Business research methods _ Notes _for MBA and other UG and PG Students by Manoj new - pdf_ppt

List ofTopics
Introduction of Research, types and
process
Data Collection Methods
Sampling Methods & Process
Scales & Scaling Technique
Data Analysis & Interpretation
Report Writing

Meaning and origins of research
• The word research is derived from the French language; its literal meaning is 'to
investigate thoroughly '.
• Research always starts with a question to which we seek an answer using
scientific methods. We define the question as a “Problem”.
• Research in common parlance refers to a search for knowledge
• The Advanced Learner’s Dictionary of Current English lays down the meaning of
research as “a careful investigation or inquiry specially through search for new facts in
any branch of knowledge.”
• Research is often described as an ‘active, diligent, and systematic process of
inquiry aimed at discovering, interpreting and revising facts’.
T.MANOJ KUMAR RESEARCH METHODOLOGY
A S S T P R O F E S S O R
S K I M T

RESEARCH
In fact, research is an art of scientific investigation.
Once can also define research as a scientific and systematic
search for pertinent information on a specific topic.
Research in common parlance refers to a search for
knowledge.
What is Research?
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RESEARCH AS AN ACADEMIC ACTIVITY
According to CliffordWoody research comprises defining and redefining problems, formulating
hypothesis or suggested solutions; collecting, organising and evaluating data; making deductions and
reaching conclusions; and at last carefully testing the conclusions to determine whether they fit the
formulating hypothesis.
Research is, thus, an original contribution to the existing stock of knowledge making for its
advancement. It is the persuit of truth with the help of study, observation, comparison and
experiment
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Definitions of research
1. Redman and Mory define research as a “systematized effort to gain
new knowledge.”
2. According to Clifford Woody research comprises defining and
redefining problems, formulating hypothesis or suggested
solutions; collecting, organising and evaluating data; making
deductions and reaching conclusions; and at last carefully testing
the conclusions to determine whether they fit the formulating
hypothesis.
3. D. Slesinger and M. Stephenson in the Encyclopaedia of Social
Sciences define research as “the manipulation of things, concepts
or symbols for the purpose of generalising to extend, correct or
verify knowledge, whether that knowledge aids in construction of
theory or in the practice of an art.T.MANOJ KUMAR RESEARCH METHODOLOGY
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OBJECTIVES OF RESEARCH
1.To gain familiarity with a phenomenon or to achieve new insights into it (studies with
this object in view are termed as exploratory or formulative research studies);
2. To portray accurately the characteristics of a particular individual, situation or a group
(studies with this object in view are known as descriptive research studies);
3. To determine the frequency with which something occurs or with which it is
associated with something else (studies with this object in view are known as diagnostic
research studies);
4. To test a hypothesis of a causal relationship between variables (such studies are known
as hypothesis-testing research studies).
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MOTIVES OF RESEARCH
1.Desire to get a research degree along with its consequential benefits;
2. Desire to face the challenge in solving the unsolved problems, i.e., concern
over practical problems initiates research;
3. Desire to get intellectual joy of doing some creative work;
4. Desire to be of service to society; and
5. Desire to get respectability.
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TYPES OF RESEARCH
1. → Descriptive research includes surveys and fact-finding enquiries of different kinds.
• The major purpose of descriptive research is description of the state of affairs as it exists at
present.
• In social science and business research we quite often use the term Ex post facto research for
descriptive research studies.
The main characteristic of this method is that
• the researcher has no control over the variables;
• he can only report what has happened or what is happening.
example,
frequency of shopping, preferences of people etc.
Ex post facto studies also include attempts by researchers to discover causes even when they cannot
control the variables.
The methods of research utilized in descriptive research are survey methods of all kinds, including
comparative and correlational methods.
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2.→ Analytical research, the researcher has to use facts or information already
available, and analyze these to make a critical evaluation of the material.
3.→ Pure or Fundamental Research – It is based on the Question ‘Why things
happen?’ fundamental research is mainly concerned with generalisations and with
the formulation of a theory. “Gathering knowledge for knowledge’s sake is termed
‘pure’ or ‘basic’ research. ”Research concerning some natural phenomenon or
relating to pure mathematics are examples of fundamental research.. Thus basic
research is directed towards finding information that has a broad base of
applications and thus, adds to the already existing organized body of scientific
knowledge.
• Basic research is also called pure research. Basic research is driven by a scientist’s
curiosity or interest in a scientific question.
• Ex: How did the universe begin
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4.→ Action or Applied Research – It is based on the Question ‘How things
happen?’ Applied research aims at finding a solution for an immediate problem
facing a society or an industrial/business organisation. It aims at certain
conclusions (say, a solution) facing a concrete social or business problem is an
example of applied research.
Research to identify social, economic or political trends that may affect a
particular institution or the copy research (research to find out whether certain
communications will be read and understood) or the marketing research or
evaluation research are examples of applied research. Thus the central aim of
applied research is to discover a solution for some pressing practical problem
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5. → Quantitative research is based on the measurement of quantity or amount. It is
applicable to phenomena that can be expressed in terms of quantity. In the era of
big data, quantitative methods used by operations analysts and economists provide
solid evidence to guide management decisions on production, distribution, marketing
and personnel management.
These methods also help managers project future business conditions, enabling them
to adjust their strategies as needed. This is research based on larger samples and is,
therefore, more statistically valid. Quantitative research is concerned with data and
addresses question such as "how many?", "how often", "who?", "when?" and
"where?"
The results of quantitative research will generally be numerical form – for example:
35% of customers rate the new product as "attractive“
3 out of 5 customers will buy a new food product after being offered a free in-store
sample
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6. → Qualitative research, is concerned with qualitative phenomenon, i.e.,
phenomena relating to quality or kind.
For instance ‘Motivation Research’, an important type of qualitative research that
studies why a person shows a behaviour uses depth interviews, word association
tests, sentence completion tests, story completion tests and similar other projective
techniques.
‘Attitude or opinion research’ designed to find out how people feel or what they
think about a particular subject or institution is also qualitative research.
Qualitative research is specially important in the behavioural sciences where the
aim is to discover the underlying motives of human behaviour.
Through such research we can analyse the various factors which motivate people
to behave in a particular manner or which make people like or dislike a particular
thing.
In practice to apply qualitative research is relatively a difficult job and therefore,
while doing such research, one should seek guidance from experimental
psychologists.
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7. → Conceptual research is that related to some abstract ideas or theory. It is generally used
by philosophers and thinkers to develop new concepts or to reinterpret existing ones.
Nowadays, conceptual research is used to answer business questions and solve real-world
problems. Researchers use analytical tools called conceptual frameworks to make conceptual
distinctions and organize ideas required for research purposes.
Conceptual research focuses on the concept or theory that explains or describes the
phenomenon being studied.
Ex :What causes disease? How can we describe the motions of the planets?
8. → Empirical research relies on experience or observation alone, often without due regard
for system and theory. It is data-based research, coming up with conclusions which are
capable of being verified by observation or experiment. We can also call it as experimental
research. Such research, begins with a working hypothesis or guess as to the probable
results.
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SOME OTHER TYPES OF RESEARCH
1. One-time research - research is confined to a single time-period.
2. Longitudinal research- research is carried on over several time-periods using a panel of
experts.
3. Field-setting research – carried out in the real field
4. Laboratory research – carried out in labs
5. Simulation research- carried out in simulated environments
6. Clinical or diagnostic research- follow case-study methods or in depth approaches to
reach the basic causal relations. Such studies usually go deep into the causes of things or
events that interest us, using very small samples and very deep probing data gathering
devices.
7. Exploratory research is non formalised and is development of hypotheses rather than their
testing.
8. Historical research utilizes historical sources like documents, dairies, autobiographies,
remains, etc. to study events or ideas of the past, including the philosophy of persons and
groups at any remote point of time
.
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CODE TO REMEMBER
Types of Research
1) DAPA-QCE
2) OLFL-SCEH
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RESEARCH PROCESS:
Research process consists of a series of steps or actions required for effectively conducting
research.The following are the steps that provide useful procedural guidelines regarding the
conduct of research:
(1) Formulating the research problem;
(2) Extensive literature survey;
(3)Writing a Primary Synopsis
(4)Identifying and LabelingVariables:
(5) Developing hypothesis;
(6) Preparing the research design
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• 6) Preparing the research design;
• (7) Determining sample design;
• (8) Collecting data;
• (9) Execution of the project;
• (10) Hypothesis testing;
• (11) Processing,Analysis and Interpretation of Data by Statistical Methods
• (12) Preparation of the report or presentation of the results.
RESEARCH PROCESS:
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RESEARCH DESIGN
• A research design is the arrangement of conditions for collection and analysis of data in a manner that
aims to combine relevance to the research purpose with economy in procedure
• Research design is the conceptual structure within which research is conducted
• Blueprint for the collection, measurement and analysis of data..
• an outline of what the researcher will do from writing the hypothesis and its operational implications
to the final analysis of data.
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Research design into the following parts:
• the sampling design which deals with the method of selecting items to be observed for the
given study;
• the observational design which relates to the conditions under which the observations are to be
made;
• the statistical design which concerns with the question of how many items are to be observed
and how the information and data gathered are to be analysed; and
• the operational design which deals with the techniques by which the procedures specified in the
sampling, statistical and observational designs can be carried out
RESEARCH DESIGN
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RESEARCH DESIGN
• Different kinds of research designs
(1)Exploratory research design
(2) Descriptive research design
(3) Experimental research design
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RESEARCH PROBLEM-NATURE
Problem means a question or an issue to be examined.A research problem refers to some kind of
problem which a researcher experiences or observes in the context of either a theoretical or practical
situation
A problem usually arises at the following condition
1) There must be an organization, a group of people or an individual experiencing some difficulties
due to one reason or other
2) There must be some objectives, personnel or organizational, to be attained
If one does not desire anything, there cannot be any problem
3) There must be at least two ways to attaining the objective
If there is only one way, there is no confusion and hence no problem.
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RESEARCH PROBLEM-NATURE
5)There must be aa environment which influences either the researcher or his/her ways
6)There must be some outcomes ( results/findings) which may have positive values
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PROBLEM IDENTIFICATION & SELECTION
PROBLEM
IDENTIFICATION
& SELECTION
Sources of
identification
Criteria for
selection
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PROBLEM IDENTIFICATION
Professional
Literature
Professional
Experience
Inferences from
theories &laws
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CRITERIA FOR A SELECTING A PROBLEM
1) The problem must be related to a profession
2) The solution to be obtained should bring in some improvement in the existing status
3) It must be feasible to find a solution to a problem before the time schedule
4) It must suite the attitude, aptitude and competency of the researcher
5) It must me practical
6) The problem must posses creativity, novelty, originality and the utility value.
7) The Research problem must be specific, meaningful & avoid controversial subject.
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RESEARCH PROBLEM
Problem means a question or an issue to be examined.A research problem refers to some kind of problem which
a researcher experiences or observes in the context of either a theoretical or practical situation
Steps for Defining and Formulating a Research Problem
1) Stating the problem in a general way
2) Understanding the nature of the problem
3) Surveying the available literature
4) Developing the ideas through discussions
5) Rephrasing the research problem
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REVIEW OF LITERATURE
Sources of Review of Literature
Books
Journels
Magazines
Other Sources-
Reports of comittes/commissions or annual reports
Proceedings of conferences, symposia, seminars and workshop
Internet ; Ex Research Gate, Google Scholar etc
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Procedures Followed in Literature Review
1) Identify the source of information
2) Decide the period of review
3) Procure cards of convenient size
4) Note down the relevant information
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5)The summary and abstract may be confined to two sides of card
6) Arrange the cards in chronological order under different heads depending upon the rsearch
problem
7) Refer the card when the report is written
8) Cross check the details after the completion of report writing.
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CONCEPTS RELATING TO RESEARCH DESIGN
Before describing the different research designs, it will be appropriate to
explain the various concepts relating to designs so that these may be better and
easily understood.
1.Variable: A concept which can take on different quantitative values is called a
variable. As such the concepts like weight, height, income are all examples of
variables. Qualitative phenomena (or the attributes) are also quantified on the
basis of the presence or absence of the concerning attribute(s). Phenomena which
can take on quantitatively different values even in decimal points are called
‘continuous variables’. But all variables are not continuous. If they can only be
expressed in integer values, they are non-continuous variables or in statistical
language ‘discrete variables’. Age is an example of continuous variable, but the
number of children is an example of non-continuous variable.
2. Dependent and independent variables:
If one variable depends upon or is a consequence of the other variable, it is
termed as a dependent variable, and the variable that is antecedent to the
dependent variable is termed as an independent variable. For instance, if we say
that height depends upon age, then height is a dependent variable and age is an
independent variable.
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3. Extraneous variable: Independent variables that are not related to the purpose of the
study, but may affect the dependent variable are termed as extraneous variables. Suppose
the researcher wants to test the hypothesis that there is a relationship between children’s
gains in social studies achievement and their self-concepts. In this case self-concept is an
independent variable and social studies achievement is a dependent variable. Intelligence may
as well affect the social studies achievement, but since it is not related to the purpose of the
study undertaken by the researcher, it will be termed as an extraneous variable. Whatever
effect is noticed on dependent variable as a result of extraneous variable(s) is technically
described as an ‘experimental error’. A study must always be so designed that the effect
upon the dependent variable is attributed entirely to the independent variable(s), and not to some
extraneous variable or variables.
4. Control: One important characteristic of a good research design is to minimise the
influence or effect of extraneous variable(s). The technical term ‘control’ is used when we
design the study minimising the effects of extraneous independent variables. In experimental
researches, the term ‘control’ is used to refer to restrain experimental conditions.
5. Confounded relationship: When the dependent variable is not free from the influence of
extraneous variable(s), the relationship between the dependent and independent variables is
said to be confounded by an extraneous variable(s).
CONCEPTS RELATING TO RESEARCH DESIGN
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HYPOTHESIS
Lundberg defines hypothesis as,“a tentative generalization, the validity of which remains to be tested
A hypothesis is a tentative statement about the relationship between two or more variables.A
hypothesis is specific, testable predictions about what is expect to happen in the study.
Research hypothesis is quite often a predictive statement, which is capable of being tested using
scientific methods that involve an independent and some dependent variables
Example
• (i) “Students who take tuitions perform better than the others who do not receive tuitions” or,
• (ii) “The female students perform as well as the male students”.
• These two statements are hypotheses that can be objectively verified and tested.Thus, they indicate
that a hypothesis states what one is looking for.
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HYPOTHESIS
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CONCEPTS IN HYPOTHESES
• 1) Null Hypothesis And Alternative Hypothesis:
• In the context of statistical analysis, hypotheses are of two types viz., null hypothesis and
alternative hypothesis.
• When two methods A and B are compared on their relative superiority, and it is assumed that
both the methods are equally good, then such a statement is called as the null hypothesis
• On the other hand, if method A is considered relatively superior to method B, or vice-versa,
then such a statement is known as an alternative hypothesis
• .The null hypothesis is expressed as H0, while the alternative hypothesis is expressed as Ha.
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2) The Level Of Significance:
• In the context of hypothesis testing, the level of significance is a very important concept. It is a
certain percentage that should be chosen with great care, reason and insight.
• If for instance, the significance level is taken at 5 per cent, then it means that H0 would be
rejected when the sampling result has a less than 0.05 probability of occurrence when H0 is
true. In other words, the five per cent level of significance implies that the researcher is willing
to take a risk of five per cent of rejecting the null hypothesis, when (H0) is actually true.
• In sum, the significance level reflects the maximum value of the probability of rejecting H0
when it is actually true, and which is usually determined prior to testing the hypothesis.
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5) One-Tailed AndTwo-TailedTests:
• These two types of tests are very important in the context of hypothesis testing.
• A two-tailed test rejects the null hypothesis, when the sample mean is significantly greater or
lower than the hypothesized value of the mean of the population.
• Such a test is suitable when the null hypothesis is some specified value; the alternative
hypothesis is a value that is not equal to the specified value of the null hypothesis.
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• 4) Type I AndType II Errors:
• As regards the testing of hypotheses, a researcher can make basically two types of errors.
He/she may reject H0 when it is true, or accept H0 when it is not true.
• The former is called asType I error and the latter is known asType II error. In other words,Type
I error implies the rejection of a hypothesis when it must have been accepted, whileType II
error implies the acceptance of a hypothesis which must have been rejected.
• Type I error is denoted by α (alpha) and is known as α error, whileType II error is usually
denoted by β (beta) and is known as β error.
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HYPOTHESIS
PROCEDURE OF HYPOTHESISTESTING
1. Making a Formal Statement:
2. Selecting A Significance Level
3. DecidingThe DistributionTo Use
4. Selection of A Random Sample And Computing An Appropriate Value
5. Calculation Of the Probability
6. ComparingThe Probability
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TYPES OF HYPOTHESIS
Descriptive Hypothesis – Describing the characteristics of a variable (may be an object, person,
organisation, event, and situation)
• Eg. Employment opportunity of commerce graduates is more than the arts students.
Relational Hypothesis – Establishes relationship between two variables. It may be positive,
negative or nil relationship.
• Eg. High income leads to high savings
Causal Hypothesis –The change in one variable leads to change in another variable i.e. Dependent and independent variables, one variable
is a cause and the other one is the effect
Statistical Hypothesis – association or difference between two variables are hypothesized
Null Hypothesis – it points out there is no difference between two populations in respect of same property.
Alternative Hypothesis- when we reject the null hypothesis, we accept another hypothesis known as alternate hypothesis.
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QUALITIES OF A GOOD RESEARCH
1. Good research is systematic: It means that research is structured with specified steps to be taken in a specified
sequence in accordance with the well defined set of rules. Systematic characteristic of the research does not rule out creative
thinking but it certainly does reject the use of guessing and intuition in arriving at conclusions.
2. Good research is logical: This implies that research is guided by the rules of logical reasoning and the logical process
of induction and deduction are of great value in carrying out research. Induction is the process of reasoning from a part to the
whole whereas deduction is the process of reasoning from some premise to a conclusion which follows from that very premise.
In fact, logical reasoning makes research more meaningful in the context of decision making.
3. Good research is empirical: It implies that research is related basically to one or more aspects of a real situation and
deals with concrete data that provides a basis for external validity to research results.
4. Good research is replicable: This characteristic allows research results to be verified by replicating the study and
thereby building a sound basis for decisions.

PROBLEMS ENCOUNTERED BY RESEARCHERS IN INDIA
1. The lack of a scientific training in the methodology of research is a great impediment for researchers in
our country. There is paucity of competent researchers. Many researchers take a leap in the dark without
knowing research methods. Most of the work, which goes in the name of research is not
methodologically sound. Research to many researchers and even to their guides, is mostly a scissor and
paste job without any insight shed on the collated materials.
2. There is insufficient interaction between the university research departments on one side and business
establishments, government departments and research institutions on the other side. A great deal of
primary data of non-confidential nature remain untouched/untreated by the researchers for want of
proper contacts.
3. Most of the business units in our country do not have the confidence that the material supplied by them
to researchers will not be misused and as such they are often reluctant in supplying the needed
information to researchers.
4. Research studies overlapping one another are undertaken quite often for want of adequate information.
This results in duplication and fritters away resources. This problem can be solved by proper
compilation and revision, at regular intervals, of a list of subjects on which and the places where the
research is going on.
5. There does not exist a code of conduct for researchers and inter-university and interdepartmental
rivalries are also quite common. Hence, there is need for developing a code of conduct for researchers
which, if adhered sincerely, can win over this problem.
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6. Many researchers in our country also face the difficulty of adequate and timely
secretarial assistance, including computerial assistance. This causes unnecessary delays in
the completion of research studies. All possible efforts be made in this direction so that
efficient secretarial assistance is made available to researchers and that too well in time.
University Grants Commission must play a dynamic role in solving this difficulty.
7. Library management and functioning is not satisfactory at many places and much of the
time and energy of researchers are spent in tracing out the books, journals, reports, etc.,
rather than in tracing out relevant material from them.
8. There is also the problem that many of our libraries are not able to get copies of old and
new Acts/Rules, reports and other government publications in time. This problem is felt
more in libraries which are away in places from Delhi and/or the state capitals. Thus,
efforts should be made for the regular and speedy supply of all governmental publications
to reach our libraries.
9. There is also the difficulty of timely availability of published data from various
government and other agencies doing this job in our country. Researcher also faces the
problem on account of the fact that the published data vary quite significantly because of
differences in coverage by the concerning agencies.
10. There may, at times, take place the problem of conceptualization and also problems
relating to the process of data collection and related things.

Data collection
Data collection is the process of gathering and measuring information on targeted variables in an
established system, which then enables one to answer relevant questions and evaluate outcomes.
Methods of Data Collection
• The primary data are those which are collected afresh and for the first time, and thus happen to be
original in character.
• The secondary data, on the other hand, are those which have already been collected by someone else
and which have already been passed through the statistical process
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DATA COLLECTION
COLLECTION OF PRIMARY DATA
We collect primary data during the course of doing experiments in an experimental research but
incase we do research of the descriptive type and perform surveys, whether sample surveys or
census surveys, then we can obtain primary data either through observation or through direct
communication with respondents in one form or another or through personal interviews.
OBSERVATION
• As the name implies, the researcher observes the situation of interest and records the relevant
facts, actions, or behaviors. Observation provides accurate data about what consumers do in
certain situations but do not provide details of why it happened. Observation becomes a
scientific tool when it is systematically planned and recorded and is subjected to checks and
controls on validity and reliability
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Methods of observation
• Structured
• Disguised
• undisguised observation
• Observation under normal setting
• Direct
• Indirect observation
• Human Observation
• Mechanical Observation
DATA COLLECTION
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Advantages
• The method eliminates subjective bias
• The information obtained under this method relates to what is current happening it is not
complicated either by past behaviour or future intentions and attitudes.
• This method is independent of respondent willingness to respondents as such is relatively less
demanded of active co-operation on the part of the respondents as happens to be the case in
interview or the questionnaire method.
• This method is particularly suitable in studies, which deal with subjects who are not capable
giving verbal reports of their feeling for one reason or the other.
DATA COLLECTION
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Disadvantages
• Its s an expensive method
• The information provided by this method is very limited.
• Sometimes unforeseen factors may interfere with the observational task.
• The fact that some people are rarely accessible to direct observation creates obstacle for this
method to collect data effectively.
DATA COLLECTION
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2. PERSONAL INTERVIEW
• An interviewer asking questions generally face-to-face to other persons conducts personal
interview.This sort of interview may be in the form of direct personal investigation or it may be
an indirect oral investigation.This method is particularly suitable for intensive investigations.
DATA COLLECTION
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Methods of Personnel Interview
• structured interviews
• unstructured interview
• Focused interview
• clinical interview
• non-directive interview
DATA COLLECTION
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Advantages
• More information and that too in greater depth can be obtained.
• Interviewer can overcome any resistance, if any, of the respondents; this interview can be made to
yield an almost perfect sample of the population.
• There is greater flexibility as questions can be restructured as when needed, especially in the
unstructured interviews.
• Observation method can supplement verbal recording of answers.
• Personal information can be obtained easily in this method.
• Sample control can be maintained, as non-response generally remains low.
• Unlike mailed questionnaire, the interviewer can usually control which persons will answer the
questions.
• The interviewer can catch the respondent off-guard and thus record the spontaneous reactions.
• The language of the interview can be changed according to the education level of the respondent.
• The interviewer can collect supplementary information about respondent's personal characteristics
and environment, which helps while interpreting, results.
DATA COLLECTION

Disadvantages
• It can be quite expensive method, especially when large and widespread geographical sample is
taken.
• Possibility of bias of interviewer and respondent is maximum.
• Certain respondents such as important officials cannot be approachable under this method.
• It is time-consuming especially when sample is large and re-calls o respondents are to be
made.
• Sometimes the presence of he interview can over-stimulate he respondent and he may give
imaginary answers to make the interview interesting.
DATA COLLECTION
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3.TELEPHONE INTERVIEWS
• This method of collecting information consists contacting information consists contacting respondents on telephone itself. It
is not a very widely used method, but plays important role in industrial surveys in developed regions.
Key Features or Advantages
• It is more flexible in comparison to mail method.
• It is faster in obtaining information than other methods.
• It is cheaper compared to personal interviews; here the cost per response is very low.
• Recall is easy; callbacks are economic and simple.
• There is higher rate of response than mailing method
• Replies can be recorded without causing embarrassment to respondents.
• Interviewer can explain requirements more easily.
• Access can be gained to respondents who otherwise cannot be contacted for one reason to other.
• No field staff is required.
• Wider distribution of sample is possible.
DATA COLLECTION
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Disadvantages
• Little time is given to respondents to answer, as these types of interviews do not last for more
than 5 minutes.
• Survey is restricted to people who have telephones.
• Cost plays a major part in extensive geographical coverage.
• It is not suitable for interviews having comprehensive answers to various questions.
• Some extent of interviewer's bias exists.
• Questions have to be short and probes are difficult to handle
DATA COLLECTION
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4) Mail Surveys
• Mail and telephone surveys are a method of collecting information by sending surveys via email or postal mail. Participants
return completed forms to the researcher. Surveys may ask respondents to rate items on a scale .
• Some surveys also allow respondents to write their feelings or attitudes about a particular event or to elaborate in more
detail on an item, or to express suggestions, etc.
Advantages
1. Mail surveys are among the least expensive.
2.This is the only kind of survey you can do if you have the names and addresses of the target population, but not their
telephone numbers.
3.The questionnaire can include pictures - something that is not possible over the phone.
4. Mail surveys allow the respondent to answer at their leisure, rather than at the often inconvenient moment they are
contacted for a phone or personal interview. For this reason, they are not considered as intrusive as other kinds of interviews.
Disadvantages
• Mail surveys take longer than other kinds.You will need to wait several weeks after mailing out questionnaires before you
can be sure that you have gotten most of the responses.
• In populations of lower educational and literacy levels, response rates to mail surveys are often too small to be useful.
DATA COLLECTION
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4. Email Surveys
• Email surveys are both very economical and very fast. More people have email than have full
Internet access.This makes email a better choice than aWeb page survey for some populations.
On the other hand, email surveys are limited to simple questionnaires, whereasWeb page surveys
can include complex logic.
Advantages
1. Speed.An email questionnaire can gather several thousand responses within a day or two.
2.There is practically no cost involved once the set up has been completed.
3.You can attach pictures and sound files.
4.The novelty element of an email survey often stimulates higher response levels than ordinary
“snail” mail surveys.
DATA COLLECTION
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Disadvantages
1.You must possess (or purchase) a list of email addresses.
2. Some people will respond several times or pass questionnaires along to friends to answer. Many
programs have no check to eliminate people responding multiple times to bias the results.The Survey
System’s Email Module will only accept one reply from each address sent the questionnaire. It eliminates
duplicate and pass along questionnaires and checks to ensure that respondents have not ignored
instructions (e.g., giving 2 answers to a question requesting only one).
3. Many people dislike unsolicited email even more than unsolicited regular mail.You may want to send
email questionnaires only to people who expect to get email from you.
4.You cannot use email surveys to generalize findings to the whole populations. People who have email
are different from those who do not, even when matched on demographic characteristics, such as age and
gender.
5. Email surveys cannot automatically skip questions or randomize question or answer choice order or
use other automatic techniques that can enhance surveys the way Web page surveys can.
DATA COLLECTION
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DATA COLLECTION
Some otherTechniques
Warranty cards:Warranty cards are usually postal sized cards which are used by dealers of
consumer durables to collect information regarding their products.The information sought is printed
in the form of questions on the ‘warranty cards’ which is placed inside the package along with the
product with a request to the consumer to fill in the card and post it back to the dealer.
2. Distributor or store audits: Distributor or store audits are performed by distributors as well as
manufactures through their salesmen at regular intervals. Distributors get the retail stores audited
through salesmen and use such information to estimate market size, market share, seasonal purchasing
pattern and so on.The data are obtained in such audits not by questioning but by observation
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3. Pantry audits: Pantry audit technique is used to estimate consumption of the basket of goods at
the consumer level. In this type of audit, the investigator collects an inventory of types, quantities and
prices of commodities consumed.Thus in pantry audit data are recorded from the examination of
consumer’s pantry.
4. Consumer panels:
An extension of the pantry audit approach on a regular basis is known as
• ‘consumer panel’, where a set of consumers are arranged to come to an understanding to maintain
• detailed daily records of their consumption and the same is made available to investigator on
demands. In other words, a consumer panel is essentially a sample of consumers who are
interviewed repeatedly over a period of time
DATA COLLECTION
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5. Use of mechanical devices:The use of mechanical devices has been widely made to collect
information by way of indirect means. Eye camera, Pupilometric camera, Psychogalvanometer,
Motion picture camera and Audiometer are the principal devices so far developed and commonly
6. Projective techniques: Projective techniques (or what are sometimes called as indirect
interviewing techniques) for the collection of data have been developed by psychologists to use
projections of respondents for inferring about underlying motives, urges, or intentions which are
such that the respondent either resists to reveal them or is unable to figure out himself. In projective
techniques the respondent in supplying information tends unconsciously to project his own attitudes
or feelings on the subject under study
DATA COLLECTION
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QUESTIONNAIRE
• Questionnaires are data collecting instruments used to ask respondents questions to secure the
desired information. Questionnaires may be administered by mail, over the telephone, by
computer, or in person.
• The design of a questionnaire depends on whether the researcher wishes to collect exploratory
information (i.e. qualitative information for the purposes of better understanding or the generation
of hypotheses on a subject) or quantitative information (to test specific hypotheses that have
previously been generated).
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The questionnaires can be classified into four types:
1. Structured – non-disguised
2. Structured disguised
3. Non-structured - non- disguised
4. Non-structured – disguised
QUESTIONNAIRE
S K I M T

QUESTIONNAIRE
steps are involved in the development of a questionnaire
• Choose and decide on the information required
• Define the target respondents
• Selection of methodology to reach the target segment
• Decide on question content
• Decide on type of questions
• Putting questions into a meaningful order and format
• Closing questions
• Physical appearance of the questionnaire
• Pilot studying the questionaire
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2) Define the target respondents
The researcher must define the population that he wishes to collect the data from. Firstly, in
marketing research, researchers often have to decide whether they should cover only existing users
of the generic product type or whether to also include non-users.
Secondly, researchers have to draw up a sampling frame.Thirdly, in designing the questionnaire we
must take into account factors such as the age, education, etc. of the target respondents.
3) Selection of methodology to reach the target segment
This will influence not only the questions the researcher is able to ask but the phrasing of those
questions.The main methods available in survey research are: personal interviews, group or focus
interviews, mailed questionnaires and telephone interviews
Among these, the first two are used more extensively than the second pair.The general rule is that
the more sensitive or personal the information, the more personal the form of data collection
should be.
QUESTIONNAIRE
S K I M T

4. Decide on question content
For each question in the questionnaire, we should pay attention to how well it addresses the content
we are trying to get at. Deciding what to ask there are three potential types of information:
Information we are primarily interested in that is, dependent variables.
Information which might explain the dependent variables-that is, independent variables.
Other factors related to both dependent and independent factors, which may distort the results and
have to be adjusted for - that is, confounding variables.
5. Decide on type of questions
The questions can be classified into two forms, i.e. closed and open-ended. So In a closed type of question, the
respondent chooses between an alternative already stated. He does not get a chance to answer in a descriptive manner. For
ex.: Do you use Brand X?Yes ________ No________.
In an open-ended question the respondent is asked to give a reply to a question in his/her own words. No answers are
suggested.These responses are explanatory in nature and give some insights from the respondents end. Example: "What do
you like most about this Product?" Open-ended questions have a lot of advantages when used in a questionnaire:
They allow the respondent to answer in his own words, with no influence by any specific alternatives suggested by the
QUESTIONNAIRE

5. Decide on type of questions
The questions can be classified into two forms, i.e. closed and open-ended. So In a closed type of question,
the respondent chooses between an alternative already stated. He does not get a chance to answer in a
descriptive manner. For ex.: Do you use Brand X?Yes ________ No________.
In an open-ended question the respondent is asked to give a reply to a question in his/her own words. No
answers are suggested.These responses are explanatory in nature and give some insights from the respondents
end. Example: "What do you like most about this Product?" Open-ended questions have a lot of advantages
when used in a questionnaire:
They allow the respondent to answer in his own words, with no influence by any specific alternatives suggested
by the interviewer.
They often reveal the issues which are most important to the respondent, and this may reveal findings which
were not originally anticipated when the survey was initiated.
Respondents can 'qualify' their answers or emphasize their opinions.
The inherent problem of an open-ended question is that they must be treated with caution as:
QUESTIONNAIRE
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6. Putting questions into a meaningful order and format
• In order to make the questionnaire effective and to ensure quality to the replies received, a
researcher must pay attention to the question-sequence in preparing the questionnaire.
• A proper question sequence reduces the chances of the questions being misunderstood
• The question sequence must be clear and smooth- moving, with questions that are easiest to
answer being put in the beginning.
• The first few questions are particularly important because they are likely to influence the
attitude of the respondent and in seeking his desired cooperation.
• Following the opening questions are the question that are rally vital to the research problem
and a connecting thread should run through successive questions.
• Relatively difficult questions must be relegated towards the end so that even if the respondent
decides not to answer such questions, considerable information would have been obtained.
QUESTIONNAIRE
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7. Closing questions
• By the time the respondent comes to the end of the questionnaire it is quite natural for a respondent to become
increasingly indifferent to the questionnaire.This is mainly
• due to impatience or fatigue. He might give careless answers to the later questions. Hence such questions should be
included in the earlier part of the questionnaire. Potentially sensitive questions should be left to the end, to avoid
respondents cutting off the interview before important information is collected.
8. Physical appearance of the questionnaire
The physical appearance of a questionnaire has a significant effect upon both the quantity and quality of marketing data
obtained. Data quality can also be affected by the physical appearance of the questionnaire with unnecessarily confusing
layouts making it more difficult for interviewers, or respondents in the case of self-completion questionnaires, to
complete this task accurately. In general it is best for a questionnaire to be as short as possible.A long questionnaire
leads to a long interview and this may lead to decreasing interest on the part of the respondent.
QUESTIONNAIRE
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Piloting the questionnaires
• Piloting is very much essential to test whether the desired responses are being obtained for the
questions. Many a time, the perception of the respondents varies from those of the researcher.
Hence these issues can be corrected in the initial stage itself so that the research process is
facilitated.
The few respondents selected for the pilot survey should be broadly representative of the type of
respondent to be interviewed in the main survey. If the questionnaire has been subjected to a
thorough pilot test, the research objective is easy to achieve
QUESTIONNAIRE
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SCHEDULE METHOD
A schedule is a device used in collecting field data when survey method is applied. It is used in
direct interviews. It is a proforma containing a set of questions and tables
Contents
of schedule
i) Introductory part
ii) Main schedule
iii) Instructions
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SCHEDULE
Construction of Schedules
Following steps are followed while constructing a schedule
1) The problem under study is defined
2) Framing of actual questions.
3) Arrangement of questions in the proper orders.
4) Pilot survey.
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DIFFERENCE BETWEEN QUESTIONNAIRES AND SCHEDULES
1.A schedule is generally filled out by the research worker or the field worker. Questionnaires are
sent by post to respondent, to answer as specified in the covering letter.
2. Schedule is relatively more costly. Questionnaire is relatively cheap.
3. In the case of schedule the response is better since the enumerators clear the doubts of the
respondents and get the replies from the respondents at the spot itself. Response to the
questionnaire is poor.
4. In the case of scheduleTime consumption is less.Time consumption is more in case of
questionnaire.
5.Through Schedule Direct personal contact is established with the respondents, In the case of
questionnaire there is no direct personal contact.
6. Schedules can be applied even if the respondents are not literate. Questionnaire can be used only
if the respondents are literates.
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SECONDARY DATA
As stated earlier, secondary data are those data which have
already been collected and analyzed by some earlier agency
for its own use, and later the same data are used by a
different agency.
According to W.A.Neiswanger,“.A secondary source is a
publication, reporting the data which was gathered by other
authorities and for which others are responsible
Sources Of Secondary Data:
The various sources of secondary data can be divided into
two broad categories:
1. Published sources, and
2. Unpublished sources
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SECONDARY DATA
1. Published Sources:
The governmental, international and local agencies publish statistical data, and chief among them are explained below:
(a) International Bublications
(b) Official Publications of Central and State Governments:
(c) Semi-Official Publication
e) Publications of various Commercial and Financial Institutions
(f) Reports of various Committees and Commissions appointed by the Government
(g) Journals and News Papers:
II. Unpublished Sources:
Unpublished data can be obtained from many unpublished sources like records maintained by various government and
private offices, the theses of the numerous research scholars in the universities or institutions etc
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ATTITUDE SCALES
Attitude scales in research measure attitudes towards an issue, they are the most distinctive concept
in modern social psychology.Attitude scales help social psychologists understand the characteristics
of individuals by placing them on a scale that best represent those characteristics. It is a special type of
questionnaire that can measure the intensity as well as direction of peoples attitude towards
something
Characteristic
Attitude scales are used to measure people’s attitude towards a product in market. Products are
often measured using product rating in consumer research.
They are also used for measuring attitude towards an idea that is related to some social issue.
Attitude scales can measure peoples attitude towards an event that happened in past.
It can also measure employee’s job-related satisfaction or performance.
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VALIDITY OF THE RESEARCH INSTRUMENT
The concept of appropriateness and accuracy as applied to a research process is called validity.
Broadly, there are two perspectives on validity:
1. Isthe research investigation providing answers to the research questions for which it was
undertaken?
2. 2. If so, is it providing these answers using appropriate methods and procedures?
There are three types of validity in quantitative research:
1. face and content validity;
2. concurrent and predictive validity;
3. construct validity.
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RELIABILITY OF THE RESEARCH INSTRUMENT
The concept of reliability in relation to a research instrument has a similar meaning: if a research
tool is consistent and stable, hence predictable and accurate, it is said to be reliable.
The greater the degree of consistency and stability in an instrument, the greater its reliability.
Therefore,‘a scale or test is reliable to the extent that repeat measurements made by it under
constant conditions will give the same result’ (Moser & Kalton 1989: 353).
The concept of reliability can be looked at from two sides: 1. How reliable is an instrument? 2. How
unreliable is it?

RELIABILITY OF THE RESEARCH INSTRUMENT
Factors affecting the reliability of a research instrument
In the social sciences it is impossible to have a research tool which is 100 per cent accurate, not only because a research
instrument cannot be so, but also because it is impossible to control the factors affecting reliability. Some of these factors
are:
The wording of questions – A slight ambiguity in the wording of questions or statements can affect the reliability of a
research instrument as respondents may interpret the questions differently at different times, resulting in different responses
The physical setting – In the case of an instrument being used in an interview, any change in the physical setting at the
time of the repeat interview may affect the responses given by a respondent, which may affect reliability.
The respondent’s mood – A change in a respondent’s mood when responding to questions or writing answers in a
questionnaire can change and may affect the reliability of that instrument.
The interviewer’s mood – As the mood of a respondent could change from one interview to another so could the mood,
motivation and interaction of the interviewer, which could affect the responses given by respondents thereby affecting the
reliability of the research instrument.
The nature of interaction – In an interview situation, the interaction between the interviewer and the interviewee can
affect responses significantly. During the repeat interview the responses given may be different due to a change in interaction,
which could affect reliability.
The regression effect of an instrument – When a research instrument is used to measure attitudes towards an issue,
some respondents, after having expressed their opinion, may feel that they have been either too negative or too positive
towards the issue.The second time they may express their opinion differently, thereby affecting reliability

ATTITUDE SCALES
Types of attitude scales in research
(i) Likert scale
(ii)The Differential/Thurstone scale
(iii)The Scalogram/Guttman scale
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Sampling-Introduction
All items in any field of inquiry constitute a ‘Universe’ or ‘Population.’ A complete enumeration of all items in the
‘population’ is known as a census inquiry. It can be presumed that in such an inquiry, when all items are covered, no
element of chance is left and highest accuracy is obtained.
In practical life, considerations of time and cost almost invariably lead to a selection of respondents i.e., selection of only
a few items.The respondents selected should be as representative of the total population as possible in order to
produce a miniature cross-section.
The selected respondents constitute what is technically called a ‘sample’ and the selection process is called ‘sampling
technique.’The survey so conducted is known as ‘sample survey’.
A sample design is a definite plan for obtaining a sample from a given population. It refers to the technique or the
procedure the researcher would adopt in selecting items for the sample.
S K I M T

SAMPLE DESIGN
Steps in Sample Design
(i) Population
(ii) Sampling Frame:
(iii) Sampling Unit
(iv) Size of sample
(v) SamplingTechnique
(vi) Draw or selecting a sample
(vi) Budgetary constraint
(vii) Sampling procedure
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STEPS IN SAMPLE DESIGN
1. Defining theTarget Population:
Defining the population of interest, for business research, is the first step in sampling process. In
general, target population is defined in terms of element, sampling unit, extent, and time frame.
The definition should be in line with the objectives of the research study.
A well defined population reduces the probability of including the respondents who do not fit
the research objective of the company. For ex, if the population is defined as all women above the
age of 20, the researcher may end up taking the opinions of a large number of women who
cannot afford to buy a micro oven
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2. Specifying the Sampling Frame:
• Once the definition of the population is clear a researcher should decide on the sampling frame.A
sampling frame is the list of elements from which the sample may be drawn. ex, an ideal sampling
frame would be a database that contains all the households that have a monthly income above
Rs.20,000.. In general, researchers use easily available sampling frames like telephone directories and
lists of credit card and mobile phone users.Whatever may be the case, an ideal sampling frame is
one that entire population and lists the names of its elements only once.
• A sampling frame error pops up when the sampling frame does not accurately represent the total
population or when some elements of the population are missing another drawback in the sampling
frame is over —representation
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3. Specifying the Sampling Unit:
A sampling unit is a basic unit that contains a single element or a group of elements of the
population to be sampled.
4. Selection of the Sampling Method
The sampling method outlines the way in which the sample units are to be selected.The choice
of the sampling method is influenced by the objectives of the business research, availability of
financial resources, time constraints, and the nature of the problem to be investigated.All
sampling methods can be grouped under two distinct heads, that is, probability and non-
probability sampling
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5. Determination of Sample Size:
The sample size plays a crucial role in the sampling process. In the case of probability sampling, however,
formulas are used to calculate the sample size after the levels of acceptable error and level of
confidence are specified. In non-probability sampling procedures, the allocation of budget, thumb rules
and number of sub groups to be analyzed, importance of the decision, number of variables, nature of
analysis, incidence rates, and completion rates play a major role in sample size determination
6. Specifying the Sampling Plan:
In this step, the specifications and decisions regarding the implementation of the research process are
outlined. Suppose, blocks in a city are the sampling units and the households are the sampling elements.
This step outlines the modus operandi of the sampling plan in identifying houses based on specified
characteristics. It includes issues like how is the interviewer going to take a systematic sample of the
houses.What should the interviewer do when a house is vacant? What is the recontact procedure for
respondents who were unavailable? All these and many other questions need to be answered for the
smooth functioning of the research process.These are guide lines that would help the researcher in
every step of the process.
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7. Selecting the Sample:
• This is the final step in the sampling process, where the actual selection of the sample elements
is carried out.At this stage, it is necessary that the interviewers stick to the rules outlined for
the smooth implementation of the business research.This step involves implementing the
sampling plan to select the sampling plan to select a sample required for the survey.
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TYPES OF SAMPLING
i) Probability sampling ii) Non Probability sampling
A probability sampling is one in which every unit in the population has a chance (greater
than zero) of being selected in the sample, and this probability can be accurately determined.The
combination of these traits makes it possible to produce unbiased estimates of population totals, by
weighting sampled units according to their probability of selection.
Non-probability sampling is defined as a sampling technique in which the researcher
selects samples based on the subjective judgment of the researcher rather than random selection
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PROBABILITY SAMPLING-TYPES
A probability sampling is one in which every unit in the population has a chance (greater than zero) of
being selected in the sample, and this probability can be accurately determined.The combination of these
traits makes it possible to produce unbiased estimates of population totals, by weighting sampled units
according to their probability of selection.
(i) Systematic Sampling
(ii) Stratified Sampling
(iii) Cluster Sampling
(iv) Area Sampling
(v) Multi-Stage Sampling
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PROBABILITY SAMPLING
1. Simple random sampling
In a simple random sample, every member of the population has an equal chance of being selected.Your
sampling frame should include the whole population.
Example
You want to select a simple random sample of 100 employees of Company X.You assign a number to
every employee in the company database from 1 to 1000, and use a random number generator to select
100 numbers.
2. Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct.
Every member of the population is listed with a number, but instead of randomly generating numbers,
individuals are chosen at regular intervals.
Example
All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly
select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6,
16, 26, 36, and so on), and you end up with a sample of 100 people.

PROBABILITY SAMPLING
3. Stratified sampling
Stratified sampling involves dividing the population into subpopulations that may differ in important ways.
It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in
the sample.
To use this sampling method, you divide the population into subgroups (called strata) based on the
relevant characteristic (e.g. gender, age range, income bracket, job role).
4. Cluster sampling
Cluster sampling also involves dividing the population into subgroups, but each subgroup should have
similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you
randomly select entire subgroups.
Example
The company has offices in 10 cities across the country (all with roughly the same number of employees
in similar roles).You don’t have the capacity to travel to every office to collect your data, so you use
random sampling to select 3 offices – these are your clusters.

TYPES OF NON-PROBABILITY SAMPLING
Non-probability sampling is defined as a sampling technique in which the researcher
selects samples based on the subjective judgment of the researcher rather than random selection
1. Convenience Sampling
2. Judgment Sampling or purposive
3. Quota Sampling
4. Snowball Sampling
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1. Convenience sampling
A convenience sample simply includes the individuals who happen to be most accessible to the
researcher.This is an easy and inexpensive way to gather initial data, but there is no way to tell if
the sample is representative of the population, so it can’t produce generalizable results.
Example
You are researching opinions about student support services in your university, so after each of
your classes, you ask your fellow students to complete a survey on the topic.This is a convenient
way to gather data, but as you only surveyed students taking the same classes as you at the same
level, the sample is not representative of all the students at your university.

2.Voluntary response sampling
Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher
choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online
survey).Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to
volunteer than others.
3. Purposive sampling or Judgmental Sampling
This type of sampling involves the researcher using their judgement to select a sample that is most useful to the purposes of
the research.
It is often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific phenomenon
rather than make statistical inferences.An effective purposive sample must have clear criteria and rationale for inclusion.
Example
You want to know more about the opinions and experiences of disabled students at your university, so you purposefully select
a number of students with different support needs in order to gather a varied range of data on their experiences with student
services.

4. Snowball sampling
If the population is hard to access, snowball sampling can be used to recruit participants via other participants.The number of
people you have access to “snowballs” as you get in contact with more people.
Example
You are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city,
probability sampling isn’t possible.You meet one person who agrees to participate in the research, and she puts you in
contact with other homeless people that she knows in the area.
5.Quota Sampling Definition
Quota sampling is an important sampling method which involves a non-probability sampling technique in which sampling is
not based upon the probability of appearance. In such a process, the researcher decides the selection of sampling based on
some quota. In quota sampling, the researcher makes sure that the final sample must meet his quota criteria.
“The sample obtained from a quota sampling method contains similar proportions of observations as the whole population
with some known traits or characteristics. In quota sampling, the researcher selects from his/her judgement or some fixed
quota. In other words, the sample observations are to be chosen based on some pre-specified virtues.Then the total sample
contains the same distribution of characteristics that were assumed to be found in the population of concern”.
S K I M T

SAMPLING ERRORS
The errors which arise due to the use of sampling survey are known as sampling errors.These are random
variation in the sample estimate around the true population parameters.
Type of sampling errors
Biased errors:These errors are occurring due to the faulty selection of sampling method due to the
prejudice of the researchers.
Unbiased errors:This type of bias is occurring due to chance difference between the items
included in the sample
Bias may arise due to,
1. Faulty process selection.
2. Faulty work during the collection of information.
3. Faulty method of analysis.
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SAMPLING ERRORS
Non-Sampling Error
Non-sampling errors are other errors which can impact the final survey estimates, caused by
problems in data collection, processing, or sample design.They include:
1. Over coverage: Inclusion of data from outside of the population.
2. Under coverage: Sampling frame does not include elements in the population.
3. Measurement error: e.g. when respondents misunderstand a question, or find it difficult to
answer.
4. Processing error: Mistakes in data coding.
5. Non-response: Failure to obtain complete data from all selected individuals.
After sampling, a review should be held of the exact process followed in sampling, rather than
that intended, in order to study any effects that any divergences might have on subsequent analysis.
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CHARACTERISTICS OF GOOD SAMPLE DESIGN
a. Representative.
b.Viable.
c.The selected sample design should not cause more errors.
d.A good sample design able to control systematic bias efficiently.
e. If the sample is well design and selected, decision makers can use this info with
Confidence

SAMPLE SIZE
Sample Size
Sample size is the number of items to be selected from the universe. It should be optimum formulas, tables,
and power function charts are well known approaches to determine sample size.
Steps in calculating sample size
STEP 1: DEFINEYOUR OBJECTIVE
STEP 2: DETERMINEYOUR DEPENDENTVARIABLE
STEP 3: DECIDE ONYOUR MARGIN OF ERROR
STEP 4: DECIDE ONYOUR SIGNIFICANCE LEVEL
STEP 5: DECIDE ON THE NECESSARY LEVEL OF POWER
STEP 6: ESTIMATE THE CURRENT AND EXPECTED LEVEL OF KEY INDICATORS
STEP 7: ESTIMATEYOUR RESPONSE AND ATTRITION RATES
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S K I M T

Measurement is defined as the assignment of numbers to characteristics of objects or
events according to rules. the definition of measurement clearly states that the
researcher should know that the measurement scale measures the characteristics of the
objects or event and not the objects or events.
Researchers normally use four level of measurement scales. they are:
a)Nominal
scale
b) Ordinal
scale
c) Interval
scale
d) Ratio
Scale
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MEASUREMENT SCALES (TYPE OF SCALE)
Nominal scale
• A set of data is said to be nominal if the values / observations belonging to it can be assigned a
code in the form of a number where the numbers are simply labels.You can count but not order
or measure nominal data. Nominal Scale is the crudest among all measurement scales but it is
also the simplest scale. In this scale the different scores on a measurement simply indicate
different categories.The nominal scale does not express any values or relationships between
variables.
• For example, labelling men as ‘1’ and women as ‘2’ which is the most common way of labelling
gender for data recording purpose does not mean women are ‘twice something or other’ than
men. Nor it suggests that men are somehow ‘better’ than women.
• The nominal scale is often referred to as a categorical scale.The assigned numbers have no
arithmetic properties and act only as labels.The only statistical operation that can be performed
on nominal scales is a frequency count
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Ordinal Scales
Ordinal Scale involves the ranking of items along the continuum of the characteristic being scaled.
In this scale, the items are classified according to whether they have more or less of a
characteristic.
For example, you may wish to ask theTV viewers to rank theTV channels according to their
preference and the responses may look like this as given below:
TV Channel Viewers preferences
Doordarshan-1 1
Star plus 2
NDTV News 3
Aaaj Tak TV 4
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The main characteristic of the ordinal scale is that the categories have a logical or ordered
relationship.
This type of scale permits the measurement of degrees of difference, (that is,‘more’ or ‘less’) but
not the specific amount of differences (that is, how much ‘more’ or less’).
This scale is very common in marketing, satisfaction and attitudinal research.Another example is
that a fast food home delivery shop may wish to ask its customers:
Using ordinal scale data, we can perform statistical analysis like Median and Mode, but not the
Mean.
How would you rate the service of our staff?
(1) Excellent • (2) Very Good • (3) Good • (4) Poor • (5) Worst
S K I M T

MEASUREMENT SCALES
Interval Scale is a scale in which the numbers are used to rank attributes such that
numerically equal distances on the scale represent equal distance in the characteristic being
measured.An interval scale contains all the information of an ordinal scale, but it also one allows
to compare the difference/distance between attributes
The interval scales allow the calculation of averages like Mean, Median and
Mode and dispersion like Range and Standard Deviation.
An interval scale is a scale of measurement where the distance between any two adjacent units
of measurement (or 'intervals') is the same but the zero point is arbitrary. Scores on an interval
scale can be added and subtracted but cannot be meaningfully multiplied or divided.
Food supplied is:
Indicate your score on
the concerned blank
and circle the appropriate
number on each line.
Fresh 1 2 3 4 5
Tastes good 1 2 3 4 5
Value for money 1 2 3 4 5
Attractive packaging 1 2 3 4 5
Prompt time delivery 1 2 3 4 5
S K I M T

Ratio Scales
Ratio Scale is the highest level of measurement scales.This has the properties of an interval
scale together with a fixed (absolute) zero point.The absolute zero point allows us to construct
a meaningful ratio.
Examples of ratio scales include weights, lengths and times. In the marketing research, most
counts are ratio scales. For example, the number of customers of a bank’s ATM in the last three
months is a ratio scale.This is because you can compare this with previous three months.
Ratio scales permit the researcher to compare both differences in scores and relative magnitude
of scores
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SCALING TECHNIQUE
S K I M T

SCALING TECHNIQUE
• In comparative scaling, the respondent is asked to compare one object with another. For
example, the researcher can ask the respondents whether they prefer brand A or brand B of a
detergent. On the other hand, in non-comparative scaling respondents need only evaluate a
single object.Their evaluation is independent of the other object which the researcher is
studying.
• Respondents using a non-comparative scale employ whatever rating standard seems
appropriate to them. Non-comparative techniques consist of continuous and itemized rating
scales. Figure below shows the classification of these scaling techniques
S K I M T

Comparative scaling techniques consist of:
a) Paired comparison scaling
b) Rank order scaling
c) Constant sum scaling and
d) Q-sort
a) Paired comparison scaling as its name indicates involves presentation of two objects and
asking the respondents to select one according to some criteria.The data are obtained using
ordinal scale..
As the number of items increases, the number of comparisons increases geometrically. If the
number of comparisons is too large, the respondents may become fatigued and no longer be able
to carefully discriminate among them.
S K I M T
SCALING TECHNIQUE

b) Rank order scaling
This is another type of comparative scaling technique in which respondents are presented with
several items simultaneously and asked to rank them in the order of priority.This is an ordinal
scale that describes the favoured and unfavoured objects, but does not reveal the distance
between the objects.
Like paired comparison, the rank order scale, is also comparative in nature.The resultant data in
rank order is ordinal data.
Example
S K I M T
SCALING TECHNIQUE

In this scale, the respondents are asked to allocate a constant sum of units such as points, rupees, or chips
among a set of stimulus objects with respect to some criterion.
For example, you may wish to determine how important the attributes of price, fragrance, packaging,
cleaning power, and lather of a detergent are to consumers. Respondents might be asked to divide a
constant sum to indicate the relative importance of the attributes using the following format.The advantage
of this technique is saving time.
The respondents may allocate more or fewer points than those specified
Example
S K I M T
SCALING TECHNIQUE

Q-Sort Scale: This is a comparative scale that uses a rank order procedure to sort objects based
on similarity with respect to some criterion.The important characteristic of this methodology is that
it is more important to make comparisons among different responses of a respondent than the
responses between different respondents.Therefore, it is a comparative method of scaling rather
than an absolute rating scale. In this method the respondent is given statements in a large number
for describing the characteristics of a product or a large number of brands of a product.
S K I M T
SCALING TECHNIQUE

SCALING TECHNIQUE
Continuous Rating Scales
It is very simple and highly useful. In continuous rating scale, the respondent’s rate the
objects by placing a mark at the appropriate position
on a continuous line that runs from one extreme of the criterion variable to the other.
Examples of continuous rating scale are given below:
Question: How would you rate the TV advertisement as a guide for buying?
Scale Type A
S K I M T

Itemised Rating Scales
Itemised rating scale is a scale having numbers or brief descriptions associated with each
category.The categories are ordered in terms of scale position and the respondents are required to
select one of the limited number of categories that best describes the product, brand, company, or
product attribute being rated. Itemised rating scales are widely used in marketing research
SCALING TECHNIQUE
S K I M T

a) Likert Scale
These scales are sometimes referred to as summated scales. It requires a respondent to indicate a degree of agreement or
disagreement with each of a series of statements related to the attitude object.
For Example:The service at a retail store is very important to me:
____ Strongly Agree ____ Agree ____ Neither Agree nor Disagree ____ Disagree ____ Strongly Disagree
To analyze a Likert Scale, each response category is assigned a numerical value.These examples could be assigned values such
as
Strongly Agree=1, through Strongly Disagree=5 or the scoring could be reversed., or a –2 through +2 system could be used.
They can be analyzed on an item-by-item basis, or they can be summed to form a single score for each individual.
Advantages
1. It is relatively easy to construct and administer.
2. Instructions that accompany the scale are easily understood; hence it can be used for mail surveys and interviews with
children.
Disadvantages
1. It takes a longer time to complete as compared to Semantic Differential Scales, etc.
2. Care needs to be taken when using Likert Scales in cross cultural research, as there may be cultural variations in
willingness to express disagreement.
SCALING TECHNIQUE
S K I M T

Example
Semantic Differential Scale
This is a seven point rating scale with end points associated with bipolar labels (such as
good and bad, complex and simple) that have semantic meaning. The Semantic Differential
scale is used for a variety of purposes. It can be used to find whether a respondent has a
positive or negative attitude towards an object. It has been widely used in comparing
brands, products and company images. It has also been used to develop advertising and
promotion strategies and in a new product development study
SCALING TECHNIQUE
S K I M T

PROCESSING OF DATA
DATA PROCESSIING
Data continues to be in raw form, unless and until they are processed and analyzed.
Processing is a statistical method by which the collected data is so organized the further analysis
and interpretation of data become easy. It is an intermediary stage between the collection of data
and their analysis and interpretation.
Processing stages
There are four important stages in the processing of data.They are;
1. Editing
2. Coding
3. Classification
4. tabulation
S K I M T

Editing
As soon as the researcher receives the data, he should screen it for accuracy. Editing is the
process of examining the data collected through various methods to detect errors and omissions
and correct them for further analysis.
Though editing, it is ensured that the collected data are accurate,consistent with other facts
gathered, uniformly entered and well-arranged so that further analysis is
made easier
EDITING
S K I M T

EDITING
Practical guidelines for editing
While editing care has to be taken to see that the data are as accurate and complete as possible.
The following points are to be noted;
1.The editor should familiarize with the copy of instructions given to the interviewers.
2.The original entry, if found incorrect, should not be destroyed or erased. On the other hand,
it should be crossed out in such a manner that it is still eligible.
3.Any, modification to the original entry by the editor must be specifically indicated.
4.All completed schedules must bear signature of the editor and the date.
5. Incorrect answer to the questions can be corrected only if the editor is absolutely sure of the
answer, otherwise leave it as such.
6. Inconsistent, incomplete or missing answers should not be used.
7. Sere that all numerical answers are converted to same units.
S K I M T

Coding
Coding is the process by which r response categories are summarized by numerals or other
symbols to carry out subsequent operations of data analysis.This process of assigning numerals or
symbols to the responses is called coding.
It facilitates efficient analysis of the collected data and helps in reducing several replies to a small
number of classes which contain the critical information
required for analysis.
In general it reduces the huge amount of information collected in to a form that is amenable to
analysis.
CODING
S K I M T

CODING
Steps in coding
1. Study the answers carefully.
2. Develop a coding frame by listing the answers and by aligning codes to each of them.
3. Prepare a coding manual with the detail of variable names, codes and instructions.
4. If the coding manual has already been prepared before the collection of the data, make the
required additions for the open ended and partially coded questions.
Coding rules
1. Give each respondent a code number for identification.
2. Provide code number for each question.
3.All responses including ‘don’t know’,‘no opinion’. Etc is to be coded.
4.Assign additional codes to partially coded questions.
S K I M T

CLASSIFICATION
Classification
Classification is the process of reducing large mass of data in to homogeneous groups for
meaningful analysis.
It converts data from complex to understandable and unintelligible to
intelligible forms.
It divides data in to different groups or classes according to their similarities and
dissimilarities.When the data are classified, they give summary of whole information.

CLASSIFICATION
Objectives of classification
1.To organize data in to concise, logical and intelligible form.
2.To take the similarities and dissimilarities s between various classes clear.
3.To facilitate comparison between various classes of data.
4.To help the researcher in understanding the significance of various classes of data.
5.To facilitate analysis and formulate generalizations

CLASSIFICATION
Types of classification
A. Classification according to external characteristics
In this classification, data may be classified either on geographical basis or periodical basis.
Classification on geographical basis
In this type of classification, the data that are collected from different places are placed in different
classes.
Classification on periodical basis (chronological classification)
In this type of classification, the data belonging to a particular time or period are put under one
class.This type of classification is based on period.
B. Classification according to internal characteristics
Data may be classified either according to attributes or according to the magnitude of variables
Classification according to Attributes
In this type data are classified on the basis of some attributes an characteristics.

CLASSIFICATION
Simple Classification
If the classification is based on one particular attribute only it is called simple classification.
Eg; classification on the basis of sex.
Manifold Classification
If the classification is based on more than one or several attributes it is called manifold or multiple
classifications. in this data are classified in several groups.
C. Classification according variables
Here the data are classified to some characteristics that can be measured. Data are classified on
the basis of quantitative characteristics such as age, height; weight etc. quantitative variables are
grouped in to two
a) Discrete variable
If the variables can take only exact value, it is called discrete variable.
b) Continuous variable
The variables that can take any numerical value within a specified range are called
continuous variable

TABULATION
Tabulation
Tabulation is the next step to classification. It is an orderly arrangement of data in rows and
columns. It is defined as the “measurement of data in columns and rows”. Data presented in tabular
form is much easier to read and understand than the data presented in the text the main purpose of
tabulation is to prepare the data for final analysis. It is a stage between classification of data and
final analysis.
Objectives ofTabulation
1.To clarify the purpose of enquiry
2.To make the significance of data clear.
3.To express the data in least possible space.
4.To enable comparative study.
5.To eliminate unnecessary data
6.To help in further analysis of the data.
S K I M T

TABULATION
Parts of a statistical table
Following are the important parts of a statistical table.
1.Title of the table
The title of the table is placed above the table. If there are more than one table in a research, each
should bear a number for easy reference.
2. Caption or title of the column
It is also termed as “box head”.There may be sub- captions under the main caption.
3. Stub (row heading)
Stub refers to the title given to rows
4. Body (main data)
This is the main body of information needed for the research work.
5. End note (foot note)
This is placed below the table to convey the expansions of abbreviations to caption, stub or main
body.
6. Source note
If the table is based on outside information, it should be mentioned in the source note below.
S K I M T

TABULATION
Types ofTables
SimpleTable
Complex table
(a) One- way table
(b) Two- way table
(c)Three-way table
(d) Manifold tables
S K I M T

ANALYSIS OF DATA
ANALYSIS OF DATA
Analysis of data is considered to be highly skilled and technical job which should be
carried out .Only by the researcher himself or under his close supervision.Analysis of data means
critical examination of the data for studying the characteristics of the object under study and for
determining the patterns of relationship among the variables relating to it’s using both quantitative
and qualitative methods.
S K I M T

ANALYSIS OF DATA
Steps in Analysis
Different steps in research analysis consist of the following.
1.The first step involves construction of statistical distributions and calculation of simple
measures like averages, percentages, etc.
2.The second step is to compare two or more distributions or two or more subgroups within a
distribution.
3.Third step is to study the nature of relationships among variables.
4. Next step is to find out the factors which affect the relationship between a set of variables
5.Testing the validity of inferences drawn from sample survey by using parametric tests of
significance.
S K I M T

ANALYSIS OF DATA
Types of Analysis
Statistical analysis may broadly classified as descriptive analysis and inferential analysis
Descriptive Analysis
Descriptive statistics are used to describe the basic features of the data in a study.They provide
simple summaries about the sample and the measures. Descriptive statistics is the discipline of
quantitatively describing the main features of a collection of data or the quantitative description
itself. In such analysis there are univariate analysis bivariate analysis and multivariate analysis.
S K I M T

Univariate analysis
Univariate analysis involves describing the distribution of a single variable, including its central
tendency (including the mean, median, and mode) and dispersion (including the range and
quartiles of the data-set, and measures of spread such as the variance and standard deviation).The
shape of the distribution may also be described via indices such as skewness and kurtosis.
Characteristics of a variable's distribution may also be depicted in graphical or tabular format,
including histograms and stem-and-leaf display.
Bivariate analysis
Bivariate analysis is one of the simplest forms of the quantitative (statistical) analysis. It involves
the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical
relationship between them. Common forms of bivariate analysis involve creating a percentage
Table or a scatter plot graph and computing a simple correlation coefficient
S K I M T

Multivariate analysis.
• In multivariate analysis multiple relations between multiple variables are examined
simultaneously.
Multivariate analysis (MVA) is based on the statistical principle of multivariate statistics, which
involves observation and analysis of more than one statistical outcome variable at a time. In design
and analysis, the technique is used to perform trade studies across multiple dimensions while
taking into account the effects of all variables on the responses of interest
Inferential Analysis
Inferential statistics is concerned with making predictions or inferences about a population from
observations and analyses of a sample.That is, we can take the results of an analysis using a sample
and can generalize it to the larger population that the sample represents.Ther are two areas of
statistical inferences (a) statistiacal estimation and (b) the testing of hypothesis
S K I M T

ANALYSIS
Steps in Analysis
Different steps in research analysis consist of the following.
1.The first step involves construction of statistical distributions and calculation of simple
measures like averages, percentages, etc.
2.The second step is to compare two or more distributions or two or more subgroups within a
distribution.
3.Third step is to study the nature of relationships among variables.
4. Next step is to find out the factors which affect the relationship between a set of variables
5.Testing the validity of inferences drawn from sample survey by using parametric tests of
significance.
S K I M T

DISPLAYING OF DATA
Graphs and Diagrams
In research, the data collected may be of complex nature. Diagrams and graphs is one of the
methods which simplifies the complexity of quantitative data and make them easily intelligible.
They present dry and uninteresting statistical facts in the shape of attracting and appealing pictures.
S K I M T

DISPLAYING OF DATA
1. Line Graphs
A line graph displays information in a series of data points that each represents an
individual measurement or piece of data.The series of points are then connected by a line to show a
visual trend in data over a period of time.The line is connected through each piece chronologically.
For eg; following data show birth rate per thousands of six countries over a period.
S K I M T

Business research methods _ Notes _for MBA and other UG and PG Students by Manoj new - pdf_ppt

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Business research methods _ Notes _for MBA and other UG and PG Students by Manoj new - pdf_ppt