Business Research Methods-Lecutre Notes-ALL UNITS

PPG INSTITUTE OF TECHNOLOGY

PPG BUSINESS SCHOOL

BATCH: 2011-2013 LECTURE NOTES - II SEMESTER ACADEMIC YEAR: 2012-2013

SUBJECT: BUSINESS RESEARCH METHODS

1.1 INTRODUCTION

BUSINESS RESEARCH DEFINITION

Research is a systematic and objective process of gathering, recording and analyzing data for aid in

making business decisions. Business research is a systematic and organized effort to investigate a

specific problem encountered in the work setting that needs a solution

SIGNIFICANCE OF RESEARCH

Research inculcates scientific thinking: Research inculcates scientific and inductive thinking and it

promotes the development of logical habits of thinking and organization.

Increasing role of research: The role of research in several fields of applied economics, whether

related to business or to the economy as a whole, has greatly increased in modern times. The

increasingly complex nature of business and government focused attention on the use of research in

solving operational problems.

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UNIT - I

1 INTRODUCTION

1.1 BUSINESS RESEARCH DEFINITION AND SIGNIFICANCE

1.2 RESEARCH PROCESS

1.3 TYPES OF RESEARCH

1.4 THEORETICAL AND EMPIRICAL RESEARCH

1.5 CROSS SECTIONAL AND TIME-SERIES RESEARCH

1.6 RESEARCH QUESTIONS/PROBLEMS

1.7 RESEARCH OBJECTIVES

1.8 RESEARCH HYPOTHESES


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Research provides the basis for nearly all government policies in economic system.

Solving operational and planning problems: Research has its special significance in solving various

operational and planning problems of business and industry. Operations research and market research,

along with motivational research, are considered crucial and their results assist, in more than one way in

taking business decisions.

Important for social scientists: Research is equally important for social scientists in studying social

relationships and in seeking answers to various social problems. It provides the intellectual satisfaction

knowing a few things just for sake of knowledge and also has practical utility for the social scientists to

know the sake of being able to do something better or in a more efficient manner.

Significance of research can also be understood keeping in view the following points:

1) To those students who are write to master’s or ph. D thesis, research, may mean a careerism

or a way to attain a high position in the social structure;

2) To professionals in research methodology, research may mean a source of livelihood;

3) To philosophers and thinkers, research may mean the outlet for new ideas and insights;

4) To literary men and women, research may mean the development of new styles and creative

work;

5) To analysis and intellectuals, research may mean the generalization of new theories

1.2 RESEARCH PROCESS

Formulating the research problem

The formulation of a general topic into a specific research problem thus constitutes the first step in a

scientific enquiry. Essentially two steps are involved in formulating the research problem, viz.,

understanding the problem thoroughly, and rephrasing the same into meaningful terms from an

analytical point of view.

Extensive Literature survey

The abstracting and indexing journals and published or unpublished bibliographies

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Formulating the working hypotheses

After extensive literature survey, researcher should state in clear terms the working hypothesis. It is

tentative assumption made in order to draw out and test its logical or empirical consequences.

Preparing the research design

Research design includes the means of obtaining the information, explanation of the way in which

selected means of obtaining information will be organized and the reasoning leading to the selection.

Determining Sample design

A sample design is a definite plan determined before nay data are actually collected for obtaining a

sample from a given population.

Collecting the data

Data are two types – Primary data and Secondary data. Primary data can be collected by observation,

through personal interview, through telephone interviews, by mailing of questionnaires, through

schedules.

Analysis of data

The analysis of data requires a number of closely related operations such as establishment of categories,

the application of these categories to raw data through coding, tabulation and then drawing statistical

inferences.

Interpret and report.

Finally, the researcher has to prepare the report of what has been done by him. The main text of the

report should have the following parts

1. Introduction

2. Summary of findings

3. Main report

4. Conclusion

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1.3 Types of research

Pure Research is undertaken for the sake of knowledge without any intention to apply it in

practice

Not necessarily problem oriented

Discovery of new theory / refinement of existing theory.

Ex : inventions like steam engine, EDP, telecomm.

Applied research is carried on to find solution to a real life problem requiring an action or

policy decision.

Problem oriented

Action directed

It seeks an immediate and practical result

Ex: Marketing research carried on for developing a new market

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Exploratory Research analyses the data and explores the possibility of obtaining as many

relationships as possible.

It is a preliminary study of an unfamiliar problem about which the researcher has little or

no knowledge.

“ To see what is there rather than to predict the relationships that will be founded”

EX: Doctor’s initial investigation of a patient suffering from an unfamiliar disease.

Descriptive research

It is a fact finding investigation with adequate interpretation.

It focuses on particular aspects or dimensions of the problem studied

Ex: Consumption behavior of people in a village

Diagnostic study

It is to discover what is happening, Why it is happening and What can be done

– It aims in identifying the cause of the problem and the possible solution for it

Evaluation studies

• It is for assessing the effectiveness of social or economic programmes implemented

– Ex: (Polio drops)

• For assessing the impact of development projects

– Ex: (irrigation projects)

Action Research

• It is a concurrent evaluation study of an action programme launched for solving a problem for

improving existing situation

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– Ex: (Creating awareness about HIV)

Experimental Research

• It is to assess the effects of particular variables on a phenomenon by keeping the other variables

constant or controlled

– To determine whether and in what manner variables are related to each other

– The factor , which is influenced , by other factors is called a dependent variable, and the

other factors , which influence it are known as independent variables

– EX: agricultural productivity (i.e) is a dependent variable and the factors such as soil

fertility, irrigation, quality of seed etc. which influences the yield are independent

variables.

Analytical studies

• It is a system of procedures and techniques of analysis applied to quantitative data

• It consists of mathematical model

• It aims in testing hypothesis and specifying and interpreting relationship

• Used to measure variables, comparing groups and examining association with factors

Historical research

• Study of past record and other information sources

• Its main objective is to draw explanations and generalizations from the past trends in order to

understand the present and to anticipate the future.

Surveys

• It is fact finding study

• It involves collection of data directly from a population

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• It requires expert and imaginative planning, careful analysis and rational interpretation of the

findings

Case Study

• It is an in-depth comprehensive study of a person, a social groups , an episode, a process

• Ex: a study of the financial health of a business undertaking

– A study of labor participation in management in a particular enterprises

– A study of life style of working women

Field studies

It is a scientific enquiries aimed at discovering the relations and interactions among sociological,

psychological and educational variables in social institutions and actual life situations like

communities, school, factories etc

• A social or institutional situation is selected and the relations among the attitudes, values,

perceptions and behaviors of individuals and groups in the selected situation are studied.

1.5 On the basis of extent theory research are two types:

Theoretical Research

Empirical Research

Theoretical research: Theoretical research generally uses the findings from existing works to develop

new ideas through analyzing existing theory and explanations. These new ideas are not tested through

collecting evidence in the form of primary data. Theoretical research is held to be a classical way of

adding something to the value of the body of knowledge.

In the business and management studies world theoretical research is not always well received. In fact

some academic researchers would argue that the process described as theoretical research should not be

regarded as “proper” academic research. The basis of such a claim is that this type of theoretical

research does not have a test component. This fact is used by those who are not enthusiastic about

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theoretical research, to imply that theories can postulated without any “proof”. However this type of

thinking is a misunderstanding of the nature of research. All research processes requires

conceptualization. One of the primary roles of theoretical research is to rework 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.

Evaluating theoretical research: theoretical research does not rely on data or evidence, collection,

analysis and synthesis it is sometimes often said to be more difficult. Theoretical research relies heavily

on creativity and imagination. Al though these attributes are still required for empirical research they are

often required to a greater extent in theoretical research.

Empirical research: empirical means based upon observation or measurement rather than theoretical

reasoning. It supports the development of new ideas through the collection of data. The researcher who

develops a theory of spot fan violence through visiting a library and developing their own explanation

through reading existing work will be undertaking theoretical research. The researcher to take this one

step further and collects data test their explanation will be undertaking empirical research. For example,

computer simulations generate scores from random number routines. The cases and measures are not

involved. Analytical researchers use mathematical operations to work from initial assumptions to

conclusions there are no cases, measures, or scores.

Empirical research involves three activities, as which are as follows:

Measurement: it involves activities associated with measuring the factors that from the expected

relationship. In other situations, a researcher may begin with measures already developed and assess

their suitability for a study at hand.

Research design: it establishes procedures to obtain cases for study and to determine how scores will be

obtained from those cases.

Analysis: empirical research also involves analysis of scores. Analyses are performed to describe

crosses on single measures and, especially, to identify relationships that may exist between scores across

different measures.

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Benefits of empirical research

1) Understand and respond to dynamics of situations.

2) Respect contextual differences.

3) Build upon what is already known to work

4) Meet accepted professional standards of research.

5) Integrate professional knowledge with empirical data to inform instructional

development decisions.

6) Establish relationship between intervention and behavioral response.

Limitations of Empirical Research

1) Time: Since empirical research requires soliciting participation and “data gathering”

from various off campus of researchers.

2) Cost: Field research requires on-sites visits by researchers may be require cash

outlays for travel, lodging, and other expenses not required in conceptual research,

which can usually be accomplished in the local academic setting.

3) Access to firms: they cannot gain access to the types of the firms necessary for their

studies.

4) Access to data: even if they gain access to business firms, such firms may be

reluctant to release any or all the data necessary for the studies.

5) Skills: they do not possess the requisite skills necessary to design such empirically

based studies, to gather and analyze the oftentimes huge data efficiently, or two

interpret the results in a manner meaningful to and rewarded by both the business and

academic worlds.

1.6 On the basis of time dimension:

Two types:

Cross-sectional Research

Longitudinal Research

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Cross-sectional research: in this research, researchers observe at one point in time. Cross-sectional

research is usually the simplest and least costly alternative. A cross- sectional designs a snapshot of the

variables included in the study, at one particular point in time. It may reveal how those variables are

related.

Longitudinal Research: Researchers using longitudinal research examine features of people or other

units at more than one time it is usually more complex and costly than cross sectional research, but it is

also more powerful, especially where researchers seek answers to questions about social change. Three

types of longitudinal research which as follows;

1) Time-series research

2) Panel study

3) Cohort study

Time series research: the time design collects data on the same variable at regular intervals (weeks,

months, year) etc

Time series designs are useful for:

Establishing a baseline measure,

Describing charges over time,

Keeping track of trends and

Forecasting future trends.

Panel study: it is a powerful type of longitudinal research. It is more difficult to conduct than time

series research. In panel study, researchers observe exactly the same people, group, or organization

across time period. Participants who are examined over repeated time points may be affected by having

previously completed the measure being used. (This is known as sensitization)

Cohort study: it is similar to the panel study, but rather than observing the exact same people, a

category of people who share a similar life experienced in a specified time period is studied.

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1.7 Research Problem

The term ‘problem’ means a question or issue to be examined.

Research Problem refers to some difficulty /need which a researcher experiences in the context

of either theoretical or practical situation and wants to obtain a solution for the same.

The first step in the research process – definition of the problem involves two activities:

Identification / Selection of the Problem

Formulation of the Problem

This step involves identification of a few problems and selection of one out of them, after

evaluating the alternatives against certain selection criteria.

Formulation is the process of refining the research ideas into research questions and objectives.

Formulation means translating and transforming the selected research problem/topic/idea into a

scientifically researchable question. It is concerned with specifying exactly what the research

problem is.

CRITERIA OF SELECTION

The selection of one appropriate researchable problem out of the identified problems requires

evaluation of those alternatives against certain criteria. They are:

Internal / Personal criteria – Researcher’s Interest, Researcher’s Competence, Researcher’s own

Resource: finance and time.

External Criteria or Factors – Researchability of the problem, Importance and Urgency, Novelty

of the Problem, Feasibility, Facilities, Usefulness and Social Relevance, Research Personnel.

SOURCES OF PROBLEMS

Reading

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Academic Experience

Daily Experience

Exposure to Field Situations

Consultations

Brainstorming

Research

Intuition

• Problem Formulation

For a researcher, the problem formulation means converting the management problem to a

research problem.

Examples

• Management problem- Want to increase the sale of product A

• Research problem- What is the current standing of the product A?

• While problem is formulated, the following should be considered

1. Determine the objectives

2. Consider environmental factors

3. Nature of the problem

4. Stating the alternatives

• Determine the objectives

To increase the sales or does it means it has improved the knowledge of the audience.

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If the advertisement by the company was indeed ineffective, what course of action does the

company intend to take?

Increase the budget for the next Ad

Use different appeal

Change the media

Go to a new agency.

Consider environmental factors

• If a company introduce a new product

Purchasing habits of consumers

Presently, who are the competitors in the market with similar product?

What is the perception of the people about other products of the company?

Size of the market and target audience.

• Nature of the problem

Initial investigation could be carried by using a focus group of consumers or sales representatives

Did the customer ever include this company’s product in his mental map?

If the customer is not buying, the reasons for that

Why did the customer turn to the competitor’s product?

• Stating the alternatives

The researcher would be better served by generating as many alternatives as possible during the

problem formulation.

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For every alternative, a hypothesis has to be developed and data to be collected and to be proved

whether it is best alternative or not.

1.8 Research objective

It’s not long term goal, but is the step towards the long term goal.

It defines the purpose of the proposed research. It should be phrased in such a way that central

hypothesis clearly grows out of it

An ideal research objective

-Hypothesis driven

-Innovative

-To study mechanism

-Realistic & focused

-Doable in the requested budge and time

1.9 Hypothesis Testing

It considered as a principal instrument in research. Hypothesis is a mere assumption to be proved or

disproved.

Hypothesis is defined as the proposition or a set of proposition set forth as an 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 right of established facts.

Characteristics of hypothesis

1) Hypothesis should be clear and precise

2) A good hypothesis is assumption or explanation of why or how something occurs

3) Hypothesis should be capable of being tested.

4) Hypothesis should state relationship between variables.

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5) Hypothesis should be limited in scope and must be specific.

6) Hypothesis should be tested in most simple terms so that the same is easily understandable by

all concerns.

7) Hypothesis should be consistent with most known facts, in other words, it should be the one

which judges accept as being the most likely.

8) Hypothesis should be agreeable to testing with a reasonable time.

9) Hypothesis must explain the facts what it claims to explain. It should have empirical reference.

Eg: Companies manufacturing washing machines spend at least 10% of their annual profits on

advertising.

Testing Hypothesis: This is a statement or proposition that we would like to verify whether it is true

or not.

Concept of Null and alternative Hypothesis

A Null hypothesis is a statement about a population parameter (such as mu) and the test is used to

decide whether or not to accept the hypothesis.

It is identified by the symbol Ho

It is always stated that “There is no significant difference between the samples.

If the H0 is false, something else must be true. That is called alternative hypothesis

It is identified by the symbol H1.

It should be clear that both Null and alternative hypotheses cannot be true and only one of them

must be true.

For any exercise, our conclusion must result into the acceptance of one hypothesis and rejection

of the other.

Eg: Suppose a person is facing a legal trial for committing a crime. The judge look into all the

evidence for and against it listens very carefully the prosecutions and defendants arguments and

then decides the case and gives his verdict.

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The verdict could be

H0: The person has not committed the crime

H1: The person has committed the crime

Procedure in Hypothesis testing

There are five steps involved in testing a hypothesis

1. Formulate a hypothesis. We have to set the null and alternative hypothesis (H0 and H1)

2. Set up a suitable significance level

3. Significance level means the confidence with which a null hypothesis is rejected or accepted

depends upon the significance level used for the purpose.

4. Select Test criterion: There are many techniques from which one is to be chosen.

Eg: If sample size > 30- Z test

If sample size <30- t-test.

5. Compute

This step involves various computations necessary for the application of that particular test.

These computations include the testing statistic as also its standard error.

6. Make decisions: This step involves in the process of accepting or rejecting the null

hypothesis at a given level of significance.

Two types of Errors

Type I error: it occurs when one rejects the null hypothesis and accepts the alternative,

when it fact the null hypothesis is true.

Type II error: it occurs when one accepts the null hypothesis when in fact the null

hypothesis is false.

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2.1 Research Design Meaning & Types

A research project conducted scientifically has a specific framework of research from the problems

identification to the presentation of the research report. This framework of conducting research is known

as the research design.

A research designs simply the framework or plan for a study that is used as a guide in collecting and

analyzing the data. It is blueprint that is followed in completing study”.

TYPES OF RESEARCH DESIGN

1) Exploratory Research

i) Literate Research/Study of Secondary Data

ii) Survey of knowledgeable persons or experience survey

iii) Case Groups

iv) Focus Groups

v) Two-Stage Design

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UNIT -II

2.1 RESEARCH DESIGN MEANING AND TYPES

2.2 EXPLORATORY RESEARCH DESIGN

2.3 CONCLUSIVE DESIGN

2.4 EXPERIMENTAL/CAUSAL RESEARCH

2.5 VARIABLES IN RESEARCH

2.6 MEASRUEMENT AND SCALING

2.7 VALIDITY AND RELIABILTIY OF INSTRUMENTS


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2) Conclusive Research

i) Descriptive

a) Longitudinal Study

b) Cross-sectional Study

ii) Experimental or Casual Research

2.2 Exploratory Research Design

Exploratory research studies are also termed as formulative research studies. The main purpose of such

studies is that of formulating a problem for more precise investigation or of developing the working

hypotheses from an operational point of view. The major emphasis in such studies is on the discovery of

ideas and insights. As such the research design appropriate for such studies must be flexible enough to

provide opportunity for considering different aspects of a problem under study.

Objectives of Exploratory Research

1) Precise formulation of the problem

2) Provide more knowledge to the researcher about the problem environment

3) Establishes priorities for further research

4) To design appropriate information collection procedure for the given situation.

5) To determine nature of relationship between various factors associated in the problem.

6) Gathering information on the problems associated with doing conclusive research.

1. Study of Secondary Data: The quickest and most economical way is to find possible hypotheses

from the available literature. The past researches may be suitable sources of information to develop new

hypotheses. The findings of marketing research are generally published in trade and professional

journals, which can be fruitful sources of information.

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2) Depth Interview: Experience survey means the survey of people who have had practical experience

with the problem to be studied. These individuals can be top executives, sales managers/executives,

wholesalers and retailers possessing valuable knowledge and information about the problem

environment.

3) Case Study: The third general type of exploratory research is the case method. This research method

has long been considered “soft” or nonscientific, but with the modern surge in qualitative research the

case method has received more attention. Indeed, the case method might be considered one variation of

the survey of individuals with ideas. It involves the comprehensive study of one, or a few, specific

situations and lends itself particularly to the study of complex situations in which the interrelations of

several individuals are import – for example, the effective management of distributor relations or what

constitutes good marketing management.

4) Focus Group: Focus group originates from sociology studies. They have been extensively used in

marketing research. Focus groups studied are generally conducted to evaluate the potential of a new

product idea or concept. A focus group comprises several persons, who are led by a trained moderator.

The moderator’s task is to lead the team in generating and exchanging ideas on a particular issue. The

process starts by issuing a topic for discussion among participants by the moderator. In such discussions,

the moderator’s role will be to silently watch the proceedings and ensure that the discussion is going on

as expected. However, the moderator needs to intervene to ensure that all individuals in the group

participate. Once the focus group’s observations and recommendations are obtains, the information is

evaluated by the moderator,. This forms the basis for further research.

5) Two-Stage Design: A two-stage design is beneficial approach for designing research. In this method,

the exploration is conducted in two stages. The first stage consists of clearly defining the research

problem, while the second stage comprises developing the research design. A two-stage design is

beneficial, when the problem is vaguely defined and the researcher is not clear about the particular topic

that has to be studied.

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2.3 Conclusive Research Design

Conclusive research provides information, which helps the executive to make a rational decision. The

marketing executive has to arrive at a suitable decision from the various alternative decisions. The

various alternative conclusions and selecting the most suitable conclusion may be done by descriptive

research design or experimental research design.

i. Descriptive Research

Descriptive studies, as their name implies, are designed to describe something – for example, the

characteristics of users of a given product; the degree to which product use varies with income, age, sex,

or other characteristics, or the number who saw a specific television commercial. A majority of

marketing studies are of this type.

Objectives of Descriptive Research

1) To describe the characteristics of relevant groups

2) To estimate the percentage of units in a specified population exhibiting a certain behavior.

3) To determine the perceptions of product characteristics.

4) To determine the degree to which marketing variable are associated.

Types of Descriptive Studies

1) Case Method: Case studies are more appropriate to exploratory research than descriptive

research. They are not widely used in descriptive research, but they are worth some comment in the

descriptive context and perhaps should be used more than they have been in the past.

2) Statistical Method: The statistical method is the most widely used method in marketing research

and is the method usually implied when a “survey” is referred to. The name comes from the

statistical techniques that are used in analyzing the data collected – techniques that vary from simple

means and percentage to very sophisticated techniques that require computers to manipulate the data.

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Uses of Descriptive Research

1) Consumer profiles

2) Market potential studied

3) Product usages studies

4) Attitude surveys

5) Sales analysis

6) Media research

7) Price surveys

Descriptive Research Analysis

1) Longitudinal Design/Panel Analysis: Longitudinal studies are based on panel data and panel

methods. A panel is a sample of respondents who are interviewed and then re-interviewed from

time to time. Generally panel data relate to the repeated measurements of the same variables.

Each family included in the panel, records its purchases of a number of product at regular

intervals, say, weekly, monthly or quarterly. Over a period of time, such data will reflect changes

in the buying behavior or families.

2) Cross-Sectional Design: A cross-sectional study is concerned with a sample of elements from a

population. Thus, it may deal with households, dealers, retail stores, or other entitles. Data on a

number of characteristics from the sample elements are collected and analyzed. The cross-

sectional study is the most frequently used descriptive design in marketing research. Cross-

sectional design involves the collection of information from any given sample of population

elements only one. They may be either single cross- sectional or multiple cross-sectional.

Types of Cross-Sectional Design

i. Field Studies ii.Survey Research

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2.4 Casual or Experimental Research

Although descriptive research in identifying co-variation between variables (e.g., blue packages

outsell red ones, consumption rate varies by education level) it cannot truly indicate causality (e.g., color

causes sales, education causes consumption). When we are in need of determining whether two or more

variables are causally related we must turn to casual research procedures. While there might be a

tendency to see many research objectives from a casual perspective (“We really want to know what

causes consumers to act that way”), there is a difference between causality in the vernacular and how it

is defined by scientists.

Types of experimental research design

Informal

Before after without control

Before-after with control

After only with control

Ex-post facto design

Panel design

Simple time series experiments

Recurrent time series experiments

Formal

Completely randomized design Randomized block design Latin square design Factorial design

Informal design

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Before and after without control

In such a design a design a single test group or area is selected and the dependent variable is measured

before the introduction of the treatment. The treatment is then introduced and the dependent variable is

measured again after the treatment has been introduced.

After only with control design

In this design two groups or areas (test are and control area) are selected and the treatment is introduced

into the test area only. Treatment impact is assessed by subtracting the dependent variable in the control

area from its value in the test area.

Before and after with control design

In this design two areas are selected and the dependent variable is measured in the both the area for an

identical time period before the treatment. The treatment is then introduced into the test area only, and

the dependent variable is measured in both for an identical time period after the introduction of the

treatment.

Ex post facto design

One variable of the “after only “design is called the ex post facto design. This differs from the “after

only” design because the experimental and control groups are selected after the experimental variable is

introduced instead of before. One advantage is that the test subject cannot be influenced, pro or con,

toward the subject by knowing they are being tested, since they are exposed to the experimental variable

before being selected for the sample.

Panel design

A permanent set of experimental units used in market research investigation is known as a ‘panel’. Panel

can be used both for exploratory and conclusive research. In such an experimental observation are

recorded at some pre d determined intervals of time and experimental variables can be introduced if and

when desired. Here any two set of successive measurements between which some experimental variable

is introduced can be considered as before and after experimental measurements.

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Simple test series experiments

Some dealers or retailers are selected for recording observation over certain period of time. Eg: to study

the impact of some advertising policy on sales, one may select some dealer or retailers. the sales during

a certain period before the advertisement and after the advertisement are recorded.

Recurrent time series design

Here the advertising policy is introduced, removed and introduced over different period of time and

sales over these periods are recorded.

Formal design

Completely randomized design

The main feature of this design is that the experimental treatments are assigned to the test units

completely at random. No prior precaution is needed to some extraneous variable before the assignment

is randomly made.

Randomized block design

It is an improvement over the CR design. In this design the principal of local control can be applied

along with the other two principles of experimental design. In this design subjects are first divided into

groups, known as blocks, such that within that group the subjects are relatively homogeneous in respect

to some selected variables

Latin square design

This design suggest that test will from a square because there will be as many tests units are treatments.

This design is used to control important extraneous influence. It is assumed that each treatment occurs

once with each store on a block. There are several stores in a block, there must be as many blocks as

there are treatments. It may be three, four and so on.

2.5 Variables in research

Dependent and Independent variables

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An independent variable is the presumed cause of the dependent variable- the presumed effect. When it can say A cause B , it means A is independent variable and B is dependent variable. The independent variable thus is one which explains or accounts variation in the dependent variable.

Experimental and measured variable

The experimental variable spell out the detail of the investigator’s manipulation while the measured variable refer to measurement. For example, rural development(measured variable) may be assessed in terms of increase in income, literacy, infrastructure......

Qualitative and quantitative variables

The Quantitative variable is one whose values or categories consist of number and differences between it’s categories can be expressed numerically. Eg: age, income, size......The qualitative variable is one which consist of discreet categories rather than numerical units

Categorical and numerical variables

Numerical variables are broken down into units in which the numbers used carry mathematical meaning. The numbers may be either discrete (1,2,3,4..)which cannot be broken down into smaller fractional quantities(no. Of children)or continuous.

2.6 Measurement and scaling

By measurement we mean the process of assigning numbers to objects or observations, the level of

measurement being a function of the rules under which the numbers are assigned.

According to kenneth D. Bailey: “Measurement is the process of determining the value or level, either

qualitative or quantitative, of a particular attribute for a particular unit of analysis”.

Basic process of measurement

I. Selecting observable empirical events

II. Developing a set of mapping rules- a scheme for assigning numbers or symbols to represent

aspect of the event being measured.

III. Applying the mapping rules to each observation of that event

Mapping rules

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In measuring, one devices some mapping rule and then translate the observation of property indicants

using this rule. For each concept or construct, several types of measurement are possible; the appropriate

choice depends on what you assume about the mapping rules. Each one has its own set of underlying

assumption about how the numerical symbols correspond to real world observation.

Importance of measurement

1) Measurement allows researchers to quantify abstract construct and variables.

2) The level of statistical sophistication used to analyze data derived from a study is directly

dependent on the scale of measurement used to quantify the variables of interest.

Functions of measurement

Empirical description: it facilitates empirical description of social and psychological phenomena. Eg:-in

a study of a tribal community, the researcher has to classify and categorize the cultural patterns and

behaviors.

1) Facilitates statistical treatment: measurement renders data amenable to statistical

manipulation and treatment. The statistical techniques for comparing groups, studying

relationship between variables.

2) Aids testing of hypothesis: measurement facilitates testing of theories and hypothesis.

3) Provide differentiation in objects: measurement enables researchers to differentiate between

objects or people in terms of specific properties they possess.

Measurement scales

1) Nominal scale

2) Ordinal scale

3) Interval scale

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4) Ratio scale

Nominal scale

It represents the most elementary level of measurement. a nominal scale assigns a value to an object

for identification or classification purposes. The value can be a number because no quantities are

being represented. In this sense, a nominal scale is truly a qualitative scale. Nominal scales are

extremely useful even though they can be considered elementary.

Marketing researchers use this scale quite often. For example, suppose three old drinks were

experimented with taste. The researcher would like the experiment to be blind, so when subject were

asked to taste one of the three cold drink, the drinks were labeled A,B or C.

Ordinal scaling

Ordinal scales have nominal properties, but they also allow things to be arranged based on how

much of some concept they possess. In other words, an ordinal scale is a ranking scale. The ordinal

scale indicates the relative position of two or more objects or some characteristics. The consumers

are asked to rank preference for several brands, flavor or package designs. The measures of such

preference are ordinal in nature.

Interval scale

The interval scale has all characteristics of the ordinal scale and in addition, the units of measures or

intervals between successive positions are equal.

Eg:- a researcher scaled brand A,B and C on an interval scale regarding the buyers ‘ degree of liking

of the brands. Brand A receives the highest liking score 6, B received 3 and C receives 2. First the

liking for brand A is more favorable than that for brand B. second the degree of liking between A

and B is three times greater than the liking between B and C.

Ratio scale

Ratio scale represents the highest form of measurement. They have all the properties of interval scale

with the additional attribute of representing absolute quantities. Interval scale represents only

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relative meaning, whereas ratio scale represents absolute meaning. In other words, ratio scale

provides iconic measurement. Zero, therefore, has meaning in that it represents an absence of some

concept. An absolute zero is a defining characteristic in determining between ratio and interval scale.

Classification of scaling ttechniques

SCALING TECHNIQUES

Comparative scales Non-comparative scales

Paired comparison Continuous rating scales Itemized rating scales

Rank order Likert

Constant sum Semantic differential

Stapel

Types of scaling Techniques

COMPARATIVE SCALES

• Involve the respondent directly comparing stimulus objects.

• e.g. How does Pepsi compare with Coke on sweetness

NON-COMPARATIVE SCALES

• Respondent scales each stimulus object independently of other objects

• e.g. How would you rate the sweetness of Pepsi on a scale of 1 to 10

Paired comparison items

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Please indicate which of the following airlines you prefer by circling your more preferred airline in each pair:

Air Canada WestJet

Air Transat Air Canada

Horizon Air WestJet

Comparative constant sum scales

• Allocate a total of 100 points among the following soft-drinks depending on how favorable you

feel toward each; the more highly you think of each soft-drink, the more points you should

allocate to it. (Please check that the allocated points add to 100.)

• Coca-Cola _____ points

• 7-Up _____ points

• Mirinda ____ points

• Fanta _____ points

• Pepsi-Cola _____ points

Total 100 points

Comparative rank order scales

Rank the following soft-drinks from 1 (best) to 5 (worst) according to your taste preference:

Coca-Cola _____

7-Up _____

Fanta _____

Pepsi-Cola _____

Mountain Dew _____

Semantic differential scale

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Here are a number of statements that could be used to describe Tesco. For each statement tick ( ) the

box that best describes your feelings about Tesco.

Modern store Old fashioned store

Low prices High prices

Unfriendly staff Friendly staff

Stapel Scale

QUALITY SERVICE VARIETY

+5 +5 +5

+4 +4 +4

+3 +3 +3

+2 +2 +2

+1 +1 +1

HIGH POOR WIDE

-1 -1 -1

-2 -2 -2

-3 -3 -3

-4 -4 -4

-5 -5 -5

Likert scale or 5 points scale

Strongly Disagree Neither agree nor Agree Strongly

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disagree disagree agree

Characteristics of good mmeasurement scales

1. Reliability

• The degree to which a measure accurately captures a true outcome without error

• synonymous with repetitive consistency

2. Validity

• The degree to which a measure faithfully represents the underlying concept (it asks the

right questions)

3. Sensitivity

The ability to discriminate meaningful differences between attitudes. The more categories the more

sensitive (but less reliable

Reliability can be more easily determined than validity

If it is reliable, it may or may not be valid

If a measure is valid, it may or may not be reliable

If it is not reliable, it cannot be valid

If it is not valid, it may or may not be reliable

Example of low validity, high reliability

Scale is perfectly accurate, but is capturing the wrong thing; for example, it measures consumers’

interest in creative writing rather than preference for kinds of stationery.

Example of modest validity, low reliability

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Scale genuinely measures consumers’ interest in kinds of stationery, but poorly worded items,

sloppy administration, data entry errors lead to random errors in data

2.6 Reliability

• Reliability refers to how consistent a measuring device is. A measurement is said to be reliable

or consistent if the measurement can produce similar results if used again in similar

circumstances.

• Validity

This refers to whether a study measures or examines what it claims to measure or examine. Valid

measures will ALWAYS be reliable…but reliable measures are not necessarily valid.

• Reliability' of any research is the degree to which it gives an accurate score across a range of

measurement. It can thus be viewed as being 'repeatability' or 'consistency'. In summary:

• Inter-rater: Different people, same test.

• Test-retest: Same people, different times.

• Parallel-forms: Different people, same time, different test.

• Internal consistency: Different questions, same construct.

• TEST-RETEST RELIABILITY

2 measures yield identical (or similar) results at 2 different times. The test-retest reliability

method is one of the simplest ways of testing the stability and reliability of an instrument

overtime. For example, a group of respondents is tested for IQ scores: each respondent is tested

twice - the two tests are, say, a month apart. Then, the correlation coefficient between two sets of

IQ-scores is a reasonable measure of the test-retest reliability of this test.

• PARALLEL FORMS RELIABILITY

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Parallel forms reliability is used to assess the consistency of the results of two tests constructed

in the same way using the same content. To create the parallel forms a large pool of test

questions that measure the same quality are created and then randomly divided into two separate

tests. Each test is given to the same sample of people and the correlation between the two parallel

forms is used as an estimate of the reliability.

Content (Face) validity

Is the degree to which a test measures an intended content area, e.g., achievement tests. Example:

to measure knowledge of parenting skills could be obtained by consulting experts such as social

workers, parents. Judgment is dependent upon the knowledge of the experts

Construct validity

Is the degree to which a test measures an intended hypothetical construct? i.e., a non-observable

trait, such as intelligence, which explains behavior

Criterion validity

Describes the extent to which a correlation exists between the measuring instrument & standard

— empirical evidence. E.g., the relationship between College Board examination and student

academic success in college. Two measures need to be taken: the measure of the test itself & the

criterion to which the test is related

Difference between reliability and validity

Reliability: the degree to which a measurement procedure produces similar outcomes when it is

repeated. E.g., gender, birthplace, mother’s name should be the same always.

Validity: tests for determining whether a measure is measuring the concept that the researcher

thinks is being measured, i.e., “Am I measuring what I think I am measuring”?

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2. INTRODUCTION:

3.1 TYPES OF DATA: The task of data collection begins after a research problem has been defined

and researches design/chalked out. While deciding about method of data collection to be used for the

study, the researcher should keep in mind two types of data viz ,primary and secondary data.

The primary data are those data, which are collected afresh and for the first time, and those happen to be

original in character. The secondary data, on the other hand, are those which have already been collected

by someone else and which have already been passed through the statistical process.

Statistical data can be classified under two categories

1) primary data

2) secondary data

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UNIT - III

3 INTRODUCTION

3.1 DATA COLLECTION-TYPES

3.2 PRIMARY DATA VS SECONDARY DATA

3.3 METHODS OF PRIMARY DATA COLLECTION

3.4 SURVEY VS OBSERVATION

3.5 SAMPLING PLAN AND SAMPLE SIZE

3.6 PROBABILITY AND NON-PROBABILITY SAMPLING METHODS


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Primary data

Primary data is the one, which is collected by the investigator himself for the purpose of a specific

inquiry or study. Such data is original in character and is generated by survey conducted by individuals

or research institutions or any organization.

The objectives of primary data are formulated on the basis of research objectives. Objectives set the

guidelines and direction research planning. Formulating the objectives offers the best feasible means of

solution.

Significance of primary data.

Reliability

Availability of a wide range of techniques

Addresses specific research issues

Greater control

Efficient spending for information

Limitations

Time consuming

High cost

Not always feasible

Large volume of data

Reluctance of respondents

Secondary data

Secondary data are those data which have been already collected and analyzed by some earlier

agency for its own use; and later the same data are used by a different agency.

Secondary data are statistics that already exist. It can be classified as

- Internal secondary data

-External secondary data

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Internal secondary data is a part of the company's record, for which research is already

conducted.

Eg: Daily production report, monthly collection report.

External Secondary data

The data collected by the researcher from outside the company.

This can be divided into four parts:

Census data

Census of the whole sale trade

Census of the retail trade

Population Census

Census of manufacturing industries

Individual project report being published

Encyclopedia of business information sources

Syndicated data is an important form of secondary data which may be

classified into

Consumer purchase data

Retailer and whole sale data

Advertising data

Advantages of secondary data

Economy

Quickness

Quality

No need of measuring instruments

Availability

Bases for comparison

Useful in exploratory research

Generates feasible alternatives

Disadvantages of secondary data

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The data may not fit into the needs of investigation

Less accuracy

Existence of obsolete information

Nondisclosure of research findings

There may be difficulties in the identification of source

Errors may be there in recording or transferring information from secondary sources

3.2 Primary data Vs secondary data

Basis of comparison Primary data Secondary data

Object Originate with the specific

research undertaking

Gathered for some other

purpose but are applicable to

present investigation

Cost involved Collection is expensive Collection is cheaper

Time consumption Collection can take weeks or

even months

Collection time usually

involves hours or days

Nature of errors Errors can be there due to

interviewer and respondent

biases

There may be inaccuracies due

to errors in recording or

transferring of the original

data

Accuracy and validity The information is more valid,

reliable and relevant

The validity of information

should be judged/evaluated

before using secondary data

Mode of collection The information is to be

generated either by

questioning the people or by

observing selected activities

Information already exists in

various sources. Data are

obtained by searching these

sources and then recording

from various sources only.

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Need of auxiliary instruments Data collection instruments

are to designed according to

the need of investigation

No need of data collection

instruments.

3.3 Methods of primary data collection

Survey techniques

Survey techniques can be divided into three broad categories as in figure below.

Interview method

Interviewing is one of the prominent methods of collection. It may be defined as a two-way systematic

conversation between an investigator and an informant, initiated for obtaining information relevant to a

Specific study.

Interviewing requires face to face contact over telephone and calls for interviewing skills. It is done by

using a structured schedule or an unstructured guide.

38

Methods of primary data collection

Survey method Observation method

Survey techniques

Schedule

Questionnaire methodInterview method


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Characteristics of interview method

Needs proper introduction

Incorporates transitory relationships

Caters to a specific purpose

Verbal interaction

Facilitates telephonic conversation

Group studies possible

Interactional process

Simultaneous recording

Types of interview

Personal interview

Unstructured and direct interview

Structured and direct interviews

Unstructured and indirect interviews

Telephone interview

Panel interview

Electronic interview

Limitations of interview method

Expensive

Subject to bias and personal traits

Ineffective in some areas

Recording complexities

demands skilled interviewers

subjective

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Questionnaire method

The questionnaire is the list of questions to be asked from the respondents. It also contains a suitable

space where the answers can be recorded.

A questionnaire is a method of obtaining specific information about a defined problem so that the data,

after analysis and interpretation, results in a better appreciation of the problem. a questionnaire

form ,which has to be completed by an interviewer, is often referred as schedule

Types of questionnaire

Structured, non-disguised questionnaire

Non-structured, non-disguised questionnaire

Non-structured, disguised questionnaire

Structured, disguised

Process of Questionnaire designing

Determine what information is needed

What type of questionnaire to be used

Decide on the type of questions

Decide on the wording of questions

Deciding on the layout

Pretest

Revise and prepare final questionnaire

Validation of questionnaire

To achieve high quality survey result, a critical component is validating the instrument (questionnaire)

reliability and validity. The validity of questionnaire is assessed by three components

1. Content validation: It often refers face validity. Face validity is determined by comparing the

questionnaire with other similar questionnaire surveys.

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2. Sampling validity: It is another component of validation. A large sample size can ensure low

sampling errors and high sampling validity.

3. Empirical validity: It examines the survey result by comparison with other studies. The aim is

to check consistency with previous results. Empirical validation of questionnaire reliability often

involves two techniques:

i. Test-retest techniques: It determines stability of measured indicators.

ii. Construct validity : It is a score to determine internal consistency –reliability, measured

by the Cronbach alpha

Schedule

It is a device in social research, which is most frequently used in collecting field data especially where

the survey method is employed. It is used in indirect interview. It contains questions and blank tables,

which are to be filled in by the investigators themselves after getting information from the respondents.

Difference between Questionnaire and schedule

Basis of difference Questionnaire Schedules

Mode The questionnaire is generally

sent through mail to

informants to be answered as

specified a covering letter, but

otherwise without future

assistance from the sender.

It is generally filled out the

researcher, who can interpret

questions when necessary.

Economy To collect data through

questionnaire is relatively

cheap and economical.

The data collection is

relatively expensive.

Chances of non-response Non-response is usually in

case of questionnaire.

Non-response is generally

very low in case of schedules.

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Identification of respondent In case of questionnaire, it is

not always clear as to who

replies.

In case of schedule the

identity of respondent is

known.

Time consumption The questionnaire method is

likely to be very slow since

many respondents do not

return the questionnaire, in

time despite several

reminders.


information is collected well

in time as they are filled in by

researcher.

Personal contact Personal contact is generally

not possible in case of

questionnaire.

In case of schedules direct

personal contact is established

with respondents.

Influence of respondents

literacy

Questionnaire method can be

used only when respondents

are literate and co-operative.


information can be gathered

even the respondents happen

to be illiterate.

Coverage and distribution Wider and more representative

distribution of sample is

possible under questionnaire

method.

In respect of schedule there

usually reminds the difficulty

in sending enumerators over a

relatively wider area.

Accuracy of information Risk of collecting complete

and right information is

relatively more under

questionnaire method.

In case of schedules, the

information collected is

generally complete and

accurate.

Success The success of questionnaire

method lies more on the

quality of questionnaire itself.

In case of schedules much

depends upon the honesty and

competence of researcher.

Appearance of questionnaire In order to attract the attention

of respondents, the physical

appearance of questionnaire

This may not be so in case of

schedules as they are to be

filled in by enumerators and

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must be quite attractive. not by respondents

Observation Method

• Structured or unstructured method

• Disguised or undisguised method

• Direct-indirect observation

• Human-mechanical observation

Structured-Unstructured Observation

Structured Observation

How many of his customers visit the hotel with their families and how many come as single

customers.

Unstructured Observation

How single customers and those with families behave and their attitude

Disguised-Undisguised Observation

In disguised observation, the respondents do not know that they are being observed

In non-disguised observation, the respondents are well aware that they are being observed.

Direct-Indirect Observation

In direct observation, the actual behavior or phenomenon of interest is observed.

In in-direct observation, the results of the consequences of the phenomenon are observed.

Eg: Inorder to know the soft drinks consumption, he may like to observe empty bottles dropped

into the bin.

3.4 Survey Vs observation method

Basis of comparison Survey method Observation method

Objective This method of collecting data

is useful when population size

is very large.

This is decidedly superior to

survey research,

experimentation, or document

study for collecting data in

behavior research.

Response Responds in survey method Response in observation is

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based on verbal answers to

limited set of questions

neither as restrictive nor as

artificial as either the survey

or experiment.

Difficulties of quantification Measurement in survey

studies generally takes the

form of the observer’s

quantitative measure.

Measurement in observational

studies generally takes the

form of the observers un

quantified perceptions.

Sample size Survey studies conducted for

large sample size.

Observational studies tend to

use a smaller sample than

survey studies, but a larger

sample than experiments.

Qualitative techniques of Data collection

There are four major techniques in Qualitative research. They are:

Depth Interview

Delphi Technique

Focus Group

Projective Technique

Depth interview

Unstructured, direct interview is known as a depth interview. It is free from restrictions imposed

by a formal list of questions.

Eg: What did you mean by that statement? Why did you feel this way? What other reasons do

you have

Advantages

o It is its ability to discover motivations

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o The second advantage of the depth interview procedure is that it encourages respondents

to express any ideas they have.

o The third advantage is that it provides a lot of flexibility to the interviewer.

Limitations

o Longer duration

o Difficult to find the qualified and trained people for conducting depth interview

o No quantifiable data is obtained in the depth interviewing process

Delphi technique

This is a process where a group of experts in the field gather together. The group members are asked to

make individual judgments about a particular subject, these judgments are compiled and returned to the

group members, so that they can compare with those of others and revise, then reach conclusion after 5

to 6 rounds.

Projective techniques

In projective techniques, respondents are asked to interpret the behavior of users, rather than describe

their own behavior. In interpreting the behavior of others, respondents indirectly project their own

motivation and feelings into the situation.

The general categories of projective techniques are:

1. Word association test

2. Completion technique

3. TAT and

4. Cartoon test

Word Association Test

This is consists of presenting a series of stimulus words to the respondent.

For eg: What brand of detergent comes to your mind first, when I mention washing of an expensive

cloth?

Completion techniques

Sentence Completion

Eg: Earnings of software professional

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Story Completion: A situation is described to a respondent who is asked to complete the story based on

his opinion and attitude.

Thematic Apperception Test

TAT is a projective technique. It is used to measure the attitude and perception of the individual.

Some picture cards are shown to respondents. The respondent is required to tell the story by looking at

the picture. When the subjects start telling the story, the researcher notices the respondents’ expression,

pauses and emotions to draw the inference.

3.5 Sampling

A sample is a part of a target population, which is carefully selected to represent the population.

Sample Frame

Sampling frame is the list of elements from wh ich the sample is actually drawn. Actually, sampling

frame is nothing but the correct list of population.

Eg: Telephone directory, Product finder, Yellow pages

Distinction between Census and Sampling

Census refers to complete inclusion of all elements in the population. A sample is a sub-group of the

population

Sampling Process:

1. Define the population

2. Identify the sampling frame.

3. Specify the sampling unit

4. Selection of sampling method

5. Determination of sample size

6. Specify sampling plan

Define the population

Elements- Company’s product

Sampling unit-Retail outlet, super market

Extent- Hyderabad and Secunderabad

Time-April 10 to May 10

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Identify the sampling frame

Sampling frame could be

Telephone Directory

Localities of a city using the municipal corporation listing

Any other list consisting of all sampling units.

Specify the sampling unit

Individuals who are to be contacted are the sampling units. If retailers are to be contacted in a

locality, they are the sampling units.

Selection of sampling method

This refers to whether a. Probability b. non-probability methods are used

Determine the sample size

We need to decide how many elements of the target population are to be chosen?

For eg: If it is an exploratory research, the sample size will be generally small. For conclusive

research, such as descriptive research, the sample size will be large.

Specify the sampling plan

A sampling plan should clearly specify the target population. Improper defining would lead to

wrong data collection.

3.6 Sampling Types/ Methods/ Techniques

Sampling is divided into two types.

Probability sampling: In a probability sample, every unit in the population has equal chances for

being selected as a sample unit.

Non-probability sampling: In the non-probability sampling, the units in the population have

unequal or negligible, almost no chances for being selected as a sample unit.

Probability sampling techniques

Random Sampling

Stratified random sampling

Systematic sampling

Cluster sampling

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Multi-stage sampling

Random Sampling

Simple random sample is a process in which every item of the population has an equal

probability of being chosen.

Lottery method:

We can now write down all the combination, put them in a box. Mix them and pull one at random.

Systematic Random sampling

Sampling interval K is determined by the following formula

K=No. of units in the population

______________________________

No. of units desired in the sample.

Stratified Random Sampling

A probability sampling procedure in which simple random sub-samples are drawn from within

different strata that are, more or less equal on some characteristics.

Proportionate stratified sampling: The number of sampling units drawn from each stratum is

in proportion to the population size of that stratum.

Disproportionate stratified sampling: The number of sampling units drawn from each stratum

is based on the analytical consideration, but not in proportion to the size of the population of that

stratum.

Cluster Sampling

The population is divided into clusters.

A simple random sample of few clusters is selected.

All the units in the selected cluster are studied

Multi-stage Sampling

The name implies that sampling is done in several stages. This is used with stratified cluster

designs. The management of a newly opened club is solicits new membership. During the first

rounds, all corporate were sent details so that those who are interested may enroll. Having

enrolled, the second round concentrates on how many are interested to enroll for various

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entertainment activities that club offers such billiards, indoor sports. After obtaining this

information, you might stratify the interested respondents

Area Sampling

If clusters happen to be some geographic subdivisions, in that case cluster sampling is better

known as area sampling.

Eg: If someone wants to measure the sales of toffee in retail stores, one might choose a city locality and

then audit toffee sales in retail outlets in those localities

Non-Probability Sampling

Judgment Sampling

The investigator uses his discretion in selecting sample observations from the universe.

Eg: Test market cities are being selected, based on the judgment sampling, because these cities are

viewed as typical cities matching with certain demographical characteristics.

Sequential Sampling

This is a method in which the sample is formed on the basis of a series of successive decisions.

Eg: If the evidence is not conclusive after a small sample, more samples are required. If the position is

still inconclusive, still larger samples are taken.

Quota Sampling

Quota sampling is quite frequently used in marketing research. Suppose, 2,00,000 students are

appearing for a competitive examination. We need to select 1% of them based on quota sampling.

Category Quota

General merit` 1,000

Sport 600

NRI 100

SC/ST 300

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Total 2000

Snow ball Sampling

In this method, the initial group of respondents is selected randomly. Subsequent respondents are being

selected based on the opinion or referrals provided by the initial respondents

Panel Samples

To give an example, suppose that one is interested in knowing the change in the consumption pattern of

households. A sample of households is drawn. These households are contacted to gather information on

the pattern of consumption. Subsequently, say after a period of six months, the same households are

approached once again and the necessary information on their consumption is collected.

Errors in Sampling/ Sampling bias

Sampling error is the gap between the sample mean and population mean.

An MNC bank wants to pick up a sample among the credit card holders. They can readily get a

complete list of credit card holders, which forms their data bank. From this frame, the desired

individuals can be chose. In this example, sample frame is identical to ideal population namely all credit

card holders. There is no sampling error in this case

Eg:2

Assume that a bank wants to contact the people belonging to a particular profession over phone to

market a home loan product. The sampling frame in this case is the telephone directory. Reasons may

be People might have migrated, Numbers have changed, Numbers may not be listed. Thus in this case,

there will be a sampling error

Non-Sampling Error/ Non-response Error

This occurs, because the planned sample and final sample vary significantly.

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Eg: Marketers want to know about the television viewing habits across the country. They choose

500 households and mail the questionnaire. Assume that only 200 respondents reply. If there is

no response, then its Non-response error.

Data Error

This occurs during the data collection, analysis or interpretation. Respondents sometimes give

distorted answers unintentionally for questions which are difficult, or if the question is

exceptionally long and the respondent may not have answer.

How to reduce Sampling Error

To choose appropriate sample size.

Non-sampling error

- Provide incentives to collect data, against the golden rule of research.

- Do not ask sensitive questions

- Training the interviewer

- Pretest the questionnaire

- Modify the sampling frame to make it a representative of the population

How will you decide the sample size?

First factor must be considered in estimating sample size, is the error permissible.

Greater the desire precision, larger will be the sample size

Higher the confidence level in the estimate, the larger the sample must be.

The greater the number of sub-groups of interest within the sample, the greater its size must be.

Cost is a factor that determines the size of the sample.

The issue to be considered in deciding the necessary sample size is the actual number of

questionnaires that must be sent out. Calculation wise, we may send questionnaires to the

required number of people, but we may not receive the response.

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1. INTRODUCTION:

4.1 Editing:

Data editing is the activity aimed at detecting and correcting errors (logical inconsistencies) in

data.

The customary first step in analysis is to edit the raw data. Editing detects errors and omissions,

corrects them when possible, and certifies that maximum data quality standards are achieved.

Alternately, recorded raw data is normally less than perfect and the first phase through which this data

must pass is editing. The editor’s purpose is to guarantee that data are:

52

UNIT - IV

4 INTRODUCTION

4.1 DATA ANALYSIS-EDITING

4.2 UNIVARIATE ANALYSIS

4.3 BIVARIATE ANALYSIS

4.4 MULTIVARIATE ANALYSIS

4.5 DISCRIMINANT ANALYSIS

4.6 FACTOR ANALYSIS

4.7 CLUSTER ANALYSIS

4.8 CONJOINT ANALYSIS

4.9 MULTI-DIMENSIORNAL SCALING

4.10 APPLICATION OF SPSS FOR DATA ANALYSIS


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1. Accurate

2. Consistent with the intent of the question and other information in the survey

3. Uniformly entered

4. Complete

5. Arranged to simplify coding and tabulation

Objectives of data editing

To ensure the accuracy of data

To establish the consistency of data

To determine whether or not the data are complete

To ensure the coherence of aggregated data; and

To obtain the best possible data available

Different stages of Editing:

The editing may be done in two stages they are as follow:

1. FIELD EDITING

2. OFFICE EDITING

Field Editing:

The Field editing is a preliminary editing done to detect the glaring omissions and inaccuracies in

the data. It is useful to controlling the field force and removing misunderstanding.

For example:

If interviewers did not follow the correct patterns or if open ended responses reflect a lack of

probing. When poor interview is detected; supervisor may train the interviewer.

Office Editing:

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It is another type of editing job of data collection performed by a centralized office staff

to perform. The researcher must set up a centralized office with all facilities for editing and coding

purpose by which coordination can be accomplished.

The office editing is done after the field editing. This implies a complete and thorough scrutiny of the

questionnaire. There should be expert editors in the office to evaluate and examine the completed returns

of the respondents.

WHAT IS DATA CODING?

“A systematic way in which to condense extensive data sets into smaller analyzable units

through the creation of categories and concepts derived from the data.”

“The process by which verbal data are converted into variables and categories of variables

using numbers, so that the data can be entered into computers for analysis.”

WHEN TO CODE?

When testing a hypothesis (deductive), categories and codes can be developed before data is

collected.

When generating a theory (inductive), categories and codes are generated after examining the

collected data.

o Content analysis

o How will the data be used?

Variables:

Categories:

Gender Age

Male Female 18-25 26-33 34-41

Do you like ice cream?

yes no

Categories and Variables

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DATA ENTRY

The process transforming data from a research project, such as answers to a survey questionnaire, to

computers is referred to as data entry. The process of entering data into a

computerized database or spreadsheet.

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4.2 Univariate Analysis

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Only one variable (Eg: Blood types) Can calculate percentage (Eg. 30% have A blood group etc.)

4.3 Bivariate analysis is the simultaneous analysis of two variables (attributes). It explores the concept

of relationship between two variables, whether there exists an association and the strength of this

association, or whether there are differences between two variables and the significance of these

differences. There are three types of bivariate analysis.

• Numerical & Numerical

• Categorical & Categorical

• Numerical & Categorical

ANOVA

• Tests the significance of group differences between two or more groups

• Tests with two or more categories only determines that there is a difference between groups, but

doesn’t tell which is different

• eg: Do CAT scores differ for low- middle- and high-income students?

Analysis of Variance (ANOVA)

The ANOVA test assesses whether the averages of more than two groups are statistically different from

each other. This analysis is appropriate for comparing the averages of a numerical variable for more than

two categories of a categorical variable.

4.4 Multivariate Analysis

• Many statistical techniques focus on just one or two variables. Multivariate analysis (MVA)

techniques allow more than two variables to be analysed at once.

• Imagine out of the five senses you only had sight. From your perspective you could see the world

but you would not be able to hear the sounds around you, smell, and taste or feel things. Your

understanding of the world would be more limited. Most of us use all of our senses to understand

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the world around us i.e. not just one “measurement” but the combination of several senses

working together. In multivariate analysis we use the information from many sources

simultaneously to get a better picture of our surroundings.

• MANOVA

The general purpose of multivariate analysis of variance (MANOVA) is to determine whether

multiple levels of independent variables on their own or in combination with one another have an

effect on the dependent variables.

4.5 Discriminate Analysis

In this analysis, two or more groups are compared. IN the final analysis, we need to find out whether

the groups differ one from another.

Eg; where discriminate analysis is used:

1. Those who buy our brand and those who buy competitors’ brand.

2. Good salesman, poor salesman, medium salesman.

3. Those who go to Food world to buy and those who buy in a kirana shop.

4. Heavy user, medium user and light user of the product.

4.6 Factor analysis

In purpose of Factor analysis is to group large set of variable factors into fewer factors. Each factor will

account for one or more component. Each factor a combination of many variables.

Customer feedback about a two-wheeler manufactured by a company.

The MR Manager prepares a questionnaire to study the customer feedback. The researcher has

identified six variables or factors for this purpose. They are as follows:

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1. Fuel efficiency (A)

2. Durability (B)

3. Comfort©

4. Spare parts availability(D)

5. Break down frequency(E)

6. Price(F)

A, B,D,E into Factor-1

F into Factor-2

C into Factor-3

Factor-1 can be termed as Technical factor

Factor-2 can be termed as price factor;

Factor-3 can be termed a s personal factor

For future analysis, while conducting a study to obtain customers’ opinion, three factors mentioned

above would be sufficient. One basic purpose of using factor analysis is to reduce the number of

independent variables in the study.

4.7 Cluster Analysis

It is used:

1. To classify personal or objects into small number of clusters or group.

2. To identify specific customer segment for the company’s brand.

Cluster analysis is a technique used for classifying objects into groups. This can be used to sort data (a

number of people, companies, cities, brands or any other objects) into homogeneous groups based on

their characteristics. The result of cluster analysis is a grouping of the data into groups called clusters.

The researcher can analyze the clusters for their characteristics and give the cluster, names based on

these.

A housing finance corporation wants to identify and cluster the basic characteristics, lifestyles and

mindset of persons who would be availing housing loans. Clustering can be done based on parameters

such as interest rates, documentation, processing fee, number of installments. Etc.

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Process

There are two ways in which Cluster Analysis can be carried out:

1. First, objects/respondents are segmented into a pre-decided number of clusters. In this case, a

method called non-hierarchical method can be used, which partitions data into the specified

number of clusters.

2. The second method is called the hierarchical method.

4.8 Conjoint Analysis

It is concerned with the measurement f the joint effect of two or more attributes that are important from

the customers’ point of view. Eg: An airline would like to know, which is the most desirable

combination of attributes to a frequent traveler: a) Punctuality b) Air fare c) Quality of food served on

the flight, and d) Hospitality and empathy shown.

Process

Design attributes for a product are first identified. For a shirt manufacturer, these could be design such

as designer shirts vs. plain shirts, this price of Rs 400 versus Rs. 800. The outlets can have exclusive

distribution or mass distribution. All possible combinations of these attribute levels are then listed out.

Each design combination will be ranked by customers and used as input data for conjoint analysis. Then

the utility of the products relative to price can be measured.

There are three steps in conjoint analysis

a. Identification of relevant products or service attributes.

b. Collection of data

c. Estimation/Evaluate the worth for the attribute chosen.

4.9 Multidimensional scaling (MDS)

It is a set of related statistical techniques often used in information visualization for exploring

similarities or dissimilarities in data. MDS is a special case of ordination. An MDS algorithm starts with

a matrix of item–item similarities, and then assigns a location to each item in N-dimensional space,

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where N is specified a priori. For sufficiently small N, the resulting locations may be displayed in a

graph or 3D visualization.

Types

MDS algorithms fall into a taxonomy, depending on the meaning of the input matrix:

1. Classical multidimensional scaling

Also known as Torgerson Scaling or Torgerson–Gower scaling, takes an input matrix giving

dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a

loss function called strain.

2. Metric multidimensional scaling

A superset of classical MDS that generalizes the optimization procedure to a variety of loss functions

and input matrices of known distances with weights and so on. A useful loss function in this context is

called stress, which is often minimized using a procedure called stress majorization.

3. Non-metric multidimensional scaling

In contrast to metric MDS, non-metric MDS finds both a non-parametric monotonic relationship

between the dissimilarities in the item-item matrix and the Euclidean distances between items, and the

location of each item in the low-dimensional space. The relationship is typically found using isotonic

regression.

Louis Guttman's smallest space analysis (SSA) is an example of a non-metric MDS procedure.

4. Generalized multidimensional scaling

An extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-

Euclidean space. In case when the dissimilarities are distances on a surface and the target space is

another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.

4.10 Application of SPSS

1. Start SPSS. Go to Windows Start menu and choose Programs, and the SPSS for Windows. Then the

Data Editor window will open.

2. Step 2

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Appearing in the list boxes will be the variable names. Often it is best when these variable titles are

in alphabetical order so you may have to change the order. From the menu choose Edit then

Options, then go to the General Tab and select Display labels in the Variables list group. Select

Alphabetical and then click OK twice.

3. Step 3

Open a Data File. From the menu choose File, Open, Data. The Open File box will display. Double

click Tutorial folder, double click sample file folders, click the file demo.sav, click Open. From the

menus choose View and then Value Labels.

4. Step 4

Run an Analysis. From the menus choose Analyze, Descriptive Statistics, then Frequencies. The

frequencies dialog box will be displayed and the icons will provide the information needed about

the data type and level of measurement.

How to use SPSS

1. Open SPSS and import your data. You can import your data set from an Excel file or any other CSV

file. To import your data, click "Open Another Type of File" and choose the file you would like to

import. If you would like to enter new data, simply click "Type in Data". When you have chosen a

data source, click "Ok."

2. Step 2

Edit your raw data in the variable view. There are two views in SPSS, (1) data view and (2) variable

view. Click the "Variable View" tab located on the bottom of the application and edit your raw data.

Here you can edit categories the name of your variable, type of variable and measurement category.

It is important to properly edit your variables, as many statistical tests will rely upon properly

formatted data.

3. Step 3

Choose a statistical test. Once you have entered and formatted your data, you will be ready to run a

statistical test. The most common tests are found in the "Analyze" tab located across the top of the

application.

4. Step 4

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Paste your syntax. Before completing a statistical test, you should always paste your syntax by

clicking "Paste" in the dialog box. This will copy your syntax into a separate file. If the application

crashes, or you need to run the same test again, you can use the syntax file to initiate a statistical

test.

5. Step 5

View your output. Once your statistical test has been run, you can view the results in an output file

that will open in a separate window

Advantages of SPSS

SPSS is the statistical package most widely used by political scientists. There seem to be several

reasons why:

SPSS has been around since the late 1960s. Statistical Package for the Social Sciences,

Of the major packages, it seems to be the easiest to use for the most widely used statistical

techniques

One can use it with either a Windows point-and-click approach or through syntax (i.e., writing

out of SPSS commands

Many of the widely used social science data sets come with an easy method to translate them

into SPSS; this significantly reduces the preliminary work needed to explore new data.

Disadvantages of SPSS

There are also two important limitations that deserve mention at the outset:

SPSS users have less control over statistical output than, But, once a researcher wants

greater control over the equations or the output, she or he will need to either choose another

package or learn techniques for working around SPSS

Once a researcher begins wanting to significantly alter data sets, he or she will have to either

learn a new package or develop greater skills at manipulating SPSS.

Overall, SPSS is a good first statistical package for people wanting to perform quantitative research in

social science because it is easy to use and because it can be a good starting point to learn more

advanced statistical packages.

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64

UNIT - V

5 INTRODUCTION

5.1 RESEARCH REPORT

5.2 TYPES OF REPORT

5.3 CONTENT OF A BUSINESS RESEARCH REPORT

5.4 EXECUTIVE SUMMARY

5.5 CHAPTERISATION

5.6 ROLE OF AUDIENCE

5.7 READABILITY, COMPREHENSION, TONE, FINAL PROOF

5.8 ETHICS IN RESEARCH

5.9 SUBJECTIVITY AND OBJECTIVITY IN RESEARCH


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1. INTRODUCTION:

5.1 RESEARCH REPORT

The research report is the compilation of findings from a piece of research

A research report is a precise presentation of the work done by a researcher while

investigating a particular problem

RESEARCH REPORT WRITING

Report writing is the final stage of the business research and it is concerned with making the

findings available to the readers with varied interests. It is important to understand as to how to write a

report. Your final report should be in accordance with the writing style used at your university.

Whatever style you adopt, the content of the research report never varies. The final report of a research

exercise takes a variety of forms.

A research report funded by an educational institution may be in the form of written document.

A research report may also take the form of an article in a professional journal.

NEED FOR RESEARCH REPORT

The purpose of research is to search for knowledge. (It is just to analyze a particular

situation and finding out some solution, that solution/result will be finished in the form

of report.)

Reporting is the process through which a basis ground is prepared for the exchange of

ideas or thoughts.

Reporting helps the researcher to make specific recommendation for a course of action

over, the phenomena, he studied. This is what actually expected in case of any study.

5.2 TYPES OF REPORT

1. Oral report

2. Written report

a) The Popular report

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b) The report for the administration

c) The technical report

d) Formal report

ORAL REPORT

The oral reporting is that the oral presentation in meetings. For example: seminars,

conferences, symposia, etc. is mainly oral presentation.

WRITTEN PRESENTATION

When compare to oral report, the preparation and presentation of written is somewhat difficult

because in case of oral report the presenter can talk in their own style, but in case of written they should

be very careful about the alignment, meaning, words, language, etc.

Written reports themselves are different types. In the context of reporting to management of a company,

reports are classified as: external and internal reports; routine and special reports; and operating and

special reports.

A useful classification of research reports seems to be the one based on the audience, i.e., the people to

whom the reports is meant. On this basis, written reports can be categorized as follows:

The popular report

The report for the administration

The technical report

Formal report

Popular report: This is the report meant to be read by public in the developments taking place around

them. For example: a researcher has worked on denudation of forest and ecological balance (research

on forest i.e., about cause and effects of cutting and destroying trees in forest).the public in this context

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would be interested in such facts as the extent of forests, denudation, impact of denudation on ecology

and specific sectors like agriculture.

The report for the administration: Many of the business reports are of this type. They may be

submitted to any level. Usually, Supervisors submit periodical reports about production, machinery

maintenance, overtime, etc. Similar reports are also submitted by the middle level managers to the top

level management

The technical reports: A technical report is written by an expert to be read by another expert. In this

sense, a thesis is a technical report intended to be read by another researcher.

Formal report: A formal report is used to document the results of an experiment, a design, or to pass on

any type of information in a formal style. When writing a formal report it is important to ensure good

English use and to follow the correct format as like as follow:

Abstract or summary

Outline

Introduction

Discussion

Conclusion

Recommendations

appendix

Research report format: The following outline is the suggested format for writing the research report

1. Title page

2. Letter of authorization

3. Summary of findings

4. Table of content

List of tables

List of figures

5. Introduction

Background to the research problem

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Objectives

Hypothesis

6. Methodology

7. Data collection

Sample and sampling method

8. Statistical or qualitative methods used for data analysis

9. Sample description

10. Findings

11. Limitations

12. Results, interpretation and conclusions.

13. Recommendation

14. Appendices

15. Bibliography

5.3 CONTENT OF A BUSINESS RESEARCH REPORT:

Problem definition

Research objectives

Background material

Methodology

Sampling design

Research design

Data collection

Data analysis

Limitation

Findings

Conclusions

Recommendations

Appendices

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Bibliography

Index

Conclusion

5.4 EXECUTIVE SUMMARY

Executive summary is a term used in business for a short document that summarizes a longer

report, in such a way that readers can rapidly become acquainted with a large body of material without

having to read it all.

Steps to write an executive summary:

Step 1: Plan to create a summary each time you write a business report exceeding four pages.

Write the summary after you write the main report, and make sure it is no more than 1/10 the length of

the main report.

Step 2: List the main points the summary will cover in the same order they appear in the main

report.

Step 3: write a simple declarative sentence for each of the main point

Step 4: Add supporting or explanatory sentences as needed, avoiding unnecessary technical

material and jargon.

Step 5: Read the summary slowly and critically, making sure it conveys your purpose, message

and key recommendations. You want readers to be able to skim the summary without missing the point

of the main report.

Step 6: Check the errors of style, spelling, grammar and punctuation. Ask a fellow writer to

proofread and edit the document.

Step 7: Ask a nontechnical person- for example, your parents or your spouse – to read the

document. If it confuses or bores them, the summary probably will have the same effect on other

nontechnical readers.

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5.5 CHAPTERISATION

Chapterisation means scanning of the entire report taken by the researcher. The subject of the

report is to be divided into different parts, arrange them in a systematic way and mention which aspects

of them in a systematic way and mention which aspects of the research will be studied in which chapter.

It should be planned that one chapter will seems to be a continuation of the previous one.

CHAPTERS AND THEIR CONTENT:

Introduction

Review of related literature

Design of the study

Analysis and interpretation of data

Main findings and recommendation

Summary

Writing a Report

During your studies you may be required to research a particular area and produce a report. For Instance depending on your area of study you might be asked to write a report on the performance.

Some of the reasons we write reports are to

• Inform

• Make proposals or recommendations for change

• Analyze and solve problems

• Present the findings of an investigation or project

• Record progress

Your lecturer or teacher will usually provide you with the following information

• The topic or subject of the report

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• The required length and due date

• A clear idea of its purpose and who will read it

• The format headings to be used and their order

5.6 ROLE OF AUDIENCE

Audience Research is an important tool to study the characteristics of target audience for various

media including demographic and psycho-graphic details of the audience, their exposure to various

media, listening/viewing/reading habits, needs and tastes for various media contents and moreover, to

estimate the size of audience for various programmes and programme ratings.

Audience Research on the one hand provides programme feedback to programme produces to

prepare audience friendly programmes and on the others provides audience share for the various media

contents to advertisers and marketers which in turn , helps in fixing rates for the various programmes

and channels. Thus, this gives consumer insight to the stakeholders and works as eyes and ears for the

media organizations.

5.7 READABILITY

Readability is the ease in which text can be read and understood. Various factors to measure

readability have been used, such as "speed of perception," "perceptibility at a distance," "perceptibility

in peripheral vision," "visibility," "the reflex blink technique," "rate of work" (e.g., speed of reading),

"eye movements," and "fatigue in reading."

Readability is distinguished from legibility which is a measure of how easily individual letters

or characters can be distinguished from each other. Readability can determine the ease in which

computer program code can be read by humans, such as through embedded documentation.

COMPREHENSION

In general usage, and more specifically in reference to education and psychology, it has roughly the

same meaning as understanding. Reading comprehension measures the understanding of a passage of

text.

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Reading comprehension

It is defined as the level of understanding of a text. This understanding comes from the interaction

between the words that are written and how they trigger knowledge outside the text.

TONE IN WRITING: In written composition, tone is often defined as what the author (rather than the

reader) feels about the subject. (What the reader feels about it, by contrast, is referred to as the mood.)

Tone is also sometimes confused with voice, which can be explained as the author’s personality

expressed in writing. Tone is established when the author answers a few basic questions about the

purpose of the writing:

Why am I writing this?

Who am I writing it to?

What do I want the readers to learn, understand, or think about?

Tone depends on these and other questions. In expository, or informative, writing, tone should be

clear and concise, confident but courteous. The writing level should be sophisticated but not pretentious,

based on the reader’s familiarity with or expertise in the topic, and should carry an undertone of

cordiality, respect, and, especially in business writing, an engagement in cooperation and mutual benefit.

FINAL PROOFREADING

After finishing the documentation, one is ready to proofread the report and to prepare final

manuscript. Proofreading is the process of checking work for errors in spelling, grammar, usage, level of

language, capitalization, punctuation, and documentation. Final editing of the report should be taken-up

after completing the writing of research report. This helps in identifying mistakes, if any, better and

correcting the mistakes.

GUIDELINES FOR PROOFREADING

Double check the spellings of proper names, such as the names of people and places.

Check to see that the quotations you have used fit grammatically into the sentences in which they

appear.

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Check to see that your language is not too informal

Check all titles of works to make sure that these rules have been followed.

Check every sentence to make sure that it has an end mark. If the sentence ends with a

parenthetical citation, make sure that the citations appears before the end mark. In the case of a

long, indented quotation , the citation should follow the end mark

Check every quotation in the body of the text to make sure that it begins and ends with quotation

mark.

Check to see that you have used points of ellipsis properly in edited quotations.

Make sure that every citation corresponds to an entry in the works cited list.

Make sure that quotation, summary, or paraphrase is followed by a parenthetical citation.

5.8 ETHICS IN RESEARCH

Although most people acquire their sense of right and wrong during childhood, moral

development occurs throughout life and human beings pass through different stages of growth as they

mature. Ethical norms are so ubiquitous that one might be tempted to regard them as simple

commonsense.

ETHICAL BEHAVIOUR OF RESEARCH

The following is a rough and general summary of some ethical principles that various codes address*:

Honest: Strive for honesty in all scientific communications. Honestly report data, results, methods and

procedures, and publication status. Do not fabricate, falsify, or misrepresent data. Do not deceive

colleagues, granting agencies, or the public.

Objectivity: Strive to avoid bias in experimental design, data analysis, data interpretation, peer review,

personnel decisions, grant writing, expert testimony, and other aspects of research where objectivity is

expected or required. Avoid or minimize bias or self-deception. Disclose personal or financial interests

that may affect research.

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Integrity: Keep your promises and agreements; act with sincerity; strive for consistency of thought and

action.

Carefulness: Avoid careless errors and negligence; carefully and critically examine your own work and

the work of your peers. Keep good records of research activities, such as data collection, research

design, and correspondence with agencies or journals.

Openness: Share data, results, ideas, tools, resources. Be open to criticism and new ideas.

Respect for Intellectual Property: Honor patents, copyrights, and other forms of intellectual property.

Do not use unpublished data, methods, or results without permission. Give credit where credit is due.

Give proper acknowledgement or credit for all contributions to research.

Confidentiality: Protect confidential communications, such as papers or grants submitted for

publication, personnel records, trade or military secrets, and patient records.

Responsible Publication: Publish in order to advance research and scholarship, not to advance just your

own career. Avoid wasteful and duplicative publication.

Competence: Maintain and improve your own professional competence and expertise through lifelong

education and learning; take steps to promote competence in science as a whole.

Legality: Know and obey relevant laws and institutional and governmental policies.

Human Subjects Protection: When conducting research on human subjects, minimize harms and risks

and maximize benefits; respect human dignity, privacy, and autonomy; take special precautions with

vulnerable populations; and strive to distribute the benefits and burdens of research fairly.

5.9 SUBJECTIVITY AND OBJECTIVITY OF RESEARCH:

SUBJECTIVITY refers to that the results are researcher -dependent. Different researchers

may reach different conclusions based on same interview. In contrast, when a survey respondent

provides a commitment score on a quantitative scale, it is thought to be more objective because the

number will be the same no matter what researcher is involved in the analysis.

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Subjectivity guides everything from the choice of topic that one studies, to formulating hypotheses, to

selecting methodologies, and interpreting data.

OBJECTIVITY pre-supposes an independent reality that can be grasped. If there is no

independent reality, or if reality cannot be apprehended, or if reality is mere the concoction of the

observer, then the notion of objectivity is moot.

DIFFICULTIES IN ACHIEVING OBJECTIVITY OF RESEARCH:

Objectivity is the first condition of research. It means willingness and ability to examine

the evidence dispassionately. In other words, objectivity, means basing conclusion on facts without any

bias judgment. This difficulty arises out of the adverse influences of:

1. Personal prejudices and bias,

2. Value judgments,

3. Ethic dilemma, and Complexity of social phenomenon

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Business Research Methods-Lecutre Notes-ALL UNITS