1. Master of Business Administration – MBA Semester 3
MB0034 Research Methodology
Assignment Set- 1
60 Marks
Note: Each question carries 10 Marks. Answer all the questions.
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. (10 marks).
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. (10 marks).
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? (10 marks).
Q 4. Briefly explain any two factors that affect the choice of a sampling technique. What are the
characteristics of a good sample?(10 marks).
Q 5. Select any topic for research and explain how you will use both secondary and primary
sources to gather the required information. (10 marks).
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. (10 marks).
2. Q 1. Give examples of specific situations that would call for the following types of research,
explaining why – a) Exploratory research b) Descriptive research c) Diagnostic research d)
Evaluation research.
Ans.: Research may be classified crudely according to its major intent or the methods. According to the
intent, research may be classified as:
Basic (aka fundamental or pure) research is driven by a scientist's curiosity or interest in a scientific question.
The main motivation is to expand man's knowledge, not to create or invent something. There is no obvious
commercial value to the discoveries that result from basic research.
For example, basic science investigations probe for answers to questions such as:
• How did the universe begin?
• What are protons, neutrons, and electrons composed of?
• How do slime molds reproduce?
• What is the specific genetic code of the fruit fly?
Most scientists believe that a basic, fundamental understanding of all branches of science is needed in order
for progress to take place. In other words, basic research lays down the foundation for the applied science
that follows. If basic work is done first, then applied spin-offs often eventually result from this research. As Dr.
George Smoot of LBNL says, "People cannot foresee the future well enough to predict what's going to
develop from basic research. If we only did applied research, we would still be making better spears."
Applied research is designed to solve practical problems of the modern world, rather than to acquire
knowledge for knowledge's sake. One might say that the goal of the applied scientist is to improve the
human condition.
For example, applied researchers may investigate ways to:
• Improve agricultural crop production
• Treat or cure a specific disease
• Improve the energy efficiency of homes, offices, or modes of transportation
Some scientists feel that the time has come for a shift in emphasis away from purely basic research and
toward applied science. This trend, they feel, is necessitated by the problems resulting from global
overpopulation, pollution, and the overuse of the earth's natural resources.
Exploratory research provides insights into and comprehension of an issue or situation. It should draw
definitive conclusions only with extreme caution. Exploratory research is a type of research conducted
because a problem has not been clearly defined. Exploratory research helps determine the best research
design, data collection method and selection of subjects. Given its fundamental nature, exploratory research
often concludes that a perceived problem does not actually exist.
Exploratory research often relies on secondary research such as reviewing available literature and/or data,
or qualitative approaches such as informal discussions with consumers, employees, management or
competitors, and more formal approaches through in-depth interviews, focus groups, projective methods,
case studies or pilot studies. The Internet allows for research methods that are more interactive in nature:
E.g., RSS feeds efficiently supply researchers with up-to-date information; major search engine search
results may be sent by email to researchers by services such as Google Alerts; comprehensive search
results are tracked over lengthy periods of time by services such as Google Trends; and Web sites may be
created to attract worldwide feedback on any subject.
The results of exploratory research are not usually useful for decision-making by themselves, but they can
provide significant insight into a given situation. Although the results of qualitative research can give some
indication as to the "why", "how" and "when" something occurs, it cannot tell us "how often" or "how many."
Exploratory research is not typically generalizable to the population at large.
3. 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 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
4. 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
• 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 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 To be read as follows
Hypotheses
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 (the researcher
must avoid the error of deriving hypotheses from the data he collects and testing the hypotheses from the
same data). In the choice of null hypothesis, the following considerations are usually kept in view:
5. a. 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.
b. 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.
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 the alternative
hypothesis in view. Why so? The answer is that on the assumption that the null hypothesis is true, one can
assign the probabilities to different possible sample results, but this cannot be done if we proceed with
alternative hypotheses. Hence the use of null hypotheses (at times also known as statistical hypotheses) is
quite frequent.
12) The Level of Significance: This is a very important concept in the context of hypothesis testing. It is
always some percentage (usually 5%), which should be chosen with great care, thought and reason. In case
we take the significance 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 error) Correct decision
The probability of Type I error is usually determined in advance and is understood as the level of significance
of testing the hypotheses. If type I error is fixed at 5%, it means there are about 5 chances in 100 that we will
reject H0 when H0 is true. We can control type I error just by fixing it at a lower level. For instance, if we fix it
at 1%, we will say that the maximum probability of committing type I error would only be 0.01.
But with a fixed sample size n, when we try to reduce type I error, the probability of committing type II error
increases. Both types of errors cannot be reduced simultaneously, since there is a trade-off in business
situations. Decision makers decide the appropriate level of type I error by examining the costs of penalties
attached to both types of errors. If type I error involves time and trouble of reworking a batch of chemicals
that should have been accepted, whereas type II 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
6. 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.
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.
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
7. 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 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 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.
8. 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 a mall or on the
street who are carrying a clipboard and who are stopping various people and asking if they could interview
them? Most likely they are conducting a purposive sample (and most likely they are engaged in market
research). They might be looking for Caucasian females between 30-40 years old. They size up the people
passing by and anyone who looks to be in that category they stop to ask if they will participate. One of the
first things they're likely to do is verify that the respondent does in fact meet the criteria for being in the
sample. Purposive sampling can be very useful for situations where you need to reach a targeted sample
quickly and where sampling for proportionality is not the primary concern. With a purposive sample, you are
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, 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.
9. • 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 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 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
10. 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 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.:
1 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 –
11 It is unique to a particular research study
22 It is recent information, unlike published information that is already available
The disadvantages are –
11. 11 It is expensive to collect, compared to gathering information from available sources
22 Data collection is a time consuming process
33 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:
11 It is always conducted in a natural setting. It is a field study.
22 It seeks responses directly from the respondents.
33 It can cover a very large population.
44 It may include an extensive study or an intensive study
55 It covers a definite geographical area.
A survey involves the following steps -
11 Selection of a problem and its formulation
22 Preparation of the research design
33 Operation concepts and construction of measuring indexes and scales
44 Sampling
12. 55 Construction of tools for data collection
66 Field work and collection of data
77 Processing of data and tabulation
88 Analysis of data
99 Reporting
There are four basic survey methods, which include:
11 Personal interview
22 Telephone interview
33 Mail survey and
44 Fax survey
Personal Interview
Personal interviewing is one of the prominent methods of data collection. It may be defined as a two-way
systematic conversation between an investigator and an informant, initiated for obtaining information relevant
to a specific study. It involves not only conversation, but also learning from the 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 less educated respondents.
It is useful for collecting a wide range of data, from factual demographic data to highly personal and intimate
information relating to a person’s opinions, attitudes, values, beliefs, experiences and future intentions.
Interviewing is appropriate when qualitative information is required, or probing is necessary to draw out 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.
7. The interview is an interactive process. The interaction between the interviewer and the respondent
depends upon how they perceive each other.
13. 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 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 –
T 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.
p Anonymity must be assured.
p 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.
d A self-addressed stamped envelope should be enclosed in the covering letter.
14. 11 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.
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.
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
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.
15. 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.
Methodology - Sample
Participants in this study (N=267) were students enrolled in 100- and 200-level English courses at a
midwestern public university. Courses that comprise the framework for this sample were selected because
they could fulfill basic studies requirements for all majors. A basic studies course is one that is listed within
the core curriculum required for all students. The researcher obtained permission from seven professors to
distribute questionnaires in the eight classes during regularly scheduled class periods. The students'
participation was voluntary; two students declined. The goal of this sampling procedure was to reach a cross-
section of students representing various fields of study. In all, 53 majors were represented.
Of the 267 students who participated in the study, 65 (24.3 percent) were male and 177 (66.3 percent) were
female. A total of 25 participants chose not to divulge their genders. Ages ranged from 17 to 56, with a mean
age of 23.6 years. This mean does not include the 32 respondents who declined to give their ages. A total of
157 participants (58.8 percent) said they were of the Caucasian race, 59 (22.1 percent) African American, 10
(3.8 percent) Asian, five (1.9 percent) African/Native American, two (.8 percent) Hispanic, two (.8 percent)
Native American, and one (.4 percent) Arabic. Most (214) of the students were enrolled full time, whereas a
few (28) were part-time students. The class rank breakdown was: freshmen, 45 (16.9 percent); sophomores,
15 (5.6 percent); juniors, 33 (12.4 percent); seniors, 133 (49.8 percent); and graduate students, 16 (6
percent).
Procedure:
After two pre-tests and revisions, questionnaires were distributed and collected by the investigator. In each of
the eight classes, the researcher introduced herself to the students as a journalism professor who was
conducting a study on students' use of newspapers and other media. Each questionnaire included a cover
letter with the researcher's name, address, and phone number. The researcher provided pencils and was
available to answer questions if anyone needed further assistance. The average time spent on the
questionnaires was 20 minutes, with some individual students taking as long as an hour. Approximately six
students asked to take the questionnaires home to finish. They returned the questionnaires to the
researcher's mailbox within a couple of day.
