements.
3.11.5 Field Experiments
This is an experiment conducted in a real life situation in which the
experimenter manipulates an independent variable in order to test a
hypothesis. Compared with laboratory experiment, a filed experiment
incorporates less control. As it is difficult to foresee and control extraneous
factors in a filed experiment, its result is subject to large uncontrolled
variation.
Application or uses: This method is well suited to both testing of theory and
finding solutions to practical social problems, It has been used for studying a
variety of social action programmes such as, improving the quality of work
life in assembly lines, post offices and insurance companies and banks; the
learning process in educational institutions and training centres; changing
group productivity; changing organization culture; changing superior-
subordinate relationship in organization overcoming resistance to change
through participative approach; changing attitude through social contract;
changing food habits; improving the effectiveness of mass education; testing
new products in markets; determining best procedures or techniques for
tasks; and so on.
“Anyone who wishes to take effective social action in any setting can
improve upon the uncontrolled, by out of new methods by the applications of
more scientific, experimental procedures. Through careful measurements,
better theorizing, the use of control groups, and other aspects of improved
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experimental design, the practical problems of social action can be solved
with greater certainty, with greater accuracy and sometimes with greater
efficiency than through common sense trial-and-error methods.
The field experiment will be more successful if preceded by a field study
which gives a more thorough knowledge of the setting and thus enables the
experimenter to manipulate and control his variables more effectively.
3.11.6 Advantages of the Field Experiments
This method has some unique virtues.
The variables in a field experiment usually have a stronger effect than
those of a laboratory experiment, as the field situation is real. Realism
increased with strength of the variables.
The field experiment permits a more unequivocal determination of
causal relations, while processes and social changes, in real life
settings.
As the field experiment deals with the total life situation, it is well suited
for studying complex syndromes and social processes where the inter-
relationships among many analytical variables are involved.
3.11.7 Limitations of Field Experimentations
The experimental method is not free from limitations:
The major limitation of this method is the practical difficulty involved in
the manipulation of independent variables and randomization in many
field situations.
Another weakness inherent in this method is lack of precision. Precise
measurement in realistic situation is not as possible as in a laboratory
setting.
A field experiment is a very difficult process, as it calls for social skills
and good contacts on the part of the experiments and also it is a time
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consuming process. Unless the organization where it has to be
conducted and the subject cooperate, it is not possible to conduct it.
Self Assessment Questions
State whether the following statements are true or false:
1. The search for answers to research questions is called collection of
data.
2. Data are facts, and other relevant materials, past and present, serving
as bases for study and analyses.
3. The data needed for a social science research may be broadly classified
into (a) Data pertaining to human beings, (b) Data relating to
organization and (c) Data pertaining to territorial areas.
4. The sources of data may be classified into (a) primary sources and (b)
secondary sources.
3.12 Summary
The sources of data may be classified into (a) primary sources and (b)
secondary sources. Secondary sources consist of not only published
records and reports, but also unpublished records. The latter category
includes various records and registers maintained by the firms and
organizations, e.g., accounting and financial records, personnel records,
register of members, minutes of meetings, inventory records etc. When a
researcher wants to use secondary data for his research, he should
evaluate them before deciding to use them.
There are various methods of data collection. There are four basic survey
methods
Personal interview
Telephone interview
Mail survey
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And fax survey
Telephone interviewing is a non-personal method of data collection. It may
be used as a major method or supplementary method. A group interview
may be defined as a method of collecting primary data in which a number of
individuals with common interest interact with each other. The mail survey
is another method of collecting primary data. This method involves sending
questionnaires to the respondents with a request to complete them and
return them by post. The panel method is a method of data collection, by
which data is collected from the same sample respondents at intervals
either by mail or by personal interview. Experimentation is a research
process used to study the causal relationships between variables. 1t aims at
studying the effect of an independent variable on a dependent variable by
keeping the other independent variable constant through some type of
control.
3.13 Terminal Questions
1. What are the sources of data for research?
2. Distinguish between primary and secondary data.
3. What are commercial surveys?
4. What is the advantage of panel study?
5. What are field experiments?
3.14 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
4. True
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TQs
1. Section 3.4.
2. Section 3.4.1 and 2
3. Section 3.9.
4. Section 3.9.2
5. Section 3.11.5
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Unit 4 Measurement Techniques in
Marketing Research
Structure
4.1 Introduction: The Concept of Measurement
Objectives
4.2 Scales of Measurement
4.3 Components of Measurement
4.3.1 Nominal Scale
4.3.2 Ordinal Scale
4.3.3 Interval Scale
4.3.4 Ratio Scale
4.4 Components of Measurement Accuracy
4.4.1 Validity
4.4.2 Reliability
4.4.3 Sensitivity
4.4.4 Generalization
4.4.5 Relevancy
4.5 Measurement Accuracy and Measurement Development
4.5.1 Questionnaire Designing
4.5.2 Plan What to Measure
4.5.3 Format the Questions
4.5.4 Question Wording
4.5.5 Sequence and Layout Design
4.5.6 Pre-test Design
4.6 Direct Response Attitude Scales
4.6.1 Single Item Scales
4.6.2 Itemized Scale
4.6.3 Comparative Scales
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4.6.4 Rank Order Scale
4.6.5 Q-Sort Scale
4.6.6 Constant Sum Scales
4.6.7 Pictorial Scales
4.6.8 Paired Comparison Scales
4.6.9 Derived Attitude Scales
4.6.10 Likert Scales
4.6.11 Thurstone Scales
4.6.12 Semantic Differential Scales
4.7 Types of Observation
4.7.1 Participant Observation
4.7.2 Non-Participant Observation
4.7.3 Direct Observation
4.7.4 Indirect Observation
4.7.5 Controlled Observation
4.7.6 Uncontrolled Observation
4.7.7 Planning Observation
4.7.8 Prerequisite of Observation
4.7.9 Observation Tools and Research Devices
4.7.10 Suitability and Application
4.7.11 Advantages of Observation
4.7.12 Limitations of Observation
Self Assessment Questions
4.8 Summary
4.9 Terminal Questions
4.10 Answers to SAQ’s and TQ’s
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4.1 Introduction: Concept of Measurement
Measurement can be defined as a standardized process of assigning
numbers or other symbols to certain characteristics of the objects of interest,
according to some pre-specified rules. Measurement often deals with
numbers, because mathematical and statistical analysis can be performed
only on numbers, and they can be communicated throughout the world in
the same form without any translation problems. For a measurement
process to be standardized process assignment, two characteristics are
necessary. First, there must be one-to-one correspondence between the
symbol and the characteristic in the object that is being measured. Second,
the rules for assignment must be invariant over time and the objects being
measured.
Objectives:
After learning this unit, the students should be able to:
discuss the scales and components of measurement
explain the components of measurement accuracy and measurement
development
understand the concept of Questionnaire design, observation, response
attitude scales etc.
discuss the types of observation
explain the observation tools and research devices
4.2 Scales of Measurement
Scaling is the process of creating a continuum on which objects are located
according to the amount of the measured characteristics they possess. An
illustration of a scale that is often used in research is the dichotomous scale
for sex. The object with male (or female) characteristics is assigned the
number 1 and the object with the opposite characteristics is assigned the
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number 0. This scale meets the requirements of the measurement process
in that the assignment is one to one and it is invariant with respect to top
time and object. Measurement and scaling are basic tools used in the
scientific method and are used in almost every marketing research situation.
4.3 Components of Measurement
The assignment of numbers is made according to rules that should
correspond to the properties of whatever is being measured. The rule may
be very simple, as when a bus route is given a number to distinguish it from
other routes. Here, the only property is identity, and any comparisons of
numbers are meaningless. This is a nominal scale. At other extreme is the
ratio scale, which has very rigorous properties. In between the extremes are
ordinal scales and interval scales.
4.3.1 Nominal Scale
In a nominal scale, objects are assigned to mutually exclusive, labelled
categories, but there are no necessary relationships among the categories;
that is , no ordering or spacing is implied. If one entity is assigned the same
number as another, they are identical with respect to a nominal variable.
Otherwise, they are just different. Sex, geographic location, and marital
status are nominally scaled variables. The only arithmetic operation that can
be performed on such a scale is a count of each category. Thus we can
count the number of automobiles dealers in the state of Karnataka or the
number of buses seen on a given route in the past hour.
4.3.2 Ordinal Scale
An ordinal scale is obtained by ranking objects or by arranging them in order
with regard to some common variable. The question is simply whether each
object has more or less of this variable than some other object. The scale
provides information as to how much difference there is between the
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objects. Because we do not know the amount of difference between objects,
the permissible arithmetic operations are limited to statistics such as the
median or mode but not median). For example, suppose a sample of 1,000
consumers ranked five brands of frozen mixed vegetables according to
quality. The results for Birds-eye brand were as follows:
Quality brands Number of respondents giving
Rankings to Bird-Eye
Highest 150
Second 300
Third 250
Fourth 200
Lowest 100
Total 1,000
The second quality category is mode; the third category is the median;
however it is not possible to compute a mean ranking, because the
differences between ordinal scaled values are not necessarily the same.
The finishing order in a horse race per class standing illustrates this type of
scale. Similarly, brands of frozen vegetables can be ranked according to
quality, from highest to lowest.
4.3.3 Interval Scale
In an interval scale the numbers used to rank the objects also represent
equal increments of the attribute being measured. This means that
differences can be compared. The difference between 1 and 2 is the same
as between 2 and 3, but is only half the difference between 2 and 4. The
location of the zero point is not fixed, since zero does not denote the
absence of the attribute. Fahrenheit and Celsius temperatures are
measured with different interval scales and have different zero points.
Interval scales have very desirable properties, because virtually the entire
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range of statistical operations can be employed to analyze the resulting
number, including addition and subtraction. Consequently, it is possible to
compute an arithmetic mean from interval-scale measures.
4.3.4 Ratio Scale
A ratio scale is a special kind of interval scale that has a meaningful zero
point. With such a scale – of weight, market share, or dollars in savings
accounts, for example, it is possible to say how many times greater or
smaller one object is than another. This is the only type of scale that permits
us to make comparisons of absolute magnitude. For example, we can say
that an annual income of Rs.80, 000/- is two times as large as an income of
Rs.40, 000/-.
There have been some contemporary efforts to adapt ratio scales to the
measurement of social opinion. Some researchers have attempted to use
magnitude estimation scales to overcome the loss of information that results
when categories arbitrarily constrain the range of opinion. Magnitude scaling
of attitudes has been calibrated through numeric estimation.
4.4 Components of Measurement Accuracy
Attitude measures, used in marketing, must be both accurate and useful.
Aspects that contribute accuracy are validity, reliability and sensitivity.
4.4.1 Validity
An attitude measure has validity if it measures what it is supposed to
measure. If this is the case, then differences in attitude scores will reflect
differences among the objects or individuals on the characteristic being
measured. How a researcher is to know whether measured differences in
the attitudes of managers, consumer activist, and consumers toward
marketing practices, regulation, and the contribution of the consumer
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movement are true. There have been three basic approaches to this
question of validity assessment.
Face, or consensus, validity is invoked when the argument is made that the
measurement so self-evidently reflects or represents the various aspects of
the phenomenon that there can be little quarrels with it. For instance,
buyer’s recognition of advertisements is usually accepted at face value as
an indication of past ad exposure.
Criterion validity is more defensible, for it is based on empirical evidence
that the attitude measure correlate with other criterion variables. If the two
variables are measured at the same time concurrent validity is established.
Better yet, if the attitude measures can predict some future event, then
predictive validity is established.
4.4.2 Reliability
The total error of a measurement consists of systematic error component
and a random error component. Random error is manifested by lack of
consistency (unreliability) in repeated or equivalent measures of the same
object or person. As a result, any measurement can be expressed as a
function of several components:
Observed Score = True Score + Systematic Error + Random Error
If the measure is to be valid, it has to be reliable. A measure is highly
reliable if random error is zero and still has a substantial systematic error
that distorts the validity. In brief, reliability is a necessary but not a sufficient
condition for validity. The basic methods for establishing reliability can be
classified according to whether they measure stability of results over time or
internal consistency of items in an attitude. Stability over time is assessed
by repeating the measurement with the same instrument and the same
respondents at two points of time and correlating the results.
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4.4.3 Sensitivity
The third characteristic of a good attitude measure is sensitivity, or the
ability to discriminate among meaningful differences in attitudes. Sensitivity
is achieved by increasing the number of scale categories; however, the
more categories there are, the lower the reliability will be
4.4.4 Generalization
Generalization refers to the ease of scale administration and interpretation
in different research settings and situations. Thus the Generalization of a
multiple- item scale is determined by whether it can be applied in a wide
variety of data collection modes, whether it can be used to obtain data from
a wide variety of individuals, and under what conditions it can be interpreted.
4.4.5 Relevancy
Relevancy of a scale refers to how meaningful it is to apply the scale to
measure a construct. Mathematically, it is represented as the product of
reliability and validity.
Relevance = reliability + validity.
If reliability and validity are evaluated by means of correlated coefficients,
the implications are:
The relevance of a scale can vary form 0 (no relevance) to 1 (complete
relevance).
If either reliability or validity is low, the scale will possess little relevance.
Both reliability and validity are necessary for scale relevance.
4.5 Measurement Accuracy and Measurement Development
4.5.1 Questionnaire Designing
Questionnaire construction is properly regarded as a very imperfect art.
There are no established procedures that will lead consistently to a good
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questionnaire. A good questionnaire accomplishes the research’s
objectives. Surveys should be custom built to the specification of given
research purposes, and they are much more than a collection of
unambiguous questions. A number of constraints are imposed on the
development of an appropriate questionnaire. For example, the number,
form, and ordering of the specific questions are partly determined by the
data collection method.
There is a sequence of logical steps that every researcher must follow to
develop a good questionnaire.
Plan what to measure
Formulate questions to obtain the needed answers
Decide on the order and wording of questions and on the layout of the
questionnaire.
Using a small sample, test the questionnaire for omissions and
ambiguity.
Correct the problems and pre-test again.
4.5.2 Plan What to Measure
The most difficult step is specifying exactly what information is to be
collected from each respondent. Poor judgment and lack of thought at this
stage may mean that the results are not relevant to the research purpose or
that they are incomplete. When a questionnaire is sent into the field that is
incomplete in important aspects, the error is irreversible. To avoid this awful
situation, careful thought is required; this is facilitated by:
1. Clear research objectives, which describe as fully as possible the kind of
information the decision maker needs, the hypotheses, and the scope of
the research
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2. Exploratory research, which will suggest further relevant variables and
help the researcher absorb the vocabulary and point of view of the
typical respondent.
3. Experience with similar studies
4. Pre-testing of preliminary versions of the questionnaire.
4.5.3 Formatting the Questions
Before specific questions are phrased, a decision has to be made as to the
degree of freedom to be given to respondents in answering the question.
The alternatives are:
i) Open-ended questions with no classification, where the interviewer
tries to record the response verbatim.
ii) Open-ended, where the interviewer uses preceded classifications to
record the response; or
iii) The closed, or structured, format, in which a question or supplementary
card presents the responses that the respondent may consider.
4.5.4 Question Wording
The wording of particular questions can have a large impact on how a
respondent interprets them. Even small changes in wording can shift
respondent’s answers, but it is difficult to know in advance whether or not a
wording change will have such an effect. The following guidelines are of
greatest value in critically evaluating and improving an existing question.
1. Is the vocabulary simple, direct and familiar to all respondents?
2. Do any words have vague or ambiguous meaning?
3. Are any questions double-barrelled?
4. Are any questions leading or loaded?
5. Are the instructions potentially confusing?
6. Is the question applicable to all respondents?
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7. Split-ballot technique: whenever there is doubt as to the appropriate
wording it is desirable to test several alternatives.
8. Are the questions of an appropriate length?
4.5.5 Sequence and Layout Design
The order or sequence of questions will be determined initially by the need
to gain and maintain the respondent’s cooperation and make the
questionnaire as easy as possible for the interviewer to administer. Once
these considerations are satisfied, attention must be given to the problem of
order bias – the possibility that prior questions will influence answers to
subsequent questions. The basic guidelines for sequencing a questionnaire
are straight forward:
i) Open the interview with an easy and non threatening question.
ii) The questionnaire should flow smoothly and logically from one topic to
the next.
iii) For most topics it is better to proceed from broad, general questions to
the more specific.
iv) Sensitive or difficult questions dealing with income status, ability and so
forth should not be placed at the beginning of the questionnaire.
4.5.6 Pre-test Design
Effective Pre-testing demands that the researcher be open to criticism and
willing to pursue the deficiencies. There are some very specific reasons for
pre-test questions. Four common tests for specific questions are as follows:
Variation: testing items for an acceptable level of variation in the target
population is one of the most common goals of pre-testing.
Meaning: the intended meaning of the question for the investigators may
not be the meaning the respondents interpret it to be.
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Task difficulty: A meaningful and clear question can still be difficult to
answer if the question requires that a respondent makes connections or
puts together information in unfamiliar ways.
Respondent interest and attention: Excessive repletion within a question
or use of the same format within a question can reduce the amount of
attention paid to questions by respondents.
4.6 Direct Response Attitude Scales
There are many ways to present a respondent with a continuum of
numbered categories that represent the range of possible attitude
judgments. They can be generally classified as single item scales and
multiple item scales.
4.6.1 Single Item Scales
Single item scales are those that have only one item to measure a
construct. Under the single item scales, the itemized category scale is the
most widely used by marketing researchers. In some situations,
comparative scales, rank order scales, or constant-sum scales have
advantages.
4.6.2 Itemized Scales
There are four categories from which respondents can choose to indicate
their overall level of satisfaction with their present health insurance plan.
– Very satisfied
– Quite satisfied
– Somewhat satisfied
– Not at all satisfied.
This satisfaction scale has the following characteristics.
All categories are labelled.
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The respondent is forced to make a choice; there is no provision for
neutral opinion or ‘don’t know’ response.
There are more favourable than unfavourable categories, so the scale is
unbalanced.
There is no explicit comparison of respondents’ present plan with other
health insurance plan.
4.6.3 Comparative Scale
Another version of the preceding scale would label the categories
“excellent”, “Very good”, “fair” and “poor” thereby eliminating the implicit
comparisons. The problem with a comparative scale is that the reference
point is unclear and different respondents may use different reference point
or standards.
4.6.4 Rank Order Scale
Rank order scale requires the respondent to arrange a set of objects with
regard to a common criterion: advertisements in terms of interest, product
features in terms of importance, or new-product concepts with regard to
willingness to buy in the future. The result is an ordinal scale with the
inherent limitations of weak scale properties. Ranking is widely used in
surveys, however, because it corresponds to the choice process occurring
in shopping environment where a buyer makes direct comparison among
competing alternatives (brands, flavours, product variations, and so on).
4.6.5 Q-Sort Scaling
When the number of objects or characteristics that are to be rated or ranked
is very large, it becomes rather tedious for the respondents to rank order or
to do pair-wise comparisons. If the respondent is forced to rank ordering or
a pair-wise comparison, a number of problems and biases creep into the
study. To deal with such situations the Q-sort scaling process is used. In
Q-sort scale the respondents are asked to sort the various characteristics or
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objects that are being compared into various groups, such that the
distribution of the number of objects or characteristics in each groups follow
a normal distribution. For example, let us take the case of a toy
manufacturing company such as Toys ‘R’ developing a new product. After a
marathon brain storming session, the new product team has come up with a
hundred different products; each with minor variations in features, and
wants to test and find out from consumers which feature combination is
most preferred and will generate maximum sales. The best scaling
procedure that can be used in this context is Q-sort scaling.
4.6.6 Constant Sum Scales
Constant sum scales require respondents to allocate a fixed number of
rating points (usually hundred) among several objects to reflect the relative
preference of each object. It is widely used to measure the relative
importance of attributes.
4.6.7 Pictorial Scales
In the pictorial scales, the various categories of the scale are depicted
pictorially. The respondents are shown a concept or read an attitudinal
statement and are asked to indicate their degree of agreement or interest by
indicating the corresponding position on the pictorial scale.
4.6.8 Paired Comparison Scale
The brands to be rated were presented two at a time, so each brand in the
category was compared once to every other brand. In each pair the
respondent is asked to divide ten points among the brands on the basis of
how much they like once compared to the other. A score was then totalled
for each brand. Although, this scale performs well on the criteria it is
cumbersome to administrate. Another possible limitation is that the frame of
reference is always the other brands in the set being tested. These brands
may change over time.
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4.6.9 Derived Attitude Scales
Attitudes towards complex objects such as health plans, automobiles, credit
instruments, or transportation modes have many facets. Thus, it is often
unrealistic to attempt to capture the full picture with one overall attitude-
scale questions. For example, the public appears to support the general
idea of income tax reforms but opposes the elimination of the most popular
tax loopholes. The most frequent employed of these methods are Likert,
Thurstone and semantic-differential scales.
4.6.10 Likert Scales
Likert scale requires a respondent to indicate a degree of agreement or
disagreement with a variety of statement related to the attitudes or objects.
They are also called summated scales, because the scores on the individual
term are summed to produce a total score for the respondent. A Likert scale
usually consists of two parts, the item part and evaluative part. The item part
is essentially a statement about a certain product, event, or attitude. The
evaluative part is a list of response categories ranking form “strongly agree
to strongly disagree” an important assumption of this scaling method is that
each of the items (statements) measures some aspects of a single common
factor: otherwise, the items cannot be legitimately summed. In other words,
the resulting scale is one-dimensional.
4.6.11 Thurstone Scale
The procedure of Thurstone scale is also known as the method of equal
appearing intervals, since the objective is to obtain a one-dimensional scale
with interval properties. The first step is to develop a large number of
statements or adjectives reflecting all degrees of favourableness toward the
attitude objects then a group of judges is given this set of items and asked
to classify them according to their favourableness or unfavourableness.
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4.6.12 Semantic – Differential Scales
Semantic differential scales are used widely to describe the set of beliefs
that comprise a person’s image of an organisation or brand. The procedure
is also an insightful procedure for comparing the images of competing
brands, stores, or services. Respondents are asked to rate each attitude
object in turn on a number of five- or seven point rating scales, bounded at
each end by polar adjectives or phrases.
Observation and Psychological Measures
Observation methods are limited to providing information on current
behaviour. Too often, this limitation becomes an excuse for not considering
observational methods; because many researchers do not use these
methods, they may not appreciate their considerable benefits. Nevertheless
there are strong arguments for considering the observation of ongoing
behaviour as an integral part of the research design.
4.7 Types of Observation
Observations may be classified in different ways. With reference to
investigator’s role, it may be classified into (a) participant observation and
(b) non-participant observation. In terms of mode of observation, it may be
classified into (c) direct observation. With reference to the rigor of the
system adopted. Observation is classified into (e) controlled observation,
and (f) uncontrolled observation
4.7.1 Participant observation: In this observation the observer is a part of
the phenomenon or group which is observed and he acts as both an
observer and a participant. For example, a study of tribal customs by an
anthropologist by taking part in tribal activities like folk dance. The persons
who are observed should not be aware of the researcher’s purpose. Then
only their behaviour will be ‘natural’. The concealment of research objective
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and researcher’s identity is justified on the ground that it makes it possible to
study certain aspects of the group’s culture which are not revealed to
outsiders.
Advantages of Participatory observation
The advantages of participant observation are
1 The observer can understand the emotional reactions of the observed
group, and get a deeper insight of their experiences.
2 The observer will be able to record context which gives meaning to the
observed behaviour and heard statements.
Disadvantages of Participatory Observation
Participant observation suffers from some demerits.
1 The participant observer narrows his range of observation. For example,
if there is a hierarchy of power in the group/community under study, he
comes to occupy one position within and thus other avenues of
information are closed to him.
2 To the extent that the participant observer participates emotionally, the
objectivity is lost.
3 Another limitation of this method is the dual demand made on the
observer. Recording can interfere with participation, and participation
can interfere with observation. Recording on the spot is not possible and
it has to be postponed until the observer is alone. Such time lag results
in some inaccuracy in recording
Because of the above limitations, participant observation is generally
restricted to those cases where non-participant observation is not practical,
e.g. study of the functioning of a mobile library or mobile bank or tribal
community etc.
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4.7.2 Non-participant observations
In this method, the observer stands apart and does not participate in the
phenomenon observed. Naturally there is no emotional involvement on the
part of the observer. This method calls for skill in recording observations in
an unnoticed manner.
4.7.3 Direct observation
This means observation of an event personally by the observer when it
takes place. This method is flexible and allows the observer to see and
record subtle aspects of events and behaviour as they occur. He is also free
to shift places, change the focus of the observation. A limitation of this
method is that the observer’s perception circuit may not be able to cover all
relevant events when the latter move quickly, resulting in the
incompleteness of the observation.
4.7.4 Indirect observation
This does not involve the physical presence of the observer, and the
recording is done by mechanical, photographic or electronic devices, e.g.
recording customer and employee movements by a special motion picture
camera mounted in a department of a large store. This method is less
flexible than direct observations, but it is less biasing and less erratic in
recording accuracy. It also provides a permanent record for an analysis of
different aspects of the event.
4.7.5 Controlled Observation
This involves standardization of observational techniques and exercises of
maximum control over extrinsic and intrinsic variables by adopting
experimental design and systematically recording observations. Controlled
observation is carried out either in the laboratory or in the field. It is typified
by clear and explicit decisions on what, how and when to observe.
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4.7.6 Uncontrolled observation
This does not involve control over extrinsic and intrinsic variables. It is
primarily used for descriptive research. Participant observation is a typical
uncontrolled one.
4.7.7 Planning Observations
The use of observation method requires proper planning.
First, the researcher should carefully examine the relevance of
observation method to the data needs of the selected study.
Second, he must identify the specific investigative questions which call
for use of observation method. These determine the data to be
collected.
Third, he must decide the observation content, viz., specific conditions,
events and activities that have to be observed for the required data. The
observation content should include the relevant variables.
Fourth, for each variable chosen, the operational definition should be
specified.
Fifth, the observation setting, the subjects to be observed, the timing
and mode of observation, recording, procedure, recording instruments to
be used, and other details of the task should be determined.
Last, observers should be selected and trained. The persons to be
selected must have sufficient concentration powers, strong memory
power and unobtrusive nature. Selected persons should be imparted
both theoretical and practical training
4.7.8 Prerequisites of Effective Observation
The pre-requisites of observation consist of:
1. Observations must be done under conditions which will permit accurate
results. The observer must be in vantage point to see clearly the objects
to be observed. The distance and the light must be satisfactory. The
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mechanical devices used must be in good working conditions and
operated by skilled persons.
2. Observation must cover a sufficient number of representative samples of
the cases.
3. Recording should be accurate and complete.
4. The accuracy and completeness of recorded results must be checked. A
certain number of cases can be observed again by another
observer/another set of mechanical devices, as the case may be. If it is
feasible, two separate observers and sets of instruments may be used in
all or some of the original observations. The results could then be
compared to determine their accuracy and completeness.
4.7.9 Observations Tools and Recording Devices
Systematic observation require the use of observation schedule , objectives
of the study, the investigative questions, hypothesis and the operational
definition of concepts and out of the data requirements, items of data to be
collected through observation are identified. A schedule is then constructed,
covering those items of data. It should be constructed in such a manner as
to make it possible to record the necessary information easily and correctly.
Enough space should be provided for recording observations for each item.
The item should appear in logical groupings and in the order in which the
observer would observe him
Field Observation Log: This may take the form of a diary or cards. Each
item of observations is recorded under appropriate sub-heading. At the time
of observation, rough noting may be made, and at the end of the day, full log
may be made. The card system is flexible and facilitates arrangement and
re-arrangement of items in any desired order.
Mechanical Devices: These may include cameras, tape recorders,
videotape and electronic devices, still, motion, sound and colour and time-
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lapse cameras give a permanent record of events. Microscopic and
telescopic lens may be used in cameras. Eye cameras are common in
advertising and package research. They record eye movements while a
consumer is looking at advertisements or packages. Other recording
instruments are: tape, disc, and wire recorders, barometer, hygrometers,
speedometers, one-way screens and mirrors, closed circuit television, light
meters, pocket calculators.
4.7.10 Suitability and Application
Observation is suitable for a variety of research purposes. It may be used
for studying (a) the behaviour of human beings and social groups, life styles
customs and manner, interpersonal relations, group dynamics, crowd
behaviour, leadership styles, managerial style, other behaviours and
actions; (b) the behaviour of other living creatures like birds, animals etc., (c)
physical characteristic of inanimate things like stores, factories residence
etc., (d) flow of traffic and parking problems; (e) movement of
materials/products through a plant.
It may be used in exploratory research to gain insights that will be
subsequently tested as hypothesis. It may be used to collect supplementary
data that may interpret or qualify findings obtained by other methods.
Observation may be used as the primary method, e.g. anthropological
studies of communities, behavioural studies of small groups
Observation may be used in studying behaviour in ‘natural’ setting or in the
laboratory, e.g. group formation may be studied as it occurs in a real life
situation or in the laboratory room.
4.7.11 Advantages of Observation
Observation has certain advantages:
1. The main virtue of observation is its directness: it makes it possible to
study behaviour as it occurs. The researcher need not ask people about
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their behaviour and interactions; he can simply watch what they do and
say.
2. Data collected by observation may describe the observed phenomena
as they occur in their natural settings. Other methods introduce
elements or artificiality into the researched situation for instance, in
interview; the respondent may not behave in a natural way. There is no
such artificiality in observational studies, especially when the observed
persons are not aware of their being observed.
3. Observations is more suitable for studying subjects who are unable to
articulate meaningfully, e.g. studies of children, tribal, animals, birds etc.
4. Observations improve the opportunities for analyzing the contextual
back ground of behaviour. Further more, verbal resorts can be validated
and compared with behaviour through observation. The validity of what
men of position and authority say can be verified by observing what they
actually do.
5. Observations make it possible to capture the whole event as it occurs.
For example, only observation can provide an insight into all the aspects
of the process of negotiation between union and management
representatives.
6. Observation is less demanding of the subjects and has less biasing
effect on their conduct than questioning.
7. It is easier to conduct disguised observation studies than disguised
questioning.
8. Mechanical devices may be used for recording data in order to secure
more accurate data and also of making continuous observations over
longer periods.
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4.7.12 Limitations of Observation
Observation cannot be used indiscriminately for all purposes. It has its own
limitations:
1. Observation is of no use, studying past events or activities. One has to
depend upon documents or narrations and people for studying such
things.
2. Observation is not suitable for studying and attitudes. However, an
observation of related behaviour affords a good clue to the attitudes.
E.g. and observations of the seating pattern of high caste and class
persons in a general meeting in a village may be useful for forming an
index of attitude.
3. Observation poses difficulties in obtaining a representative sample. For
interviewing and mailing methods, the selection of a random sampling
can be rapidly ensured. But observing people of all types does not make
the sample a random one.
4. Observation cannot be used as and when the researcher finds it
convenient to use. He has to wait for the event to occur. For example,
an observation of folk dance of a tribal community is possible, only when
it is performed.
5. A major limitation of this method is that the observer normally must be at
the scene of the event when it takes place. Yet it may not be possible to
predict where and when the event will occur, e.g., road accident,
communal clash.
6. Observation is slow and expensive process, requiring human observers
and/or costly surveillance equipments
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Self Assessment Questions
State whether the following statements are true or false:
1. Measurement can be defined as a standardized process of assigning
numbers or other symbols to certain characteristics of the objects of
interest, according to some pre-specified rules.
2. Scaling is the process of creating a continuum on which objects are
located according to the amount of the measured characteristics they
possess.
3. The assignment of numbers is made according to rules that should
correspond to the properties of whatever is being measured.
4. In a nominal scale, objects are assigned to mutually exclusive, labelled
categories, but there are no necessary relationships among the
categories; that is , no ordering or spacing is implied
4.8 Summary
Measurement can be defined as a standardized process of assigning
numbers or other symbols to certain characteristics of the objects of interest,
according to some pre-specified rules. Scaling is the process of creating a
continuum on which objects are located according to the amount of the
measured characteristics they possess. The assignment of numbers is
made according to rules that should correspond to the properties of
whatever is being measured. In a nominal scale, objects are assigned to
mutually exclusive, labelled categories, but there are no necessary
relationships among the categories; that is, no ordering or spacing is
implied. An ordinal scale is obtained by ranking objects or by arranging them
in order with regard to some common variable. In an interval scale the
numbers used to rank the objects also represent equal increments of the
attribute being measured. A ratio scale is a special kind of interval scale that
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has a meaningful zero point. Attitude measures, used in marketing, must be
both accurate and useful. Aspects that contribute accuracy are validity,
reliability and sensitivity. An attitude measure has validity if it measures what
it is supposed to measure. The total error of a measurement consists of
systematic error component and a random error component. The third
characteristic of a good attitude measure is sensitivity, or the ability to
discriminate among meaningful differences in attitudes. Sensitivity is
achieved by increasing the number of scale categories; however, the more
categories there are, the lower the reliability will be. Generalization refers to
the ease of scale administration and interpretation in different research
settings and situations. Relevancy of a scale refers to how meaningful it is to
apply the scale to measure a construct. Questionnaire construction is
properly regarded as a very imperfect art. There are no established
procedures that will lead consistently to a good questionnaire.
There are many ways to present a respondent with a continuum of
numbered categories that represent the range of possible attitude
judgments. They can be generally classified as single item scales and
multiple item scales. Single item scales are those that have only one item to
measure a construct. Under the single item scales, the itemized category
scale is the most widely used by marketing researchers. In some situations,
comparative scales, rank order scales, or constant-sum scales have
advantages. There are four categories from which respondents can choose
to indicate their overall level of satisfaction with their present health
insurance plan. Rank order scale requires the respondent to arrange a set
of objects with regard to a common criterion: advertisements in terms of
interest, product features in terms of importance, or new-product concepts
with regard to willingness to buy in the future. Constant sum scales require
respondents to allocate a fixed number of rating points (usually hundred)
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among several objects to reflect the relative preference of each object. It is
widely used to measure the relative importance of attributes.
Attitudes towards complex objects such as health plans, automobiles, credit
instruments, or transportation modes have many facets. Likert scale
requires a respondent to indicate a degree of agreement or disagreement
with a variety of statement related to the attitudes or objects. They are also
called summated scales, because the scores on the individual term are
summed to produce a total score for the respondent. Semantic differential
scales are used widely to describe the set of beliefs that comprise a
person’s image of an organisation or brand. Observation methods are
limited to providing information on current behaviour. Observations may be
classified in different ways. With reference to investigator’s role, it may be
classified into (a) participant observation and (b) non-participant
observation. In terms of mode of observation, it may be classified into (c)
direct observation. With reference to the rigor of the system adopted.
Observation is classified into (e) controlled observation, and (f) uncontrolled
observation
4.9 Terminal Questions
1. What are the concepts of measurement techniques used in marketing
research?
2. Explain different scales of measurement.
3. How is a questionnaire designed?
4. What are derived attitude scales?
5. What are the types of observation?
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4.10 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
4. True
TQs
1. Section 4.1
2. Section 4. 5
3. Section 4. 4.5.1
4. Section 4.6.9
5. Section 4.6.7
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Unit 5 Sampling and Data Analysis
Structure
5.1 Introduction: Meaning of sampling
Objectives
5.1.1 Census and sample
5.2 The sampling process
5.2.1 Criteria for sampling procedure
5.3 Different types of sampling techniques
5.3.1 Probability or random sampling
5.3.2 Non-probability or Non-random sampling
5.4 Random sampling
Self assessment questions
5.5 Summary
5.6 Terminal questions
5.7 Answers to SAQs and TQs
5.1 Introduction: Meaning of Sampling
A part of the population is known as sample. The method consisting of the
selecting for study, a portion of the ‘universe’ with a view to drawing
conclusions about the ‘universe’ or ‘population’ is known as sampling. A
statistical sample ideally purports to be a miniature model or replica of the
population constituted of all the items that the study should principally
encompass, that is, the items which potentially hold promise of affording
information relevant to the purpose of a given research.
Sampling helps in time and cost saving. It also helps in checking their
accuracy. But on the other hand it demands exercise of great care and
caution; otherwise the results obtained may be incorrect or misleading.
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Objectives:
After studying this unit, the students should be able to:
explain the census and sample
discuss the sampling process and criteria for sampling procedure
explain the different types of sampling techniques
5.1.1 Census and Sample Survey
The process of designing a field study, among other things, involves a
decision to use sampling or not. The researcher must decide whether he
should cover all the units. When all the units are studied, such a complete
coverage is called a census survey. When only a sample of the universe is
studied, the study is called a sample survey.
In making this decision of census or sampling, the following factors are
considered:
1) The size of the population: If the population to be studied is relatively
small, say 50 institutions or 200 employees or 150 households, the
investigator may decide to study the entire population. The task is easily
manageable and the sampling may not be required. But, if the
population to be studied is quite large, sampling is warranted.
However, the size is a relative matter. Whether a population is large or
small depends upon the nature of the study, the purpose for which it is
undertaken, and the time and other resources available for it.
2) Amount of funds budgeted for the study: The decision regarding
census or sampling depends upon the budget of the study. Sampling is
opted when the amount of money budgeted is smaller than the
anticipated cost of census survey.
3) Facilities: The extent of facilities available – staff, access to computer
facility and accessibility to population elements - in another factor to be
considered in deciding to sample or not. When the availability of these
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facilities is extensive, census survey may be manageable. Otherwise,
sampling is preferable.
4) Time: The time limit within which the study should be completed is
another important factor to be considered in deciding the question of
census or sample survey. This, in fact, is a primary reason for using
sampling by academic and marketing researchers.
5.2 Sampling Process
5.2.1 Criteria of Sampling Procedure
The decision process is complicated one. The researcher has to first identify
the limiting factor or factors and must judiciously balance the conflicting
factors. The various criteria governing the choice of the sampling technique:
1. Purpose of the Survey: What does the researcher aim at? If he intends
to generalize the findings based on the sample survey to the population,
then an appropriate probability sampling method must be selected. The
choice of a particular type of probability sampling depends on the
geographical area of the survey and the size and the nature of the
population under study.
2. Measurability: The application of statistical inference theory requires
computation of the sampling error from the sample itself. Probability
samples only allow such computation. Hence, where the research
objective requires statistical inference, the sample should be drawn by
applying simple random sampling method or stratified random sampling
method, depending on whether the population is homogenous or
heterogeneous.
3. Degree of Precision: Should the results of the survey be very precise,
or even rough results could serve the purpose? The desired level of
precision as one of the criteria of sampling method selection. Where a
high degree of precision of results is desired, probability sampling
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should be used. Where even crude results would serve the purpose
(E.g., marketing surveys, readership surveys etc) any convenient non-
random sampling like quota sampling would be enough.
4. Information about Population: How much information is available
about the population to be studied? Where no list of population and no
information about its nature are available, it is difficult to apply a
probability sampling method. Then exploratory study with non-probability
sampling may be made to gain a better idea of population. After gaining
sufficient knowledge about the populations through the exploratory
study, appropriate probability sampling design may be adopted.
5. The Nature of the Population: In terms of the variables to be studied, is
the population homogenous or heterogeneous? In the case of a
homogenous population, even a simple random sampling will give a
representative sample. If the population is heterogeneous, stratified
random sampling is appropriate.
6. Geographical Area of the Study ad the Size of the Population: If the
area covered by a survey is very large and the size of the population is
quite large, multi-stage cluster sampling would be appropriate. But if the
area and the size of the population are small, single stage probability
sampling methods could be used.
7. Financial resources: If the available finance is limited, it may become
necessary to choose a less costly sampling plan like multistage cluster
sampling or even quota sampling as a compromise. However, if the
objectives of the study and the desired level of precision cannot be
attained within the stipulated budget, there is no alternative than to give
up the proposed survey. Where the finance is not a constraint, a
researcher can choose the most appropriate method of sampling that fits
the research objective and the nature of population.
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8. Time Limitation: The time limit, within which the research project should
be completed, restricts the choice of a sampling method. Then, as a
compromise, it may become necessary to choose less time consuming
methods like simple random sampling instead of stratified
sampling/sampling with probability proportional to size; multi-stage
cluster sampling instead of single-stage sampling of elements. Of
course, the precision has to be sacrificed to some extent.
9. Economy: It should be another criterion in choosing the sampling
method. It means achieving the desired level of precision at minimum
cost. A sample is economical if the precision per unit cost is high or the
cost per unit of variance is low. The above criteria frequently conflicts
and the researcher must balance and blend them to obtain a good
sampling plan. The chosen plan thus represents an adaptation of the
sampling theory to the available facilities and resources. That is, it
represents a compromise between idealism and feasibility. One should
use simple workable methods instead of unduly elaborate and
complicated techniques.
5.3 Different Types of Sample Techniques
Sampling techniques or methods may be classified into two generic types:
5.3.1 Probability or Random Sampling
Probability sampling is based on the theory of probability. It is also known as
random sampling. It provides a known non-zero chance of selection for each
population element. It is used when generalization is the objective of study,
and a greater degree of accuracy of estimation of population parameters is
required. The cost and time required is high and thus the benefit derived
from it should justify the costs. The following are the types of probability
sampling.
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Simple random sampling: This sampling technique gives each element an
equal and independent chance of being selected. An equal chance means
equal probability of selection. An independent chance means that the draw
of one element will not affect the chances of other elements being selected.
The procedure of drawing a simple random sample consists of:
Enumeration of all elements in the population,
Preparation of a List of all elements, giving them numbers in serial order
1, 2, 3, and so on, and
Drawing sample numbers by using (a) lottery method, (b) a table of
random numbers or (c) a computer.
Suitability: This type of sampling is suited for a small homogeneous
population.
Advantages: The advantage of this is that it is one of the easiest methods,
all the elements in the population have an equal chance of being selected,
simple to understand, does not require prior knowledge of the true
composition of the population.
Disadvantages: It is often impractical because of non-availability of
population list or of difficulty in enumerating the population, does not ensure
proportionate representation and it may be expensive in time and money.
The amount of sampling error associated with any sample drawn can easily
be computed but it is greater than that in other probability samples of the
same size, because it is less precise than other methods.
Stratified random sampling: This is an improved type of random or
probability sampling. In this method, the population is sub-divided into
homogenous groups or strata, and from each stratum, random sample is
drawn. E.g., university students may be divided on the basis of discipline,
and each discipline group may again be divided into juniors and seniors.
Stratification is necessary for increasing a sample’s statistical efficiency,
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providing adequate data for analyzing the various sub-populations and
applying different methods to different strata. The stratified random sampling
is appropriate for a large heterogeneous population. Stratification process
involves three major decisions. They are stratification base or bases,
number of strata and strata sample sizes. Stratified random sampling may
be classified into:
Proportionate stratified sampling: This sampling involves drawing a
sample from each stratum in proportion to the latter’s share in the total
population. It gives proper representation to each stratum and its statistical
efficiency is generally higher. This method is therefore very popular. E.g., if
the final year MBA students of the Management Faculty of a University
consist of the following specialization groups:
Specialization stream
No. of students Proportion of each stream
ProductionFinance MarketingRural development
40203010
0.40.20.30.1
100 1.0
The research wants to draw an overall sample of 30. Then the strata sample
sizes would be:
Strata Sample size
ProductionFinance MarketingRural development
30 x 0.430 x 0.230 x 0.330 x 0.1
12 6 9 3
30
Advantages: Stratified random sampling enhances the representative ness
to each sample, gives higher statistical efficiency, easy to carry out, and
gives a self-weighing sample.
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Disadvantages: A prior knowledge of the composition of the population and
the distribution of the population, it is very expensive in time and money and
identification of the strata may lead to classification of errors.
Disproportionate stratified random sampling: This method does not give
proportionate representation to strata. It necessarily involves giving over-
representation to some strata and under-representation to others. The
desirability of disproportionate sampling is usually determined by three
factors, viz, (a) the sizes of strata, (b) internal variances among strata, and
(c) sampling costs.
Suitability: This method is used when the population contains some small
but important subgroups, when certain groups are quite heterogeneous,
while others are homogeneous and when it is expected that there will be
appreciable differences in the response rates of the subgroups in the
population.
Advantages: The advantages of this type are that it is less time consuming
and facilitates giving appropriate weighing to particular groups which are
small but more important.
Disadvantages: The disadvantage is that it does not give each stratum
proportionate representation, requires prior knowledge of composition of the
population, is subject to classification errors and its practical feasibility is
doubtful.
Systematic random sampling: This method of sampling is an alternative to
random selection. It consists of taking kth item in the population after a
random start with an item form 1 to k. It is also known as fixed interval
method. E.g., 1st, 11th, 21st ……… Strictly speaking, this method of sampling
is not a probability sampling. It possesses characteristics of randomness
and some non-probability traits.
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Suitability: Systematic selection can be applied to various populations such
as students in a class, houses in a street, telephone directory etc.
Advantages: The advantages are that it is simpler than random sampling,
easy to use, easy to instruct, requires less time, it’s cheaper, easier to
check, sample is spread evenly over the population, and it is statistically
more efficient.
Disadvantages: The disadvantages are that it ignores all elements between
two kth elements selected, each element does not have equal chance of
being selected, and this method sometimes gives a biased sample.
Cluster sampling: It means random selection of sampling units consisting
of population elements. Each such sampling unit is a cluster of population
elements. Then from each selected sampling unit, a sample of population
elements is drawn by either simple random selection or stratified random
selection. Where the population elements are scattered over a wide area
and a list of population elements is not readily available, the use of simple or
stratified random sampling method would be too expensive and time-
consuming. In such cases, cluster sampling is usually adopted. The cluster
sampling process involves: identify clusters, examine the nature of clusters,
and determine the number of stages.
Suitability: The application of cluster sampling is extensive in farm
management surveys, socio-economic surveys, rural credit surveys,
demographic studies, ecological studies, public opinion polls, and large
scale surveys of political and social behaviour, attitude surveys and so on.
Advantages: The advantages of this method are that it is easier and more
convenient, cost of this is much less, promotes the convenience of field
work as it could be done in compact places, it does not require more time,
units of study can be readily substituted for other units and it is more
flexible.
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Disadvantages: The cluster sizes may vary and this variation could
increase the bias of the resulting sample. The sampling error in this method
of sampling is greater and the adjacent units of study tend to have similar
characteristics than do units distantly apart.
Area sampling: This is an important form of cluster sampling. In larger field
surveys cluster consisting of specific geographical areas like districts, taluks,
villages or blocks in a city are randomly drawn. As the geographical areas
are selected as sampling units in such cases, their sampling is called area
sampling. It is not a separate method of sampling, but forms part of cluster
sampling.
Multi-stage and sub-sampling: In multi-stage sampling method, sampling
is carried out in two or more stages. The population is regarded as being
composed of a number of second stage units and so forth. That is, at each
stage, a sampling unit is a cluster of the sampling units of the subsequent
stage. First, a sample of the first stage sampling units is drawn, then from
each of the selected first stage sampling unit, a sample of the second stage
sampling units is drawn. The procedure continues down to the final
sampling units or population elements. Appropriate random sampling
method is adopted at each stage. It is appropriate where the population is
scattered over a wider geographical area and no frame or list is available for
sampling. It is also useful when a survey has to be made within a limited
time and cost budget. The major disadvantage is that the procedure of
estimating sampling error and cost advantage is complicated.
Sub-sampling is a part of multi-stage sampling process. In a multi-stage
sampling, the sampling in second and subsequent stage frames is called
sub-sampling. Sub-sampling balances the two conflicting effects of
clustering i.e., cost and sampling errors.
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Random sampling with probability proportional to size: The procedure
of selecting clusters with probability Proportional to size (PPS) is widely
used. If one primary cluster is twice as large a population as another, it has
twice the chance of being selected. If the same number of persons is then
selected from each of the selected clusters, the overall probability of any
person will be the same. Thus PPS is a better method for securing a
representative sample of population elements in multi-stage cluster
sampling.
Advantages: The advantages are that the clusters of various sizes get
proportionate representation, PPS leads to greater precision than would a
simple random sample of clusters and a constant sampling fraction at the
second stage, equal-sized samples from each selected primary cluster are
convenient for field work.
Disadvantages: PPS cannot be used if the sizes of the primary sampling
clusters are not known.
Double sampling and multiphase sampling: Double sampling refers to
the subsection of the final sample form a pre-selected larger sample that
provided information for improving the final selection. When the procedure is
extended to more than two phases of selection, it is then, called multi-phase
sampling. This is also known as sequential sampling, as sub-sampling is
done from a main sample in phases. Double sampling or multiphase
sampling is a compromise solution for a dilemma posed by undesirable
extremes. “The statistics based on the sample of ‘n’ can be improved by
using ancillary information from a wide base: but this is too costly to obtain
from the entire population of N elements. Instead, information is obtained
from a larger preliminary sample nL which includes the final sample n.
Replicated or interpenetrating sampling: It involves selection of a certain
number of sub-samples rather than one full sample from a population. All
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the sub-samples should be drawn using the same sampling technique and
each is a self-contained and adequate sample of the population. Replicated
sampling can be used with any basic sampling technique: simple or
stratified, single or multi-stage or single or multiphase sampling. It provides
a simple means of calculating the sampling error. It is practical. The
replicated samples can throw light on variable non-sampling errors. But
disadvantage is that it limits the amount of stratification that can be
employed.
5.3.2 Non-Probability or Non Random Sampling
Non-probability sampling or non-random sampling is not based on the
theory of probability. This sampling does not provide a chance of selection
to each population element.
Advantages: The only merits of this type of sampling are simplicity,
convenience and low cost.
Disadvantages: The demerits are that it does not ensure a selection
chance to each population unit. The selection probability sample may not be
a representative one. The selection probability is unknown. It suffers from
sampling bias which will distort results.
The reasons for usage of this sampling are when there is no other feasible
alternative due to non-availability of a list of population, when the study does
not aim at generalizing the findings to the population, when the costs
required for probability sampling may be too large, when probability
sampling required more time, but the time constraints and the time limit for
completing the study do not permit it. It may be classified into:
1. Convenience or accidental sampling: It means selecting sample units
in a just ‘hit and miss’ fashion E.g., interviewing people whom we happen to
meet. This sampling also means selecting whatever sampling units are
conveniently available, e.g., a teacher may select students in his class. This
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method is also known as accidental sampling because the respondents
whom the researcher meets accidentally are included in the sample.
Suitability: Though this type of sampling has no status, it may be used for
simple purposes such as testing ideas or gaining ideas or rough impression
about a subject of interest.
Advantage: It is the cheapest and simplest, it does not require a list of
population and it does not require any statistical expertise.
Disadvantage: The disadvantage is that it is highly biased because of
researcher’s subjectivity, it is the least reliable sampling method and the
findings cannot be generalized.
2. Purposive (or judgment) sampling: This method means deliberate
selection of sample units that conform to some pre-determined criteria. This
is also known as judgment sampling. This involves selection of cases which
we judge as the most appropriate ones for the given study. It is based on
the judgement of the researcher or some expert. It does not aim at securing
a cross section of a population. The chance that a particular case be
selected for the sample depends on the subjective judgement of the
researcher.
Suitability: This is used when what is important is the typicality and specific
relevance of the sampling units to the study and not their overall
representative ness to the population.
Advantage: It is less costly and more convenient and guarantees inclusion
of relevant elements in the sample.
Disadvantage: It is less efficient for generalizing, does not ensure the
representative ness, requires prior extensive information and does not lend
itself for using inferential statistics.
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3. Quota sampling: This is a form of convenient sampling involving
selection of quota groups of accessible sampling units by traits such as sex,
age, social class, etc. It is a method of stratified sampling in which the
selection within strata is non-random. It is this ‘Non-random’ element that
constitutes its greatest weakness.
Suitability: It is used in studies like marketing surveys, opinion polls, and
readership surveys which do not aim at precision, but to get quickly some
crude results.
Advantage: It is less costly, takes less time, no need for a list of population,
and field work can easily be organized.
Disadvantage: It is impossible to estimate sampling error, strict control if
field work is difficult, and subject to a higher degree of classification.
4. Snow-ball sampling: This is the colourful name for a technique of
building up a list or a sample of a special population by using an initial set of
its members as informants. This sampling technique may also be used in
socio-metric studies.
Suitability: It is very useful in studying social groups, informal groups in a
formal organization, and diffusion of information among professional of
various kinds.
Advantage: It is useful for smaller populations for which no frames are
readily available.
Disadvantage: The disadvantage is that it does not allow the use of
probability statistical methods. It is difficult to apply when the population is
large. It does not ensure the inclusion of all the elements in the list.
5.4 Random Sampling
The importance of randomness is that sampling needs no emphasis. It is a
means for securing a representative sample. Experience shows that the
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human being is an extremely poor instrument for the conduct of a random
selection. To ensure true randomness, the method of selection must be
independent of human judgment. There are three basic procedures:
The Lottery Method: This is the simplest and most familiar procedure of
random sampling. In a sampling procedure, there are two alternatives. After
a number is selected by draw, it may be replaced, and consequently it has a
chance of being selected again. Such a method is known as sampling with
replacement. This is usually referred to as unrestricted random sampling.
Alternatively, the selected number is set aside, and so in the subsequent
draw, it does not get a chance of being selected again. This type of
sampling is known as sampling without replacement. This is a form of
restricted sampling. Lottery Method is used for drawing a small form a small
population. But it would be time consuming and tedious if the population is
very large.
The use of Table of Random Numbers: This is a less cumbersome but
equally valid procedure of sample selection. Tables of random numbers
have been developed by Kendall and Smith (1939), Fisher and Yates (1963)
and Tippett (1927). One of them is usually found in a standard book on
Statistics or Methodology of Research. To select a random sample out of a
given frame, one should simply start to read numbers form a Table of
Random Numbers at any randomly selected point and pick out numbers
within the range of the frame.
Use of computer: If the population is very large and if computer facilities are
available, a computer may be used for drawing a random sample. The
computer can be programmed to print out a series of random numbers, as
many as the researcher desires.
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Self Assessment Questions – I
State whether the following statements are true or false:
1. Sampling helps in time and cost saving.
2. The decision regarding census or sampling depends upon the budget of
the study.
3. Simple random sampling technique gives each element an equal and
independent chance of being selected.
5.5 Summary
A statistical sample ideally purports to be a miniature model or replica of the
population constituted of all the items that the study should principally
encompass, that is, the items which potentially hold promise of affording
information relevant to the purpose of a given research. Sampling helps in
time and cost saving. It also helps in checking their accuracy. But on the
other hand it demands exercise of great care and caution; otherwise the
results obtained may be incorrect or misleading. The process of designing a
field study, among other things, involves a decision to use sampling or not.
The researcher must decide whether he should cover all the units. When all
the units are studied, such a complete coverage is called a census survey.
When only a sample of the universe is studied, the study is called a sample
survey. If the population to be studied is quite large, sampling is warranted.
However, the size is a relative matter. Whether a population is large or small
depends upon the nature of the study, the purpose for which it is
undertaken, and the time and other resources available for it.
The decision regarding census or sampling depends upon the budget of the
study. Sampling is opted when the amount of money budgeted is smaller
than the anticipated cost of census survey. The extent of facilities available
– staff, access to computer facility and accessibility to population elements -
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is another factor to be considered in deciding to sample or not. When the
availability of these facilities is extensive, census survey may be
manageable. Otherwise, sampling is preferable. The time limit within the
study should be completed is another important factor to be considered in
deciding the question of census or sample survey. This, in fact, is a primary
reason for using sampling by academic and marketing researchers. The
application of statistical inference theory requires computation of the
sampling error from the sample itself. Probability samples only allow such
computation. Hence, where the research objective requires statistical
inference, the sample should be drawn by applying simple random sampling
method or stratified random sampling method, depending on whether the
population is homogenous or heterogeneous.
Probability sampling is based on the theory of probability. It is also known as
random sampling. It provides a known non-zero chance of selection for each
population element. Simple random sampling technique gives each
element an equal and independent chance of being selected. An equal
chance means equal probability of selection. Stratified random sampling
is an improved type of random or probability sampling. In this method, the
population is sub-divided into homogenous groups or strata, and from each
stratum, random sample is drawn. Proportionate stratified sampling
involves drawing a sample from each stratum in proportion to the latter’s
share in the total population. It gives proper representation to each stratum
and its statistical efficiency is generally higher. Disproportionate stratified
random sampling does not give proportionate representation to strata. It
necessarily involves giving over-representation to some strata and under-
representation to others. The desirability of disproportionate sampling is
usually determined by three factors, viz, (a) the sizes of strata, (b) internal
variances among strata, and (c) sampling costs. Systematic random
sampling method is an alternative to random selection. It consists of taking
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kth item in the population after a random start with an item from 1 to k. It is
also known as fixed interval method. Cluster sampling means random
selection of sampling units consisting of population elements. Each such
sampling unit is a cluster of population elements. Then from each selected
sampling unit, a sample of population elements is drawn by either simple
random selection or stratified random selection. In Area sampling larger
field surveys cluster consisting of specific geographical areas like districts,
taluks, villages or blocks in a city are randomly drawn. As the geographical
areas are selected as sampling units in such cases, their sampling is called
area sampling. It is not a separate method of sampling, but forms part of
cluster sampling. Double sampling and multiphase sampling refers to the
subsection of the final sample from a pre-selected larger sample that
provided information for improving the final selection. When the procedure is
extended to more than two phases of selection, it is then, called multi-phase
sampling. This is also known as sequential sampling, as sub-sampling is
done from a main sample in phases. Replicated or interpenetrating
sampling involves selection of a certain number of sub-samples rather than
one full sample from a population. All the sub-samples should be drawn
using the same sampling technique and each is a self-contained and
adequate sample of the population. Non-probability or non random
sampling is not based on the theory of probability. This sampling does not
provide a chance of selection to each population element. Purposive (or
judgment) sampling method means deliberate selection of sample units
that conform to some pre-determined criteria. This is also known as
judgment sampling. This involves selection of cases which we judge as the
most appropriate ones for the given study. It is based on the judgment of the
researcher or some expert. Quota sampling is a form of convenient
sampling involving selection of quota groups of accessible sampling units by
traits such as sex, age, social class, etc. it is a method of stratified sampling
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in which the selection within strata is non-random. Snow-ball sampling is
the colourful name for a technique of building up a list or a sample of a
special population by using an initial set of its members as informants. This
sampling technique may also be used in socio-metric studies.
5.6 Terminal Questions
1. Distinguish between Census and sample survey
2. Explain the Sampling process
3. What are the types of Probability or random sampling?
4. Explain Multi-stage and sub-sampling?
5. What is Random sampling with probability proportional to size?
6. Distinguish between Double sampling and multiphase sampling:
7. What is replicated or interpenetrating sampling?
8. What is Non-probability or non random sampling?
9. What is Purposive (or judgment) sampling?
10. What is Quota sampling?
11. What is Snow-ball sampling?
12. What is Random sample?
5.7 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
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TQs
1. Section 5.11
2. Section 5.2
3. Section 5.3.1.
4. Section 5.3.1.
5. Section 5.3.1
6. Section 5.3.1
7. Section 5.3.1
8. Section 5.3.2
9. Section 5.3.2.
10. Section 5.3.2
11. Section 5.3.2.
12. Section 5.6.
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Unit 6 Hypothesis Formation and Testing
Structure
6.1 Introduction: Meaning of hypothesis
Objectives:
6.2 Criteria for hypothesis construction
6.3 Nature of hypothesis
6.4 Features of hypothesis
6.5 Characteristics of hypothesis
6.6 Concept of testing hypothesis
6.7 Procedure and flow diagram for testing hypothesis
6.8 Test of hypothesis
6.9 Important parametric tests
Self assessment questions
6.10 Summary
6.11 Terminal Questions
6.12 Answers to SAQs and TQs
6.1 Introduction: Meaning of Hypotheses
A hypothesis is an assumption about relations between variables. It is a
tentative explanation of the research problem or a guess about the research
outcome. Before starting the research, the researcher has a rather general,
diffused, even confused notion of the problem. It may take long time for the
researcher to say what questions he had been seeking answers to. Hence,
an adequate statement about the research problem is very important. What
is a good problem statement? It is an interrogative statement that asks: what
relationship exists between two or more variables? It then further asks
questions like: If A related to B or not? How are A and B related to C? Is A
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related to B under conditions X and Y? Proposing a statement pertaining to
relationship between A and B is called a hypothesis.
According to Theodorson and Theodorson, “a hypothesis is a tentative
statement asserting a relationship between certain facts. Kerlinger describes
it as “a conjectural statement of the relationship between two or more
variables”. Black and Champion have described it as “a tentative statement
about something, the validity of which is usually unknown”. This statement is
intended to be tested empirically and is either verified or rejected. If the
statement is not sufficiently established, it is not considered a scientific law.
In other words, a hypotheses carries clear implications for testing the stated
relationship, i.e., it contains variables that are measurable and specifying
how they are related. A statement that lacks variables or that does not
explain how the variables are related to each other is no hypotheses in
scientific sense.
Objectives:
After learning this unit, the students should be able to:
explain the criteria for hypothesis construction
discuss the nature of hypothesis, features of hypothesis, characteristics
of hypothesis and testing hypothesis
discuss the important parametric tests
6.2. Criteria for Hypotheses Construction
Hypothesis is never formulated in the form of a question. The standards to
be met in formulating a hypothesis:
1. It should be empirically testable, whether it is right or wrong.
2. It should be specific and precise.
3. The statements in the hypotheses should not be contradictory.
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4. It should specify variables between which the relationship is to be
established.
5. It should describe one issue only.
6. 3 Nature of Hypotheses
A scientific and justified hypothesis must meet the following criteria:
It must accurately reflect the relevant sociological fact.
It must not be in contradiction with approved relevant statements of
other scientific disciplines.
It must consider the experience of other researchers.
6.4 The Functions of Hypotheses
1. A hypothesis gives a definite point to the investigation, and it guides the
direction on the study.
2. A hypothesis specifies the sources of data, which shall be studied, and
in what context they shall be studied.
3. It determines the data needs.
4. A hypothesis suggests which type of research is likely to be most
appropriate.
5. It determines the most appropriate technique of analysis.
6. A hypothesis contributes to the development of theory
6.5 Characteristics of Good Hypotheses
Conceptual Clarity
Specificity
Testability
Availability of Techniques
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Theoretical relevance
Consistency
Objectivity
Simplicity
6.6 Concept of Testing Hypotheses
Basic concepts in the context of testing of hypotheses need to be explained
1) Null hypotheses and alternative hypotheses: In the context of
statistical analysis, we often talk null and alternative hypotheses. If we
are to compare method A with method B about its superiority and if we
proceed on the assumption that both methods are equally good, then
this assumption is termed as null hypotheses. As against this, we may
think that the method A is superior, it is alternative hypotheses.
Symbolically presented as:
Null hypotheses = H0 and Alternative hypotheses = Ha
Suppose we want to test the hypotheses that the population mean is
equal to the hypotheses mean (µ H0) = 100. Then we would say that the
null hypotheses are that the population mean is equal to the
hypothesized mean 100 and symbolical we can express as: H0: µ= µ
H0=100
If our sample results do not support this null hypothesis, we should
conclude that something else is true. What we conclude rejecting the
null hypotheses is known as alternative hypotheses. If we accept H0,
then we are rejecting Ha and if we reject H0, then we are accepting Ha.
For H0: µ= µ H0=100, we may consider three possible alternative
hypotheses as follows:
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Alternative Hypotheses To be read as follows
Ha: µ≠µ H0 (The alternative hypotheses is that the population mean is not equal to 100 i.e., it may be more or less 100)
Ha: µ>µ H0 (The alternative hypotheses is that the population mean is greater than 100)
Ha: µ< µ H0 (The alternative hypotheses is that the population mean is less than 100)
The null hypotheses and the alternative hypotheses are chosen before
the sample is drawn (the researcher must avoid the error of deriving
hypotheses from the data he collects and testing the hypotheses from
the same data). In the choice of null hypotheses, the following
considerations are usually kept in view:
a. Alternative Hypotheses is usually the one which wishes to prove and
the null hypotheses are one that wishes to disprove. Thus a null
hypotheses represent the hypotheses we are trying to reject, the
alternative hypotheses represents all other possibilities.
b. If the rejection of a certain hypotheses when it is actually true
involves great risk, it is taken as null hypotheses because then the
probability of rejecting it when it is true is α (the level of significance)
which is chosen very small.
c. Null hypotheses should always be specific hypotheses i.e., it should
not state about or approximately a certain value.
Generally, in hypotheses testing we proceed on the basis of null
hypotheses, keeping the alternative hypotheses in view. Why so? The
answer is that on assumption that null hypotheses is true, one can
assign the probabilities to different possible sample results, but this
cannot be done if we proceed with alternative hypotheses. Hence the
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use of null hypotheses (at times also known as statistical hypotheses) is
quite frequent.
2) The Level of Significance: This is a very important concept in the
context of hypotheses testing. It is always some percentage (usually
5%) which should be chosen with great care, thought and reason. In
case we take the significance level at 5%, then this implies that H0 will
be rejected when the sampling result (i.e., observed evidence) has a
less than 0.05 probability of occurring if H0 is true. In other words, the 5%
level of significance means that researcher is willing to take as much as
5% risk rejecting the null hypotheses when it (H0) happens to be true.
Thus the significance level is the maximum value of the probability of
rejecting H0 when it is true and is usually determined in advance before
testing the hypotheses.
3) Decision rule or test of hypotheses: Given a hypotheses H0 and an
alternative hypothesis H0 we make rule which is known as decision rule
according to which we accept H0 (i.e., reject Ha) or reject H0 (i.e., accept
Ha). For instance, if (H0 is that a certain lot is good (there are very few
defective items in it) against Ha that the lot is not good (there are many
defective items in it), that we must decide the number of items to be
tested and the criterion for accepting or rejecting the hypotheses. We
might test 10 items in the lot and plan our decision saying that if there
are none or only 1 defective item among the 10, we will accept H0
otherwise we will reject H0 (or accept Ha). This sort of basis is known as
decision rule.
4) Type I & Type II errors: In the context of testing of hypotheses, there
are basically two types of errors that we can make. We may reject H0
when H0 is true & we may accept H0 when it is not true. The former is
known as Type I & the later is known as Type II. In other words, Type I
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error mean rejection of hypotheses which should have been accepted &
Type II error means accepting of hypotheses which should have been
rejected. Type I error is donated by α (alpha), also called as level of
significance of test; and Type II error is donated by β(beta).
Decision
Accept H0 Reject H0
H0 (true) Correct decision Type I error (α error)
Ho (false) Type II error (β error) Correct decision
The probability of Type I error is usually determined in advance and is
understood as the level of significance of testing the hypotheses. If type
I error is fixed at 5%, it means there are about chances in 100 that we
will reject H0 when H0 is true. We can control type I error just by fixing it
at a lower level. For instance, if we fix it at 1%, we will say that the
maximum probability of committing type I error would only be 0.01.
But with a fixed sample size, n when we try to reduce type I error, the
probability of committing type II error increases. Both types of errors can
not be reduced simultaneously. There is a trade-off in business
situations, decision-makers decide the appropriate level of type I error
by examining the costs of penalties attached to both types of errors. If
type I error involves time & trouble of reworking a batch of chemicals
that should have been accepted, where as type II error means taking a
chance that an entire group of users of this chemicals compound will be
poisoned, then in such a situation one should prefer a type I error to a
type II error means taking a chance that an entire group of users of this
chemicals compound will be poisoned, then in such a situation one
should prefer a type II error. As a result, one must set very high level for
type I error in one’s testing techniques of a given hypotheses. Hence, in
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testing of hypotheses, one must make all possible effort to strike an
adequate balance between Type I & Type II error.
5) Two Tailed test & One Tailed test: In the context of hypotheses testing
these two terms are quite important and must be clearly understood. A
two-tailed test rejects the null hypotheses if, say, the sample mean is
significantly higher or lower than the hypnotized value of the mean of the
population. Such a test is inappropriate when we haveH0: µ= µ H0 and
Ha: µ≠µ H0 which may µ>µ H0 or µ<µ H0. If significance level is % and
the two-tailed test to be applied, the probability of the rejection area will
be 0.05 (equally split on both tails of curve as 0.025) and that of the
acceptance region will be 0.95. If we take µ = 100 and if our sample
mean deviates significantly from µ, in that case we shall accept the null
hypotheses. But there are situations when only one-tailed test is
considered appropriate. A one-tailed test would be used when we are to
test, say, whether the population mean in either lower than or higher
than some hypothesized value.
6.7 Procedure and Flow Diagram for Testing Hypotheses
To test a hypotheses means to tell (on the basis of the data researcher has
collected) whether or not the hypothesis seems to be valid. In hypothesis
testing, the main question is: whether or not to accept the null hypothesis?
Procedure for hypothesis testing refers to all those steps that we undertake
for making a choice between the two actions i.e., rejection and acceptance
of a null hypothesis. The various steps involved in hypothesis testing are
stated below:
i. Making a formal statement: The step consists in making a formal
statement of the null hypothesis (Ho) and also of the alternative
hypothesis (Ha). This means that hypothesis should be clearly stated,
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considering the nature of the research problem. For instance, Mr. Mohan
of the Civil Engineering Department wants to test the load bearing
capacity of an old bridge which must be more than 10 tons, in that case
he can state his hypothesis as under:
Null hypotheses HO: µ =10 tons
Alternative hypotheses Ha: µ >10 tons
Take another example. The average score in an aptitude test
administered at the national level is 80. To evaluate a state’s education
system, the average score of 100 of the state’s students selected on the
random basis was 75. The state wants to know if there is a significance
difference between the local scores and the national scores. In such a
situation the hypothesis may be stated as under:
Null hypotheses HO: µ =80
Alternative hypotheses Ha: µ ≠ 80
The formulation of hypothesis is an important step which must be
accomplished with due care in accordance with the object and nature of
the problem under consideration. It also indicates whether we should
use a tailed test or a two tailed test. If Ha is of the type greater than, we
use alone tailed test, but when Ha is of the type “whether greater or
smaller” then we use a two-tailed test.
ii. Selecting a significant level: The hypotheses are tested on a pre-
determined level of significance and such the same should have
specified. Generally, in practice, either 5% level or 1% level is adopted
for the purpose. The factors that affect the level of significance are:
a) The magnitude of the difference between sample ;
b) The size of the sample;
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c) The variability of measurements within samples;
d) Whether the hypotheses is directional or non – directional (A
directional hypotheses is one which predicts the direction of the
difference between, say, means). In brief, the level of significance
must be adequate in the context of the purpose and nature of
enquiry.
iii. Deciding the distribution to use: After deciding the level of
significance, the next step in hypothesis testing is to determine the
appropriate sampling distribution.. The rules for selecting the correct
distribution are similar to those which we have stated earlier in the
context of estimation.
iv. Selecting a random sample & computing an appropriate value:
Another step is to select a random sample(S) and compute an
appropriate value from the sample data concerning the test statistic
utilizing the relevant distribution. In other words, draw a sample to
furnish empirical data.
v. Calculation of the probability: one has then to calculate the probability
that the sample result would diverge as widely as it has from
expectations, if the null hypothesis were in fact true.
vi. Comparing the probability: Yet another step consists in comparing the
probability thus calculated with the specified value for α, the significance
level. If the calculated probability is equal to smaller than the value in
case of one tailed test (and α/2 in case of two-tailed test), then reject the
null hypothesis (i.e. accept the alternative hypothesis), but if the
probability is greater, then accept the null hypothesis. In case we reject
H0 we run a risk of (at most level of significance) committing an error of
type I, but if we accept H0, then we run some risk of committing error
type II.
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Flow diagram for testing hypotheses
Specify the level of significance
Decide the correct sampling distribution
Sample a random sample and workout an appropriate value
Calculate the probability that sample result would diverge as widely as
it has form expectations, if H0 were true
Is this probability equal to or smaller than α value in case of one-tailed
test and α/2 in case of two-tailed test
Run the risk of Committing type I error committing type II error
6.8 Test of Hypotheses
The hypotheses testing determine the validity of the assumption (technically
described as null hypotheses) with a view to choose between the conflicting
hypotheses about the value of the population hypotheses and about the
value of the population parameter. Hypotheses testing helps to succeed on
the basis of a sample data, whether a hypotheses about the population is
State H0 as well as Ha
Reject H0 Accept H0
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likely to be true or false. Statisticians have developed several tests of
hypotheses (also known as tests of significance) for the purpose of testing
of hypotheses which can be classified as:
a) Parametric tests or standard tests of hypotheses ;
b) Non Parametric test or distribution – free test of the hypotheses.
Parametric tests usually assume certain properties of the parent population
from which we draw samples. Assumption like observations come from a
normal population, sample size is large, assumptions about the population
parameters like mean, variants etc must hold good before parametric test
can be used. But there are situation when the researcher cannot or does not
want to make assumptions. In such situations we use statistical methods for
testing hypotheses which are called non- parametric tests because such test
do not depend on any assumption about the parameters of parent
population. Besides, most non-parametric test assumes only nominal or
original data, where as parametric test require measurement equivalent to at
least an interval scale. As a result non-parametric test needs more
observation than a parametric test to achieve the same size of Type I &
Type II error.
6. 9 Important parametric tests
The important parametric tests are:
1) z-test
2) t-test
3) x2-test
4) f-test
All these test are based on the assumption of normality i.e., the source of
data is considered to be normally distributed. In some cases the population
may not be normally distributed, yet the test will be applicable on account of
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the fact that we mostly deal with samples and the sampling distributions
closely approach normal distributions.
z-test is based on the normal probability distribution and is used for judging
the significance of several statistical measures, particularly the mean. The
relevant test statistic is worked out and compared with its probable value (to
be read from the table showing area under normal curve) at a specified level
of significance for judging the significance of the measure concerned. This is
a most frequently used test in research studies. This test is used even when
binomial distribution or t-distribution is applicable on the presumption that
such a distribution tends to approximate normal distribution as ‘n’ becomes
larger. Z-test is generally used for comparing the mean of a sample to some
hypotheses mean for the population in case of large sample, or when
population variance is known as z-test is also used for judging the
significance of difference between means to of two independent samples in
case of large samples or when population variance is known z-test is
generally used for comparing the sample proportion to a theoretical value of
population proportion or for judging the difference in proportions of two
independent samples when happens to be large. Besides, this test may be
used for judging the significance of median, mode, co-efficient of correlation
and several other measures.
t-test is based on t-distribution and is considered an appropriate test for
judging the significance of sample mean or for judging significance of
difference between the two means of the two samples in case of samples
when population variance is not known (in which case we use variance of
the sample as an estimate the population variance). In case two samples
are related, we use paired t-test (difference test) for judging the significance
of them mean of difference between the two related samples. It can also be
sued for judging the significance of co-efficient of simple and partial
correlations. The relevant test statistic, t, is calculated from the sample data
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and then compared with its probable value based on t-distribution at a
specified level of significance for concerning degrees of freedom for
accepting or rejecting the null hypotheses it may be noted that t-test applies
only in case of small sample when population variance is unknown.
x2-test is based on chi-square distribution and as a parametric test is used
for comparing a sample variance to a theoretical population variance is
unknown.
f-test is based on f-distribution and is used to compare the variance of the
two-independent samples. This test is also used in the context of variance
(ANOVA) for judging the significance of more than two sample means at
one and the same time. It is also used for judging the significance of multiple
correlation coefficients. Test statistic, f, is calculated and compared with its
probable value for accepting or rejecting the H0.
Self Assessment Questions
State whether the following statements are true or false:
1. A hypothesis is an assumption about relations between variables.
2. A hypothesis gives a definite point to the investigation, and it guides the
direction on the study.
3. A hypothesis specifies the sources of data, which shall be studied, and
in what context they shall be studied.
4. A two-tailed test rejects the null hypotheses if, say, the sample mean is
significantly higher or lower than the hypnotized value of the mean of the
population.
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6.10 Summary
A hypothesis is an assumption about relations between variables. It is a
tentative explanation of the research problem or a guess about the research
outcome. Before starting the research, the researcher has a rather general,
diffused, even confused notion of the problem. A hypothesis gives a definite
point to the investigation, and it guides the direction on the study. A
hypothesis specifies the sources of data, which shall be studied, and in what
context they shall be studied. In the context of hypotheses testing, these two
terms are quite important and must be clearly understood. A two-tailed test
rejects the null hypotheses if, say, the sample mean is significantly higher or
lower than the hypnotized value of the mean of the population.
Z-test is based on the normal probability distribution and is used for judging
the significance of several statistical measures, particularly the mean. The
relevant test statistic is worked out and compared with its probable value (to
be read from the table showing area under normal curve) at a specified level
of significance for judging the significance of the measure concerned. This is
a most frequently used test in research studies.
T-test is based on t-distribution and is considered an appropriate test for
judging the significance of sample mean or for judging significance of
difference between the two means of the two samples in case of samples
when population variance is not known (in which case we use variance of
the sample as an estimate of the population variance).
X2-test is based on chi-square distribution and as a parametric test is used
for comparing a sample variance to a theoretical population variance is
unknown.
F-test is based on f-distribution and is used to compare the variance of the
two-independent samples.
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6.11 Test Questions
1. What is the meaning of Hypothesis?
2. What are the criteria for Hypotheses Construction?
3. What are the characteristics of Good Hypotheses?
4. What are the types of Hypotheses?
5. What is Type I & Type II Errors?
6. What is Two Tailed Test & One Tailed Test?
7. What are the procedure and Flow Diagram for Testing Hypotheses?
8. Which are the important Parametric Tests?
6.12 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
4. True
TQs
1. Section 6.1
2. Section 6.2
3. Section 6.5
4. Section 6.6
5. Section 6.6
6. Section 6.6
7. Section 6.7
8. Section 6.9
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Unit 7 Techniques of Quantitative Data Analysis
Structure
7.1 Introduction: Cross tabulation
Objectives
7.1.1 Setting up of cross tabulation
7.1.2 Choosing for variable for cross tabulation
7.1.3 Two-way cross tabulation
7.2 Multiple regressions
7.3 Perceptual mapping
7.4 Conjoint analysis
Self Assessment Questions
7.5 Summary
7.6 Test Questions
7.7 Answers to SAQs and TQs
7.1 Introduction: Cross Tabulation
It is true, that if all sets of marketing data are viewed only in terms of
frequency distribution and measures of central tendency, only a fraction of
the possible significant findings will be revealed. Cross tabulation, is the
most frequently used analytical technique in marketing research, which
involves examination of one variable crossed by one or more other
variables.
Objectives
After learning this unit, the students should be able to:
discuss the setting up of cross tabulation
understand the choosing for variable for cross tabulation
explain two-way cross tabulation, multiple regressions, perceptual
mapping and conjoint analysis.
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7.1.1 Setting up Cross Tabulation
We usually think of a cross-tabulation table as looking at two or possibly
three variables simultaneously. This is most often the case when a firm
conducts the research project itself, including processing the data and using
some software program such as SPSS to run each cross-tabbed table. If the
researcher has contracted with an independent marketing research firm to
do the survey, the firm will likely b3e doing the data processing, including
setting up the cross-tabs per the researcher’s instructions. If the firm is
responsible for the data collection and processing, then it will probably
include as part of the contracted price what will be referred to as an
eighteen points or twenty point “banner”. This means the firm will provide a
set of computer printout pages that are eighteen or twenty columns wide for
every question in the survey. Each question in the survey would appear
down the side of a page, called the stub.
The appeal of banners is obvious – they provide a simple way to look at
survey questions crossed by the most important variables the researcher is
interested in exploring.
An eighteen-point banner means any combination of column headings that
total eighteen can be used. It is usual practice to devote the left-most
column to grand total which in effect gives the frequency distribution for
each question. The remaining seventeen columns may be divided in any
combination of columns desired.
It is customary to have the banner headings consist of independent
variables that the research purpose, questions, and hypotheses suggest are
most likely to be influencing the dependent measures of interest (such as
attitudes, consumption rate, likelihood of purchase, membership or non-
membership, etc.)
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Another common practice in setting up a banner heading or single cross-
tabbed table is to consistently move from low to high, moving from left to
right in the banner; and from high to low, moving down the stub.
7.1.2 Choosing Variables for Cross- tabulations
It is tempting to ask the data processors to “cross everything by everything”
so that a cross-tabbed table for every combination of two questions in the
survey is available. That way, one or more tables are available to look at, to
help the researcher determine the relationship between the variables of
interest.
A cross-tabbed table is merely inserting data into a dummy table, so the
plan of analysis primarily consists of determining which variables should be
simultaneously examined for possible relationships to get answers to
questions, test hypothesis, or fill in dummy tables. Although the researcher
will think of relationships, he or she wishes to examine during the analysis
and not before, there is also a point at which the researcher must get on
with the implications and alternative decision suggestions, which is the
purpose of the research and its analysis. Ts are easy to become distracted
by the almost infinite number of possible relationships so that one avoids
drawing the conclusions necessary to move through the process.
7.1.3 Two-Way Cross Tabulation
I will pay extra Re. 1 for my favourite brand
Substitute Consumption Rate
Low Medium High
# % # % # %
Strongly agree or agree 113 34 239 54 308 67
Neither agree or disagree 107 33 108 24 101 22
Strongly disagree or disagree 108 33 98 22 54 11
Total 328 100 445 100 463 100
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7.2 Multiple Regressions
The error term includes the effects on the dependent variable of variables
other than the independent variable. It may be desirable to include explicitly
some of these variables in the model. As predictions, their inclusion will
improve the model’s ability to predict and will decrease the unexplained
variation; in terms of understanding, they will introduce the effect of other
variables and therefore elaborate and clarify the relationships.
7.3 Perceptual Mapping
Multidimensional scaling addresses the problem of identifying the
dimensions upon which customers perceive or evaluate phenomena
(products, brands, or companies) in a perceptual map. Multidimensional
scaling techniques result in perceptual maps that describe the positioning of
companies or brands that are compared relative to the position they occupy
in the minds of customers according to key attributes. These maps allow the
decision maker to examine underlying criteria or dimensions that people
utilize to form perceptions about similarities between and preferences
among various products, services, or companies. The question of
positioning by multidimensional scaling ( MDS) and perceptual mapping,
deals with how a firm compares to its competitors on key attributes, what the
ideal set of attributes sought by the customers might be, or what positioning
or repositioning strategy should be developed for a specific sector of the
marketplace. A medium sized bank might learn for example, that the most
effective way to compete for commercial loan business with larger, more
prestigious banks with a wider range of services, is by focusing on the
genuine concern communicated by loan supervisors as well as the expertise
they develop in their knowledge of their client’s sub-sector of industry.
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7.4 Conjoint Analysis
Conjoint analysis is also known as trade-off analysis, is used for evaluating
judgmental data where choices between attributes are involved. It is more
commonly used for measuring the trade-off values of purchases selection
factor attributes. Specifically, conjoint analysis is concerned with the joint
effect of two or more independent variables on the ordering of a dependent
variable. In essence, this method allows a determination of how consumers
value various levels of purchase criteria and the extent to which they might
tend to forego a high level of one attribute in order to obtain a high level of
another. Fro example, the trade-off values of holding power, scent, non-
stickiness, brand name, and price for hair spray might be cause for a
conjoint evaluation.
Self Assessment Questions
State whether the following statements are true or false:
1. A cross-tabbed table is merely inserting data into a dummy table, so the
plan of analysis primarily consists of determining which variables should
be simultaneously examined for possible relationships to get answers to
questions, test hypothesis, or fill in dummy tables.
2. Multidimensional scaling techniques result in perceptual maps that
describe the positioning of companies or brands that are compared
relative to the position they occupy in the minds of customers according
to key attributes.
3. Perceptual maps allow the decision maker to examine underlying criteria
or dimensions that people utilize to form perceptions about similarities
between and preferences among various products, services, or
companies.
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4. Conjoint analysis is also known as trade-off analysis, is used for
evaluating judgmental data where choices between attributes are
involved
7.5 Summary
Cross tabulation, is the most frequently used analytical technique in
marketing research, which involves examination of one variable crossed by
one or more other variables. A cross-tabbed table is merely inserting data
into a dummy table, so the plan of analysis primarily consists of determining
which variables should be simultaneously examined for possible
relationships to get answers to questions, test hypothesis, or fill in dummy
tables. Multidimensional scaling techniques result in perceptual maps that
describe the positioning of companies or brands that are compared relative
to the position they occupy in the minds of customers according to key
attributes. These maps allow the decision maker to examine underlying
criteria or dimensions that people utilize to form perceptions about
similarities between and preferences among various products, services, or
companies. Conjoint analysis is also known as trade-off analysis, is used for
evaluating judgmental data where choices between attributes are involved.
It is more commonly used for measuring the trade-off values of purchases
selection factor attributes
7.6 Terminal Questions
1. What is cross tabulation?
2. What are multiple regressions?
3. What are perceptual maps?
4. Explain the use of conjoint analysis.
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7.7 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
4. True
TQs
1. Section 7.1
2. Section 7.2
3. Section 7.3
4. Section 7.4
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Unit 8 Writing a Research Report
Structure
8.1 Introduction: Meaning of Research Report
Objectives
8.2 Purpose of Research Report
8.3 Characteristics of Research Report
8.4 Functions of Research Report
8.5 Types of Research Report
8.5.1 Technical Reports
8.5.2 Popular Reports
8.5.3 Interim Reports
8.5.4 Summary Reports
8.5.5 Research Abstract
8.5.6 Research Article
8.6 Preparing the Research Report and Approaches to Research Report
8.6.1 Communicate To Specific Audience
8.6.2 Structure the Presentation
8.6.3 Create Audience Interest
8.6.4 Be Specific and Visual
8.6.5 Address Validity and Reliability Issues
8.7 Written Presentations
8.8 Organization of a Research Report
8.9 Reading Research Reports
8.9.1 Don’t Read
8.9.2 Use Visual Aids
8.9.3 Make Sure the Start Is Positive
8.9.4 Avoid Distracting the Audience
8.9.5 Involve the Audience
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Self Assessment Questions
8.10 Summary
8.11 Terminal Questions
8.12 Answers To SAQs And TQs
8.1 Introduction: Meaning of Research Report
Research report is a means for communicating research experience to
others. A research report is a formal statement of the research process and
it results. It narrates the problem studied, methods used for studying it and
the findings and conclusions of the study.
Objectives:
After learning this unit, the students should be able to:
explain the purpose, characteristics and functions of research report
discuss the types of research report
explain the preparation of the research report and approaches to
research report
discuss the written presentations
understand the organization of a research report and reading research
reports
8.2 Purpose of the Research Report
The purpose of the research report is to communicate to interested persons
the methodology and the results of the study in such a manner so as to
enable them to understand the research process and to determine its
validity. The aim is not to convince but to convey what was done, why and
what was its outcome.
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8.3 Characteristics of Research Report
Research report is a narrative and authoritative document on the outcome of
a research effort. It represents highly specific information for a clearly
designated audience. It is simple, readable and accurate form of
communication.
8.4 Functions of Research Report
It serves as a means for presenting the problem studied, methods and
techniques used for collecting and analyzing data, findings and conclusions
and recommendations. It serves as a basic reference material for future use.
It is a means for judging the quality of research project.
It is a means for evaluating researcher’s competency.
It provides a systematic knowledge on problems and issues analyzed.
8.5 Types of Research Report
Research reports can be classified as:
Technical reports
Popular reports
Summary reports
Research abstract
Research article
These differ in terms of the degree of formality, physical form, scope, style
and size.
8.5.1 Technical Reports
In a technical, comprehensive full report of the research process and its
outcome. It covers all the aspects of the research process. A description of
the problem studied, the objectives of the study, method and techniques
used, a detailed account of sampling filed and other research procedures,
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sources of data, tools for data collection, methods of data processing and
analysis, detailed findings and conclusions and suggestion.
8.5.2 Popular Reports
In popular report the reader is less interested in the methodological details,
but more interested in the findings of the study. Complicated statistics are
avoided and pictorial devices are used. After a brief introduction to the
problem and the objectives of the study, an abstract of the findings of the
study, conclusion and recommendations are presented. More headline,
underlining pictures and graphs may be used. Sentences and paragraphs
should be short.
8.5.3 Interim Report
When there is a time lag between data collection and presentation of the
result, the study may lose significance and usefulness. An interim report in
such case can narrate what has been done so far and what was its
outcome. It presents a summary of the findings of that part of analysis which
has been completed.
8.5.4 Summary Reports
Summary report is meant for lay audience i.e., the general pubic. It is written
in non-technical, simple language with pictorial charts, objectives, findings
and its implications. It is a short report of two to three pages.
8.5.5 Research Abstract
Research abstract is a short summary of technical report. It is prepared by a
doctoral student on the eve of submitting his thesis. It contains a brief
presentation of the statement of the problem, the objectives of the study,
methods and techniques used and an overview of the report. A brief
summary of the results of the study may also be used.
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8.5.6 Research Article
Research article is designed for publication in a professional journal. A
research article must be clearly written in concise unambiguous language. It
must be logically organised. Progression from a statement of a problem and
purpose of the study, through analysis of evidence to the conclusions and
implications are given in the report.
8.6 Preparing the Research Report and Approaches to Research
Report
Along with the related skill of working with and motivating people, the ability
to communicate effectively is undoubtedly the most important attribute a
manager can have. Effective communication between research users and
research professional is extremely important to the research process. The
formal presentation usually plays a key role in the communication effort.
Generally presentations are made twice during the research process. First,
there is the research proposal presentation. Second there is the
presentation of the research results.
Guidelines for successful presentations
In general, a presenter should –
Communicate to a specific audience.
Structure the presentation.
Create audience interest
Be specific and visual
Address validity and reliability issues
8.6.1 Communicate to a Specific Audience
The first step is to know the audience, its background, and its objectives.
Most effective presentations seem live conversations or memos to a
particular person as opposed to an amorphous group. Audience
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identification affects presentation decisions such as selecting the material to
be included and the level of presentation. Excessive detail or material
presented at too low a level can be boring. The audience can become
irritated when material perceived as relevant is excluded or the material is
presented at too high level. In an oral presentation, the presenter can ask
audience whether they already know some of the material.
Frequently, a presentation must be addressed to two or more different
audiences. There are ways to deal with such a problem. In a written
presentation, an executive summary at the outset can provide an overview
of the conclusions for the benefit of those in the audience who are not
interested in details. The presentation must respect the audience’s time
constraints. An appendix can be used to reach some people selectively,
without distracting the others. Sometimes, introduction to a chapter or a
section can convey the nature of the contents, which certain audiences may
bypass. In an oral presentation, the presence of multiple audiences should
be recognized
8.6.2 Structure the Presentation
Each piece of presentation should fit into the whole, just as individual pieces
fit into a jigsaw puzzle. The audience should not be muttering. The solution
to this is to provide a well-defined structure. The structure should include an
introduction, a body, and a summary. Further, each of the major sections
should be structured similarly. The precept is to tell the audience what you
are going to say, say it and then tell them what you said. Sometimes you
want to withhold the conclusion to create interest.
Introduction should play several roles. First, it should provide audience
interest. The second function is to identify the presentation’s central idea or
objective. Third, it should provide a road map to the rest of the presentation
so that the audience can picture is organisation and flow.
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It is better to divide the body of the presentation into two and five parts. The
audience will be able to absorb only so much information. If that information
can be aggregated into chunks, it will be easier to assimilate. Sometimes
the points to be made cannot be combined easily or naturally. In that case, it
is sometimes necessary to use a longer list.
One way to structure the presentation is by the research questions. Another
method that is often useful when presenting the research proposal is to
base it on the research process. The most useful presentations will include
a statement of implications and recommendations relevant to the research
purpose. However, when researcher lacks information about the total
situation because of the research study addressing only a limited aspect of
it, the ability to generate recommendations may be limited.
The purpose of the presentation summary is to identify and underline the
important points of the presentations and to provide some repetition of their
content. The summary should support the presentation communication
objectives by helping the audience to retain the key parts of the content. The
audience should feel that there is a natural flow from one section to another
8.6.3 Create Audience Interest
The audience should be motivated to read or listen to the presentation’s
major parts and the individual elements of each section the audience should
know as to why the presentation is relevant to them and why each section
was included. A section that cannot hold interest should be excluded or
relegated to appendix.
The research purpose and objectives are good vehicles to provide
motivation. The research purpose should specify decisions to be made and
should relate to the research questions. A presentation that focuses on
those research questions and their associated hypothesis will naturally be
tied to relevant decisions and hold audience interest. In contrast, a
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presentation that attempts to report on all the questions that were included
in the survey and in the cross-tabulations often will be long, uninteresting
and of little value.
As the analysis proceeds and presentation is being prepared, the
researcher should be on the lookout for results that are exceptionally
persuasive, relevant, interesting, and unusual. Sometimes the deviant
respondent with strange answers can provide the most insight in his or her
responses are pursued and not discarded.
8.6.4 Be Specific and Visual
Avoid taking or writing in the abstract. If different members of the audience
have different or vague understandings of important concepts, there is a
potential problem. Terms that are ambiguous or not well known should be
defined and illustrated or else omitted.
The most interesting presentations usually use specific stories, anecdotes,
studies, or incidents to make points. They will be much more interesting and
graphic that generalization, however accurate and scientific.
8.6.5 Address Validity and Reliability Issues
The presentation should help the audience avoid misinterpreting the results.
The wording of the questions, the order in which they are asked, and the
sampling design are among the design dimensions that can lead to biased
results and misinterpretations. The presentation should not include an
exhaustive description of all the design considerations. Nobody is interested
in a textbook discussion of the advantages of telephone over mail surveys,
or how you located homes in an area sampling design.
The presentation should include some indication of the reliability of the
results. At a minimum, it always should be clear what sample size was
involved. The key results should be supported by more precise information
in the form of interval estimates or a hypothesis test. The hypothesis test
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basically indicates, given the sample size, what probability exists that the
results were merely an accident of sampling. If the probability of the latter is
not low, then the results probably would not be repeated. Do not imply more
precision than is warranted.
8.7 Written presentation
The general guidelines discussed so far are applicable to both written and
oral presentations. However, it is important to generate a research report
that will be interesting to read. Most researchers are not trained in effective
report writing. In their enthusiasm for research, they often overlook the need
for a good writing style. In writing a report, long sentences should be
reconsidered and the critical main points should stand out.
Here are some hints for effective report writing.
Use main heading and subheadings to communicate the content of the
material discussed.
Use the present tense as much as possible to communicate information.
Whether the presentation is written or oral, use active voice construction
to make it lively and interesting, passive voice is wordy and dull.
Use computer-generated tables and graphs for effective presentations.
Use informative headings.
Use variations to communicate respondent’s comments.
Use double-sided presentation as possible. For example, tables or
graphs could be presented on the left side of an open report and their
descriptions on the right side.
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8.8 Organisation of the Research Report
The outline of a research report is given below:
I. Prefatory Items
Title page
Declaration
Certificates
Preface/ acknowledgements
Table of content
List of tables
List of graphs/ figures/ charts
Abstract or synopsis
II. Body of the Report
Introduction
Theoretical background of the topic
Statement of the problem
Review of literature
The scope of the study
The objectives o the study
Hypothesis to be tested
Definition of the concepts
Models if any
Design of the study
Methodology
Method of data collection
Sources of data
Sampling plan
Data collection instruments
Field work
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Data processing and analysis plan
Overview of the report
Limitation of the study
Results: findings and discussions
Summary, conclusions and recommendations
III. Reference Material
Bibliography
Appendix
Copies of data collection instruments
Technical details on sampling plan
Complex tables
Glossary of new terms used.
8.9 Reading Research Reports
Preparing Oral Presentation
The ability to communicate orally is extremely important to effective
management in general and to the marketing research function in particular.
Five suggestions are discussed in this regard:
Don’t read
Use visual aids
Make sure the start is positive
Avoid distracting the audience
Involve the audience
8.9.1 Don’t Read
The biggest problem with reading is that it is boring for the reader and the
audience. Very few can make a script sound interesting and those few do
even better without a script. It is necessary to develop the ability to
communicate orally in front of a group without a script, to prepare for those
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occasions when there is no time to prepare a script or when the presenter
must adapt to new developments in the middle of a presentation. If you rely
too heavily on a script and use it in what may be limited opportunities to give
presentations, you will not develop this important capability.
8.9.2 Use Visual Aids
Visual aids perform several functions. First, they give impact to the
information and focus attention on important points. Second, ideas that are
extremely difficult to express in words often can be communicated easily
with visual aids. Finally, they help to give the presentation variety. Visual
aids include computer-assisted presentations, transparencies, charts, hand-
outs, slides, video-tapes, films, samples, demonstrations, and role-playing.
Transparencies, charts, slides, and hand-outs are probably the most widely
used.
8.9.3 Make Sure the Start is Positive
The start should be positive in tone, confident, and involving. Sometimes the
silence can be used effectively to get attention. It is useful to stimulate and
involve the audience immediately, perhaps by a provocative question or
statement. Absolutely never apologize at the outset, even in jest.
8.9.4 Avoid Distracting the Audience
The presenter needs to be aware that the audience is easily distracted. The
following do’s and don’ts address some common causes of distraction.
Take everything out of your pockets and make sure there is nothing on
the lectern other than your notes. Remove pens, pointers, keys, clips –
everything. If often happens, without your being aware of it, that you will
pick up objects and manipulates them until the audience is severely
distracted.
Try to avoid the extremes of either obvious pacing or hiding behind a
lectern. It can be distracting to see a speaker clutch a lectern for support
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as to see someone pace back and forth. The speaker’s movement
should be purposeful and natural, such as stepping aside to point to a
chart, standing or sitting beside the lectern, or moving closer to the
audience for a short portion of the presentation.
Maintain good eye contact. This allows audience feedback, stimulates
trust and confidence in what you are saying, and involves the audience.
A speaker who avoids eye contact by looking up or down or somewhere
else risks distracting the audience.
Be concerned about the sound of your voice. Listen to a tape of your
presentation if possible. A presentation can be distracting if it is too soft,
loud, fast, slow or monotonous. Be sure to use pauses to break up the
presentation and to allow the audience to digest the material.
8.9.5 Involve the Audience
An involved audience will be more interested. An effective technique is to
intersperse questions throughout. If time does not permit a discussion, a
pause at least gives the audience a chance to reflect.
The question – and-answer part of the presentation is particularly important.
This often concludes the talk, but it can be permitted to occur during the
presentation. Pause and make sure the question is understood. Then if
possible give a short positive or negative response and as compact an
explanation as possible. A good technique is to write the question down so
you do not forget it. Anticipate questions before hand and rehearse the
answers. Sometimes it is even effective to leave things out of the
presentation if they can be covered more effectively during the question-
and-answer period.
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Self Assessment Questions
State whether following statements are true or false:
1. Research report is a means for communicating research experience to
others.
2. The purpose of the research report is to communicate to interested
persons the methodology and the results of the study in such a manner
as to enable them to understand the research process and to determine
its validity.
3. Research report is a narrative and authoritative document on the
outcome of a research effort. It represents highly specific information for
a clearly designated audience.
8.10 Summary
Research report is a means for communicating research experience to
others. The purpose of the research report is to communicate to interested
persons the methodology and the results of the study in such a manner as
to enable them to understand the research process and to determine its
validity. Research report is a narrative and authoritative document on the
outcome of a research effort. It represents highly specific information for a
clearly designated audience. It serves as a means for presenting the
problem studied, methods and techniques used for collecting and analyzing
data, findings and conclusions and recommendations. It serves as a basic
reference material for future use. It is a means for judging the quality of
research project. It is a means for evaluating researcher’s competency. It
provides a systematic knowledge on problems and issues analyzed. In a
technical report a comprehensive full report of the research process and its
outcome. It covers all the aspects of the research process. In popular report,
the reader is less interested in the methodological details, but more
interested in the findings of the study. An interim report in such case can
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narrate what has been done so far and what was its outcome. It presents a
summary of the findings of that part of analysis which has been completed.
Summary report is meant for lay audience i.e., the general pubic. It is written
in non-technical, simple language with pictorial charts it just contains
objectives, findings and its implications. It is a short report of two to three
pages. Research abstract is a short summary of technical report. It is
prepared by a doctoral student on the eve of submitting his thesis. Research
article is designed for publication in a professional journal. A research article
must be clearly written in concise unambiguous language.
8.11 Terminal Questions
1. What is a research report?
2. What are the contents of research report?
3. What are the types of research reports?
8.12 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
TQs
1. Section 8.1
2. Section 8.8
3. Section 8.5
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Unit 9 Ethical Issues in Marketing Research
Structure
9.1 Introduction: Nature of Ethical Issues in Marketing Research
Objectives
9.2 Overt and Covert Purposes
9.3 Dishonesty in Dealing with Suppliers
9.4 Misuse of Research Information
9.5 The Supplier’s Ethics
9.6 Abuse of Respondents
9.7 Respondent’s Ethics and Rights
9.8 Corporate Espionage
Self Assessment Questions
9.9 Summary
9.10 Terminal Questions
9.11 Answers to SAQ’s and TQ’s
9.1 Introduction: Nature of Ethical Issues in Marketing Research
Ethics refers to moral principles or values that generally govern the conduct
of the individual or group. Researchers have responsibilities to their
profession. Clients and respondents must adhere to high ethical standards
to ensure that both the function and the information are not brought into
disrepute. The Marketing Research Association, Inc. (Chicago, Illinois) has
instituted a code of ethics that serves as a guideline for marketing ethical
decisions. The Council of American Research Organisation (CASRO) has
also established a detailed code of marketing research ethics to which its
members adhere.
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Normally, three parties are involved in a marketing project:
1. The client who sponsors the project
2. The supplier who designs and executes the research
3. The respondent who provides the information.
Below given is the Code of Professional Ethics and Practices instituted by
the Marketing Research Association.
1. To maintain high standards of competence and integrity in marketing
and survey research.
2. To maintain the highest level of business and professional conduct
and to comply with the Federal State and local laws, regulations and
ordinances applicable to business practices and those of the company.
3. To exercise all reasonable care and to observe the best standards of
objectivity and accuracy in the development, collection, processing
and reporting of marketing and survey research information.
4. To protect the anonymity of respondents and hold all information
concerning an individual respondent privileged, such that this
information is used only within the context of the particular study.
5. To thoroughly instruct and supervise all persons for whose work I am
responsible in accordance with study specifications and general
research techniques.,
6. To observe the rights of ownership of all materials received from and /
or for clients and to keep in confidence all research techniques, data
and other information considered confidential by the owners.
7. To make available to clients such details on the research methods and
techniques of an assignment as may be reasonable required for
proper interpretation of the data, providing this reporting does not
violate the confidence of respondents or clients.
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8. To promote the trust of the public for marketing and survey research
activities and to avoid any procedures which misrepresents the
activities of the respondents, the rewards of cooperation or the uses of
data
9. To refrain from referring to membership in this organisation as proof of
competence, since the organisation does not certify any person or
organisation.
10. To encourage the observance of principles of this code among all
people engaged in marketing and survey research.
Source: Marketing Research Association, Inc., Chicago, Illinois
Objectives:
After learning this unit, the students should be able to:
explain the Overt and Covert Purposes
discuss dishonesty in dealing with suppliers
understand the supplier’s ethics
explain the abuse of respondents, respondent’s ethics and rights
explain the corporate espionage
9.2 Overt and Covert Purposes
Most researchers have encountered situations where the main purpose of
their efforts was to serve someone’s organisational goals. Thus research
can be used to postpone an awkward decision or to lend respectability to a
decision that has been made already. A related purpose is to avoid
responsibility. When there are competing factions, the manager who must
make a difficult choice looks to research to guide the decision. This has the
further advantage that if the decision is later proven wrong, the manager can
find someone else to blame.
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Sometimes a covert purpose will open the way to ethical abuses that
present difficult dilemmas to researchers. The most serious abuses are
created when there is subtle (or not so subtle) pressure to design research
to support a particular decision or enhance a legal position.
9. 3 Dishonesty in Dealing with Suppliers
A few client companies have been known to indulge in “picking the brains”
of research suppliers by asking them to submit elaborate bids that detail the
research design and methodology the supplier would adopt in conducting
the research. Later, the client-firm uses these ideas and conducts the
research itself. Another technique that client firms sometimes use is to make
a false promise of future contracts in an effort to obtain low price for the
current project
9.4 Misuse of Research Information
The client firm should not misuse information gathered through marketing
research projects. For example, data base about consumer preference are
used in target marketing to identify the people who are most likely to buy or
use a product.
The common form of misuse comes from comparative advertisement or
product performance claims that stem from data that are statically not
significant, though puffery in advertisements is a normal practice; gross
misuse of research data is ethically unacceptable.
Too often researchers find themselves dealing with demands by sales and
other professional for access to result and the names and telephone
numbers of respondents. The intention of course is to use the research
study for the entirely different – and usually unethical – purpose of
generating sales leads. The only time this is acceptable is when the
interviewer asks specifically whether the respondent will accept a follow up
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sales call or would like more information, and acts precisely on respondents
answer, sadly there are a number of situation where the research study is
simply a disguise or a selling pitch. Many people have received phone calls
ostensibly to ask some research questions that lead only to a canned sales
message for life insurance, an encyclopaedia for a mutual fund. This is not
only unethical behaviour because it has no merits on its own; it is also a
serious abuse of respondent rights. Not surprisingly, respondents are more
suspicious after a few of these encounters and may refuse to participate in
any research study.
9.5 Supplier’s Ethics
The more common ethical issues for the research supplier are:-
Violating client confidentiality: disclosing any information about the client
that the supplier has gathered from the research project amounts to a
violation of client confidentiality.
Improper execution of research: suppliers are required to conduct
marketing research project in an objective manner, free from personal
biases and motives. Improper execution also includes using biased
samplings, ignoring relevant data or misusing statistics all of which lead to
erroneous and misleading results
9.6 Abuse of Respondents
Abuse of respondents is perhaps the most frequent and controversial
problem that crops up regarding ethics in conducting research. Any form of
violation of a respondents rights amounts to unethical treatment or abuse of
the respondent.
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9.7 Respondent’s Ethics and Rights
A respondent who of his or her own free will agrees to participate in a
marketing research project has the ethical obligation to provide the supplier,
and hence the client with honest and truthful answers. The respondent can
abstain from answering a sensitive question, but falsifying the answers is
ethically improper.
Any respondent who participates in a research project has the following
rights:-
The right to privacy.
The right to safety
The right to know the true purpose of the research
The right to the research results
The right to decide which questions to answer.
9.8 Corporate Espionage
Over the past few years, there has been increasing concern for protecting
the rights of subjects used in research projects. This is a potential problem
in all studies involving human subjects. The researcher should give careful
consideration to the potential negative effects on those participating in an
experiment to avoid violating the subjects’ rights and deflect potential
lawsuits.
Self Assessment Questions
State whether the following statements are true or false:
1. Ethics refer to moral principles or values that generally govern the
conduct of the individual or group. Researchers have responsibilities to
their profession.
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2. Clients and respondents must adhere to high ethical standards to
ensure that both the function and the information are not brought into
disrepute.
3. Most researchers have encountered situations where the main purpose
of their efforts was to serve someone’s organisational goals. The client
firm should not misuse information gathered through marketing research
projects
4. Disclosing any information about the client that the supplier has
gathered from the research project amounts to a violation of client
confidentiality
9.9 Summary
Ethics refers to moral principles or values that generally govern the conduct
of the individual or group. Researchers have responsibilities to their
profession. Clients and respondents and must adhere to high ethical
standards to ensure that both the function and the information are not
brought into disrepute. Most researchers have encountered situations where
the main purpose of their efforts was to serve someone’s organisation goals.
Thus research can be used to postpone an awkward decision or to lend
respectability to a decision that has been made already. A related purpose
is to avoid responsibility. The client firm should not misuse information
gathered through marketing research projects. Disclosing any information
about the client that the supplier has gathered from the research project,
amounts to a violation of client confidentiality. Suppliers are required to
conduct marketing research project in an objective manner, free from
personal biases and motives. Any form of violation of a respondents rights
amounts to unethical treatment or abuse of the respondent. A respondent
who of his or her own fee will agrees to participate in a marketing research
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project has the ethical obligation to provide the supplier, and hence the
client with honest and truthful answers
9.10 Terminal Questions
1. What are some ethical problems that marketing researchers face in
designing and conducting field studies?
2. What is sponsor’s ethics?
3. How research information is misused?
4. What is dishonesty in dealing with suppliers?
5. What is supplier’s ethics?
6. What is abuse of respondents?
7. What are the rights of respondents in marketing research?
9.11 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
4. True
TQs
1. Section 9.1
2. Section 9.1.
3. Section 9.4
4. Section 9.3
5. Section 9.5
6. Section 9.6
7. Section 9.7