mb0034 research methodology-set 1

8/8/2019 MB0034 Research Methodology-Set 1

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NAME:

DEEPASHREE V P

SUBJECT:

Research Methodology

ROLL NUMBER:

510923576

ASSIGNMENT:

MB0034– Set 1

STUDY CENTRE:

02908

DATE OF SUBMISSION:

NOVEMBER, 2010



ASSIGNMENTS

Subject code: MB 0034

Set 1

SUBJECT NAME: Research Methodology

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."

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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

• 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 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. 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.

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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 assignment

results 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 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 of a

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

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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.

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 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 “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 researcher’s agenda to those more driven by

participants;

• Those that are motivated primarily by instrumental goal attainment to those motivated

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

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• 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

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 (µ 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: µ= µ H0=100

If our sample results do not support this null hypothesis, we should conclude that something else

is true. What we conclude rejecting the null hypothesis is known as an alternative hypothesis. If

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we accept H0, then we are rejecting Ha and if we reject H0, then we are accepting Ha. For H0:

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

Alternative

Hypotheses

To be read as follows

Ha: µ≠µ H0 (The alternative hypothesis is that the population mean is not equal to 100

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

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

100)

Ha: µ< µ 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 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 the

hypothesis 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 α (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

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chosen with great care, thought and reason. In case we take the significance level

at 5%, then this implies that H0 will be rejected when the sampling result (i.e.,

observed evidence) has a less than 0.05 probability of occurring if H0 is true. In

other words, the 5% level of significance means that 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 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

may accept 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 α (alpha),

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

Decision

Accept H0 Reject H0

H0 (true) Correct decision Type I error (α error)

Ho (false) Type II error (β Correct decision

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error)

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 error means taking a chance that an entire group of users of this chemicals compound will be poisoned,

then in such a situation one should prefer a type I error to a type II error. 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 balance between

Type I & Type II error.

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: µ= µ H0 and Ha: µ≠µ H0 which

may µ>µ H0 or µ<µ H0. If significance level is 5 % and the two-tailed test is to be applied, the

probability of the rejection area will be 0.05 (equally split on both tails of the curve as 0.025) and

that of the acceptance region will be 0.95. If we take µ = 100 and if our sample mean deviates

significantly from µ, in that case we shall accept the null hypothesis. But there are situations

when only a one-tailed test is considered appropriate. A one-tailed test would be used when we

are to test, 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 probability

distribution and makes inferences about the parameters of the distribution most well known

elementary statistical methods are parametric.

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Generally speaking parametric methods make more assumptions than non-parametric methods. If

those extra assumptions are correct, parametric methods can produce more accurate and precise

estimates. They are said to have more statistical power. However, if those assumptions are

incorrect, parametric methods can be very misleading. For that reason they are often not

considered robust. On the other hand, parametric formulae are often simpler to write down 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 not

specified a priori but is instead determined from data. The term nonparametric is not meant to

imply that such models completely lack parameters but that the number and nature of the

parameters 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 based

on kernels, splines, and wavelets.

Data Envelopment Analysis provides efficiency coefficients similar to those obtained

by Multivariate Analysis without any distributional assumption.

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 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 direct

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

a dominant focus on sales transactions.

10

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


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

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

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


http://en.wikipedia.org/wiki/Kernel_(statistics)

http://en.wikipedia.org/wiki/Spline_(mathematics)


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


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

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

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

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

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


http://en.wikipedia.org/wiki/Kernel_(statistics)



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

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

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




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 company’s 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, 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

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

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



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 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

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

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

http://en.wikipedia.org/wiki/#cite_note-7



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


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

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

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

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


http://en.wikipedia.org/wiki/Copula_(statistics)

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

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




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

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

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


http://en.wikipedia.org/wiki/Copula_(statistics)



social research there may be circumstances where it is not feasible, practical or theoretically

sensible to do random sampling. Here, we consider a wide range of non-probabilistic alternatives.

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

Accidental or purposive.

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

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

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

approaches.

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 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 and

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

most likely 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

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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

likely to 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,

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expert sampling is essentially just a specific sub case of purposive sampling. But the other reason

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

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

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

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

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

appropriateness and validity. The advantage of doing this is that you aren't out on your own

trying to defend your decisions -- you have some acknowledged experts to back you. The

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

• Quota Sampling

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

types of quota sampling: proportional and non proportional . 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 the

minimum number of sampled units you want in each category. Here, you're not concerned with

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

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

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

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

• 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

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

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

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

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

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

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

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

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

• 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 appropriate

probability sampling method must be selected. The choice of a particular type of

probability sampling depends on the geographical area of the survey and the size and

the nature of the population under study.

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

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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,

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 an

exploratory 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 no

alternative 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

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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 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

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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 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, the researcher

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.

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information is 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

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:

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.

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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 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, 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:

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,

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 concerned

professional association.

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

backs to make.

4 Group Interviews A group interview may be defined as a method of collecting primary data in

which a number of individuals with a common interest interact with each other. In a personal

interview, the flow of information is multi dimensional. The group may consist of about six to

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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 for

evidence of common elements of attitudes, beliefs, intentions and opinions among individuals in

the group. At the same time, he must be aware that a single comment by a member can provide

important insight. Samples for group 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 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 sponsor’s 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.

1 After a few days from the date of mailing the questionnaires to the respondents, the

researcher can expect the return of completed ones from them. The progress in return may be

watched and at the appropriate stage, follow-up efforts can be made.

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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 answer them

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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Q 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

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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

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.

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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 on

entertainment, 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 slight

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

interested in news about public affairs.

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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 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 minutes, with some

individual students taking as long as an hour. Approximately six students asked to take the

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questionnaires home to finish. They returned the questionnaires to the researcher's mailbox within

a couple of day.

mb0034 research methodology-set 1

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