MMS-Business-Research-Methods-munotes

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INTRODUCTION TO RESEARCH
Unit Structure
1.0 Objectives
1.1 Introduction
1.2 Meaning and Definition of Research
1.3 Objectives of Research
1.4 Characteristics of Research
1.5 Scope of Research in Business
1.6 Types of Research
1.6.1 On the Basis of Applic ation
1.6.2 On the Basis of Objectives
1.6.3 On the Basis of Extent of Theory
1.6.4 On the Basis of Time Dimension
1.6.5 On the Basis of Enquiry Made
1.6.6 Other Types of Research
1.7 Approaches to Research
1.7.1 Qualitative Approach
1.7.2 Quantitative App roach
1.8 The Process of Research
1.9 Research Applications in Business
1.10 Research Applications in Social Sciences
1.11 Features of a Good Research Study
1.12 Self-Assessment Questions
1.13 Summary
1.14 Key Words
1.15 Answers to Self -Assessment Question s
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Business Re search Methods 1.0 OBJECTIVES
After studying this module, you should be able to:
 Understand the meaning and nature of research
 Define the purpose of research
 Assess the different types of research
 Know about the various research approaches
 Generalize the Process of re search
 Understand the significance of research in business decision making
 Know about the criteria of good research
1.1 INTRODUCTION
In the modern complex world, society today is faced with varied social,
economic & political problems. These problems need systematic,
intelligent and practical solutions. Problem solving is technical process
and requires the accumulation of new knowledge. The quest for
knowledge is a never ending process and in its simplest form this process
has been called as ‘research’. In other words, research is a systematic
effort of gathering analysis & interpretation of problems confronted by
humanity. The world has evolved as a result of consistent efforts to
discover new things. In the current times, research has become an
organized a nd specialized field. Newer methods to conduct research have
come up. However, whatever be the field in which research is being
carried out, the research methodology parameters remain the same, even if
the objectives and the population to which the problem is being addressed
is different. Certain basic rules, often referred to as standard operating
procedures, are common to all fields, although they can be easily
developed for scientific research and are fuzzy for social science research.
Differences exist between one subject and other, but there is also
interdependence. It has been seen that there are inputs of theoretical
considerations in empirical studies and in a theoretical study; we look for
empirical evidences to support our theory. Understanding res earch
concepts and the methods used in it is important before any researcher
picks up the initiative of undertaking research. Research is a thinking
process and scientific method of studying a problem and finding solution.
It is an in -depth analysis based on reflective thinking. The current module
provides an insight into the basic research concepts.
1.2 MEANING AND DEFINITION OF RESEARCH
From a novice's point of view, research can be defined as the search of
knowledge. Oxford dictionary defines research as ‘the systematic
investigation and study of materials and sources in order to establish facts
and reach new conclusions’. Research is pursued in almost all the
professions. More than a set of skills, it is a critical way of observing,
examining, thinking, questioning and formulating principles that hold true
at least for the given space. Almost all professions affirm the need of
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Introduction to Research
of knowledge. Whatever profession we are in, we ask ourse lves a lot of
questions for finding new knowledge and ideas. For example, consider
that you are running a retail store; there are a lot of questions that may
help you in increasing your business:
 How many customers do I can handle daily?
 Which are the most purchased groceries?
 Which groceries combination is more popular?
 What time does the customers hit to maximum at store?
 How the customers rate our store?
 What is the average money a customer spends on a purchase?
Just by finding answers of these, one can always say that, a very valid
investigation has been done for the domain and the results. This is a very
raw example of research that we practice in everyday life. Essentially
speaking research involves a well -developed plan, a systematic approach
to deve loping new theories of finding solutions to various problems.
Redman and Mory defined research as ‘a systematized effort to gain new
knowledge’. Some professionals consider research as a movement, a
movement from the known to the unknown. It is actually a voyage of
discovery with pleasure and satisfaction. While considering research as an
academic activity, it involves a lot of steps such as problem definition, to
solve the problem, literature review, data collections, analysis, drawing
inference, making hy pothesis and arriving at a solution. Research is not
just gathering of information from books and other sources. The
transportation of knowledge from one form to another will neither
constitute a good research. In short and simple, we can define research a s
‘the systematic process of collecting and analysing information (data)in
order to increase our understanding of the phenomenon about which we
are concerned or interested’.
Zina O' Leary defined research as a 'creative and strategic thinking
process that involves constantly assessing, reassessing and making
decisions about the best possible means for obtaining trustworthy
information, carrying out appropriate analysis and tracing credible
solutions.'
Thus, research is actually a journey of discovery. Huma ns since aeons
have been trying to discover better methods of doing routine things, a
better explanation for why things happen in a particular manner and better
answers to recurring problems. The technique which is employed in the
search of this knowledge is termed as ‘research’.
Research provides us with right kind of information that helps us in
successfully dealing with problems. Clifford Woody has very
comprehensively defined research as 'a method for the discovery of truth
which is really a method of critical thinking. It comprises of defining and munotes.in

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Business Re search Methods redefining problems, formulating hypothesis or suggested solutions;
collecting and organizing and evaluating data; making deductions and
reaching conclusions; and at last, carefully testing the conclusions to
determine whether they fit the formulating hypothesis.' It can be
concluded that research involves:
(i) A clear definition of the problem
(ii) Formulation of hypothesis
(iii) Collection and analysis of data and
(iv) Relating the findings to existing th eories and earlier formulated
hypothesis.
Thus, research is re -search, i.e. a revisit on the earlier findings with the
intention of correlating them with newly discovered facts. The
Encyclopedia of Social Sciences has described research as a ‘critical and
exhaustive investigation or experimentation having as its aim the revision
of accepted conclusions in the light of newly discovered facts."
1.3 OBJECTIVES OF RESEARCH
The objective of research is to discover answers to questions through the
application o f scientific procedure. The main aim of research is to find out
the truth which is hidden and which has not been discovered as yet. It is
primarily concerned with production of knowledge. Though each research
study has its own specific purpose, some genera l objectives of research
below:
1. To Explore: Research can be carried out with the purpose of
gaining familiarity with a particular topic or to gain insight into
unexplored areas. Such a research is termed as exploratory or formative
research. It is often ca rried out before formulating a hypothesis e.g. a
domestic company may think of setting up its manufacturing operations
abroad. This kind of investment is new to the company and the initial
research conducted to explore the possibility of this new idea can be
termed as exploratory research.

2. To Describe: Quite often a research can be carried out with the
objective of describing a particular situation, event or an individual e.g. a
study can be carried out to study the voting pattern in a particular state on
the basis of gender, economic status, religion etc. as observed in the
previous election. Such researches are termed as descriptive studies .
Since these studies are about events that have already taken place, these
studies are also called as ex-post facto studies .

3. To Diagnose: When a study is carried out with the objective of
finding out how frequently a particular event is associated with another
event, it is termed as diagnostic study e.g. a fast food chain has conducted
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Introduction to Research
The chief objective of this study is to find out that how often people eat
their meal outside when planning to watch a movie. Doctors frequently
employ diagnostic methods to discover what it is that ails the patient.
Numerous questions are asked from the patient and through symptomatic
and clinical investigation, the doctors can then declare a diagnosis. Such
studies are called as diagnostic studies .

4. To Establish Causal Relationship: A research can be done wi th
the objective of finding out the causal relationship between the dependent
variables with independent variables. Such research are called as
hypothesis testing research e.g. a research carried out so establish the
relationship between polio vaccine (ind ependent variable) and its
effectiveness in controlling the occurrence of polio (dependent variable) is
a hypothesis testing research.
In simple words, the objectives of research may be:
 To identify and find solutions to the problems (e.g. "Why is that
demand for a product is falling"? "Why is there a business fluctuation
once in three years"?)

 To help making decisions (e.g. should we maintain the advertising
budget same as last year?)

 To develop new concepts (e.g. Customer Relationship Management,
Horizontal Marketing, Multi -Level Marketing, e -tailing etc.)

 To find alternate strategies (e.g. should we follow pull strategy or push
strategy to promote the product.)
1.4 CHARACTERISTICS OF RESEARCH
An understanding of the meaning of research puts us in a p osition to list
the characteristics of research. From the above explanations, the following
characteristics of research can be summarized:
1. Research is directed toward the solution of a problem.

2. Research is purposive i.e. it deals with a well -defined signi ficant
problem.

3. Research is based upon observable experience or empirical evidences.

4. A research gathers new knowledge and brings to the forefront hitherto
unexplored and unexplained phenomenon.

5. Research involves collection of primary data from first ha nd sources
or involves use of existing data for a new purpose.

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Business Re search Methods 6. Research activities are carefully detailed and clearly outlined through
a research design. These activities are defined by carefully designed
procedures and analysis tools.

7. Research emphas izes the development of generalizations, principles,
or theories that will be helpful in predicting future occurrences.

8. Research requires a degree of expertise and skill. A research worker is
expected to be knowledgeable about the intricacies involved in
carrying out a research.

9. Research strives to be objective and logical, applying every possible
test to validate the procedures employed the data collected and the
conclusions reached. The findings should be free from bias and the
results should be carefu lly verified.

10. Every process, term and tool used in the research should be carefully
documented and reported.

11. The research should target towards the discovery of general principles
or theories which can find application to a wide range of problems in
the present and future context.
1.5 SCOPE OF RESEARCH IN BUSINESS
Business research refers to any type of research done when starting and
inaugurating any type of business organisation. Simply speaking, the
application of research, its tools and techniques in business decision
making constitutes business research. According to Zikmund, “B usiness
research is a management tool that companies use to reduce uncertainty. It
is a manager’s source of information about organisational and
environmental conditions, an d covers topics ranging from long -range
planning to the most ephemeral tactical decision”. Looking upon this, the
scope of business research includes the following areas. However, the list
given below is not exclusive rather indicative.
1. Business Environmen t: The marketing activities are influenced by
several internal and external environments. Internal environments include
price, promotion, product and place (distribution), whereas the external
environments include economic, sociological, political, legal a nd
government motives.

2. Consumption Pattern: The pattern of consumption is to be
assessed by the management. The study of buyers' behaviour, attitudes and
capacity to purchase is very important in research. The purchasing power
of a consumer depends upon his disposable personal income. Thus, the
total purchasing power of a country or geographical area can be assessed
by the disposable income of the place. The research reveals all the factors
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Introduction to Research
3. Controlling: Researc h is used as a control technique of
management to find out the weaknesses and shortcoming of the
management decisions to re -orient the planning and performance
techniques.

4. Decision -Making: Research is useful for taking management
decisions. It provides n ecessary information and data in analysed and
processed forms for making decisions in various business areas. With
advanced technology, higher production functions and increasing
complexities in market, the research has become an indispensable tool for
taking appropriate decisions.

5. Finance: Research in finance helps financial experts and those other
individuals involved to study how the financial industry is affected by
market changes and trending. The research on finance and financial
management mainly r evolved around estimating financial requirement,
financial management, deciding capital structure, selecting a source of
finance, selecting a pattern of investment, proper cash management,
implementing financial controls, proper use of surplus etc.

6. Growin g Complex Markets: The advancement of science and
technology and the standard of living of consumers necessitate closer
touch with the growing markets. The size and specialisation within the
business unit and the intervention of numerous middlemen between the
manufacturer and customers created a wide communication gap. The
widening gap requires marketing research to fill up the communication
gap between the consumer and the producer.

7. Human Resources Management: Research on HRM activities
provides an unders tanding of what does work, what does not work, what
needs change, the nature and the extent of change. The human resource
research seeks to discover the basic relationships which may lead to
improved personnel decision -making in such areas as turnover,
absenteeism, compensation levels and structure, job satisfaction, employee
morale, assessment of managerial potential, training effectiveness,
grievance handling, labour relations and collective bargaining.

8. Management Planning: Research is used for managemen t
planning. It deals with business opportunities, i.e. those opportunities
which are viable to be exploited by management. Thus, management can
assess the resources that will be useful for the business.

9. Marketing Strategy: Marketing management has to lay down
appropriate marketing strategies to meet competition, to pursue growth in
the market and to attain organisational objectives. The policies and
programmes related to pricing, distribution, sales promotion, product etc.
can be made with proper research.

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Business Re search Methods 10. Problem -Solving: Starting from problem identification to
formulation of alternative solutions, and evaluating the alternatives in
every area of management, is the problem -solving action of research.
Problem -solving research focuses on the short -range a nd long -range
decisions that must be taken with respect to the elements of the business
viz. marketing, HRM, production, finance etc. It can help managements
bring about prompt adjustment and innovations in the above areas of
business.

11. Production Manageme nt: Research helps large -scale production
by providing suitable decisions to be undertaken by the producers to
exploit the existing production resources to meet the growing markets.
The resources of production and market potentials are properly assessed
by research. The research performs an important function in product
development, diversification, introducing a new product, product
improvement, process technologies, choosing a site, new investment etc.
1.6 TYPES OF RESEARCH
This part specifically focuses on the types of research. Research can be
classified from various perspectives. A detailed description of the same
can be had from the figure below and the description that follows:
1.6.1. On the Basis of Application
On the basis of application, research i s of two types:
i) Pure/ Basic/ Fundamental Research: Developing scientific theories or
basic principles are called 'pure' or 'basic' or 'fundamental' research . This
research is concerned with quest for

knowing more about the phenomenon without concern for its practical use
and also with developing and testing hypotheses and theories. Pure
research takes place to explore a particular concept, or issue, without
regards for a specific problem, and may be carry out to simply gain a
better understanding of t he overall concept. It is said, there is nothing as
practical as a good theory. It is conducted to satisfy any curiosity such as:
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What makes things happen?
Why society changes, and
Why social relations are in a certain way.
In fact, it is the source of most new theories, principles, and ideas. To
sum-up, basic research is purely theoretical to increase our understanding
of certain phenomena or behaviour but does not seek to solve any existing
problem. It is essentially positive and not normative. This m ay take the
form of the discovery of totally new idea, invention and reflection where
an existing theory is re -examined possibly in a different social context.
ii) Applied Research: It is also termed as practical, need based or action
research. The object ive of this research is to find the solutions to problems
that are faced by government, society or the business. Applied Research,
thus, is more concerned with actual life. It also suggests remedial
measures to alleviate various types of problems. This res earch is concern
with search for ways of using scientific knowledge to solve practical
problems. The findings become basis of framing programme and policies,
based on principles of pure research. According to Horton and Hunt , this
research is an investigat ion for ways of using scientific knowledge to
solve practical problems. This type of research is conducted on
interdisciplinary basis also.
Examples include like evaluating the impact of a training programme on
employee performance, examining consumer resp onse to direct marketing
programmes. Although the purpose of these two research forms varies,
there is not much difference in the research methods and tools used for
their conduct. In the present world situation, more emphasis is being given
to applied res earch to solve problems arising out of various environmental
changes.
1.6.2. On the Basis of Objectives
On the basis of fundamental objective, research is designed in following
ways:
(i) Exploratory Research: This type of research is carried out at the ve ry
beginning when the problem is not clear or is vague. In exploratory
research, all possible reasons which are very obvious are eliminated,
thereby directing the research to proceed further with limited options. The
main aim of exploratory studies (also k nown as formulative research ) is
to gather initial information which helps to define problems and
recommend hypothesis. It often relies on secondary research such as
reviewing available literature, or qualitative approaches such as informal
discussions wit h consumers, employees, management or competitors, and
more formal approaches through in -depth interviews, focus groups,
projective methods, case studies or pilot studies. It is important to bear in
mind that it can mainly be conducted when researchers lac k clear idea of
the problem. The outcomes of this research are not generally useful for
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Business Re search Methods (ii) Descriptive Research: Descriptive research, also known as statistical
research , describ es data and characteristics about the population or
phenomenon being studied. Descriptive research answers the questions
who, what, where, when, and how. The description is used for
frequencies, averages, and other statistical calculations. Often the best
approach, prior to writing descriptive research, is to conduct a survey
investigation. In short descriptive research deals with everything that can
be counted and studied. But there are always restrictions to that. The
research must have an impact to the l ives of the people around the
researcher. This research is applicable to problem which specifies certain
criteria and data is clearly available for accurate objectives.
(iii) Causal/Experimental Research: It is commonly used in sciences
such as sociology a nd psychology, physics, chemistry, biology, medicine,
etc. It is commonly used in sciences such as sociology and psychology,
physics, chemistry, biology, medicine, etc. However, it may also be
carried out in social sciences if such research enables us to q uantify the
findings, to apply the statistical and mathematical tools and to measure the
results thus quantified. It is also classified under conclusive research .
In many problems, there are many variables involved or influential. It is
not always possible or feasible to study all variables simultaneously, so to
study limited variables this type of research is conducted. Here, one
variable (under study) keep open whereas other variables are kept constant
and then open variable effect is studied. The relatio nship between
dependent and independent variables is observed and describe in
connecting hypothesis. The variable which is influenced is known as
dependent and the variable which influence other is known as independent
variable. For example, effect of inve stment decision (independent) on
investment returns (dependent), effect of advertisement (independent) on
sales (dependent). Causal research is used to obtain evidence of cause -
and-effect (causal) relationships.
1.6.3. On the Basis of Extent of Theory
On the basis of extent of theory, research is of two types:
(i) Theoretical Research: Theoretical research generally uses the findings
from existing theory and explanations to develop new ideas. These new
ideas are not tested through collecting evidence in th e form of primary
data. Theoretical research is held to be a classical way of adding
something of value to the body of knowledge. One of the primary roles of
theoretical research is to re -work already established ideas in order to
improve insights into the subject matter. Such improvements could well -
constitute adding something of value to the body of knowledge. A
researcher who develops a theory through visiting a library and
developing their own explanation through reading existing work will be
undertakin g theoretical research.
(ii) Empirical Research: This is a data based research where we collect
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Introduction to Research
hypothesis. It is a way of gaining knowledge by means of direct
observation or experience. E mpirical evidence (the record of one's direct
observations or experiences) can be analysed quantitatively or
qualitatively. It is based on observation and experience more than upon
theory and abstraction. Empirical means based upon observation or
measureme nt rather than theoretical reasoning. Pharmaceutical companies
use empirical research to try out a specific drug on controlled groups or
random groups to study the effect and cause. This way they prove certain
theories they had proposed for the specific dr ug. Such research is not just
useful in science but in many other fields like history, social sciences,
business, etc.
1.6.4. On the Basis of Time Dimension
On the basis of time dimension, research can be of two types:
(i) Cross -Sectional Research: Cross -sectional study is popular in the
field of business and marketing research. Cross -sectional research design
involves the collection of information from a sample of a population at
only one point of time. Cross -sectional research is usually the simplest and
least costly alternative. In this study, various segments of the population
are sampled so that the relationship among the variables may be
investigated by cross tabulation. Sample surveys are cross -sectional
studies in which the samples happen to be a re presentative of the
population. It may reveal how these samples are represented in a cross -
section of a population. The cross -sectional study generally involves large
samples from the population; hence, they are sometimes referred as
“sample surveys.” Cross -sectional research can be exploratory,
descriptive, or explanatory but it is most consistent with a descriptive
approach to research. “What is the effectiveness of an advertisement
campaign for an air conditioner?” is an example of cross -sectional study.
ii) Longitudinal Research: Longitudinal study involves survey of the
same population over a period of time. It is usually more complex and
costly than cross -sectional research, but it is also more powerful,
especially when researchers seek answers to quest ions about social
change. In this study, the sample remains the same over a period of time.
“How have consumers changed their opinion about the performance of air
conditioner as compared with that last summer?” is an example of
longitudinal study. Longitud inal surveys usually combine both extensive
(quantitative) and intensive (qualitative) approaches. Descriptive and
explanatory researchers use longitudinal approaches. They consider three
types of longitudinal research which can be described as follows:
(a) Time -Series Research: A time series design collects data on the same
variable at regular intervals (weeks, months, years, etc.) in the form of
aggregate measures of a population. Measurements are taken on each
variable over two or more distinct time per iods. This allows the researcher
to measure change in variables over time. For example, the Consumer
Price Index (CPI), unemployment rates, poverty rates, etc. Time series
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Business Re search Methods over time , keeping track of trends, and forecasting future (short -term)
trends. Time series data are nearly always presented in the form of a chart
or graph: The horizontal (or x) axis is divided into time intervals, and the
vertical (y) axis shows the values of th e dependent variable as they
fluctuate over time.
(b) Panel Study: It is a powerful type of longitudinal research. A panel is
a sample of respondents who have agreed to provide responses over a
specified time interval. In a panel study, the researcher obs erves exactly
the same people, group, or organisation across time periods. Panels are
also of two types: traditional panels and omnibus panels . In case of
traditional panels, same questions are asked to the respondents on each
panel measurement. For exampl e, firms are interested in knowing the
change in attitude, opinion, feeling, or emotion of the customers about a
particular product over a specific time interval. In the case of omnibus
panels, different set of questions are asked to the respondents on eac h
panel measurement. Hence, different set of information is obtained using
omnibus panels. Use of panels is based on the objective of the research
and the nature of the problem.
(c) Cohort Study: A cohort is a group of respondents who experiences
the same events within the same time interval. The word “cohort” means a
group of people. It is similar to the panel study, but rather than observing
the exact same people, a category of people who share a similar life
experience in a specified time period is studi ed. Cohort analysis is
‘explicitly macro analytic’, which means researchers examine the category
as a whole for important features. In cohort study, the individuals
examined over time may not be the same but they should be representative
of a particular gr oup (or cohort) of individuals who have shared a common
experience. For example, cohort analysis used to predict changes in voter
opinions during the polls.
Cohort studies can be forward -looking of backward -looking. A forward -
looking cohort study is also k nown as a prospective cohort study.
‘Prospective’ means that it relates to the future. A backward -looking
cohort study is also called as retrospective cohort study. ‘Retrospective’
means that it relates to the past. To carry out prospective cohort studies,
researchers identify a group of people to study and plan the research in
advance, collecting data over time. In retrospective cohort studies,
researchers use data that are already available for a particular group.
1.6.5. On the Basis of Enquiry Made
On the basis of enquiry to be made, research can be of two types:
(i) Quantitative Research/ Structured Approach: It usually involves the
collection and converting of numerical data into numerical form so that
statistical calculations can be done which help in drawing conclusions to
answer a specific research question. Quantitative research is applicable to
phenomena that are measurable so that they can be expressed in terms of
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researchers seek to avoid their own presence, behaviour or attitude
affecting the results (e.g., by changing the circumstances being studied or
causing participants to behave differently). The aim of quantitative
research is to develop mathematical models, theories r elated to
phenomenon. Quantitative research is mainly used in social sciences. It
may involve correlation study, ex -post facto study, longitudinal study,
meta -analysis and survey
(ii) Qualitative Research/ Unstructured Approach: Qualitative research
is usually related with the social constructivist concept which emphasizes
the socially constructed nature of reality. This research is designed to find
out how people feel or what they think about a particular subject. The
sequence to data collection and analy sis is logical but allows for greater
flexibility in all aspects of the research process. Data is collected in textual
form on the basis of observation and communication with the participants,
e.g., through participant observations, in -depth interviews and focus
groups. The data is not converted into numerical form and is not
statistically analysed. It is more appropriate to explore the nature of a
problem, issue or phenomenon without quantifying it. The prime objective
of such researches is to describe the variation in a phenomenon, situation
or attitude. The qualitative research attempts to answer ‘why’ and ‘how’
aspects of decision -making rather than ‘what’ and ‘when’ aspects.
1.6.6. Other Types of Research
(i) Ex -Post Facto: In this type of research, an examination of relationship
that exists between independent and dependent variable is studied. It is
similar to empirical research. In this method, the researcher has no control
over an independent variable. Ex -post facto literally means ‘from what is
done afterwards’ . In this research, a variable ‘A’ is observed. Thereafter,
the researcher tries to find a causal variable ‘B’ which caused ‘A’. It is
quite possible that ‘B’ might not have been caused ‘A’. In this type of
analysis, there is no scope for the r esearcher to manipulate the variable.
The researcher can only report ‘what has happened’ and ‘what is
happening’. Ex post facto research is the process beginning with a
phenomenon and going backward in time to identify casual factors.
(ii) Historical Rese arch: The name itself indicates the meaning of the
research. Historical study is a study of past records and data in order to
understand the future trends and development of the organisation or
market. There is no direct observation. The research has to de pend on the
conclusions or inferences drawn in the past. Historical research is the
systematic collection, critical evaluation, and interpretation of historical
evidence (i.e., data relating to past occurrences). In general, historical
research is undertak en to answer questions about causes, effects, or trends
relating to past events that may shed light on present behaviours or
practices. For example, study of epics like Ramayana and Mahabharata for
TV serial or movie making, biographical research, historie s of institutions
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Business Re search Methods (iii) Diagnostic Research: It may be said as another name for descriptive
research. This research is conducted to establish whether two or more
variables are associated and their degree of association. In a diagnost ic
research, the researcher is trying to evaluate the cause of a specific
problem or phenomenon. This research design is used to understand
more in detail the factors that are creating problems in the company.
Diagnostic research design includes three step s viz. t he inception of
the issue, diagnosis of the issue and solution for the issue.
(iv) Case -Study Research: This type of research is concerned with
exploring and analysing the life or functioning of a social or economic
unit, such as a person, a family , a community, an institution, a firm or an.
Industry. The objective of case study method is to examine the factors that
cause the behavioural patterns of a given unit and its relationship with the
environment. A researcher conducting a study using the cas e study
method attempts to understand the complexity of factors that are operative
within a social or economic unit as an integrated totality.
(iv) Evaluation Research: Evaluation research aims at evaluating
programme that have been implemented or actions that have taken in
order to get an objective. Evaluation means some sort of measurement of
the end -product and impact of an effort in the light of the stated goals for
which the programmed undertaken. There are so many programmes in
which economic gains ar e not visible, the evaluation of which calls for
special techniques. There are three types of evaluations made in research
namely concurrent evaluation, periodic evaluation, and terminal
evaluation.
(v) Conclusive Research: As the name suggests, conclusive research is
carried out to provide information that is useful in reaching conclusions or
decision -making. The purpose of conclusive research is to provide a
reliable or representative picture of the population through the use of a
valid research instrumen t. Conclusive research design provides a way to
verify and quantify findings of exploratory studies. Conclusive research
usually involves the application of quantitative methods of data collection
and data analysis. Moreover, conclusive studies tend to be deductive in
nature and research objectives in these types of studies are achieved via
testing hypotheses. Conclusive research can be sub -divided into two major
categories i.e. descriptive or statistical research, and causal research.
1.7APPROACHES TO RESE ARCH
Approaches to research consists of making a suitable decision regarding
research components like types of research, measurement and scaling,
development of questionnaire, sample size -determined sampling
techniques and data analysis plan. A research ap proach can vary
significantly depending on what is to be studied. If it is a scientific
method, it would be appropriate to use similar methods or other scientists
who have attempted the experiment. However, if the research was is in
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carry -out surveys, look into past surveys, etc. The general research
approach acts as an overall guide for conducting the research work.
There are two major approaches in research literature, i.e., Qualita tive
and Quantitative . In a generalized view, we can add logical and
participatory approaches along with aforementioned above.

1.7.1 Qualitative Approach
Qualitative research is a research method used extensively by scientists
and researchers studying h uman behaviour and habits. Qualitative
methods are used to find and confirm the presence and absence of an
element. Qualitative research is often regarded as a precursor to
quantitative research, in that it is often used to generate 'possible leads and
ideas which can be used to formulate a realistic and testable hypothesis.
This hypothesis can then be comprehensively tested and mathematically
analysed, with standard quantitative research methods. For these reasons,
these qualitative methods are often close ly allied with survey design
techniques and individual case studies, as a way to reinforce and evaluate
findings over a broader scale.
One example of a qualitative research design might be a survey
constructed as a precursor to the paper towel experiment. A study
completed before the experiment was performed would reveal which of
the multitude of brands were the most popular. The quantitative/
experiment could then be constructed around only these brands, saving a
lot of time, money and resources. Qualitati ve research can be further
classified under two types namely:
 Direct Qualitative Research
 Indirect Qualitative Research
In direct qualitative research , in-depth interview is the norm. The group
is prepared with the help of a selected few research participa nts. This is
more like a brain storming session. The topic is discussed amongst the
focus group with the help of a moderator from amongst the focus group
participants only. Subsequently, the group interview is conducted and a munotes.in

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There is one more approach other than these two and it is mixed approach as for any research it is very difficult to apply exclusively only one
approach. Many number of times objectives and effectiveness of research is
more important than the approach and so combination of approaches is the best way to adopt. final report is prepared by th e researcher based on the outcomes of the
discussion. For example, in -depth interviews, focus groups, etc.
In indirect qualitative research, the participants are encouraged to come
out with their own versions and understandings about the issue/s being
taken up by the researcher. The respondents are supposed to 'project' their
feelings or attitudes about the situation. For example, word association,
sentence completion, role playing, etc.
1.7.2 Quantitative Approach
The quantitative approach involves the col lection of quantitative data,
which are put to rigorous quantitative analysis in a formal and rigid
manner. This approach further includes experimental, inferential, and
simulation approaches to research. They are most commonly used by
physical scientists, in social sciences, education and management. It is the
opposite of qualitative research.
Quantitative experiments use a standard format, with a few minor inter -
disciplinary differences, of generating a hypothesis to be proved or
disproved. This hypothes is must be testable by mathematical and
statistical methods, and is the basis of which the whole experiment is
designed. Quantitative methods are used to measure the degree of an
element already present. A sound quantitative design should only
manipulate o ne variable at a time, or statistical analysis becomes
cumbersome and open to question. Ideally, the research should be
conducted in a manner that allows others to repeat the experiment and
obtain similar results. A common perception of quantitative resear ch is
that the emphasis is on proof rather than discovery.
Quantitative experiments are useful for testing the results gained by a
series of qualitative experiments, leading to a final answer, and a
narrowing down of possible directions for follow -up rese arch.










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1.8 THE PROCESS OF RESEARCH
According to Horton and Hunt , the scientific method of research
comprises the following basic steps:
(i) Defining the problem
(ii) Review of literature
(iii) Formulation of hypothesis
(iv) Developing a research design
(v) Collection of data
(vi) Analysis of data
(vii) Drawing conclusions
(viii) Replicate the study for generalisations
These steps give us an idea of the essential steps involved in conducting
research. However, there is no rigid sequence of research process steps.
Depending on each situ ation, certain steps can be skipped, can he repeated
or circumvented. However, inspire of all these variations, it is possible for
us to develop a sequence of research process. Although each of the steps
discussed here are studied in greater detail in subs equent modules, a brief
overview can be provided at this stage. Figure given below shows the
detailed sequence of research process. Each of these steps is the natural
outcome of the previous steps, but these steps are not mutually exclusive.
It is possible for instance to develop our research objectives and working
hypothesis simultaneously.
The various steps are:
Step 1: Discover the Problem Area
To start a research, we first of all need to discover the problem which
demands solution. The best way to iden tify the problem would be to look
for an unresolved query, a gap in the existing knowledge or an unfulfilled
need within the chosen subject. Although the world is filled with unsolved
problems, yet not every problem is suitable for research. Researcher
should take care that the problem should be one which can be clearly
identified and formulated. Further, while choosing the research area the
researcher should look into the availability of information relevant to the
topic. Mere availability is not enough, i t should also be accessible.
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Step 2: Review of Literature
The next step is to become familiar with the problem and formulate it
clearly. Litera ture review involves a comprehensive review of published
and unpublished work from the secondary sources of data available in the
relevant area of study. The researcher at this stage may review all the
available conceptual literature concerning the theorie s and concepts
related to the problem as well as the empirical literature comprising of
studies done earlier and bearing similarity to the problem under study.
Literature review helps the researcher in two ways; firstly , it helps him in
specifying his rese arch problem in a meaningful context, secondly , it
would provide him with an insight into the methods and techniques
adopted for handling such problems. The researcher can access
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Introduction to Research
name of the author, title of the book, publisher, year, volume and page
number. He can also use abstract database which along with bibliographic
citations also provides him with an abstract of the article. The researcher
can also use full text databases whi ch contain the entire text.
Step 3: Problem Formulation and Definition
Initially, the focus of the problem is not clearly defined. However, after
the literature review, the researcher is now in a position to formulate his
problem clearly. In the words of Albert Einstein, "The formulation of a
problem is far more essential than its solution, which may be merely a
matter of mathematical or experimental skill". A problem well formulated
will alone yield fruitful results. It is a clear, precise, and concise st atement
of the matter that is to be investigated. The problem statement is a fact
oriented information gathering question. The objectives of the study at this
stage are clearly developed. While problem is being formulated, the
following should be taken int o account:
 Determine the objective of the study
 Consider various environment factors
 Nature of the problem
 State the alternative
Step 4: Developing a Working Hypothesis
A hypothesis is a tentative assumption regarding the solution to the
problem under stud y. The hypothesis is the focal point around which the
future research efforts will be directed. The kind of data to be collected,
the tools of analysis are influenced by the hypothesis. The hypothesis is a
predictive statement which is made in the light, of the available facts
relating to the problem under study. For example, a study conducted to
find the amount of research investment being done by the companies will
have the following hypothesis:
Ho: Companies invest 1% of their sales revenue in research a ctivities.
H1: Companies invest less than 1% of their sales revenue in research
activities.
The H o is called as the null hypothesis which assumes there is no
difference between the population parameter and the sample mean and the
H1 is called as the alter native hypothesis which presents the alternative
solution. A hypothesis thus presents a relationship between the different
variables. In case of social research relating to human behaviour, the
hypothesis helps us in making a prediction about the populatio n
parameter.
Step 5: Research Proposal
A research proposal is a brief summary outlining the objectives of study
and the modus operandi of conducting the research. In case of a thesis, the
research proposals are in the form of a synopsis stating the resea rch
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Business Re search Methods with a detailed bibliography. In case of business or government
organizations, the research proposal, in addition to the above information,
will contain information about the res earcher's qualification, the time and
cost schedules, the resources and special facilities required during
research. Essentially, they contain an executive summary, right in the
beginning stating the objective of the study and benefit derived thereon.
Rese arch proposal is very important and should be prepared carefully
especially when it is to be reviewed by the concerned authorities for
approval to conduct further research.
Step 6: Research Design
After approval of proposal, the next step is to work out the research
design. Research design outlines the conditions for collection and analysis
of data. The what, when, where, how much and the method of data
collection are detailed in the research design. It will specifically contain
information about:
(a) The Sampling Design
(b) Data Collection Design
(c) Instrument Design
(a) The Sampling Design: A sample design is a definite plan determined
before any data is actually collected for obtaining a sample from a given
population. If a research involves the stu dy of each and every unit of the
population, it is termed as a census survey. However, it is generally not
feasible to conduct a census survey, especially if the universe of the study
is very vast. E.g. TRPs of TV programmes are developed on a sample
basis rather than covering all the households of an area. A sampling
design will include a decision on the sampling unit, the sample size and
the sampling method.
'Sampling unit' is the most elementary unit which would be a part of the
study e.g. in a survey on newspaper readership pattern, a single household
comprising of all the members of the household can be regarded as a
sampling unit..
Next, the decision on ‘sample size' is taken. The size depends on factors
like the availability of time and funds to the researcher, the ability of the
researcher, the size of the population and the nature of the population. The
important thing to remember is that the sample size should be such as to
adequately represent the population.
Finally, a decision on the ‘sampling method’ is to be taken. A researcher
can use a non -probability method or a probability method of sampling.
Non-probability method of selecting a sample involves an element of
bias. The probability of a unit being a part of the sample is not known.
Under th is method, one can adopt various methods like convenience
sampling (easily accessible), purposive sampling (specific people for
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Introduction to Research
Probability method of sampling involves giving every m ember a known
and unbiased chance of being a part of the sample. Few probability
methods are simple random sampling, stratified sampling, cluster
sampling, area sampling, and systematic sampling.
(b) Data Collection Design: The method of gathering the data is planned
here. The data can be collected through an experiment conducted in
controlled settings or it can he conducted through field survey. The survey
can be a simple one involving data collection from one source or it could
involve areas spread all ov er the world. Hence, a well -planned data
collection design becomes necessary. Briefly speaking, some of the data
collection techniques available to a researcher are:
(i) Questionnaire: A set of questions pertaining to the topic under
study are compiled and the questionnaire is then mailed to the respondent
through mail. This method of data collection is particularly used in
situations where a large number of respondents are to be covered and they
are spread over a wide area.

(ii) Interview: An interview method inv olves a direct interaction
between the respondent and the researcher/field workers. The interview
can be a physical interview where the researcher personally asks questions
from the respondent or it can be a telephonic or virtual interview. The
telephonic or virtual method is adopted where the respondents are spread
over a wide area and time available is very less. An important thing to
remember is that while conducting an interview the researcher uses an
interview schedule which is like a questionnaire.

(iii) Observation: Observation involves collecting data visually and
recording the event. Observation besides visual aspect also involves
listening, smelling and touching. All behavioural activities as well as non -
behavioural activities like physical condition a nalysis, processes, and
records can be analysed through observation. An observation also involves
the use of observation schedules which contains a list of all the items that
are to be observed.

(c) Instrument Design: Instrument refers to the questionnai re or the
schedule that the researcher would use to collect data. The researcher
while designing the questionnaire must think in terms of following issues:

(i) Type of Data: It determines whether the data will be collected in a
nominal, ordinal, interval or ratio form. A nominal data has no order,
distance or origin, ordinal data has an order but no distance and origin, an
interval data has order and distance but no origin and a ratio data has
order, distance as well as a unique origin.

(ii) Communication Approa ch: Before designing the instrument the
researcher has to decide on how to collect the data i.e. through mailed
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Business Re search Methods (iii) Question Structure: This decides the type of questions and their
order. The instrument can be completel y direct structured, indirect
unstructured questionnaire or combination of these two approaches.

(iv) Question Wording: It should be the endeavour of every researcher
to have questions with simple words leaving no scope for ambiguity. Long
questions are to be avoided and the wording should not be biased.

After finalizing instrument, it will now be designed and subject it to pilot
testing . Pilot testing detects the weakness in the design and contents of the
instrument. It involves selecting a small sample fro m the target population
and simulating the procedures for data collection that have been designed.
Step 7: Data Collection
From this stage, the researcher moves ahead to data gathering stage. This
involves sending questionnaires to respondents, training f ield workers in
interview and observation methods. A careful control over the data
collection process is maintained. Respondents who have remained
incommunicado in the first round of communication are once again
contacted. The basic aim in this stage is th at the data is collected in the
correct form and within the specified schedules.
Step 8: Analysis of Data
The data gathered in the previous step does not have any meaning until it
has undergone for data analysis. Processing of this raw data will yield
some kind of relevant information. The raw data when it is aggregated,
organized and analysed yields us some information which helps us in
decision making. Data analysis is concerned with reducing the bulk of
accumulated data to a manageable size. Generally, i t involves the
following procedures:
(a) Coding: Under this process, the various responses of the respondents
are coded using symbols/ legends e.g. respondents can be classified in
term of education using symbol of L (Literate) or IL (Illiterate). The basi c
purpose of coding is to group the responses in well -defined categories
which then become easy to tabulate.
(b) Editing: The next step is the editing of responses. Many times the
response given by the respondent is either incomplete, incomprehensible
or is written in short hand. Editing removes ambiguities regarding
responses, shunts out the invalid responses and thus improves the quality
of data for statistical analysis.
(c) Tabulation: It is the process of putting the classified data in the form
of tab les. Tables can be one dimensional where data is tabulated in terms
of one feature e.g. sales figure in terms of time. They can be
multidimensional where data is tabulated using two or more features e.g.
sales figure in terms of time, region and product ar e depicted
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Introduction to Research
(d) Statistical Analysis: In the last stage, the tabulated data is analysed
using various statistical techniques like averages, percentages, trend
analysis, correlation and regression techniques etc. Statistical analy sis
these days has become highly dependent on computers and softwares e.g.
IBM SPSS Statistics, e -views .
Step 9: Hypothesis Testing
After analysing the data, the next step is to test the hypothesis that had
been formulated in the beginning of the research process. There are
various parametric and non -parametric tests like t -test, z -test, f -test,
Kruskal -Wallis test, Wilcoxon -Man-Whitney test etc. The choice of test
selected for hypothesis testing depends on factors like the nature and
objective of research, characteristics of population distribution, the
sampling technique, type of data etc. Hypothesis testing will help a
researcher in establishing the validity of his results. It will help in
determining whether the difference is real or simply an outcome of
random fluctuations.
Step 10: Data Interpretation and Generalisation
In case of no -hypothesis testing research, data interpretation is done with
the intention of seeking explanation for the research results on the basis of
existing theories. The results are interpreted in the light of existing
theories and doors are thrown open for newer explanations and
possibilities for further research. In case of hypothesis testing research,
after the data has been analysed, generalizations are made to builda new
theory. Such generalizations come up with better explanation and new
theories for existing phenomenon and greatly contribute to the existing
data bank.
Step 11: Reporting of Results
The last step is concerned with bringing in public the results of the
researc h so that the findings can be put to application. The style and
method of reporting would depend on the target audience, the purpose and
the time of reporting the results. Any research report whether it is
presented in a detailed form or in the form of a b rief note should
essentially have the following contents.
 The preliminary section containing the title of the report, table of
contents, list of tables, graphs, preface and an executive summary,
which gives briefly the research objectives and The findings and
importance of the study.

 A main text section which contains the problem background, research
objectives, a note on research methodology used, the importance of
conducting the research and the conclusion arrived thereof. A special
mention of the recom mendation given by the researcher in light of the
findings made by him should also be included.

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Business Re search Methods  The last section which includes appendices supporting the research
with items like questionnaires and schedules used, glossary of terms
and any other matter which although not a part of the main research
but required in order to support the research can be included.
Thus, this is a brief listing of the steps involved in a research process. A
researcher should keep in mind that these steps are not rigid. Their
sequence can be altered; steps can overlap or jumped depending on the
topic of research.
1.9 RESEARCH APPLICATIONS IN BUSINESS
For effective planning and implementation of business decisions, accurate
information about the internal business environment and the external
business environment is of primary importance. The key objective of any
business research is to provide accurate, relevant and timely information
to the top management, so that they can make effective decisions. The
business managers have the option of taking decisions either intuitively or
randomly, or under the directions of an authority or through rational
analysis. The best approach is to rationally analyse the problem which
requires that the managers should have access to the right kin d of
information. This information could have been gathered by others at some
time in the past (secondary data) or it could he gathered by the business
managers themselves specifically with the objective of solving the
problem at hand (primary data). This information is gathered by a
manager through research only.A business manager lives in three time
dimensions:
(a) The past -The objectives achieved and those left unaccomplished are
dealt in this dimension.
(b) The present -There is a perpetual evaluation of what is being currently
accomplished.
(c) The future - An eye on what would be the challenges in future and what
endeavours would be required to handle them.
Research is needed in order to gather data regarding the accomplishments
and lacunae of the pa st and the performance level in the present times in
order to take strategic decisions relating to future. The process of decision
making is a complex one which can be seen in the diagram below. It can
be broadly classified into following five steps. Each of these steps requires
information that can be collected through research. Let us review these
steps:
Step 1: Defining the problem: Research is needed to understand the
environment in which the organisation is operating e.g. information
gathered on work relationships between the employees may help an
organisation to define the problem of interpersonal conflict.
Step 2: Gathering information: In this stage, information through
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Introduction to Research
affected by the problem. Opinions of experts, top level managers can be
gathered through interviews to thoroughly understand the dimensions and
impact of the problem.
Step 3: Developing and selecting the best alternative: This step
involves looking at the problem from a different perspective. Techniques
like brain storming are used to generate a free flow of ideas. The, ideas are
generally evaluated to see which suits the problem at hand. Once the ideas
have been generated, research can be used to further draw a co nsensus on
the best alternatives.
Step 4: Implementation: Implementation converts an intention into a
solution. It involves a careful and step -by-step set of actions carried out
for solving the problems. The implementation process is carried out within
a well-defined framework of time and resource allocation. Research can
be used to obtain an inventory of available resources and decide on
optimal allocation.
Step 5: Evaluation : A continuous monitoring is required to judge the
success of implementation. Eval uation is the systematic acquisition and
assessment of information to provide useful feedback about some object
or action(s). A research that is carried out with the objective of appraising
the extent to which a given set of actions have managed to achieve their
targets within the given time and resource framework is called as
evaluation research .
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Thus, research is needed at every step of decision making. It is through
research only that a manager can remove the uncertainty surrounding a
decision. The app lication of research in business encompasses almost all
the business processes of an organisation. In contemporary times it is
expected of the organisations to be research oriented. Research is carried
out in almost all functional areas e.g. advertising re search, marketing
research, consumer behaviour research, financial research, performance
monitoring research, evaluation research, B2B, B2C marketing research
etc.
Research in business is being actively carried out:
(i) To evaluate the day to day performa nce of employees;
(ii) To monitor the organisational effectiveness to improve efficiency and
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Introduction to Research
(iv) To review and update existing information which is a key resource;
(v) To identify business opportunitie s;
(vi) To avail potential investment options and maximize returns; and
(vii) To plan for staff appraisal and development.
Thus business research is needed to place the organisation competitively
within the market.
1.10 RESEARCH APPLICATIONS IN SOCIAL
SCIENCES
The term ‘social research’ has been defined by different scholars
differently. A broad comprehensive definition of social research has been
given by P.V. Young which says that “It a scientific undertaking which by
means of logical and systematized techniques, aims to discover new factor
verify a test old facts, analyse their sequence, interrelationship and causal
explanation which were derived within an appropriate theoretical frame of
reference, develop new scientific tolls, concepts and theories which would
facilities reliable and valid study of human behaviour. A researcher’s
primary goal distant and immediate is to explore and gain an
understanding of human behaviour and social life and thereby gain a
greater control over time”.
Social research has great social importance because it helps in solving
many social problems. It aids the economic policies of a country, both for
government and business. Social Research helps to consider the basic
necessity of people and thereby provide sufficient alloc ation of a nation’s
resources. A new social research into society and its people helps us to
find the truth about various problems in our social setups and
relationships. It helps to understand the different social institutions and
their functions in socie ty. It provides an overview of the changing trends
in social institutions around the world. Moreover, social research h elps to
compare and contrast among different countries.
Social sciences include various disciplines dealing with human life,
human behavi our, social groups and social institutions. They consist of
Anthropology, Behaviour Science, Commerce, Demography, Economics,
Education, Geography, History, Law, Linguistics management, Political
Science, Psychology, Public Administration , Sociology and So cial Work.
Though these sciences are treated as separate branches of knowledge for
the purpose of study, they are interdependent studies of the different
aspects of the same object, viz. man. All the branch of social sciences
makes use of research to find solution to their problems and enquiries. The
contribution made by different discipline should be integrated and the
interface between them should become stronger by social researcher.
In context of research in social sciences there are two important thing s that
need to be mentioned; firstly that importance needs to be given to the
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Business Re search Methods paradigm needs to be reworked in the light of profound changes taking
place in the field of social science. The importance to methodology in
social science stems from the fact that the quality and credibility of
research depends on the quality and credibility of the methodology. A
philosopher, Heizenberg, has said that 'we observe nature not as it is but as
it is exposed to us by our method of questioning'. This implies that it is
only if we develop the right questions and state the problem correctly that
we can get the right set of answers. A problem correctly identified, a
question rightly stated is half the r esearch accomplished. If we know what
it is that we want to do, only then can we design the method to achieve it.
The social research need not restrict itself to the questions only but should
concentrate on following three aspects:
(i) Method of questioni ng
(ii) Method of observation
(iii) Method of interpretation
The last aspect i.e. method of interpretation assumes importance in social
science research because social science data is amenable to diversified
interpretations. Information technology has c reated a virtual reality
through television, internet etc. where people believe what they are made
to believe. This has made the social reality more complex and dynamic.
The effect of such developments on research methodology is that there
cannot be a sing le methodology for all social sciences.
1.11 FEATURES OF A GOOD RESEARCH STUDY
Whatever may be the types of research works and studies, one thing that is
important is that they all meet on the common ground of scientific method
employed by them. One expec ts scientific research to satisfy the following
criteria:
1. The purpose of the research should be clearly defined and common
concepts be used.
2. The research procedure used should be described in sufficient detail to
permit another researcher to repeat the research for further
advancement, keeping the continuity of what has already been
attained.
3. The procedural design of the research should be carefully planned to
yield results that are as objective as possible.
4. The researcher should report with c omplete frankness, flaws in
procedural design and estimate their effects upon the findings. Good
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Introduction to Research
5. The analysis of data should be sufficiently adequat e to reveal its
significance and the methods of analysis used should be appropriate.
The validity and reliability of the data should be checked carefully.
6. Conclusions should be confined to those justified by the data of the
research and limited to thos e for which the data provide an adequate
basis.
7. Greater confidence in research is warranted if the researcher is
experienced, has a good reputation in research and is a person of
integrity.
8. Related state -of-the-art literature should be studied in de pth to avoid
reinvention of wheel.
9. It should be time -bound and realistic.
In other words, we can state the qualities of a good research as under:
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 deduc tion 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 i s related
basically to one or more aspects of a real situation and deals with concrete
data that provide a basis for external validity to research results. Validity
and reliability of data should be checked and researchers should consider
an adequate amoun t of data.
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.
5. Good Research has Utility: The ultimate objective of any research
program s hould be oriented towards providing benefit to the society/
business. The research work should either form foundation for further
advancement in the domain, draw some concrete conclusions or it should
be beneficial from the social, commercial, or education al point of view.
6. Good Research is Creative: Creativity is the most important factor in
research proposal. Ideally no two research proposals should be identical to
each other. Research proposal should be designed meticulously so as to
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Business Re search Methods the formulation and structure of two research programs results in
difference in creativity and also in findings. Any sorts of guessing or
imagination should be avoided in arriving at conclusions of a research
program.
1.12 SELF ASSESMENT QUESTIONS
1. Fill in the blanks with appropriate words:
(a) The __________ approach relies on direct observation and
experimentation in the acquisition of new knowledge
(b) Business research comes within the purview of ...................... research.
(c) ...................... methods are concerned with attempts to quantify social
phenomena and collect and analyse numerical data.
(d) The purpose of research is to find solutions through the application of
........... ........... and...................... methods.
(e) Gathering knowledge for knowledge's sake is known as ...................
research.
(f) In exploratory research, all possible reasons which are ................... are
eliminated.
(g) In .................. . research, an examination of relationship that exists
between independent and dependent variable is studied.
(h) It is better for the researcher to generate as many alternatives as
possible during problem..........................
(i) The................. ... must decide if data is to be collected by observation
method or by interviewing.
(j) ………………….is the best type of research type for gathering causal
information.
2. State true or false for the following statements:
(a) The purpose of doing research is t o identify problem and find the
solution.
(b) In an experimental design, the dependent variable is the one that is not
manipulated and in which any changes are observed
(c) Research conducted to find solution for an immediate problem is
fundamental researc h.
(d) Identification of problem is the first step in starting the research
process
(e) In the process of conducting research ‘Formulation of Hypothesis” is
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Introduction to Research
(f) “Controlled Group” is a term used in historical resea rch.
(g) 'Research methodology' refers to the chain of association between the
research question and the research design.
(h) The two main approaches of research are sampling and recording.
(i) There are various types of research designed to obtain differe nt types of
information. Descriptive Research is used to define problems and
suggest hypotheses.
(j) In a longitudinal study, measures are taken from same participants on
different occasions usually over extended period of time.
3. Match the following:
A. Research Proposal (i) Expected relationship between two or more
variables
B. Hypothesis (ii) Data collection methods used in research
C. Dependent Variable (iii) Qualitative and quantitative research
D. Questionnaire (iv) Descr iption of research process for a research
project
E. Time dimension (v) Variable that changes due to the action of
another variable
4. Answer the following:
a) Define the term ‘Research’, Enumerate the characteristics of research.
Give a comprehensive def inition of research.

b) Define business research and explain its application in managerial
decision making.

c) What do you mean by Research Methodology? Explain its
significance and compile the different types of research.

d) Describe the various classification of research, Differentiate between
fundamental research and action research. Elaborate your answer with
examples.

e) Explain the steps in research process with the help of flow chart of the
research process.

f) What type of research would you undertake in orde r find why middle
income groups go to a particular retail store to buy their products?

g) Which type of research would you conduct when the problem is not
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Business Re search Methods h) Business research methods are tools for decisio n making in the hands
of a researcher. Justify the statement.

i) What are the different methods in conducting an exploratory research?

j) What is descriptive research and when do researchers conduct it?

k) Give a detailed essay on application of research in busi ness and social
sciences.

l) Enlist the features of a good research study.
1.13 SUMMARY
This module presents an introductory discussion about research.
Researchers systematically collect, compile, analyse, and interpret data to
provide quality information ba sed on which the decision maker will be
able to take a decision in an optimum manner. In fact, research is tool in
the hands of a decision maker to make an optimum decision in an
environment of uncertainty. Conducting research to deal with any problem
is a scientific, systematic, and interlinked exercise, which requires sound
experience and knowledge. This module is an attempt to understand the
nature and scope of the research. The purpose of research is to contribute
to or develop a body of knowledge.
Rese arch may broadly be classified on the basis of objectives, extent of
theory, time dimension, enquiry made and others. Basic/ pure research is
generally not related to a specific problem and its findings cannot be
immediately applied. Applied research direc tly addresses the problem at
hand. Applied research is launched by the firm, agency, or the individual
facing the specific problem. Exploratory research is mainly used to
explore the insight of the general research problem. It is used in obtaining
backgrou nd information, research problem formulation or defining it more
precisely, identifying and defining key research variables, and developing
hypotheses. Exploratory research is conducted through secondary data
analysis, expert survey, focus group interviews , case analysis, and
projective techniques. Descriptive research is conducted to describe the
business or market characteristics. Cross -sectional research design
involves collection of information from a sample of a population at only
one point of time. Lo ngitudinal study involves survey of the same
population over a period of time. Causal research is conducted to identify
the cause -and-effect relationship between two or more business (or
decision) variables. There are two approaches to research namely;
qualitative approach and quantitative approach.
The ability to take an informed decision is generated through a systematic
study that is conducted through various interrelated stages . A research
design is the detailed blueprint used to guide a research study towards its
objective. A good research is conducted through these steps; discover the
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Introduction to Research
developing a working hypothesis, research proposal, research design, data
collection, analysis of data, hypothesis testing, data interpretation and
generalisation and reporting of results.
There is wide spectrum of application of research in business and social
sciences. On one side, research is always required by business managers
for solving business problems and decision making. On other side, social
scientists makes use of research in various disciplines of social sciences to
find social phenomenon, link social sciences, analysing social trends etc.
A good research must be systematic, logical, empir ical, replicable, useful
and creative.
1.14 KEY WORDS
 Applied Research: It refers to scientific study and research that seeks
to solve practical problems .

 Case -Study Research: It is concerned with exploring and analysing
the life or functioning of a socia l or economic unit, such as a person, a
family, a community, an institution, a firm or an. Industry.

 Causal/ Experimental Research: A research carried out so establish
the relationship between independent variable and dependent variable.

 Cohort Study: It is about observing a category of people who share a
similar life experience in a specified time period.

 Conclusive Research: Conclusive research is carried out to provide
information that is useful in reaching conclusions or decision -making.

 Cross -Secti onal Research: Cross -sectional research design involves
the collection of information from a sample of a population at only one
point of time.

 Descriptive Research: It is used to describe characteristics of a
population or phenomenon being studied.

 Diag nostic Research: This research is conducted to establish whether
two or more variables are associated and their degree of association.

 Empirical Research: Research based on first -hand gathering of data
through interviews, questionnaires, ethnographies, pa rticipant
observation, action research and so on.

 Evaluation Research: Evaluation research aims at evaluating
programme that have been implemented or actions that have taken in
order to get an objective.

 Exploratory Research: It is a methodology that inv estigates research
questions that have not previously been studied in depth .
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Business Re search Methods  Ex-Post Facto: Ex post facto research is the process beginning with a
phenomenon and going backward in time to identify casual factors.

 Historical Research: Historical study is a study of past records and
data in order to understand the future trends and development of the
organisation or market.

 Hypothesis: A hypothesis is a tentative assumption regarding the
solution to the problem under study.

 Longitudinal Research: Longitud inal study involves survey of the
same population over a period of time.

 Panel Study: Panel study is concerned with observing exactly the
same people, group, or organisation across time periods.

 Pure Research: It is a type of scientific research with th e aim of
improving scientific theories for better understanding and prediction of
natural or other phenomena.

 Qualitative Research: Qualitative research is designed to find out
how people feel or what they think about a particular subject.

 Quantitative R esearch: It usually involves the collection and
converting of numerical data into numerical form to made statistical
calculations which help in drawing conclusions to answer a specific
research question.

 Research Design: Research design refers to the fram ework
of market research methods and techniques that are chosen by a
researcher. The design that is chosen by the researchers allows
them to utilize the methods that are suitable for the study.

 Research Proposal: A research proposal is a brief summary ou tlining
the objectives of study and the modus operandi of conducting the
research.

 Research: The systematic investigation and study of materials and
sources in order to establish facts and reach new conclusions.

 Theoretical Research: This research uses t he findings from existing
theory and explanations to develop new ideas.

 Time -Series Research: A time series design collects data on the same
variable at regular intervals (weeks, months, years, etc.) in the form of
aggregate measures of a population.



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Introduction to Research
1.15 ANSWERS TO SELF ASSESMENT QUESTIONS
1. (a) empirical (b) social science(c) Quantitative(d) systematic, scientific
(e) basic (f) very obvious(g) ex -post facto (h) formulation hypothesis
(i) researcher (j) Experimental
2. (a) True (b) False (c) False (d) True (e) True (f) False
(g) True (h) False (i) True (j) True
3. A. (iv) B. (i) C. (v) D. (ii) E. (iii)



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RESEARCH PROBLEM AND
FORMULATION OF RESEARCH
HYPOTHESES
Unit Structure
2.0 Objectives
2.1 Introduction
2.2 Research Problem: Meaning and Definition
2.3 Importance of Problem Definition
2.4 Sources of Research Problem
2.5 Criteria of Determining a Res earch Problem
2.5.1 Internal Criteria
2.5.2 External Criteria
2.6 Management Decision Problem vs. Management Research Problem
2.7 Problem Identification Process
2.8 Components of the Research Problem
2.9 Self-Assessment Questions (A)
2.10 Research H ypothesis: Meaning and Definition
2.11 Sources of Hypothesis
2.12 Characteristics of Good Hypothesis
2.13 Formulation of Research Hypothesis
2.13.1 Process of Formulating Research Hypothesis
2.13.2 Generation of Research Hypothesis
2.14 Types of Research Hypothesis
2.15 Research Proposal
2.16 Writing a Research Proposal
2.17 Contents of a Research Proposal
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Research Problem and
Formulation of Research
Hypotheses 2.19 Self-Assessment Questions (B)
2.20 Summary
2.21 Key Words
2.22 Answers to Self -Assessment
2.0 OBJECTIVES
After studying this module, you should be able to:
 Understand the concept, importance of and the process used for
defining research problem.
 Apply deductive and inductive reasoning strategies to formulate
research problem.
 Understand the difference of mana gement decision problem and
management research problem.
 Describe research hypothesis and its formulation procedure.
 Identify propositions and convert them into workable research
hypothesis.
 Know about the research proposal and its contents.
 Develop the un derstanding about various types of research proposals
and their importance.
2.1 INTRODUCTION
Selecting the research problem is the first and important step in executing
the research work. The success of a research effort lies in choosing the
correct proble m. This problem setting may not be necessary for
exploratory or formative researches where the research work does not start
with the formulation of the problem and such studies often precede the
setting up of problem. However, in other forms of research it is logical
that the researcher must know what is that he wants to do. It is essential
that a researcher identifies a problem that demands an answer, a need that
requires a solution, a demand that requires satisfaction. The world around
has lots of problem e.g. in the social field the problems of crime, poverty,
housing, in government the problem of bureaucratic delays and
complexities, in business of work culture and technical deficiencies. It is
not difficult to find problem areas, the difficulty lies in choosing a
problem area where specific problem can be clearly delineated and yet the
problem is not a frequently researched one. This module focuses on the
conditions under which decision -making occurs and the managers use to
clearly define business proble ms and opportunities. Once the researcher
knows what his problem is, he can make a guess or number of guesses.
The guesses he makes are the hypothesis which either solve the problem
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Business Research Methods midpoint of research. From this midpoint one can go back to the problem
and also look forward to the date. If the hypothesis is proved, the solution
can be found. If it is not proved, alternative hypotheses need to be
formulated and tested. The researcher p roceeds to test on the basis of
facts; already known or that can be made known. The most difficult task
in conducting the research is making the research proposal. A research
proposal is the representation of the research work in a short and precise
manner . Research proposal is made to make it clear what the researcher
intends to do in his research. Research proposal is also necessary to be
submitted for approval of the concerned authorities i.e. the guide or the
client or the funding agency etc. This modul e would highlight on defining
and formulating a problem, hypothesis and describe how to structure a
research proposal.
2.2 RESEARCH PROBLEM: MEANING AND
DEFINITION
Primary step in the process of research is selection of a research problem.
A person or grou p of persons having a distinct difficulty with regard to a
phenomena and tries to find solution using scientific method may broadly
be defined as research problem . A problem does not mean that
something is seriously wrong with the current situation that ne eds to be
rectified immediately. A problem could indicate that its solution might
help to improve the situation. Thus, it is useful to define the problem.
Basic researchers define their problems for investigation.
A research problem, in general, refers to some difficulty which a
researcher faces in the context of practical situation and wants to obtain
solution for the same. It is important that systems of problems are not
defined as the real problem. For example, a manager may have tried to
increase produ ctivity by increasing the piece rate, but with little success.
Here the problem may be the low morale, and motivation of employees.
Here, low productivity may be symptom of morale and motivation
problem. Thus, finding the ‘right’ answers to the wrong probl em
definition will not help. Hence, identification of correct problem is
essential for finding the solutions to critical issues.
The researcher needs to have a vast knowledge in the domain that he
wishes to work. Only then, he can analyze the gaps in the p resent situation
and put forward a new proposal. The term problem in its Greek form,
proballein , means anything through forward; anything proposed for
solution; a matter stated for examination. We can visualize this process
like a magician's magic box. Whe never we open the box, it contains
another one. Similarly, the process continues and he takes something very
precious from the innermost boxes. Likewise, in research we start with a
big domain and we narrow it down to smaller pieces and finally gets to the
core and obtains the real research problem.
Northrop during 1966 has explained the research problem in best way.
He stated that ‘inquiry starts only when something is unsatisfactory, when
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Research Problem and
Formulation of Research
Hypotheses to resolve one's uncertainties are not known, when the likely relevant
hypotheses are not even imagined. What one has at the beginning of
inquiry is merely the problem".
Bajpai Naval describes research problem as “It is somewhat information
orien ted and focuses mainly on the causes and not on the symptoms.”
Malhotra Naresh defines it as “A broad statement of the general problem
and identification of the specific components of the marketing research
problem.
Woodworth defines problem as “a situatio n for which we have no ready
and successful response by instinct or by previously acquired habit. We
must find out what to do.”
In simple words, we can say that research problem defines the destination
before starting the journey. It specifies; what to do, how to do, where to do
and what the outcomes are. Thus without a properly defined problem, the
research cannot progress. Formulation of research problem is not just
finding a topic within our interest. It is the remodeling, reshaping or even
reconstructio n of facts, theories or hypothesis. The problem thus
formulated should also be compact for data collection and analysis. The
sole aim of problem definition is creation of research question and
creation of hypothesis. We can summarize the goal of formulatin g
research problem as the method of generating measurable, well -defined,
directed and in -scope research questions for creating desired hypothesis.
There are certain guidelines that need to be followed while formulating a
research problem. The single statem ent that needs to be in every
researcher’s mind is that there is no short cut to the research -it is only the
hard work and determination that gives you the perfect result. Many
scholars hastily skip the primary step of problem formulation, which
makes them to face difficulties in later stage.
The definitions of problem given by some other notable authors are:
“To define a problem means to put a fence around it, to separate it by
careful distinctions from like questions found in related situations of
need.” -Whitney
“A problem is a question proposed for a solution generally speaking a
problem exists when there is a no available answer to same question. ” -
J.C. Townsend
“A problem is an interrogative sentence or statement that asks: What
relation exists between two or more variables?” -F.N. Kerlinger
“To define a problem means to specify it in detail and with precision each
question and subordinate question to be answered is to be specified, the
limits of the investigation must be determined. Frequently, it is n ecessary
to review previous studies in order to determine just what is to be done.
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Business Research Methods theory on which the investigation is to be based. If certain assumptions are
made, they must be expli citly noted.” -Monero and Engelhart
From the above discussion, it can be concluded that problem definition is
a clear and precise statement of the questions that are to be investigated.
Problem definitions may pertain to:
1. Existing business problem that is to be solved by the manager.
2. Situations (currently not creating problem) that have scope for
improvement.
3. Areas where conceptual clarity is needed.
4. Situations in which researcher is trying to answer a research question.
The first two cases r elate to applied research while remaining two cases
come under basic research.
2.3 IMPORTANCE OF PROBLEM DEFINITION
Defining a research problem properly is a prerequisite for any study and is
a step of the highest importance. Generally, we all hear that a problem
clearly defined is a problem half solved. This statement signifies the need
for defining a research problem. While defining the problem, it should be
noted that definition should be unambiguous i.e. must be defined clearly.
Example of an ambiguous definition: “Why is the productivity in China
is much higher than that in India?”
In this type of question, a number of ambiguities are there, such as:
i. What sort of productivity is to be specified; is it men, machine,
materials?
ii. To which type of industry is the productivity related to?
iii. In which time -frame are we analysing the productivity?
Example of an unambiguous definition: “What are the factors
responsible for increased labour productivity in Chinese mobile
manufacturing industries during 2010 -15 relat ive to Indian mobile
industries?”
The significance of clearly defined problem can be judged as under:
1. A proper definition of research problem will enable the researcher to
be in the right direction whereas ill -defined problem may create
hurdles.
2. A well -defined problem gives the answers to the questions like
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Research Problem and
Formulation of Research
Hypotheses b. Which appropriate techniques are to be used to generate alternative
solutions ?
c. What relations, among variables, are to be explored?
d. What kind of study is required?
3. A well-defined problem provides basic for research design.
4. Problem definition facilitates research process.
5. A problem definition indicates a specific managerial decision area that
will be clarified by answering some research questions.
As a matter of fact , formulation of a problem (problem defining) is more
essential than its solution.
2.4 SOURCES OF RESEARCH PROBLEM
One of the initial tasks is to look for the unresolved questions. There are a
lot of potential problem areas that require the immediate atte ntion of the
researcher. If problem has not been identified in right manner, it is very
difficult for researcher to find the right solution of the issue. A researcher
may find a potential research problem through any of these sources:
(i) Personal, Profes sional and Academic Experience: An individual's
personal, professional and academic experience is one of the best sources
of problem. It generates greater interest on the part of the researcher
because he has encountered it at some point in his life and re alizes the
importance of solving the problem. Academic experience helps the
researcher to develop critical thinking towards happenings.
(ii) Review of Literature: It is another good source to look for research
problems. By going through research studies/ w orks of others, researcher
identifies the new dimensions of studies. Many studies have a special
section detailing the possibilities of further research. Thus it is important
that a researcher goes through literature in his area of interest.
(iii) Confere nces and Seminars: One can also get good information
about potential research problem by attending conferences, seminars etc.
Generally such events involve a discussion by experts on problems and
issues relevant to the conference. After having brainstormin g and
intellectual discussions, researcher get to know many new aspects related
to specific aspect of the study.
(iv) Imagination and Creativity: These are the best brainteasers. They
guide an individual to newer hitherto unexplored areas. It empowers the
individual to perceive the routine things in a different perception and look
for new answers to existing accepted solutions.
(v) Technical and Social Changes: These changes are constantly
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Business Research Methods technology changes overnight and so do the expectations of the users.
Hence, these developments offer a good source of research opportunities.
Apart from above, some other sources may by intuition of researcher,
consultations with experts, and daily experiences of life and field
situations observed by researcher.
These sources throw open numerous potential possibilities of research.
However not every problem qualifies for research. Hence the selection of
the problem is a very careful and well thought out process.
2.5 CRITERIA OF DETERMINING A RESEARCH
PROBLEM
Like musical appreciation, choice of the problem depends upon one's own
taste. It should be a ‘problem’ which researcher faces and wants to find
solution. If the problem springs from others and t he researcher wants to
find solution then identification or belonging will not be there. However,
now days, either in business, or in governmental sector, the research work
is carried out for the specific problems set for specific purposes. In such
cases t he problem is not researcher's ‘own’ but organizational. Here
researcher is only an employed person and personal likes and dislikes are
not counted.
Goode and Hatt enumerate the following criteria for the selection of a
research problem:
(1) The Researche r's interest, intellectual curiosity and drive
(2) Practicability
(3) Urgency of the problem
(4) Anticipation of the outcomes
(5) Resources, training, expertise, availability of resources and facilities
etc.
However, to generalize it, the criteria for fo rmulation of good research
problem out of identified problems can be grouped into:
1. Internal criteria
2. External criteria
2.5.1 Internal Criteria
a. Interest and Curiosity of Researcher: The problem should be
captivating and interesting enough to hold the interest of the researcher.
Without interest in problem, it becomes very difficult for researcher to
sustain continuity in research and it will not be able to reach its logical
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Research Problem and
Formulation of Research
Hypotheses b. Resources of Researcher: Research requires a lot of time and money.
The researcher should take care that he has the financial resources
required to undertake the research. If researcher does not have enough
money and he is unable to manage external finance, researcher should not
go for research. Further, research requires more time; he should carefully
evaluate the time schedule required to complete the project and make sure
that he has the ability and resources to complete it with in the specified
time.
c. Researcher's Abilities: A mere interest in research will not work. The
researcher must be competent to plan and carry out a study of the problem.
The researcher should choose a problem that is within his capabilities.
Researcher shou ld have relevant knowledge of subject matter, relevant
methodology and statistical procedures. He should be able to carry out the
research confidently and get the required cooperation and guidance from
the right sources.
2.5.2 External Criteria
a. Unambiguit y and Research -ability: Too narrow or too vague
problems should be avoided. To be researchable, a problem must be one
for which observation or other data collection in real world can provide
the answer. The problem must be clearly stated and expresses rela tionship
between variables succinctly e.g. ‘Studying the relationship between
family conditions and performance of students’ is a poorly stated problem.
In this problem the term 'family conditions' is very vague and does not
specify the meaning of family c onditions.
b. Importance and Urgency: Issues requiring investigation are
unlimited but available research efforts are very much limited. Therefore,
relative importance and significance of problem is required. The outcome
or the results of the research should find an application in solving a
problem being faced by the society, organization or a government.
Important and urgent issues should be given priority over an unimportant
one. The problem should contribute towards the existing knowledge
database.
c. Novelty and Originality: A problem on which a lot of research work
has been done should not be considered for research. A researcher should
keep in mind is that a good research problem is original and is not a
duplication of existing work. Duplication does not mea n that past studies
cannot be researched. A researcher may work on existing studies but with
the objective of re -interpreting the known findings.
d. Feasibility: Novelty of the problem is, not sufficient. If it is not
feasible to conduct the study on problem in real world means existence of
facts is not there. Even to the novel problems, we should make a small
feasibility study first and proceed only after this if study allows.
e. Facilities Available: Well -equipped library, proper guidance, data
analysis etc. are basic facilities which are required to carry on any
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Business Research Methods f. Research Staff: Availability of adequate research personnel like
investigators and research officers is very important for data collection,
which is a major issue in many developing countri es like India
The researcher must be aware of three principle components of research -
problem:
(1) What the researcher wants to know?
(2) Why a particular problem is being put?
(3) Possible, alternative solutions to the problem.
2.6 MANAGEMENT DECISION P ROBLEM VS.
MANAGEMENT RESEARCH PROBLEM
The management decision problem pertains to the decision makers in
which there is ambiguity in the mind of decision makers. It asks what the
decision makers need to do. Whereas, the management research problem
is the quest for searching the solution. It asks what information is needed
and how it can be obtained effectively and efficiently. For making the
sound decisions, the necessary information can be provided by research.
The management decision problem is action or iented. It is concerned with
the possible actions the decision makers can take. For example, how can
the loss of market share be coped? How the market can be segmented
differently? Whether a new product can be introduced? Is there any need
to increase the promotional budget? In comparison to this, the
management research problem is information oriented. Whereas, the
management decision problem focuses on symptoms, the management
research problem focuses on underlying causes.
For better understanding of conc ept, let’s have an example. There is the
loss of market share for a particular product line in a company. The
decision maker’s decision problem is how the company recover this loss
and gain market share again. The various alternative courses of action may
include changes in existing product line, launching new products,
changing other components of the marketing mix and segmenting the
market differently. Suppose the decision maker and the researcher agrees
upon that this problem is the result of inappropria te segmentation of the
market and want research on this issue to provide more information. Then,
the management research problem would become the identification and
evaluation of an alternative basis for segmenting the market. Note that this
process requir es much interaction, in the sense that both parties critically
evaluate, develop and defend each other’s ideas to clarify the nature of
decision and research problems, and to ensure there is a clear and logical
connection between them. The following exampl e will give clearer
distinction between the management decision problem and the
management research problem. It also illustrates the interactive nature of
identifying the management decision problem and the research problem,
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Research Problem and
Formulation of Research
Hypotheses ABC Bank : It has been observed that our market share is declining in India
in corporate banking.
Researcher : Only India?
ABC Bank : No, but the majority of our business is there in India and the
decline of share in that ter ritory is causing us the greatest amount of
concern.
Researcher : Do you have any idea regarding the reasons behind losing
market share?
ABC Bank : We wish we knew!
Researcher : What about your competitors? How are they coping?
ABC Bank : We doubt that many ot her Indian banks are facing same
problem, and the multinational banks are capturing market share.
Researcher : What is the feeling of your consumers regarding the quality
of services you deliver?
ABC Bank : Last year only, we have received the prestigious qu ality
certification ‘ISO:9000’ from an international agency, which we are proud
of!
Researcher : But how does your service delivery compare with your
competitors?
After such lengthy and sequence of discussions with key persons, analysis
of secondary data an d intelligence sources within the bank and from other
sources, the problem was identified as follows:
Management Decision Problem: To improve the relationship experience
with clients at all contact points, in order to arrest the decline in market
share of ABC Bank.
Management Research Problem: To determine the relative strengths and
weaknesses in terms of relationship experiences of ABC Bank, vis -à-vis
other major domestic and international competitors in India. It will be
carried out with respect to factor s that influence a corporate’s choice of a
bank to handle its transactions.
However different, the management decision problem need to be closely
linked to the management research problem. A conceptual map (as
diagram given below) is a good way of linking the broad statement of the
decision problem with the research problem. munotes.in

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Business Research Methods

A conceptual map involves the following three components:
Management decision maker wants to ( take an action )
Therefore, we should study ( topic )
So that, we can explain ( question ).
The first line states the rationale for the question and the project which is
the management decision problem. The second line of the conceptual map
declares what broader topic is being investigated. The third line implies
the qu estion being investigated – the who/ how/ why that needs to be
explained. Thus, the second and third lines define the broad management
research problem. An example of the conceptual map for the study of
affluent class, assuming that the US luxury brand Calvin Klein was
developing marke ting strategies to develop its brand in India is as follows:
Management decision maker wants to ( deliver differentiated in -store
customer experiences for particular types of affluent class individuals )
Therefore, we should study ( ways to segment different types of affluent
class individuals in India ) munotes.in

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Research Problem and
Formulation of Research
Hypotheses So that, we can explain ( the essential demographic, geograp hic,
psychographic, behavioural and psychological factors that could shape
differentiated in -store consumer experiences for luxury goods and
services ).
The above example provides valuable definitions of the management
decision problem and the broad management research problems that are
closely linked. The problem now looks upon a research approach and
research design that will generate understanding and measurements of
different types of affluent class individuals in India. This distinction and
linkage between the management decision problem and the management
research problem helps us in understanding how the marketing research
problem should be defined.
2.7 PROBLEM IDENTIFICATION PROCESS
The problem identification process invariably starts with the decision
maker and some difficulty or decision dilemma faced by him. This is
action oriented problems that answer the question of what the decision
maker shou ld do. Sometimes, the problem might be related to actual and
current difficulties faced by the manager (applied research) or gaps
experienced in the existing body of knowledge (basic research). The broad
decision problem has to be narrowed down to informat ion oriented
problem which focuses on the information required to arrive at any
meaningful conclusion. Management decision problem will give way to
the management research problem.
Once the audit process of arriving at management decision problem is
over, the researcher now focuses and identifies the issues of concern,
which needs to be investigated further, in the form of an unambiguous and
clearly -defined research problem. Identifying all possible dimensions of
the problem might be a monumental and imposs ible task for the
researcher. The researcher must be able to isolate the underlying issues
from the symptoms of the problem. Researchers can make two common
errors in problem identification. The first, when the research problem is
defined too broadly. A bro ad definition does not provide clear guidelines
for the subsequent steps involved in the project. The second error is just
the opposite: the research problem is defined too narrowly. A narrow
focus may preclude consideration of some courses of action, part icularly
those that are innovative and not obvious...
The research problem can be stated in two ways:
(a) As an interrogative statement, or
(b) As a declarative statement
Identification of a research problem involves several interrelated, which
can be des cribed as follows:
1. Ascertaining decision -Maker's objectives
2. Understand the background of the problem munotes.in

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Business Research Methods 3. Isolating and identifying the problem not the Symptoms
4. Determining the unit of analysis
5. Determining the relevant variables
6. Stating the research questio ns and research objectives.
1. Ascertaining Decision -Maker's Objectives -The research investigator
must make an attempt to satisfy the objectives of decision maker who
requested the project. Management theorists suggest the decision maker
should express hi s or her goals to the researcher in measurable terms.
However, it is unfortunately said that the decision maker seldom
formulates his objective accurately. Consequently, objectives usually have
to be extracted by the researcher. In doing so, the researcher may well be
performing his most useful services to the decision maker. Often
exploratory research can highlight the nature of problem and help the
managers clarify their objectives and decisions.
Iceberg Principle - Why do so many business research problem s begin
without clear objectives or inadequate problem definitions? Managers are
logical people, and it seems logical that definition of the problem is the
starting point for any enterprise. Frequently, managers and researchers
cannot discover the actual p roblem because they lack detailed information.
Here, Iceberg principle serves as a useful analogy. A sailor in the sea
notices only a small part of an iceberg. Only 10 percent of it is above the
surface of the water, and 90 percent is under the water. Simi larly, the
dangerous part of many business problems, like the under the sea portion
(submerged) of the iceberg, is neither visible to nor understood by
managers. If the submerged portion of the problem is due to problem
definition, the decisions based on t he research may be less than desired.
2. Understanding the Background of the Problem - The iceberg
principle illustrates that understanding the background of a problem is
vital. Often experienced managers know more about a situation, and they
can provide t he researchers with considerable background information
about previous events and why those events happened. Institutions, where
decision maker's objectives are clear, the problem may be diagnosed by
exercising managerial judgment. In other situations, whe re information is
inadequate or managers have trouble identifying the problem, a situation
analysis is the logical first step in defining the problem.
The researcher should develop a conceptual framework of the problem.
The background of the study, underl ying theoretical and conceptual basis
should be carefully understood. The researcher should make a critical
examination of the related studies.
3. Isolating and Identifying the Problem not the Symptoms - The
anticipation of all dimensions of a problem is i mpossible for any
researcher or an executive. For example, a firm may have a problem with
its advertising effectiveness. The possible causes of this problem may be
low awareness, the wrong brand image use of the wrong media, or perhaps munotes.in

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Research Problem and
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Hypotheses low budget allocatio n. Management's job is to isolate and identify the
most likely causes. Sometimes, certain happenings that appear to be ‘the
problem’ may be only symptoms of a deeper problem.
Other problems may be identified only after a research of background
information and after conducting exploratory research. The researcher
should develop a conceptual framework of the problem. The background
of the study, underlying theoretical and conceptual basis should be
carefully understood. How does one ensure that the fundament al problem
has been identified? There is no easy or simple answer to this question.
The researcher should make a critical examination of the related studies.
Executive judgment and creativity must be exercised.
4. Determining the Unit of Analysis - In the next step, the researcher
should state the unit of analysis. It would help him to define the limits of
his study. The researcher must specify whether the level of investigation
will focus on the collection of data about organizations, departments, work
groups, individuals, or objects. The unit of analysis could be an individual,
a social structure like a household or an organization etc. e.g. in studies of
home buying, the husband -wife is one rather than the individual is the unit
of analysis because the pu rchase decision is jointly made by husband and
wife. It must always be kept in mind that the unit of analysis suits our
purpose of study. In another example, in case of a study on vehicle
ownership, a household can be a unit of analysis.
5. Determining the Relevant Variables - The researcher should now
determine the essential and relevant variables. A Variable is defined as
anything that varies or changes in value. It is something that can be
observed, manipulated and changes in value in response to certain stimuli.
Because a variable represents a quality that can exhibit differences in
value, it may be said that a variable is anything that may assume different
numerical or categorical values. The variable that is to be predicted or
explained called as the d ependent variable and the variable that influences
the dependent variable is called as the independent variable. The research
may also need to identify the extraneous variables i.e. those variables that
are not directly a part of the study but may influenc e the outcome of the
study e.g., a study on relationship between tuitions (independent variable)
and performance (dependent variable) may have IQ as an extraneous
variable. A clear identification of the variables will help in formulating the
correct relati onships and controlling the extraneous variables effectively.
Key variables should be identified in the problem definition stage. To
address the specific problem, managers and researchers should be careful
to include all of the relevant variables that must be studied. Similarly,
variables that are superfluous (i.e., not directly relevant to the problem)
should not be included in the study.
6. Stating the Research Questions and Research Objectives - . Now the
researcher must specify the relationship which in his opinion exists
between the variables. Once the problem to be tackled has been finalized,
the variables identified, the relationship stated, the researcher should now munotes.in

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Business Research Methods formulate a tentative solution to the problem. Both managers and
researchers expect t hat problem solving efforts should result in statement
of research question and research objectives. Depending on the purpose of
the study the researcher has to decide which relationship would form a
part of the study and which are to be ignored. A well th ought decision will
help the researcher in having a precise set of objectives and the resources
would be optimally utilized on the stated objectives.
7. Developing a Hypothesis: The last stage is of developing a hypothesis.
These proposed solutions are cal led as hypothesis, which the researcher
would proceed to formulate on the basis of facts known or collected by
him. Whether these relationships are scientifically maintainable or not,
will be decided after the researcher collects and analyses his data. In case
of studies, which are not hypothesis -testing, the researcher may frame a
set of research objectives. After the formulation of the working
hypothesis, the researcher is now ready to prepare the research proposal.
A researcher should take care that the problem chosen is not an excuse for
the personal enlightenment of the researcher i.e. is not being used to
enhance the knowledge of the researcher alone. While identification he
should take care that it is not too narrow or broad in its scope and matches
with his experience, qualifications and resources.
2.8 COMPONENTS OF THE RESEARCH PROBLEM
A research problem, in general, refers to some difficulty which a
researcher experiences in the context of either a theoretical or practical
situation and wants to ob tain a solution for the same. Once the topic
for research is selected, research can not immediately be started unless the
specific research problem is formulated. It is investigated by scientific
methods. Needless to say that the formulation of enquiry mus t recognize
some difficulty whether it is practical or theoretical.
R. L. Ackoff in his work discussed about the following five most
important components of research problem:
1. Research Consumer
2. Research -Consumer’s Objective
3. Alternative Means to Meet the Objective
4. Doubt in Regard to Selection of Alternatives
5. There must be one or More Environ ments
1. Research Consumer: It is first and most important component of
research problem. There must be individuals, groups or organisations
which have some difficulty or problem. The individuals, groups or the
organisations themselves may be researchers. There are other participants
in the problem. All are affected by the decision on the part of the research munotes.in

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Research Problem and
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Hypotheses consumer. The individual or the organisation, as the case may be , occupies
an environment, which is defined by values of the uncontrolled variables.
2. Research -Consumer’s Objective: There must be some objectives to
be attained as the research consumer must have something he wants to get
it. It one wants nothing, one c annot have a problem. There must be at
least two courses of action to be pursued. A course of action is defined by
one or more values of the controlled variables.
3. Alternative Means to Meet the Objective: There must be alternative
means or the courses o f action for achieving the desired objective. Means
are courses of action. A course of action may involve the use of various
objects. Objects are the instruments. This means that there must be at least
two means available to a researcher. He has no choice or means, he cannot
have a problem. Of the two possible outcomes of the course of action, of
which one should be preferable to the other. In other words, this means
that there must be at least one outcome that the researcher wants, i.e., an
objective.
4. Doubt in Regard to Selection of Alternatives: The existence of
alternative courses of action is not enough. To experience a problem, the
researcher must have some doubt as to which alternative to select. Without
such a doubt, there can be no problem. This m eans that research must
answer the question concerning the relative effi ciency of the possible
alternative. The courses of action available must provide some chance of
obtaining the objective, but they cannot provide the same chance,
otherwise the choice would not matter. In simple words, we can say that
the choices must have unequal efficiencies for the desired outcomes.
5. There must be one or More Environ ments: There must be some
environments to which the difficulty or problem pertains. A change in the
environment may produce or remove a problem. A researcher may have
doubts as to which will be the most efficient means in one environment
but may entertain no such doubt in another. Some problems are quite
general. Thus, a research problem is one which re quires a researcher to
find out the best solution for the given problem i.e., to find out by which
course of action the objective can be attained optimally in the context of a
given environment.
There are several factors which may result in making the prob lem
complicated. For instance, the environment may change affecting the
efficiencies of the courses of action or the values of the outcomes; the
number of alternative courses of action may be very large; persons not
involved in making the decision may be a ffected by it and react to it
favorably or unfavorably, and similar other factors. All such elements (or
at least the important ones) may be thought of in context of a research
problem.
Over and above these conditions, the individuals or the organisations can
be said to have the problem only if they does not know what course of
action is best, i.e. they must be in doubt about the solution. Thus, an
individual or a group of persons can be said to have a problem which can munotes.in

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Business Research Methods be technically described as a researc h problem, if they (individual, group,
the organisation), having one or more desired outcomes, are confronted
with two or more courses of action that have some but not equal efficiency
for the desired objective(s) and are in doubt about which course of act ion
is best.
2.9 SELF ASSESMENT QUESTIONS
1. Fill in the blanks with appropriate words:
(a) If you have a specific idea about what is to be researched you should
formulate ……………………

(b) Of all the steps in the research process, the one that typically takes the
most time is formulating ……………….

(c) Developing a researchable question would ……………..
(involve/not involve) deciding what statistical software to use.

(d) R. L. Ackoff in his work discussed about the …… most important
components of research problem

(e) The research pr oblem can be stated in two ways: one is as an
interrogative statement and other is as a ……………. Statement.
2. State true or false for the following statements:
(a) The formulation of a research problem is the most crucial part of the
research journey as the qua ntity and vanity of the project entirely
depends upon it.

(b) A variable that changes due to the action of another variable is
known as the independent variable

(c) The research problem determines what methodology will be used.
(d) Knowledge of the subject area help s when developing a research
question.
(e) A research problem is feasible only when it is researchable, new and
adds something to knowledge and has utility and relevance.
3. Answer the following:
(a) Describe why a research problem is the heart of the research
process?
(b) Do you agree that formulating research problem should be the first
thing to do in undertaking a research project? Discuss.
(c) Elaborate in detail the various steps of research problem
identification? munotes.in

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Research Problem and
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Hypotheses (d) Write an essay on the various components of research problem?
How they help in better definition of research problem?
(e) What are the major criteria for selecting a research problem?
2.10 RESEARCH HYPOTHESIS: MEANING AND
DEFINITION
When a researcher observes known facts and takes up a problem for
analysis, he first has to start somewhere and this point of start is
hypothesis. The term hypothesis has been derived from the ancient Greek,
hypotithenai , meaning “to put under” or “to suppose”. The development of
hypothesis is a very vital aspect of a research. A res earch work without
hypothesis is a rare phenomenon. It gives direction and focus to the
research. It refines the process research. Without the hypothesis, research
is unfocussed and random empirical wandering. When a research problem
is articulated a resea rcher will have in his mind a tentative generalisation
about possible outcome of the research. This tentative generalisation may
be proved or disproved based upon the analysis of data or material that is
processed for research. This is generally called the hypothesis . Bachrach
opined, "A researcher observes an event, wonders about it, formulate
some tentative ideas about it, and sets out to test the accuracy of his
ideas." While giving provisional answer to the problem, the researcher
tries to establish rel ationship between two variables.
In other words, one has to proceed to formulate tentative solutions as soon
as the problem to be talked is finalised. These proposed solutions or
explanations constitute the hypothesis which the researcher proceeds to
test on the basis of facts; already known or that can be made known. Even
collection of facts merely for the sake of collecting such that they are for
or against some point of view of proposition. Such a point of view or
proposition ( statement ) is the hypothes is.
The dictionary meaning of hypothesis indicates that, ‘Hypo’ means ‘less
than’, 'Thesis' means ‘generally held view’, so ‘Hypothesis’ means '’less
than generally held view’.
The definitions of hypothesis given by several authors are as follows:
Accordi ng to Moshin , “Hypothesis is a conjectural statement about a
relationship among two or more variables.”
According to George A. Lundberg , “A hypothesis is a tentative
generalisation, the validity of which remains to be tested. In its most
elementary stage, the hypothesis may be very hunch, guess, imaginative
data, which becomes the basis for action or investigation.”
According to Goode and Hatt , “a hypothesis looks forward. It is a
proposition which can be put to a test to determine its validity. It may
seem contrary to, or in accord with common -sense. It may prove to be
correct or incorrect. In any event, however, it leads to an empirical test.” munotes.in

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Business Research Methods According to Webster's New International Dictionary, “A hypothesis is a
proposition, condition or principle which is assumed, perhaps without
belief, in order to draw out it its logical consequences and by this method
to test its accord with facts which are known or may be defined.”
Comprehensively, we can explain hypothesis as “a proposition or a set of
propositions set forth as explanation for the occurrence of some specified
group of phenomena either asserted merely as a provisional conjecture to
guide some investigation or accepted as highly probable in the light of the
established facts. Quite often a research hyp othesis is a predictive
statement, capable of being tested by scientific methods, that relates an
independent variable to some dependent variable.”
Hypothesis and Research Question
All the research works start with tentative generalisations. These
generali sations may be in the form of either research question or
hypothesis. A research question and hypothesis are similar in nature
except for the aspect that a research question does not predict the outcome
of the research where as a hypothesis predicts the ou tcome. Research
questions are generally used in the exploratory research or in the areas
where a phenomenon is studied marginally. The research is carried to
know indications rather than causality. Hypothesis is tentative, testable
generalisation regarding the relationship between variables. For e.g.,
advertising & increase in sales.
Relationship between Theory and Hypothesis
Formulation of deductions from the existing theory constitute hypothesis.
If these deductions are proved they become part of theory . According to
William H. George, “theory is an elaborate hypothesis”. Every worthwhile
theory permits the formulation of hypothesis. Hypothesis is necessary link
between theory and investigations, which leads to discovery of addition of
knowledge.
2.11 SOURCES OF HYPOTHESIS
According to Goode and Hatt, sources of hypothesis are as under:

(1) General Culture: The general culture in which a science develops
furnishes many of its basic hypotheses. Hypothesis develops based upon
the researcher's attention w hich generally will be influenced by cultural
values. In India, for example - religion and custom dominate the way of
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Research Problem and
Formulation of Research
Hypotheses in various walks of life. Such a situation could give rise to any number of
sociological , cultural, political and economic hypotheses .
(2) Scientific Theories : Hypothesis originates in science itself. The
history of science provides testimony to the fact that personal experiences
of the scientist contributes a great deal to the type and form of questions
he may ask as also to the kinds of tentative answers to these questions,
(hypothesis) that he can provide. Science is twined with values and it
influences the tentative generalization. Learning experience always
influences the sketching of the hypothesis. Socialization process in
learning a science also affects the hypothesis which will be developed by
the scientists.
(3) Analogies: Analogies are often a spring of valuable hypothesis.
Students of sociology in the cour se of their studies would have come
across analogies wherein a society is compared to a biological organism ,
the natural law to the social law, thermodynamics to social dynamics, etc.
Julian Huxley opined that casual observations in nature or in the
framew ork of another science may be a fertile source of hypothesis.
(4) Personal Experience : Personal experience and individual reaction
may give rise to hypothesis. For example - Mr. Lambrosa - developed the
concept ‘In born criminal type’ when he was working wit h soldiers.
Hypothesis
5) Another Sources:
I. “Cases which are exceptions to accepter theory” is also the source for
formulating hypothesis.
II. Another source of hypothesis is folk (people in general) wisdom or
current popular beliefs (faith) and practices sug gesting both the
problems and the hypothesis. It is also consequences of personal,
idiosyncratic experiences.
III. Hypothesis may also rest on the findings of other studies . Comparative
studies are energetic sources of research.
IV. Theory is indeed and extremely f ertile seed bed of hypothesis. It gives
direction to research by stating what is known. Logical deduction from
the theory leads to new problems.
2.12 CHARACTERISTICS OF GOOD HYPOTHESIS
All hypotheses are not equally helpful or workable to the enquiry and
some are perhaps not at all. The researcher therefore has to separate the
more from the less useful ones. A good usable hypothesis is the one which
satisfies many of the following criteria:
(1) Relevant and Available Technique - An investigation to be practi cal
should relate the hypothesis to the investigational techniques feasible in
the particular discipline. This requires, in the researcher, a sound munotes.in

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Business Research Methods knowledge of techniques to test the hypothesis and thus, to formulate
practical questions.
(2) Conceptual C larity - It is advisable that the concepts embodied in
hypothesis be clearly defined in a manner commonly accepted and
communicable. The conceptual clarity is required in both formally as well
as operationally. It should be operationally defined.
(3) Closest to Things Observable —Hypothesis should be closest to the
things observable. Failing this, it would not possible to test their accord
with empirical facts. Hypothesis must be an adequate answer to the
problem. It must be compatible with the current knowled ge in the area
concern.
(4) Brevity -The hypothesis should be brief so that its observation may be
possible. It would help the better understanding of the underlying concept
and meaning. It should be precise.
(5) Empirically Testable (Easy to experience) - A hypothesis should be
capable of being tested, have empirical references . It should be so stated
that it is possible to deduce logically certain inferences from it, in which
term can be tested by observation in the field.
(6) Specific in Nature - The hypot hesis must be specific in nature and
precise i.e. it must help in detail all the operations and predictions
connected with it in the process of investigation. It should not involve the
investigator into unnecessary roaming about and discussion.
(7) Relate d to a Body of Theory - The hypothesis should be related to a
body of theory or some theoretical orientation. If hypothesis is related to
some theory, research will help to quality, support, correct, or refute
(reject) the theory. It should establish relati onship between variables.
(8) Simple - The hypothesis should be simple to the point. Insight of the
phenomenon is essential for simplicity. According to P. V. Yong , ‘The
more insight the researcher has into the problem, the simple will be the
hypothesis abo ut it.’ The better hypothesis is the simple one requiring
fewer conditions or assumptions.
2.13 FORMULATION OF RESEARCH HYPOTHESIS
Hypothesis formulation is mostly related to causal research or empirical
research. Usually, exploratory and its similar resea rches are exceptions,
where a hypothesis is not a necessity. But, in most researches, the
researcher has an ‘intelligent guess’ about the outcome of his research.
This ‘guess’ paves way to a formal hypothesis. However, hypotheses are
not unique to research . Hypotheses are constantly generated in the human
mind as we work to understand day -to-day phenomena. By formulating a
series of reasonable guesses of cause and effect, we are able to understand
and explore the events in our surrounding environment. Hypot hesis
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Hypotheses Formulation of the hypothesis basically varies with the kind of research
conducted. Causal studies tend to study the relationship that exists within
the variable while qualitat ive studies tend to generalize upon the subject.
Qualitative studies are characterized by:
 Use of words - what, how.
 Use of non -directional wording in the question.
 The questions are under continual review and reformulation - will
evolve and change during s tudy.
 Use of a single focus.
On the other hand, quantitative research is characterized by:
 The testable proposition to be deduced from Theory.
 Independent and dependent variables to be separated and measured
separately.
Based on above explanation, rese archer has to use two logical
approaches to drawn upon in developing a hypothesis. The processes are
known as:
a) Deductive Approach, and
b) Inductive Approach.
For qualitative studies 'deductive approach' is more useful in formulation
of a hypothesis. The deduc tive method tries to establish a pattern after
observation and formulate a tentative hypothesis (which gets modified as
the research progresses) and eventually leading to theory. Archimedes
observation that there is a rise in the level of water, when an ob ject is
immersed in water, let him to formulate a hypothesis that ‘there is a
change in the level of water when a body is immersed in water.’ Further
observation made him refine the hypothesis from change in the level of
water to 'volume of water dispersed '. And finally after this hypothesis was
found to be true for different objects, the hypothesis was established as a
theory.
In quantitative studies, an ‘inductive approach’ is used for hypothesis
formulation. The inductive method contrary to the deductiv e method starts
from an established theory and the hypothesis is formulated prior to the
observation and the hypothesis is, consequently, confirmed, e.g., the
Demand theory states that the demand for a product or service is related to
its price. A research er is interested in knowing if the theory of demand
holds true for a market segment of quality conscious upper -middle class
consumers. He will use the inductive method in formulating the
hypothesis. Figure given below represents the two approaches to the
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Business Research Methods

Based on the above research approaches, there are two methods of
hypothesis formulation:
1. Qualitative
2. Quantitative
Qualitative Methods
It is very frequent in the use of research questions against the objectives.
The questions mainly start with what or how and seek to explore or
describe the experiences. These questions describe to compare groups or
variables. The questions under continual review are revised at the time of
study. Usually, questions are open ended which will not refer any
literature or theory. The qualitative methods include naturalistic inquiry
(e.g. study real -world situations), emergent design flexibility (e.g. avoid
rigid designs) and purposeful sampling (e.g. case studies with
organizations or people). Selec ted characteristics of qualitative research
methods are as follows:
 It is an effort to understand situations in their uniqueness as part of a
particular context and the interactions there.
 The researcher is the primary instrument for data collection and
analysis.
 Qualitative research involves fieldwork for data collection.
 The uses of a deductive research strategy and the research build
abstractions, concepts, hypothesis or theories rather than tests existing
theory.
Quantitative Methods
It is very frequ ent of getting research questions in the objectives of survey
projects. The use of hypothesis is also more frequent in quantitative
experimental research in variables. The comparison and relationship
between variables are represented in experiments. Theori es are useful to
deduce testable propositions. Dependent and independent variables are
separated at the experiments and measured separately. Combinations of
objectives and hypothesis are not considered. Make an alternate forms of
experiments crated to focu s to the audience of research. It is analysed
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Research Problem and
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Hypotheses Selected characteristics of quantitative research methods are:
 This method emphasize on collecting and analysing information in the
form of numbers.
 It emphasis on collecting scores that calculate distinct attributes of
people and organizations.
 This method emphasizes on the procedures of comparing groups or
relating factors about people or groups in experiments, correlation
studies and surveys.
2.13.1 Pr ocess of Formulating Research Hypothesis
The process of formulating a research hypothesis is usually made up of
two phases.
Phase 1: Addressing Primary Problems
Phase 2: Phrase the Hypothesis
Phase 1: Addressing Primary Problems
In the first phase, the res earcher has to establish the main issues that
should be addressed in the hypothesis. It is in this phase that the researcher
decides on the variables or other phenomena that should be addressed in
the hypothesis. In other words, a researcher has to refer t o the primary
problems that are supposed to be addressed in the research when
managing this part of the process of formulating hypothesis.
Phase 2: Phrase the Hypothesis
In the second phase, the researcher has to phrase the hypothesis in a
language that i s logical, complete, and reflective of the theoretical
foundations of the research. A language is regarded as logical when it
clearly elucidates the main issues of concern. In this case, it makes sense
for a research hypothesis to be framed in a language t hat clearly states the
proposed relationship among variables. For example, if a researcher
frames the hypothesis in a superfluous language, then the researcher may
note easily test it. Similarly, it is important for the researcher to clearly
identify the r elationship that they intend to test in the hypothesis.
These two phases of the process of formulating a research hypothesis have
to be adequately addressed for the entire research process to be completed
successfully.
2.13.2 Generation of Research Hypothe sis
The normal approach is to set two hypotheses instead of one, in such a
way, that if one hypothesis is true, the other is false. Alternatively, if one
hypothesis is false or rejected, then the other is true or accepted. Figure
given below shows how a r esearch hypothesis is formulated. Take an
example, a hypothesis is formulated with two -sample variable association
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Business Research Methods 1. If there is an association between x and y, then "x and y are associated".
2. If x is dependent on y, then "y is related to x".
3. Increase in the values of x appears to result fall in value of y, then "x
increases, y decreases".
From this scenario, we have different hypothesis formulations from the
two variables. We can formulate three different hypotheses:
1. A simple stat ement of association between two variables x and y. There
is no indication in the association of variables x and y that cause change
in any other variable.
2. A simple statement of association between two variables x and y. It is
the conditional of the va lues of y and contingent upon the condition of
the variable x.
3. Consider a relation between variables x and y with reference to its
values. The values may be depending on nature of association between
the variables.

Difficulties in Formulating Hypoth esis
Goode and Hatt observed that the researcher often suffers from the
following deficiencies which pose problems in formulating good, definite
and testable hypothesis.
(1) Lack of knowledge of scientific methods
(2) Lack of a clear theoretical framework.
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Research Problem and
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Hypotheses (4) Failure to be acquainted with available research techniques resulting
in inability to phrase the hypothesis properly.
2.14 TYPES OF RESEARCH HYPOTHESIS
Theoretically, there should be only one ty pe of hypothesis that is the
research hypothesis - the basis of any investigation. However, because of
the conventions in scientific enquiries and because of the wording used in
the construction of a hypothesis, hypotheses can be classified into several
types. These are as follows:
Simple Research Hypotheses: Simple research hypotheses predict the
relationship between a single independent variable and a single dependent
variable.
Complex Research Hypotheses: Complex hypotheses predict the
relationship betwee n two or more independent variables and two or more
dependent variables.
Directional Hypotheses: If, in stating the relationship between two
variables or comparing two groups, terms such as positive, negative, more
than, less than, and the like are used, t hen these hypotheses are directional
because the direction of the relationship between the variables
(positive/negative) is indicated (e.g. The greater the stress experienced in
the job, the lower the job satisfaction of employees ), or the nature of the
difference between two groups on a variable (more than/less than) is
postulated (e.g. Women are more motivated than men ).
Non-directional Hypotheses: These hypotheses do postulate a
relationship or difference, but offer no indication of the direction of the se
relationships or differences. In other words, though it may be conjectured
that there would be a significant relationship between two variables, we
may not be able to say whether the relationship would be positive or
negative (e.g. there is a relationsh ip between age and job satisfaction ).
Likewise, even if we can conjecture that there will be differences between
two groups on a particular variable, we will not be able to say which
group will be more and which less on that variable (e.g. there is a
difference between the work ethic values of American and Asian
employees ).
Associative Hypotheses: Such hypotheses propose relationships between
variables - when one variable changes, the other changes. They do not
indicate cause and effect.
Statistical Hypothe sis: To test whether the data support or reject the
research hypothesis, it needs to be translated into a statistical hypothesis.
It is given in statistical terms. In the context of inferential statistics, it is
statement about one or more parameters that are measures of the
population under study. To use inferential statistics, we need to translate
the research hypothesis into a testable form. A testable hypothesis
contains variables that are measurable or able to be manipulated. It can be
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Business Research Methods 1. Null Hypothesis: The null hypothesis is a proposition that states a
definitive, exact relationship between two variables. That is, it states that
the population correlation between two variables is equal to zero or that
the difference in the mean s of two groups in the population is equal to
zero (or some definite number). In general, the null statement is expressed
as no (significant) relationship between two variables or no (significant)
difference between two groups. It is denoted byH o or H n. Null hypotheses
can be:
 Simple or Complex
 Associative or Causal
2. Alternate Hypothesis: It is the opposite of the null hypothesis, is a
statement expressing a relationship between two variables or indicating
differences between groups. It is denoted by H 1 or H a. It is only reached if
Ha is rejected.
Descriptive Hypotheses: This is simply a statement about the magnitude,
trend or behaviour of a population under study. Based on past records, the
researcher makes some presumptions about the variable under stu dy. For
example, the current advertisement for the soft drink will have a 20 –25
per cent recall rate; the literacy rate in the city of Mumbai is 100 per cent.
Relational Hypotheses: These are the typical kind of hypotheses which
state the expected relatio nship between two variables. While stating the
relation if the researcher makes use of words such as increase, decrease,
less than or more than, the hypothesis is stated to be directional or one -
tailed hypothesis. For example, higher the likeability of the advertisement,
the higher is the recall rate; ban on smoking has an impact on the cigarette
sales. Such hypotheses are of two types viz. Causal or Correlational:
1. Causal or Explanatory Hypotheses: Such hypotheses propose a
cause and effect interaction betw een two or more variables. The variable
which causes or influences change is called independent, causal or
explanatory variable and the other variable which gets influenced is called
dependent variable. The independent variable is manipulated to cause
effect on the dependent variable. The dependent variable is measured to
examine the effect created by the independent variable. For example,
change in sales turnover is caused or explained by change in advertising
expenses.
2. Correlational Hypotheses: Such hypo theses are used when we want
to test whether there is any correlation between two variables. For
example, return on a stock and return on BSE Sensex/ NIFTY; marks in
entrance examination and final MBA grade.
Universal Hypothesis: Based on statistical signi ficance, if a hypothesis is
used to cover all the phenomena, then it is regarded as a universal
hypothesis. It is one, which denotes that, the stated relationship holds for
all specified variables for all times at all places .The universality of the
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Hypotheses between variables under all circumstances and at all times. For example,
sun rises from the east.
Existential Hypothesis: Based on statistical significance, the hypotheses
that are restrict ed to phenomena that meet particular criteria are regarded
as existential. It is one in which the stated relationship is said to exist for at
least one particular case . It is clearly stated that the supposed relationship
between the variables only exists fo r a single case or under specific
conditions which may not necessarily be universal.
Question -based Hypothesis: In this hypothesis, the researcher formulates
a question and then endeavours to answer the question by conducting the
research study. The action that follows depends on whether the researcher
accepts the question which is posed as the hypothesis of the research
study.
Working Hypotheses: While planning the study of problem hypotheses
are formed. Initially they may not be very specific. In such cas es, they are
referred to as ‘working hypotheses’, which are subject to modification as
the investigation proceeds.
Common Sense Hypotheses: These represent the common sense ideas.
They state the existence of empirical uniformities perceived through day
to day observations. Common sense statements are often a confused
mixture of clichés and moral judgments. Researchers have a large -scale
job in transforming and testing them. This requires three tasks . Firstly, the
removal of value judgment; secondly, the cla rification of terms; and
thirdly, the application of validity tests.
Analytical Hypotheses: These are concerned with the relationship of
analytic variables. These hypotheses occur at the highest level of
abstraction. These specify relationship between chan ges in one property
and changes in another.
2.15 RESEARCH PROPOSAL
The next stage after developing the hypothesis is to prepare a research
proposal for submission to the management. By making a research
proposal, the researcher puts himself, his intention s and his ideas in front
of management and also in front of himself for any queries, new ideas,
criticism or improvements in his research project plan. It also helps in
creating the budget estimates -, costs, time etc. statements. When a
researcher needs ap proval and/ or financial support for an intended
research, he prepares all format proposals and submits it to an appropriate
approving/sponsoring authority. It is a bid for undertaking research. The
proposal is the form of a research design, which is the b lue print for
conducting and controlling research. It can also be considered a research
plan or a research projects.
Broadly speaking, a research proposal encompasses the methodology of
conducting the research to solve the formulated research problem. The
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Business Research Methods committee that one is undertaking a good research on to support his aims
and objectives of the research to make the thesis or a dissertation worthy
of it.
A research proposal is a w ritten account of the plan for the research
project. It presents an argument as to why a particular problem should be
investigated and what the appropriate research design is to investigate it. It
sets out what the researcher intends to do - how, why, where , when and at
what cost. For quantitative research, the research proposal is like a pattern
for a garment or a blueprint for a building in that it assists the researcher
to follow a process that has been laid down. For qualitative research, the
research pr oposal is much more flexible because the method tends to
evolve with the research.
Research proposal may be defined as a document that sets out the purpose
of the study and the research design details of the investigation to be
carried out by the researche r.
2.14.1 Objectives/Purposes of Research Proposal
The purpose of a research proposal can be understood as the following:
I) Prevents Distraction from Actual Topic: A thoroughly prepared
research plan or project proposal prevents distraction from the actua l
topic. A research plan also promotes the (continual) fit between the parts
of the research. The research problem, research questions, purposes,
sample, data collection, analysis and reporting should be tuned to each
other.
II) Important For Third Partie s: A research proposal not only has a
function for a researcher but also extremely important for third parties,
like instructors, supervisors, granting organisations, commissioners,
ethical committees and so on.
III) To Convince Others: A research proposa l is intended to convince
others that one has a worthwhile research project and that one has the
competence and the work -plan to complete it.
IV) Focus and Define Research Plans: The purpose of the proposal is to
help to focus and define the research plan s. These plans are not binding, in
that they may well change substantially as one progress in the research.
V) Other Purposes: There are several other purposes of research proposal
which are being discussed below:
 To present the management question to be researched and relate its
importance.
 To discuss the research efforts of others who have worked on related
management questions.
 To suggest the data necessary for solving the management question
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Research Problem and
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Hypotheses  To present the researcher's plan, services and credentials in the best
possible may to encourage the proposal's selection over competitors.
 To help the researcher to define the contents and to plan and execute
his research project.
 To inform potential collaborators and supporters about the topic and
the expected quality of the research.
A research proposal is critical, for it makes the researcher think of the
possible roadblocks on the way and alternate by passes to be taken in such
cases. Another ver y important use of a research proposal is that it helps he
decision -maker and the researcher to arrive at agreements on the problem
with regard to objectives, information required, and the methods of
analysis.
2.16 WRITING A RESEARCH PROPOSAL
While referri ng previous researches, one has to be totally objective and
abstain from any iota of past bias. Only facts and figures are to be
supplied without any comment. Based on these facts and figures and the
developments that have taken place or those that are bei ng envisaged, one
has to justify the relevance and the benefit of the proposal. The emphasis
has to be on ‘Improvement’ that is visualised in future rather than
‘Criticising’ what happened in the past. In fact, one could endeavour to
appreciate the past ef forts and results, while indicating scope for further
improvement in quality or expanding and creating new products/ services/
systems. Use of such appreciative phrases makes the proposal as
‘constructive’. One has to remember that the strategies, their re levance
and effectiveness keep on changing with time; therefore the emphasis has
to be on improvement rather than on criticism.
It is said that a research is as good as one’s proposal. A good quality
proposal, in addition to the increased chances of accept ance by the
concerned authorities also creates a good impression as well as establishes
credibility of the researcher. It is, therefore, necessary to put in best efforts
to ensure high degree of acceptance of the proposal and its smooth
execution.
A resear ch proposal serves the purpose of convincing that the research is
worthwhile and the researcher has the requisite competence and ability to
complete the project as per schedule.
It should reflect good grasp of various issues related to the topic supported
by survey of relevant literature . Accordingly, it should answer the
following questions:
 What is the objective to be achieved?
 What is its relevance and importance?
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Business Research Methods  What is the plan and schedule of completion?
 What are t he scope and limitations?
 What is the extent to which the objectives might be achieved?
2.17 CONTENTS OF A RESEARCH PROPOSAL
The contents of a research proposal differ on the type of research
conducted and proposal given. The proposal can be tailored to su it the
intended audience. The proposal can be structured under the following
heads:
1. Title - The title should indicate the gist or theme of the research.
However, it should be catchy, and should instantaneously arouse the
curiosity and interest of the rea der. This would induce further reading of
the proposal with favourable disposition towards the proposal.
2. Preamble or Executive Summary - The executive summary is an
informative abstract. It highlights the essential points of the proposal.
Executives can understand the meaning without going into detail. The
goal of summary is to secure a positive evaluation by the executive who
will pass the proposal for full evaluation.
3. Statement of the Problem - This section of research proposal clarifies
the manageme nt question (problem) and importance of answering the
management question. In this section, it should also be stated that a
particular area of a management question will not be studied. Be sure that
problem statement is clear without the use of idioms. As a matter of fact,
after reading this section of research proposal, potential sponsor or
researcher should know the actual problem
4. Objective/s of the Research - This section of research proposal states
the objectives of investigation. In a descriptive st udy, the objectives can
be stated as the research question (Investigative question). If the proposal
is for a causal study, then the objectives can be stated as a hypothesis.
It is best to prepare a list of objectives from the problem statement. It will
give real picture of goals to the sponsor. The research objective section is
the basis for selection of proposal.
5. Review of Literature - The literature review section examines recent
researches, company data or industry reports that acts as a basis for t he
proposed study. If the problem has historical background, begin with
earliest references.
Avoid the excessive details; a brief review of the information may be
undertaken. If you find something of interest in a quotation, find the
original publication and ensure you understand it. In this way, you will
avoid any errors of interpretation. Emphasize the important results,
relevant data and trends from previous research, and particular methods or
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Research Problem and
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Hypotheses 6. Benefi ts of the Study - In this section, you describe benefits that will
accrue from your study. The importance of ‘doing study now’ should be
emphasised. If you find it difficult to explain the benefits, then you have
not adequately clarified the problem. You sh ould convince the sponsoring
organisation that your plan will meet the needs.
7. Research Design - The research design describes that what you are
going to do in technical terms. This section should include the phases of
project. Research design includes i nformation relating to sample selection,
size of sample, data collection method, procedures and ethical
requirements etc.
8. Analysis of Data - In this section, you will describe the proposed
handling of data and the theoretical basis for using selected tec hniques.
The objective of this selection is to assure the sponsor that you are
following correct assumptions and sound data analysis procedures.
9. Forms of Results - In this section, the sponsor should be able to know
that goal of study has been covered. One should also specify the types of
data to be collected and interpretations that will be made in the analysis.
This section also contains the contractual statement telling the sponsor
about the type of information to be received.
10. Researcher’s Brief: When hiring a contract researcher, this element
of proposal should be taken into consideration. Two elements are critical
in this regard:
 Professional Research Competence (relevant research experience,
the highest academic degree held, and membership in b usiness and
technical societies).
 Relevant Management Experience
There are so many individuals and research firms providing research
services. Therefore, it is essential that researchers should be professionally
competent. Past research experience is the b est barometer of competence,
followed by the highest academic degree.
11. Budget - This section will include the statement of proposed expenses
and financial liabilities. Budget statement in an internal research proposal
is based on employee and overhead c osts. However, in case of external
research, budget is decided on the basis of charges to be charged by
contracting firms. Hence, budget for research proposal should be
presented in the form of sponsor requests.
12. Schedule - Schedule must include the majo r phases of the project, their
timetables etc. It should also specify the completion time of a project. For
example, the major phases may be:
 Exploratory Interviews
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Business Research Methods  Questionnaire Revision
 Field Interviews
 Editing
 Data Analysis
 Report Preparation
It may be helpful to you and your sponsor if you chart your schedule. If
the project is large and complex, a critical path method (CPM) of
scheduling may be used.
13. Special Facilities and Resources -Often, projects will require speci al
facilities or resources. However, these requirements may vary from study
to study. The proposal must carefully list the relevant facilities and
resources to be used. The costs of such facilities must be recorded on your
budget. E.g. research laboratory, computer and ICT facilities, special
softwares.
14. Project Management - The purpose of the project management is to
do the project efficiently. A master plan is required for complex projects
in order to show how all the phases will be completed. A master plan
includes
 Management Procedures
 Control Techniques for Executing the Research Plan
 Financial and Legal Responsibility
 Management Competence
 The Research Team's Organisation
 Example of Management and Technical Reports.
Tables and charts are most h elpful in presenting the master plan. This
section also discusses details such as printing facilities, clerical help or
information processing capabilities to be provided by the sponsor.
15. Bibliography -For all projects, that require literature review, a
bibliography is necessary. Use the bibliographic format required by the
sponsor. If none is specified, a standard style manual will provide the
details necessary to prepare bibliography.
16. Appendices - Glossary of term should be included whenever there ar e
many words you need to the research topic and not understood by the
general management community. This is a simple section consisting of
glossary, measurement instrument and other reference materials which are
not the part of main text.
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Research Problem and
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Hypotheses 2.18 TYPES OF RESEARCH PROPOSALS
The research proposal can be classified in a number of ways. Following
are some of the basis of classifying research proposals:
On the Basis of Origin
This classification is on the basis of place of origin of proposal.
1. Internal Proposal s: These are proposals generated within an
organisation or agency and submitted to its management for approval or
funding. They are responses to specific management needs of problem
solving or product or process development, and are funded internally. The
emphasis is on solving the immediate problem or developing new
product/process or modifying old ones. They do not emphasise literature
reviews. An executive summary is required in these proposals for quick
management appreciation. Schedule of funds and tim e frame for
completion should also be included.
2. External Proposals: A proposal generated within an organisation
and directed to an outside customer, organisation, or funding agency, is an
external proposal. This may be against an advertisement or solicitat ion
from the customer. Then the proposal becomes a competing bid. The
researcher is generally from outside and the proposal is much more formal
in structure. In case of an external proposal, apart from the objectives,
methodology, time and budget schedules it essentially contains
information about the researcher's qualifications and experience. An
external proposal may be solicited or unsolicited.
On the Basis of Solicitation
This classification is on the basis of solicitation or invitation of the
proposal .
1. Solicited Proposals (RFP): Sometimes organizations make a
Request for Proposal (RFP) for a particular research problem that they
have in mind. The proposal is a document that identifies a specific
research problem of interest to the funding agency for wh ich they are
seeking a solution. Interested investigator then submits a proposal, briefly
outlining their proposed solution to the problem. The researchers include a
background on the problem, the objectives, research methodology, the
time schedule, the co st and resource requirements as well as the
researcher's qualifications and experience in handling similar problems.
The solicited proposal competes with numerous other proposals.
2. Unsolicited Proposals: In this case the proposing researcher or
organisation makes the proposal based on a perceived general/ natural
need after a preliminary/ feasibility study. Under such circumstances , the
proposal plays a key role in securing an affirmation for the research to go
ahead. Since the proposal has not been invited hence the research proposal
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Business Research Methods study is stated as succinctly as possible. A non -solicited proposal has the
advantage of not competing with others.
On the Basis of Content
This classificati on is on the basis of contents of the proposal.
1. Basic Proposals: If the proposal is for a basic research it is called as a
basic research proposal
2. Applied Proposals: If the proposal is for an applied research it is
termed as applied research proposal.
On t he Basis of Time
This classification is on the basis of time needed to complete of the
proposal.
1. Short -Term Proposals: When the proposal is for solving the
immediate problem and sponsoring organisation has less time for
completion of the project then it ca n be termed as short -term proposal.
2. Long -Term Proposals: When the proposal is for strategic problem or
long term objectives and sponsoring organisation has enough time for
completion the project then it can be termed as long -term proposal.
On the Basis of Teaming
This classification is on the basis of investigators involved in carrying out
the research.
1. Single Investigator Proposals: The research where only a single
researcher works on the problem are called as single investigator
proposals.
2. Multiple Inves tigator Proposals: The research where a team of
researchers work on the problem are called as multiple investigator
proposals. In a multiple investigator proposal, the proposal must contain
complete information on the qualifications and experience of all t he
researchers. It should also contain information on who would be the
project leader and what all project management techniques will be used by
him to manage his team effectively.
2.19 SELF ASSESMENT TEST QUESTIONS (B)
1. Fill in the blanks with appropri ate words:
(a) A ……………….. is a tentative generalisation, the validity of which
remains to be tested.
(b) “Officers in my organization have higher than average level of
commitment.” Such a hypothesis is an example of……………
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Research Problem and
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Hypotheses (c) “There is no relationship betwe en higher motivation level and higher
efficiency.” is an example of …………… hypothesis
(d) In quantitative studies, an …………….. approach is used for
hypothesis formulation.

(e) …………………. presents a problem, discusses related research
efforts, outlines the data needed for solving the data and shows the
design used to gather and analyze the data.

(f) In case of proposals, the proposing researcher or organisation makes
the proposal based on a perceived general/ natural need after a
preliminary/ feasibility study.

(g) ………………..… hypothesis is simply a statement about the
magnitude, trend or behavior of a population under study.

(h) ……………….are proposals generated within an organisation or
agency and submitted to its management for approval or funding.
2. State true or false for the fo llowing statements:
(a) Deductive thought demands generating a conclusion beyond the
available fact and information.
(b) All hypotheses are always formulated in question form.
(c) If one is formulating a proposition about the magnitude or behaviour
of a particular pop ulation, we call it a descrip tive hypothesis.
(d) A hypothesis that has two sub -hypotheses is called two -directional
hypothesis.
(e) A deductive approach is one that involves testing an explicitly
defined hypothesis.
(f) A research proposal is just like a research re port and written before
the research project.
(g) A research proposal doesn’t show that the researcher is capable of
successfully conducting the proposed research project.
(h) Every research proposal, regardless of length should include two
basic sections namely r esearch question and research methodology
3. Match the following:
A. Deductive Approach (i) quantitative studies
B. Inductive Approach (ii) Null Hypothesis, Alternate Hypothesis
C. Statistical Hypothesis (iii) H1 or H a
D.Variables (iv)qualitative studi es
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Business Research Methods 4. Answer the following:
(a) There is an advantage in stating the hypothesis both in the null and
in the alternate; it adds clarity to our thinking of what we are
testing. Explain.

(b) What is a research hypo thesis? Do all researchers require hypotheses
formulation? Explain

(c) Write a detailed note on the various types of hypothesis.

(d) Why is the research proposal considered as a technical part of
research? Explain.

(e) What do you mean by RFP? What are the differe nt bases of
classification of research proposal?

(f) Explain the various contents that would be the part of a research
proposal.
2.20 SUMMARY
Defining the research problem is the most important step in a research
project. Problem definition is a difficult ste p, because, frequently, decision
makers have not determined the actual problem or only have a vague
notion about it. The researcher’s role is to help decision makers identify
and define their marketing research problem. The formal ways in which
decision ma kers and researchers communicate their perspectives on a
research problem and how to solve it are through the development of a
research brief and a research proposal. To develop these documents fully,
researchers should be proactive in arranging discussion s with key decision
makers, which should include a problem audit whenever possible. They
should also conduct, where necessary, interviews with relevant experts,
and secondary data collection and analyses. These tasks should lead to an
understanding of the environmental context of the problem.
The management decision problem asks what the decision maker needs to
do, whereas the management research problem asks what information is
needed and how it can be obtained effectively and efficiently. The
researcher s hould avoid defining the management research problem either
too broadly or too narrowly. An appropriate way of defining the
management research problem is to make a broad statement of the
problem and then identify its specific components.
Hypothesis formul ation is mostly related to causal research or empirical
research. The term hypothesis has been derived from the ancient Greek,
hypotithenai , meaning to put ‘under’ or ‘to suppose’. A hypothesis is a
“supposition made as a starting point for further investi gation from known
facts”. It provides an investigator with a relational statement that is
directly testable in a research study. One of the major purposes for which
a hypothesis is formulated is defining the relationship between the
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Hypotheses treatment, the outcome measure and the control group. Two approaches
are used in formulating a hypothesis, the inductive approach or the
deductive approach. Hypothesis is formulated in pairs that are mutually
complimentary of each other. These are called is null and alternate
hypothesis. After the hypothesis has been formulated, the hypothesis
needs to be tested for its truthfulness.
The next stage after developing the hypothesis is to prepare a research
proposal . The proposal is the form of a research design, which is the blue
print for conducting and controlling research. It can also be considered a
research plan or a research projects. A good research proposal serves the
purpose for answering various questions like objective to be achieved ,
relevance and importance , methodology to be used , plan and schedule of
completion , scope and limitations and the extent to which the objectives
might be achieved . The contents of a model research proposal includes
title, exec utive summary, statement of the problem, objective/s of the
research, review of literature, benefits of the study, research design,
analysis of data, forms of results, researcher’s brief, budget, schedule,
special facilities and resources, project manageme nt, bibliography and
appendices . The research proposal can be of many types’ viz. internal
proposals, external proposals, solicited proposals, unsolicited proposals,
basic proposals, applied proposals, short -term proposals, long -term
proposals, single inve stigator proposals and multiple investigator
proposals.
2.21 KEY WORDS
 Alternate Hypothesis: An educated conjecture that sets the parameters
that one expects to find. The alternate hypothesis is tested to see
whether or not the null is to be rejected.
 Associative Hypotheses: Such hypotheses propose relationships
between variables - when one variable changes, the other changes.
 Causal Hypothesis: The hypotheses which propose a cause and effect
interaction between two or more variables.

 Correlational Hypothe sis: Such hypotheses are used when we want to
test whether there is any correlation between two variables.

 Deductive Approach: The process of arriving at conclusions based on
the interpretation of the meaning of the results of data analysis.
 Descriptive H ypotheses: This is simply a statement about the
magnitude, trend or behaviour of a population under study.
 Directional Hypothesis: An educated conjecture as to the direction of
the relationship, or differences among variables, which could be
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Business Research Methods  External Proposals: A proposal generated within an organisation and
directed to an outside customer, organisation, or funding agency, is an
external proposal.
 Hypothesis: A hypothesis is a proposition, condition or pr inciple
which is assumed, perhaps without belief, in order to draw out it its
logical consequences and by this method to test its accord with facts
which are known or may be defined.

 Inductive Approach: The process by which general propositions based
on observed facts are established.
 Internal Proposals: These are proposals generated within an
organisation or agency and submitted to its management for approval
or funding.
 Management Decision Problem: It pertains to the decision makers in
which there is amb iguity in the mind of decision makers. It asks what
the decision makers need to do.
 Management Research Problem: It is the quest for searching the
solution. It asks what information is needed and how it can be
obtained effectively and efficiently.
 Non-directional Hypothesis: An educated conjecture of a relationship
between two variables, the directionality of which cannot be guessed.
 Null Hypothesis: The conjecture that postulates no differences or no
relationship between or among variables.
 Research Proble m: A statement about a problematic situation that
identifies the issues which researcher is trying to address.
 Research Proposal: It is an offer to produce or render a service to the
potential buyer or sponsor. The research proposal presents a problem,
discusses related research efforts, outlines the data needed and shows
the research design.
 Solicited Proposals: When organizations make a Request for Proposal
(RFP) for a particular research problem that they have in mind and ask
for bids from outside resear chers.
 Statistical Hypothesis: Given in statistical terms, to test whether the
data support or reject the research hypothesis, it needs to be translated
into a statistical hypothesis.
 Unsolicited Proposals: In this case the proposing researcher or
organis ation makes the proposal based on a perceived general/ natural
need after a preliminary/ feasibility study.

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Research Problem and
Formulation of Research
Hypotheses 2.22 ANSWERS TO SELF ASSESMENT QUESTIONS
A. 1. (a) research objectives (b) the problem (c) not involve (d) five
(e) declarative
2. (a) False (b) False (c) True (d) True (e) True
B.1. (a) hypothesis (b) descriptive (c) null (d) inductive
(e) Research Proposal (f) unsolicited (g) Descriptive (h) Internal
proposals
2. a) False (b) False (c) True (d) False (e) True (f) True
(g) True (h) True
3. A (i) B (iv) C (ii) D (v) E (iii)


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RESEARCH DESIGN
Unit Structure
3.0 Objectives
3.1 Meaning of Research Design
3.2 Nature and Classifications of research design
3.3 Exploratory Research Design
3.3.1 Secondary Resource Analysis
3.3.2 Case Study Method
3.3.3 Expert Opinion Survey
3.3.4 Focus Group Discussion
3.4 Descriptive research design
3.4.1 Cross Sectional Studies
3.4.2 Longitudinal Studies
3.5 Experimental Design
3.6 Errors affecting research design
3.7 Conclusion
3.8 Self - Assessment Questions
3.0 OBJECTIVES
1. To understand p rocess of research design as an essential part of
research.
2. To explore classifications of research design and understand
application of each of the type in various research situation.
3. To explore various methods of exploratory research design.
4. To learn cros s sectional studies and longitudinal studies as an
important types of descriptive research design.
5. To understand framework and application of experimental research
design .
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3.1 MEANING OF RESEARCH DESIGN
Research design refers to Framework or blue print for conducting research
project. It specifies the details of the procedures necessary for obtaining
the information needed to structure and solving research problem.
Research design is heart and soul of the research project, it outlines how
the research proje ct will be conducted and guides data collection, analysis,
and report preparation. Research design is arrangements of conditions for
collection and analysis of data in a manner that aims to combine relevance
to the research purpose with economy in procedur e. Research design is
conceptual structure within which research is conducted. As such the
design includes an outline of what the researcher will do from writing the
hypothesis and its operational implicat ions to final analysis of data e .g.
Course structur e of academic program, Building construction plan,
research project.
Let us take the example of course structure of MMS program offered by
IDOL of Mumbai University, here you will find that the course is divided
in four semesters and each semester consist of few subjects, in second
year you will be introduces to subjects of your choice from various
functional specializations such as marketing management, financial
management and human resources management besides others. Then
credits allocated to each of th e course such as Managerial economics can
be understood from Course structure, further how many items to be
covered in continues assessments and how many items to be covered in
end semester examinations can be understood with the help of course
structure, simply course structure is a n outline for each and every activity
to be carried out in the entire two years of MMS program. Similarly,
research design is a blue print which will guide you in entire research
process about what to do and what to choose. It i s a frame work available.
It can also be understood with the help of one more example. Let us
consider that you want to construct a home. Before construction of home
you will visit architect and explain him about your requirements and
budget. The architect will provide you building plan consist of number of
rooms, ventilations, beams, foundations, materials to be used and lot of
similar information along with drawings. This plan presented by building
architect is highly helpful for you throughout your const riction activities,
similarly research design is helpful to you in understanding the
requirements of research projects and various phases with detailing about
the activities.
3.2 NATURE OF RESEARCH DESIGN
The research design is crucial element in entire re search process. Better
research design gives clear idea about following questions.
What type hypothesis should be formed?
What type of Secondary data to be analyzed?
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Business Research Methods Where will the study carried out?
Where can the required data are found?
What will Sample design?
What techniques of data collections to be used?
How will the data be analyzed?
It is well said that Clarity about research design will help you half solve
your research journey, as research design is that framework which wi ll
brief about your entire research process. Audience or public or
beneficiaries of your research can understand about methods and
techniques you have adopted in your research. It is also very helpful for
reviewer of the research thesis as it gives underst and of researcher about
his research study. We need to understand that any research objectives are
associated with some problem or opportunity in the present situation and
you want better understanding of the situation. Based on objectives of
research stu dy, you are supposed to form hypothesis statements. In line
with type of your hypothesis you should adopt measurement and scales
design, based on your measurement and scales design your next role is to
develop questionnaire, based on responses of questionn aire you should
adopt right coding and tabulation technique which will facilitate required
data for already chosen test of hypothesis, after this the findings of the
research should be drawn from you research data.
Various elements of research design are a s follows
Define the information needed
Choose the appropriate research design type.
Measurement and scale design
Construct and pretest questionnaire
Sampling Process and Sampling Size.
Data Analysis Plan.
Let us consider the above mentioned elements of research design in detail.
Management problems can be translated to research problem with the help
of researchers. Based on problems we may have clarity on information
needed for drawing conclusions and making decisions. Based upon the
type of informat ion needed we can choose appropriate research design
from exploratory research design and conclusive research design.
Research situation which present requirement of better insights will be
completed by exploratory studies. In situation where you have exac t idea
about problem statement and wants to take decision for a problem
statement then you should choose amongst various types of conclusive
research designs such as descriptive and causal research design.
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using right scales. We can measure physical variables such as weight in
kgs but behavioral variables such as happiness measurement is difficult as
we don’t have any set standard to measure happiness as we have kg in
case of weight meas urement. These behavioral variables can be measured
with the help of scales. There are four types of scales such as nominal
scale, ordinal scale, interval scale, ratio scale. Nominal data and interval
data is generally known as non metric data. Such type o f data is useful in
analyzing the categories such as studies involving behavior of rural and
urban, male & female, various regions etc. here rural and urban can be
classified as 1 & 2 respectively. Numbers 1 & 2 is only symbolic and does
facilitate any mat hematical calculations. You cannot use many statistical
tests on such non metric data. In contrast to this metric data, which can be
obtained from interval and ratio scale facilitates common test of
hypothesis such as t test, z test, f test etc. It is to b e noted that many a
times similar information can be obtained with the help of different scales
for example we can collect the information about respondent’s height in
exact centimeters such as 170 cm or 172 cm, similarly we may give
classes like tall and short and collect information about height in the form
of categorical data.
As mentioned required data can be collected in various forms, care should
be taken so that stated statistical test can be applie d to the collected data.
We can incorporate all suc h things while designing questionnaire.
Different types of questions can be asked such as multiple choice
questions, L inier scale questions, Likert scale questions and so on. Choice
of questions selected based on objective, hypothesis and data analysis
techniques to be used. The designed questionnaire has to be tested with
few respondents in pilot survey, data so obtained in pilot survey to be
checked for weather it is in line with the required format of tabulation,
further it has to be checked with few sta tistical test for its validity, minor
corrections can be done in questionnaire if required and data from sample
so selected can be collected with the help of validated questionnaire.
Sampling refers choose few representative elements from the given
popula tion. It can again be done in two styles, probability sampling and
non probability sampling. Based on situation and constrain you can
choose the sampling technique. Sample size can be estimated with the
help available references.






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Business Research Methods 3.2.1 Classifications Of r esearch design


As shown in figure, research design is broad ly classified in three classes,
exploratory research design, Descriptive research design and causal
research. Descriptive r esearch design can be of Cross S ectional Design or
longitudinal design . Further cross sectional design can be classified as
single cross sectional design and multiple cross sectional design. Let us
discuss each of this type in detail.
3.3 EXPLORATORY RESEARCH DESIGN
It is one type of research design which has as its primary objective the
provision of insights in to and comprehension of problem situation
confronting the researcher. E.g. post pandemic academic abilities of
students, Entrepreneurship environment and placement situation.
Exploratory research studies are also term ed as formulate research studies.
The main purpose of studies is that formulating a problem for more
precise investigation or of developing a working hypothesis from an
operational point of view. Research question is not exactly pre defined in
case of expl oratory studies. The major emphasis of such studies is on the
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enough to provide opportunity for different aspects of a problem under
study. In built flexibility in research design is need ed because the research
problem broadly defined initially is transformed into one with more
precise meaning in exploratory studies, which may necessitate change in
research procedure for gathering relevant data.
Let us discuss the above mentioned examples in detail. The example of
research is to be carried out on estimating impact of pandemic on
academic abilities of students. As the pandemic has brought many new
biological and economical challenges which the world has never faced , it
is to be understood th at many educational institutes went online so as to
minimize the academic loss of the students, teaching learning and
evaluation process in pandemic was altogether different. Suppose we want
to study post pandemic academic abilities of students such as lea rning,
paying attention, listening and writing. We know the said variables but in
case because of over exposure to the mobile eye sight of few students is
reduced. Their attention in offline class is less than the attention in pre
covid era. This aspect of eyesight was earlier not known, it may also be
possible that many students are facing many problems related to brain
such as Migraine and more such variables were not known but they do
have impact on academic abilities of students, by studding this situat ion
government or university may grant additional time for writing answers in
end semester examination.
It is well known fact that Indian economy is driven by high levels of
consumption and its huge consumption size present many
entrepreneurship opportunit ies. This opportunity is well understood by the
government and many schemes which will motivate people to start their
entrepreneurship careers. It is noticed that entrepreneurship has grown
only in few packets of India and not all around the country, few p ackets
such as western Maha rashtra, part of Gujrat, Tamil Na du, and Karnataka
might be doing well in case of entrepreneurship but few other packets
such as Madhya Pradesh, Bihar, Vidharbha region might not do equally
well. Now government is concerned and t hey are providing much support
in various areas such as technology, finance and marketing, export,
banking and others. While doing survey these variables are known but fear
of loss, insecurity, changing tastes, dynamic changes in market structure
and unlaw ful demands from some pressure groups can be few more
variables which are restricting growth of entrepreneurship in above
mentioned areas. These variables were not known earlier but while
exploring we may come across such variables.
Thus in an explorato ry research study which merely leads to insights or
hypothesis, whatever method or research design outlined above is
adopted, the only thing essential is that it must continue to remain flexible
so that many different facets of a problem may be considered as and when
they arise and come to the notice of researcher. Exploratory research
design is helpful in proving insight in subject of interest and develop
hypothesis. It can be executed by various methods namely secondary
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Business Research Methods 3.3.1 Secondary Resource Analysis
Secondary resource analysis happens to be most simple and fruitful
method of formulating precisely the research problem or developing
hypothesis. Hypothesis developed by earlier workers may be reviewed and
their usefulness may be evaluated as a basis for further research. It may
also be considered weather the already stated hypothesis suggest new
hypothesis. In this way the researcher should review and build upon the
work already done by others, but in case hypothesis have not been yet
formulated, his task is to review the available literature and define the
hypothesis from it.
Besides, the bibliographical survey of studies, already made in once area
of interest may as we ll be made by the researcher for precisely
formulating problem. He should also make an attempt to apply his
understanding in different research context to the area in which he is
working. Many a times referring from others thesis or research projects
gives an idea about development of working hypothesis from it.
3.3.2 Case Study Method
In the case study method, a unit under study may be an individual,
department of the organization or a company is taken. All the factors
relating to research area are explor ed in detail and the study is done. Case
studies generally focus on collecting information from specific object like
individual or a business unit. The case can be any one or few from
individual, event, group, organization or it may also be situation. The
objective of case studies is to have a clear picture of a problem by
examining real life situation from various angles and perspective using
multiple methods of data collection. Case studies are empirical
investigation of particular contemporary phenomenon within its real life
context using multiple methods of data collection. Case studies leads to
generation or collection of qualitative and quantitative data both. Case
study method is highly useful to develop the hypothesis. E.g. A case study
can be done f or particular branch of bank for challenges in effective
implementation crop loan scheme of government.
3.3.3 Expert Opinion Survey
In this method, group of people having expertise in particular domain are
surveyed. The object of the survey is to obtain in sight in to the
relationship between variables and new ideas relating to research problem.
To conduct such survey a group of people having expertise is carefully
selected. The selected respondents are generally interviewed instead of
questionnaire to get f urther insight in to problem. Interviews should be
unstructured instead of structured interviews so that various views of these
expert people can be accommodated in research problem. Every interview
may last for several hours based on expertise of a partic ular respondent. It
is preferred to inform the respondent well in advance so that he can
arrange his schedule and be prepared for the said topic. It is general
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that he can be well prepa red. Thus an expert opinion survey may enable
the researcher to define the problem more concisely and help in the
formation of hypothesis. This survey may as well provide information
about practical possibilities for doing different types of research. E.g.
survey of bank managers can be taken so as to design new product
focused on consumer durable loans from the bank. In this survey
challenges and opportunities in this segment can be explored by the
researcher. The survey can be in the form of interviews.
3.3.4 Focus Group Discussion
A focus group discussion is an interview conducted by a trained
moderator among a small group of respondents in an unstructured and
natural manner. In this method of research, a moderator acts as a
stimulator in the group discu ssion. The group selected consists of 7 to 12
people from experts. Here many a time unexpected finding may emerge
from the discussion of these experts. Such focus group discussion can be
carried out online or offline. Focus group is the most important qual itative
research procedure. They are every popular and many times used as
synonymous with qualitative research. Various online meeting platforms
such as zoom meetings, google meetings, Jio meetings or some subscribed
platforms can be used for discussion. I n this method the respondents are
informed in advance about the research topic and well studied respondents
with the help of moderator participate in the discussion forum. The output
in these methods depends heavily on art of moderator to explore the visio n
of participating respondents. The focus group selected is from similar
demographic background. If you have selected working class of women
then they should not be combined with college going girls as the lifestyle
of both the groups might be different and research may not get exact
output from such focus group. E.g. A focus group of university professors
may be selected for a discussion on providing students online learning and
evaluation platform. In this focus group various university professors of
about 7 to 12 can be selected and moderator can lead to discussion about
challenges in online education.
 Conclusive research Design
As main objective of exploratory research is get insight in research
problem, it is helpful in understanding nature of problem , conclusive
research design are those research design where finding are very helpful in
decision making unlike exploratory design. Conclusive research can be
descriptive research design and causal research design.




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Business Research Methods  Difference between Exploratory research a nd conclusive research
Parameter Exploratory Conclusive
Objective To provide insights and
understanding To test specific hypothesis
Characteristics Information needed is
defined only loosely,
research process is
flexible and
unstructured. Study is
quali tative and sample
size is small. Information needed is clearly
defined. Research process is
structured. Quantitative data
analysis is used. Sample size
is large.
Findings/
Results Tentative Conclusive
outcomes Followed by conclusive
research Findings a re used for
decision making.

3.4 DESCRIPTIVE RESEARCH DESIGN
It is type of conclusive research that has as its major objective the
description of some things usually market characteristics or functions. E.g.
A research conducted about customer satisfact ion, employee satisfaction,
brand preference, brand awareness etc. Special characteristic of
descriptive research is that, researcher do not have any control on the
situation, he is only passive observer and describe what situation is? He
will not emphasiz e on what situation ought to be? Majority of marketing
research studies are descriptive in nature. Let us discuss the given example
in detail. A motor cycle company wants to study level of customer
satisfaction for its servicing units. After motor cycle is serviced then with
the billing customer satisfaction form may be presented at service outlet,
customer is requested to fill out the form and data so gathered may be
analyzed with the help of predefined descriptive and inferential analysis
and conclusions from the data can be drawn. Here researcher is not
supposed to control the situation but just the collect real information from
the customers of service department. Descriptive research design is further
classified as cross sectional studies and longitudin al studies.
3.4.1 Cross Sectional Studies
A type of research design involving the collect ion of information from
any given sample of population elements only once. Cross sectional
design is further classified as single cross sectional design and multiple
cross sectional design as explained below.

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3.4.1.1 Single Cross sectional design
A cross sectional design in which one sample of respondents is drawn
from the target population and the information is obtained from the
sample once. E.g . we want to conduct survey of MMS students about their
Industrial exposure. We can finalize Industrial exposure as dependent
variable and Industrial visits, guest lectures and internship as independent
variables. The survey can be made out of respondents from MMS final
year p assed students and conclusions can be drawn based on findings of
the study.
3.4.1.2 Multiple Cross S ectional D esign
Multiple cross sectional design in which there are two or more sample of
respondents and information from each sample is collected only once .
Here we can conduct a similar survey as explained above on various post
graduate students in university. Similar questionnaire can be given to
respondents from various departments such as commerce, management,
chemistry and physics. Data obtained from fi nal year passed students of
various courses. Such research is known as multiple cross sectional study.
3.4.1.3 Cohort Analysis
It is multiple cross sectional design consist of series of surveys conducted
at appropriate time intervals . The cohort refers to group of respondents
who experience the same events within the same time intervals.
3.4.2 Longitudinal Studies
A type of research design involving a fixed sample of population elements
that is measured repeatedly. The sample remains the same over time, thu s
providing a series of picture that when vied together; portray a vivid
illustration of the situation and the changes that are taking place over time.
Let us consider the same example of MMS students and their industrial
exposure as explained above. Depen dent and independent variables
remain same but we now want to understand growth in industrial exposure
semester wise. To fulfill this objective, we will conduct this survey five
times starting with survey immediately after starting of MMS program,
second s urvey can be carried out at the end of semester I and similarly at
the end of each semester till final year. We can see the output of industrial
exposure at the end of each semester and compare with industrial
exposure of same students at the time of joini ng MMS program. From
such study we can understand impact of each semester on increase in
industrial exposure of students.
3.5 CAUSAL RESEARCH DESIGN
It is a type of conclusive research where the major objective is to obtain
evidence regarding cause and eff ect relationship. It is also known as
experimental design. Consider an example of estimating consumer
demand for shoes. Here dependent variable is consumer demand and
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Business Research Methods competition and substitu tes available with their respective prices. We
know that when ever price decreases quantity demanded increases. Here
quantity demanded is effect caused by change in price so here we can state
this as an example of cause and effect. In many type of research , we are
interested to understand cause and effect and strength of relationship
between cause and effect e.g. Demand depends on price as well as income
but if we see strength of relationship, data shows stronger relationship
with price of the product. Such relationship can be stated well with the
help of regression and correlation analysis. Greater the values of
coefficients of regression and correlation stronger are the relation between
the variables. Research in which the independent variable is manipulat ed
is termed as experimental design. Further principle of replication,
principle of randomization and principle of local control is used in case of
experimental design. Experimental design is further classified as informal
experimental and formal experimen tal design explained as follows.
Informal Experimental Design
 Before and after experimental design
o After only with control design
o Before and after with control design.
 Formal Experimental design
o Completely randomized design
o Randomized block design
o Latin sq uare design
o Factorial design
3.6 ERRORS AFFECTING RESEARCH DESIGN
Several potential sources of error can affect a research design. A good
research design take care of potential sources of errors and try to avoid or
control errors. Total errors can be samp ling errors and non sampling
errors. Total error is the variation between true mean value in the
population of the variable of interest and the observed mean value
obtained in the marketing research project. Random sampling error is due
to the particular s ample selected being an imperfect representation of the
total population of interest. It may be defined as the variation between true
mean values for the sample and true mean value of the population. Non
sampling errors are errors that can be attributed to sources other than
sampling, and they can be random or non random.
3.7 CONCLUSION
Research design refers to Framework or blue print for conducting research
project. It specifies the details of the procedures necessary for obtaining
the information needed to structure and solving research problem.
Research design is broad ly classified in three classes, exploratory research
design, Descriptive research design and causal research. Exploratory
research design is helpful in proving insight in subject of interes t and
develop hypothesis. It can be executed by various methods namely
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Research Design
method and focus group interviews. Descriptive research design can be of
Cross Sectional Design or longitudinal design. Further cross sectional
design can be classified as single cross sectional design and multiple cross
sectional design. Research in which the independent variable is
manipulated is termed as experimental design. Further principle of
replication, principle of randomization and principle of local control is
used in case of experimental design. Experimental design is further
classified as informal experimental and formal experimental design.
Several potential sources of error can affect a research design. Tota l errors
can be sampling errors and non sampling errors.
3.8 SELF ASSESSMENT QUESTIONS
Q1. Explain meaning and significance of research design.
Q2. What are the various types of research design?
Q3. What are the potential sourc es of errors in research de sign?
Q4 Present a differentiation between Exploratory, descriptive and causal
research design .
Q5. Fill In the Blanks
1. Research design refers to ______ for conducting research project
(System / Blueprint)
2. Sampling refers choose _______ elements from t he given
population. (All / Few)
3. Descri ptive research involves ____of respondents. (Correction
/Observation)
4. Research design is broadly classified in _____ classes . (Three/ five)
5. Random sampling error is due to the particular _____ selected
(Sample / Pop ulation)
Q6. MCQ Questions.
1. Research design involves techniques for…
a. Collection of Data
b. Analysis of data
c. both A & B
d. None Of the Above

2. Various elements of research design are ___
a) Define the information needed
b) Choose the appropriate research design typ e.
c) Measurement and scale design
d) All of These

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Business Research Methods 3. “Provision of insights in to and comprehension of problem situation” is
feature of__
a) Exploratory Research design
b) Descriptive Research design
c) Causal Research design
d) All Of these

4. Group of respondents who ex perience the same events within the same
time intervals refers to__.
a) Sample
b) Cohort
c) Population
d) None of these

5. All the factors relating to research area are explored in detail and the
study is done in ___type of Experimental research design.
a) Case study Met hod
b) Focus Group Interview
c) Expert Opinion Survey
d) None of these


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4
BUSINESS RESEARCH METHODS
Unit Structure
4.0 Objectives
4.1 Primary and Secondary Data
4.2 Classification of Data
4.2.1. Types of Data Classification
4.2.2. Objectives of Data Classification
4.3 Primary Data
4.3.1. Primary Data Sources
4.3.2 . Advantages of Primary Data
4.3.3. Disadvantages of Primary Data
4.4. Observation Method
4.4.1. Advantages of Observation Method
4.4.2. Disadvantages of Observation Method
4.5 Focus Group Discussion
4.5.1. Advantages of focus group discussion
4.5.2. Disadvantages of focus group discussion
4.6 Interview Method
4.6.1. Structured Interview
4.6.2. Semi – Structured Interview
4.6.3. Unstructured Interview
4.7 Secondary Data
4.7.1 Uses of Secondary Data
4.7.2. Advantages of Secondary Data
4.7.3. Limitatio ns of Secondary Data
4.7.4. Types of Secondary Data
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Business Research Methods 4.0 OBJECTIVES
By the end of the chapter, you should be able to
1) Understand and differentiate between primary and secondary data
2) Identify the advantages and disadvanta ges of primary and secondary
data
3) Classify types of primary and secondary data collection techniques.
4) Distinguish between the various types and sources of secondary data
4.1 PRIMARY AND SECONDARY DATA
Introduction
Data can be defined as different ki nds of information formatted in a
particular way to enable ease of use. Data collection is the process of
collecting, measuring and examining accurate data for research using
standard validated tools and techniques. Data is a very important part of
any res earch. Without data, research cannot be conducted. Data collection
is the primary and most important step for research, irrespective of the
field of research.
The approach of data collection is different for different fields of study,
depending on the req uired information. Along with research, data
collection is also required for any kind of business decision making,
ensure quality assurance, and keep research integrity in check. Hence data
collection methods and process is given utmost importance as it is the first
step towards achieving the above mentioned objectives.
During data collection, the researchers must identify the data types, the
sources of data, and what methods would be used to obtain the data.
Before a researcher begins collecting data, they must be able to answer a
few basic questions:
 What’s the objective or purpose of this research?
 What kinds of data is to be gathered?
 What methods and procedures will be used to collect, store, and
process the information collected?
 How would the data be presented for analysis of the study?
Data Collection – Why do we need it?
Before a judge makes a ruling in a court case or a general creates a plan of
attack on the enemy, they should have as many relevant and accurate facts
as possible. The best courses o f action come from informed decisions, and
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Whether one is in the world of academia, trying to conduct research, or
part of the corporate sector, thinking of how to promote a new product,
one requires da ta collection to help make informed and better choices.
4.2 CLASSIFICATION OF DATA
Classification of data is can be defined as the process of organizing data
by relevant categories so that it may be used more efficiently during the
research process. This h elps to locate the required data more easily.
Classifying data into correct categories can help avoid duplication of data
along with easy presentation.
4.2.1. Types of Data Classification
Data classification can be carried out based on content, context, or user
selections:
 Content -based classification —involves reviewing files and
documents, and classifying them.
 Context -based classification —involves classifying files based on meta
data like the application that created the file (for example, accounting
software), the person who created the document (for example, finance
staff), or the location in which files were authored or modified (for
example, finance or legal department buildings).
 User -based classification —involves classifying files according to a
manu al judgement of a knowledgeable user. Individuals who work with
documents can specify how sensitive they are —they can do so when
they create the document, after a significant edit or review, or before
the document is released.
4.2.2. Objectives of Data Cla ssification
 It compresses the volume of data in an easily understandable form
such that the similarities and variations can be instantly recognised.
 It reduces unnecessary details in the data.
 It helps in comparisons and highlights the important aspects of the
data.
 It helps in statistical processing of the data gathered by converting it
into tables, graphs or bars for easy understanding.
 It gives importance to the prominent data collected and helps in
classifying the data of lesser importance.
4.3 PRIMARY DATA
Primary data refers to the firsthand data collected by the researcher
himself. This type of data is collected with some specific purpose in mind
or a particular study. It is sometimes a very long process with lots of
involvement. In order to get effe ctive and accurate data, the researcher has
to spend a lot of time and energy into collecting this type of data. While
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Business Research Methods collected by this method is of crude, that is unfiltered natu re, meaning it
has to be filtered and categorized before analyzing and making inferences
out of it.
Primary data is also very specific to the researchers needs as per his
research and methodology and area of interest and hence may or may not
be useful for other research projects or areas. A lot of elaborate pre
planning and designing is involved before a researcher begins to collect
data by this method. The researcher also has the flexibility to change or
alter his method of data collection during the proc ess as he is obtaining
this information at the very basic level.
In terms of cost effectiveness, the primary method of data collection can at
most times be an expensive and time consuming process.
There are different methods by which this type of data can be collected.
4.3.1. Primary Data Sources
The primary data can be collected by using
Quantitative Methods or
Qualitative Methods
Quantitative Methods
Survey is one of the most popular techniques of primary data collection by
using quantitative methods. A survey is a method for collecting
quantitative information about items in a sample population. The
information is collected by using different kinds of interview questions
addressed to a large amount of population. Surveys may have different
approaches li ke personally administered surveys or a telephonic survey, a
mail survey or an electronic survey. All surveys are basically conducted
using a prefixed format of questions, termed as a questionnaire.
Qualitative Methods
This type of data collection does no t necessarily use questionnaires. The
emphasis of such methods is to have a flexible approach while collecting
data. This method is usually used when the research is in the exploratory
stage and due to the lack of enough research conducted on the topic, th e
researcher does not have enough knowledge about the research topic. In
such situations, it is extremely difficult for a researcher to design and draft
a questionnaire and follow a systematic approach. In all such situations
that require exploring new ins ights and perspectives from the respondents,
the qualitative methods of data collection are considered the most
appropriate method.
Qualitative methods of data collection include observations, focus groups
unstructured and semi structured interviews etc.
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4.3.2. Advantages of Primary Data
 Obtain specific research
Performing your own research allows the researcher to address and
resolve issues specific to the research topic. The collected information is
the exact information that the researcher wants and he is able to present it
in a way that benefits his research.
 Better accuracy
Primary data is much more accurate because it is directly collected from a
given population.
 A higher level of control
The researcher can control easily the research design and meth od. In
addition, He also has a higher level of control over how the information is
gathered.
 Up-to-date information
The primary market research is a great source of latest and up -to-date
information as you collect it directly from the field.
 Information ownership
Information collected by the researcher is their own property and the
researcher reserves the right to share the information.
4.3.3. Disadvantages of Primary Data
 Expensive process
It could be very expensive to obtain primary data collection
metho ds because the research team has to start from the beginning. They
have to follow the whole study procedure, organizing materials, process
and etc.
 Time -consuming
It takes a lot of time to conduct the research from the beginning to the end.
Often it is muc h longer in comparison with the time needed to collect
secondary data.
 Limitation of other factors
Primary data is limited to the specific time, place or number of
participants. To compare, secondary data can come from a variety of
sources to give more det ails.
4.4 OBSERVATION METHOD
This is a qualitative method of data collection. In this technique, the
information is captured by observing variety of factors like, objects,
human behaviour, systems, processes, structures etc. For example, in a munotes.in

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Business Research Methods study about a product, instead of asking consumers a fixed set of questions
about their attitude towards the product, it would be more beneficial to
appoint a person as an observer in a store to observe and analyse the
behvaiour of the consumers while they make their p urchase decision.
Although this method could turn out to be time consuming and an
expensive matter, the results obtained through this method would be
reliable. The observer generally does not interfere in the process of data
collection and because of that the observer’s bias is also eliminated to an
extent. This technique may reveal some important information that
otherwise may not be disclosed in the other form of data collection.
Few examples of Observation Method are
A Doctor watching a patient after g iving him an injection.
An astronomer looking at the night sky to view movement of objects.
An observer looking at a customer’s buying behaviour towards a product.
4.4.1. Advantages of Observation Method
 The researcher can collect, check and record accurat e data by
observing himself first -hand.
 It generates a permanent record of the observation activity and the
researcher or others can refer to it later
 The organization method is one of the simplest methods of data
collection. It does not require too much t echnical knowledge
 Observation method is one of the most common methods used in all
sciences and is very easy to follow and accept
 The observation method does not require the willingness of the
participant to record his observation. The researcher can obse rve from
a distance and record his findings
4.4.2 Disadvantages of Observation Method
 This method of data collection takes a longer time frame compared to
other data collection methods
 There is a chance of higher observer bias in the observation method
 Several personal behaviors are not open for observation and this
proves a limitation in case of observation method
 Many of the incidents are abstract like love, affection and the
researcher can’t gain an exact and correct account of those
 Observation method i s considered an expensive affair as it requires
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4.5 FOCUS GROUP DISCUSSION
This is a type of interviewing technique. A small, selected group of
participants are chosen who are interviewed by a researcher. The
participants are generally from a target research audience whose opinion is
of interest to the said researcher. The discussion is generally a
collaboration of experiences, attitudes, perception, ideas and how they feel
about a certain topic. The researcher gene rally moderates the discussion in
a direction that will lead to some quality opinions by the participants.
The discussion during this activity is generally free and open, sometimes
allowing the researcher to a new chain of thoughts. The researcher may
also receive varied ideas and insights, enabling him to increase his quality
of research work.
It is however important that the selection of the participants be given due
importance. Smaller groups are generally pref erred for a natural and well -
coordinated discussion. The participants are to be selected, as far as
possible, from a similar economic, social and cultural background. This
would minimize any conflict that could arise within the group and
contribute towards achieving the set objectives. The research er’s skills are
extremely important in keeping the discussion relevant without getting
involved in any controversy or bias. The researcher should have adequate
knowledge of the topic that would be discussed and should be able to
follow and utilize effectiv ely, the opinions provided in the focus group
discussion.
4.5.1. Advantages of focus group discussion
 It is an inexpensive and fast method of obtaining valuable data.
 Co-workers and friends are more comfortable in voicing views in each
other’s company than on their own with the researcher.
 Participants are given a chance to reflect or react to the viewpoint of
others with which they may disagree or of which they’re unaware.
 The dynamic discussion between participants stimulates their thoughts
and reminds th em of their own thoughts regarding the research
subject.
 All individuals along with the researcher have a chance to ask
questions, and these will produce more information when compared
with individual interviews.
 Informants can build on the answers of othe rs.
4.5.2. Disadvantages of focus group discussion
 The researcher sometimes face problem controlling discussion and
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Business Research Methods  A few individuals could possibly be introverts while others take
control of the debate and impact the end result, or possibly even
introduce bias.
 Recording data can present difficulties; it is actually not possible to
record when so many participants are speaking at the same time.
 Data analysis could be time consuming and challengin g task.
 Focus group discussions cannot be repeated. The validity and
dependability of the findings are tough to determine.
4.6 INTERVIEW METHOD
This is the most commonly used method of data collection. A prefixed set
of questions are designed by the resear cher in order to ask the target
participants. The analysis is further done on the answers provided by
them.
There are basically three types of interviews.
1. Structured Interview
2. Semi – Structured interview
3. Unstructured Interview
4.6.1. Structured Interview
Structured interviews are the most systematized type of interview.
Opposite to semi -structured or unstructured interviews, the interviewer
uses predetermined questions in a set order.
Structured interviews are mostly closed -ended. They can be divided,
which means asking participants to answer “yes” or “no” to each question,
or multiple -choice options.
Asking a fixed set questions in a set order allows you to easily compare
responses between participants in a uniform context. This can help you see
patterns a nd highlight areas for further research. A structured interview is
straightforward to conduct and analyze. Asking the same set of questions
lowers potential biases and leads to fewer uncertainties in analysis. It is an
undertaking you can likely handle as an individual, provided you remain
organized.
A survey is generally conducted by a structured interview where the set of
questions do not change for the entire population of participants the
survey is taken from.
Advantages of structured interviews
 Reduced bias
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minimizes the risk of introducing bias via the order or nature of questions
asked, or via any environm ental factors.
 Increased credibility, reliability and validity
Structured interviews are thought to be more credible than other types of
interviews because they are very carefully planned and set. All
participants are presented with the same questions in t he same order,
which makes it easier to compare the answers. This contributes to
their reliability and validity.
 Simple, cost -effective and efficient
Similar to questionnaires and surveys, structured interviews introduce
more variation and diversity to the topic being studied without
representing too much more work for the interviewer. Relatedly, there is
less preparation needed for the interviewee, so the process is also less
time-consuming on their part.
Disadvantages of structured interviews
 Formal in na ture
The fixed structure of such an interviews means that there is very little
opportunity to build a bond between the interviewer and the participant.
The method of structured interviews can cause participants to feel
uncomfortable or nervous, which can a ffect their answers.
 Limited flexibility
Once the questions are selected, they cannot be altered or removed
without affecting the quality of the interview. Even if a question is poorly
worded, excess, or unnecessary, it still has to be presented to all
respondents.
 Limited scope
Since most structured interviews are closed -ended, their scope is limited.
Participants cannot go into much detail with their answers, and there is
little room for any kind of explanation. If a participant doesn’t truly
identify wit h any of the multiple -choice answers, it can be difficult to
know how much their answer reflects their true opinions.
4.6.2. Semi – Structured Interview
This method is used when the researcher asks the respondents some basic
questions, the researcher somet imes interferes wherever necessary. In this
method, the interviewer sets some simple guidelines for the questions to
be asked. The succession questions are generally on the basis of the
preceding questions. Hence we can say that in a semi structured interv iew,
while there is some flexibility to the questioning path, there is also some
fixed structure to the questions being asked to the respondents. A ‘job
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This type of interview allows the interviewer to get opinions and get a feel
of general attitudes of the respondents. The questions drafted by the
researcher are extremely flexible. There is no pre fixed set of questions.
Since the questioning takes place in a very flexible mode, a researcher is
able to get deeper insight into the subject. This can help the researcher
understand the respondents’ views and opinions better than in structured
questions. Such interviews are generally used in exploratory research a nd
are usually time consuming. One of the biggest challenge of this method is
that the data generated is in a much unstructured format, this makes it very
difficult for quantitative analysis. The segregation of this type of data is
not only time consuming but also expensive. Coding such kind of raw data
is extensive to allow methodical analysis and needs expertise. However,
there are multiple software available for this purpose in order to cut short
such a tedious task.
4.7 SECONDARY DATA
Secondary data is research data that has previously been collected by
another source and can be accessed by researchers. This is the opposite of
primary data, which is data collected directly from its source. Secondary
data is almost always past data which is inexpensive an d less time
consuming to identify. Although secondary data is economical to get, it
may or may not be specific to the researchers needs. A lot of filtering and
scanning may be required in order to arrive at the correct data as per
researchers ’ topic of int erest. However, the ultimate source of any
secondary data has to be primary data. The most common example of
secondary data are the data collected from published sources like
newspapers, magazines/ journals, books, reports, publications, /economic
survey a nd census published by Government of India.
Secondary data can be of 2 types, depending on the kind of research:
 Quantitative data – data that can be expressed as a number or can be
quantified. Examples – the weight and height of a person, the number
of w orking hours, the volume of sales per month, etc. Quantitative
data can be easily modified to statistical manipulation.
 Qualitative data – data that can’t be expressed as a number and can’t
be measured. Qualitative data consist of words, pictures, observat ions,
and symbols, not numbers. It is about qualities. Examples – colour of
the eyes (black, brown, blue, green), your socioeconomic status,
customer satisfaction, loyalty towards a product, etc. (To measure this
type of data we create tools that help us q uantify the above
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4.7.1 Uses of Secondary Data
Secondary data can be used for a number of purposes such as
1. To identify the research problem
2. To develop strategies and plans that will lead to solutions.
3. To help develop a good research design
4. To help find answers to certain research questions and support
hypothesis testing.
5. To help analyze primary data.
6. To validate the results of primary data.
7. To identify possible problems if any.
8. To get more background information and improve the credibility of t he
study.
4.7.2. Advantages of Secondary Data
There are several advantages to secondary data.
1. The data available is quick and easy to use. Therefore, for a study with
limitations of time, it is easier to use secondary data method than the
primary data meth od.

2. If the source of data collected is reliable such data can avoid the errors
that otherwise could come in while collecting primary data.

3. In many cases secondary data could be less expensive to obtain than
the primary data because it does not involve elab orate processes like in
primary data collection.

4. Secondary data enables the researcher to cover a wider geographic,
cross -sectional range

5. Information gathered by secondary data can generate more accurate
data than that obtained through primary research.

6. Secondary data allows you to generate new insights from previous
analysis and helps to come at relevant conclusions.

7. Secondary data allows you to carry out a longitudinal analysis which
means the studies are performed spanning over a large period of time.
This can help you to understand different trends. In addition to this,
sometimes you can also find secondary data from many years back up
to the latest month. It also allows you to compare data over time.
It is basically an economic use of time and resources that also comes with
an ease of access. This is because the data is already prepared, segmented
and categorized for the researcher to use and analyze and incorporate into
his study.
4.7.3. Limitations of Secondary Data
While there are many advantages to o btaining and using secondary data
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Business Research Methods 1. One of the biggest challenges while using secondary data is that the
data should be relevant, recent and reliable to the researcher for the
given purp ose of his study.

2. The research conducted by primary data collection methods is
generally controlled by the researcher himself. However, if the data
used is secondary data or not collected directly by the researcher
himself, the origin of the data may be qu estionable if the data is not
verified appropriately.

3. Secondary data does not address the specific needs of the researchers.
For example, if the researcher wants the demographic profile in a
specific category like income, the categories defined by the rese archer
may not match with the categories defined in the secondary data.

4. Since the secondary data available may not be specifically required by
the researcher, it can be considered as inefficient spending of
resources for the secondary data. The quality con trol of the data is also
not possible.

5. Since the secondary data is available to many others, the exclusiveness
of the information derived from the data is lost.

6. The secondary researcher may have to comprise on the forecasted
objectives and scope of the stu dy depending on the availability of data
that is available via this method.

7. The secondary data might lack quality. The source of the information
may be questionable, especially when you gather the data via the
Internet and other such secondary sources. As the researcher is much
dependent on secondary data as a part of the research process , one
must evaluate the reliability of the information by finding out how the
information was collected and analysed.
One should note that the choice between collecting pri mary data or using
secondary data depends on the objective, confidentiality or exclusiveness
of the study among other objectives in mind.
4.7.4. Types of Secondary Data
There are two types of secondary data, based on the data source:
 Internal sources of data: information gathered within the researcher’s
company or organization (examples – a database with customer
details, sales reports, marketing analysis, your emails, your social
media profiles, etc).
 External sources of data : the data collected outside t he organization
(i.e. government statistics, mass media channels, newspapers, etc.)
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Examples of secondary data are
 Tax records and social security data
 Census data
 Electoral statistics
 Health records
 Books, journals, or other print media
 Social media monit oring, internet searches, and other online data
 Sales figures or other reports from third -party companies
 Libraries and electronic filing systems
 App data, e.g. location data, GPS data, timestamp data, etc.
Sources of Secondary Data
Sources of secondary da ta include:
 Government departments
 Public sector organizations
 Industry associations
 Trade and industry bodies
 Educational institutions
 Private companies
 Market research providers
Internal secondary data
Secondary data does not only come from different o rganizations. It can
also come from within an organization itself.
Sources of internal secondary data include:
 Sales reports
 HR filings
 Annual accounts
 Quarterly sales figures
 Customer relationship management systems
 Emails and metadata
 Website cookies
4.8 SELF ASSESSMENT QUESTIONS
Q1. Distinguish between primary and secondary methods of data
collection.
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Business Research Methods Q3. Explain the different types of Interviews, what are the advantages of
structured i nterviews?
Q4. What is the purpose and use of primary data and secondary data?
Q5. You plan to export semi -precious stones from Jaipur to countries like
South Africa, Canada, USA. What would be the nature of
information required by you? How can you use sec ondary data
sources in this example?
Q6. Ritesh wants to plan a holiday to a few countries in Europe to
experience the winter weather. How will he go about making his
decisions of choosing the right places to enjoy his vacation? What
kind of sources would b e helpful?
Q7. A school teacher wants to set up a primary school in a small town.
How should she proceed to collect data for the same?



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ATTITUDE MEASUREMENT AND
SCALING
Unit Structure
5.0 Objectives
5.1 Introduction
5.2 Attitude
5.3 Measurement
5.4 Measurement Level/Scales or Classification of Scales
5.5 Single item Vs Multiple Items
5.6 Comparative Vs Non -Comparative Scales
5.7 Measurement Error
5.8 Criteria for good Measurement
5.9 Exercise
5.0 OBJECTIVES:
After studying this unit students should be able to :
1. Introduce the concept of Attitude measurement or scaling and its types.
2. Provide the detail s of the various scales
3. Identify the difference between single item Vs multiple items and
Comparative Vs Non -Comparative Scales.
4. Learn the Measurement errors.
5. Present the Criteria for Good Measurement.
5.1 INTRODUCTION:
In our day -to-day life, when we h ave to measure the weight, height, or
some other features of a physical object or we have to judge the person or
his / her qualities, song, or painting then we use some yardstick. Thus, we
measure physical objects as well as abstract concepts. In organizat ions,
many decisions are taken in each of the functional areas of management:
production, marketing, finance and personnel. Some of the examples of
decisions in the area are - acquisition or disposal of materials,
manufacturing, and marketing of products a nd services promoting or
demoting the employees, opening new plants, or closing down the existing
ones, and potential buyers or customers for the company’s products or
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Business Research Methods cases or example s, the businessman or decision -maker needs to learn
something about the attitude of present or potential consumers. Most of
the time in the business the attitude measures can be used to learn which
features of a new product or service concept are acceptabl e or
unacceptable. The study and measurement of attitudes are important since
it is assumed that there is a relationship between attitude and behavior.
The research, however, indicates that such a relationship holds more at the
aggregate level than at the individual level.
In every research information or data is the lifeline of the research as the
entire project outcome is dependent upon this data. If everything else is in
place but the data are not collected keeping in mind the measurement
aspects, then t he entire efforts of the researcher go waste. Hence it is very
important to understand the way different scales are to be constructed to
measure the qualitative data.
5.2 ATTITUDE:
In the business, research scales are generally constructed to measure
beha vior, knowledge, and attitude. Attitude may be defined as the degree
of positive or negative affect associated with some psychological object. It
is a pre -disposition of the individuals to evaluate some object or symbol or
aspect of his world favorably or unfavorably.
Attitude comprises three components.
1. A cognitive component - a person's belief or information about the
object.
2. An affective component - a person's feeling about the object such as
"like" or "dislike", `"good" or " bad"
3. A behav ioral component - a person's readiness to respond behaviorally
to the object.
The number of definitions of ‘attitude’ that have been proposed, the
following are a few definitions:
1) “Attitude is a mental and neural state of readiness expecting a
directive i nfluence upon individual’s response to all objects and
situations with which it is related”.

2) “Attitude is the probability of occurrence of a defined behavior in a
defined situation”.

3) “Attitude is an enduring system of three components centering about a
single object: positive or negative evaluations or beliefs (the cognitive
component), emotional feelings (affective component), and disposition
to take action (action tendency component)”.

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Attitude Measurement and
Scaling 4) L.L. Bernard: “The behavior which we define attitudinal or attitude is
a certain observable ‘set’ organism or relative tendency preparatory to
and indicative of more complete adjustment”.

 Attitude is a psychological construct and is a very of conceptualizing
the intangible. So, attitude scales are among the most difficult t o
construct. Attitude Measurement is a relatively complex and
demanding task especially when it concerns qualitative or abstract
phenomena. This chapter covers the concept of attitude measurement
and its procedures that will help the researcher to unders tand the
criteria for a good measurement scale and expected measure errors,
which will help them to select or design appropriate measures for their
research.

 An attitude is an individual’s enduring perceptual knowledge -based
evaluation and action -oriented process concerning an object or
phenomenon.

 Attitude is a learned tendency of individuals to respond consistently to
a given object of orientation.
Elements of measuring attitude are as follows:
 Persons, objects, events, concepts, or states to be observ ed
 Environmental conditions of the study
 Instruments to be used to perform the steps
 Observations to be collected.
5.3 MEASUREMENT:
Meaning:
Measurement means assigning numbers or other symbols to certain
characteristics of the objects of interest, accor ding to some pre -specified
rules. Measurement is the actual assignment of a number from 1 to 100 to
each respondent.
The term scaling is applied to the attempts to measure the attitude
objectively. Attitude is a result of several external and internal fac tors.
Depending upon the attitude to be measured, appropriate scales are
designed. Scaling is a technique used for measuring qualitative responses
of respondents such as those related to their feelings, perception, likes,
dislikes, interests and preference s.
Definitions:
1) George Lundberg: “Measurement is the way of defining things and not
only a process”
2) Kenneth D. Biley: “Measurement is the process of determining the
value or level, either qualitative or quantitative, of a particular attribute
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Business Research Methods 3) G.C. Helmstadter: “Measurement is a process of obtaining a numerical
description of the extent to which a person or object possesses some
characteristics.”
4) Blalock: “Measurement is a systematic assignment of numbers to a set
of obs ervations to reflect the status of each member of the set -in terms
of the various properties”.
5.4 MEASUREMENT LEVEL/SCALES OR
CLASSIFICATION OF SCALES :
Levels or scale of Measurement is a Yardstick. It is similar to an
instrument for finding weight or vo lume. The scale doesn’t possess the
properties related to most physical measures. Scales are the methods most
often used to turn responses obtained in surveys into numerical indices to
be used within the analysis. Inconclusive research, the researcher has to
rely on quantitative techniques to express in numeric terms the difference
in responses. Hence, a scale is used to represent the item being measured
in the spectrum of possibilities. The values assigned in the measuring
process can then be manipulated i n line with bound mathematical rules.
As per, the dictionary meaning of Measurement scale , in Britannica:
it is the type of information provided by numbers. Each of the four scales
(i.e., nomi nal, ordinal, interval, and ratio) provides a distinct kind of
information. Measurement refers to the assignment of numbers in a very
important manner and understanding measurement scales is very
important to interpreting the numbers assigned to people, objects, and
events.




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Attitude Measurement and
Scaling 1) Nominal scales:
The nominal scale is the simplest method of measurement. This scale is
simply a system of assigning number symbols to events to label them. The
nominal scale classifies individuals, companies, products, brands, or other
entities into two or more categories, Nominal scale is the least powerful
level of measurement. It indicates no order or distance relationship. A
Nominal Scale simply describes diffe rences between things by assigning
them categories.
Gender is an example of a nominal measurement in which a number (e.g.,
1) is used to label one gender, such as males, and a different number (e.g.,
2) is used for the other gender, females. Numbers do not mean that one
gender is better or worse than the other; they simply are used to classify
persons. Any other numbers could be used because they do not represent
an amount or a quality.
The numb ers have no arithmetic properties and act only as labels. The
only measure of average which can be used is the mode because this is
simply a set of frequency counts. In addition, in nominal measurement, the
numerical values just “name” the attribute unique ly.
Some examples of variables that can be measured on a nominal scale
include:
 Gender: Male, female
 Eye color: Blue, green, brown
 Hair color: Blonde, black, brown, grey, other
 Blood type: O-, O+, A-, A+, B-, B+, AB-, AB+
 Place you live: City, suburbs, rural
2) Ordinal scales
Ordinal scales involve the ranking of individuals, attitudes, or items along
the continuum of the characteristic being scaled. It is more powerful than
a nominal scale in that the numbers possess the property of tank order.
The ordin al scale provides a rank order of categories and arranges objects
according to their magnitude in an ordered relationship. For example,
ranking the products of a company according to the satisfaction of
customers, ranking operators in a shop according to t heir skills, ranking
the products on a shop floor according to their quality, etc.
Transformations that do not change the order of properties are permissible
at this level of measurement. Reacher can perform any statistical
operations that do not change t he order of properties that are permissible
at this level of measurement.

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Business Research Methods Rate your experience while using products:
Product Packaging:


Product Design:


In ordinal scales, numbers represent rank order and indicate the order of
quality or quantity, but they do not provide an amount of quantity or
degree of quality.
Some examples of variables that can be measured on an ordinal scale
include:
 Satisfaction: Very unsatisfied, unsatisfied, neutral, satisfied, very
satisfied
 Socioeconomic status: Low income, medium income, high income
3) Interval Scale:
The interval scale has all characteristics of the ordinal scale and in
addition, the units of measurement or intervals between successive
positions are equal. The interval scales are also termed Rating sc ales.
Interval means space between two objects. In the interval scale, numbers
are assigned to objects or events which can be categorized, ordered and
assumed to have an equal distance between scale values. An interval level
of measurement embodies the cha racteristics of both the nominal and
ordinal scales. This is the first quantitative application of numbers.
The centigrade thermometer and Fahrenheit thermometer are some
examples of an interval scale. In a centigrade thermometer, the minimum
number is 00C and the maximum number is 1000C. between these two
numbers the numbers are placed at equal distances in a Fahrenheit
thermometer, the minimum number is 320F and the maximum number is
2120F. munotes.in

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Attitude Measurement and
Scaling The Interval scale provides a more powerful measurement than the ordinal
scales as it incorporates the concept of equality of interval. The statistical
tools range, mean and standard deviation are used in research studies,
especially in collecting attitudinal and overall brand rating information.
Some examples of variables that can be measured on an interval scale
include:
 Temperature: Measured in Fahrenheit or Celsius
 Income Level (Rs): 0 to 10,000, 10,001 to 20,000, 20,001 to 30,000,
and so on.
 Credit Scores: Measured from 300 to 850
 SAT Scores: Measured from 400 to 1,600
4) Ratio scales:
The highest level of measurement is a ratio scale. This has the properties
of an interval scale together with a fixed origin or zero point.
Measurement of physical dimensions like height, weight, and age are
examples of ratio levels . The mathematical and statistical operations can
be performed at this level of measurement. A ratio scale has a natural
zero point and further numbers are placed at equally appearing intervals.
The numbers on the scale indicate the actual amounts of the p roperty that
are measured.
A score of 0 means there is a complete absence of height or weight. A
person who is 1.2 meters (4 feet) tall is two -thirds as tall as a 1.8 -meter (6 -
foot-) tall person. Similarly, a person weighing 45.4 kg (100 pounds) is
two-thirds as heavy as a person who weighs 68 kg (150 pounds).
Some examples of variables that can be measured on a ratio scale include:
 Height: Can be measured in centimeters, inches, feet, etc. and cannot
have a value below zero.
 Weight: Can be measured in kilograms, pounds, etc. and cannot have
a value below zero.
 Length: Can be measured in centimeters, inches, feet, etc. and cannot
have a value below zero.





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Business Research Methods 5.5 SINGLE ITEM VS MULTIPLE ITEMS

5.6 COMPARATIVE VS NON -COMPARATIVE SCALES

Scaling technique s:
The scaling technique is a method of placing respondents in continuation
of gradual change in the pre -assigned values, symbols, or numbers based
on the features of a particular object as per the defined rules. All the
scaling techniques are based on fou r pillars, i.e., order, description,
distance and origin.
1) Comparative Scale:
In the comparative scaling, the respondent is asked to compare one object
with another. A comparative scale is an ordinal or rank order scale that munotes.in

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Attitude Measurement and
Scaling can also be referred to as a non-metric scale. Respondents evaluate two or
more objects at one time and objects are directly compared with one
another as part of the measuring process. For Example, the researcher can
ask the respondents whether they prefer brand A or Brand B of a
detergent. Or in another example, If the researcher asks the respondent to
express his attitude regarding the reasonableness of the price of one face
cream against that of another brand of face cream it becomes a
comparative scale.
Following are the differen t types of comparative scaling techniques: a)
paired comparison Scale, b) Rank order Scales, c) Consent Sum Scale and
d) Q sort Scale.
a) Paired comparison Scale: This is the simplest case of a ranking
scale. In this pared Comparison rating scale, the re spondent is asked to
rate between the two items at a time. Paired comparison is a widely used
scaling technique wherein a respondent has presented a pair of objects to
which he is supposed to provide his/her preference for the object from a
pair. A paired comparison symbolizes two variables from which the
respondent needs to select one. This technique is mainly used at the time
of product testing, to facilitate the consumers with a comparative analysis
of the two major products in the market.
For Example, a respondent may be asked to indicate his/her preference for
Mobiles in a paired manner.
Mobile Carbon Apple Mi Samsung
Carbon * + + +
Apple - * - -
Mi - + * +
Samsung - + - *
No of time a brand is preferred 0 3 1 2

‘+’: Column brand is preferred ov er row brand
‘-’: Row Brand is preferred over column brand
Based on the column totals the ordinal ranking of the 4 brands is as
follows:
1st - Apple
2nd - Samsung
3rd - Mi
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Business Research Methods This method is useful when the number of brands i s limited, as it requires
direct comparison and clear choice.
b) Rank Order Scale: The rank order Scale is a very popular method
of scaling. This method is also called as Force ranking method. This
technique is used in business research wherein the resp ondents are offered
different objects simultaneously for ranking from a list of objects
presented. This technique of scaling is different from the pattern of rating
scales. In this rank order/force ranking rating scale, the respondent is
given a set of att ributes in terms of verbal statements for a single item and
he decides which one or ones, represents the individual being rated most
appropriately and accurately. In rank order scaling the respondent needs to
rank or arrange the given objects according to his or her preference.
(To understand the concept easily we will continue with the same example
of mobile.) For example, a mobile manufacturing company conducted a
rank order scaling to find out the orderly preference of the consumers. It
asked the responde nts to rank the following brands in the sequence of their
choice:
Mobile Brand Rank
Carbon 4
Apple 1
Mi 3
Samsung 2

The above scaling shows that Apple brand Mobile is the most preferred
brand, followed by Samsung, then Mi and the least preferred one is the
Carbon.
c) Constant Sum Scale: In this method, the respondent has to allocate
a given number of points among the items according to some criterion.
The technique involves asking the respondents to assign 10 points to
attributes/features of a Produc t utility. If the attribute is not much
important then the respondents would want to enter zero. The attributes
are scaled by counting the points assigned to each one by all the
respondents and dividing the number of responses. The constant sum
scaling met hod allows the discrimination among stimulus objects without
requiring too much time. Normally a hundred - or two -hundred -point
scale is used for this.
For Example, the following are the 10 attributes of a newly launched
model of a Mobile. Please indicat e out of 100 points you will assign to
indicate your relative preference of the particular attribute.
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Attitude Measurement and
Scaling Attributes points
1 Price 8
2 Speed (RAM) 15
3 Storage Capacity 10
4 Camera (Mega Pixel) 5
5 Battery Backup 12
6 Colour 9
7 Weight 8
8 Warran ty 14
9 Service 12
10 Discount 7
Total Points 100

d) Q-Sort Scale: In this, the purpose of sorting is to get an individual’s
view or attitude towards the object under consideration. The method is
widely applied in the study of personality. Q-sort scaling is a technique
used for sorting the most appropriate objects out of a large number of
given variables. It emphasizes the ranking of the given objects in a
descending order to form similar piles based on specific attributes. In Q -
sort scaling the re spondents are asked to sort the various characteristics or
objects that are being categorized into various groups, such that the
distribution of the numbers of objects or characteristics in each group
follows a normal distribution. It uses a rank order pro cedure and the
objects are sorted into piles based on similarity concerning some criteria.
The number of objects to be sorted should be 60 to 140 approximately.
For example, we are taking 10 brands of biscuits. On the basis of taste, we
classify the brands into tasty, moderate and non -tasty. We can classify on
the basis of price also low, medium, and high. Then we can attain the
perception of people whether they prefer a low -priced brand, high or
moderate. We can classify sixty brands or piles into three pi les. So, the
number of objects is to be placed in three piles -low, medium, or high.
Thus, the Q -sort technique is an attempt to classify subjects in terms of
their similarity to the attribute under study.
2) Non-Comparative Scales:
Non-comparative scalin g requires respondents to evaluate only a single
object. Their evaluation is independent of the other object which the
researcher is studying. A non -comparative scale is used to analyze the
performance of an individual product or object on different parame ters. munotes.in

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Business Research Methods When the opinion of the respondent is sought to be obtained without
reference to a particular product, it becomes a non -comparative scale.
For example, mobile -like Apple companies I phone is reasonability priced
or not. While giving the opinion the respondent is free to compare the
price of, I phone with that of any other mobile. If there is no guideline
about comparative instruction mentioned then the respondent is free to
compare the stimulus with any other stimulus that comes to his mind.
Followin g are some of its most common types:
a) Continuous Rating Scales and b) itemized Rating Scale
a) Continuous Rating Scales: It is also called a graphic rating scale
and it is used to indicate ratings of a particular attribute. It consists of
points on a continuum (such as a line) and the respondents rate the objects
by placing a mark at the appropriate position on a line. In a graphical
rating scale, the respondents are free to place the object in a position of
their choice. It is done by selecting and ma rking a point along the vertical
or horizontal line which ranges between two extreme criteria.
For example, a restaurant owner used a continuous rating scale to evaluate
the service quality of a restaurant.
Service Quality of a Restaurant

Continuous Rat ing Scale
The above diagram shows a non -comparative analysis of the service
quality of a restaurant. Thus, making it very clear that the customers are
quite satisfied with the quality of service provided in the restaurant.
Advantages of the Continuous Rati ng Scale are i) it got developed
within less time, ii) This scaling technique allows to conduct a quantitative
comparison, iii) This technique is easy to use and not more costly to
develop.
Disadvantages of the Continuous Rating Scale are i) It doesn’t hav e
uniformity as different persons will use the graphic scales in different
ways, and ii) difficulties in interpretation of scale item meaning and scale munotes.in

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Attitude Measurement and
Scaling ranges, iii) Scores received from this scale are difficult to use in all the
parameters.
b) Itemized Ra ting Scale: Itemized scale is another essential
technique under the non -comparative scales. It emphasizes choosing a
particular category among the various given categories by the respondents.
Each class is briefly defined by the researchers to facilitate s uch selection.
In this scale, numbers and descriptions, both factors are used. The
respondent is provided with a scale having a number and corresponding
brief description associated with each category.
How Easy or difficult do you find to operate new mobi le handset?

Now a days these sales are widely used in research. Variations among the
itemised rating scales are – i) Likert Scale, ii) Semantic Scale, iii) Staple
Scale.
i) Likert Scale: A Likert scale is termed as summated instrument
scale. This means that the items making up a liker scale are summoned to
produce a total score. Likert Scale was developed by Rensis Likert in
1932. This Scale is most widely used sale in business research particularly
in testing models. In the Likert scale, the researcher provides some
statements and ask the respondents to mark their level of agreement or
disagreement over these statements by selecting any one of the options
from the five given alternatives. A Likert scale measures attitudes and
behaviours using answer choi ces that range from one extreme to another.
In this scale the respondents are asked to respond to each of the statement
in terms of several degree of agreement or disagreement e.g. 1) Strongly
Agree 2) Agree 3) Undecided 4) Disagree 5) Strongly Disagree. These
five points constitute the scale; where at one end is strong agreement and
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Business Research Methods For example, A Mobile manufacturing company adopted the Likert scale
technique for its new mobile range named V Pro. The purpose is to know
the agreement or disagreement of the respondents. Attributes 5
Strongly
Agree 4
Agree
3
Neither
Agree Nor
Disagree 2
Disagree
1
Strongly
Disagree Price range of
mobile is
appropriate After Sales
Service is Good Ad Campaign is
effect ive Show room
demonstration is
proper Sales executives
are cooperative

The above illustration will help the company to understand what the
customers think about newly launched mobile. Also, whether there is any
need for improvement or no t.
Procedure for Likert Scale:
1) First step is to collect relevant statements to the attitude being
investigated.
2) Pre-test for the respondent expressing opinion of each of the item as
per the categories of response in conducted.
3) Respondents are asked to indicate their responses to statement on the
five-point scale as stated above,
4) By adding item score total respondent score is obtained.
5) By analysing pre -test, items to be included final test are decided.
6) The final step is to array these total scores and find out those
statements which have a high discriminatory power.

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Attitude Measurement and
Scaling Advantages of Likert Scale:
1) This Scale method permits the use of items not clearly related to the
attitude being studied.
2) It is relatively easy to construct Liker t-type scale in comparison to
other scales.
3) These scales are considered more reliable as these scales measure the
degree of response.
4) The range of responses permitted to a statement in the Likert scale
provides more precise information about the individual’s responses.
5) These sales are highly suitable that are respondent and stimuli
oriented.
ii) Semantic Differential Scale: The semantic differential scale is an
attitude measurement device developed by Charles E. Osgood, G. J. Suci
and P.H. Te nnenbnum (1975). This is an attempt to measure the
psychological meaning of an object to an individual. The semantic
differential (SD) scale may be defined as, ‘a collection of subscales in
which absolute ratings of concept are done’. A bi -polar seven -point non -
comparative rating scale is where the respondent can mark on any of the
seven points for each given attribute of the object as per personal choice.
Respondents describe their feelings about the products or brands on the
scales with semantic labels. W hen bipolar adjectives are used at the end
points of the scales, these are termed as semantic differential scales. Thus,
depicting the respondent’s attitude or perception towards the object. In
this scale the term ‘concept’ refers to the object which is to be rated.
For example, a well -known brand for mobile, carried out semantic
differential scaling to understand the customer’s attitude towards its
product. The pictorial representation of this technique is as follows:

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Business Research Methods Semantic Differential Scale
From t he above diagram, we can analyze that the customer finds the
mobiles of superior quality; however, the brand needs to focus more on
the styling of its watches.
Advantages of Semantic Differential:
1) While developing the image profile this scale is simpl e to construct
and it provides a good basis for comparing images of two or more
items.
2) The scale provides a very convenient and quick way of gathering
impressions on one or more than one concept.
3) It is Easy and fast to administer.
4) Since the sco res are summed over the different scales, they tend to
average out the peculiarities, if any among the scales as well as
provide a basis for finer discrimination among the individuals.
Limitations of Semantic Differential:
1) The appropriateness of pairs o f adjectives is questionable and little
consensus exists among the experts regarding the suitability of the
pairs selected.
2) It is not appropriate for children, unless presented in a simplified form.
3) Responses given by the subjects are at a superfici al and verbal level.
iii) Stapel Scale: This Scale was developed by Jan Staple. This Scale
method is a variation of semantic differential scale with unipolar rating
scale having generally 10 categories from -5 to 5 without a zero point. A
Stapel scale is that itemized rating scale which measures the response,
perception or attitude of the respondents for a particular object through a
unipolar rating. It is easy to administer. The higher number indicates more
accurate description of the object and lower nu mber indicates lower
description of the object.
Following are the features of this Staple Scale:
1) For every item there is only one word or phrase indicating the
dimension in represents.
2) Each Item has ten response categories.
3) For every item an even number of categories are allotted.
4) There is numerical labels are given not verbal labels.
For example, a tours and travel company asked the respondent to rank
their holiday package in terms of value for money and user -friendly
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Attitude Measurement and
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Stapel Scale

With the help of the above scale,
the management or the company
owner can take certain policy
decisions to improve its package
in terms of value for money.
However, the decisive point is that
the interface is quite user -friendly
for the custom ers so overall we
can say that customer are quite
satisfied.


Advantages of Staple Scale:
1) This scale is very easy for developer and respondent as only one
descriptive word or phrase at a time must be considered.
2) Staple scale designed to measure bot h the direction and intensity of
attitudes simultaneously.
3) It enables the researcher to avoid the task of creating bipolar adjective
pairs.
4) Scale also permits finer discrimination in measuring attitudes.
5) The data obtain staple scale can be analy sed by using procedures
similar to the ones for semantic differential scales.
6) Overall attitude scores can be computed for the respondents by
summing their rating on the individual items.
7) In this staple scale there is no need to develop complete st atement.
Limitations of Staple Scale:
1) Descriptive words may bias respondent if phrased in a positive or
negative manner
2) A drawback is that descriptor adjectives can be phrased in a positive,
neutral or negative vein and the choice of phrasing has been shown to
affect the scale results.
5.7 MEASUREMENT ERROR:
The research study should be free of any measurement errors.
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Business Research Methods study. Measurement Error (also called Observational Error) is th e
difference between a measured quantity and its true value.
Definition:
1) “The measurement error is defined as the difference between the
actual value and the measured value. The true value is the average of
the infinite number of measurements, and th e measured value is the
precise value.”
2) “Measurement error is the difference between the observed value of
a Variable and the true, but unobserved, the value of that Variable .”
3) “Measurement error causes the recorded values of Variables to be
different from the true ones.”
Sources of Erro r in Measurement:
The researcher should be aware of the causes of measurement errors,
however , four major possi ble sources of error in measurement are as
follows:











1) The Respondent:
Sometimes the respondent may be reluctant to reveal their st rong negative
feelings or he may have very little understanding of the situation but he
never admits his ignorance. All this reluctance is likely to result in an
interview of ‘guesses.’ Transient factors like fatigue, boredom, anxiety,
etc. could limit the power of the respondent to report accurately and
absolutely.

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2) Situation:
Sometimes due to stress and strain, the respondent does not give a proper
response which hurts data collection and affects the correct measurement.
Situational factors may also come in the way of correct measurement. Any
condition which places a strain on the interview can have serious effects
on the interviewer -respondent rapport. For example, if the superior is
present at the time of the interview, he can distort responses by j oining in
or merely by being present. If the respondent feels that secrecy about his
opinion or views is not assured, he may be reluctant to express the facts
about the situation and actual feelings.
3) Measurer:
The interviewer or the person collecting d ata may arise some errors. The
body language of the measurer the voice and the tone may have an impact
on the data collection process. The stereotype appearance and actions of
the respondent may create bias. Errors may also take place, particularly in
the data analysis stage because an incorrect recording of data, incorrect
coding, faulty tabulation and statistical calculations leads to errors.
4) Instrument:
Errors may arise because of the imperfect measuring instrument. The use
of complex words and ja rgon used may not be understood by the
respondent. Ambiguous meanings, poor priority, inadequate space for
replies, response choice deletions, etc. are many effects that make the
measuring instrument imperfect and may affect the dimension of expected
resul ts.
The researcher must know that the correct dimension depends on
successfully meeting all of the problems listed over. He must try to
exclude errors or else deal with all the possible errors of measurement so
that the final results may not be defiled.
Types of Errors in Measurement
The error that may arise from different sources is usually classified into
the following types. These types are 1) Gross Errors, 2) Systematic Errors
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Business Research Methods

These types are explained below in detail.
A. Gross Errors:
The gross error occurs owing to human mistakes. Such errors occur when a
mistake is made while recording data results, using a measurement
instrument, or calculating measurement. They are usually caused by
abrupt changes in the prevailing physical circumstances, system
faults/bugs, or operator errors.
For example, take into account that the experimenter uses the instruments
wrongly and takes the incorrect reading, or they will record the incorrect
data. Such type of error comes under gross error. Fo r example – The
experimenter reads the 40.5ºC reading while the actual reading is 50.5Cº.
This happens because of the oversights. The experimenter takes the wrong
reading and because of this, an error occurs in the measurement. The gross
error can only be avoided by taking the reading carefully.
Such type of error is incredibly common in measurement. The complete
elimination of such type of error is not possible. Some of the gross errors
are easily detected by the experimenter but some of them are difficul t to
find.
The following methods can remove the gross error.
1) The reading should be taken very carefully.
2) Two or more readings should be taken of the measurement quantity.
3) The readings are taken by the different experimenters and at different
points for removing the error.
B. Systematic Errors
A systematic error means that your measurements of the same thing will
vary in predictable ways: every measurement will differ from the true
measurement in the same direction, and even by the same amoun t in some
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Attitude Measurement and
Scaling Systematic error is also referred to as bias because your data is skewed in
standardized ways that hide the true values. This may lead to inaccurate
conclusions.
Systematic error is caused by many factors that consistently affect the
measu rement of the variable across the sample. Systematic errors are
errors that have a clear cause and can be eliminated for future
experiments.
The systematic errors are mainly classified into three categories.
a) Instrumental Errors
b) Environmental Errors
c) Observat ional Errors

Image Source: - Caroline Monahan
There are four different types of systematic errors:
1) Instrumental Errors:
When the instrument being used doesn’t operate properly inflicting error within
the experiment (such as a scale that reads 2g qui t the particular weight of the
thing, causing the measured values to read too high consistently ).These errors
primarily arise because of the three main reasons.
(a) Inherent Shortcomings of Instruments – Such varieties of errors
in square measure constitu tional in instruments owing to their mechanical
structure. They will ensure the production, activity, or operation of the
device. These errors might cause the error to read too low or too high. munotes.in

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Business Research Methods For example – If the instrument uses a weak spring, then it wi ll result in a
high value of measuring quantity. The error happens within the instrument
because of the friction or physical phenomenon loss.
(b) Misuse of Instrument – The error happens within the instrument
because of the fault of the operator. A proper or decent instrument used in
an unintelligent way may give an enormous result.
For example – the misuse of the instrument might cause the failure to
regulate the zero of instruments, poor initial adjustment, and victimization
resulting in too high resista nce. These improper practices might not cause
permanent harm to the instrument; however, all are identical land they
cause errors.
(c) Loading Effect –It is the foremost common type of error that is
caused by the instrument in measurement work. For instan ce, once the
meter is connected to the high resistance circuit it offers a misleading
reading, and once it’s connected to the low resistance circuit, it offers a
dependable reading. This suggests the meter includes a loading impact on
the circuit. The erro r caused by the loading impact will be overcome by
using the meters intelligently. For instance, when measuring a low
resistance by the ammeter -voltmeter technique, a meter having a high
value of resistance ought to be used.
2) Environmental Errors:
Envir onmental errors will happen because of the outside situation of the
measurement instruments. Such kinds of errors primarily occur because of
the effect of temperature, pressure, humidity, dust, vibration, or as a result
of the magnetic or electric field. When the surrounding environment (such as
a lab) causes errors within the experiment. The corrective measures utilized
to eliminate or scale back these undesirable effects are:
a) The arrangement ought to be created to stay the conditions as constant
as attain able.
b) Using the instrumentation that is free from these effects.
c) By using the techniques which eliminate the result of those
disturbances.
d) By applying the computed corrections.
3) Observational Errors:
The observational errors square measure because of w rong observation of the
reading or the fault study of the instrument reading, and therefore the sources of
those errors square measure several. For example, the indicator of a meter
retunes a touch over the surface of the scale. As a result, a fault happen s except
the line of the image of the witness is accurately on top of the indicator. To
reduce the parallax error extremely precise meters are offered with reflected
scales. When the scientist inaccurately reads a measuring wrong (such as when
not standing straight -on when reading the quantity of a f l a s k i n f l i c t i n g t h e
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Attitude Measurement and
Scaling C. Random Errors:
Random errors are the result of unpredictable changes. They are not like
systematic errors; random errors can cause varied results. One moment a
reading can be too high and the next moment the reading is simply too
low. You’ll be able to account for random errors by repeating your
measurements. Taking repeated measurements permits you to use
statistics to calculate the random error.
An error that is caused by unexpected changes within the climatic
condition, such type of error is termed a random error. A random
measurement error stems from fluctuation within the conditions among a
system being measured that has nothing to do with the true signal being
measured. These types of errors remain even after the removal of the
systematic error. Hence such type of error is also known as residual error.
An example of random error is putting the same weight on an electronic
scale many times and getting readings that change in an exceedingly
random fashion from one reading to the next. The var iations between
these readings and also the actual weight correspond to the random error
of the scale measurements. Random error (also known as unsystematic
error, system noise, or random variation) has no pattern.
There are two types of random errors

Image Source: - Caroline Monahan
1) Observational random error: Random observational errors don’t
seem to be predictable. They fluctuate between being too high or too low.
An example would be an instrument's reading unsteady/fluctuating. If you
were to tak e the mid -point of the fluctuations, you’ll be too high on one
measurement but too low on the next. When the observer makes consistent
observational mistakes (such as not reading the scale correctly and writing
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Business Research Methods 2) Environmental random error: Environmental errors are caused by
the laboratory environment. An example can be a malfunctioning
instrument. In my freshman chemistry lab, I had a pH meter that may not
stay calibrated. After five minutes , the pH v alues would fluctuate
erratically when unpredictable changes occur in the environment of the
experiment (such as students repeatedly opening and closing the door
when the pressure is being measured, causing fluctuations in the reading).
Some common sources of random error include:
 natural variations in real -world or experimental contexts.
 imprecise or unreliable measurement instruments.
 individual variations between participants or units.
 poorly controlled experimental procedures.
Reducing random error
Rand om error is almost always present in research, even in extremely
controlled settings, whereas , you can’t eradicate it, you will be able to
reduce random error using the following subsequent strategies.
a) Take repeated/continual measurements: A simple way to increase
precision is by taking repeated/continual measurements and using their
average. For example, you might measure the wrist circumference of a
participant three times and obtain slightly different lengths every time.
Taking the mean of the three m easurements, instead of using just one,
brings you much closer to the true value.
b) Increase sample size: Large samples have less random error than
small samples. That’s as a result of the errors in different directions
cancelling each other out more eff iciently when you have more data
points. Collecting data from a large sample increases precision and
statistical power.
c) Control variables: In controlled experiments, you should carefully
control any extraneous variables that could impact your
measureme nts. This ought to be controlled for all participants so that
you take away key sources of random error across the board.
5.8 CRITERIA FOR GOOD MEASUREMENT:
In standard research, any score received by a measuring instrument is the
total of both the ‘true score’, which is unknown, and the ‘error’ in the
measurement process. If the error margins are low and reporting of results
of the research are of high standards, no doubt the research will be fruitful.
As per the opinion of Kimberlin & Winter stein , 2008 ‘If the measurement
is very accurate then a researcher will find a true score’. The basis of good
research is the trustworthiness (reliability and validity) of the
data/information to make decisions; otherwise, a good decision cannot be
made. Thatcher, 20 10; Twycross & Shields, 2004 emphasized that in
quantitative research a measurement can be reliable, but invalid; however,
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Attitude Measurement and
Scaling Measurement criteria may be a system of metrics that defines what
program a nd project success are and t he way they should be measured
significantly, the measurement criteria must be aligned with the program
objectives and therefore the stakeholders' desires and expectations. The
adequacy of scale is judged in an exceedingly manne r on having a
measuring of acceptable quality, t he overall usefulness of the scale
depends upon its validity, reliability, and sensitivity.
These are mentioned below:
1) Validity: A scale is said to be valid if it measures correctly what it
is expected to measure. As we know attitudes are multifaceted, therefore
single item scales are usually deficient in this criterion. In other words, an
attitude scale is valid only when it is real -and correct: There are several
differences.
Types of validity as disc ussed below.
a) Content Validity: This is also known as face validity. Here the
contents of an attitude scale should cover all relevant facets of an issue
that influence the respondent's attitude. Suppose we have a -scale to
measure the job satisfaction of employees in, an organization. The scale, -
covers various dimensions like nature of work, pay, security and superior.
The scale, however, lacks content validity as it does not cover an
important dimension like company policies and practices. This could
happen because what dimensions to be included in a scale would depend
on the judgment of the researcher which is likely to vary from individual
to individual. Therefore, to avoid this, it would be better to approach a
group of knowledgeable persons rather than leaving it to one person.
b) Construct Validity: It is known that it is not possible to measure
attitude directly. It is inferred indirectly from the responses given by the
respondents. Construct validity involves understanding the theoretical
rationale underlying the obtained measurements. The content validity of
an attitude can be assessed quantitatively by finding its correlation with
measures of other constructs that one would expect to be strongly
associated with the attitude and measures of con structs that would not be
correlated to the attitude. A strong correlation of attitude with the
construct is an example of convergent validity whereas a low correlation
of attitude with the construct is an example of discriminating validity. A
scale with c onstruct validity should have both convergent and
discriminating validity.
c) Predictive Validity: It involves the ability of a measured market
phenomenon at one point in time to predict successfully another market
phenomenon at another point in time. If the correlation between the two is
high, the initial measure is said to have a high predictive, validity. An
opinion questionnaire that forms the basis for correctly forecasting, the
demand for a product has high predictive validity.
2) Reliability: A sc ale is said to be reliable. When it gives the same
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Business Research Methods every time, it would be reliable. However, such a scale cannot be valid as
we know that the validity depends upon correct measurement. Reliability
is achieved when the scale is free from erratic measurements. The
following two methods are used for testing reliability. Test -Retest
Reliability: It is concerned with how stable the ratings are when the scale
is administered to the same group of persons at two _different points of
time. If there is a high correlation between two sets of scores
(consistency), the test -retest reliability is very high. One should be
cautious while using this method to test reliability. If the time difference
betwe en two sets of observations is long, the attitude may likely change.
Further, if the time difference is too small, the respondents are likely to
remember their earlier responses and therefore memory effect may distort
the reliability test. There are no gui delines for determining what should be
the ideal time interval between two observations to take care of these
problems. A rule of thumb is to use the time interval between two to four
weeks. Split -Halt Reliability: It can be assessed only for multiple -item
scales. Here the scale items are split randomly into two equal parts. At
times splitting may be done by putting an even number of items on one
side and an odd number on the other side. The correlation coefficient
between respondents' total scores derived from two sets of items is
computed. A high degree of correlation indicates a high split -half
reliability of the scale.
3) Sensitivity: The sensitivity of a scale is closely related to its
reliability. A scale can be able to discriminate between responden ts who
differ even slightly in terms of their attitude toward something. An
essential of a rating scale is that it should have a sufficient range of
numbers to help detection of fine variations in attitude.
5.9 EXERCISE
Long Answer Questions:
1) Define ‘measurement’ in research. What are the different levels of
measurement?
2) What are the various comparative scaling techniques?
3) What are the various non comparative scaling techniques?
4) What are the various sources of measurement of errors?
5) Explain the different sources of error in measurement.
6) Explain the criteria for good measurement.


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QUESTIONNAIRE DESIGN
Unit Structure
6.0 Objectives
6.1 Questionnaire method
6.2 Types of Questionnaires
6.3 Process of Questionnaire Designing
6.4 Advantages and Disadvantages of Questionnaire Method
6.5 Sample Questionnaire
6.6 Self-Assessment Question s
6.0 OBJECTIVES
1. To understand the different methods to design the questionnaire.
2. To understand the step -by-step procedure used to construct the
questionnaire .
3. To learn the usage of various types of questions during
questionnaire design.
4. To stud y the benefits and drawbacks of different types of
questionnaires.
5. To identify the content of the questions asked during questionnaire
development in order to avoid bias and to get accurate responses .
6.1 QUESTIONNAIRE METHOD
In research studies, one of the most popular methods of data collection
is through questionnaires. This method is used for conducting academic
research, business research as well as research by the government. The
questionnaire's purpose is to gather valuable information about the study
by asking questions based on the hypothesis. This method is especially
useful in instances when conducting interviews with a large group of
people is impossible.
It consists of a set of questions which are arranged systematically and
these ques tions are asked to respondents to obtain useful information
related to the study. In this method, data can be gathered in a number of
ways, including online, over the phone, on paper, or in person. If the
data gathered from the questionnaire is accurate an d unbiased, then it
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Business Research Methods the questionnaire should be simple to grasp because if the respondent
does not understand the question, he may offer an incorrect response
that will affect the data analysis.
The questionnaire approach is simple to use, and the results can be
statistically analyzed. An adequate number of questions should be
covered in the questionnaire. With this method, different categories of
information can be gathered from a speci fic group of people. Before we
conduct the main survey, it is advised to conduct a pilot study. This pilot
study is very similar to the actual survey; it is done to understand the
strengths and weaknesses of a questionnaire. Errors might occur during
the q uestionnaire design, so it is important to conduct a pilot study for a
small group of people so that modifications can be made to the
questionnaire before it is floated to a large group of people. The pilot
study aids in the detection and elimination of er rors in the questionnaire.
Benefits of Questionnaire Method
1. The main benefit of the questionnaire method is that it is cost -
effective.
2. Different varieties of questions can be asked in the questionnaire
method.
3. Questions can also be written in the local l anguage.
4. Data can be gathered by sharing the questionnaire with local as well
as global audiences.
5. When the questionnaire is made through an online platform, it
reduces the time of data coding.
6. Questionnaires can be used to collect numeric (quantitative) o r non -
numeric (qualitative) data.
7. Since the researcher is not required to be present while the surveys
are completed, data can be gathered swiftly.
6.2 TYPES OF QUESTIONNAIRES
The following are the various types of questionnaires:
1. Online Questionnaire
In an online questionnaire, questions can be prepared using an online
portal and sent over an internet channel. The advantage of an online
questionnaire is that it is not restricted by location. It may be delivered to
responders from all over the world. Anoth er benefit of an online
questionnaire is that the respondent may respond to the questions at his or
her convenience. When a questionnaire is delivered over an online
channel, such as email, the return rate is low. This might occur if the
respondent chooses to ignore the mail. In such cases, the researcher may
decide to send respondents follow -up emails. To complete the online
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educated and skilled in using the internet platform. It is a cos t-effective
method of getting responses to the questions. Through an online
questionnaire, data can be gathered in real -time.

2. Telephone Questionnaire

In a telephonic questionnaire, the researcher connects with a potential
responder via telephone to get r esponses to the questions. When the
study's sample size is small, a telephone questionnaire is the ideal option.
The respondent might not feel comfortable responding to a large number
of questions via telephone. Hence, this method is suitable when the
numb er of questions is less. When compared to online questionnaires,
telephone questionnaires have a higher response rate. The disadvantage of
telephone questionnaires is that they are more expensive and time
consuming than other approaches.

3. Paper Questionnai re
The paper -based questionnaire is the most traditional technique, in which
the questions are presented to potential responders on paper. Instead of
having connection to the internet, this method needs the respondent to
have access to a pen or pencil. Paper questionnaire are more expensive
than other forms of questionnaire. The biggest downside using paper
questionnaires is the danger of data loss, which might have a significant
influence on the study. This method of Data collection is more suitable
where the sample size is small.
4. Mail Questionnaire

This approach was widely used in the early years since it was the simplest
form of data collection. The researcher sends the questionnaire to the
potential responder by mail, enclosed in an envelope. Since the
questionnaire is in paper format, the respondent must be qualified to read
and answer the questions. After completing the questionnaire, the
respondent must mail it to the researcher. The respondent does not require
access to technology for this metho d. This approach has the benefit of
covering a vast geographic region, allowing respondents to reply at their
convenience, and putting less pressure on respondents than a telephone
questionnaire. The downside to this approach is that the responder is
unabl e to clarify his queries, and the response rate is low.
Type of Questions
a) Open ended questions
An open ended question allows the respondent to reply to the question in
detail rather than just one word. The questions may begin with who, what,
why, when, an d so on in order to provoke a long conversation. This type
of question allows the respondent to respond in whatever way they like.
Open -ended questions provide more information, new insights, and the
opportunity to probe. Example:
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Business Research Methods a) What is the purpose of e ducation?
b) Why are you purchasing this product?
c) What are your thoughts on your workplace's culture?

b) Close ended questions
Closed -ended questions require the respondent to choose between two or
more alternatives. In this type of question, the respondent ca n make a
quick decision since his options are limited. The main advantage of Close -
ended questions over open -ended questions is that the responses can be
easily coded. If the options provided for the question are insufficient, the
researcher may include an open -ended question at the bottom of the
questionnaire so that the respondent can provide extra information not
covered by the alternatives. Examples:

a) Do you enjoy drinking coffee?
I. Yes
II. No

b) How old are you?
I. 18-25
II. 26-35
III. 36-45
IV. Above 45

c) Which smart phone do you use?
I. Oppo
II. One Plus
III. Xiaomi
IV. Samsung
V. Other (please elaborate):__________

c) Dichotomous Questions.
A dichotomous question is a question that has just two possible responses,
for example, male or female. When compared to other types of questions,
dichot omous questions take less time to respond to. Another benefit of this
type of question is that it is simple to code, analyse, and interpret.
A dichotomous question allows respondents to make a quick, simple, and
direct response. Example:

a) Do you own a vehi cle?
I. Yes
II. No

b) Instagram is a social networking platform.
I. Agree
II. Disagree

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d) Likert Questions
In Likert scale questions, the respondent's attitude or opinion is usually
measured using a five -to-seven -point scale. A question using a Likert
scale determines if the respondent agrees or disagrees with the statement.
While designing the Likert scale questions, symmetry is maintained by
having an equal number of positive and negative questions. The middle
option on the Likert scale is "neutral" or "neither agree nor disagree", and
the responder picks this option when he is indecisive. Example:
5-point Likert Scale
a) I would recommend this product to others.
(1) Strongly disagree
(2) Disagree
(3) Neutral
(4) Agree
(5) S trongly agree
b) I enjoy my current job.
(1) Strongly disagree
(2) Disagree
(3) Neither agree nor disagree
(4) Agree
(5) Strongly agree
7-point Likert Scale
a) I believe that putting in more effort at work will help me advance in my
profession.
(1) Strongly disagree
(2) Disagree
(3) Somewhat disagree
(4) Neutral
(5) Somewhat agree
(6) Agree
(7) Strongly agree
b) When a new task at work is assigned to me, I like to do it first.
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Business Research Methods (3) Somewhat disagree
(4) Neither agree nor d isagree
(5) Somewhat agree
(6) Agree
(7) Strongly agree
6.3 PROCESS OF QUESTIONNAIRE DESIGNING
The process of designing the questionnaire is the most crucial stage of the
study. The questionnaire forms the centre of the study, and if there is an
error in the questionnaire, it will have an impact on the analysis part and
the conclusion part. Hence, the questions must be designed carefully and
bias needs to be avoided. The respondent should be able to understand
each and every question. Every question in the questionnaire should be
simple for the respondent to read and comprehend. It is important to
ensure that the number of questions asked is not more than what is
required throughout the questionnaire design process. In addition, the
questions should be able to extract precise responses.
Guidelines for developing an effective questionnaire
1. Length of the questions
The length of the question is desired to be short and precise rather than
long. If the question is excessively lengthy, the respondent may skip
part of the question and provide an incorrect answer. In order to
prevent this scenario, Oppenhein (1986) recommended that
researchers stick to a rule of thumb of keeping questions under 20
words or one-line long.
2. Sequence of the questions
While sequencing t he questions, in the beginning, the questions should
be asked to establish a good rapport with the respondent. The initial
questions should be general so that the respondent does not have to
think too hard to answer them. The general questions are followed by
specific questions designed to extract detailed information from the
respondent and demand the respondent's thoughts, opinions, and
judgements on a certain subject. The questionnaire should start with
easy-to-answer questions and then go on to more cha llenging
questions about sensitive topics. It is advised to avoid asking positive
and negative questions about the same theme one after the other.
3. Layout of the Questionnaire
The following information should be provided in the questionnaire's
introduction :
 The Title of the research.
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 A concise summary and the purpose of the study.
 Guidelines for responding to the questions.
The questionnaire should be designed in such a way that answering
the questions is made as simple as possible. It is vital to establish a
rapport with the respondent in order to encourage them to answer the
questions. The respondent must be assured that their response and
identity will be treated confidentially. Each sectio n of the
questionnaire should have instructions so that the respondent can
easily answer the questions.
4. Wordings of the questions
The wording selected when designing the questions will have an
impact on the response. As a result, it is critical to careful ly design
each question because a minor modification in phrasing may result in
a different response from the respondent. The question's wording
should be chosen in such a manner that it expresses the same meaning
to all respondents and is interpreted in th e same way by all
respondents. The question should not include more than one concept
since the respondent may find it difficult to comprehend. It is
recommended that the language be kept simple so that questions may
be easily understood and accurate respon ses can be acquired.

Designing the questionnaire using Google Forms

There are several online tools for designing and developing questions.
Google Forms is one such well -known tool with several features. A
researcher can make each and every question manda tory to answer,
and the sequence of questions for each respondent can also be
changed. This will assist in avoiding bias while the respondent fills
out the questionnaire. A Google form also allows researchers to make
a questionnaire in sections, and it als o has a feature to include images,
upload documents, or capture the email address of the respondent. If
the respondent does not answer a particular question in the
questionnaire, it will create a blank space during data coding. In such
a situation, this bl ank space can be given a neutral value during
coding. In order to avoid the situation, all the questions can be made
mandatory in the Google form, but care has to be taken to ensure that
each and every question is easy to understand for the respondent.
Designing the questionnaire and data collection through Google
Forms is both cost -effective and time -saving.

Preliminary testing of the Questionnaire
The questionnaire survey is developed tentatively at first, and then,
after a preliminary test, further modi fications are made. The
population selected for conducting the preliminary test or pilot study
is the same one that will be used for the final study. A preliminary test
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Business Research Methods improvements may be made before carrying out the survey for the
entire targeted population. This test assists researchers in identifying
questions that respondents might either misinterpret or are unable to
comprehend.
6.4 ADVANTAGES AND DISADVANTAGES OF
QUESTIONNAIRE METHO D
Advantages
1. The questionnaire method enables researchers to gather a large
amount of data with ease.
2. In this method, the researcher doesn't have to be present during
surgery. The researcher may choose to send the questionnaire to the
respondents via mail or social media platform and the respondent fills
it out without monitoring. It eliminates the need for investigators to be
trained.
3. Data can be gathered in an online format effortlessly.
4. This method is not limited to a specific region. Researchers may
choose to send the questionnaire across the globe to a targeted group
of people through online mode.
5. One of the major advantages of this method is that it is cost -effective.
6. Different varieties of questions can be asked in this method.
7. Questions can also be w ritten in the local language.
8. If the questionnaire is created through an online platform, it reduces
the time of coding the data.
9. The responder has adequate time to complete the questionnaire.
10. This method allows the researcher to ask the respondent persona l
questions, which is difficult to do during a face -to-face interview.
Disadvantages
1. The respondent may not be truthful while answering the questions.
2. If the question is not designed properly, the respondent may find it
difficult to interpret it.
3. If the number of questions is excessively high or the questions are
unrelated to the study, the respondent may experience survey fatigue.
4. If the respondent doesn't read the complete question, he may give an
inaccurate answer.
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6. The responder must be educated in order to complete the questionnaire.
7. The questions in this method are structured, and asking the same set of
questions to various respondents may not match their profiles.
8. In comparison to the interview method, a questionnaire cannot
completely capture a respondent's emotional responses.
9. It is impossible to tell whether the respondent really comprehended
the question using this method.
10. This method gives respondents the option to modify their earlier
answer if it conflicts with the subsequent responses.
6.5 SAMPLE QUESTIONNAIRE
Questionnaire 1
This questionnaire is based on Facebook usage.
1. Your Name :
2. Your Age :
3. Gender :
 Male
 Female
 Other

4. Education Qualification :
 10th
 12th
 Graduation
 Post-Graduation
 Ph.D.

5. Occupation
 I am working.
 I have my own Business.
 I am Student.
 Others

6. Do you use Facebook?
 Yes
 No munotes.in

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Business Research Methods 7. What is your purpose for using Facebook?
 To learn about other people's lives.
 For entertainment.
 To request help regardi ng personal or professional issues.
 Searching for information.
 Any other reason (Please elaborate) :__________

8. Apart from Facebook, what other social networking sites have you
joined?
 Instagram
 Twitter
 LinkedIn
 Reddit

9. Why did you decide to join Facebook?
 My friends requested me to join Facebook.
 Due to advertisements.
 To stay in touch with friends.
 Any other reason (Please elaborate) :__________

10. How long have you been on Facebook?
 <1 Year
 1-2 Years
 2-3 Years
 >3 Years

11. How frequently do you use Facebook ?
 Daily
 Weekly
 Monthly
 Quarterly



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Questionnaire Design
12. What is it about Facebook that you enjoy?
(Please note: 1 - Minimum value and 5 - Maximum value)
Features of Facebook 1 2 3 4 5 News Feed
Sending someone a friend request.
The wall where users' content is displayed.
Sending messages to friends.
Using groups for discussion.
Availability of Likes and Reactions feature.
Timeline feature which displays content according to
year, month and date.

13. Do you feel comfortable updating y our profile or uploading a photo?
 Yes
 No
 Maybe
14. How likely are you to tell your friends about Facebook?
 Extremely likely
 Likely
 Neutral
 Unlikely
 Extremely unlikely
15. How frequently do you update your Facebook profile with new
content?
 Once in a Day
 Once i n a Week
 Once in a Month
 Once in a Year
 Never munotes.in

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Business Research Methods 16. Please rate your overall Facebook experience.
 Very Satisfied
 Satisfied
 Neutral
 Dissatisfied
 Very dissatisfied
Questionnaire 2

This questionnaire is related to Mobile Service Provider.

1. Name: _________ ___

2. Email ID: __________

3. Location: __________

4. Age: ______________

5. Gender:
 Male
 Female
 Others
6. Occupation
 Student
 Working
 Looking for Job
 Own Business
 Home maker
 Others :___________
7. Monthly Salary
 Not applicable
 Less than 10,000 /-
 10,000 -20,000/ -
 20,000 -30,000/ -
 More than 30,000/ -
8. How long have you been using a mobile phone?
 Less than a year
 2 Years
 2-3 Years
 3-4 Years
 More than 4 Years munotes.in

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Questionnaire Design
9. Which mobile service provider do you use?
 Reliance Jio
 Airtel
 Idea
 BSNL
 Other (pl ease specify):__________
10. How long have you been using the mobile service provider mentioned
above?
 Less than a year
 2 Years
 2-3 Years
 3-4 Years
 More than 4 Years
11. Please specify what type of connection you are using.
 Pre-paid
 Post-paid
 Other (plea se specify):__________
12. Rate the following services provided by your mobile service provider.
(Please note: 1 - Minimum value and 5 - Maximum value)
Services 1 2 3 4 5
Calling
Internet
SMS
International Call
Subscriptions to App s
Caller tune

13. What aspects do you consider while choosing a mobile service
provider?
 Data plans
 4G availability
 Good Service
 Network availability
 The Quality of Audio and Video Calls
 Other (please specify):__________ munotes.in

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Business Research Methods 14. Have you porte d your number to the current mobile service provider?
 Yes
 No
15. How satisfied are you with your current mobile service provider?
 Very Satisfied
 Satisfied
 Neutral
 Dissatisfied
 Very dissatisfied
16. Would you recommend switching to your mobile service provider to a
friend, family member, or co -worker?
 Very unlikely
 Somewhat unlikely
 Neither likely nor unlikely
 Somewhat likely
 Very likely
17. Please specify your mobile network.
 2G
 3G
 4G
 Others
18. What do u like the most about your mobile service pr ovider?
 Internet Speed
 Audio call quality
 Affordable price
 Network availability in your area
 Excellent service

Questionnaire 3
This questionnaire is based on consumer behaviour towards online
shopping. The questions are asked to capture the following v ariables:
Attitude, Perceived Usefulness, Perceived ease of use, and Buying
Intention.
Question nos. 1 to 8 are demographic questions which are asked to
understand the background of the respondents. Question nos. 9 to 24 are
asked to capture the four varia bles of the study.

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Questionnaire Design
Demographic questions
1. Name:
2. Email Id:
3. Contact Number:
4. Your age:
 Under 19
 19-25
 26-35
 36-50
 50+

5. Your marital status :
 Single
 Married
 Divorced
 Others

6. Gender:
 Male
 Female
 Other

7. What is your highest level of educ ation?
 Prefer not to say
 Bachelor's Degree
 Master's Degree
 Ph.D.

8. Please specify your location
 Mumbai
 Bangalore
 Delhi
 Pune
 Any other location (Please specify) :__________
Attitude
9. I think online shopping is more effective than offline shopping.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree

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Business Research Methods 10. I prefer online shopping for purchasing household products.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree

11. I feel online shopping saves time.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree

12. Online buying is quite beneficial.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree
Perceived Usefulness
13. I believe online shopping doesn’t require a lot of mental effort.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree

14. Online s hopping is more useful as it saves time and money.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree

15. I believe online shopping provides more information about the product.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree

16. Shopping online i ncreases my ability to purchase the product I want.
 Strongly Agree
 Agree
 Neutral
 Disagree
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Questionnaire Design
Perceived ease of Use
17. I think shopping online makes it easier for me to purchase products.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagre e

18. My search is more effective when I shop online.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree

19. I feel shopping products online is easy for me.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree

20. I feel I have no problem using internet for shopping.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree
Buying Intention
21. I would purchase products online.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree

22. I intend to use the internet to make a purchase decision towards a
product .

 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree


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Business Research Methods 23. I am willing to purchase a product through the internet rather than the
offline mode.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree

24. In the near future, I plan to purchase the produc t online.
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree

6.6 SELF ASSESSMENT QUESTIONS
1. What is a questionnaire?
2. What are the steps involved in designing the questionnaire?
3. Which rules must be followed while constructing the questionnair e?
4. How would you create a questionnaire to measure consumer
satisfaction towards banking services?
5. What are advantages and disadvantages of questionnaires method?
6. Write a short note on types of questions.
7. Distinguish between open ended questions and close ended
questions.
8. What is preliminary testing of the Questionnaire ?
9. Distinguish between 5 point and 7 point Likert scale with example.
10. Write a short note on benefits of Questionnaire m ethod .

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SAMPLING AND DATA PROCESSING
Unit Structure
7.0 Objectives
7.1 Introduction
7.2 Sampling Concepts
7.3 Sampling Design
7.4 Sample Size
7.5 Sampling Methods
7.6 Editing and Coding of Data
7.7 Classification and Tabulation of Data
7.8 Summary
7.9Self Assess ment Questions
7.0 OBJECTIVES
After analyzing this module, you should be able to:
● Understand the meaning and nature of sampling concepts
● How to design a sample for the research
● Importance of correct sample size
● Understand the significance of sampling met hods
● Selection of right sampling method
● Knowledge about editing and coding of data
● Knowledge about classification and tabulation of data
7.1 INTRODUCTION
In most of the research projects, the responses by the respondents
determine the accuracy and relevanc e of research. Sample helps to collect
the vital information. Today’s world is highly dynamic. Any study is
expected to be completed in a short time to maintain the relevance of
situations and characteristics before it gets changed. The technique
through w hich a sample is selected is crucial. Sampling helps to collect
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Business Research Methods Selection of participants is an important process. The way we select
participants will determine the population. It will determine the population
to which w e may generalize the findings of the research. If the job is done
poorly at the sampling stage of the process of research, the integrity and
the relevance of the whole research is at risk.
A sample is a small version of a larger group. The size of the sam ple plays
an important role in research. Sample is a group of people taken from a
large group for the measurement of the findings of the research. The
following factors are important while determining the size of the sample -
1. Nature of the universe
2. Nature of the study
3. Availability of time
4. Financial availability and options
5. Standard of accuracy and reliability
6. Size of the questionnaire
7. Method of sampling
8. Types of sampling
A sample will not be considered representative due to wrong stratification,
small size , non -random, wrong selection of the population, purposive
selection etc.
7.2 SAMPLING CONCEPTS
It is not possible to survey the population. Cost and time are some
important elements. With the help of sampling, the objective of covering
people in research can be achieved. Sampling is the process of selecting
units from a population of the universe. In the first place, the major
question that motivates sampling is generalization. For better
understanding, let’s have a look into the various important concept s of
sampling -
01. Sampling
Sampling is a technique through which the predetermined number of
observations are taken from a large population for the purpose of research.
This technique is used in statistical analysis. It is an act of selecting a
representativ e from the population for determining the features of the
whole population.
02. Population or Universe
The population or universe represents the entire aggregation of items from
which samples can be selected for the research. It represents the entire
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Sampling and D ata Processing 03. Sampling size
A sampling size consists of the various units taken for the research in
numbers from the whole population to conduct research. It is usually
represented by “n”.
04. Sample Design
Sample design represents a definit e plan for obtaining a sample from a
given population. A sample design can be described by sampling methods
and estimators.
05. Sampling Methods
Sampling methods refers to the procedures by which some elements of the
population are included in the sample for conducting research.
06. Estimator
For calculating sample statistics, the estimation process is used. This
process is called the estimator. Different estimators are used for different
sampling.
07. Sampling Frame
It is the list of items from which the sample ma y be drawn.
7.3 SAMPLING DESIGN
Sample design represents a definite plan for obtaining a sample from a
given population. A sample design can be described by sampling methods
and estimators. Sampling methods refers to the procedures by which some
elements of the population are included in the sample for conducting
research. For calculating sample statistics, the estimation process is used.
This process is called the estimator. Different estimators are used for
different sampling.
It is a definite plan fo r obtaining a sample from a given population. The
design gives meaning and direction to the research. The best design
depends on the objectives and the resources available for the research.
Many factors guide the choice of sample design. To get a desired l evel of
accuracy and precision in research, a researcher might select the most
appropriate design for selecting a sample.
7.3.1 Features of Good Sample Design
The following can be considered as the features of good sampling design -
01. Systematic bias can be controlled in a better way through proper
sampling design.
02. The sampling design must represent the research objectives for
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Business Research Methods 03. It must be designed in such a way which results in a small sampling
error.
04. In the co ntext of funds, the sample design must be viable.
05. It must give the results of the sample study.
06. It must result in a truly representative sample.
With the help of sampling design, a large number of units can be studied.
A good sampling design also saves t ime, energy and money for the
researcher. The sampling design involves some important steps which
starts from defining the target population to executing the sampling
process. A sampling error pops up when the process is not well defined or
executed.
7.3.2 STEPS IN SAMPLING DESIGN
Sampling represents the segment of the population that is selected for the
purpose of investigation for research. The process of designing a sample
should be planned and well executed properly. The following are the steps
involve s in sampling design -

Fig. 7.1: Steps in Sampling Design
01. Defining the Target Population
The first step in sampling design is defining the population of interest. In
terms of sampling unit and time, the population for the sample can be
defined. The popu lation or universe should be selected by keeping in view
the objectives of the research study. The right selection of population
reduces the probability of selecting wrong respondents who are not
beneficial for research. The definition of the population fo r the research
must be cleared and corrected.
02. Specifying the Sampling Frame and Unit
After defining the population for the research, a researcher should select
and decide on the sampling frame. Sampling frame is the list of items from
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Sampling and D ata Processing sampling frame is not clearly specified. When the sampling frame does not
represent the total population, the chances of sampling frame error may
arise.
Sampling unit contains a single element or a group of ele ments from
which a sample may be drawn or selected. It is a basic unit containing a
single element or group of elements of the population. The population to
be sampled is a sampling unit. The units can be in the form of
geographical, demographic, construct ion, groups or individual units
suitable for the research.
03. Selecting the Sampling Technique
After specifying the sampling frame and unit, the selection of sampling
techniques is considered as the fourth step. The technique shows the way
in which the sampl es are to be drawn. It is a very important step of the
research process. Using an appropriate technique for selecting a sample
can generalize the results of the research in a better way. The choice of the
sampling technique depends upon the research object ives, financial
resources, time availability and the nature of the problem.
There are essentially two types of sampling methods -
● Probability Sampling Methods
● Non-probability Sampling Methods
04. Determination of Sample Size
A sampling size consists of th e various units taken for the research in
numbers from the whole population to conduct research. It is usually
represented by “n”. It plays a crucial role in the sampling design. The
calculation of sample size is a simple procedure. Due to errors, the
estimation may not come exactly. Type I error and Type II error are some
of the ingredients in a sample size calculation.
05. Execution - Selecting the sample
The final step in the sampling design is selecting the sample. For the
smooth implementation of th e whole process, it is necessary that the
interviewers should follow the outline for the smooth functioning and
implementation of the research. The execution step involves implementing
the whole process to draw a sample. The sample which is required for th e
research is drawn in the final step of the research design.
7.4 SAMPLE SIZE
A sample size is a part of the total population which is drawn for the
purpose of conducting research. A sampling size consists of the various
units taken for the research in num bers from the whole population to
conduct research. It is usually represented by “n”. Determination of
sample size plays an important role in the sampling process. There are
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Business Research Methods In the case of prob ability sampling, the sample size is determined with the
help of formulas. The acceptable error and level of confidence are
specified. In the case of non -probability sampling, many factors like
budget allocation, analysis of subgroups, and number of variab les are
some important things to be considered while determining the sample size.
The sample size involves the study of the purpose of the research and the
size of the population. Along with these, the following criteria is also used
to determine the sampl e size -

Fig. 7.2: Sample Size Criteria
7.5 SAMPLING METHODS
Sampling methods refers to the procedures by which some elements of the
population are included in the sample for conducting research. . The
technique shows the way in which the samples are to be drawn. It is a very
important step of the research process. Using an appropriate technique for
selecting a sample can generalize the results of the research in a better
way. The choice of the sampling technique depends upon the research
objectives, fin ancial resources, time availability and the nature of the
problem.
There are essentially two types of sampling methods -
01. Probability Sampling Methods
02. Non -probability Sampling Methods
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Sampling and D ata Processing

Fig. 7.3: Methods of Sampling
01. Probability Sampling
In probabi lity sampling, each element of population has a chance of being
chosen for the sample. In this technique of sampling, every member of the
population has a probability of being included in the sample. The
probability of getting chosen is an important featur e of probability
sampling.
The approach of probability sampling is quite desirable as there are
chances for elements of being included in the sample. The following are
the techniques of probability sampling -
(i) Simple Random Sampling
A simple random sam pling is a technique where each element in the target
population is randomly selected to form a sample. In this technique, every
element from the target population has an equal probability of inclusion in
the sample. This technique is suitable when the siz e of the target
population is small.
(ii) Systematic Sampling
Systematic sampling is often used as a substitute for simple random
sampling. The selection of the elements from the target population is
systematic. Except for the first element, the selection is on the basis of a
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Business Research Methods sampling. It involves the selection of every kth element. The selection is
done from a sampling frame. ‘K’ represents the skip interval.
(iii) Stratified Random Sa mpling
Before sampling, the process of grouping the members of the population
in a similar feature group is known as stratification. In this technique, the
elements of the population are divided into small subgroups. The division
of the group is based on t he similarity. After stratification, the elements
are randomly selected from each of these strata. The elements within the
group are homogeneous and heterogeneous among the group formed.
(iv) Cluster Sampling
In this technique, the population is grouped i nto various clusters and the
few clusters are selected for the study. The use of this technique is suitable
for the studies which cover geographical areas. A researcher can choose,
one level or multilevel cluster sampling.
02. Non-probability Sampling
Non-probability sampling is also known as purposive sampling. The
elements in this technique are selected on the basis of factors. There is no
chance of getting selected randomly. The selection of the element is
purposive and based on some factors. Quota sampling , convenience
sampling, judgment sampling and snowball sampling are few examples of
non-sampling sampling.
(i) Quota Sampling
Quota sampling is a technique through which the number of respondents
that are to be drawn from each of several categories is men tioned and
specified in advance. The final selection of the respondents is left to the
researcher. The process proceeds until the quota for each category is filled.
(ii) Convenience Sampling
As the name suggests, convenience sampling is based on the ease and
convenience of the researcher to further carry research. Based on the easy
availability and accessibility when the researcher selects the units from the
population to draw samples, it is known as convenience sampling. In
convenience sampling, there is no set criteria for selecting the sample. It
depends upon the ease and accessibility of the researcher.
(iii) Judgment Sampling
Judgment sampling is a technique through which an experienced
researcher selects the units from the population based on the judg ment for
their research. The sample units are selected based on the population's
parameters.

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Sampling and D ata Processing (iv) Snowball Sampling
Snowball sampling involves the selection of additional respondents. The
technique of snowballing is used on rare populations or low incid ence
populations. The process of sampling under this technique is based on the
chain of referrals.
7.6 EDITING AND CODING OF DATA
After the process of data collection, the data which is collected needs to be
edited. The data for the research can be obtain ed through schedules,
interviews, questionnaire, observations or through secondary sources like
books, journals, websites, published data and reports etc. the following
process is adopted while preparing the data -

Fig. 7.4 Process of Data Preparation
01. Data Editing
Data Editing is the first step. It is a focal point in the preparation of
statistics. The goal of data editing is to improve the quality of the
information. When the responses are collected through open -ended
questions of questionnaires, unstru ctured observations and interviews, the
editing of data is required. The information must be coded systematically.
Lack of clarity or goal at first stage will result later in confusion and goal
deviation.
The following are the reasons which advocates the need for data editing -
01. It provides consistency.
02. It provides uniformity to the whole process.
03. It provides completeness.
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Business Research Methods 02. Data Coding
Coding is the development and use of a language. The language will be
used to transfe r data. The coding plays a vital role in transferring data
from the instrument which was used in the process of data collection.
Coding is the process of mixing the data with a code label to easily
retrieve at a later stage for further comparison, interpre tation and analysis.
The codes can be based on topics, ideas, concepts, themes, phrases, terms
and keywords. The codes are given the relevant names that provide an
indication of the idea or concept. Researchers have some codes already in
mind and are also looking for other ideas to draw something from the data.
The following questions are important or suggested to ask about the data
for coding -
1. What is happening/going on?
2. What is the person’s behavior saying?
3. How can the actions be taken for granted?
4. Wha t are people doing?
03. Classification of Data
Data classification is an act of organizing data by relevant categories to
purposely use it for research analysis. The data is classified on the basis of
characteristics, level of sensitivity and impact of the study. The
classification of data helps to determine the relevant and irrelevant data.
The relevant data is used for further research.
The data should be classified into three categories - Restricted Data,
Private data and Public data. Restricted data is one which cannot be
disclosed or altered. Private data is data which has a moderate level of
risk. A reasonable level of security controls should be applied to private
data. The public data is published and available for all. It can be easily
accessible f or all. Some level of control is required to prevent
unauthorized alteration.
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Sampling and D ata Processing

Fig. 7.5: Process of Data Classification
04. Tabulation of Data
Tabulation of data means systematic arrangement of data in rows and
columns. Tabulation is a layout of the dat a in tabulated form to show with
the help of different rows and columns. Tabulation of data is a systematic
process of showing the data in terms of figures to analyze. When the data
are tabulated for one characteristic, it is said to be simple tabulation.
While, the data is tabulated according to the two characteristics, it is said
to be double tabulation.
When the data is tabulated on the basis of multiple characteristics, it is
said to be multiple or complex tabulation. The main purpose of data
tabulatio n is to simplify the presentation. With the help of tabulated data,
data can easily be understood.
Preparation of tables is an art. For tabulating data, this art is required to a
researcher for handling and presenting data. The preparation of table
involv es general principles -
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Business Research Methods

Fig.7.6: General Principles of Tabulation of Data
05. Analysis of Data
Data analysis is an important step of the research process. It is the process
of applying techniques to describe, condense and evaluate data. Data
integrity i s an important part of data analysis. Data analysis is the process
through which data is collected, modeled and analyzed data that supports
decision making.
It involves the process of organization, summarization and categorization
of data. It is defined a s a process of cleaning, modeling and transforming
data to support the decision making and findings related to research.

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Sampling and D ata Processing 7.8 SUMMARY
● Sampling is a technique through which the predetermined number of
observations are take n from a large population for the purpose of
research. This technique is used in statistical analysis. It is an act of
selecting a representative form the population for determining the
features of the whole population.
● The population or universe represent s the entire aggregation of items
from which samples can be selected for the research. It represents the
entire group which is the focus of the study.
● Sample design represents a definite plan for obtaining a sample from a
given population. A sample desig n can be described by sampling
methods and estimators. Sampling methods refers to the procedures by
which some elements of the population are included in the sample for
conducting research. For calculating sample statistics, the estimation
process is used. This process is called the estimator. Different
estimators are used for different sampling.
● Sampling represents the segment of the population that is selected for
the purpose of investigation for research. The process of designing a
sample should be pla nned and well executed properly.
● A sample size is a part of the total population which is drawn for the
purpose of conducting research. A sampling size consists of the
various units taken for the research in numbers from the whole
population to conduct re search. It is usually represented by “n”.
Determination of sample size plays an important role in the sampling
process. There are various methods through which a sample size can
be determined.
● Sampling methods refers to the procedures by which some element s of
the population are included in the sample for conducting research. .
The technique shows the way in which the samples are to be drawn. It
is a very important step of the research process.
● After the process of data collection, the data which is collect ed needs
to be edited. The data for the research can be obtained through
schedules, interviews, questionnaires, observations or through
secondary sources like books, journals, websites, published data and
reports etc.
● The goal of data editing is to improve the quality of the information.
When the responses are collected through open -ended questions of
questionnaires, unstructured observations and interviews, the editing
of data is required.Coding is the process of mixing the data with a code
label to easily retrieve at a later stage for further comparison.,
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Business Research Methods ● Data classification is an act of organizing data by relevant categories
to purposely use it for research analysis. The data is classified on the
basis of characteristics, lev el of sensitivity and impact of the study.
● Tabulation of data means systematic arrangement of data in rows and
columns. Tabulation is a layout of the data in tabulated form to show
with the help of different rows and columns. Tabulation of data is a
syste matic process of showing the data in terms of figures to analyze.
● Data analysis involves the process of organization, summarization and
categorization of data. It is defined as a process of cleaning, modeling
and transforming data to support the decision making and findings
related to research.
7.9 SELF ASSESSMENT QUESTIONS
Section A - Descriptive Questions
01. What is the meaning of sample? Explain the process of designing a
sample.
02. Explain the importance of techniques of research by keeping in view
its rel evance in social and scientific research.
03. Describe the various steps in the sampling process.
04. Distinguish between probability sampling and non -probability
sampling.
05. What is data editing and coding? Explain its relevance in research.
06. Why is data editing co nsidered as a focal point of research? Support
your answer with the help of a few examples.
07. What is coding? Explain the process of coding.
08. What do you mean data classification? Why is data integrity
important in data classification?
09. Describe this statemen t - “Data editing, coding, classification,
tabulation and analysis are important elements of processing of
data”.
Section B - Multiple Choice Questions
01. Which of the following is not a type of non -probability sampling?
a) Quota sampling
b) Convenience samp ling
c) Snowball sampling
d) Stratified random sampling
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Sampling and D ata Processing 02. Among these, which sampling is based on equal probability?
(a) Simple random sampling
(b) Stratified random sampling
(c) Systematic sampling
(d) Probability sampling

03. The distribution that is formed by all possible values of a statistics is
known as:
(a) Hyper geometric distribution
(b) Normal distribution
(c) Sampling distribution
(d) Binomial distribution

04. Of the following sampling methods, which is a probability method?
a) Judgment
b) Quota
c) Sim ple random
d) Convenience

05. Sample is a representative unit of the population. Is this statement
true or false?
a) True
a) False

06. What are the various classifications of sampling?
a) Random and purposive
b) Stratified and cluster
c) Probability and non -probability
d) Multi -stage and sequential



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UNIVARIATE AND BIVARIATE
ANALYSIS OF DATA
Unit Structure
8.0 Objectives
8.1 Introduction
8.2 Descriptive vs Inferential Analysis
8.3 Descriptive Analysis of Univariate data
8.4 Nominal Scale
8.5 Ordinal Scale
8.6 Measures of Central Tendency
8.7 Measures of Dispersion
8.8 Descriptive Analysis of Bivariate data
8.9 Summary
8.10 Self Assessment Questions
8.0 OBJECTIVES
After studying this module, you should be able to:
● Concepts of univariate and bivariate analysis
● Know about the descriptive and inferential analysis
● How to do descriptive analysis of univariate data
● Knowledge about nominal and ordinal scale
● Understand the meaning and application of measures of central
tendency and dispersion
● How to do descriptive analysis of bivariate data
● Difference between univariate and bivariate analysis
8.1 INTRODUCTION
Research is defined as the creation of new knowledge. From problem
statements to conclude the research, each and every step plays an
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Univariate and Bivariate
Analysis of Data data analysis. Data analysis is the process through which data is collected,
modeled and analyzed data that supports decision making.
Data analysis is an important step of the research process. It is the process
of applying techniques to describe, c ondense and evaluate data. Data
integrity is an important part of data analysis. It involves the process of
organization, summarization and categorization of data. It is defined as a
process of cleaning, modeling and transforming data to support the
decis ion making and findings related to research.
Fig. 8.1: Process of Data Analysis
Data analysis involves the application of raw data into categories with the
help of coding and tabulation. For the further analysis, the irrelevant data
is removed to conduct the further research on the basis of relevant data.
The purpose of data analysis is to classify the raw data into some usable
categories. The data transformation and coding, editing, tabulation and
statistical inferences are some important elements of dat a analysis.
Transforming data is an important step. Coding is the process of mixing
the data with a code label to easily retrieve at a later stage for further
comparison, interpretation and analysis. The codes can be based on topics,
ideas, concepts, them es, phrases, terms and keywords. The codes are given
the relevant names that provide an indication of the idea or concept.
Researchers have some codes already in mind and are also looking for
other ideas to draw something from the data.
Data editing is a focal point in the preparation of statistics. The goal of
data editing is to improve the quality of the information. When the
responses are collected through open -ended questions of questionnaires,
unstructured observations and interviews, the editing of d ata is required.
Tabulation of data means systematic arrangement of data in rows and
columns. Tabulation is a layout of the data in tabulated form to show with
the help of different rows and columns. Tabulation of data is a systematic
process of showing th e data in terms of figures to analyze.
8.2 DESCRIPTIVE VS. INFERENTIAL ANALYSIS
8.2.1 Descriptive Analysis
Descriptive analysis is also referred to as one dimensional analysis. The
act of organizing, analyzing and presenting data in a meaningful way is
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Business Research Methods variable. The analysis may be based on the one variable, two variable or
multi variable. Under study, descriptive analysis helps to describe, show
and summarize data. With the help of thi s analysis, the profile of the
various companies, departments, organizations etc. can be taken for
further study.
The descriptive analysis helps to describe a situation. Measures of central
tendency like mean, median and mode and measures of variability l ike
range and standard deviation are the types of descriptive analysis.


Fig. 8.2: Types of Descriptive Analysis
8.2.2 Inferential Analysis
Inferential analysis helps to compare, test and predict data. This analysis
allows researchers to begin making inf erences as the name suggests about
the hypothesis based on the data collected for the research. It involves
tests of significance for the testing of the hypothesis. A researcher can
draw inferences and come to conclusions about the population at large
with the help of inferential analysis.
With the help of this analysis, the data validity can be determined which
can further lead to draw some conclusions. Inferential analysis takes an
active part and helps in the interpretation of data. It involves the anal ysis
of a random sample of data which is taken from the population. Further, it
helps to describe and make inferences about the population.
The main purpose of this analysis is to draw conclusions from a sample.
Late, to generalize them to the population . With the help of probability
theory, it determines the probability of the characteristics of the sample
which can be generalized obn the whole population.
Hypothesis testing, analysis of variance, estimation of parameters are
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Univariate and Bivariate
Analysis of Data

Fig. 8.3: Types of Inferential Analysis
8.3 DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
As mentioned earlier, descriptive analysis helps to describe, show and
summarize data. Univariate data means your data has only one variable. It
is the most basic form of statistical data analysis technique. The
descriptive analysis of univariate data is used when the data contains only
one variable and it does not deal with the cause and effect relationship.
The main purpose of descriptive analysis of u nivariate data is to simply
describe the data to find the patterns within the data. With the help of
central tendency, dispersion, range, variance etc. the patterns can be
described found in univariate data. Several options can be used for
describing data for univariate data -
● Bar charts (Graph with rectangular bars)

Fig. 8.4: Bar Graphs
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Business Research Methods ● Frequency distribution table (It tells how often something happened)
Number of
Products (x) Tally Frequency (f)






Fig. 8.5: Frequency Distribution T ables
● Histogram (Way to display counts of data)

Fig. 8.6 : Histogram




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Univariate and Bivariate
Analysis of Data ● Frequency Polygon ( can be used to compare sets of data )


Fig. 8.7: Frequency Polygon
● Pie Chart (It displays data in a circular graph)

Fig. 8.8: Pie Chart
8.4 NOMINAL SCALE
The name of this scale is derived from the Latin word ‘Nomen’ which
means name. The numbers on the nominal scale don’t have values. A
nominal scale can have both qualitative variables and quantitative
variables. It is a scale of measurement. The scale is used to assign events
or objects into discrete categories. The use of numeric value or categories
ranked by class is not required in this type of scale. This scale is
considered as the most basic form of measurement. The scale uses tags or
labels to associate v alue with the rank. It differentiated items. The scale
deals with non -numeric attributes.
Nominal scales are used to categorize and evaluate data in many fields. An
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Business Research Methods Que. 1 What is your occupation?
● Business
● Service
● Professiona l
● Other

Que. 2 What is your gender?
● Male
● Female
● Transgender
8.4.1 Analysis of Nominal scale data with only one possible response
and two possible responses
Nominal scales are qualitative in nature. The numbers which are assigned
to the attributes have no numerical values. This makes the analysis
confusing. There is no arithmetic computation and no numerical value can
be assigned to the data obtained through this scale.
With the help of Mode and Percentage, the analysis of nominal scale data
can be perform ed. Equality or set membership can be used to analyze
nominal data. The qualities of the attribute can be put in order. Ranking
the variables is meaningless.
For the graphical representation of the data, Pie charts and Bar charts can
be used for the anal ysis of nominal data. Pie chart to represent the
percentage value of your findings and Bar chart to represent the frequency
of the categories according to the responses.
To assess the nominal data, two categories can be used - The Matched
sample and the U nmatched sample. In the Matched sample, the data with
similar characteristics are paired together. While in an unmatched sample,
the random pairs are chosen for the purpose of analysis.
8.5 ORDINAL SCALE
The term 'ordinal' means order. It includes the typ e of statistical data
where variables are in rank or order. There is no degree of difference
between categories. The scale places variables in order and permits to
measure the value at lower or higher in scale. Ordinal scale is the second
level of measurem ent. The data can be grouped, named or ranked with the
help of this scale.
The ordinal scale shows the relative rank of variables. The non -numeric
attributes like happiness, health, sadness can be measured with the help of
ordinal scale. It helps to ident ify the rank of variables. The degree of
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Univariate and Bivariate
Analysis of Data An example of ordinal scale is
01. How satisfied are you with our services?
● 1- Totally Satisfied
● 2- Satisfied
● 3- Neutral
● 4- Dissatisfied
● 5- Totall y Dissatisfied
How happy are you with the performance of the product?
● 1- Very Unhappy
● 2- Unhappy
● 3- Neutral
● 4- Unhappy
● 5- Very Unhappy
8.5.1 Analysis of Ordinal Scaled Questions
The analysis of the ordinal scale questions can be done with the help of
ranki ng the data. With the help of rating surveys, the impact can be
assessed. Use of Likert scale is found to be very convenient and famous in
ordinal scale. The Likert scale is a variant of the ordinal scale that is used
to calculate satisfaction level.
The example of Likert Scale is -
Que. How satisfied are you with our products?
● 4- Strongly Agree
● 3- Agree
● 2- Neutral
● 1- Disagree
● 0- Strongly Disagree
Positional measures like Median and Percentile are considered as
appropriate methods for ordinal scale. Classification method is also used
for measuring the data collected through ordinal scale. The class ification
process includes the segmentation of data so that each observation is
similar to the other. After classification, dispersion is measured and
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Business Research Methods 8.6 MEASURES OF CENTRAL TENDENCY
A m easure of central tendency attempts to describe a whole set of data
with a single value that represents the middle or center of its distribution.
It is an attempt to describe a set of data by analyzing and identifying the
central position within the set of data. There are three main measures of
central tendency. Out of these three, Mean or Average is the most familiar
one as compared to median and mode.
These measures explain a different position of the central value in the
distribution. In the distributio n, the central tendency aims to provide an
accurate description of the entire data. It also helps to ascertain how to
calculate them and under what conditions they are most convenient and
appropriate to be used.

Fig. 8.9: Measures of Central Tendency

01. Mean
The mean or average is the most popular measure of central tendency.
Mean can be used with both discrete and continuous data, although its use
is most often with continuous data.. We can simply add all the values in a
data set and divide it by the tota l number of values to calculate the mean.
The formula of mean This formula is usually written in a slightly different
manner using the Greek capital letter, ∑, pronounced "sigma", which
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Univariate and Bivariate
Analysis of Data

Some other measures of mean used to find the ce ntral tendency are as
follows:
● Geometric Mean
● Harmonic Mean
● Weighted Mean
02. Median
Median represents the middle value. It is the middlevalue in distribution
when the values are arranged in ascending or descending order. It divides
the distribution in half. I n simple words, we can say that median is the
middle score for a set of data that has been arranged in order of
magnitude. Median when ‘n’ is odd and when ‘n’ is even can be calculated
with the help of this formulas,

03. Mode
Mode represents the most frequen t values. It represents the frequently
occurring value in the dataset. Mode represents the most common value.
The most frequent value in the given dataset is considered as mode. It is
the most commonly occurring value in a distribution. Over the median an d
the mean, the mode has an advantage as it can be found for both numerical
and categorical (non -numerical) data.
Mode can be calculated as,

8.7 MEASURES OF DISPERSION
Dispersion is a statistical term. The term ‘Dispersion’ is used to describe
the exten t to which data is scattered. Measures of dispersion help to
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Business Research Methods are used to quantify the dispersion of data. Measures of dispersion help to
interpret the variability of data. It helps to understand how homogeneous
or heterogeneous the data is.
There are two main types of dispersion methods in statistics which are:
● Absolute Measure of Dispersion
● Relative Measure of Dispersion
01. Absolute Measure of Dispersion
The method expresses the variat ions in terms of the average of deviations
of observations like standard or mean deviations. It contains the same unit
as the original data set. The absolute method of dispersion measures
usually expresses variations in a data set with respect to the avera ge of the
deviations of the observations. The most commonly used absolute
measures of deviation are listed below -
Fig. 8.10: Categories of Absolute Measures of Dispersion
01. Range
The range can be defined as the difference between the maximum value
and the minimum value. The difference shows the range between the
minimum value and maximum value. Range is the difference between the
maximum value and the minimum value given in a data set.
02. Varia nce
Variance checks the spread of the data about the mean. Variance is known
as the average squared deviation from the mean of the given data set.
03. Mean Deviation
The central points of mean deviation could be mean, median or mode. It
gives the average of th e data's absolute deviation about the central points.
In this way, the Mean Deviation is calculated -
1. Calculate the average of the observations
2. Calculate the difference of each observation from the mean
3. Average all the deviations.
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Univariate and Bivariate
Analysis of Data 04. Standard Deviat ion
Standard deviation measures the differences in the values about the mean.
It measures the variation of the data about the mean. The square root of
the variance gives the standard deviation. In simple words, the square root
of the variance is known as t he standard deviation.
05. Quartile Deviation
The quartiles are values that divide a list of numbers into quarters. The
quartile deviation is half of the distance between the third and the first
quartile. It can be defined as half of the difference between the third
quartile and the first quartile in a given data set.
02. Relative Measures of Dispersion
Relative Measures of Dispersion measure compares values without
units. They are used to compare the distribution of two or more data
sets. When the data of sepa rate sheets have different units and their
comparison needs to be done, the use of relative measures takes place.
The relative measures of dispersion are expressed in the form of ratios
and percentages. Let’s have a look at this diagram which shows the
various measures of relative measures of dispersion -
Fig. 8.11 Categories of Relative Measures of Dispersion
01. Coefficient of Range
The Coefficient of Range is the ratio of the difference betwee n the highest
and lowest value in a data set to the sum of the highest and lowest value.
02. Coefficient of Variation
The Coefficient of Variation is the ratio of the standard deviation to the
mean of the data set. It is expressed in the form of a percentage .
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Business Research Methods 03. Coefficient of Mean Deviation
The Coefficient of Mean Deviation is defined as the ratio of the mean
deviation to the value of the central point from which it is calculated.
04. Coefficient of Quartile Deviation
The Coefficient of Quartile Deviation is the ratio of the difference
between the third quartile and the first qu artile to the sum of the third and
first quartiles.
8.8 DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
Univariate analysis is the analysis of one variable. In Bivariate analysis,
the analysis of exactly two variables takes place. Multivariate analysis is
the analysis of more than two variables. Bivariate analysis means analysis
of bivariate or analysis of two variables used to find out if there is a
relationship betw een two sets of values. It usually involves the variables X
and Y. It is one of the simplest methods of ascertaining two variables.
In this statistical analysi s, the two variables are observed. One variable is
dependent while the other is independent. As mentioned earlier, it usually
involves the variables X and Y. in order to conclude the impact and cause
of study involving two variables, this analysis is used. The study of
bivariate studies explores the relationship of two variables. The study is
stated to be an analysis of any concurrent relation between two variabl es
or attributes.
The following are the types of Bivariate analysis -

Fig. 8.12: Types of Bivariate Analysis

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Univariate and Bivariate
Analysis of Data 01. Scatter Plots
Scatter plots give a visual idea of the pattern that the variables follow. The
dots in the scatter plots represent the values for two different numeric
variables.
02. Regression Analysis
The Regression Analysis allows t he researcher to examine the relationship
between two or more variables of interest.
03. Correlation Coefficient
Correlation Coefficient measures the relationship between th e two
variables. It is used to measure how strong a relationship is between two
variables . Correlation coefficient formulas are used to find how strong a
relati onship is between data. The formulas return a value between -1 and
1, where:
● 1 indicates a strong positive relationship.
● -1 indicates a strong negative relationship.
● A result of zero indicates no relationship at all.
8.9 SUMMARY
● Data analysis is an importa nt step of the research process. It is the
process of applying techniques to describe, condense and evaluate
data. Data integrity is an important part of data analysis. It involves
the process of organization, summarization and categorization of
data. It is defined as a process of cleaning, modeling and
transforming data to support the decision making and findings
related to research.
● Descriptive analysis is also referred to as one dimensional analysis.
The act of organizing, analyzing and presenting data in a meaningful
way is descriptive analysis. It involves the study of the distribution
of one variable. The analysis may be based on the one variable, two
variable or multi variable.
● Inferential analysis helps to compare, test and predict data. This
analysis allows researchers to begin making inferences as the name
suggests about the hypothesis based on the data collected for the
research. It involves tests of significance for the testing of the
hypothesis.
● Univariate data means your data has only one va riable. It is the most
basic form of statistical data analysis technique. The descriptive
analysis of univariate data is used when the data contains only one
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Business Research Methods ● A nominal scale can have bo th qualitative variables and quantitative
variables. It is a scale of measurement. The scale is used to assign
events or objects into discrete categories. The use of numeric value
or categories ranked by class is not required in this type of scale.
● The ord inal scale shows the relative rank of variables. The non -
numeric attributes like happiness, health, sadness can be measured
with the help of ordinal scale. It helps to identify the rank of
variables. The degree of agreement or disagreement can be analyzed
with the help of this scale.
● A measure of central tendency attempts to describe a whole set of
data with a single value that represents the middle or center of its
distribution. It is an attempt to describe a set of data by analyzing
and identifying the c entral position within the set of data.
● Measures of dispersion help to describe the variability in data. There
are certain types of measures that are used to quantify the dispersion
of data. Measures of dispersion help to interpret the variability of
data. It helps to understand how homogeneous or heterogeneous the
data is.
● Bivariate analysis means analysis of bivariate or analysis of two
variables used to find out if there is a relationship between two sets
of values. It usually involves the variables X and Y. It is one of the
simplest methods of ascertaining two variables.
8.10 SELF ASSESSMENT QUESTIONS
Section A - Descriptive Questions
01. Explain the Univariat e and Bivariate Analysis of Data.
02. What is descriptive analysis and inferential analysis? Explain its
various types.
03. Elaborate the descriptive analysis of univariate data. Also explain the
several options that can be used for describing data for univariate
data.
04. What is the nominal scale? How are nominal scales used to
categorize and evaluate data in many fields?
05. What is the ordinal scale? How can analysis of the ordinal scale
questions be done?
06. What is the measure of central tendency? Explain the various
measures of central tendency.
07. What are Measures of Dispersion? Explain the types of dispersion
methods.
08. Describe the various types of analysis of bivariate. munotes.in

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Univariate and Bivariate
Analysis of Data Section B - Multiple Choice Questions
01. For which type of measurement, the coefficient of variation can be
computed?
a) Nominal Scale
b) Ordinal Scale
c) Interval Scale
d) Ratio Scale

02. Which of the following are types of correlation?
a. Positive and Negative
b. Simple, Partial and Multiple
c. Linear and Nonlinear
d. All of the above

03. The original hypothesis is known as ___ ___.
a. Alternate hypothesis
b. Null hypothesis
c. Both a and b are incorrect
d. Both a and b are correct

04. Which of the following statements is true for correlation analysis?
a. It is a bivariate analysis
b. It is a multivariate analysis
c. It is a univariate analysis
d. Both a and c

05. Which of the following techniques is an analysis of the relationship
between two variables to help provide the prediction mechanism?
a. Standard error
b. Correlation
c. Regression
d. None of the above

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TESTING OF HYPOTHESES, CHI -
SQUARE ANALYSIS & ANALYSIS OF
VARIANCE (ANOVA)

Unit Structure
9.0 Objectives
9.1 Hypothesis Testing:
9.2 Chi -Square Test
9.3 Analysis of Variance (ANOVA)
9.4 Self Assessment Questions
9.0 OBJECTIVES
1. Define null hypothesis, alternative hypothesis, level of significance, test
statistic.
2. Distinguish between a one -tailed and a two -tailed test.
3. Formulate statistical hypothesis for testing.
4. To determine whether the difference between the observed and
expected values is s tatistically significant using Chi - Square test.
5. To know and apply the one way & two -way ANOVA and interpret the
results.
9.1 HYPOTHESIS TESTING:
9.1.1 Introduction
In Quantitative research, a researcher is trying to answer a question set by
him/her. h ypothesis testing is a process of evaluating the research
question. The main idea of hypothesis testing is to prove or disprove
research questions. Hypothesis testing is termed significant testing.
Researchers need to state Null and alternative hypothesi s to use hypothesis
testing.
Statistical hypothesis is a statement that is based on experience and is
believed to be proved i.e. known as the null hypothesis and the burden of
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Testing of hypotheses, Chi -
Square analysis & Analysis
of variance (Anova) assumption made regarding the distribution of one or more population
characteristics.
Let’s see some statements,
i) One-day cricket matches are not good for cricketers.
ii) There is a vast difference in the performance of players in ODI and
Test matches.
iii) The mobile phone is not good for the health of users.
iv) The life of individual decreases with the use of the mobile phone.
Statements ii) and iv) are statistical hypothesis as they are based on
population characteristic, and statement i) and iii) are simple hy pothesis.
9.1.2Null Hypothesis
The null hypothesis is the hypothesis that there is no relation between two
or more variables. The null hypothesis can be disproved, and rejected by
researchers. it is denoted by
(H-zero).
a statistical hypothe sis in which there is no significant difference between
the set of variables is a null hypothesis.
9.1.3Alternative Hypothesis
A statistical hypothesis, which states that there is a significant difference
between the set of variables, is an alternative hyp othesis. A hypothesis
other than the null hypothesis is an alternative. It is denoted by
(H-
one). In other words, an alternating hypothesis is a contradictory statement
to the null hypothesis.
Rejection of null hypothesis means that it is fa lse but the decision of
accepting
does not mean that it is true and therefore when we set the
hypothesis, we write the statement in the null hypothesis that we want to
reject.
As long as there is no contradiction, we retain
but, when we have some
observation contradicting
we could expect that
is valid. So
therefore
has benefit of doubt but
has burden of justification.
The researcher aims to prove an alternative hypothesis but in an indirect
way. When the null hypothesis gets rejected the same time alternative
hypothesis gets accepted. They are mutually exclusive and exhaustive.
9.1.4 Errors in Hypothesis Testing
Hypothesis testing may lead to two types of errors. Type I err or and type
II error.
Type I error: When the null hypothesis is true and the researcher rejects it,
a type I error occurs. The probability of committing a type I error is called
the significance level.
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Business Research Methods Type II error: When the null hypothesis is false an d the researcher fails to
reject it, a type II error occurs. The probability of committing a type II
error is called the power of the test.
Decision ↓
(True)
(False)
Accept
Correct decision Type II error
Rejec t
Type I error Correct decision

The type I error is usually pre -defined.
If the type I error is fixed at 10 percent, it means that there are about 10
chances in 100 that we will reject
when
is true. If a type I e rror is
fixed at a lower level, then it can be controlled.
For example, if we fix type I error at 5 percent, we will say that the
maximum probability of committing a Type I error would only be 0.05.
With a fixed sample size, n, the probability of committi ng Type II error
increases, whe n we try to reduce Type I error, simultaneously we cannot
reduce both types of errors.
9.1.5 Types of Hypothesis Testing
1) Parametric Tests
2) Non-parametric Tests
1) Parametric Tests: It is a statistical test that depends on an assu mption
about the distribution of the data, that the data are normally distributed. In
the case of a normal distribution of a population, these features are known
as parameters.
Parametric analysis can only be used on quantitative data, as only
quantitativ e data can have a normal distribution.
Parametric tests generate more information about the whole population as
compared to non -parametric tests.
2) Non-parametric Tests: There is no need for any particular
distribution for the data in the non -parametric t ests of the null hypothesis.
Even though quantitative data is not normally distributed or data is of any
kind, non -parametric tests can be used.
Parametric tests should be used to find differences between the study
groups if they exist. Hence, the Normal distribution of data plays an
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Testing of hypotheses, Chi -
Square analysis & Analysis
of variance (Anova) To carry out the parametric analysis, non -normally distributed data should
transform into normally distributed data. Logarithmic transformation is the
most commonly used method. Sample s ize and normal distribution of data
are the main factors in hypothesis testing. For small -size data, non -
parametric tests are used. Parametric tests can be used for large -size data,
as large -size data will be more normally distributed and variation will be
less.
9.1.6 Steps for Testing Hypothesis:
1) Setting up the hypothesis: This means that making a formal and clear
statements of the null and alternating hypothesis.
2) Selecting the level of significance: significance level should be
specified in advance a s hypothesis are tested on pre -defined
significance level. Generally, 5% or 1% level is used for testing.
3) Test Statistic: Hypothesis will be tested using formulae of test statistics
for mean, proportion and variance.
4) Critical value: we obtain critical va lue using test statistic, level of
significance (
) and the type of test (one -tailed or two -tailed).
5) Decision making: decision of rejection or not rejection of null
hypothesis will be made after comparison of value of test statistic and
critic al value.

Null hypothesis will be rejected when
a) Value of test statistic < lower critical value or Value of test statistic >
lower critical value, in case of two -tailed test.
b) Value of test statistic < critical value, in case of left -tailed test.
c) Value of test statistic > critical value, in case of right -tailed test.
9.1.7 Hypothesis testing for Mean:
Here we test, whether population mean and hypothesized mean are same
of different. Let,
be the hypothesized mean, we may test any of the
followi ng:
(i)
against

(ii)
against
or
against

(iii)
against
or
against

a) S.D. is given
Test statistic is given by
here
: sample mean,
: standard deviation of population and
: sample size

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Business Research Methods b) S.D. is not given
Test statistic is given by
where

here
: sample mean,
: standard deviation of population and
:
sample size
Example:
Ten employees are selected from a firm and their weights are found to be,
in kgs., 50, 52, 52, 53, 55, 56, 57, 58, 58 and 59. At 5% level of
significance, check whether previous average weight i.e. 5 4 kg is equals
current average weight or not.
Solution:
Here we want to test
against
, we have to check it is
changed or not, hence it is two tailed test.
Here,
average weight is not changed.
average weight is changed.
Also, S.D. is not given.
We calculate mean, S.D. for given data.
x x-x\ (x-x\)2
50 -5 25
52 -3 9
52 -3 9
53 -2 4
55 0 0
56 1 1
57 2 4
58 3 9
58 3 9
59 4 16
550 86

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Testing of hypotheses, Chi -
Square analysis & Analysis
of variance (Anova)
,


Test statistic is

Now, we calculate critical value with d.f. = (n -1) = 9 and significance
level = 0.05
From student’s t -distribution table we get
Critical value =2.262
Here value of test statistic < critical value
So, we reject null hypothesis i.e. we reject ‘average weigh t is not
changed’.
Therefore, conclusion is average weight is changed from 54.
9.1.8 Hypothesis testing for Difference of Two Mean:
Here we test, whether population means of two samples are same of
different. These population samples may be drawn from dif ferent
populations. Let,
be sample drawn from one population
with population mean
and population variance
and

be sample drawn from another population with population mean
and
population va riance
.
We may test any of the following:
(i)
against

(ii)
against
or
against

(iii)
against
or
against

Let us see, di fferent situations and test statistic under each situation:
c) Both samples are independent of each other both population variances
are known.
Let,
be sample means and
be variances of two samples with
sample size
from two populations respectively.
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Business Research Methods Test statistic is given by

This test is also known as Z -test.
d) Both samples are independent of each other both population variances
are not known.

When population variances are unknown, student’s t -test to be used.
Student’s t -test with
degree of freedom has two conditions
viz. both populations have normal distribution and their variances are
equal.

Unknown variances are calculated by pooled sample variance given
by

Test statistic is given by


e) Both samples are related.
Two samples are related means they are paired observations. So,
clearly
. Here we use t -distribution with
degree of
freedom (d.f.). Test statistic is

where,
,
&

Example:
In a survey of daily wages of workers, 400 workers are chosen at random
in a company ABC. Their average daily wages are Rs. 250 with standard
deviation of Rs.40. For 400 workers chosen from some other company
XYZ, the average daily wages are Rs. 220 with standard deviation of
Rs.55. Are the average daily wages of workers in both companies same?
Test at 5% level of significance.
Solution;
Here we wish to test
against
, we have to check it
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Testing of hypotheses, Chi -
Square analysis & Analysis
of variance (Anova) Let,
average daily wages of workers in both companies are not same
average daily wages of workers in both companies are same
Given that,

Test statistic is

Now we find critical value at 5% significance level an d d.f. = 400 (infinity)
Critical values = -1.96 and 1.96 for two tailed test.
Since, calculated test statistic > Critical value
Therefore, we reject the null hypothesis and conclusion is daily wages of
workers are same in both companies.
9.1.9 Hypothesis testing for Proportion:
Population has two mutually exclusive and exhaustive classes or groups
based on certain attributes. One class possesses that attribute and another
class not possesses that attribute. Here, population proportion is the
parameter.
We may test any of the following:
(i)
,
(right tail test)
(ii)
,
(left tail test)
(iii)
,
(two tail test)
Test statistic is given by
here
: sample proportion and
: sample size
Example:
A sample of 500 persons selected at random from a large city gives the
results that 53% males are employed. Is there reason to doubt the
hypothesis that males and females are in equal numbers in employment
sector? Us e 1% level of significance.


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Business Research Methods Solution:
We wish to test,
against
(right tailed test)
Given that,
Sample proportion = 0.53
Sample size = 500

Critical value with 0.01 significance level is 2.326
Since, test statistic < critical va lue
So null hypothesis not rejected. So number of male and females are same
in employment sector.
9.1.10 Hypothesis testing for Difference of Two Proportions:
Here we compare two population proportions
and
with sample
proporti ons
and
taken from two independent samples (large enough,
>30) drawn from each population respectively.
We may test any of the following:
(i)
against

(ii)
against
or
against

(iii)
against
or
against

The test statistic is
where,

Example: There are 100 students in a university college and in the whole
university, inclusive of this college, the number of students is 2000. In a
random sample study 20 were found smokers in the college and the
proportion of smokers in the university is 0.05. Is there a significant
difference between the proportion of smokers in the college and
universi ty? Test at 5 per cent level.
Solution:
Let
there is no difference between sample proportion and
population proportion)
and
(there is difference between the two proportions) munotes.in

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Testing of hypotheses, Chi -
Square analysis & Analysis
of variance (Anova) and on the basis of the given information, the test st atistic z can be worked
out as under:

As the
is two -sided, we shall determine the rejection regions ap plying
two-tailed test at 5 per cent level and the same works out to as under, using
normal curve area table:
R : | z | > 1.96
The observed value of z is 7.143 which is in the rejection region and as
such we reject
and conclude that there is a significant difference
between the proportion of smokers in the college and university.
9.2 CHI -SQUARE TEST
9.2.1 Introduction
A confiden ce interval of the unknown population variance is estimated by
using the Chi -Square distribution. Here we use non -parametric tests which
are not based on any parameter like mean, variance, proportion, etc. with
the help of Chi -Square distribution.
These n on-parametric tests a re a) T est for equality of more than two
population proportions. b) T est for independence of variable c) test for the
goodness of fit
9.2.2 Test for equality of more than two population proportions.
Example: A food industry wishes to test if the proportion of its customers
in three age groups are the same or not.
Industry conducts a sample survey of 1000 people in each age group and
find that there are 400, 450 and 300 customers in the sample in three
different age(in yrs.) groups (15 -25, 25 - 40 and above 40).
Here, we wish to test
= proportion of its customers in three age groups are same.
= proportion of its customers in three age groups are not same.
Age group (yrs.) → 15 - 25 25 - 40 Above 40
Like 400 450 300
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Business Research Methods The above table shows the number of individuals in respective age groups
who like or dislike products of that food industry.
Test statistics Chi -square is,

Where,
= Observed frequency of the ith row and jth column
= Expected frequency of ith row and jth column
=

For contingency table having r -rows and c - columns,
follows chi -
Square distribution with
degree of freedom with three
conditions
(1) each cell has an expected frequency of at least 5
(2) total sample size is at least 50
(3)

For the above example,
Age group
(yrs.) → 15 - 25 25 - 40 Above 40
Like
= 400
= 383.33
= 450
= 383.33
= 300
= 383.33
Dislike
= 600
= 616.66
= 550
= 616.66
= 700
= 616.66

=

=

We then compare the test statist ic to the critical Chi -square value.
Here we set alpha value = 0.05 (i.e. 5% ) and the degrees of freedom=
(2-1) (3-1) = 2
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Testing of hypotheses, Chi -
Square analysis & Analysis
of variance (Anova) For d.f. 2 and 5% significance level critical value is 5.991
Since
>

Therefore, we reject t he null hypothesis H_0 at a 5% significance level
and conclude that the Subject and mode of learning are not independent.
9.2.3 Test for the independence of variable
The Chi -square test of independence is a statistical hypothesis test used
to determine wh ether two categorical or nominal variables are likely to be
related or not. This test does not measure the degree of relationship
between two attributes. If the calculated value of
is less than the table
value at a certain level of significan ce for a given degree of freedom, we
conclude that the null hypothesis is rejected.
Example :
Consider the data for 500 students at college. For each student, we know
the subject and mode of learning, whether online or on campus.
Our variables are the sub ject and whether online or on -campus learning.
Both variables are categorical.
The last requirement is at least five expected values for each combination
of the two variables. To confirm this, we need to know the total counts for
each subject learned and the total counts for whether online or on -campus
learning.
Here we wish to test,

= ‘subject’ and ‘mode of learning’ are independent.

= ‘subject’ and ‘mode of learning’ are not independent.
Subject learned Online On-campus
Mathematics 60 75
Communication Skills 85 95
History 90 30
Information Technology 45 20

To find expected counts for each subject -mode of learning combination,
we first need the row and column totals, which are shown below:
Contingency table for subject -mode of learning combination with row and
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Business Research Methods
Subject learned Online On-campus Row totals
Mathematics 60 75 135
Communication
Skills 85 95 180
History 90 30 120
Information
Technology 45 20 65
Column totals 280 220 GRAND
TOTAL = 500

Here a re the actual and expected counts for each subject -mode of learning
combination. In each cell below, the expected count appears in bold below
the actual count. The expected counts are rounded to the nearest whole
number.
Expected frequency of any cell = [( (Row total for the row of that
cell)*(Column total for the column of that cell)] / (Grand Total)
Contingency table for subject -mode of learning combination showing
actual count vs. expected count
Subject learned Online On-campus Row totals
Mathematics 60
76 75
59 135
Communication Skills 85
101 95
79 180
History 90
67 30
53 120
Information Technology 45
36 20
29 65
Column totals 280 220 GRAND
TOTAL = 500
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Testing of hypotheses, Chi -
Square analysis & Analysis
of variance (Anova) The expected counts use the row and column totals. If we look at each of
the cells, we can see that some expected counts are close to the actual
counts but most are not. If there is no relationship between the subject
learned and the mode of learning, the actual and expected counts will be
similar. If there is a relationship, the actual and expected counts will be
different.
Lastly, to get our test statistic,
=

= 3.29+3.52+5.81+6.21+12.65+13.52+9.68+10.35=65.03
We then compare the test statistic to the critical Chi -square value.
Here we set alpha value = 0.05 (i.e. 5% ) and the degrees of freedom= (4 -
1)(2-1) = 3
For d.f. 3 and 5% significance level critical value is 7.815.
Since
>

Therefore, we reject the null hypothesis
at a 5% significance level and
conclude that the Subject and mode of learning are not independent.
9.2.4 Test for the goodness of fit
The Chi -squar e goodness of fit test checks whether your sample data is
likely to be from a specific theoretical distribution. We have a set of data
values and an idea about how the data v alues are distributed. The test
gives us a way to decide if the data values have a “good enough” fit to our
idea, or if our idea is questionable.
Conditions for continuity of
test i.e. for the validity of
test of
goodness of fit between theory and experiment the following conditions
must be satisfied
1) Sample observations are drawn independently
2)

3) Total frequency should be very large i.e. at least 50
4) No theoretical cell frequency should be less than 5
Example:
We coll ect a random sample of five boxes. Each box has 60 pens and four
colors. We hypothesize that the proportions of the four colors in each box
are the same.
= proportion of the four colors in each box is the same.
= proportion of t he four colors in each box is not the same. munotes.in

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Business Research Methods We have a simple random sample of 5 boxes of pens. Our categorical
variable is the color of the pen. We have the count of each color in 5 boxes
of pens.
Each bag has 60 pens. Each box has four colors of pens. W e expect to
have equal numbers for each color. This means we expect 60 / 4 = 15 pens
of each color from each box.
For 5 boxes in our sample, we expect 5 x 15 = 75 pens of each color. This
is more than the requirement of five expected values in each catego ry.
Expected frequency of any cell =


Color Number of Pens (05 bags) Expected
Number of
pens Observed –
Expected Squared
Difference Squared Difference
/ Expected Number
Red 70 75 5 25 25 / 75 = 0.33
Blue 65 75 10 100 100 / 75 = 1.33
Black 70 75 5 25 25 / 75 = 0.33
Green 95 75 20 400 400 / 75 = 5.33
Total 300 300 – – 7.32

Finally, we calculate the test statistic = 7.32
We then compare the test statistic to the critical Chi -square value.
Here we set alpha value = 0.05 (i.e. 5%) and the degrees of freedom= (4 -
1)(4-1) = 9
For d.f. 9 and 5% significance level critical value is 16.919
Since
<

Therefore, we accept the null hypothesis
at a 5% significance level and
conclude that proportion of the four co lors in each box may be the same.
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Testing of hypotheses, Chi -
Square analysis & Analysis
of variance (Anova) 9.3 ANALYSIS OF VARIANCE (ANOVA)
9.3.1 Introduction
ANOVA was developed and practiced by Professor R. A. Fisher later on
professor Snedecor and many others contributed in development of
ANOVA. The difference between diff erent groups of data for
homogeneity is tested by using ANOVA techniques.
“The essence of ANOVA is that the total amount of variation in a set of
data is broken down into two types, that amount which can be attributed to
chance and amount which can be con tributed to specified causes.”There
may be variation between samples and also within sample items.
To compare more than two populations, ANOVA plays an important role.
To compare yield of crop from different types of soils, to compare diet
habits of peopl e from different age groups, etc. In such cases, one
generally does not want to consider all possible combinations of two
populations at a time for that would require a large number of tests before
we would be able to arrive at a decision. This testing req uires lots of
money and time, even after these tests some relationship between
population or attributes remains unidentified. Through this technique one
can explain whether various varieties soils or age groups etc differ
significantly so that a policy dec ision could be taken accordingly.
In general ANOVA technique helps to investigate any number of factors
which are hypothesized or said to influence the dependent variable.
Also, the differences amongst various categories within each of these
factors be i nvestigated by this technique.
If we take only one factor and investigate the differences amongst its
various categories having numerous possible values, we are said to use
one-way ANOVA and in case we investigate two factors at the same time,
then we use two-way ANOVA.
The basic principle of ANOVA is to test differences among the means of
the populations examining the amount of variation within each of these
samples, relative to the amount of variation between the samples. The total
variance in the joint sample is partitioned into two parts (a) between
samples variance, and (b) within samples variance. Between samples
variance is due to different treatments, while within samples variance is
due to the random unexplained disturbance.
Test statistic is def ined as

Using this method, we wish to test
: all population means are the same (i.e., effects of all treatments are
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Business Research Methods Against
: all population means are not the same (i.e., effects of all treatments
are not the same)
When the effects of all the treatments will be different, between samples
variance will be large. In such cases calculated
will be large and hence
we reject null hypothesis. Therefore, ANOVA is right tailed test.
9.3.2 ONE WAY ANOVA:
Calcula tion Steps:
For k samples, having
number of items respectively,

1) calculate mean of each sample
i.e. calculate

2) calculate mean of the sample means
i.e. calculate

3) calculate sum of squares for varian ce between the samples or SS
between
SS between =

4) calculate mean square between samples
MS between =
where,
is degree of freedom between
samples
5) calculate sum of squares for variance within samples
SS within =
for

6) calculate mean square within samples
MS within =
where,
is degree of freedom within samples
7) calculate sum of squares of deviations for total variance
SS for total varianc e =

Also
SS for total variance = SS between + SS within
and d.f. for total variance = d.f. for between + d.f for within
=
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Testing of hypotheses, Chi -
Square analysis & Analysis
of variance (Anova) 8) finally, calculate test statistic


9) calculate Critical value from F -distribution table.
Critical value = F -distribution (d.f. for between, d.f for within)
here,

10) conclusion
Now we can decide whether to reject or not reject the null hypothesis.
9.3.3 TWO WAY ANOVA:
Two-way ANOVA technique is used when the data are classified with
respect to two factors.
For example, the agricultural yield may be classified with respect to
different varieties of seeds and also with respect to different varieties of
fertilizers used. A business firm may have its sales data class ified with
respect to different salesmen and also with respect to sales in different
regions. In a factory, the various units of a product produced during a
certain period may be classified with respect to different varieties of
machines used and also with respect to different situation of weather. Such
a two -way design may have repeated measurements of each factor or may
not have repeated values. The ANOVA technique is little different in case
of repeated measurements where we also compute the interaction
variation .
9.3.3.1 One Observation per Cell:
We have to calculate this residual or error variation by subtraction, once
we have calculated (just on the same lines as we did in the case of one -
way ANOVA) the sum of squares for total variance and for varian ce
between varieties of one treatment as also for variance between varieties
of the other treatment.
Calculation Steps:
1) Calculate T = total of the values of individual items in all the samples.
2) Calculate Correction factor =

3) Calculate sum of squares of deviations for total variance or total SS
Total SS =

4) Take the total of different columns and then obtain the square of each
column total and divide such squared values of each column by the
number of items in the concern ing column and take the total of the result
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Business Research Methods obtain the sum of squares of deviations for variance between columns or
(SS between columns).
SS between columns =

5) Take the total of different rows and then obtain the square of each row
total and divide such squared values of each row by the number of items in
the corresponding row and take the total of the result thus obtained.
Finally, subtract the correction factor from this total to obtain the sum of
squares of deviations for variance between rows (or SS between rows).
SS between rows =

6) Calculate Sum of squares of deviations for residual or error variance
SS for residual or error variance = Total SS – (SS between columns + SS
between rows)
7) Calculate Degrees of freedom (d.f.)
d.f. for total variance = (c. r – 1)
d.f. for variance between columns = (c – 1)
d.f. for variance between rows = (r – 1)
d.f. for residual variance = (c – 1) (r – 1)
where c = number of columns and r = number of rows
8) Calculate Mean squares




here we wish to test
a)
: the varieties of first factor have the same effect
Against
: the varieties of first factor are significantly differ ent
The test statistic is

It follows F distribution with
d.f.
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Testing of hypotheses, Chi -
Square analysis & Analysis
of variance (Anova) b)
: the varieties of second factor have the same effect
Against
: the varieties of second factor are significantly different
The test statistic is

It follows F distribution with
d.f.
Example:
The following table gives the monthly sales (in thousand rupees) of a
certain firm in three states by its four salesmen:
States Salesmen
Total
A B C D
X 5 4 4 7 20
Y 7 8 5 4 24
Z 9 6 6 7 28
Total 21 18 15 18 72

Calculate F -coefficients and state whether the difference between sales
affected by the four salesmen and difference between sales affected in
three States are significant.
Solution:
Here no. of rows (r) = 3, no. of columns(c) = 4, total no. of observations
(n) = 12
Sum of all observations = T = 72
Correction factor =

Total SS =


SS between columns =

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