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ASSIGNMENT SET-1 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) Diagnosticresearch d) Evaluation research.

Ans.: Research may be classified crudely according to its major intent or the methods. Accordingto the intent, research may be classified as:Basic (aka fundamental or pure) research is driven by a scientist's curiosity or interest in ascientific question. The main motivation is to expand man's knowledge, not to create or inventsomething. There is no obvious commercial value to the discoveries that result from basicresearch.

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

y How did the universe begin?

y What are protons, neutrons, and electrons composed of?

y How do slime molds reproduce?

y What is the specific genetic code of the fruit fly?

Most scientists believe that a basic, fundamental understanding of all branches of science isneeded in order for progress to take place. In other words, basic research lays down thefoundation for the applied science that follows. If basic work is done first, then applied spin-offsoften eventually result from this research. As Dr. George Smoot of LBNL says, "People cannotforesee the future well enough to predict what's going to develop from basic research. If we onlydid applied research, we would still be making better spears."

A pplied research is designed to solve practical problems of the modern world, rather than toacquire knowledge for knowledge's sake. One might say that the goal of the applied scientist is toimprove the human condition.

For example, applied researchers may investigate ways to:

y Improve agricultural crop production

y Treat or cure a specific disease

y 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 basicresearch and toward applied science. This trend, they feel, is necessitated by the problemsresulting 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. Itshould 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 helpsdetermine the best research design, data collection method and selection of subjects. Given itsfundamental nature, exploratory research often concludes that a perceived problem does notactually exist.Exploratory research often relies on secondary research such as reviewing available literatureand/or data, or qualitative approaches such as informal discussions with consumers, employees,management or competitors, and more formal approaches through in-depth interviews, focusgroups, projective methods, case studies or pilot studies. The Internet allows for researchmethods that are more interactive in nature: E.g., RSS feeds efficiently supply researchers withup-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 lengthyperiods of time by services such as Google Trends; and Web sites may be created to attractworldwide feedback on any subject.The results of exploratory research are not usually useful for decision-making by themselves, butthey can provide significant insight into a given situation. Although the results of qualitativeresearch can give some indication as to the "why", "how" and "when" something occurs, it cannottell 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 theconditions 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 differencebetween 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 commonexample compares smokers and non-smokers with regard to health problems. Causalconclusions can't be drawn from such a study because of other possible differences between thegroups; e.g., smokers may drink more alcohol than non-smokers. Other unknown differencescould exist as well. Hence, we may see a relation between smoking and health but a conclusionthat smoking is a cause would not be warranted in this situation. (Cp)Descriptive research, also known as statistical research, describes data and characteristicsabout 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 describewhat caused a situation. Thus, descriptive research cannot be used to create a causalrelationship, where one variable affects another. In other words, descriptive research can be saidto have a low requirement for internal validity.The description is used for f requencies, averages and other statistical calculations. Often the bestapproach, prior to writing descriptive research, is to conduct a survey investigation. Qualitativeresearch 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 arealways restrictions to that. Your research must have an impact to the life of the people aroundyou. For example, finding the most frequent disease that affects the children of a town. Thereader 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 towardsdiscovering what is happening and what can be done about. It aims at identifying the causes of aproblem and the possible solutions for it. It may also be concerned with discovering and testingwhether certain variables are associated. This type of research requires prior knowledge of theproblem, its thorough formulation, clear-cut definition of the given population, adequate methodsfor collecting accurate information, precise measurement of variables, statistical analysis and testof 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 theproject area. It is thus directed to assess or appraise the quality and quantity of an activity and itsperformance and to specify its attributes and conditions required for its success. It is concernedwith causal relationships and is more actively guided by hypothesis. It is concerned also with

change over time. Action research is a reflective process of progressive problem solving led by individuals workingwith others in teams or as part of a "community of practice" to improve the way they addressissues 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 designersand stakeholders, researchers work with others to propose a new course of 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 ³action research´ in about 1944, and it appears inhis 1946 paper ³Action Research and Minority Problems´. In that paper, he described action



research as ³a comparative research on the conditions and effects of various forms of socialaction and research leading to social action´ that uses ³a spiral of steps, each of which iscomposed 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 actionsimplemented in a collaborative context with data-driven collaborative analysis or research tounderstand underlying causes enabling future predictions about personal and organizationalchange (Reason & Bradbury, 2001). After six decades of action research development, manymethodologies have evolved that adjust the balance to focus more on the actions taken or moreon the research that results from the reflective understanding of the actions. This tension existsbetween

those that are more driven by the researcher¶s agenda to those more driven by

participants;

y Those that are motivated primarily by instrumental goal attainment to thosemotivated primarily by the aim of personal, organizational, or societal transformation;and

y 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), aimedprimarily at improving the group; and µscholarly¶ research aimed primarily attheoretical generalization and/or large scale change.

Action research challenges traditional social science, by moving beyond reflective knowledgecreated by outside experts sampling variables to an active moment-to-moment theorizing, datacollecting, and inquiring occurring in the midst of emergent structure. ³Knowledge is alwaysgained through action and for action. From this starting point, to question the validity of socialknowledge is to question, not how to develop a reflective science about action, but how todevelop genuinely well-informed action ² how to conduct an action science´ (Tolbert 2001).

Q 2.In the context of hypothesis testing, briefly explain the difference betweena) 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 -1) Null Hypotheses and Alternative Hypotheses: In the context of statistical analysis,

we often talk about null and alternative hypotheses. If we are to compare thesuperiority of method A with that of method B and we proceed on the assumption thatboth 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 analternative hypothesis.

These are symbolically represented as:Null hypothesis = H0 and Alternative hypothesis = HaSuppose we want to test the hypothesis that the population mean is equal to the hypothesizedmean ( H0) = 100. Then we would say that the null hypothesis is that the population mean isequal 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 elseis true. What we conclude rejecting the null hypothesis is known as an alternative hypothesis. If we 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:

AlternativeHypotheses

To be read as follows



Ha: � H0 (The alternative hypothesis is that the population mean is not equal to 100i.e., it may be more or less 100)

Ha: > H0 (The alternative hypothesis is that the population mean is greater than100)

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

The null hypotheses and the alternative hypotheses are chosen before the sample is drawn (theresearcher must avoid the error of deriving hypotheses from the data he collects and testing thehypotheses from the same data). In the choice of null hypothesis, the following considerations areusually kept in view:

a. The alternative hypothesis is usually the one, which is to be proved, and the nullhypothesis is the one that is to be disproved. Thus a null hypothesis representsthe hypothesis we are trying to reject, while the alternative hypothesis representsall other possibilities.

b. If the rejection of a certain hypothesis when it is actually true involves great risk, itis 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.c. 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 thealternative hypothesis in view. Why so? The answer is that on the assumption that the nullhypothesis is true, one can assign the probabilities to different possible sample results, but thiscannot be done if we proceed with alternative hypotheses. Hence the use of null hypotheses (attimes also known as statistical hypotheses) is quite frequent.2) The Level of Significance: This is a very important concept in the context of hypothesistesting. 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 berejected 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 trueand is usually determined in advance before testing the hypothesis.3) Decision Rule or Test of Hypotheses: Given a hypothesis Ha and an alternative hypothesisH0, 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 arevery few defective items in it), against Ha, that the lot is not good (there are many defective itemsin it), then we must decide 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 rejectH0 (or accept Ha). This sort of basis is known as a decision rule.4) 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 may accept H0 when it is nottrue. The former is known as Type I and the latter is known as Type II. In other words, Type Ierror means rejection of hypotheses, which should have been accepted, and Type II error meansaccepting 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 5chances in 100 that we will reject H0 when H0 is true. We can control type I error just by fixing itat 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 committingtype II error increases. Both types of errors cannot be reduced simultaneously, since there is atrade-off in business situations. Decision makers decide the appropriate level of type I error byexamining the costs of penalties attached to both types of errors. If type I error involves time andtrouble of reworking a batch of chemicals that should have been accepted, whereas type II error means taking a chance that an entire group of users of this chemicals compound will bepoisoned, 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 one¶s testing techniques of a given hypothesis.Hence, in testing of hypotheses, one must make all possible efforts to strike an adequate balancebetween Type I & Type II error.5) 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 whichmay > H0 or < H0. If significance level is 5 % and the two-tailed test is to be applied, theprobability of the rejection area will be 0.05 (equally split on both tails of the curve as 0.025) andthat of the acceptance region will be 0.95. If we take = 100 and if our sample mean deviatessignificantly from , in that case we shall accept the null hypothesis. But there are situations whenonly a one-tailed test is considered appropriate. A one-tailed test would be used when we are totest, say, whether the population mean is either lower or higher than some hypothesized value.Parametric statistics is a branch of statistics that assumes data come from a type of probabilitydistribution and makes inferences about the parameters of the distribution most well knownelementary statistical methods are parametric.Generally speaking parametric methods make more assumptions than non-parametric

methods. If those extra assumptions are correct, parametric methods can produce more accurateand precise estimates. They are said to have more statistical power. However, if thoseassumptions are incorrect, parametric methods can be very misleading. For that reason they areoften not considered robust. On the other hand, parametric formulae are often simpler to writedown and faster to compute. In some, but definitely not all cases, their simplicity makes up for their non-robustness, especially if care is taken to examine diagnostic statistics.Because parametric statistics require a probability distribution, they are not distribution-free.Non-parametric models differ from parametric models in that the model structure is notspecified a priori but is instead determined from data. The term nonparametric is not meant toimply that such models completely lack parameters but that the number and nature of theparameters are flexible and not fixed in advance.Kernel density estimation provides better estimates of the density than histograms.Nonparametric regression and semi parametric regression methods have been developed basedon kernels, splines, and wavelets.Data Envelopment Analysis provides efficiency coefficients similar to those obtainedby Multivariate Analysis without any distributional assumption.

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



give 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 of marketing developed from directresponse 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 recognizesthe long term value of customer relationships and extends communication beyond intrusiveadvertising and sales promotional messages.

With the growth of the internet and mobile platforms, Relationship Marketing has continued toevolve and move forward as technology opens more collaborative and social communicationchannels. This includes tools for managing relationships with customers that goes beyond simpledemographic and customer service data. Relationship Marketing extends to include InboundMarketing efforts (a combination of search optimization and Strategic Content), PR, Social Mediaand Application Development.

Just like Customer relationship management(CRM), Relationship Marketing is a broadlyrecognized, widely-implemented strategy for managing and nurturing a company¶s interactionswith clients and sales prospects. It also involves using technology to, organize, synchronizebusiness processes (principally sales and marketing activities) and most importantly, automatethose marketing and communication activities on concrete marketing sequences that could run inautopilot (also known as marketing sequences). The overall goals are to find, attract, and win newclients, 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, 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 dueto chance) or causative association (one variable causes the other). The information given by a correlation coefficient is not enough to define the dependencestructure between random variables. The correlation coefficient completely defines thedependence structure only in very particular cases, for example when the distribution is amultivariate normal distribution. (See diagram above.) In the case of elliptic distributions itcharacterizes the (hyper-)ellipses of equal density, however, it does not completely characterizethe dependence structure (for example, a multivariate t-distribution's degrees of freedomdetermine the level of tail dependence).

Distance correlation and Brownian covariance / Brownian correlation[8][9]

were introduced toaddress the deficiency of Pearson's correlation that it can be zero for dependent randomvariables; zero distance correlation and zero Brownian correlation imply independence.

The correlation ratio is able to detect almost any functional dependency, or the entropy-basedmutual information/total correlation which is capable of detecting even more generaldependencies. The latter are sometimes referred to as multi-moment correlation measures, incomparison 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 thecorrelation between theorised latent variables.



One way to capture a more complete view of dependence structure is to consider a copulabetween them.

Q 4. Briefly explain any two factors that affect the choice of a sampling technique. Whatare 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 thatnon-probability samples aren't representative of the population? Not necessarily. But it doesmean that non-probability samples cannot depend upon the rationale of probability theory. Atleast with a probabilistic sample, we know the odds or probability that we have represented thepopulation well. We are able to estimate confidence intervals for the statistic. With non-probabilitysamples, we may or may not represent the population well, and it will often be hard for us to knowhow well we've done so. In general, researchers prefer probabilistic or random sampling methodsover non probabilistic ones, and consider them to be more accurate and rigorous. However, inapplied social research there may be circumstances where it is not feasible, practical or theoretically sensible to do random sampling. Here, we consider a wide range of non-probabilisticalternatives.

We can divide non-probability sampling methods into two broad types: Accidental or purposive.

Most sampling methods are purposive in nature because we usually approach thesampling problem with a specific plan in mind. The most important distinctions among these typesof 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. Iwould 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 aquick (although non representative) reading of public opinion. I would also argue that the typicaluse of college students in much psychological research is primarily a matter of convenience. (Youdon't really believe that psychologists use college students because they believe they'rerepresentative of the population at large, do you?). In clinical practice, we might use clients whoare 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 evidencethat they are representative of the populations we're interested in generalizing to -- and in manycases we would clearly suspect that they are not.

Purposive Sampling

In purposive sampling, we sample with a purpose in mind. We usually would have one or more specific predefined groups we are seeking. For instance, have you ever run into people in amall or on the street who are carrying a clipboard and who are stopping various people andasking if they could interview them? Most likely they are conducting a purposive sample (andmost likely they are engaged in market research). They might be looking for Caucasian femalesbetween 30-40 years old. They size up the people passing by and anyone who looks to be in thatcategory they stop to ask if they will participate. One of the first things they're likely to do is verifythat the respondent does in fact meet the criteria for being in the sample. Purposive sampling canbe very useful for situations where you need to reach a targeted sample quickly and wheresampling for proportionality is not the primary concern. With a purposive sample, you are likely toget 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 samplingmethods. 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 locateotherwise, as in snowball sampling. In all of these methods we know what we want -- we aresampling with a purpose.

y Modal Instance SamplingIn statistics, the mode is the most frequently occurring value in a distribution. In sampling, whenwe do a modal instance sample, we are sampling the most frequent case, or the "typical" case. Ina lot of informal public opinion polls, for instance, they interview a "typical" voter. There are anumber 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 youknow that those three variables -- age, education, income -- are the only or even the mostrelevant 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.

y Expert SamplingExpert sampling involves the assembling of a sample of persons with known or demonstrableexperience and expertise in some area. Often, we convene such a sample under the auspices of a "panel of experts." There are actually two reasons you might do expert sampling. First, because

it would be the best way to elicit the views of persons who have specific expertise. In this case,expert sampling is essentially just a specific sub case of purposive sampling. But the other reasonyou might use expert sampling is to provide evidence for the validity of another samplingapproach you've chosen. For instance, let's say you do modal instance sampling and areconcerned that the criteria you used for defining the modal instance are subject to criticism. Youmight convene an expert panel consisting of persons with acknowledged experience and insightinto that field or topic and ask them to examine your modal definitions and comment on their appropriateness and validity. The advantage of doing this is that you aren't out on your own tryingto defend your decisions -- you have some acknowledged experts to back you. The disadvantageis that even the experts can be, and often are, wrong.

y Quota SamplingIn quota sampling, you select people non-randomly according to some fixed quota. There are two

types of quota sampling: proportional and non proportional . In proportional quota sampling youwant to represent the major characteristics of the population by sampling a proportional amountof each. For instance, if you know the population has 40% women and 60% men, and that youwant a total sample size of 100, you will continue sampling until you get those percentages andthen 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 willnot sample them because you have already "met your quota." The problem here (as in muchpurposive sampling) is that you have to decide the specific characteristics on which you will basethe 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 withhaving numbers that match the proportions in the population. Instead, you simply want to haveenough 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 usedto assure that smaller groups are adequately represented in your sample.

y Heterogeneity SamplingWe sample for heterogeneity when we want to include all opinions or views, and we aren'tconcerned about representing these views proportionately. Another term for this is sampling for diversity . In many brainstorming or nominal group processes (including concept mapping), wewould use some form of heterogeneity sampling because our primary interest is in getting broadspectrum of ideas, not identifying the "average" or "modal instance" ones. In effect, what wewould 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 thepopulation of people who have the ideas. Clearly, in order to get all of the ideas, and especiallythe "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.

y Snowball SamplingIn snowball sampling, you begin by identifying someone who meets the criteria for inclusion inyour study. You then ask them to recommend others who they may know who also meet thecriteria. Although this method would hardly lead to representative samples, there are times whenit may be the best method available. Snowball sampling is especially useful when you are tryingto reach populations that are inaccessible or hard to find. For instance, if you are studying thehomeless, you are not likely to be able to find good lists of homeless people within a specificgeographical area. However, if you go to that area and identify one or two, you may find that theyknow 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 conflictingfactors. The various criteria governing the choice of the sampling technique are:

1. Purpose of the Survey: What does the researcher aim at? If he intends to generalizethe 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 sizeand 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. Only probability samples allow suchcomputation. Hence, where the research objective requires statistical inference, thesample should be drawn by applying simple random sampling method or stratifiedrandom 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 could evenrough results serve the purpose? The desired level of precision is one of the criteriafor sampling method selection. Where a high degree of precision of results is desired,probability sampling should be used. Where even crude results would serve thepurpose (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 itsnature 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 theexploratory study, an appropriate probability sampling design may be adopted.

5. The Nature of the Population: In terms of the variables to be studied, is thepopulation homogenous or heterogeneous? In the case of a homogenous population,even simple random sampling will give a representative sample. If the population isheterogeneous, stratified random sampling is appropriate.

6. Geographical Area of the Study and the Size of the Population: If the area coveredby 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 populationare 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 quotasampling as a compromise. However, if the objectives of the study and the desiredlevel 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 theresearch objective and the nature of population.





and tribal communities, sociological studies of social problems and social institutions, marketingresearch, leadership studies, opinion polls, attitudinal surveys, radio listening and T.V. viewingsurveys, knowledge-awareness practice (KAP) studies, farm management studies, businessmanagement 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 froma population or a sample at a particular time. A survey has certain characteristics: It is always conducted in a natural setting. It is a field study. It seeks responses directly from the respondents. It can cover a very large population. It may include an extensive study or an intensive study It covers a definite geographical area.

A survey involves the following steps - 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

There are four basic survey methods, which include: Personal interview Telephone interview Mail survey and Fax surveyPersonal InterviewPersonal 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 obtaininginformation relevant to a specific study. It involves not only conversation, but also learning fromthe respondent¶s 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 lesseducated respondents. It is useful for collecting a wide range of data, from factual demographicdata to highly personal and intimate information relating to a person¶s opinions, attitudes, values,beliefs, experiences and future intentions. Interviewing is appropriate when qualitative informationis required, or probing is necessary to draw out the respondent fully. Where the area covered 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 talkthan 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 thebehavioral context of the data furnished by the respondents. It permits the investigator to seekclarifications and brings to the forefront those questions, which for some reason or the other therespondents do not want to answer. Interviewing as a method of data collection has certaincharacteristics. They are:

1. The participants ± the interviewer and the respondent ± are strangers;hence, the investigator has to get himself/herself introduced to therespondent in an appropriate manner.



2. The relationship between the participants is a transitory one. It has afixed 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 aconversation with a specific purpose, viz., obtaining information relevantto a study.

4. The interview is a mode of obtaining verbal answers to questions putverbally.

5. The interaction between the interviewer and the respondent need notnecessarily be on a face-to-face basis, because the interview can alsobe conducted over the telephone.

6. Although the interview is usually a conversation between two persons, itneed not be limited to a single respondent. It can also be conducted witha group of persons, such as family members, or a group of children, or agroup of customers, depending on the requirements of the study.

7. The interview is an interactive process. The interaction between theinterviewer and the respondent depends upon how they perceive eachother.

8. The respondent reacts to the interviewer¶s appearance, behavior,gestures, facial expression and intonation, his perception of the thrust of

the questions and his own personal needs. As far as possible, theinterviewer should try to be closer to the social-economic level of therespondents.

9. The investigator records information furnished by the respondent in theinterview. This poses a problem of seeing that recording does notinterfere with the tempo of conversation.

10. Interviewing is not a standardized process like that of a chemicaltechnician; it is rather a flexible, psychological process.

3 Telephone InterviewingTelephone interviewing is a non-personal method of data collection. Itmay be used as a major method or as a supplementary method. It will be useful in the followingsituations:

11. When the universe is composed of those persons whose names arelisted 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,

provided the 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 concernedprofessional association.

15. When the respondents are widely scattered and when there are manycall backs to make.

4 Group Interviews A group interview may be defined as a method of collecting primary data inwhich 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 eightindividuals with a common interest. The interviewer acts as the discussion leader. Freediscussion 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 beobtained through self-administered questionnaire or interview, with the discussion serving as aguide 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 inthe group. At the same time, he must be aware that a single comment by a member can provideimportant 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 methodinvolves sending questionnaires to the respondents with a request to complete them and returnthem by post. This can be used in the case of educated respondents only. The mailquestionnaires should be simple so that the respondents can easily understand the questions andanswer them. It should preferably contain mostly closed-ended and multiple choice questions, sothat it could be completed within a few minutes. The distinctive feature of the mail survey is thatthe questionnaire is self-administered by the respondents themselves and the responses arerecorded by them and not by the investigator, as in the case of personal interview method. It doesnot involve face-to-face conversation between the investigator and the respondent.Communication is carried out only in writing and this requires more cooperation from therespondents than verbal communication. The researcher should prepare a mailing list of theselected respondents, by collecting the addresses from the telephone directory of the associationor organization to which they belong. The following procedures should be followed - a coveringletter should accompany a copy of the questionnaire. It must explain to the respondent thepurpose of the study and the importance of his cooperation to the success of the project.

Anonymity must be assured. The sponsor¶s identity may be revealed. However, when suchinformation may bias the result, it is not desirable to reveal it. In this case, a disguisedorganization name may be used. A self-addressed stamped envelope should be enclosed inthe covering letter. 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 andat the appropriate stage, follow-up efforts can be made.

The response rate in mail surveys is generally very low in developing countries like India. Certaintechniques have to be adopted to increase the response rate. They are:

1. Quality printing: The questionnaire may be neatly printed on quality light colored 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 attractand hold the interest of the respondent. It must anticipate objections and answer thembriefly. It is desirable to address the respondent by name.

3. Advance information: Advance information can be provided to potential respondents by atelephone call, or advance notice in the newsletter of the concerned organization, or by aletter. 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 the mail questionnaire.5. 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.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 mayhelp the researcher to secure an effective sample size closer to the required size.

Q 6. Case Study: You are engaged to carry out a market survey on behalf of a leadingNewspaper that is keen to increase its circulation in Bangalore City, in order toascertain reader habits and interests. Develop a title for the study; define theresearch 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 feelapprehensive, confused and ill at ease. It is the demarcation of a problem area within a certaincontext involving the WHO or WHAT, the WHERE, the WHEN and the WHY of the problemsituation.



There are many problem situations that may give rise to research. Three sources usuallycontribute to problem identification. Own experience or the experience of others may be a sourceof problem supply. A second source could be scientific literature. You may read about certainfindings 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 clarifyingcontradictory findings, at correcting a faulty methodology, at correcting the inadequate or unsuitable use of statistical techniques, at reconciling conflicting opinions, or at solving existingpractical problems

Types of questions to be asked :For more than 35 years, the news about newspapers andyoung readers has been mostly bad for the newspaper industry. Long before any competitionfrom cable television or Nintendo, American newspaper publishers were worrying about decliningreadership among the young.

As early as 1960, at least 20 years prior to Music Television (MTV) or the Internet, mediaresearch scholars1 began to focus their studies on young adult readers' decreasing interest innewspaper 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 occupiedhouseholds in a geographic market that are served by a newspaper.2 Simply put, populationgrowth 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 examinessome 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 readersevaluate 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 readersshowed increased interest in national news, weather, sports, and classified advertisements over the decade between 1984 and 1994, while older readers ranked weather, editorials, and foodadvertisements higher. Interest in international news and letters to the editor was less amongyounger readers, while older readers showed less interest in reports of births, obituaries, andmarriages.

David Atkin explored the influence of telecommunication technology on newspaper readershipamong students in undergraduate media courses.17 He reported that computer-relatedtechnologies, 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 electronicnewspapers 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 assesseddimensions of interest in newspapers, magazines, and books,19 exploring the influence of mediause, non-media leisure, and academic major on newspaper content preferences. The studydiscovered 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 preferencesfor 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 aslight correlation between age and the public affairs readership index, with older readers moreinterested in news about public affairs.

Methodology

Sample

Participants in this study (N=267) were students enrolled in 100- and 200-level English courses ata midwestern public university. Courses that comprise the framework for this sample wereselected because they could fulfill basic studies requirements for all majors. A basic studiescourse 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 duringregularly scheduled class periods. The students' participation was voluntary; two studentsdeclined. The goal of this sampling procedure was to reach a cross-section of studentsrepresenting 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 (66.3percent) 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 respondentswho declined to give their ages. A total of 157 participants (58.8 percent) said they were of theCaucasian 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 (.4percent) 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 (5.6percent); juniors, 33 (12.4 percent); seniors, 133 (49.8 percent); and graduate students, 16 (6percent).

Procedure

After two pre-tests and revisions, questionnaires were distributed and collected by theinvestigator. 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, andphone number. The researcher provided pencils and was available to answer questions if anyoneneeded further assistance. The average time spent on the questionnaires was 20 minutes, withsome individual students taking as long as an hour. Approximately six students asked to take thequestionnaires home to finish. They returned the questionnaires to the researcher's mailboxwithin a couple of day.



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