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Q 1. Give examples of specific situations that would call for the following types of

research, explaining why a) Exploratory research b) Descriptive research c) Diagnostic

research d) Evaluation research.

Ans.: Research may be classified crudely according to its major intent or the methods.According to the intent, research may be classified as:

Basic (aka fundamental or pure) research is driven by a scientist's curiosity or interest in a

scientific question. The main motivation is to expand man's knowledge, not to create or invent

something. There is no obvious commercial value to the discoveries that result from basic

research.

For example, basic science investigations probe for answers to questions such as:

How did the universe begin?

What are protons, neutrons, and electrons composed of?

How do slime molds reproduce?

What is the specific genetic code of the fruit fly?

Most scientists believe that a basic, fundamental understanding of all branches of science is

needed in order for progress to take place. In other words, basic research lays down the

foundation for the applied science that follows. If basic work is done first, then applied spin-offs

often eventually result from this research. As Dr. George Smoot of LBNL says, "People cannot

foresee the future well enough to predict what's going to develop from basic research. If we only

did applied research, we would still be making better spears."

Applied research is designed to solve practical problems of the modern world, rather than to

acquire knowledge for knowledge's sake. One might say that the goal of the applied scientist is

to improve the human condition.

For example, applied researchers may investigate ways to:

Improve agricultural crop production

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Treat or cure a specific disease

Improve the energy efficiency of homes, offices, or modes of transportation

Some scientists feel that the time has come for a shift in emphasis away from purely basic

research and toward applied science. This trend, they feel, is necessitated by the problems

resulting from global overpopulation, pollution, and the overuse of the earth's natural resources.Exploratory research provides insights into and comprehension of an issue or situation. It

should draw definitive conclusions only with extreme caution. Exploratory research is a type of

research conducted because a problem has not been clearly defined. Exploratory research

helps determine the best research design, data collection method and selection of subjects.

Given its fundamental nature, exploratory research often concludes that a perceived problem

does not actually exist.

Exploratory research often relies on secondary research such as reviewing available literature

and/or data, or qualitative approaches such as informal discussions with consumers, employees,

management or competitors, and more formal approaches through in-depth interviews, focus

groups, projective methods, case studies or pilot studies. The Internet allows for research

methods that are more interactive in nature: E.g., RSS feeds efficiently supply researchers with

up-to-date information; major search engine search results may be sent by email to researchers

by services such as Google Alerts; comprehensive search results are tracked over lengthy

periods of time by services such as Google Trends; and Web sites may be created to attract

worldwide feedback on any subject.

The results of exploratory research are not usually useful for decision-making by themselves, but

they can provide significant insight into a given situation. Although the results of qualitative

research can give some indication as to the "why", "how" and "when" something occurs, it

cannot tell us "how often" or "how many."

Exploratory research is not typically generalizable to the population at large.

A defining characteristic of causal research is the random assignment of participants to the

conditions of the experiment; e.g., an Experimental and a Control Condition... Such assignmentresults in the groups being comparable at the beginning of the experiment. Any difference

between the groups at the end of the experiment is attributable to the manipulated variable.

Observational research typically looks for difference among "in-tact" defined groups. A common

example compares smokers and non-smokers with regard to health problems. Causal

conclusions can't be drawn from such a study because of other possible differences between the

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groups; e.g., smokers may drink more alcohol than non-smokers. Other unknown differences

could exist as well. Hence, we may see a relation between smoking and health but a conclusion

that smoking is a cause would not be warranted in this situation. (Cp)

Descriptive research, also known as statistical research, describes data and characteristics

about the population or phenomenon being studied. Descriptive research answers the questions

who, what, where, when and how.Although the data description is factual, accurate and systematic, the research cannot describe

what caused a situation. Thus, descriptive research cannot be used to create a causal

relationship, where one variable affects another. In other words, descriptive research can be

said to have a low requirement for internal validity.

The description is used for frequencies, averages and other statistical calculations. Often the

best approach, prior to writing descriptive research, is to conduct a survey investigation.

Qualitative research often has the aim of description and researchers may follow-up with

examinations of why the observations exist and what the implications of the findings are.

In short descriptive research deals with everything that can be counted and studied. But there

are always restrictions to that. Your research must have an impact to the life of the people

around you. For example, finding the most frequent disease that affects the children of a town.

The reader of the research will know what to do to prevent that disease thus; more people will

live a healthy life.

Diagnostic study: it is similar to descriptive study but with different focus. It is directed towards

discovering what is happening and what can be done about. It aims at identifying the causes ofa problem and the possible solutions for it. It may also be concerned with discovering and

testing whether certain variables are associated. This type of research requires prior knowledge

of the problem, its thorough formulation, clear-cut definition of the given population, adequate

methods for collecting accurate information, precise measurement of variables, statistical

analysis and test of significance.

Evaluation Studies: it is a type of applied research. It is made for assessing the effectiveness of

social or economic programmes implemented or for assessing the impact of development of the

project area. It is thus directed to assess or appraise the quality and quantity of an activity and

its performance and to specify its attributes and conditions required for its success. It is

concerned with causal relationships and is more actively guided by hypothesis. It is concerned

also with change over time.

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Action research is a reflective process of progressive problem solving led by individuals

working with others in teams or as part of a "community of practice" to improve the way they

address issues and solve problems. Action research can also be undertaken by larger

organizations or institutions, assisted or guided by professional researchers, with the aim of

improving their strategies, practices, and knowledge of the environments within which they

practice. As designers and stakeholders, researchers work with others to propose a new courseof action to help their community improve its work practices (Center for Collaborative Action

Research). Kurt Lewin, then a professor at MIT, first coined the term action research in about

1944, and it appears in his 1946 paper Action Research and Minority Problems. In that paper,

he described action research as a comparative research on the conditions and effects of

various forms of social action and research leading to social action that uses a spiral of steps,

each of which is composed of a circle of planning, action, and fact-finding about the result of the

action.

Action research is an interactive inquiry process that balances problem solving actions

implemented in a collaborative context with data-driven collaborative analysis or research to

understand underlying causes enabling future predictions about personal and organizational

change (Reason & Bradbury, 2001). After six decades of action research development, many

methodologies have evolved that adjust the balance to focus more on the actions taken or more

on the research that results from the reflective understanding of the actions. This tension exists

between

those that are more driven by the researchers agenda to those more driven byparticipants;

Those that are motivated primarily by instrumental goal attainment to those

motivated primarily by the aim of personal, organizational, or societal transformation;

and

1st-, to 2nd-, to 3rd-person research, that is, my research on my own action, aimed

primarily at personal change; our research on our group (family/team), aimed primarily

at improving the group; and scholarly research aimed primarily at theoretical

generalization and/or large scale change.

Action research challenges traditional social science, by moving beyond reflective knowledge

created by outside experts sampling variables to an active moment-to-moment theorizing, data

collecting, and inquiring occurring in the midst of emergent structure. Knowledge is always

gained through action and for action. From this starting point, to question the validity of social

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knowledge is to question, not how to develop a reflective science about action, but how to

develop genuinely well-informed action how to conduct an action science (Tolbert 2001).

Q 2.In the context of hypothesis testing, briefly explain the difference between a) Null and

alternative hypothesis b) Type 1 and type 2 error c) Two tailed and one tailed test d)

Parametric and non-parametric tests.

Ans.: Some basic concepts in the context of testing of hypotheses are explained below -

11) Null Hypotheses and Alternative Hypotheses: In the context of statistical analysis,

we often talk about null and alternative hypotheses. If we are to compare the

superiority of method A with that of method B and we proceed on the assumption that

both methods are equally good, then this assumption is termed as a null hypothesis.

On the other hand, if we think that method A is superior, then it is known as an

alternative hypothesis.

These are symbolically represented as:

Null hypothesis = H0 and Alternative hypothesis = Ha

Suppose we want to test the hypothesis that the population mean is equal to the hypothesized

mean ( H0) = 100. Then we would say that the null hypothesis is that the population mean is

equal to the hypothesized mean 100 and symbolically we can express it as: H0: = 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 hypothesis is known as an alternative hypothesis. Ifwe accept H0, then we are rejecting Ha and if we reject H0, then we are accepting Ha. For H0:

= H0=100, we may consider three possible alternative hypotheses as follows:

Alternative

Hypotheses

To be read as follows

Ha: H0 (The alternative hypothesis is that the population mean is not equal to

100 i.e., it may be more or less 100)

Ha: > H0 (The alternative hypothesis is that the population mean is greater than

100)Ha: < H0 (The alternative hypothesis is that the population mean is less than 100)

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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 hypothesis, the following considerations

are usually kept in view:

1a. The alternative hypothesis is usually the one, which is to be proved, and the null

hypothesis is the one that is to be disproved. Thus a null hypothesis represents thehypothesis we are trying to reject, while the alternative hypothesis represents all

other possibilities.

2b. If the rejection of a certain hypothesis when it is actually true involves great risk, it

is taken as null hypothesis, because then the probability of rejecting it when it is

true is (the level of significance) which is chosen very small.

3c. The null hypothesis should always be a specific hypothesis i.e., it should not state

an approximate value.

Generally, in hypothesis testing, we proceed on the basis of the null hypothesis, keeping the

alternative hypothesis in view. Why so? The answer is that on the assumption that the null

hypothesis 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 use of null hypotheses (at

times also known as statistical hypotheses) is quite frequent.

12) The Level of Significance: This is a very important concept in the context of

hypothesis testing. It is always some percentage (usually 5%), which should be

chosen with great care, thought and reason. In case we take the significance levelat 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 the researcher is willing to

take as much as 5% risk rejecting the null hypothesis 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

hypothesis.

23) Decision Rule or Test of Hypotheses: Given a hypothesis Ha and an alternative

hypothesis H0, we make a rule, which is known as a 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), then we must decide

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the number of items to be tested and the criterion for accepting or rejecting the

hypothesis. 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 a decision rule.

34) Type I & 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 and we mayaccept H0 when it is not true. The former is known as Type I and the latter is

known as Type II. In other words, Type I error means rejection of hypotheses,

which should have been accepted, and Type II error means accepting of

hypotheses, which should have been rejected. Type I error is denoted by (alpha),

also called as level of significance of test; and Type II error is denoted by (beta).

Decision

Accept H0 Reject H0

H0 (true) Correct decision Type I error (

error)Ho (false) Type II error ( 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 5

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 cannot be reduced simultaneously, since 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 and

trouble of reworking a batch of chemicals that should have been accepted, whereas type II errormeans 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. As a result,

one must set a very high level for type I error in ones testing techniques of a given hypothesis.

Hence, in testing of hypotheses, one must make all possible efforts to strike an adequate

balance between Type I & Type II error.

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15) Two Tailed Test & One Tailed Test: In the context of hypothesis testing, these two terms

are quite important and must be clearly understood. A two-tailed test rejects the null hypothesis

if, say, the sample mean is significantly higher or lower than the hypothesized value of the mean

of the population. Such a test is inappropriate when we have H0: = H0 and Ha: H0 which

may > H0 or


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Q 3. Explain the difference between a causal relationship and correlation, with an

example of each. What are the possible reasons for a correlation between two variables?

Ans.: Correlation: The correlation is knowing what the consumer wants, and providing it.

Marketing research looks at trends in sales and studies all of the variables, i.e. price, color,

availability, and styles, and the best way to give the customer what he or she wants. If you cangive the customer what they want, they will buy, and let friends and family know where they got

it. Making them happy makes the money.

Casual relationship Marketing was first defined as a form ofmarketing developed from direct

response marketing campaigns, which emphasizes customer retention and satisfaction, rather

than a dominant focus on sales transactions.

As a practice, Relationship Marketing differs from other forms of marketing in that it recognizes

the long term value of customer relationships and extends communication beyond intrusive

advertising and sales promotional messages.

With the growth of the internet and mobile platforms, Relationship Marketing has continued to

evolve and move forward as technology opens more collaborative and social communication

channels. This includes tools for managing relationships with customers that goes beyond

simple demographic and customer service data. Relationship Marketing extends to include

Inbound Marketing efforts (a combination of search optimization and Strategic Content), PR,Social Media and Application Development.

Just like Customer relationship management(CRM), Relationship Marketing is a broadly

recognized, widely-implemented strategy for managing and nurturing a companys interactions

with clients and sales prospects. It also involves using technology to, organize, synchronize

business processes (principally sales and marketing activities) and most importantly, automate

those marketing and communication activities on concrete marketing sequences that could run

in autopilot (also known as marketing sequences). The overall goals are to find, attract, and win

new clients, nurture and retain those the company already has, entice former clients back into

the fold, and reduce the costs of marketing and client service. [1] Once simply a label for a

category of software tools, today, it generally denotes a company-wide business strategy

embracing all client-facing departments and even beyond. When an implementation is effective,

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people, processes, and technology work in synergy to increase profitability, and reduce

operational costs

Reasons for a correlation between two variables: Chance association, (the relationship is

due to chance) or causative association (one variable causes the other).

The information given by a correlation coefficient is not enough to define the dependence

structure between random variables. The correlation coefficient completely defines the

dependence structure only in very particular cases, for example when the distribution is a

multivariate normal distribution. (See diagram above.) In the case of elliptic distributions it

characterizes the (hyper-)ellipses of equal density, however, it does not completely characterize

the dependence structure (for example, a multivariate t-distribution's degrees of freedom

determine the level of tail dependence).

Distance correlation and Brownian covariance / Brownian correlation [8][9] were introduced to

address the deficiency of Pearson's correlation that it can be zero for dependent random

variables; zero distance correlation and zero Brownian correlation imply independence.

The correlation ratio is able to detect almost any functional dependency, or the entropy-based

mutual information/total correlation which is capable of detecting even more general

dependencies. The latter are sometimes referred to as multi-moment correlation measures, in

comparison to those that consider only 2nd moment (pairwise or quadratic) dependence.

The polychoric correlation is another correlation applied to ordinal data that aims to estimate the

correlation between theorised latent variables.

One way to capture a more complete view of dependence structure is to consider a copula

between them.

Q 4. Briefly explain any two factors that affect the choice of a sampling technique. What

are the characteristics of a good sample?

Ans.: The difference between non-probability and probability sampling is that non-probability

sampling does not involve random selection and probability sampling does. Does that mean that

non-probability samples aren't representative of the population? Not necessarily. But it does

mean that non-probability samples cannot depend upon the rationale of probability theory. At

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http://en.wikipedia.org/wiki/Multivariate_normal_distributionhttp://en.wikipedia.org/wiki/Distance_correlationhttp://en.wikipedia.org/wiki/Brownian_covariancehttp://en.wikipedia.org/wiki/#cite_note-7http://en.wikipedia.org/wiki/#cite_note-8http://en.wikipedia.org/wiki/Correlation_ratiohttp://en.wikipedia.org/wiki/Information_entropyhttp://en.wikipedia.org/wiki/Mutual_informationhttp://en.wikipedia.org/wiki/Total_correlationhttp://en.wikipedia.org/wiki/Polychoric_correlationhttp://en.wikipedia.org/wiki/Copula_(statistics)http://en.wikipedia.org/wiki/Multivariate_normal_distributionhttp://en.wikipedia.org/wiki/Distance_correlationhttp://en.wikipedia.org/wiki/Brownian_covariancehttp://en.wikipedia.org/wiki/#cite_note-7http://en.wikipedia.org/wiki/#cite_note-8http://en.wikipedia.org/wiki/Correlation_ratiohttp://en.wikipedia.org/wiki/Information_entropyhttp://en.wikipedia.org/wiki/Mutual_informationhttp://en.wikipedia.org/wiki/Total_correlationhttp://en.wikipedia.org/wiki/Polychoric_correlationhttp://en.wikipedia.org/wiki/Copula_(statistics)


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least with a probabilistic sample, we know the odds or probability that we have represented the

population well. We are able to estimate confidence intervals for the statistic. With non-

probability samples, we may or may not represent the population well, and it will often be hard

for us to know how well we've done so. In general, researchers prefer probabilistic or random

sampling methods over non probabilistic ones, and consider them to be more accurate and

rigorous. However, in applied social research there may be circumstances where it is notfeasible, practical or theoretically sensible to do random sampling. Here, we consider a wide

range of non-probabilistic alternatives.

We can divide non-probability sampling methods into two broad types:

Accidentalorpurposive.

Most sampling methods are purposive in nature because we usually approach the sampling

problem with a specific plan in mind. The most important distinctions among these types of

sampling methods are the ones between the different types of purposive sampling approaches.

Accidental, Haphazard or Convenience Sampling

One of the most common methods of sampling goes under the various titles listed here. I would

include in this category the traditional "man on the street" (of course, now it's probably the

"person on the street") interviews conducted frequently by television news programs to get a

quick (although non representative) reading of public opinion. I would also argue that the typical

use of college students in much psychological research is primarily a matter of convenience.

(You don't really believe that psychologists use college students because they believe they're

representative of the population at large, do you?). In clinical practice, we might use clients who

are available to us as our sample. In many research contexts, we sample simply by asking for

volunteers. Clearly, the problem with all of these types of samples is that we have no evidence

that they are representative of the populations we're interested in generalizing to -- and in many

cases we would clearly suspect that they are not.

Purposive Sampling

In purposive sampling, we sample with apurpose in mind. We usually would have one or more

specific predefined groups we are seeking. For instance, have you ever run into people in a mall

or on the street who are carrying a clipboard and who are stopping various people and asking if

they could interview them? Most likely they are conducting a purposive sample (and most likely

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they are engaged in market research). They might be looking for Caucasian females between

30-40 years old. They size up the people passing by and anyone who looks to be in that

category they stop to ask if they will participate. One of the first things they're likely to do is verify

that the respondent does in fact meet the criteria for being in the sample. Purposive sampling

can be very useful for situations where you need to reach a targeted sample quickly and where

sampling for proportionality is not the primary concern. With a purposive sample, you are likelyto get the opinions of your target population, but you are also likely to overweight subgroups in

your population that are more readily accessible.

All of the methods that follow can be considered subcategories of purposive sampling methods.

We might sample for specific groups or types of people as in modal instance, expert, or quota

sampling. We might sample for diversity as in heterogeneity sampling. Or, we might capitalize on

informal social networks to identify specific respondents who are hard to locate otherwise, as in

snowball sampling. In all of these methods we know what we want -- we are sampling with a

purpose.

Modal Instance Sampling

In statistics, the mode is the most frequently occurring value in a distribution. In sampling, when

we do a modal instance sample, we are sampling the most frequent case, or the "typical" case.

In a lot of informal public opinion polls, for instance, they interview a "typical" voter. There are a

number of problems with this sampling approach. First, how do we know what the "typical" or

"modal" case is? We could say that the modal voter is a person who is of average age,educational level, and income in the population. But, it's not clear that using the averages of

these is the fairest (consider the skewed distribution of income, for instance). And, how do you

know that those three variables -- age, education, income -- are the only or even the most

relevant for classifying the typical voter? What if religion or ethnicity is an important

discriminator? Clearly, modal instance sampling is only sensible for informal sampling contexts.

Expert Sampling

Expert sampling involves the assembling of a sample of persons with known or demonstrable

experience and expertise in some area. Often, we convene such a sample under the auspices of

a "panel of experts." There are actually two reasons you might do expert sampling. First,

because it would be the best way to elicit the views of persons who have specific expertise. In

this case, expert sampling is essentially just a specific sub case of purposive sampling. But the

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other reason you might use expert sampling is to provide evidence for the validity of another

sampling approach you've chosen. For instance, let's say you do modal instance sampling and

are concerned that the criteria you used for defining the modal instance are subject to criticism.

You might convene an expert panel consisting of persons with acknowledged experience and

insight into that field or topic and ask them to examine your modal definitions and comment on

their appropriateness and validity. The advantage of doing this is that you aren't out on your owntrying to defend your decisions -- you have some acknowledged experts to back you. The

disadvantage is that even the experts can be, and often are, wrong.

Quota Sampling

In quota sampling, you select people non-randomly according to some fixed quota. There are

two types of quota sampling:proportionaland non proportional. In proportional quota

sampling you want to represent the major characteristics of the population by sampling a

proportional amount of each. For instance, if you know the population has 40% women and 60%

men, and that you want a total sample size of 100, you will continue sampling until you get those

percentages and then you will stop. So, if you've already got the 40 women for your sample, but

not the sixty men, you will continue to sample men but even if legitimate women respondents

come along, you will not sample them because you have already "met your quota." The problem

here (as in much purposive sampling) is that you have to decide the specific characteristics on

which you will base the quota. Will it be by gender, age, education race, religion, etc.?

Non-proportional quota sampling is a bit less restrictive. In this method, you specify theminimum number of sampled units you want in each category. Here, you're not concerned with

having numbers that match the proportions in the population. Instead, you simply want to have

enough to assure that you will be able to talk about even small groups in the population. This

method is the non-probabilistic analogue of stratified random sampling in that it is typically used

to assure that smaller groups are adequately represented in your sample.

Heterogeneity Sampling

We sample for heterogeneity when we want to include all opinions or views, and we aren't

concerned about representing these views proportionately. Another term for this is sampling for

diversity. In many brainstorming or nominal group processes (including concept mapping), we

would use some form of heterogeneity sampling because our primary interest is in getting broad

spectrum of ideas, not identifying the "average" or "modal instance" ones. In effect, what we

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would like to be sampling is not people, but ideas. We imagine that there is a universe of all

possible ideas relevant to some topic and that we want to sample this population, not the

population of people who have the ideas. Clearly, in order to get all of the ideas, and especially

the "outlier" or unusual ones, we have to include a broad and diverse range of participants.

Heterogeneity sampling is, in this sense, almost the opposite of modal instance sampling.

Snowball Sampling

In snowball sampling, you begin by identifying someone who meets the criteria for inclusion in

your study. You then ask them to recommend others who they may know who also meet the

criteria. Although this method would hardly lead to representative samples, there are times when

it may be the best method available. Snowball sampling is especially useful when you are trying

to reach populations that are inaccessible or hard to find. For instance, if you are studying the

homeless, you are not likely to be able to find good lists of homeless people within a specific

geographical area. However, if you go to that area and identify one or two, you may find that

they know very well whom the other homeless people in their vicinity are and how you can find

them.

Characteristics of good Sample: The decision process is a 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 are:

11. 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 appropriateprobability 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.

22.Measurability: The application of statistical inference theory requires computation of

the sampling error from the sample itself. Only probability samples 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.

33.Degree of Precision: Should the results of the survey be very precise, or could even

rough results serve the purpose? The desired level of precision is one of the criteria

for sampling method selection. Where a high degree of precision of results is desired,

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probability sampling 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.

44. 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 anexploratory study with non-probability sampling may be done to gain a better idea of

the population. After gaining sufficient knowledge about the population through the

exploratory study, an appropriate probability sampling design may be adopted.

55. 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 simple random sampling will give a representative sample. If the population is

heterogeneous, stratified random sampling is appropriate.

66. Geographical Area of the Study and 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.

77. 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 noalternative but 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.

88. 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;

or multi-stage cluster sampling, instead of single-stage sampling of elements. Of

course, the precision has to be sacrificed to some extent.

99. 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

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frequently conflict with each other 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.

Q 5. Select any topic for research and explain how you will use both secondary and

primary sources to gather the required information.

Ans.: Primary Sources of Data

Primary sources are original sources from which the researcher directly collects data that has

not been previously collected, e.g., collection of data directly by the researcher on brand

awareness, brand preference, and brand loyalty and other aspects of consumer behavior, from a

sample of consumers by interviewing them. Primary data is first hand information collected

through various methods such as surveys, experiments and observation, for the purposes of the

project immediately at hand.

The advantages of primary data are

1 It is unique to a particular research study

2 It is recent information, unlike published information that is already available

The disadvantages are

1 It is expensive to collect, compared to gathering information from available

sources

2 Data collection is a time consuming process

3 It requires trained interviewers and investigators

2 Secondary Sources of Data

These are sources containing data, which has been collected and compiled for another purpose.

Secondary sources may be internal sources, such as annual reports, financial statements, sales

reports, inventory records, minutes of meetings and other information that is available within the

firm, in the form of a marketing information system. They may also be external sources, such as

government agencies (e.g. census reports, reports of government departments), published

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sources (annual reports of currency and finance published by the Reserve Bank of India,

publications of international organizations such as the UN, World Bank and International

Monetary Fund, trade and financial journals, etc.), trade associations (e.g. Chambers of

Commerce) and commercial services (outside suppliers of information).

Methods of Data Collection:

The researcher directly collects primary data from its original sources. In this case, theresearcher can collect the required data precisely according to his research needs and he can

collect them when he wants and in the form that he needs it. But the collection of primary data is

costly and time consuming. Yet, for several types of social science research, required data is not

available from secondary sources and it has to be directly gathered from the primary sources.

Primary data has to be gathered in cases where the available data is inappropriate, inadequate

or obsolete. It includes: 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

management studies, business management studies etc.

There are various methods of primary data collection, including surveys, audits and panels,

observation and experiments.

1 Survey Research

A survey is a fact-finding study. It is a method of research involving collection of data directly

from a population or a sample at a particular time. A survey has certain characteristics:1 It is always conducted in a natural setting. It is a field study.

2 It seeks responses directly from the respondents.

3 It can cover a very large population.

4 It may include an extensive study or an intensive study

5 It covers a definite geographical area.

A survey involves the following steps -

1 Selection of a problem and its formulation

2 Preparation of the research design

3 Operation concepts and construction of measuring indexes and scales

4 Sampling

5 Construction of tools for data collection

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6 Field work and collection of data

7 Processing of data and tabulation

8Analysis of data

9 Reporting

There are four basic survey methods, which include:1 Personal interview

2 Telephone interview

3 Mail survey and

4 Fax survey

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 respondents gestures, 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 persons opinions, attitudes, values,

beliefs, experiences and future intentions. Interviewing is appropriate when qualitative

information is required, or probing is necessary to draw out the respondent fully. Where the areacovered for the survey is 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 rapport 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

behavioral 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 other the

respondents do not want to answer. Interviewing as a method of data collection has certain

characteristics. They are:

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1. The participants the interviewer and the respondent are strangers;

hence, the investigator has to get himself/herself 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. The interview is not a mere casual conversational exchange, but a

conversation with a specific purpose, viz., obtaining information relevant to a

study.

4. The 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 the interview can also be

conducted over the telephone.

6. Although the 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. The interview is an interactive process. The interaction between the

interviewer and the respondent depends upon how they perceive each

other.8. The respondent reacts to the interviewers appearance, behavior, 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. The investigator records information furnished by the respondent in the

interview. 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 Telephone Interviewing Telephone interviewing is a non-personal method of data collection.

It may be used as a major method or as a supplementary method. It will be useful in the

following situations:

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11.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.

12.When the study requires responses to five or six simple questions, e.g. a

radio or television program survey.

13.When the survey must be conducted in a very short period of time, providedthe units of study are listed in the telephone directory.

14.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.

15.When the respondents are widely scattered and when there are many call

backs to make.

4 Group InterviewsA 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. 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 forevidence 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 interviews can be obtained through schools, clubs and

other organized groups.

5 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-ended 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

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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. 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. The following procedures should be followed -

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 must be assured. The sponsors identity may be revealed. However,

when such information may bias the result, it is not desirable to reveal it. In this case, a

disguised organization name may be used. A self-addressed stamped envelope should be

enclosed in the covering letter.

1After 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.

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:

11. Quality printing: The questionnaire may be neatly printed on quality light colored paper,

so as to attract the attention of the respondent.

22. 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 answerthem briefly. It is desirable to address the respondent by name.

33. Advance information:Advance information can be provided to potential respondents by

a telephone call, 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.

44. Incentives: Money, stamps for collection and other incentives are also used to induce

respondents to complete and return the mail questionnaire.

55. Follow-up-contacts: In the case of respondents belonging to an organization, they may

be approached through someone in that organization known as the researcher.

66. 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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7

8Q 6. Case Study: You are engaged to carry out a market survey on behalf of a leading

Newspaper that is keen to increase its circulation in Bangalore City, in order to

ascertain reader habits and interests. Develop a title for the study; define the

research problem and the objectives or questions to be answered by the study.

Ans.:Title: Newspaper reading choices

Research problem: A research problem is the situation that causes the researcher to feel

apprehensive, confused and ill at ease. It is the demarcation of a problem area within a certain

context involving the WHO or WHAT, the WHERE, the WHEN and the WHY of the problem

situation.

There are many problem situations that may give rise to research. Three sources usually

contribute to problem identification. Own experience or the experience of others may be a

source of problem supply. A second source could be scientific literature. You may read about

certain findings and notice that a certain field was not covered. This could lead to a research

problem. Theories could be a third source. Shortcomings in theories could be researched.

Research can thus be aimed at clarifying or substantiating an existing theory, at clarifying

contradictory findings, at correcting a faulty methodology, at correcting the inadequate or

unsuitable use of statistical techniques, at reconciling conflicting opinions, or at solving existing

practical problems

Types of questions to be asked :For more than 35 years, the news about newspapers and

young readers has been mostly bad for the newspaper industry. Long before any competition

from cable television or Nintendo, American newspaper publishers were worrying about

declining readership among the young.

As early as 1960, at least 20 years prior to Music Television (MTV) or the Internet, media

research scholars1 began to focus their studies on young adult readers' decreasing interest in

newspaper content. The concern over a declining youth market preceded and perhaps

foreshadowed today's fretting over market penetration. Even where circulation has grown or

stayed stable, there is rising concern over penetration, defined as the percentage of occupied

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households in a geographic market that are served by a newspaper.2 Simply put, population

growth is occurring more rapidly than newspaper readership in most communities.

This study looks at trends in newspaper readership among the 18-to-34 age group and

examines some of the choices young adults make when reading newspapers.

One of the underlying concerns behind the decline in youth newspaper reading is the question of

how young people view the newspaper. A number of studies explored how young readers

evaluate and use newspaper content.

Comparing reader content preferences over a 10-year period, Gerald Stone and Timothy

Boudreau found differences between readers ages 18-34 and those 35-plus.16 Younger readers

showed increased interest in national news, weather, sports, and classified advertisements over

the decade between 1984 and 1994, while older readers ranked weather, editorials, and food

advertisements higher. Interest in international news and letters to the editor was less among

younger readers, while older readers showed less interest in reports of births, obituaries, and

marriages.

David Atkin explored the influence of telecommunication technology on newspaper readership

among students in undergraduate media courses.17 He reported that computer-related

technologies, including electronic mail and computer networks, were unrelated to newspaper

readership. The study found that newspaper subscribers preferred print formats over electronic.

In a study of younger, school-age children, Brian Brooks and James Kropp found that electronic

newspapers could persuade children to become news consumers, but that young readers would

choose an electronic newspaper over a printed one.18

In an exploration of leisure reading among college students, Leo Jeffres and Atkin assessed

dimensions of interest in newspapers, magazines, and books,19 exploring the influence of

media use, non-media leisure, and academic major on newspaper content preferences. The

study discovered that overall newspaper readership was positively related to students' focus onentertainment, job / travel information, and public affairs. However, the students' preference for

reading as a leisure-time activity was related only to a public affairs focus. Content preferences

for newspapers and other print media were related. The researchers found no significant

differences in readership among various academic majors, or by gender, though there was a

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slight correlation between age and the public affairs readership index, with older readers more

interested in news about public affairs.

Methodology

Sample

Participants in this study (N=267) were students enrolled in 100- and 200-level English courses

at a midwestern public university. Courses that comprise the framework for this sample were

selected because they could fulfill basic studies requirements for all majors. A basic studies

course is one that is listed within the core curriculum required for all students. The researcher

obtained permission from seven professors to distribute questionnaires in the eight classes

during regularly scheduled class periods. The students' participation was voluntary; two students

declined. The goal of this sampling procedure was to reach a cross-section of students

representing various fields of study. In all, 53 majors were represented.

Of the 267 students who participated in the study, 65 (24.3 percent) were male and 177 (66.3

percent) were female. A total of 25 participants chose not to divulge their genders. Ages ranged

from 17 to 56, with a mean age of 23.6 years. This mean does not include the 32 respondents

who declined to give their ages. A total of 157 participants (58.8 percent) said they were of the

Caucasian race, 59 (22.1 percent) African American, 10 (3.8 percent) Asian, five (1.9 percent)

African/Native American, two (.8 percent) Hispanic, two (.8 percent) Native American, and one

(.4 percent) Arabic. Most (214) of the students were enrolled full time, whereas a few (28) were

part-time students. The class rank breakdown was: freshmen, 45 (16.9 percent); sophomores,

15 (5.6 percent); juniors, 33 (12.4 percent); seniors, 133 (49.8 percent); and graduate students,

16 (6 percent).

Procedure

After two pre-tests and revisions, questionnaires were distributed and collected by the

investigator. In each of the eight classes, the researcher introduced herself to the students as a

journalism professor who was conducting a study on students' use of newspapers and other

media. Each questionnaire included a cover letter with the researcher's name, address, and

phone number. The researcher provided pencils and was available to answer questions if

anyone needed further assistance. The average time spent on the questionnaires was 20

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minutes, with some individual students taking as long as an hour. Approximately six students

asked to take the questionnaires home to finish. They returned the questionnaires to the

researcher's mailbox within a couple of day.

Assignment Set- 2

Q 1.Discuss the relative advantages and disadvantages of the different methods of

distributing questionnaires to the respondents of a study.

Ans.: There are some alternative methods of distributing questionnaires to the respondents.

They are:

1) Personal delivery,

2) Attaching the questionnaire to a product,

3) Advertising the questionnaire in a newspaper or magazine, and

4) News-stand inserts.

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,

the completed questionnaires can be collected 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 therespondents may return the completed questionnaires through mail.

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. A gift

or a discount coupon usually rewards the respondent.

Advertising the questionnaire: The questionnaire with the instructions for completion may be

advertised on a page of a magazine or in a 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 for collecting information from the customers of

commercial banks in India. This method may be useful for large-scale studies on topics of

common interest. Newsstand inserts: This method involves inserting the covering letter,

questionnaire and self addressed reply-paid envelope into a random sample of newsstand

copies of a newspaper or magazine.

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Advantages and Disadvantages:

The advantages of Questionnaire are:

this method facilitates collection of more accurate data for longitudinal studies than any other

method, because under this method, the event or action is reported soon after its occurrence.

this method makes it possible to have before and after designs made for field based studies.

For example, the effect of public relations or advertising campaigns or welfare measures can bemeasured by collecting data before, during and after the campaign.

the panel method offers a good way of studying trends in events, behavior 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 laborers 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 also

possible to find out how the constituency of the various economic and social strata of society

changes through time and so on.

A panel study also provides evidence on the causal 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 - favorable attitude or promotion. A panel study can provide data for finding an answer to

this question.

It facilities depth interviewing, because panel members become well acquainted with the field

workers and will be willing to allow probing interviews.

The major limitations or problems of Questionnaire method are:

this method is very expensive. The selection of panel members, the payment of premiums,

periodic training of investigators and supervisors, and the costs involved in replacing dropouts,

all add to the expenditure.

it is often difficult to set up a representative 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 dropouts. Persons with similar characteristics may replace the dropouts. However,

there is no guarantee that the emerging panel would be 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

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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 operation for some time. Cheating by panel members or investigators may be a problem insome cases.

Q 2. In processing data, what is the difference between measures of central tendency and

measures of dispersion? What is the most important measure of central tendency and

dispersion?

Ans.: Measures of Central tendency:

Arithmetic Mean

The arithmetic mean is the most common measure of central tendency. It simply the sum of the

numbers divided by the number of numbers. The symbol m is used for the mean of a population.

The symbol M is used for the mean of a sample. The formula for m is shown below: m=

X

NWhere X is the sum of all the numbers in the numbers in the sample and N is the number of

numbers in the sample. As an example, the mean of the numbers 1+2+3+6+8=

20

5=4 regardless of whether the numbers constitute the entire population or just a sample from

the population.

The table, Number of touchdown passes, shows the number of touchdown (TD) passes thrown

by each of the 31 teams in the National Football League in the 2000 season. The mean number

of touchdown passes thrown is 20.4516 as shown below. m=X

N=

634

31=20.4516

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37 33 33 32 29 28 28 2322 22 22 21 21 21 20 2019 19 18 18 18 18 16 1514 14 14 12 12 9 6Table 1: Number of touchdown passes

Although the arithmetic mean is not the only "mean" (there is also a geometric mean), it is by far

the most commonly used. Therefore, if the term "mean" is used without specifying whether it is

the arithmetic mean, the geometric mean, or some other mean, it is assumed to refer to the

arithmetic mean.

Median

The median is also a frequently used measure of central tendency. The median is the midpoint

of a distribution: the same number of scores is above the median as below it. For the data in the

table, Number of touchdown passes, there are 31 scores. The 16th highest score (which equals

20) is the median because there are 15 scores below the 16th score and 15 scores above the

16th score. The median can also be thought of as the 50th percentile.Let's return to the made up example of the quiz on which you made a three discussed previously

in the module Introduction to Central Tendency and shown in Table 2.

Student Dataset 1 Dataset 2 Dataset 3

You 3 3 3John's 3 4 2Maria's 3 4 2Shareecia's 3 4 2Luther's 3 5 1Table 2: Three possible datasets for the 5-point make-up

quizFor Dataset 1, the median is three, the same as your score. For Dataset 2, the median is 4.

Therefore, your score is below the median. This means you are in the lower half of the class.

Finally for Dataset 3, the median is 2. For this dataset, your score is above the median and

therefore in the upper half of the distribution.

Computation of the Median: When there is an odd number of numbers, the median is simply the

middle number. For example, the median of 2, 4, and 7 is 4. When there is an even number of

numbers, the median is the mean of the two middle numbers. Thus, the median of the numbers2, 4, 7, 12 is

4+7

2=5.5.

Mode

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The mode is the most frequently occurring value. For the data in the table, Number of

touchdown passes, the mode is 18 since more teams (4) had 18 touchdown passes than any

other number of touchdown passes. With continuous data such as response time measured to

many decimals, the frequency of each value is one since no two scores will be exactly the same

(see discussion ofcontinuous variables). Therefore the mode of continuous data is normally

computed from a grouped frequency distribution. The Grouped frequency distributiontableshows a grouped frequency distribution for the target response time data. Since the interval with

the highest frequency is 600-700, the mode is the middle of that interval (650).

Range Frequency

500-600 3600-700 6700-800 5800-900 5900-1000 01000-1100 1Table 3: Grouped frequency

distribution

Measures of Dispersion: A measure of statistical dispersion is a real numberthat is zero if all

the data are identical, and increases as the data becomes more diverse. It cannot be less than

zero.

Most measures of dispersion have the same scale as the quantity being measured. In otherwords, if the measurements have units, such as metres or seconds, the measure of dispersion

has the same units. Such measures of dispersion include:

Standard deviation

Interquartile range

Range

Mean difference

Median absolute deviation

Average absolute deviation (or simply called average deviation)

Distance standard deviation

These are frequently used (together with scale factors) as estimators ofscale parameters, in

which capacity they are called estimates of scale.

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All the above measures of statistical dispersion have the useful property that they are location-

invariant, as well as linear in scale. So if a random variableXhas a dispersion ofSX then a

linear transformationY= aX+ b forreala and b should have dispersion SY= |a|SX.

Other measures of dispersion are dimensionless (scale-free). In other words, they have no

units even if the variable itself has units. These include:

Coefficient of variation

Quartile coefficient of dispersion

Relative mean difference, equal to twice the Gini coefficient

There are other measures of dispersion:

Variance (the square of the standard deviation) location-invariant but not linear in

scale.

Variance-to-mean ratio mostly used forcount data when the term coefficient of

dispersion is used and when this ratio is dimensionless, as count data are themselves

dimensionless: otherwise this is not scale-free.

Some measures of dispersion have specialized purposes, among them theAllan variance and

the Hadamard variance.

Forcategorical variables, it is less common to measure dispersion by a single number. See

qualitative variation. One measure that does so is the discrete entropy.

Sources of statistical dispersion

In the physical sciences, such variability may result only from random measurement errors:

instrument measurements are often not perfectly precise, i.e., reproducible. One may assume

that the quantity being measured is unchanging and stable, and that the variation between

measurements is due to observational error.

In the biological sciences, this assumption is false: the variation observed might be intrinsicto

the phenomenon: distinct members of a population differ greatly. This is also seen in the arena

of manufactured products; even there, the meticulous scientist finds variation.The simple model

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http://en.wikipedia.org/wiki/Random_variablehttp://en.wikipedia.org/wiki/Linear_transformationhttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Dimensionlesshttp://en.wikipedia.org/wiki/Coefficient_of_variationhttp://en.wikipedia.org/wiki/Quartile_coefficient_of_dispersionhttp://en.wikipedia.org/wiki/Relative_mean_differencehttp://en.wikipedia.org/wiki/Gini_coefficienthttp://en.wikipedia.org/wiki/Variancehttp://en.wikipedia.org/wiki/Variance-to-mean_ratiohttp://en.wikipedia.org/wiki/Count_datahttp://en.wikipedia.org/wiki/Coefficient_of_dispersionhttp://en.wikipedia.org/wiki/Coefficient_of_dispersionhttp://en.wikipedia.org/wiki/Dimensionlesshttp://en.wikipedia.org/wiki/Allan_variancehttp://en.wikipedia.org/w/index.php?title=Hadamard_variance&action=edit&redlink=1http://en.wikipedia.org/wiki/Categorical_variablehttp://en.wikipedia.org/wiki/Qualitative_variationhttp://en.wikipedia.org/wiki/Information_entropyhttp://en.wikipedia.org/wiki/Accuracy_and_precisionhttp://en.wikipedia.org/wiki/Observational_errorhttp://en.wikipedia.org/wiki/Random_variablehttp://en.wikipedia.org/wiki/Linear_transformationhttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Dimensionlesshttp://en.wikipedia.org/wiki/Coefficient_of_variationhttp://en.wikipedia.org/wiki/Quartile_coefficient_of_dispersionhttp://en.wikipedia.org/wiki/Relative_mean_differencehttp://en.wikipedia.org/wiki/Gini_coefficienthttp://en.wikipedia.org/wiki/Variancehttp://en.wikipedia.org/wiki/Variance-to-mean_ratiohttp://en.wikipedia.org/wiki/Count_datahttp://en.wikipedia.org/wiki/Coefficient_of_dispersionhttp://en.wikipedia.org/wiki/Coefficient_of_dispersionhttp://en.wikipedia.org/wiki/Dimensionlesshttp://en.wikipedia.org/wiki/Allan_variancehttp://en.wikipedia.org/w/index.php?title=Hadamard_variance&action=edit&redlink=1http://en.wikipedia.org/wiki/Categorical_variablehttp://en.wikipedia.org/wiki/Qualitative_variationhttp://en.wikipedia.org/wiki/Information_entropyhttp://en.wikipedia.org/wiki/Accuracy_and_precisionhttp://en.wikipedia.org/wiki/Observational_error


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of a stable quantity is preferred when it is tenable. Each phenomenon must be examined to see

if it warrants such a simplification.

Q 3. What are the characteristics of a good research design? Explain how the research

design for exploratory studies is different from the research design for descriptive and

diagnostic studies.

Ans.: Good research design:Much contemporary social research is devoted to examining

whether a program, treatment, or manipulation causes some outcome or result. For example, we

might wish to know whether a new educational program causes subsequent achievement score

gains, whether a special work release program for prisoners causes lower recidivism rates,

whether a novel drug causes a reduction in symptoms, and so on. Cook and Campbell (1979)

argue that three conditions must be met before we can infer that such a cause-effect relation

exists:

1. Covariation. Changes in the presumed cause must be related to changes in the

presumed effect. Thus, if we introduce, remove, or change the level of a treatment or

program, we should observe some change in the outcome measures.

2. Temporal Precedence. The presumed cause must occur prior to the presumed effect.

3. No Plausible Alternative Explanations. The presumed cause must be the only

reasonable explanation for changes in the outcome measures. If there are other factors,

which could be responsible for changes in the outcome measures, we cannot beconfident that the presumed cause-effect relationship is correct.

In most social research the third condition is the most difficult to meet. Any number of factors

other than the treatment or program could cause changes in outcome measures. Campbell and

Stanley (1966) and later, Cook and Campbell (1979) list a number of common plausible

alternative explanations (or, threats to internal validity). For example, it may be that some

historical event which occurs at the same time that the program or treatment is instituted was

responsible for the change in the outcome measures; or, changes in record keeping or

measurement systems which occur at the same time as the program might be falsely attributed

to the program. The reader is referred to standard research methods texts for more detailed

discussions of threats to validity.

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This paper is primarily heuristic in purpose. Standard social science methodology textbooks

(Cook and Campbell 1979; Judd and Kenny, 1981) typically present an array of research

designs and the alternative explanations, which these designs rule out or minimize. This tends to

foster a "cookbook" approach to research design - an emphasis on the selection of an available

design rather than on the construction of an appropriate research strategy. While standard

designs may sometimes fit real-life situations, it will often be necessary to "tailor" a researchdesign to minimize specific threats to validity. Furthermore, even if standard textbook designs

are used, an understanding of the logic of design construction in general will improve the

comprehension of these standard approaches. This paper takes a structural approach to

research design. While this is by no means the only strategy for constructing research designs, it

helps to clarify some of the basic principles of design logic.

Minimizing Threats to Validity

Good research designs minimize the plausible alternative explanations for the hypothesized

cause-effect relationship. But such explanations may be ruled out or minimized in a number of

ways other than by design. The discussion, which follows, outlines five ways to minimize threats

to validity, one of which is by research design:

1. By Argument. The most straightforward way to rule out a potential threat to validity is to

simply argue that the threat in question is not a reasonable one. Such an argument may

be made eithera prioriora posteriori, although the former will usually be more convincingthan the latter. For example, depending on the situation, one might argue that an

instrumentation threat is not likely because the same test is used for pre and post test

measurements and did not involve observers who might improve, or other such factors. In

most cases, ruling out a potential threat to validity by argument alone will be weaker than

the other approaches listed below. As a result, the most plausible threats in a study

should not, except in unusual cases, be ruled out by argument only.

2. By Measurement or Observation. In some cases it will be possible to rule out a threat

by measuring it and demonstrating that either it does not occur at all or occurs so

minimally as to not be a strong alternative explanation for the cause-effect relationship.

Consider, for example, a study of the effects of an advertising campaign on subsequent

sales of a particular product. In such a study, history (i.e., the occurrence of other events

which might lead to an increased desire to purchase the product) would be a plausible

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alternative explanation. For example, a change in the local economy, the removal of a

competing product from the market, or similar events could cause an increase in product

sales. One might attempt to minimize such threats by measuring local economic

indicators and the availability and sales of competing products. If there is no change in

these measures coincident with the onset of the advertising campaign, these threats

would be considerably minimized. Similarly, if one is studying the effects of specialmathematics training on math achievement scores of children, it might be useful to

observe everyday classroom behavior in order to verify that students were not receiving

any additional math training to that provided in the study.

3. By Design. Here, the major emphasis is on ruling out alternative explanations by adding

treatment or control groups, waves of measurement, and the like. This topic will be

discussed in more detail below.

4. By Analysis. There are a number of ways to rule out alternative explanations using

statistical analysis. One interesting example is provided by Jurs and Glass (1971). They

suggest that one could study the plausibility of an attrition or mortality threat by

conducting a two-way analysis of variance. One factor in this study would be the original

treatment group designations (i.e., program vs. comparison group), while the other factor

would be attrition (i.e., dropout vs. non-dropout group). The dependent measure could be

the pretest or other available pre-program measures. A main effect on the attrition factor

would be indicative of a threat to external validity or generalizability, while an interaction

between group and attrition factors would point to a possible threat to internal validity.Where both effects occur, it is reasonable to infer that there is a threat to both internal and

external validity.

The plausibility of alternative explanations might also be minimized using covariance

analysis. For example, in a study of the effects of "workfare" programs on social welfare

caseloads, one plausible alternative explanation might be the status of local economic

conditions. Here, it might be possible to construct a measure of economic conditions and

include that measure as a covariate in the statistical analysis. One must be careful when

using covariance adjustments of this type -- "perfect" covariates do not exist in most

social research and the use of imperfect covariates will not completely adjust for potential

alternative explanations. Nevertheless causal assertions are likely to be strengthened by

demonstrating that treatment effects occur even after adjusting on a number of good

covariates.

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5. By Preventive Action. When potential threats are anticipated some type of preventive

action can often rule them out. For example, if the program is a desirable one, it is likely

that the comparison group would feel jealous or demoralized. Several actions can be

taken to minimize the effects of these attitudes including offering the program to the

comparison group upon completion of the study or using program and comparison groups

which have little opportunity for contact and communication. In addition, auditing methodsand quality control can be used to track potential experimental dropouts or to insure the

standardization of measurement.

The five categories listed above should not be considered mutually exclusive. The inclusion of

measurements designed to minimize threats to validity will obviously be related to the design

structure and is likely to be a factor in the analysis. A good research plan should, where

possible. make use of multiple methods for reducing threats. In general, reducing a particular

threat by design or preventive action will probably be stronger than by using one of the other

three approaches. The choice of which strategy to use for any particular threat is complex and

depends at least on the cost of the strategy and on the potential seriousness of the threat.

Design Construction

Basic Design Elements. Most research designs can be constructed from four basic elements:

1. Time. A causal relationship, by its very nature, implies that some time has elapsed

between the occurrence of the cause and the consequent effect. While for some

phenomena the elapsed time might be measured in microseconds and therefore might be

unnoticeable to a casual observer, we normally assume that the cause and effect in social

science arenas do not occur simultaneously, In design notation we indicate this temporal

element horizontally - whatever symbol is used to indicate the presumed cause would be

placed to the left of the symbol indicating measurement of the effect. Thus, as we read

from left to right in design notation we are reading across time. Complex designs might

involve a lengthy sequence of observations and programs or treatments across time.

2. Program(s) or Treatment(s). The presumed cause may be a program or treatment

under the explicit control of the researcher or the occurrence of some natural event or

program not explicitly controlled. In design notation we usually depict a presumed cause

with the symbol "X". When multiple programs or treatments are being studied using the

same design, we can keep the programs distinct by using subscripts such as "X 1" or "X2".

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