All Research Methods- Reading Material (PGS) Dr Gete2-2

Research Methods in Business Studies: A Practical Guide (PGS)–Dr Getie A. Page 1

Introduction to Research Methods

Just close your eyes for a minute and utter the word research to yourself. What kinds of

images does this word conjure up for you? Do you visualize a lab with scientists at work

with Bunsen Burners and test tubes, or an Einstein –like character writing dissertation

on some complex subject, or someone collecting data to study the impact of a newly

introduced day-care system on the morale of employees? Most certainly, all these

images do represent different aspects of research. Research is somewhat intimidating

term for some, is simply the process of finding solutions to a problem after a thorough

study and analysis of the situational factors.

Business research is an organized , systematic , data based , critical , objective ,

scientific enquiry or investigation into a specific problem undertaken with the purpose

of finding answers or solutions to it In essence , research provides the needed

information that guides managers to make informed decisions to successfully deal

with problems.

Managers in organizations constantly engage themselves in studying and analyzing

issues and hence are involved in some form of research activity as they make decisions

at the workplace. As is well known, sometimes managers make good decisions and the

problem gets solved, sometimes they make poor decisions and the problem persists,

and on occasions they make such colossal blunders that the organization gets stuck in

the mire.

The difference between making good decisions and committing blunders lies in how

managers go about the decision-making process. In other words, good decision making

fetches a “yes” answer to the following questions.

Do managers identify where exactly the problem lies ,

Do they correctly recognize the relevant factors in the situation needing

investigation,

Do they know how to make use of information so collected and draw

appropriate conclusions to make the right decision ,and finally ,


Do they how to implement the results of this process to solve the problem?

This is what this reading material is all about.

This teaching material is organized under 13 chapters:

Chapter-1 addresses the Nature & Scope Of Research; chapter -2 deals with structural

elements of a Research Proposal; chapter -3 is concerned with a critical review

guideline of related Literature ; chapter -4 discusses issues related to Problem

Definition & Setting Of Objectives; chapter -5 explores various form of Research

Designs and the level of interventions by the researchers ; Chapter -6 describes

pertinent issues related to Sampling designs and procedures; chapter -7 highlights

different Qualitative Research Methods; chapter -8 deals with various methods used

in Quantitative Research ; chapter -9 is concerned with Measurement In Business

Research; chapter -10 describes skills used in Designing Data Collection Forms

/Questionnaires; chapter -11 is dealing with the Data Preparation Process For Analysis;

chapter -12 Data Analysis & Interpretation; and chapter -13 highlights the main issues

incorporated in Report Generation & Presentation.

Good Luck!!!

(Dr. Getie A.)


Chap-1 An overview of Business Research

1. Definition of Research

1.1 What Is Research?

Research is an organized inquiry carried out to provide information for the solution of a

problem. Research is an organized and systematic way of finding answers to

questions.

Research

directed toward the solution of a problem;

is based upon observable experience or empirical evidence;

demands accurate observation and description;

involves gathering new data from primary or first-hand sources or using

existing data for a new purpose;

is characterized by carefully designed procedures, always applying

rigorous analysis.

requires expertise;

is characterized by patient and unhurried activity;

is carefully recorded and reported;

sometimes requires courage;

emphasizes the development of generalizations, principles, or theories

that will be helpful in predicting future occurrences;


Strives to be objective and logical, applying every possible test to

validate the procedures employed, the data collected, and the

conclusion reached.

The purpose of scientific research is problem solving – practical or theoretical

nature.

Seeking solutions to practical or theoretical problems involves:

Describing phenomena/pictorial account of the phenomena

Explaining phenomena/cause -and -effect relationships/

Predicting phenomena/predicting what will happen in the future/

Controlling phenomena/intervention packages

Comparing phenomena/comparing 2 or more groups on a certain

behavior or even within the same group

Business Research is an organized, systematic, data based on critical, objective,

scientific enquiry or investigation into a specific problem, undertaken with the purpose

of finding answers or solutions to it. In essence research provides the needed

information that guides managers to make informed decisions to successfully deal with

problems.

The basic differences between research methods and research methodology

A method is a particular research technique or way to gather evidence about

a phenomenon.

Methods are the specific tools to gather data such as surveys, interviews,

participant observations.

Methodology describes “the theory of how inquiry should proceed”

Methodology encompasses our entire approach to research – how we

design and implement research studies.


Research methods are rules and procedures, and can be seen as “tools or ways of

proceeding to solve problems “research methods play several roles, such as:

“logic” or ways of reasoning to arrive at solutions;

Rules for communication, i.e. to explain how the findings have been achieved;

Rules of intersubjectivity, i.e. outsiders should be able to examine and

evaluate research findings.

The role and salient characteristics of marketing research may be described by the

Acronym RESEARCH.

R recognition of information needs

E ffective decision making

S ystematic and objective

E xude or disseminate information

A analysis of information

R commendations for action

C collection of information

H helpful to managers.

1.2 Is research a means to an end or an end by it-Self?

Information is often required to solve a specific problem to minimize risk and

uncertainty which makes research a means to end (applied research).Research can

also be used to increase knowledge or understanding. This makes research an end in

itself. ( Basic Research).

1.3 What are the basic Features of Scientific Research?

Scientific research is concerned with observed facts systematically classified, and

includes trustworthy methods to discover truths. This method of inquiry is a very


important aspect of science. Observation, hypothesis & verification are the three

important components of scientific enquiry.

1.4 Working Assumption of Science

In searching for causes, a researcher will adopt any one of these assumptions:

Realism- the philosophy that perceived objects has an existence outside the

mind.

Rationality- a view that reasoning is the basis for solving problems.

Regularity- a belief that phenomena exist in recurring patterns that conform

to universal laws.

Discoverability- the belief that is possible to find solution to questions posed.

Determinism: the doctrine that all events happen because of preceding

causes.

1.5 Characteristics of Scientific Methods

Two general traits characterize the scientific method:

Validity: is the characteristics used to describe research that

measures what it claims to measure For example , to measure TV-

viewing audiences people meter –mechanical device is put on TV sets

to determine when they are turned on.

Reliability: is the characteristic of a research methodology that allows

it to be repeated again and again by any researcher –always with

the same results.

Scientists working in their laboratories control all aspects of their experiments and report

them in detail so that other scientists can attempt the same study and confirm the

results.

1.6 Distinction between the Scientific and Non-Scientific Methods

The main differences between scientific and nonscientific methods include the following:


Objectivity of the Researcher: researchers must base their judgment on

facts, not on preconceived notions or intuitions.

Accuracy of Measurement: the scientific method attempts to obtain the

most accurate measurement possible.

Continuing & exhaustive Nature of investigation: a scientific investigation

considers all facts pertinent to the problem at hand.

1.7 Classification /Categories of Researches

There are different categories of research:

The Basis of Classification includes the following:

goal of research

1. Basic research

2. Applied research

Specific Objectives of Research

1. Descriptive

2. Explanatory

3. exploratory

approaches of research

1. Qualitative research

2. Quantitative research

Designs

1. Experimental

2. Quasi-experimental

3. Non-experimental

the type of data used in research

1. Primary or field research

2. Secondary or desk research

Fields of study.

1. natural science research,

2. social science research,

3. educational research,

4. behavioral science research,


5. health science research, etc

Basic vs. Applied Research

Basic Research/Pure Research/Fundamental Research

The investigation of problems to further and develop existing Knowledge. It is mainly

concerned with generalization and formulations of theories. Gathering information and

acquiring knowledge for knowledge‘s sake is termed basic research. It aims to solve

perplexing questions of a theoretical nature that have little direct impact on action,

performance, or policy decisions. Leads to major scientific breakthroughs.

The major aims of basic research include:

Obtaining and using empirical data to formulate, expand, or evaluate theory;

and

Discovery of knowledge solely for the sake of knowledge.

Basic research:

Represents a rigorous and structured type of analysis;

Employs careful sampling procedures in order to extend the findings beyond the

group or situation;

Basic research lays down the foundation for the applied research that follows;

and

Has little concern for the application of the findings or social usefulness of the

findings

Problem That Calls For Basic Research

1) Right from her days as a clerical employee in a bank , Belyou had observed

that her colleagues , though extremely knowledgeable about the nuances and

intricacies of Banking , were exerting very little effort to improve the efficiency

and effectiveness of the bank in the area of customer relations and services.


They took on the minimum amount of work load, availed of long tea and lunch

breaks, and seamed not motivate d in their dealings with the customers or the

management. That they were highly knowledgeable about banking policies and

practices was clearly evident from their mutual discussions about these as they

processed applications from customers . Belyou herself was very hardworking

and enjoyed her work with customers. She always used to think what a huge waste

it was for talented employees to goof off rather than contribute to GNP. When she

left the bank and did her dissertation for her Ph.D.’ her topic of investigation was

job involvement, or the ego investment of people, in their jobs. The conclusion of

her investigation was that the single most important contributory factor to job

involvement is the fit or match between the nature of the job and the personality

predispositions of the people engaged in performing it. For example, challenging

jobs allowed employees with high capabilities to get job –involved, and people –

oriented employees got job-involved with service activities. Belyou then understood

why the highly intelligent bank employees could not get job-involved with service

activities. Belyou then understood why the highly intelligent bank employees could

not get job-involved or find job satisfaction in the routine jobs that rarely called for

the use of their abilities. Subsequently, when Belyou joined the internal research

team of a Fortune 500 company, she applied this knowledge to solve problems of

motivation, job satisfaction, job involvement, and the like, in the organization.

The above is an instance of basic research, where knowledge was

generated to understand a phenomenon of interest to the researcher.

Most research and developments in various industries , as well as

many professors in colleges and universities , do basic or fundamental

research , so that more knowledge is generated in particular areas of

interest to industries , organizations, and researchers.

Though the objectives of engaging in basic research is primarily to

equip oneself with additional knowledge of certain phenomena and

problems that occur in several organizations many professors in

colleges and universities , do basic or fundamental research , so that

more knowledge is generated in particular areas of interest to


industries , organizations , with a view to finding solutions , the

knowledge generated from such research is often applied later for

solving organizational problems.

2) An investigation on the causes of Global warming.

3) A general electric company engaged on generating knowledge on the different

application of electrical energy under the Motto “we bring good things to life.”

4) How to improve the effectiveness of information systems ,integrate and the

like.ate technology into the overall strategic objectives of an organization, assess

the impact of logos, increase the productivity of employees in service industries ,

Monitor sexual harassment incidents at the work place , increase the effectiveness

of small businesses , evaluate alternative inventory valuation methods , change

the institutional structures of the financial and capital market .University professors

engage in basic research in an effort to understand and generate more knowledge

about the various aspects of business-which these findings later become useful for

application in business situations.

Applied Research

Research done with the intention of applying the results of the findings to solve specific

problems currently being experienced in the organisation.It has a practical problem

solving emphasis, although the problem solving is not always generated by negative

circumstances. Problem solving based as it is conducted to reveal answers to specific

questions related to action, performance, or policy needs. It is undertaken to solve a

specific problem. Attempts to solve specific management problems.

Applied research is designed to solve practical problems of the modern world,

rather than to acquire knowledge for knowledge's sake.

The goal of the applied scientist is to improve the human condition. It is

undertaken to solve immediate practical problem and the goal of adding to the

scientific knowledge is secondary.

The primary purpose for applied research is discovering, interpreting, and the

development of methods and systems for solving practical problems.

Problem That Calls For Applied Research


1) Oxford health plans Inc. Saw trouble brewing. It was a company in distress

experiencing computer problems. Turnover among Oxford’s programmers was,

unusually high and processing of claims become a big nightmare. Clients started

cancelling their policies , claims for bypass surgery and such were way up, and

premiums paid out relative to clients’ medical expenses, on a percentage basis ,

was close to 85%.

It is obvious that Oxford has a multitude of problems and an

outside consultant- researcher would perhaps be able to design

a scientific study that would look into them.

Presumably, this would be a lengthy investigation that could then

consider them, make the right decision, and thereby solve Oxford’s

problems.

This problem illustrates the need for applied research and can be

solved through investigation and good managerial decision

making.

2) Xerox is insular and is not ready for the increasingly Competitative, high tech

world. Xerox still relies on old fashioned and slow- selling analog copiers , for

more than half its revenue and despite its double –digit growth in digital

products and services , its sales rose just 4 % .In this situation, Xerox also

needs to look into the efficacy of the analog technology used in copiers

and examine what should be done to increase efficiency and promote its

sales. This problem illustrates the need for applied research and can be

solved through investigation and good

In sum , both applied and basic business research are scientific in nature , the main

difference being that the former is undertaken specifically to solve a current business

problem whereas the latter is primarily resorted to because of the importance of the

subject to the researcher. A deeper understanding of the phenomenon would be useful

for its own sake as well as for application later, as needed. It is also possible that some

applied research could have a shorter time frame than some basic research.


Descriptive, Explanatory, & Exploratory Research

Descriptive research

It sets out to describe and to interpret what is.

It looks at individuals, groups, institutions, methods and materials in order to

describe, compare, contrast, classify, analyze and interpret the entities and

the events that constitute the various fields of inquiry.

It aims to describe the state of affairs as it exists.

the goal of descriptive research is to describe some aspect of a

phenomenon, i.e., the status of a given phenomenon.

Methods of Descriptive Research

Surveys

gathers data on a one-shot basis and hence is economical and efficient;

represents a wide target population

generates numerical data;

provides descriptive, inferential and explanatory information;

manipulates key factors and variables to derive frequencies; and

presents material which is uncluttered by specific contextual factors.

Surveys can be distinguished as cross-sectional and longitudinal.

Correlation studies

Correlational studies trace relationships among two or more variables in order

to gain greater situational insight.

E.g. Price and sales, advertising and sales, etc.


The purpose of correlational studies is not to establish cause-effect

relationship among variables but to determine whether the variables under

study have some kind of association or not.

Variables being studied may have positive or negative relationship or they

may not have relationship at all.

Observation studies

Observation studies involve observing and recording of behavior or trait or

attribute as it occurs in its natural settings.

observation study has the following important features:

The first and most fundamental principle is that of

noninterference.

Second, observation study involves the observation and

detection of invariants, or behavior patterns or other

phenomena that exist in the real world.

Third, observation study is particularly useful when we know

little or nothing about a certain subject.

Finally, observation study is basically descriptive. Although it

can provide a somewhat detailed description of a phenomenon,

it cannot tell us why the phenomenon occurred.

Case studies

Case studies emphasize detailed contextual analysis of a limited number of

events or conditions and their relationships.

They are largely descriptive examinations, usually of a small number of sites

Case studies can provide very engaging, rich explorations of a project or

application as it develops in a real-world setting.


Explanatory Research

Explanatory research aims at establishing the cause and effect relationship

between variables.

The researcher goes beyond merely describing the characteristics, to analyze

and explain why or how something is happening.

Explanatory or analytical research aims to understand phenomena by

discovering and measuring causal relations among them.

Explanatory research builds on both exploratory and descriptive researches.

It involves:

Explaining things not just reporting. Why? Elaborating and

enriching a theory's explanation.

Determining which of several explanations is best.

Determining the accuracy of the theory; test a theory's predictions

or principle.

Providing evidence to support or refute an explanation or

prediction.

Testing a theory's predictions or principles.

Quantitative Vs. Qualitative Research Vs. Pluralistic Research

Quantitative Research: Based on the measurement of quantity or amount. Applicable

to phenomenon that can be expressed in terms of quantity.

Quantitative research is the systematic and scientific investigation of

quantitative properties and phenomena and their relationships.


The objective of quantitative research is to develop and employ

mathematical models, theories and hypotheses pertaining to natural

phenomena.

The process of measurement is central to quantitative research.

Quantitative research involves surveys and experiments

Quantitative approach typically concentrates on measuring or counting and

involves collecting and analyzing numerical data and applying statistical

tests.

Qualitative Research: Concerned with qualitative phenomena. Is important in behavioral

sciences where the aim is to discover the underlying motives, interests, personality and

attitudes of human beings.

Qualitative research is a type of empirical enquiry that entails purposive sampling for

gathering data. It involves:

in-depth interviews,

group discussions,

projective techniques,

Observations without formal measurement.

A case study, which is an in-depth examination of one person, is a form of

qualitative research.

Pluralistic Research: Defined as the combination of qualitative & quantitative research

methods in order to gain the advantages of both. With pluralistic research , it is

common to begin with exploratory qualitative techniques as, for example , in-depth

interviews of selected dealers or a series of group discussion with customers in order

to understand how they perceive your product and service as compared to those of

competitors. The qualitative phase serves as the foundation for the quantitative phase

of the research project because it provides the researcher with first-hand knowledge of


the research problem. Armed with this knowledge, the researcher’s design and

execution of the quantitative phase is invariably superior to what it might have been

without the qualitative phase. With pluralistic research , the qualitative phase serves to

frame the subsequent quantitative phase , and in some cases , a qualitative phase is

applied after a Quantative study to help the researcher understand findings in the

quantitative phase.

Conceptual Vs. Empirical Research

Conceptual Research: Related to some abstract idea or theory. Philosopher’s

thinkers generally use it to develop new concepts or to interpret existing ones.

Empirical Research: Data based, coming up with conclusions that are capable of

being verified, by observation or by experiment. The researcher should collect enough

that to prove or disprove his hypothesis. Appropriate when proof that certain variable

affect others variables in some way is sought. Evidence gathered through experiment or

empirical studies provides the most powerful support possible for a given hypothesis.

Induction and Deduction

A researcher observes and faithfully records what is seen without any prejudice. Some

of these statements of observations are established as true and serves as the basis for

theories and laws.

There are two ways of establishing what is true or false and to draw conclusions:

Induction and Deduction.

Induction

Induction is based on empirical evidence, while deduction is based on logic. Through

induction we draw general conclusion from our empirical observations. In this type of

research the process goes from observations findings theory

building, as findings are incorporated back into existing knowledge to improve theories.

This type of research is often associated with qualitative type of research. This process

goes from assumption to conclusions and is illustrated as follows:


Assumption: psychiatrists have found that psychological problems in patents depend

upon their experiences in childhood.

Conclusions: All psychological problems are based on experiences in childhood.

Deduction

By deduction mean that we draw conclusion through logical reasonining. In this case,

it need not be true in reality, but it is logical. The researcher in this type of research

builds /deduces hypothesis from the existing knowledge which can be subjects to

empirical scrutiny and this can be accepted or rejected.

The researcher’s main job is not only to build hypothesis from existing knowledge but

also to present them in operational terms, to show how information can be collected to

test these hypothesis and the concepts used.

The process of deduction goes as follows:

Assumption: al materials expand when heated.

Assumption: rail tracks are built of metal.

Conclusion: Rail tracks will expand when heated.

The above examples explain the difference between induction and deduction. The

difference is that facts acquired through observations lead us theories and hypothesis,

while with deduction we accept or reject these theories and hypothesis. This

acceptance and the rejection then help us to explain or predict.

1.8 Internal Vs. External Researchers

There Two Groups Of Consultants/ Researchers: Internal and External.

1.8.1 Advantages and Disadvantage Internal Consultants / Researchers


Some organization has their own consulting or research department, which might be

called the management services Department.

I) Advantages of Internal Consultants /Researchers

There are at least four advantages’ in engaging an internal team to do the research

project.

The internal team would stand a better chance of being readily accepted

by the employees in the subunit of the organization where research needs

to be done.

The team would require much less time to understand the structure, the

philosophy and climate, and the functioning and work systems of the

organization.

They would be available for implementing their recommendations after the

research findings are accepted. This is very important because any “bugs”

in the implementation of the recommendation s could be removed with

their help.

The internal team might cost considerably less than an external team for

the department enlisting help in problem solving , because they will need

less time to understand the system due to their continuous involvement

with various units.

II) Disadvantages of Internal Consultants

There are also certain disadvantages to engaging internal research teams for

purposes of problem solving:

In view of their long tenure as internal consultants, the internal team may

quite possibly fall into a stereotyped way of looking at the organization

and its problems. This would inhibit fresh ideas and perspectives that

might be needed to correct the problem.

There is scope for certain powerful coalitions in the organization to

influence the internal team to conceal, distort, or misrepresent certain

facts. In other words, certain vested interests could dominate, especially

in securing a sizeable portion of the available scant resources.


There is also a possibility that even the most highly qualified internal

researcher teams are not perceived as “experts” by the staff and

management, and hence their recommendations do not get the

consideration and attention they deserve.

Certain organizational biases of the internal research team might in

some instances make findings less objective and consequently less

scientific.

1.8.2 Advantages & Disadvantages of External Consultants

The disadvantages of an internal research teams turn out to be the advantages of the

external teams, and the former’s advantages work out to be the disadvantages of the

latter. However, the specific advantages and disadvantages of the external teams may

be highlighted.

I) Advantages Of External Consultants

The external team can draw a wealth of experience from having worked

with different type of organizations that have had the same or similar types

of problems. This wide range of experience would enable them to think

both divergently and convergent rather than hurry to an instant solution on

the basis of the apparent facts in the situation. They would be able to

ponder over several alternative ways of looking at the problem because of

their extensive problem solving experiences in various other

organizational settings.

The external teams , especially those from established research and

consulting firms , might have more knowledge of current sophisticated

problem-solving models through their periodic training programs, which

the teams within the organization may not have access to. Because

knowledge obsolescence is a real threat in the consulting area , external

research institutions ensure that their members are current on the latest

innovations through periodic organized training programmes.

II) Disadvantages Of External Consultants

The major disadvantages in hiring an external research team are as follows:


The cost of hiring an external research team is usually high and is the

main deterrent, unless the problems are very critical.

In addition to the considerable time the external team takes to understand

the organization to be researched, they seldom get a warm welcome, nor

are readily accepted by employees. Departments and individuals likely to

be affected by the research study may perceive the study team as a threat

and resist them.

The external team also charges additional fees for their assistance in the

implementation and evaluation phases.

The Research Process

The research process includes a number of practical steps which may include the

following.

Define The Research Problem,

Establish The Research Objectives,

Develop Hypothesis And Research Questions,

Identify Information Types And Sources

Determine Research Design,

Determine Sample Plan And Size

Qualitative methods

Quantitative methods

Development of measurement scales

Development of data collection designs (Questionnaire)

Collecting Data

Analyze Data

Prepare and Present The Final Research Report

Although the list does strongly imply an orderly, step-by-step process, it is rare that a

research project follows these steps in the exact order.


Actually, research practices are more of an iterative process whereby a

researcher, by discovering something in a given step, may move backward

in the process and begin again at another step.

Finding some new information while collecting data, may cause the

researcher to establish different research objectives.

Any given research project may not involve each and every step shown

above.

The research problem could be resolved, by review of a secondary data,

thereby eliminating the need to determine a sample plan or size.

What is important for you to know is that, although almost every research

project is different, there are enough commonalities among the various

procedures and activities to enable us to specify the basic steps of the

research process.

Review Questions

1. For what specific purposes as basic research Important?

2. When is applied research, as distinct from basic research, useful?

3. Why is it important to be adept in handling the manager-researcher relationship?

4. Explain, giving reasons, which is more important, applied or basic research.

5. Explain, giving reasons, which is more important, applied or basic research.

6. Give two specific scenarios where an external research team would be useful

and two other sections when an internal research team will be deployed, with

adequate explanations as to why each scenario is justified for an external or

internal team.

7. Given the situation below a) discuss with reasons whether they fall into the

category of applied or basic research, and b) for scenario 1, explain with reasons

who will conduct the research.

Scenario 1

To Acquire or Not To Acquire: That Is the Question


Companies are very interested in acquiring other firms even when the latter operate in

totally unrelated realms of business. For example, Gencore industries manufacturing

asphalt plants for road construction acquired Ingersoll –Rand in 1996, and later

acquired yet another company engaged in the business of food processing. Such

acquisitions are claimed to “work miracles.” However, given the volatility of the stock

market and the slowing down of business, many companies are not sure whether such

acquisitions involve too much risk. At the same time, they also wonder if they are

missing out on a great business opportunity if they fail to take such risk. Some research

is needed here!

Scenario 2

Reasons for Absenteeism

A university professor wanted to analyze in depth the reasons for absenteeism of

employees in organizations. Fortunately, a company within 20 miles of the campus

employed her as a consultant to study that very issue.

Scenario 3

Effects of Nasal Spray on Flu

A research scientist surveys 1,000 employees in different organizational settings to

study the efficacy of several types of nasal sprays in controlling the flu virus. He

subsequently publishes his findings in a highly respected medical journal.


Chap- 2 The Research Proposal

2.1 What is a research proposal?

A research proposal is an activity that incorporates decisions made during early

research-project planning phase of the study including management –research

questions hierarchy and exploration. A research proposal is a work plan, outline,

prospectus, statement of intent, draft plan.

A proposal is an individual’s or a company’s offer to produce a product or render a

service to a potential buyer or sponsor.

2.2 Purpose of Research Proposal

The purpose of a research proposal is to:

Present the management question to be researched and its importance.

Discuss the research efforts of others who have worked on related

management questions.

Suggest the data necessary for solving the management questions and how

the data will be gathered, treated, and interpreted.

2.3 Types of Research Proposals

Proposals can be internal or external.

I) Internal Proposals

Internal proposals are more succinct than external ones.

An internal proposal is done for the corporation by staff specialist or by the

research department of the firm.

It is a memo from the researcher to management outlining the problem

statement, study objectives, research design, and schedule enough to start

an exploratory study.


Privately and publically held firms are concerned with how to solve a

particular problem, make a decision, or improve an aspect of their business.

In the small scale proposal, the literature review and bibliography are

consequently not stressed and can often be stated briefly in the research

design.

For the smaller scale projects, descriptions are not required for facilities and

special resources, nor is there a need for a glossary.

II) External proposals

An external proposal is either solicited or unsolicited.

A solicited proposal is often in response to REP.

The proposal is likely competing against others for a contract or grant.

An unsolicited proposal has the advantage of not competing against

others but the disadvantage of having to speculate on the ramification of a

problem facing the firm’s management.

The writer of the unsolicited proposal must decide to whom the document

should be sent.

The external summary of an external proposal may be included within

about project management and the facilities and special resources.

Complexity Level of a Proposal

There are three general levels complexity.

Type least………………………………………….Most

i) Management

proposal

internal exploratory

study

small-

scale

study

large-scale

study

external exploratory

contract

research

small-

scale

contract

research

Large–

scale

cochntract

research

Government

Sponsored


ii) Student Proposals Term

Paper

Master’s

Thesis

Doctoral

Thesis

Structure of an Internal Proposal

The major components of an internal proposal include:

1. Title Page

A title ought to be well studied and give a definite and concise indication of

what is to come.

The title of a research proposal should state your topic exactly in the

smallest possible number of words.

Put your name, the name of your department/faculty/college, the name of

your advisor(s) and date of delivery under the title.

First impressions are strong impressions: make your title an attention

grabber.

2. Summary/Abstract

The abstract is a one page brief summary of the thesis proposal.

It needs to show a reasonably informed reader why a particular topic is

important to address and how you will do it.

Specify the question that your research will answer, establish why it is a

significant question; show how you are going to answer the question.

Do not put references, figures, or tables in the abstract.

The abstract is a concise summary of the material presented in the

proposal.

Though it appears at the front of the proposal, it is written last.

A well-prepared summary enables the reader to


Identify the basic content of a document quickly and accurately,

Determine its relevance to their interests, and

Decide whether they need to read the document in its entirety.

3. Introduction/Background

The introduction is the part of the proposal that provides readers

with the background information for the research proposal.

The introduction should address the following points:

Sufficient background information to allow the reader to understand

the context and significance of the question you are trying to

address.

Proper acknowledgement of the previous work on which you are

building.

Sufficient references

The introduction should be focused on the research question(s).

All cited work should be directly relevant to the goals of the

research.

Explain the scope of your work, what will and will not be included.

A verbal "road map" or verbal "table of contents" guiding the reader

to what lies ahead.

4. Statement of the problem

Statement of the problem encapsulates the question you are trying

to answer.

Effective problem statements answer the question “Why does this

research need to be conducted.”


5. Literature Review

The literature review asks how similar and related questions have been

answered before. A literature review can be organized as:

Introduction: define the topic, together with your reason for selecting

the topic.

Body: this is where you discuss your sources.

Conclusion: summarize the major contributions, evaluating the current

position, and pointing out flaws in methodology, gaps in the research,

contradictions, and areas for further study

6. Objective/Aim Of The Study

The objectives of a research delineate the ends or aim which the

inquirer seeks to bring about as a result of completing the research

undertaken.

An objective may be thought of as either a solution to a problem or a

step along the way toward achieving a solution; an end state to be

achieved in relation to the problem.

The objectives of a research project summarize what is to be achieved

by the study.

Objectives should be closely related to the statement of the problem.

After statement of the primary objective, secondary objectives may be

mentioned.

Objectives should be

simple (not complex),

specific (not vague),


stated in advance (not after the research is done), and

stated using “action verbs” that are

Specific enough to be measured.

Commonly, research objectives are classified into general objectives

and specific objectives.

7. Hypotheses /Questions

Hypotheses and questions are linked to the speculative proposition of

the problem statement.

the term hypotheses implies a derivation, within a hypothetic-deductive

theoretical system, of a particular assertion or prediction.

Hypotheses are tentative statements/solutions or explanations of the

formulated problem

The hypothesis is subject to test, i.e., to confirmation or rejection on

empirical grounds.

The term question implies an interrogative statement that can be

answered by data.

The formulation of objectives will help you to:

Focus the study (narrowing it down to essentials);

Avoid the collection of data which are not strictly necessary for

understanding and solving the problem you have identified; and

Organize the study in clearly defined parts or phases.

8. Research Methods, Materials And Procedures

What belongs in the "methods" section of a research proposal?


Information to allow the reader to assess the believability of your

approach.

Information needed by another researcher to replicate your experiment.

Description of your materials, procedure, theory.

Calculations, technique, procedure, equipment, and calibration plots.

Limitations, assumptions, and range of validity.

Description of your analytical methods, including reference to any

specialized statistical software.

Sampling design

Sampling is the process of selecting a number of study units from a defined

study population.

Consider the following questions:

What is the study population you are interested in from which we

want to draw a sample?

How many subjects do you need in your sample?

How will these subjects be selected?

The key reason for being concerned with sampling is that of validity—the

extent to which the interpretations of the results of the study follow from the

study itself and the extent to which results may be generalized to other

situations with other people.

Sampling is critical to external validity—the extent to which findings of a

study can be generalized to people or situations other than those observed

in the study.


Another reason for being concerned with sampling is that of internal

validity—the extent to which the outcomes of a study result from the

variables that were manipulated, measured, or selected rather than from

other variables not systematically treated.

Representative ness and accuracy

Probability Sampling

Simple random sampling

Systematic sampling

Cluster sampling

Stratified sampling

Non probability Sampling

Convenience sampling

Referral sampling

Judgment sampling

Quota sampling

9. Work Plan

Work plan is a schedule, chart or graph that summarizes the different

components of a research proposal and how they will be implemented.

In the work plan

Different components/phases/stages of the study should be stated

Description of activities in each phase

Time required to accomplish the various aspects of the study should also

be indicated


THE GANTT CHART

A GANTT chart is a planning tool that depicts graphically the order in which

various tasks must be completed and the duration of each activity.

The GANTT chart indicates:

the tasks to be performed;

who is responsible for each task; and

the time each task is expected to take.

10. Budget And Funding

Budget items need to be explicitly stated

Cost for every budget item should be quantitatively shown

Their might be a need for budget justification of certain costs whose

requirement is not obvious

Direct costs:

Personnel: Salaries and wages

Consumable supplies:

Equipments

Travel

Communications

Publication

Other direct costs

Indirect costs

Overhead costs for institutions or associations


General administrative cost

Operational and maintenance

Depreciation and use allowance

11. References

References may be made in the main text using index numbers in brackets (Vancouver

style) or authors name (Harvard style).

For a journal paper give:

o the names of the authors,

o the year of publication,

o the title of the paper,

o the title of the journal,

o the volume number of the journal,

o the first and last page numbers of the paper.

For a book give:

the author,

the year of publication,

the title, and the edition number if there is one,

the name of the publisher,

the page numbers for your reference.

For an internet reference give:

the author of the web page,

the title of the item on the web page,

the date the item was posted on the web page

the date the item was accessed from the web page

the complete and exact URL.

12. Appendices/Annexes

Include in the appendices of your proposal any additional information you think might be

helpful to a proposal reviewer. Example,


Appendix includes:

Questionnaire & other collection forms

Dummy tables

Biographical data on the principal investigator

The consent form (if any)

Major Structure of an External Proposal

The major components of an external proposal include:

1. Title page

A title ought to be well studied and give a definite and concise indication of

what is to come.

The title of a research proposal should state your topic exactly in the

smallest possible number of words.

2. Executive Summary

Allows a busy manager or sponsor to understand quickly the thrust of

the proposal.

It is essentially an informative abstract, giving executives the chance to

grasp the essentials of the proposal without having to read the details.

The goal of the summary is to secure a positive evaluation by the

executive who will pass the proposal on to the staff for a full evaluation.

It should include brief statements of the management dilemma and

management question, the research objectives/research question(s),

and the benefits of your approach.

If the proposal is unsolicited, a brief description of your qualifications is

also appropriate.

3. Problem Statement

It should convince the sponsor to continue reading the proposal.


It should capture the reader’s attention by stating the management

dilemma, its background and consequences, and the question starts

the research task.

It should include any restrictions or areas of the management question

that won’t be addressed.

4. Research Objective

Lays out exactly what is being planned by the proposed research.

In a descriptive study, the objectives can be stated as the research

question.

The research questions can be further broken down into investigative

questions.

If the proposal is for a descriptive or causal study, then the objectives can

be restated as a hypothesis.

The objective naturally flows from the problem statement, giving the

sponsor specific, concrete, achievable goals.

The research questions or hypothesis, if appropriate should be set off from

the flow of the text so they can be found easily.

The basis for judging the remainder of the proposal and ultimately the final

report.

5. Literature Review

Examines recent research studies, company data, or industry reports

that acts as a basis for the proposed study.

Discussion of literature should move from a comprehensive

perspective to more specific studies that are associated with your

problem.

If the problem has a historical background, begin with the earliest

references.

Avoid the extraneous details of the literature, do a brief review of the

information, not a comprehensive report.

Always refer to the original source.


If you find something of interest in a quotation, find the original

publication and ensure you understand it.

Avoid any errors of interpretation transcription.

Emphasize the important results and conclusions of other studies, the

relevant data and trends from previous research, and particular

methods or design that could be duplicated or should be avoided.

Discuss how the literature applies to the study you are proposing,

show the weaknesses or faults in the design , discussing how you

would avoid similar problems.

If your proposal deals solely with secondary data, discuss the

relevance of the data and the bias or lack of bias inherent in it.

It should explain the need for the proposed work to appraise the

shortcomings and informational gaps in secondary data sources.

This analysis may go beyond scrutinizing the availability or conclusion

of past studies and their data , to examining the accuracy of

secondary sources , the credibility of these sources , and the

appropriateness of earlier studies.

Close the literature review section by summarizing the important

aspects of the literature and interpreting them in terms of your problem.

Refine the problem as necessary in light of your findings.

6. Importance / Benefits of The Study

Allows describing explicit benefits that will accrue from your study.

The importance of “doing the study now” should be emphasized.


If you find it difficult to write, then you will have probably not

understood the problem adequately.

Return to the analysis of the problem and ensure , through additional

discussions with your research team , or

By the reexamination of the literature, that you have captured the

essence of the problem.

It requires you to understand what is most troubling to your sponsor.

It is more important to the unsolicited external proposal.

7. Research Design

The design module describes what you are going to do in technical

terms.

It should include so many subsections as needed to show the phases

of the project.

Provide information on your proposed design for tasks such as sample

selection and size, data collection method, instrumentation,

procedures, and ethical requirements.

When more than one way exists to approach the design discuss the

methods you rejected and why your selected approach is superior.

8. Data Analysis

With smaller projects, the proposed data analysis would be included

within the research design section.

For large –scale contract research projects and doctoral thesis a

separate brief section of data analysis is appropriate.

Describe your proposed treatment and the theoretical basis for using

the selected techniques.

Assure the sponsor that you are following correct assumptions and

using theoretically sound data analysis procedures.

Use sample charts and dummy tables to make it easier to understand

your data analysis.

You should contact an expert to review the latest technique s available

for your use.


If there is statistical or analytical expertise within your company, be

prepared to hire a professional to help with this activity.

9. Nature & Form Of Results

The sponsor should be able to go back to the problem statement and

research objectives and discover that each of the goals of the study

has been covered.

One should also specify the types of data to be obtained and the

interpretations that will be made in the analysis.

If the data are to be turned over to the sponsor for proprietary reasons,

make sure that this is reflected.

Alternatively, if the report will go to more than one sponsor, that should

be noted.

It also contains the contractual agreement telling the sponsor exactly

what types of information will be received.

Statistical conclusions, applied findings, recommendations, action

plans, models strategic plans, and so forth are ………forms of results.

10. Qualification Of Researchers

It should begin with the principal investigator.

It is also customary to begin qualifications with the highest academic

degree held.

Experience in carrying out previous research is important, especially in

the corporate market place, so a concise description of similar projects

should be included.

Relevant business and technical societies to which the researcher

belongs can be included where the information is particularly relevant

to the research project.

The entire curriculum vitae of each researcher should not be included

unless required by the RFP.

Instead, refer to the relevant areas of experience and expertise that

make the researchers the best selection task.


Complete curriculum vitae information is often placed in an appendix

for review by interested sponsors.

11. Research Budget

The budget should be presented in the form the sponsor requests.

The budget should be no more than 1-2 pages.

The budget statement in an internal research proposal is based on

employee and over head costs.

The budget presented by an external research organisation is not just

the wages or salaries of their employees but the person –hour price

that the contracting firm charges.

Salaries (research director’s, research assistances, secretarial

services), other costs such as travel per diem, office supplies,

telephone expenses , rent, other equipments , publication and storage

costs etc has estimated and presented in a presentable format.

Additional information, back up details, quotes from vendors, and

hourly time and payment calculations should be put into an appendix if

required or kept in the researcher’s file for future reference.

The detail presented may vary depending on both the sponsor’s

requirements and the contracting research company’s policy.

The budget section of an external proposal states the total fee payable

for the assignment.

12. Research Schedule

Your schedule should include the major phase of the project, their

time tables, and the milestones that signify completion of a phase.

Major phase may be:

Exploratory interviews

Final research proposal

Questionnaire revision

Field interviews


Editing & coding

Data analysis , and

Report generation

You can use Gant chart.

13. Facilities & Special Resources

Often projects will require facilities or resources that should be

described in detail.

A contract exploratory study may need specialized facilities for focus

group sessions.

Computer assisted telephone or other interviewing facilities may be

required.

Alternatively , your proposed data analysis may require sophisticated

computer algorithms , and therefore, you need access to an adequate

system . These requirements will vary from study to study. The

proposal should carefully list the relevant facilities and resources that

will be used.

14. Project Management

This section shows the sponsor that the research team is organized in

a way to do the project efficiently.

A master plan is required for complex projects to show how the phase

will be brought together.

The plan includes:

The research team ‘s organization

Management procedures and controls for executing the

research plan.

Examples of management and technical reports.

Research team relationship with the sponsor.

Financial and legal responsibility.

Management competence

Tables and charts are most helpful in presenting the master plan.


Sponsors must know that the director is an individual capable of

leading the team and being a useful liaison to the sponsor.

Procedures for information processing, record control and expense

control are critical to large operations and should be shown as part of

the management procedures.

The type and frequency of progress reports should be recorded so the

sponsor can expect to be kept up-to-date and the researchers can

expect to be left alone to do research.

The sponsor’s limits on control during the process should be

delineated.

Details such as printing facilities, clerical help, or information-

processing capabilities that are to be provided by the sponsor are

discussed.

Rights to the data, the result, and authority to speak for the researcher

and for the sponsor are included.

Payment frequency and timing are also covered in the master plan.

Proof of financial responsibility and overall management competence

are provided.

15. Bibliography

For all projects that require literature review, a bibliography is

necessary.

Use the bibliographic format required by the sponsor.

16. Appendices Glossary

A glossary of terms should be included whichever there are many

words unique to the research topic and not understood by the general

management community.

This is a simple section consisting of terms and definitions.

Also, define any acronyms that you use, even if they are defined within

the text.

17. Measurement Instrument


For large projects, it is appropriate to include samples of the

measurement instruments if they are available when you assemble the

proposal.

This allows the sponsor to discuss particular changes in one or more of

the instruments.

If exploratory work precedes the selection of the measurement

instruments, you will not use this appendix section.

Evaluating the Research Proposal

Proposals are subjected to formal and informal reviews.

The formal methods have some variations, but its essence is described as

follows.

Before the proposal is received, criteria are established and each is given

weights or points.

The proposal is evaluated with a check list or criterion in hand.

Points are recorded for each category reflecting the sponsor’s assessment

of how well the proposal meets the category’s established criteria.

Several people, each of whom is assigned to a particular section, typically

review long and complex proposals.

After the review, the category scores are added to provide a cumulative

total.

The proposal with the highest of points will win the contract.

The formal method is most likely to be used for competitive government,

university, or public sector grants and also for large –scale contracts.

Small –scale contracts are more prone to informal evaluation, the project

needs, and thus the criteria, are well understood but not usually well

documented.

In contrast to the formal method, a system of points is not used and the

criteria are not ranked.


Beyond the required modules, there are factors that can quickly eliminate a proposal

from consideration and factors that improve the sponsor’s reception of the proposal.

The proposal must be neatly presented.

Although a proposal produced on a work processor and

bound with an expensive cover will not overcome design or

analysis deficiencies.

A poorly presented, unclear or disorganized proposal will not

get serious attention from the reviewing sponsors.

The proposal’s major topics should be easily found & logically

organized

The reviewer should be able to page through the proposal to

any section of interest.

The proposal also must meet specific guidelines set by the

sponsoring company or agency.

The proposal also must meet specific guidelines set by the

sponsoring company or agency.

Theses include budgetary restrictions and schedule

deadlines.

The technical writing style of the proposal.

The problem statement must be easily understood.

The research design should be clearly outlined and the

methodology explained.

The importance /benefits of the study must allow the sponsor

to see why the research should be funded.

The objectives and results sections should communicate

exactly the goals and concrete results that will come from the

study.

Budget and schedule considerations must be kept in mind.


A late proposal won’t be evaluated.

A schedule that doe not meet the expected deadlines will

disqualify the proposal.

A budget that is too high for the allocated funds will be

rejected.

Conversely, a low budget compared to competing proposal

suggests that something is missing or there is something

wrong with the researchers.

Factors to be considered in Choosing a Research Topics

There are a number of factors you need to take into account when choosing the

subject of your thesis.

Interest and Relevance: you should choose a topic that interests and

even possibly excites you, otherwise you will have trouble sustaining

the motivation and commitment necessary to complete the project. It

should also be of interest to some external audience as well.

Durability: will the project last the length of the course? Organizations

are capable of making very rapid changes in direction and policy. It

may be that the topic you choose could become obsolete because of a

change in organizational strategy, ownership or other strategy.

Breadth of research questions: is there enough substance to your

topic?

Topic adequacy :check the assessment criteria used on your course

, against which your work will be marked ,and ask yourself whether

the topic you have in mind will enable you to do well against the

criteria.

Access: you may have an excellent topic in mind , but unless you can

get access to the people who can answer your research questions,

whether by questionnaire, interview or whatever , then the project will

be a non-starter.


Micro politics: whenever you research a business issue there is a

danger that you may become a partisan in the management debates

and politics that surround it.

Risk and security: bearing in mind the previous criterion, you can not

avoid all risk. If you choose a topic that is totally safe it will probably be

so bland that neither you nor anyone else will be interested in the

outcomes.

Resources : literature make sure there is enough written about

your topic , or about the general academic field in which it is located ,

for you to be able to do the critical literature review. IT, Software and

Skills- your topic may require access to, and skill in , various software

packages

Stages In The Process Of Choosing a Topic

The following is a six stage process for you to follow when choosing a topic.

Identify Broad Topic And Academic Discipline(S)

Determine The Scope

Brainstorm Issues , Puzzles And Questions

Map and Structure The Issues

Conduct A Reconnaissance

Frame Your Research Question(S)

Identify Broad Topic and Academic Discipline (S)

The starting point is to decide your broad area of interest. It might be to give some

examples:

Implementation of mission statements and strategy in multinational

companies;

The problems caused by a lack of cooperation between GPs and the

other health professionals in primary care;

Performance –related pay and flexible benefits.


Determine the Scope

This is very often a practical matter of where you can get access. You need to decide

whether you will be:

Studying one part of an organization;

Making a comparison of several parts of an organization;

Studying one organization;

Making a comparison of two or more organization;

Studying a secure.

Brainstorm Issues, Puzzles and Questions

Now go into brain storming mode and list as many different issues, problems and

questions that arise from the broad topic area as you possibly can. Do not evaluate

them by saying,” No, that is unimportant,”. Just make the list as long as possible

Map and Structure the Issues

Now you have a pile of issues, they need structuring and organizing. Sort and cluster all

your research issues and questions in a relevance tree or hierarchical diagram. The

relevance tree provides a map of all the issues and questions you could research under

broad areas of interest.

Conduct a Reconnaissance

Having arrived at a clear view about what your research topic is going to be, it is

sensible to discuss it with others. Discuss it with tutors, colleagues and other managers

to see whether they agree that the issue is important and coherent. You should also do

an initial trowel of the literature, if you have not already done so, to see what work

others have done on the issues that concern you.

Frame Your Research Question(S)

The final stage is to ensure that you are clear in what you are doing by framing your

research questions in plain English. The suspicion is that if you can ask your research

questions without using management jargon, then you are probably not clear what you

are asking and you need to think about it some more.


If you research questions resembles this- I am addressing the issues relevant to

leveraging human resources competency to produce turnaround to world- class status

and to diagonally integrate professional functionalities – you probably do not know

what you are talking about.

Structures of a research Proposal

You have, of course, freedom to structure your proposal as you please, but should you

need guidance; the following structure covers most of the issues:

Introduction

The objectives and purpose of the project’( what)

A brief overall description of the project’s context

The strategic question that guides the project

The objectives of the project.

The justification for the project (why?)

The research question ( what?- again, but in more detail):

Identify and A hypothesis is a speculation about an association

between two or more variable .It is important discuss the research

questions that you will answer in the project.

If you are taking a realist research approach you might frame your

research question as a hypothesis.

A hypothesis is a speculation about an association between two or

more variables. It is important that the hypothesis can be tested by

research to see whether it can be disproved.

An overview of the appropriate literature.

Mapping the main writers in the field and their arguments.

Definition of key concepts and outlining of conceptual framework if

necessary and possible. This is the how –conceptually? Quadrant

of the watsen Box.)

Research design:

o What methodological approach are you going to

adopt?


o Research methods samples, methods for analyzing

research material.

Practical and ethical issues

Does the research raise any ethical concerns that need to be

resolved?

Are there any potential problems of research access?

Are there any resources issues such as access to specialist

databases or particular research software?

Are there issues of commercial confidentially or intellectual property

rights?

A plan or time table:

Consider drafting a Gantt chart that plots against a timeline when

the major elements of the projects will be done.

Critical Thinking Discussion Question

1. Define research proposal.

2. Identify the purpose of research proposals

3. Discuss types of research proposals.

4. Discuss the structure of research proposal

5. Discuss the formal and informal evaluation /review techniques.


Chap- 3 Writing a Critical Literature Review

Chapter objectives

After completing this chapter the student will be able to:

Search library and other information resources to find appropriate

literatures for their dissertation

Map out the literature and identify the key concepts, theories and arguments

they are necessary for their project

Make an overall assessment of the critical quality of a book or articles.

Carry out a forensic critique of the core theories, concepts and arguments

that they have drawn from the literature.


Have an awareness of radical critical perspective and be able to make

radical critiques of key themes and topics --- presented in the literature.

3.1 The Sources

Since Literature review is critical component of the research process it has to be

addressed properly.

The first task in doing a literature review is to find some literature the need a

mixture of materials, but the precise combinations will vary from topic to topic.

Try to find some material from most of the categories ---below. Students often

ask: How many books and articles should I----my dissertation?

It is impossible to give a number in answer, although it can be tempting to quote

a very large number to scare them and to encourage them not to ask silly

questions in future.

It all depends on what literature is available and how you use the ideas, material

and theory it contains.

It is possible to suggest that a list of references with less than 20 items is

probably too short.

Books

There are three sorts of management books as far as researchers are Concerned:

a. Text books

Are useful to help you orient yourself in a field of literature and as a

source of references that can be followed up.

They may be recognized from their characteristic use of learning

device such as learning objectives, summaries, exercises and text

books containing illustrative material.

They should not be relied upon too heavily.


b. Academic Monographs

AM are often difficult to read, but they do alert the reader to

development arising from recent research.

These books should be used selectively.

c. ‘Airport Bookstall’ Books

Are mostly written in an accessible style and have attractive jackets.

When they are not based on the biographies or experiences of

successful managers, they are often distillations of more academic

work.

They are mostly presented in a ‘can do’ format, offering practical

guidance for business success. Normally they have attention-grabbing

titles that appear to offer solutions to all business problems.

They can be useful materials, but they should not be used uncritically

because they often oversimplify and contain uncorroborated

assumptions.

Journals

Journals fall into two main categories and you should use both. Academic

journals contain articles that have been peer reviewed.

This means that two or more expert referees approved the papers before

they were accepted for publication.

The general information or style pages, often on the inside of the front or

back cover of journal, should say whether it is peer reviewed.

Non-peer-reviewed journals are often professional or trade journals.

The articles in these journals are commonly simpler and shorter than those

in the peer-reviewed journals.

They can be useful for identifying trends, fashions and current concerns

within business, but many are derivative and of questionable quality. Non-

peer-reviewed journals should not be used exclusively.

Managers doing MBAs and other Master’s courses in management often

look at the more academic papers in horror and declare, ‘We are managers,


and we cannot be expected to cope with this hugely complex academic

writing style.

’Well, I have some sympathy because many papers do appear to be written

in a style designed to fool, impress or confuse.

Nevertheless, there will be much pertinent and good material in such papers

and you will need to make the effort to understand them.

There are some journals, such as the Harvard Business Review, that are

academically prestigious but are designedly accessible to managers as well

as to academics.

Journals will most likely be your main source for a research dissertation,

probably being more important than books.

This is because the most up-to-date research and debates will be found in

journals.

The World Wide Web

The World Wide Web is a very useful source of material.

The better search engines can be valuable in tracking down material.

The Web has to be used with care, however, because it includes both high-

quality material and utter tosh.

Distinguishing between the two is not always easy.

The first few pages read very sensibly and then the web page slowly

became an obsessive polemic in which semantic differentials were

presented as diabolic devices that bureaucrats were using to remove the

liberties of the individual.

In the public sector, where much of the materials is in the form official publications and

reports, the Web can be the most convenient means access to material. The Direct

Gov. Government website http://www.direct.gov.uk/Homepage/fs/en is a good starting

point.

Dissertations


Business schools keep copies of past Master’s dissertations produced by

their students. Some are commercials confidential and cannot be – suited

without the author’s permission.

Many students find a comforting to see others’ efforts, and it may be worth

asking to –some.

Try to make sure that you are given ones that passed comfortably; otherwise

you may gain a dangerously low impression of the standard you need to

reach.

Searching For Literature

Looking for literature these days if mostly an electronic activity of searching

through a virtual library.

Using a library’s on line catalogue of books and other materials makes

searching for books a lot easier.

It is possible to access the online catalogues of academic libraries across the

country.

NISS (National Information Services and Systems) provides a website of links

to online library catalogues at http/www.niss.ac.hk/lis/opacs.html. the

advantages of searching for journal articles and papers by using electronic

library resources in that it takes much less time, you are more likely to find

what you need and. If you are lucky, the papers may be available online and

you can download them or print them off.

You should spend some time becoming familiar with all the services that your

university or institutional library ca provide by exploring both the physical

library and the virtual library.

You may need various user names and passwords to access the latter, and

the library staff will be able to help you with these.

Do not become so carried away with online searching that you forget about

bibliographic serendipity, by which I mean wandering around the library

shelves and current journal stands, flicking through books and reading the

contents lists on the back pages of journals.


It is surprising how often you chance upon useful material that you would

otherwise have missed.

Many academic libraries have joined a cooperative scheme so that students

who live or work at a distance from their university can use libraries closer to

them.

A website listing the universities in the scheme can be found at

http://uklibraries plus.ac.uk/.

Electronic Resources

There is a range of electronic resources that might be of help. These can be

searched one at a time but, increasingly, electronic libraries provide meta-

search facilities that allow many databases to e searched at the same time.

If you want to do a search using advanced search facilities it is often best to

search databases individually and not to use meta-search engines.

Electronic Journals

Most libraries will subscribe to journals that are published and or unavailable

online.

Check out which relevant publication are provided by you library.

Full-Text Databases

These are databases that allow you to make searches for materials using

key words.

They are provide all the bibliographic details such as authors titles, journal

titles, data of publication, volume, issue and page number.

They will also include abstracts of papers’ and articles’ contents. In addition,

in many but not all cases they give you access to an html or ---copy of the

articles it self that you can read on screen, download, and print off or email to

yourself.

The full-text databases that you are mostly likely to use are listed below:


Business Source Premier-a good all-round database Emerald-includes all

the journals published by MCB Press.

European Business ASAP- covers finance, acquisition and mergers new

Technologies and products, HRM, marketing and management generally.

Ingenta- a general database of papers from a number of publishers

Universities have to buy licenses to give students access to databases which

databases universities choose to buy licenses for will, of course, vary from

institution to institution.

You need to explore your institution’s list of electronic resources to see what

is available to you.

Bibliographic Database

These databases can be searched in the same way as full-text database

and provide you with all the bibliographic information and with abstracts,

but they do not provide full copies of the papers and articles.

If you want them you will have to find the hard copy of the journal in the

library, and if the library does not have a copy you will have to order the

item on inter-library loan.

Some databases you might want to use are listed below:

Zetoc – provides bibliographic information on journal held by the British

Library

PsycINFO- covers the field of psychology, but there are many articles

that are relevant to management topics.

Econlit – covers the field of economics, and again there are many

relevant references especially for people working on finance topics.

If you are interested in management in health care and medicine, then Medline

may be worth consulting. If you are working in the public sector especially in the

social service, then ASSIA would be worth a look.

Newspapers in Electronic Formats


Newspapers are valuable source of up-to-date information on many

management topics.

A number of newspapers are available as searchable database in libraries.

Newspapers, including the Financial Times, are also available as online

editions on the World Wide Web.

Financial and Marketing Databases

Strictly speaking these are primary resources rather than secondary

materials and so would find their place in the ‘results and analysis’ part of

the dissertation rather than in the literature review.

Nevertheless, here is a convenient place to mention them.

FAME is the main financial database.

It provides update financial information of 130,000 companies in the United

Kingdom and Ireland.

Mintel is one of the main marketing databases.

These or other databases will be available at your university library.

Building Up Your List of References

It might be worthwhile to check up on he Harvard system of referencing

before you starts collecting material.

Then you can be sure to record all the necessary bibliographic details as

you identify the books the papers.

This will safe you much time.

As I have discovered to my cost, it can take hours, after you have finished

writing the dissertation to track down the missing details (year of

publication, page numbers for quotation of material you have cited but that

you forgot to record properly.

This is time you can ill afford when you are struggling to get the dissertation

finished by the deadline.

It is also worth considering whether you on going to use some specialist

citation software to help you with building up your bibliography.


Mapping and Describing the Literature

In most cases the literature search identifies a lot of material probably more

than can be sensibly dealt with in a Master’s dissertation.

So the next step in conducting a literature review is to map out and identify

the key works and material in the literature.

This exercise has two purposes

It is necessary for you to show the person who marks your dissertation that

you are aware of the breadth of literature relating to your topic.

But it is also necessary, for your sanitary, as well as to meet the marker’s

expectations show that you can priorities the literature and identify the key

works theories Or concepts that will be of value in the conduct of your

project.

Describing the Literature

Describing and mapping the literature relevant to your research project in a step-by-step

process that moves from the general to the specific.

It is an editing process.

1. Prepare a ‘map’ showing the location of all the appropriate literatures

The first step involves identifying the different fields of literature that may

be appropriate to the study without, at this stage, looking at any of it in

detail.

By literature I mean a collection of books and papers that deal with the

same issues and that respond to each other in the developing debates

about a topic.

It follows from this definition that there will be many literatures rather one

solitary and unified literature.

But it is up to you to draw the boundaries between them.

This can be done by skimming through textbooks and following up the

references they give, and by dipping into the journal literature via the

abstracts in the bibliographic databases.


Figure 1.1 shows a ‘map’ of the literatures that I thought were relevant

when I was planning some research into the fates and attitudes of the

survivors of downsizing.

The main theme of the dissertation if shown in the middle of the map in

bold font.

There are a number of key researchers who have written research papers

specifically on the subject.

There is also a large literature, mostly in journals, that focuses on

downsizing as a strategy and on its financial consequences for companies.

This literature suggests that the demoralization of survivors may be a

contributor factors the poor performance of organizations post-downsizing.

This literature on redundancy concentrates on the managerial processes

involved in chooses in people for redundancy.

The organizational justice literature assesses such processes and

evaluates them for fairness.

As it is likely that people’s judgments about the fairness of redundancy

processes will be a factor in their reactions to it, this literature is also

relevant.

The literature on emotion in organization is not directly relevant to

downsizing; but because downsizing is an emotional issue there may be

some insights from the literature that will be helpful in the study.

Finally, in psychology there is a well-established literature on people’s

reactions to trauma.

It suggests a sequence of reaction beginning with disbelief, moving through

shock and anger and culminating with readjustment.

As being a survivor of down sizing may be as much a trauma as being a

victim of it, this literature might provide some helpful frameworks.


Figure 1.1 Mapping the Literature

In the literature review it is important to identify the major literatures relevant

to a project, without going into detail, and to explain why they might be

significant.

The important skill at this stage is to be able to condense or précis large

volumes of literature so that the essential views or arguments they contain

can be presented in a small number of words.

2. Present an argument about which literatures you are going to concentrate on

Normally the map of the literature will contain more fields than can be

managed in the literature review.

It is important to reduce it to two three.

In the first step of the literature review, credit may be scored for showing

an awareness of the broad scope of the literature, in this phase credit may

gained by choosing the key literatures that will be helpful to the particular

study being conducted.

In the example of the survival of downsizing study’, I chose the literature

on survival and psychology of grieving and emotion in organization as the

key ones for the study wished to do.

Literature on redundancy

DownsizingLiterature Literature on emotion

in organizations

Literature on Survivors

Organizational

Psychology of grieving


The choice of literature will reflect the angle or perceptive you wish to take

on a topic.

3. Provide an Overview of the Chosen Literature

At the stage it is necessary to give a more detailed description of the

literature chosen.

When you come to write up this chapter the overview will provide the bulk

of he material.

It is important to think carefully about how you will structure it.

The way not to structure an account of the literature is to work through a list

of works one at a time.

The citations are technically correct but padded out.

Do we really need to have the title of the work or the magazine spelt out in

the text?

The writer telling us whether they enjoyed a work does not constitute a

critique, although it may be a criticism.

But most importantly there is no accounting given o the ideas, theories and

materials there are presented in the works cited.

Your account of the literature should be structured thematically.

You should draw out of the works you are using the main themes,

questions or issues that are discussed.

These should become the sub-headings within the chapter.

Under each sub-heading it is necessary to give an account of the relevant

theories and the evidence provided in support of them.

The discussion should be comparative, weight one writer’s views and

evidence against those presented by others.

Similarities and differences should be identified and their significance, if

any, explained.

Structuring Literature Thematically

One way of thematically structuring an account of the literature is shown below:


a. Identify The Main ‘Camps’, ‘Waves’, ‘Schools’, Ideological Stances Or

Positions.

There are always arguments and debates within a field of

literature.

It is necessary to describe the nature of these arguments.

Identify the dominant or loudest voice in the debates.

b. Compare And Contrast Them

Decide the depth of the disagreement between the parties and

their arguments.

Sometimes the differences are great and significant. On other

occasions the difference are trivial to all except those involved

in the argument.

c. Evaluate Their Strengths And Weaknesses

If the differences are significant, decide which side of the

argument you believe to be stronger.

Literature reviews can often become detached from the project they are in support of.

The discussion of theories and authors may develop its own momentum, and no links

are made to the research questions that the dissertation is seeking to answer.

The way to overcome this is to state the relevance of each piece of theory to the topic of

the dissertation by using sentences similar to the following:

Mintzberg’s theory of structure in fives provides a means of analyzing the

organizational changes that are the subject of this dissertation.

… this quotation mirrors the situation that has motivated the research

project

Hamel and Prahalad’s work identifies a number of factors that should be

considered when researching…


An insight into employees’ responses to downsizing in my case study

organizations can be gained from Broeckner’s work on ---

Socio-technical theory integrates the two key aspects of this study into…

It is important to follow up these up statements with an account of their significance for

your project. There is nothing more frustrating to the reader than to be informed that

something is ‘critical’ and then left without and explanation why.

If you cannot think of a way in which a piece of theory or literature aids your thinking

and research into your topic, then it is likely you need not include it. A dissertation is not

a place to display your encyclopedic knowledge of a topic, unless it is relevant to your

theme.

4. Provide an argument to explain and justify the shortlist of theories, concepts,

frameworks or techniques that you have chosen for use in you project

By this stage a small part of the literature will have been identified that will

be useful to the project.

It may e down to a handful of concepts, theories and arguments.

These will provide basic working materials for the project.

At this stage the literature review merges into the process of developing a

conceptual framework that will be considered in the next chapter.

5. Provide a critical account of the chosen concepts, theories or arguments

This is a detailed review of particular theories designed to test their fitness of purpose

for use in the project. One student was researching the introduction of team working at

his place of work. This sentence could be found in his draft literature review chapter:

Dunphy and Bryant (1996), for example, identify three forms of team, each of

which is most suited to the pursuit of a particular aspect of performance. Which

simple, multi-skilled teams are thus most likely to impact most heavily on an


organization’s costs, self-managed teams will have their main effects on value,

and self-led teams innovation.

At face value it appears that this piece of theory might be very helpful in

understanding the introduction of team working into a particular

organization.

It would allow question to be asked such as whether the organization had

chosen the most appropriate form of team working for introduction.

However, this sentence was all that was said; and it was inadequate.

A more critical and extensive account needed to be given (assuming that the

theory was going to be helpful) by answering some of the following questions:

How were the three types of teams defined? In particular, what is the

difference between self-led and self-managed teams? At first glance

these last two seem similar.

How does this threefold analysis of team types differ from other writers

classifications?

Is the claim that each type of team delivers a different strategic out come

(cost reduction, value and innovation) based on sound research

How well supported it is by other researcher and writers?

Are these claims logically and consistently argued for in the work?

How useful might this theory be to the student’s own projects?

The best literature reviews are those where themes and ideas are taken from the

literature, evaluated, and then woven into a coherent argument about the subject

matter of the dissertation.

Note Taking

Describing the content of the books and articles you find in your literature

search means you will have to take notes.


Unfortunately, simply using a highlighter pen on the book or article you are

reading will not be sufficient. (It can actually be a problem in not taking if, as I

have often seen, nearly all the sentences in a work are highlighted.)

Highlighting is very helpful stage in making notes but the passages

highlighted need to be thought about and transferred into one’s own notes

using one’s own words for learning to take place.

Note taking is process of editing or ‘gutting’ a work to identify the key material and

involves looking for the following things in the work:

How does the work being annotated fit into the wider academic debate on

the subject?

What are the main findings, conclusions or arguments presented in the

work?

For each of the above:

What arguments are presented to support the finding or conclusion?

What evidence is presented?

What authorities (i.e. well-established authors) are quoted as supporting

the finding or conclusion? `

Are there any particular data, arguments and/or quotations that might be

useful and should be note?

Are there any books, articles, electronic resources cited in the list of

references that might be worth chasing up?

There are three main aspects of criticizing the literature

I. Assessing the Quality of an Article or Book

This first aspect of critiquing a book or article involves making an over

judgment of its worth so as to be able to decide whether it deserves more

detailed forensic critique.

You need to decide whether the piece good quality and ought to be

included in your literature review.


This ---- be done by skim reading the piece and considering the following

--- and questions.

The Provenance

Some journals, and some publishers of academic books, have more ----

than others.

We have already seen that some journals are --- reviewed and others are

not. In general, articles in peer-reviewed journal should be more reliable

than those in non-peer-reviewed publications. This is not an infallible rule,

however.

It would be foolish to ignore a paper just because it was published by an

unregarded journal.

The status of the journal is just one piece of information you have to

consider.

If you wish you can further establish the credentials of journal by looking

at one of the league tables of academic journals that are available.

These league tables claim to rank academic journals according to their

quality.

The list published by Bristol Business School can be found on the World

Wide Web http://www.uwe.ac/uk/bbs/research/jlists.shtm1#top.

References

The next step is to turn to the back of the book or article and look at the list of

references. This will give a rough and ready guide to how well the author (s) has used

the literature.

Are there many references? But bear in mind that too many can be much of

a weakness as too few.

Check the ratio of books to journal articles. Generally the higher the

proportion of articles, the more up to date and embedded in the literature

the piece will be.

Are the journal references to well-established and regarded journals?

Precision of the Writing


When you skim read the book or article consider the care that the authors

take in expressing their argument.

Are there many sweeping statements --- many unsubstantiated

assumptions?

Statements such as ‘corporate real-estate management is the critical factor

in organizational success’, especially if it comes near the beginning of a

piece, is probably both sweeping and unsubstantiated; and does not

suggest that great care is being taken in developing arguments.

Also consider whether the key concepts used in the work have been

carefully defined.

For example if someone is writing about virtual teams in organizations and

claims that they can be of any size, indeed they can have an almost infinite

number of members, and can be formed of anonymous participants, then

the definition is so all-encompassing that the term ‘virtual teams’ loses

most of its value.

It becomes impossible to make any useful generalizations about such an

amorphous concept.

The more precise and careful writers are with their language, the better the

quality of their work.

Description or Analysis

A good piece of academic writing will have a mixture of description and

analysis.

Descriptive writing tells us what is but does not attempt to classify and

organize the material; nor does it seek to draw any theoretical inferences

from the maternal.

A book or article that is purely descriptive is likely to be of lesser quality

thane one which adds analysis to description.

If a researcher has conducted many interviews we do not simply want to

be told what the interviewees said, although of course that is of interest,

we also want to be told what is significant and what is trivial in what they


said. We also need to know what insights and generalizations might be

drawn from their testimonies.

This often requires that the findings are discussed in the light of the

theories available in the literature.

One should not be too prim abut this matter. Sometimes we use an article or a

book not because of its analysis or theorizing but because it provides raw

material, research findings that we can use to strengthen the analysis of our own

research material. Quite often we will cite a book or article because of a single

fact or insight it contains and we will ignore the bulk of its contents.

Research Evidence

Check whether the book or article contains research evidence that is used to

support its arguments and contentions.

It is perfectly proper, of course, to write a purely theoretical piece that is

based on the literature.

Indeed, C Wright Mills (1959:205) in the appendix on intellectual

craftsmanship wrote:

Now I do not like to do empirical work if I can possible avoid it there is no

more virtue in empirical enquiry as such that reading as such.

Nevertheless, research has to be conducted to resolve the matters of fact that

the preliminary reading and reasoning have identified.

In general terms a piece of work that includes some appropriate and well-

conducted research will be more useful than a purely theoretical piece.

Theoretical pieces often have one advantage to the business student,

however, which is that they can provide a helpful resume and evaluation of

the literature on a particular topic.

A consideration of the preceding points should help you make a judgment

about the value of a particular piece of academic writing.

II. Forensic Analysis/Critique


The term ‘forensic critique’ has been invented for this chapter one definition

of forensic is ‘pertaining to public debate.’

This is --- the evaluation of literature because evaluation is a contribution to a

debate on arguments and theories that writers have placed in the domain.

But forensic also has an emerging definition, based on the of forensic

science, which means a minute, detailed and logical exaction of evidence.

This definition is also relevant to doing a liter review. Once you have

identified the key concepts, theories and ---that you will use in your research,

t is important to put them ---scrutiny before relying n them.

So, in this section, forensic critique in the process of testing academic ideas

to assess their usefulness.

There are two ways of tacking a forensic critique.

The first is to --- the key arguments in a piece of work and to

evaluate the soundness the logic they utilize.

The second is to scour the piece for examples weak

argumentation.

Both approaches will be considered in turn.

Soundness of Arguments

Books and articles contain many things accounts, descriptions, summaries,

instructions, and polemics and so on.

The most important item however, is the arguments they contain.

When a book or article is evaluated the main arguments it presents have to

be dissected out from the bull of the text and a judgment made about their

soundness and robustness.

All arguments have three components. These can help you recognize when, in a text,

an argument and not an assertion or an assumption is being presented:


Premises – these are assumptions or claims that something is true or are a

‘fact’. Most arguments are based on several premises, but they can be

founded on one only.

Inference Words – these are the indicators that the writer is about to draw a

logical inference or conclusion from the premises presented. Examples are

‘thus’, ‘therefore,’ ‘because’, ‘implies’, ‘hence’, it follows that’, ‘so’, ‘then’ and

‘consequently’.

Conclusions – a conclusion is an arguable statement. It is either a

statement about the relationship between the premises or it is an inference

about the likely consequences given the circumstance and the premises.

It should be possible, using his threefold classification and by looking for the inference

words, to identify and summaries the main arguments presented in a book or an article.

It may even be possible to lay out the arguments as a series of flow charts.

Judging the strength of an argument is a two-pronged activity:

Assessing the truthfulness of the premises. In books and articles, the

truthfulness, or validity, of a premise may be supported by research

evidence. If this is so, then it is necessary to judge whether the research

methods have been sensibly chosen and competently applied.

Assessing the logical strength of the conclusions drawn from them. When

considering the logical strength of an argument, it is likely that conclusions

that are drawn deductively will be stronger than those drawn inductively.

These terms need definition.

Deduction is when a conclusion is drawn that necessarily follows in logic from the

premises that are stated. So a deduction does not depend on observation or

experience; it is simply a matter of logic. So, assuming the premises are true, the

following is an example of a logically strong piece of deduction.

Premise 1 – the selection panel thought Jane was a better candidate for

the job than John

Premise 2- the selection panel thought John a better candidate for the job

than Mark.


Conclusion, by deduction – Jane muse be a better candidate for the job

than Mark.

If you are evaluating a piece of theoretical writing then many of the conclusions

drawn will be based on such logic. The question to be asked is whether the logic

is true.

‘Deduction’

‘Deduction’ is a troublesome word.

It has least two meanings.

In the analysis of arguments it has the meaning given here; that is, arriving

at a conclusion based on logic and not on experimentation or experience.

It follows (an inference phrase) that there can be no doubt about a

deduced conclusion.

A deduction is certain as long as the premises are true and the world is

rational. However, deduction is also defined as making a specific inference

from a general law.

This definition does not hold in the instance.

In arguments, deduction can proceed from the general to the particular, from

particular to particular (as in the example above) or from general to general

(Hart, 1998:82).

Induction

Induction is when a conclusion is drawn from past experience or experimentation. The

assumption is made that because things have always been so, then that is how they will

be in the future. The strength of inductive arguments is often weaker than that of

deductions, however, the more supporting evidence there is, the stronger the probability

that the inference is true. Deductions are certainties but inductive conclusions are

probabilities. The trick is to make sure that the balance of probabilities the favorable.

Consider the following example:


Premise 1 – Companies that have the ISO 9000 certification have in

the past Made better supply chain partners than those that have not

Premise 2 – Companies that have been selected using the selecting

supply chain partner software ‘prove to be better collaborative partners

nine times out of ten.

Premise 3 – In the past, small companies have made better supply

chain partners than large ones.

Premise 4 – Skegmouth Logistics Co. has ISO 9000, has been

accepted by the software and is a small company

Conclusion, inferred by indications – Skegmouth Logistics Co.

likely makes a good supply chain partner.

When inductive arguments have been identified in a piece of writing important to

assess whether the conclusion is based more on wishful thinking than on a carefully

judge balance of probabilities.

3.2 Evaluating Arguments

Another way of judging the strength of arguments is to see whether the authors have

made any of a number of well-known logical errors. What follows is a list of the most

common flaws in argument, based largely on Thouless (1968) and Warburton (1996).

1. The Use Of Emotionally Toned Words

‘The standards of modern examinations have woefully declined. They fail

hopelessly to prepare students for work in modern organizations. They

cripple a child’s chances of later career success.”

Such arguments use a form of emotional blackmail.

Who, after all, would wish to cripple a child’s chances in life?

The argument also rehashes a much used cliché in management: that if

things are not modern, they are bad.


It is particularly tempting to use such arguments in the early years of a

new millennium preparing ourselves for the challenges of the new

millennium and so on.

2. Making A Statement In Which All Is Implied But Some Is True

In a debate in some parts of Scotland during the early years of the past

century, about whether to continue the prohibition of alcohol on Sundays,

a poster proclaimed that people should vote against prohibition because

any loss of liberty meant slavery (Thouless, 1968:24).

This statement is only true if all liberty is lost.

The loss of only some liberty (to drink a pint of beer on a Sunday) is not

the same as slavery.

3. Proof By Selected Instances

Where you think someone has chosen only those cases, from all those

available to them, that make their point.

4. Challenging An Opponent’s Point By Extension Or Contradiction

In an argument over the relative merits of markets and bureaucracies in

delivering public services, a person who believes that there are some

services that are best delivered by private sector organizations is drawn

into extending their argument into, first that most services are best

delivered so and, second, that all services are best delivered so.

Extended positions are always vulnerable and can be easily demolished

with a few well-chosen facts.

5. Proof By Inconsequent Argument

You make a statement in support of a proposition that is not logically

related to it but at first glance looks as if it might be.

This can often be ensured by making the supporting statement a

reference to a learned theory of which one’s opponent will be afraid to

confess his [sic] ignorance, or at any rate, making the supporting

statement in a manner so obscure that one’s opponent fears that it

would show shameful ignorance if he confessed that he did not see the

connection.


Fairbairn and winch (1996:185) refer to this ploy as crafty conflation.

The following statement provides an example:

‘Organizations that fail to modernize will not survive in the twenty- first

century. The acceptance of flexible career patterns and of the loss of

careers for like is consequence essential for organizational survival.’

This argument conflates the idea of modernization and the acceptance

of career flexibility. It is not necessarily the case that the latter is

necessary to achieve the former.

It may or may not be; but the point is worth of discussion.

6. The argument that we should not make efforts against X, which is

Admittedly evil, because there is a worse evil, Y, against which our efforts should be

directed.

In the debate about the extradition of General Pinochet from the United

Kingdom in 1998, one of the arguments against extradition was that

Pinochet was a lesser tyrant that the Chinese’s leadership and that he

should not be tried for crimes against humanity if the Chinese leadership

was not.

A failure to deal with one wrong is no reason for not acting against

another.

7. The recommendation of a position because it is a mean between two extremes

There may be occasions when the mean is golden, but it rather depends

the choice of the two extremes

8. The Use Of Logically Unsound Argument

There are many forms of illogicality. Here is just one.

The fact of an association between two things does not prove a causal or

logical relationship. It may be that there is a statistical relationship

between the number of ‘quality stations’ in a work place (with their


progress charts, quality awards and photos of successful quality circles)

and high out put.

But the association does not prove that one causes the other or, if it

does, which way round the causal chain works.

Do the workstations cause the staff to be more highly motivated or does

the fact that people are successful – them to treasure their quality

stations?

9. Begging The Question

One can prove that all swans are white by refusing to accept that black

swans are truly swans.

Failing To Distinguish Factual Statements From Opinion

The nation of facts is a little difficult to accept if you are of a postmodern

persuasion.

Nevertheless, it should still be possible to separate those things that are

generally accepted as if they were true, and for which some research

evidence can be presented, and personal opinion.

In management research this error occurs when people do not distinguish

between what they have discovered about a problem and their beliefs

about how it can be solved, as in ’my research shows that the

implementation of staff empowerment will enable the organization to

become a world-class business’.

10.Tautology

A tautology is saying the same thing twice. Stylistically in involved

redundant words (‘Lloyds TSB Bank’).

By extension a tautological argument is one in which a thing is said twice

but in such a way as to make it appear that a logical deduction is being

made, as in ‘the increased pace of change means that things are moving

faster.

12. Inferring what should be form what the arguer thinks ought to be and assuming

that something is right because it is commonly done or is useful


The fact that lot s of organizations are doing 260-degree appraisals does’

not necessarily mean they are a good thing.

Equality, the author’s belief that a proposition will be of practical benefit

does not mean that it is right or should be commended.

13. Changing the Meaning Of A Term During The Argument

Thouless (1968:77) gives the following example.

Crowds exhibit cruelty, unintelligence and irresponsibility.

Democratic government is rule by the crowd. Therefore, democratic

government is cruel, unintelligent and irresponsible. This argument

involves a subtle change in the definition of the word ‘crowd’.

14. The Use of the Fact of Continuity between Two Things to Throw Doubt On The

Real Difference Between Them.

Using the practical difficult of determining where, precisely, in terms of

number of staff or amount of turnover, to draw the distinction between

large and small organizations to suggest that the distinction is not a useful

one.

15. Disguising A Lack Of Clear Thought By The Extensive Use Of Jargon

Too much use of business and professional jargon, or of obscure and

lengthy words, raises suspicions that author is seeking to disguise their

confusion and lack of clear thought.

16. The Appeal to Mere Authority

This false type of argument is that a well-established writer in the field has

said x and so it must be true.

This should not be seen as a license not be cite authors in your

dissertation.


The error is when an author is cited --- lazy substitute for making a case or

presenting an argument.

17. Argument by Imperfect Analogy

The keen edge of intellect will be blunted by too frequent use.’ This --- be

a tempting maxim for a hard-pressed MBA student. However, --- intellects

are honed, not blunted, by use. foo frequent a resort to argument by

analogy can lead to a hermetic paranoia in which everything seen as part

of a vast and secret conspiracy.

Read Umberto Eco’s (1989) novel Foucault’s Pendulum for an insight into

the place such thinking can lead.

(The reference is to Leon Foucault (1819-1868) and not to the Michel

Foucaular who is discussed on p. 112; although Eco no doubt intended

any confusion that might have arisen.)

18. AD Hominem Arguments

Attacking he arguer and not the arguments.

This is normally resorted to when a critic can find no weak points in an

argument and so argues than the writer is a bad person and that for this

reason their arguments should be discounted. In 2001 and 2002, for

example, there was a correspondence in a literary journal about Foucault.

The debate was started by. Tallis (2001) and continued by Hargreaves

(2002) and Sennett (2001), among others.

Foucault was accused of deliberately infecting his partners with AIDS.

It was also alleged that he did not believe in objective truth. This led to an

implication that the latter justified the former.

In such debates, arguments about a person’s theories and arguments

become confused with arguments about the morality of the person.

19. The Use Of Persuaders

Fairbairn and winch (1996:182) describe these as ‘words and phrases

that are put into an assertion illicitly to persuade the reader to accept

what is said by other than rational means.


For example, ‘surely all accountants must realize that their job is one of

the most important in the country’ or ‘It is the exception that proves the

rule’.

There are some criticisms commonly made of books and articles that are

not fair.

One is ‘death by a thousand snips’. This is done by accumulating a large

number of petty errors, an author’s name misspelt here, a date wrong by

one year there and so on.

The overall effect is to cast doubt on the author’s arguments without

actually challenging them.

It is a form of ad bominem criticism because it is challenging the author’s

abilities rather than their arguments.

Another unfair criticism is to claim that the themes the author has chosen

to focus on are the wrong ones.

This is tantamount to saying the author should have written a different

book or article from the one they chose to write.

III. Radical Critique

There is a managerialist mainstream in business, management and

organizational studies that accepts the necessity of the capitalist system

and concentrates on making it work effectively.

The system, the mainstream writers would argue, is to the benefit of all and

it is only necessary to focus on making small improvements.

There is an alternative view that challenges the system itself. This critical

perspective appears in several of the chapters in this guide.

3.3 The Critical Approach

Alistair Mutch

What do we mean by the word ‘Critical’? In one sense we would expect all

academic work to be critical; that is, we should always be questioning our

assumptions and approaches.


This king of questioning has been discussed in the previous section.

However, the word has tended to be appropriated by those who would use

it in a broader sense.

This sense is of challenging the basic assumptions on which our social

world is organized.

It is also, to a greater or lesser extent, used to indicate a need for radical

change in these basic principles.

The approach originally associated with this sense of the word is Marxism.

(Indeed, one current approach, the ‘Frankfurt School’, attempted to

appropriate not only the word ‘critical’ but also the word ‘theory’.

If you see the two together a Critical Theory you will know what is being

attempted and you will see the difference that capitalization can make!) In

this respect, Marxism can be seen a continuing the Enlightenment project.

This was the powerful intellectual traditional that emerged in the late

eighteenth century.

It stressed the importance of Reason and Science (they were found of

capital letters too) over religion and traditional as ways of knowing the

world.

To this faith in the power f ideas to reveal the truth about the world Marx

added a radical political dimension.

We have become much more skeptical about notion of truth, and more

sophisticated currents in modern Marxism have dropped notions such as

‘false consciousnesses’ (the idea that the only reason for the lack of radical

change was people holding the ‘wrong’ ideas.

However, Marxism has lost what was once a dominant position in the

‘critical’ camp, wit other voices now being heard.

One of the most important of these critical voices has bee that of feminism.

Reacting both to material social changes and to the failure of Marxism to

address these changes feminism has brought a new set of challenges, in

this case to received gender roles.


This has meant shifts to both the topics examined and the way in which

they are examined.

There are many currents with in feminism. Some strive for change---

existing arrangements, arguing for the need to change organization –

release the talent of women (and make them better place for men).

An example of a writer in this tradition who has had influence within the

‘mainstream’ is Rosabeth Moss Kanter.

Others reject what they see as patriarchy and call for more fundamental

change – Pollert (1996) has a useful overview. One impact of feminist

writing has been to emphasis the importance of identify in current critical

writing.

Identify is central in the intellectual tradition of postmodernism. This spurns

what it calls ‘grand narratives’ such as Marxism (that is, in there terms,

theories that purport to have ‘all the answers’).

Instead, it stresses the fragmentation of current life that is composed of a

shifting kaleidoscope of fluid identities.

An influential thinker has been the French social theorist Michel Foucault.

His emphasis on power as a property of networks and relationships has

been a strong influence on thinking in many areas, not least the field of

management.

It has led to a focus on the micro-politics of local situations and to a

divorce between critical analysis and prescriptions for change.

This has led to charges (from more traditional critical theorists, such as

Marxists) of academic self-indulgence and a loss of relevance.

For others, however, this is a realistic adjustment to the contemporary

social world and a cause for celebration.

What is does seem to produce is complex and difficult language and a

tendency to agonized introspection. (For an attempt to reconcile these


forms of critical thinking by direct application to management, see Alvesson

and Wilmott, 1996.)

Other social theorists try to steer something of a middle way. Some of

these, such as Giddens (lots of work, but see 1990 for a short introduction)

and Castells (2000) have important things to say about the nature of the

world of work, even if sometimes their detail fails to convince.

Finally, the tradition known as Critical Realism (more appropriation! See p.

284) claims to be clearing the way for a more transparent use of concepts

–Reed (1997) and Savage et al (1992) provide some applications in the

management field, although these are ‘works in progress’.

What does all this mean for management researchers? It means that there

is a rich body of resources upon which to draw.

A little recognized aspect of management history is the way in which ideas

that are now mainstream (in change management, for example) had their

origins on the left (Cooke, 1999).

Alternatively, the challenging of basic assumptions can help, even if it

ends up by confirming your belief in the soundness of those assumptions.

Some of these areas, notably feminism, have had more direct impact on

the management field.


It is helpful to be aware of he nuances that lie behind the use of this little

word ‘critical’, as you will come across it all the time in the academic

literature.

This simply reinforces the need for you to be skeptical and challenging in

your use of such literature in a word, to be critical!

Developing a Radical Critique

To summarize the above discussion: a radical critique is one that

challenges assumptions and conventional ways of doing things.

It normally does so from a moral stance based on perceptions of

injustice; so for instance, a radical critique could be Marxist (Justice to the

workers or feminist (justice to women) or green (justice to Gaia) or based

on an anti globalization position that would incorporate all of the

proceeding. This section looks at how a radical critique of a managerial

theme might be constructed using a loose Marxist perspective.

Remember, radical critique – are not a necessary part of a literature

review.

The case of computerization of administrative and clerical work is used as an example

to illustrate the process.

Stage 1 – Identify a Conventional Position

Take a conventional and standard argument that is used in the --- and in

public debate.

Waddington (1977:16) created the acronym --- DUNG, which stands for

the conventional with wisdom of the dominant group ---OK, it does not quite

work as an acronym but it defines the concept well.

A few year ago, for example, it was widely held that the increasing use of

computers in the field of clerical and administrate work would make

employees’ lives more interesting by removing the drudgery fro their work.

Stage 2 - Problematise It


Problematising the issue means revealing that the issue is more difficult

than commonly thought and probably raises questions of values or ethics.

Computerization may make work more interesting, but management does

not want information technology for this reason alone.

Management also wants to improve efficiency and profits and this means

making staff redundant as computers do more and more of the work.

This process causes that which was straightforward to appear morally

ambiguous.

Stage 3 - Identify Contradictions and Negation

Look for contradictions and negations within the process or issue being

studied.

A negation is a self-destructive feature of a process. Computers are meant

to make life more interesting for staff by taking away the boring chores from

their work.

But computer software not only does sums and clerical chores- it can also

make decisions by comparing particular situations against decosopm-

making guidelines.

Computes can remove decision making form people and so make their work

less interesting and less responsible.

Computerization also disempowers staff by removing their discretion to

amend procedures.

Staffs have to work in the ways required by the software.

By enabling management to monitor their work rates, staff are put under an

intense scrutiny that prevents them from being flexible in their work methods

and rates (i.e. if staff are working on computers, then it is easy for

management to see how many transactions they have processed in a shift).

So, in summary, computerization was meant to make employees’ lives

more interesting and ends up making them less so. This is a form of

dialectical analysis that is discussed further on p. 303.


Stage 4 - Spot the Effects of False Consciousness (This Is an Optional Stage)

False consciousness may be out of fashion (see p. 105) but it is often a

useful element in a radical critique.

False consciousness arises when the opposite of what people want is

happening but they do not realize it.

It is a Marxist concept to describe the tendency of the proletariat to fail to

recognize the nature of its interest.

More generally it is any form of ideology or self-imagery that is held to be

inappropriate to the real or objective situation of an actor.

So, for example, imagine you have interviewed a number of clerical staff

whose work has been computerized.

To your surprise they tell you that they feel the process has made their work

more interesting because they have learnt new skills. They have learnt how

to use the new software, but they remain oblivious to the fact that the

software is insidiously removing responsibility, self-control and higher-

control and higher- level skills from their jobs.

Stage 5 – End with an Aporia

Aporia means being in a state where everything is so complex and

ambiguous that you are at a loss about what to do.

The critique is ended with a view that something is wrong but that it is difficult

to see what can be done about it, short of changing human nature and

sensibility.

Things, you argue, are so complicated that anyone who believes there can be

a quick fix for the issue must be deluded. Here is a typical example taken

from a paper that gives a critique of the idea of business ethics:

The problem if find it impossible to address in the rest of the

paper will be what options are left for a project like ‘Business

Ethics’ if the entire above are accepted. It would simply be

inappropriate for me to tie up a paper like this at the end as if I

really did have a medical solution to these problems.


However, there is often a final claim that our increasing understanding of the complexity

of things will enable humanity to become better, Parker ends his paper in such a way:

Recognizing the paradoxes within ‘Ethics’ and Business Ethics’ is one way

to stop these words form having so much hold onus. Perhaps we can begin

to develop ways of expressing our dreams and nightmares that do not fall

back into the agon so easily.

It may not be easy to see quite what this means (the use of the word agon makes it

difficult it means the endless arguments between different and incomparable ways of

viewing a topic), but as an ending it sounds --- in an ambiguous sort of a way.

Stage 6- Appropriate Reference and Citation

Try to make references to Michel Foucault and/or Jurgen Habermas in the

text. Surprisingly, references to Jeremy Bentham, the eighteenth- century

utilitarian philosopher, and his notion of the panoptican, published 1791 but

more accessible in Bozovic (1995), are also well received the panoptican was

a design for a circular prison.

The cells were to be around the circumference of the building and at the

centre there was to be a watchtower.

This design would allow a warder to observe all the prisoners with ease.

An additional element in the design was a series of baffles and blinds.

These meant that the warder could see the prisoners but the prisoner could

not be sure whether they were being observed.

The effect of the panoptican is the inmate would behave as if they were being

observed even though they did not know whether they were.

The risk that they could be under observation was sufficient, Benhtham

argued modify their behavior.


The idea of the panopticon was adopted by Foucault as a metaphor by the

way that organization and institutions seek to control individual’s works well

as a trope for analyzing information technology in offices.

When an employee is working at a computer terminal that is networked to

others in the organization, it is technically possible for the management to

monitor the employee’s work.

The employee knows that they may be being monitored, but they can never

be sure whether they are.

The metaphor works even better with roadside speed cameras, which reduce

average speeds because drivers do not know whether the box really does

contain a camera.

So they proceed on the basic that it might.

A panoptican therefore provides a very efficient method of social control.

The following citation of Foucault, citing Bentham, achieves a double whammy effect:

‘The contemporary popularity of Foucault’s version of the

panoptican seems to illustrate something rather profound’ (Parker

1998 31). The panoptican is discussed in Foucault (1991).

The six stages of radical critique are simply a teaching aid. No one would follow such a

mechanistic process in their writing. Nevertheless, these different aspects can be found

in academic papers such as Parker (1998). If you have the time and inclination, you

might want to locate the six stages in his article.

Summary

Do

Identify and discuss key and landmark studies –include as much up-to date

material as possible.

Get the details right


Try to be reflective;

Try to be reflexive –examine your own bias and make it known;

Critically evaluate the material and show your analysis;

Use extracts , illustrations and examples to justify your arguments ;

Be analytical, critical and evaluative in your review ;

Make it interesting.

Don’t

Omit classic works or Discuss core ideas without reference;

Discuss outdated or rubbish stuff , or only old material;

Use concepts to impress or without definition;

Produce a list of items , even if annotated , a list is not a review;

Accept anything at face value or believe everything that is written;

Only produce a description of the content of what you have read;

Drown in information. (based on Hart, 1998:219)

Exercise

Reviewing Arguments

Visit the library or a bibliographic database and choose an article that interest you from

a peer-reviewed journal. Read the article and draft a critical review of it. Use the

following questions (based on Hughes, 1992) as guideline:

Identify The Main Arguments In The Paper

What is the main point of the arguments?

Identify the ‘inference indicators’, the words such as ‘therefore’

that --- that a conclusion is being drawn from the evidence

presented.

Assess the context for the argument


Identify The Premises From Which The Conclusions Are Drawn

Locate the premises in the text

Check The Acceptability of These Premises.

Are they well supported by evidence?

Are they well supported by citations from the literature?

Are you convinced they are true?

Check The Logical Strength of The Conclusions Drawn From The

Premises.

Are the conclusions logically based on the premises?

How strong are the conclusions?

Look For Counter Arguments

Could the premises support a counter-conclusion?


Chap -4 Problem Definition and Setting Objectives

Chapter Objectives

After completing this chapter the student shall be able:

Define the Research Problem

Establish the Research Objectives

Identify type of Information Needed to Answer the Research Questions.

Develop Hypothesis and Research Questions.

3.1 Introduction to Defining the Problem and Determining Research Objectives

Before discussing the research problem is s pertinent to discuss the need for research

and situations in which research may not be inappropriate.

The Need for Research

To establish the need for research, all organizations should monitor their surrounding

environments on a continuous basis using a monitoring system. The primary objective

of a monitoring system is to bring operating information to management. Such

information allows management:

To evaluate whether their current operating information results in meeting

performance objectives.

If proposed legislation has an impact on consumer spending or other

industry interests,

Whether changes in consumer values and lifestyles are occurring , or

If new strategies are being implemented by competitors.

Note: Monitoring may be accomplished either formally or informally and in a variety of

ways.

A firm may have a sophisticated formal management information system

(MIS).


Another firm may have a more traditional control system that primarily relies on

financial statements as feedback.

A small business owner /manager may diligently observe the environments

that affect his or her firm.

Situations in Which Research May Not Be Needed

The four major situations where research may not be needed include the following:

1. Information Is Already Available

If management knows its markets, competition, and the product/services,

they may have the necessary information to make an informed decision

without commissioning a research study.

It is possible to have information on sales, costs, and profitability

available by product, customer, region, salesperson, and so on, at the

touch of a key.

2. There Is Insufficient Time For Research

Occasionally, a problem is discovered that requires an immediate

response on the part of management.

When competitive pressure or customer shifts demand quick

management actions, there may not be enough time to carry out a

properly conducted research project.

Although research will be helpful, circumstances argue strongly against

performing it.

3. Resources Are Not Available

Oftentimes resources are not available for research.

If conducted in-house, research requires a commitment of personnel,

facilities, and budget.

If conducted by an outside firm, money as well as some personnel time is

needed.


If there is not enough money to devote to the research, management

must simply make the decision that those resources are better spent

elsewhere.

4. Costs Outweigh The Value Of The Research

Even when funds and other resources are available to conduct research,

management must always weight the costs of conducting the research

with the potential value of conducting the research.

Some decisions have relatively little impact on company sales, profits,

consumer loyalty, dealer goodwill, and so on, and, as a result, they

simply do not justify the expenditure.

Once we are very clear about the above two –steps, we will then shift to define the

marketing problem.

3.2 Problem Definition

After the interviews and the literature review, the researcher is in a position to narrow

down the problem from its original broad base and define the issues of concern more

clearly. It is critical that the focus of further research, or in other words, the problem, be

unambiguously identified and defined. No amount of good research can find solutions to

the situation, if the critical issues or the problem to be studied is not clearly pinpointed.

A Problem does not necessarily mean that something is seriously wrong with a current

situation that needs to be rectified immediately. A “problem” could simply indicate an

interest in an issue where finding the right answers might help to improve an existing

situation.

Thus, it is fruitful to define a problem as any situation where a gap exists between the

actual and the desired ideal seats. Basic researchers usually define their problems for

investigation from this perspective. For instance, we would ideally like to see zero

defects, low inventory of unsold goods, and high share quotation in the stock market,

and so on. These “problems “could then very well become the foci of research. Thus,

problem definitions could encompass both existing problems in a current setting, as well

as the quest for idealistic states in organization.


Problem definition or problem statements ,as it is also often referred to, is a clear ,

precise ,and succinct statement of the question or issue that is to be investigated with

the goal of finding an answer or solution. As mentioned earlier, problem definition could

pertain to:

Existing business problems where a manger looking for a solution,

Situations that may not pose any current problems but which the

manager feels have scope for improvement ,

Areas where some conceptual clarity is needed for better theory building,

or

Situations in which a researcher is trying to answer a research question

empirically because of interest in the topic.

The first two fall within the realm of allied research, and the latter two under

basic research.

Defining the problem is critical to setting the direction for all subsequent phases of the

research process. This is particularly true for custom designed research, as opposed to

standardized / syndicated research.

Standardized /syndicated research is generic research that is

provided in identical fashion to all buyers by the research supplier.

Custom- designed research is research that is fashioned to address

a unique management problem confronting a client manager.

Custom- designed research requires that the researcher fully

understand the circumstances of the manager‘s problem.

It is common for the manager and the researcher to define the problem. If the problem

is not defined correctly, satisfactory performance at the other stages in the research

process will not remedy the situation.

Broad problem areas that a manager could observe at the work place are as

follows:


Training programs are perhaps not as effective as anticipated.

The sales volume of a product is not picking up.

Minority group members in organisations are not advancing in their careers.

The daily balancing of accounting ledgers is becoming a continuing concern.

The newly installed information system is not being used by the managers

for whom it was primarily designed.

The introduction of flexible work hours has created more problems than a

has solved in many companies.

The anticipated results of a recent merger have not been forthcoming.

Inventory control is not effective.

The installation of an MIS keeps getting stalled.

The management of a complex, multi-departmental team project is getting

out of hand in the R & D department of a firm.

The broad problem area would be narrowed down to specific issues for investigation

after some preliminary data are gathered by the researcher. This may be through

interviews and literature research.

Examples of Well-Defined Problems

To what extent do the structure of the organisation and type of information

system installed account for the variance in the perceived effectiveness of

managerial decision making?

To what extent has the new advertising campaign been successful in

creating the high-quality, customer –centered corporate image that it was

intended to produce?

How has the new packaging affected the sales of the product?

Has the new advertising message resulted in enhanced recall?

How do price and equality rate on consumer’s evaluation of products?

Is the effect of participative budgeting on performance moderated by control

system?


Does better automation lead to greater asset investment per Dollar of

output?

Does expansion of international operations result in an enhancement of the

firm’s image and value?

What are the effects of downsizing on the long-Range growth patterns of

companies?

Can cultural differences account for the differences in the nature of

hierarchical relationships between superiors and subordinates in Germany,

India, Japan, Singapore, and the United States?

What are the components of “quality of life?”

What are the specific factors to be considered in creating a data warehouse

for a manufacturing company?

What network systems are best suited for Smith Pharmaceuticals?

Differences between Managers & Researchers

The fundamental differences between Mangers & researchers include the following:

S/N Area Of Difference Mangers Researchers

1. Organisation Position Line Staff

2. Responsibility To Make Profits To Generate

Information

3. Training General Decision

Making

Technique

Application

4. Disposition Toward Knowledge Wants Answers To

Questions

Wants To Ask

Question

5. Orientation Pragmatic Scholarly

6. Involvement Highly Involved ,

Emotional

Detached ,

Unemotional

7. Use Of Research Political Nonpolitical

8. Research Motivation To Make Symptoms To Find The Truth


Disappear

Guidelines to Resolve Differences between Managers & Researchers

Alreck and Settle have formulated six (6) questions that managers and Nine (9)

questions that researchers should ask themselves to avoid potential misunderstandings

as they discuss the problem and determine the objectives of the research involved.

S/N Questions For Managers Questions For Researchers

1) What is the background of the problem? What are the capabilities and

limitations of the research being

considered?

2) What issues or questions prompted

management to consider undertaking

research?

What is the history of the operations,

policies, and procedures of the client?

3) What types of information could clarify

or solve the problem?

What problems or issues appear to be

the focus of the research, and what is

uncertain or unknown about them?

4) What decision, choices, or actions will

be based on the research results of

findings?

What decisions, choices, actions are to

be based on the results of the

proposed research?

5) How much is the information worth;

what risks might be reduced or what

opportunities might gained?

What is a preliminary assessment of

worth of the research information to

the client?

6) What is the time horizon and level of

resources available to perform the

research?

What are the time requirements for the

research and what resources can be

devoted to it?

7) What degrees of cooperation and

participation will be expected of the

clients?

8) What ethical issues are associated


with the research?

9) How can I gain the client’s confidence

and trust in a professional and ethical

manner?

Defining The Management and Research Problems

Defining a problem involves:

Specifying the symptoms

Itemizing the possible causes of the symptoms, and

Listing the reasonable alternative courses of action that the manager can

undertake to solve the problem.

There are two types problems with which a research must contend:

a) The Management Problem and

b) The Research Problem

Management Problem

A management problem is defined in one of the two ways:

If the symptoms of the failure to achieve an objective are present , the manager

must select a course of action to regain the objective, and

If the symptoms of the likelihood of achieving an objective are present, the

manager must decide on how best to seize the opportunity (opportunity

identification).

Poor problem definition can expose research to a range of undesirable

consequences, including incorrect research design s and inappropriate or

needlessly expensive and collection, assembly of incorrect or irrelevant data, and

poor choice of the sample selection.

It is critical that the management problem be defined accurately and fully.


Once the management problem is fully defined, the researcher must wrestle with the

research problem.

Assement areas to define a research problem

There are eight assessment areas that help the researcher properly define the

Managent problem.

1. Assess The Background of The Company , Product , And Market

The manager informs the researcher regarding the company

history, its organizational structure, ownership situation, and

overall mission.

The researcher is also informed about performance history

and any strategy changes.

2. Understand The Decision- Maker’s Circumstances , Objectives, And

Resources

It will certainly help to know the reasons the manager has requested

research help.

The researcher would know the manager’s objectives, which are

tangible statements of a company’s expectations.

Gaining a feel for the manager’s specific objectives will help the

researcher understand the gravity of the problem.

By comparing recent sales, market share, or profitability level, for

instance, with objectives, the researcher can compare how far the

brand has strayed from acceptable performance.

3. Clarify The Symptoms of The Problem

The manager & researcher must distinguish between symptoms, which

are changes in the level of some key monitor that measures the

achievement of an objective, and their causes.


A symptom may also be a perceived change in the behaviour of some

market factor that suggests adverse consequences or, perhaps,

implies an emerging opportunity.

Formal monitors that are helpful in alerting managers to possible

problems include:

Productivity: unit of output produced from a given

unit of inputs.

Employee’s turnover: the percent of workforce

leaving the organisation.

Market share: the percent of total industry sales

accounted for by the company’s brand.

Profit: the total profit generated by a brand.

Complaints: the level of consumer complaints

registered.

Competitors: actions of the competitions such as

price reduction, introduction of new products , style

changes , expansion into new territories , etc;

Even if formal monitors are in place, they may not present sufficiently

detailed or complete pictures of what has happened.

This is sometimes referred to as the “iceberg” phenomenon, a

situation in which you must look beneath the surface to see the

complete picture.

The researcher may request a breakdown by sales territory, say, to

see if market share has fallen in all areas.

An astitute researcher will ask questions that sometimes tax the

manager’s patience, but they are necessary to gain an understanding

of all of the facts of the problem.

4. Pinpoint Suspected Causes of The Problem

Problems do not arise out of the blue-there always is an underlying

cause.


For examples: Sales do not drop without buyers doing something

different from what they have been doing in the past,

Customer complaints do not increase unless some dissatisfaction

has occurred and

Retailers do not report returns unless there is a flaw in the product.

In other words, changes in market behavior do not occur randomly.

Any factor that changed prior to or concurrent with the symptoms was

a probable cause of the problem.

5. Specify Actions That May Alleviate The Problem

Managers have at their disposal certain resources, and these

resources constitute the solutions that may attempt to employ to

resolve the decision problem at hand.

Possible solution includes any actions that the manager thinks may

resolve the problem.

It is during this phase that the researcher brain storm possible

solutions.

Sometimes, this phase may be moderated by the researcher.

At other times, the manager may independently generate several

tactics that might resolve the problem once the probable cause is

identified.

6. Speculate On Anticipated Consequences of These Actions

Research on anticipated consequences of each action under

consideration will help determine whether the solution is correct or not.

To avoid resolving the problem incorrectly, the manger asks “what if

“questions regarding possible consequences of each action being

considered.

These questions include:


What will be the impact not only on the problem at

hand, but throughout the program, if specific action

is implemented?

What additional problem s will be created if a

proposed solution to the current problem is

implemented?

7. Identify The Manager’s Assumptions About The Consequences ; And

As they define the problem, the manger and the researcher make

certain assumptions, which are assertions that certain conditions exist

or that certain reactions will take place if the considered actions are

implemented.

The manager’s assumptions be analyzed for accuracy.

Assumptions beer researcher attention because they are the glue that

holds the decision problem together.

Given a symptom, the manager assumes that certain causes are at

fault.

She /he assume that, by taking corrective actions, the problem will be

resolved and that the symptoms will disappear.

If the manager is completely certain of all of these things , there is no

need for research.

If uncertainty prevails, and critical assumptions about which the

manger is uncertain will ultimately factor in heavily when the

researcher addresses the research problem.

Research will help eliminate this uncertainty.

At the same time, there may be disagreement on key assumptions

within the manager’s company, and research is needed to determine

which competing assumption is true.

8. Assess The Adequacy Of Information On Hand.


As the manger attempts to formulate a decision problem, available

information varies in both quantity and quality.

The manager may have information that greatly reinforces his or her

own belief s, or he or she may not have anything more than a “gut

feeling”.

If the manger knows something with a high degree of certainty, it is of

little value for the researcher to conduct research to reiterate that

knowledge.

The researcher assesses the existing information state, which is the

quantity and quality of evidence a manager possesses for each of his

or her assumptions.

During this assessment, the researcher should ask questions about the

current information’s state and determine what the desired information

state is.

Conceptually, the researcher seeks to identify information gaps, which

are discrepancies between the current information level and the

desired level of information at which the manger feels comfortable

resolving the problem at hand.

Ultimately, information gaps are the basis for research objectives.

Research objectives are specific bits of knowledge that need to be

gathered and that serve to close information gaps.

These objectives become the basis for the researcher‘s work.

In order to formulate the research objectives, the researcher considers

all the current information surrounding the management problem.

1) Specify Constructs and Operational Definitions

A construct is a research term or concept that is somehow involved in the

management problem that will be researched.

Although managers and researchers share the language of constructs, the

researcher translates the construct into an operational definition, which

describes how a researcher measures the construct.


Operational definition implies a specific question format that will be used in

a survey to gather information about the construct at hand.

A Representative List of Constructs Often Investigated By Researchers.

S/N Constructs Operational Definitions

1. Brand awareness Percent of respondents having heard

of brand

2. Recall, recognition of advertising The number who remember seeing a

specific ad

3. Knowledge of product , feature What they can tell about the product

4. Brand familiarity Those who have seen the brand or

tried it

5. Comprehension of product benefits What respondents think the product

does for them

6. Attitude , feelings toward brand The number who feel positive,

negative or , neutral

7. Intensions to purchase The number that are planning to buy it

8. Past purchase or use The percent that bought it

9. Satisfaction How they evaluate its performance

10. Importance of some factor What factors determined their

purchase choice

11. Demographics How old , what gender , and so on

12. Disposal of product and knowledge What they did with the box , wrapping

, or product

13. Brand loyalty How many times bought the brand in

the last six months.

Project Research


Management Decision Problem (MDP): MDP is the problem confronting the decision

maker. It asks what the decision maker needs to do.

A Research Problem: A problem that entails determining what information is needed

and how it can be obtained in the most feasible way.

Other illustrative examples between management decision problems and the research

problems include the following:

S/N Management Decision Problem Research Problem

1 Should a new product be introduced? To determine consumer

preference s and purchases

intentions for the proposed

new product?

2 Should the advertising campaign be changed? To determine the

effectiveness of the current

advertising campaign?

3 Should the price of the brand be increased? To determine the price

elasticity of demand and

the impact on sales and

profits of various levels of

price changes.

Well-Defined Problems

To what extent do the structure of the organisation and type of information

system installed account for the variance in the perceived effectiveness of

managerial decision making?

To what extent has the new advertising campaign been successful in creating the

high-quality, customer –centered corporate image that it was intended to

produce?

How has the new packaging affected the sales of the product?


Has the new advertising message resulted in enhanced recall?

How do price and equality rate on consumer’s evaluation of products?

Is the effect of participative budgeting on performance moderated by control

system?

Does better automation lead to greater asset investment per Dollar of output?

Does expansion of international operations result in an enhancement of the firm’s

image and value?

What are the effects of downsizing on the long-Range growth patterns of

companies?

Can cultural differences account for the differences in the nature of hierarchical

relationships between superiors and subordinates in Germany, India, Japan,

Singapore, and the United States?

What are the components of “quality of life?”

What are the specific factors to be considered in creating a data warehouse for a

manufacturing company?

What network systems are best suited for Smith Pharmaceuticals.

Process of Defining a Problem

DM: we have seen a decline in the patronage of our store.

R: How do you know that?

DM: well, it is reflected in our sales and market share.

R: Why do you think your patronage is declined?

DM: I wish I knew.

R: what about competition?

DM: I suspect we are better than competition on some factors and worse on others.

R: How do the customers view your stores?


DM: I think most of them view it positively, although we may have a weak area of one or

two.

After a series of dialogues with the decision maker and other key managers analysis

of secondary data and qualitative research, the problem was identified as follows.

Management Decision Problem

What should be done to improve the patronage of the Store?

Marketing Research Problem

Determine the relative strengths and weaknesses of the store vis-à-vis other major

competitors with respect to factors that influence store patronage.

Specifically, Research should provide information on the following questions:

What criteria do households use when selecting department stores?

How do households evaluate Tana department store and competing stores

in terms of the choice criteria identified in question 1?

Which stores are patronized when shopping for specific product

categories?

What is the market share of Tana and its competitors for specific product

categories?

What is the demographic and psychological profile of the customers of

Tana? Does it differ from the profile of customers of competing stores?

Can store patronage and preference be explained in terms of store

evaluations and customer characteristics?

2) Specify Research Objectives

A good way of setting research objectives is to ask, “what information is

needed in order to solve the problem?’

A key aspect of the research objectives step is the specification of the

specific types of information useful to the managers as they look for a

solution to the management problem at hand.


Research objectives address information gaps that must be closed in order

for the manager to go about resolving the current problem.

Generally, the researcher prepares an itemized listing of the information

objectives agreed upon by the manger as essential for this purpose.

Each research objectives must be precise, detailed, clear and operational.

Research Questions & Development of Hypothesis

Research Questions: are refined statements of the specific components of the

problem.

Hypothesis: an unproven statement or proposition about a factor or phenomenon that

is of interest to the researcher.

Specific Research Questions:

RQ1: what foods are considered to be comfort foods?

H1: potato Chips are considered comfort foods.

H2: Ice cream is considered comfort food.

RQ2: when do people eat comfort foods?

H3: people eat comfort foods when they are in a good mood.

H4: people eat comfort foods when they are in a bad mood.

RQ3: How do people become attached to comfort foods?

H5: People are attached to comfort foods that are consistent with

their personality.

H6: People are attached to comfort foods because of past association.

A General Procedure for Hypothesis Testing


Null hypothesis is a statement in which no difference or effects are expected. If the null

hypothesis is not rejected, no changes will be made.

The null hypothesis is a proposition that states a definitive, exact relationship

between two variables.

It states that the population correlation between two variables is equal to

zero or that the difference in the means of two groups in the populations is

equal to zero or the difference in the means of two groups in the population

is equal to zero( or some definite number).

In general, the null statements are expressed as no (significant) relationship

between two variables or no (significant) difference between two groups.

Alternative Hypothesis: is a statement that some difference or effect is expected.

Accepting the alternative hypothesis will lead to changes in opinions or actions.

Directional and Non –Directional Hypothesis

Directional Hypothesis (DP)

DH is refers to those hypothesis that deals with relationships between

two variables or comparing two groups.

In stating such hypothesis terms such as positive, negative, more

than, less than, and the like are used.

Non Directional Hypothesis (NDH)

NDH are those that do no postulate a relationship or difference, but offer no

indication of the direction of these relationships or differences.

In other words, though it may be conjectured that there would be a

significant relationship between two variables, we may not be able to say

the relationship would be positive or negative.

Parametric tests: hypothesis –testing procedures that assume that the variables of

interest are measured on at least an interval scale.


Nonparametric test: hypothesis testing procedures that assume that the variables are

measured on a nominal or ordinal scale.

The following steps are involved in hypothesis testing:

Formulate the null hypothesis H0 and the alternative hypothesis HA.

Select an appropriate statistical technique and the corresponding test

statistic.

Choose the level of sigiinificance, α.

Determine the sample size and collect the data .Calculate the value of the

test statistics.

Determine the probability associated with the test statistics under the null

hypothesis using the sampling distribution of the test statistics. Alternatively,

determine the critical values associated with the test statistics that divide the

rejection and non rejection regions.

Compare the probability associated with the test statistics with the level of

significance specified. Alternatively, determine whether the test statistics

has fallen into the rejection or the non rejection region.

Make the statistical decision to reject or not to reject the null hypothesis.

Express the statistical decision in terms of the marketing research problem.

Testable Statements

If the pilots are given adequate training to handle midair crowded situations,

air –safety violations will be reduced.

If employees are more healthy, then they will take sick leave less

frequently.(DH)

The greater the stress experienced in the job, the lower the job satisfaction

of employees.(DH)

There is a relationship between age and job satisfaction.(NDH)

There is a difference between the work ethic values of American and Asian

employees.(NDH)


The greater the extent of gender stereotyping in organisations, the fewer will

be the number of women at the top.(DH)

Male managers have more access to critical information than women

managers in the same ranks.(DH)

There will be a significant positive correlation between access to information

and chances for promotion to top –level positions.(DH)

The more the sex-role stereotyping, the less the access to critical

information for women.(DH)

Sex-role stereotyping and access to critical information will both significantly

explain the variance in promotional opportunities for women to top-level

positions.(NDH)

Schematic diagram of 6-10

Dependent Variable

Independent variables

Review Questions

1. How would you describe the research process?

2. Explain the preliminary data collection methods?

3. Why is important to gather information on the background of the organization?

Sex-role stereotyping

Access to information

Advancement of women to the top.


4. Should a researcher always obtain information on the structural aspects and

job characteristics from those interviewed? Give reasons for your answer with

examples.

5. How do you go about doing a literature survey in the area of business ethics?

6. What is the purpose of a literature survey?

7. What is appropriate citation important? What are the consequences of not

giving credit to the source from which materials are extracted?

8. “The problem definition stage is perhaps more critical in the research process

than the problem solution stage .” Discuss this statement.

9. Why should one get hung up on problem definition if one already knows the

broad problem area to be studied?

10.List five differences between managers and researchers.

11.What constructs, who used them, and how are they envisioned by the

researcher?

12.How does a symptom differ from a cause? How does a cause differ from a

potential solution?

13.How do management problem and the research problem differ?

14.What are the major steps that have to be followed to define the research

problem in clear and concise terms?

15.What is the problem statement in the following situation?

Employee Loyalty

Companies benefits through employee loyalty. Crude downsizing in

organizations during the recession crushed the loyalty of millions. The

economic benefits of loyalty embrace lower recruitment and training costs,

higher productivity of workers, customer satisfaction, and the boost to

morale of fresh recruits. In order that these benefits are not lost, some

companies while downsizing try various gimmicks. Flex leave , for

instance , is one . This helps employees receive 20% of their salary, plus

employer provided benefits, while they take a 6 to 12 month sabbatical,

with a call option on their services. Others try alternatives like more

communications, hand holding, and the like.


16.How do you define the problem in the following case?

Accounting Gets Radical

The GAAP (Generally Accepted Accounting Principles) do an unacceptable job

of accounting for the principle activities of the information age companies. Today,

investors are in the dark because the accounting is irrelevant. The basic purpose

of accounting is to provide useful information to help investors make rational

investment, credit, and similar decisions, but today’s most important assets and

activities –intellectual capital and work knowledge –are totally ignored.

Professor Robert A. Howell wants to reform the accounting system with the goal

of making clear the measurement of how companies produce cash and create

value.


Chap - 5 The Research Design/The Research Methodology /The Research Approach

5.1 Definition of Research Design

A research design is a framework for conducting the marketing research project. It

specifies the details of the procedures necessary for obtaining the information needed

to structure and /or solve marketing research problems.

A Research Design is the detailed blueprint used to guide a research study toward its

objectives.

A research design is the specification of procedures for collecting and analyzing the

data necessary to help identify or react to a problem or opportunity, such that the

difference between the cost of obtaining various levels of accuracy and the expected

value of the information associate d with each level of accuracy is maximized.

Several aspects of this definition deserve emphasis:

Research design requires the specification of procedures. These

procedures involve decisions on what information to generate, the data

collection method, the measurement approach, the object to be measured,

and the way in which the data are to be analyzed.


The data are to be collected to help identify or react to a problem or

opportunity. All data collected should eventually relate to decisions faced

by management. Obviously, the efficient collection of data relevant to a

decision requires a clear definition of the problem /opportunity.

The information has value. Information acquires values as it helps

improve decisions.

The varying levels of accuracy of information can be generated in

response to the same problem. Information accuracy is affected by the

occurrence of a number of potential errors.

The goal of applied research design is not to generate the most

accurate information possible. Rather, the objective is to generate the

most valuable information in relation to the cost of generating the

information.

5.2 Classification of Research Design

Research Design Can Be Classified Into Two:

Exploratory Research And

Conclusive Research.

1) Exploratory research is one type of research design, which has as its primary

objective the provision of insights into and comprehension of the problem situation

confronting the researcher.

Example:

The manager of a multinational corporation is curious to know if the work ethic

values of employees working in its subsidiary in London city will be different from

that of pennathur a small city in northern Indies .there is very little information

about London , and since there is considerable controversy about what work ethic

alues mean to people in other cultures , the manager’s curiosity can be satisfied

only by an exploratory study , interviewing the employees in the organization in

Pennathur.Relgion, political , economic , and social conditions , upbringing , cultural

values, and so on play a major role in how people view their work in different parts


of the world. Here, since very little is known about work ethic values in India’s, an

exploratory study will have to be undertaken.

Exploratory studies can be done by interviewing individuals and through focus

groups. Foe instance , if a company manufacturing cosmetics wants to obtain a

thorough understanding of what it is that arouses emotive appeal for the product an

induces people to buy cosmetics , several focus groups can be convened to

discuss the related issues. This exploratory study will offer the needed preliminary

information for a full-fledged study on the matter, later.

Exploratory research could be used for any of the following purposes:

1) Gain background information.

When very little is known about the problem or when the problem

has not been clearly formulated, exploratory research may be used

to gain much-needed background information.

This is easily accomplished d in firms having information system in

which a review of internal information tracked over time can provide

useful insights into the background of the firm, brand, sales

territories, and so on.

Even for very experienced researchers it is rare that some

exploratory research is not undertaken to gain current, relevant

background information.

2) Formulate a problem or define a problem more precisely.

Gain insights for developing an approach to the problem.

By conducting exploratory research to define a question such as

“what is image?”

The researcher quickly learns that “image “is composed of several

components-perceived convenience of location, friendliness of

employees, and so on.


For example, not only would exploratory research identify the

components of bank image but it could also demonstrate how

these components may be measured.

3) Clarify problems and Develop hypothesis.

Isolate key variables and relationships for further examination

Exploratory research is used to clarify and problems and

hypothesis. For example, bank image reveals the issue of different

groups of bank customers.

Banks have three types of customers, commercial customers, and

other banks for which services are performed for fees.

Exploratory research can also be beneficial in the formulation of

hypothesis, which are statements describing the speculated

relationships among two or more variables.

4) Establish priorities for further research.

Exploratory research can help a firm prioritize research topics in

order of importance, especially when it is faced with conducting

several research studies.

A review of customer complaint letters may indicate which product

or services are most in need of management’s attention.

5) Identify alternative sources of action.

Exploratory Research is most commonly unstructured, informal research that is

undertaken to gain background information about the general nature of the research

problem.

By unstructured we mean that exploratory research does not have a

formalized set of objectives, sample plan, or questionnaire.

Exploratory research is aimed at gaining additional information about a topic

and generating possible hypothesis to test.


Such research may consist of going to the library and reading published

secondary data; of asking customers, sales persons, and prices; or simply

observing everyday company practices.

Exploratory research is systematic, but it is very flexible in that it allows the

researcher to investigate whatever sources he or she desires and to the

extent he or she feels is necessary in order to gain a good feel for the

problem at hand.

Methods of Conducting Exploratory Research

A variety of methods are available to conduct exploratory research. These include:

1) Secondary Data Analysis

Secondary data analysis refers to the process of searching for and

interpreting existing information relevant to the research problem.

Data that have been collected for some other purposes.

Libraries and internet are full of secondary data, which includes

information found in books, journals, magazines, special reports,

bulletins, news letters, and so on.

An analysis of secondary data is often the “core” of exploratory research.

2) Experience surveys

Experience survey refers to gathering information from those thought to

be knowledgeable on the issues relevant to the research problem.

If the research problem deals with difficulties encountered when buying

infant clothing, then surveys of mothers or fathers with infants may be in

order.

If the research problem deals with forecasting the demand for sulphuric

acid over the next 2 years, researchers may begin by making a few calls

to some “experts” on this issue.

3) Case Analysis

Case analysis refers to a review of available information about a former

situation (s) that has some similarities to the present research problem.


There are few research problems that do not have some similarities to

some situation in the past.

Even when the research problem deals with a radically new product,

there are often some similar past experiences that may be observed.

4) Focus groups and

Focus groups are groups of people brought and guided by a moderator

through an unstructured, spontaneous discussion for the purpose of

gaining information relevant to the research problem.

The moderator should ensure the discussion is “focused “on some

general area of interest.

5) Projective techniques.

Projective techniques seek to explore hidden consumer motives for

buying goods and services by asking participants to project themselves

into a situation and then to respond to specific questions regarding the

situation.

For example, sentence completion test may be used.

2) Conclusive Research. Research designed to assist the decision maker in

determining, evaluating, and selecting the best course of action to take in a given

situation.

is typically more formal and structured than exploratory research. It is base

don large, representative samples, and the data obtained are subjected to

quantitative analysis.

The findings from the research are considered to be conclusive in nature in

that they are used as input into managerial decision-making.

However, it should be noted that from the perspective of the philosophy of

science, nothing can be proven and nothing is conclusive.

Conclusive research can be either descriptive or causal and descriptive

research can be either cross-sectional or longitudinal.

2.1 Descriptive Research


A Descriptive Research is a type of conclusive research that has as its major objective

the description of something-usually market characteristics or functions. Provides

answers to questions such as who, what, where, when and how, as they are related to

the research problem. Typically, answers to these questions, are found in secondary

data or by conducting surveys.

The decision makers often need answers to these basic questions before they can

formulate effective business strategies:

Who may be defined as the firm’s (competitor’s) customers?

What may be defined as the products, brands, size, and so on that are

being purchased?

Where may be defined as the places the customers are buying these

products?

How may mean the questions such as why sales increase or decrease if

we increase or decrease advertising or

Why one ad garners attention than another r are questions that must be

answered through causal research designs.

For Example

A bank manager wants to have a profile of the individuals who have loan

payments outstanding for 6 months and more .It would include details of their

average age, earnings, nature of occupations, full-time /part time employment

status, and the like. This might help him to elicit further information or decide right

away on the types of individuals who should be made ineligible for loans in the

future.

A CEO may be interested in having a description of organizations in her industry

that follow the LIPO system. In this case, the report might include the age of the

organizations, their locations, their locations, their production levels, assets,

sales, inventory levels, suppliers, and profits. Such information might allow

comparison later of the performance levels of specific types of companies.


A marketing manager might want to develop a pricing, sales, distribution,and

advertising strategy for her product. With this in mind, she might ask for

information regarding the competitors, with respect to the following:

The percentage of companies who have prices higher and lower than

the industry norm, a profile of the terms of sale ,and the percentage

where prices are controlled regionally instead of from central

headquarters.

The percentage of competitors hiring in-house staff to handle sales and

those who use independent agents.

Percentage of sales groups organized by product line , by accounts,

and by region.

The types of distribution channels used and the percentage of

customers using each.

Percentage of competitors spending more dollars on advertising

/promotion than the firm and those spending less; a categorization of

their target audience, and the types of media most frequently used.

Percentage of those using the web (“dot coms”) to sell the product.

Descriptive research is conducted for the following reasons:

To describe the characteristics’ of relevant groups, such as consumers,

salespeople, organizations, or market areas. For example we can develop a

profile of the “heavy users” (frequent shoppers) of prestigious department stores.

To estimate the percentage of units in a specified population exhibiting a

certain behaviour. For example, we might be interested in estimating the

percentage of heavy users of prestigious department stores who also patronize

discount department stores.

To determine the perceptions of product characteristics’. For example, how

do households perceive the various department stores in terms of salient factors

of the choice criteria?


To determine the degree to which marketing variables are associated. For

example, to what extent is shopping at department stores related to eating out?

To make specific predictions. For example, what will be the retail sales of wow

international (a specific store) for fashion clothing (specific product category) in

Bole area (specific region)?

Descriptive Research can be further classified into cross-sectional and longitudinal

research.

i) Cross-Sectional Designs: A type of research design involving the collection of

information from any given sample of population elements only once.

a) Single cross-sectional design

A type of research design involving the collection of information from

any given sample of population elements only once.

In this type of design only one sample of respondents is drawn from the

target population, and information is obtained from this sample only once.

b) Multiple-cross-sectional design:

A cross sectional design in which there are two or more samples of

respondents, and information from each samples is obtained only once.

Often, information from different samples is obtained at different times

over long intervals.

It allows comparisons at the aggregate level but not at the individual

respondent’s level.

Because a different sample is taken each time a survey is conducted,

there is no way to compare the measures on individual respondents

across surveys. For example cohort analysis.

A cohort analysis: is a multiple cross-sectional design consisting of a series of

surveys conducted at appropriate time intervals. The cohort refers to the group of

respondents who experience the same event within the same time interval.

Example birth or age cohort is a group of people who were born during the same

time interval such as 1961 through 1970.


The term cohort analysis refers to any study in which there are measures of

some characteristics’ of one or more cohorts at two or more points in time.

Cohort analysis is also used to predict changes in voter opinions during political

campaigns. It is unlikely that any of the individuals studied at time one will also

be in the sample at time two. For example, the age cohort of people between 8

and 19 years old was selected and their soft drink consumption was examined

every 10 years for 30 years. In other words, every 10 years a different sample of

respondents was drawn in this study from the population of those who were then

between 8 and 19 years old.

II) Longitudinal Design: A type of research design involving a fixed sample of

population elements that is measured repeatedly on the same variables. The sample

remains the same over time, thus providing a series of pictures which, when viewed

together, portray a vivid illustration of the situation and the changes that are taking place

over time. Longitudinal data are used for tracking markets. Market tracking studies are

those that measure some variable(s) of interest like market share or unit sales overtime.

For example, by having representative data on brand market shares, a business

manager can “track” how his or her brand is doing relative to a competitive brand’s

performance.

A Panel: A sample of respondents who have agreed to provide information at specified

intervals over an extended period. (Some times a panel is used interchangeably with

longitudinal design).There are two types of panels: Traditional Panels

Ask panel members the same questions on each panel measurement.

They are also demographically matched to some larger entity, implying

representativeness as well.

Usually, firms are interested in using data from traditional panels

because they can gain insights into changes in consumer’s purchases,

attitudes, and so on.

For example, data from traditional panels can show how members of

the panels switched brands from one time period to the next.


Studies examining how many consumers’ switched brands are known

as brand-switching studies.

Omnibus Panels

Vary questions from one panel measurement to the next.

May be used for a variety of purposes, and the information collected by

an omnibus panel varies from one panel measurement to the next.

Essentially, the omnibus panel’s primary usefulness is that it represents

a large group –people, stores, or some other entity-that is agreeable to

providing marketing research information.

Omnibus panels are also demographically matched to some larger

entity, implying representativeness as well.

Relative advantages and disadvantages of longitudinal and cross-sectional

design

Evaluation Criteria Cross-Sectional Design Longitudinal Design

Detecting change - +

large amount of data

collection

- +

accuracy - +

representative sampling + -

response bias + -

Note: A + indicates a relative advantage over the other design, where as a –

indicates a relative disadvantage.

2.2 Causal Research

A type of research where the major objective is to obtain evidence regarding cause –

and effect (causal) relationships. Causality may be thought of as understanding a

phenomenon in terms of conditional statements in the form of “if X, then Y.”

These,” if -then” statements become our way of manipulating variables of

interest.

For example, if the thermostat is lowered, then the air will get cooler.


If I spend more on advertising, then sales will rise.

Commercial managers are always trying to determine what will cause a

change in consumer satisfaction, a gain in market share, or an increase

in sales.

Our desire to understand our world in terms of causal, if-then statements

is very difficult, if not impossible because there are formal conditions that

must be in place before a researcher can attest to causality.

Example

A causal study question:

Does smoking cause cancer?

A correlational study question:

Are smoking and cancer related?

OR

Are smoking, drinking and chewing tobacco associated with cancer? If so, which of

these contributes most to the variance in the dependent variable?

Fears of an earthquake predicted recently in the new Madrid fault zone were

instrumental or causal in an unprecedented number of house owners in the Midwest

region taking out of the earth quake insurance policy.

This example indicates a causal relationship between the earthquake predication and

insurance.

Increases in interest rates and property taxes, the recession, and the predicted

earthquake considerably slowed down the business of real estate agents in the

Midwest. This example indicates that several factors, including the predicted earthquake

influenced not caused the slowdown of real estate agents business. This is a

correlational study, which was not intended to establish a cause –and –effect

relationship.

Causal Research is Appropriate for the Following Purposes:


To understand which variables is the cause (independent variables)

and which variables are the effect (dependent variables) of a

phenomenon.

To determine the nature of the relationship between the causal

variables and the effect to be predicted.

Formal Requirements of Causality

1) Co Variation

It must be demonstrated that the causal variable occurs with the

caused variable and that the two variables have an orderly

relationship

For example as prices goes down sales goes up.

2) Time Sequence

It must be demonstrated that the causal variables changed prior to or

simultaneous with observed changes in the caused variable.

For example, prices were lowered on Monday, and sales go up for

Monday, and sales go up for Monday and all other days when prices

were lower.

3) Systematic Elimination

It must be demonstrated that all other possible causal variables are

eliminated from candidacy.

For example, if an advertising campaign began on the days we

lowered prices, we could not eliminate the ad campaign as a cause

of sales going up.

4) Experimental Design

It must be demonstrated that a valid experiment has been conducted

in order to state the variable is unequivocally causal.

For example, a formal market test would be designed and conducted

in order to determine the effect of a price reduction on sales.

Relationships among Exploratory, Descriptive, and Causal Research


A given marketing research project may involve more than one type of research

design and serve several purposes. When combination of research design should be

employed depends on the nature of the problem.

General guidelines for choosing research designs include the following:

1. When little is known about the problem situation, it is desirable to begin with

exploratory research. Exploratory research is appropriate when the problem

needs to be defined more precisely , alternative courses of action identified ,

research questions or hypothesis developed , and key variables isolate d and

classified as dependent or independent.

2. Exploratory research is the initial step in the overall research design

framework. It should, in most instances, be followed by descriptive or causal

research. For example, hypothesis developed via exploratory research should be

statistically tested using descriptive or causal research. Exploratory research in the

form of secondary data analysis and focus groups was conducted to identify the

social causes that Ethiopian businesses should be concerned about. Then a

descriptive cross-sectional survey was undertaken to quantify the relative salience

of these causes.

3. It is not necessary to begin the survey research design with exploratory

research. It depends upon the precision with which the problem has been defined

and the researcher‘s degree of certainty about the approach to the problem. A

research design could well begin with descriptive or causal research. To illustrate, a

consumer satisfaction survey is conducted annually need not begin with or include

an exploratory phase each year.

4. Although exploratory research is generally the initial step, it need not be.

Exploratory research may follow descriptive or causal research. For example,

descriptive or causal research results in findings that are hard for mangers to

interpret. Exploratory research may provide more insights to help understand these

findings.

Differences between Exploratory and Conclusive Research


S/N Factors Exploratory Causal

1) Objective to provide

insights and

understanding

to test specific

hypothesis and

examine

relationship

2) Characteristics Information

needed is

defined only

loosely.

Research

process is

flexible and

unstructured.

sample is small

and non

representative .

analysis of

primary data is

qualitative

Information

needed is clearly

defined.

Research

process is

formal and

structured.

sample is large

and

representative

Data analysis is

quantitative.

3) Findings/Results Tentative conclusive

4) Outcomes Generally

followed by

further

exploratory or

conclusive

research.

findings used as

input into

decision making

A Comparison of Basic Research Design

S/N Factors Exploratory Descriptive Causal

1) Objective discover ideas and

insights

describe

market

determine

cause and


characteristic

s or functions

effect

relationships

2) Characteristics Flexible

Versatile

often the front end of

total research design

Marked by the

prior

formation of

specific

hypothesis.

preplanned

and

structured

design

Manipulating

of one or

more

independent

variables.

control of

other

mediating

variables

3) Methods expert surveys

pilot surveys

secondary data(analyzed

quantitatively)

qualitative research

secondary

data

(analyzed

quantitatively)

surveys

panels

observational

and other

data

experiments

Extent of Researcher Interference with the Study

The extent of interference by the researcher with the normal flow of work at the

workplace has a direct bearing on whether the study undertaken is causal or

correlational.

A correlational study is conducted in the natural environment of organization with

minimum interference by the researcher with the normal flow of work .For example, if

the researcher wants to study the factors influencing training effectiveness, all that

individual has to do is is develop a theoretical framework ,collect the relevant data ,and

analyze them to come up with findings. Though there is some disruption to the normal

flow of work in the system as the researcher interviews employees and administers


questionnaires at the workplace , the researcher ‘s interference in the routine

functioning of the system is minimal as compared to that caused during casual studies.

There are three varying degrees of interference –minimal, moderate, and excessive.

Minimal Interference

A hospital administrator wants to examine the relationship between the perceived

emotional support in the system and the stresses experienced by the nursing staff. In

other words, she wants to do a correlational study. Here the administrator /researcher

will collect data from the nurses to indicate how much emotional support they get in the

hospital and to what extent they experience stress. By correlating the two variables, the

answer that is being sought can be found.

In this case, beyond administering a questionnaire to the nurses, the researcher has not

interfered with the normal activities in the hospital. In other words, researcher

interference has been minimal.

Moderate Interference

The same researcher is now no longer content with finding the correlation, but wants to

firmly establish a causal connection. That is , the researcher wants to demonstrate

that if the nurses had emotional support , this indeed would cause them to experience

less stresses. If this can be established, then the nurse’s stress can definitely be

reduced by offering them emotional support.

To test the causes –and –effect relationship , the researcher will measure the stress

currently experienced by the nurses in three wards in the hospital ,and then deliberately

manipulate the extent of emotional support given to the three groups of nurses in the

three wards for perhaps a week ,and measure the amount of stress at the end of that

period. For one group , the researcher will ensure that a number lab technians and

doctors help and comfort the nurses when they face stressful events-for example ,

when they care for the patients suffering excruciating pain and distress in the ward.

Under a similar setup, for a second group of nurses in another ward , the researcher

might arrange for them only a moderate amount of emotional support and employing


only the lab technians and excluding Doctors. The third ward might operate without any

emotional support.

If the experimenter’s theory is correct, then the reduction in the stress levels before and

after the one-week period should be greatest for the nurses in the first ward, moderate

for those in the second ward, and nil for the nurses the third ward.

Excessive Interference

The above researcher, after conducting the previous experiments, feels that the results

may or may not be valid since other external factors might have influenced the stress

levels experienced by the nurses. For example, during that particular experimental

week, the nurses in one or more wards may not have experienced high levels of stress

because there were no serious illnesses or deaths in the ward. Hence, the emotional

support received might not be related to the level of stresses experienced. The

researcher might now want to make sure that such extraneous factors as might affect

the cause –and –effect relationship are controlled. so she might take three groups of

medical students ,put them in different rooms, and confront all of them with the same

stressful task. For example, she might ask them to describe in the minutest detail , the

surgical procedures in performing surgery on a patient who has not responded to

chemotherapy and keep bombarding them with more and more questions even as they

respond. Although all are exposed to the same intensive questioning, one group might

get help from a doctor who voluntary offers clarifications and help when students

stumble. In the second group, a doctor might be nearby, but might offer clarifications

and help only if the group seeks it. In the third group, there is no Doctor present and no

help is available.

Critical Thinking Discussion Questions

1. What are the basic research design issues? Describe them in some detail.

2. Why is it important to consider basic design issues before conducting the study and

even as early as the time of formulating the research question?


3. Is a field study out of the question if one is trying to establish cause-and –effect

relationships?

4. “An exploratory study is just as useful as a predictive study,” Discuss this statement.

5. Why is the unit of analysis an integral part of the research design?

6. Discuss the relationships among non contrived setting , the purpose of the study ,

type of investigation, researcher interference ,and time horizon of study.

7. Below are three scenarios. Indicate how the researcher should proceed in each

case that is , determine the following, giving reasons:

The purpose of the study

The type of investigation

The extent of researcher interference

The study setting

The time horizon for the study

The unit of analysis

Scenario A

Ms. Meron , the owner of a small business ( a women’s dress Boutique ), has

invited a consultant to tell her business is different from similar small businesses

within a 60-mile radius with respect to use of the most modern computer

technology , sales volume , profit margin, and staff training.

Scenario B

Mr. Yonas, the owner of several restaurants on the southern tip of Addis Ababa,

is concerned about the wide differences in their profit margins. He would like to

try some incentive plans for increasing the efficiency levels of those restaurants

that lag behind. But before he actually does this, he would like to be assumed

that the idea would work. He ask a researcher to help him on this issue.

Scenario C

A manager is intrigued why some people seem to derive joy from work and get

energized by it, while others find it troublesome and frustrating.

8. A foreman thinks that the low efficiency of the machine tool operators is directly

linked to the high level of fumes emitted in the workshop. He would like to prove this

to his supervisor through a research study.


Would this be a causal or a correlational study? Why?

Is this an exploratory, descriptive, or hypothesis –testing (analytical or

predictive) study? Why?

What kind of a study would this be: field of study, lab experiment, or field

experiment? Why?

What would be the unit of analysis? Why?

Would this be a cross-sectional or a longitudinal study? Why?

9. How would you match research designs with various research objectives?

10.What types of research design answers the questions of who, what, where, when,

and how?

11.What differences between and cross-sectional studies longitudinal studies?

12. In what situations would a traditional panel be more suitable that an omnibus panel?

13.Explain why studies of the “if –then” variety are considered to be “causal” studies.

Chap -6 Sampling Design &Procedures

6.1 What Is Sampling?

Sampling is the process of using a small or parts of a larger population to make

conclusions about the whole population.

Sampling is one of the components of research design. Sampling design involves

several basic questions:

Should a sample be taken?

If so, what process should be followed?

What kind of sample should be taken?

How large it should be?


What can be done to control and adjust for non response errors?

6.2 Basic Terminologies in Sampling

1. Population (Universe): The aggregate of all elements, sharing some common

set of characteristics that comprises the universe for the purpose of marketing

research problem.

2. Target population: the specific complete group relevant to the research group.

3. Population element: an individual member of a specific population.

4. Census: a complete enumeration of the elements of a population or study

objects.

5. Sample: a subgroup of the elements of the population selected for participation

in the study.

6. Sampling Frame: a representation of the elements of the target population. It

consists of a list or set of directions for identifying the target population.

7. Representative Frame: a sample that reflects the population accurately so that

it is a microcosm of the population.

8. Probability Sampling: a sampling procedure in which each element of the

population has a fixed probabilistic chance of being selected for the sample.

9. Non-Probability Sampling: sampling techniques that do not use chance

selection procedures, rather, they rely on the personal judgment of the

researcher.

10.Sampling With Replacement: a sampling technique in which an element can

be included in the sample more than once.

11.Sampling Without Replacement: a sampling technique in which an element

cannot be included in the sample more than once.

12.Sampling Units: a single element or group of elements subject to selection in

the sample. (PSU)

13.Primary Sampling Unit (PSU): a unit selected in the first stage of the

sampling.


14.Secondary Sampling Unit (SSU): a unit selected in the second stage of

sampling.

15.Sample Size: The number of elements to be included in a study.

16.Sampling Error: the difference between a sample and the population from

which it is selected, even though a probability sample has been selected.

17.Non-Sampling Error: differences between the population and the sample that

arise either from deficiencies in the sampling approach, such as an adequate

sampling frame or non-response, or from such problems.

18. Non –Response: a source of non-sampling error that is particularly likely to

happen when individuals are being sampled. It occurs whenever some

members of the sample refuse to cooperate, can not be contacted, or for some

reason can not supply the required data, because of mental incapacity.

5.3 Probability Sampling Vs. Non-Probability Sampling

Sampling technique can be broadly classified into two broad categories: Probability and

Non-probability sampling.

1) Probability Sampling Technique

Probability sampling techniques vary in terms of sampling efficiency. Sampling

efficiency is a concept that reflects a trade-off between sampling cost and precision.

Precision refers to the level of uncertainty about the characteristics being measured.

Precision is inversely related to sampling errors but positively related to cost. The

greater the precision the greater the cost and most studies require a trade –off. The

researcher should strive for the most efficient sampling design, subject to the budget

allocated.

Probability sampling techniques includes the following:

a) Simple Random Sampling(SRS)

SRS is a probability sampling technique in which each element in the

population has a known and equal probability of selection. Every element


is selected independently of every other element and the sample is drawn

by a random procedure from a sampling frame.

To draw a simple sample, the researcher first compiles a sampling frame

in which each element is assigned a unique identification number.

Probability of selection = sample size

Population Size

There are different methods in simple random sampling:

The “blind Draw “method: involves blindly choosing participants by

their names of some other unique designation.

The table of random numbers method: a more sophisticated

application of simple random sampling is to use a table of random

numbers which is a listing of numbers whose random order is assured. If

you look at a table of random numbers, you will not be able to see any

systematic sequence of numbers regardless of where in the table you

begin and whether you go up, down, left right, or diagonally across the

entries.

Advantages of SRS

It embodies the requirements necessary to obtain a probability sample

and there for to derive unbiased estimates of the population‘s

characteristics.

It guarantees that every member of the population has a known and equal

chance of being selected in to the sample; the resulting sample, no matter

what the size, will be a valid representation of the population.

Disadvantages of SRS

To use either the blind draw or the table of random numbers approach, it

is necessary to pre designate each population number.


In the blind draw sample, each student’s name was written on a 3-by-5

card,

Where as in the random numbers example, each student was a specific

number.

In essence, simple random sampling necessarily begins with a complete

listing of the population, a current and complete listings are often difficult

to obtain.

It is also cumbersome to provide unique designation s for each population

member.

Numbering from 1 through an unknown total population size (N) is tedious

and invites administrative errors.

Using names is unsatisfactory due to duplicates.

b) Systematic Sampling (SS): is a probabilistic sampling technique in which the

sample is chosen by selecting a random starting point and then picking every point

and then picking every ith elements in succession from the sampling frame. A SS

involves selecting every nth unit after a random start. To use systematic sampling the

researcher decides on a “skip interval”, which is calculated by dividing the number of

names on the list by sample size.

Skip interval ( I) = Population list size

Sample size

For example, there are 100,000 elements in the population and a sample of 1000 is

desired. In this case, the sampling interval, I, is 100. Random number between 1 and

100 is selected. If, for example, this number is 23, the sample consists of elements

23,123, 233, 423, 523, and so on.

Prerequisites of Systematic Sampling

A prerequisite for applying systematic sampling is that the units in the population can be

ordered in some way:

Some examples are presented below:

Records that are ordered in a file.


Names that are ordered alphabetically in a telephone directory.

Houses that are ordered along a road.

Customers who walk one by one through an entrance, and so on.

Advantages

It has “economic efficiency” (can be applied with less difficulty.)

The method is simple, but probably the most important advantage is that

a frame is not always needed.

The method can therefore be used, for instance, to interview a sample of

persons passing by a corner during a particular day.

The units in the sample will be spread evenly over the ordered

population. (Sometimes this will increase precision.)

It can be accomplished in a shorter time period than can simple random

sampling.

It has the potential to create a sample that is almost identical in quality to

samples created from simple random sampling.

Disadvantages

The most important potential drawback is the danger of hidden

periodicities, for example, that a deficiency in producing a specific

product occurs at specific intervals.

If one happens to get an unfortunate starting point the whole sample

could consist of defective products.

c) Stratified Sampling: is a probability sampling technique that uses a two-step

process to partition the population into sub populations, or strata. Elements are

selected from each stratum by random procedures. The strata should be mutually

exclusive and collectively exhaustive in that every population elements should be

omitted. Next, elements are selected from each stratum by random procedures,

usually SRS. Technically, only SRS should be employed in selecting the elements

from each stratum.


In practice, sometimes systematic sampling and other probability sampling procedures

are employed. Stratified sampling differs from quota sampling in that sample elements

are selected probabilistically rather than based on convenience or judgment. A major

objective of stratified sampling is to increase precisions without increasing cost.

Stratified sampling can be further divided into two:

I) Proportionate: In proportionate stratified sampling the size of the sample

drawn from each stratum is proportionate to the relative size of that stadium

in the total population.

II) Disproportionate: in disproportionate stratified sampling , the size of the

sample from each stratum is proportionate to the relative size of the stratum

and to the standard deviation of the distribution of the characteristics of

interest among all the elements in that stratum.

The logic behind disproportionate sampling is simple.

First, strata with relative larger sizes are more influential in

determining the population mean, an d these strata should also

exert a greater influence in delivering the sample elements.

Consequently more elements will be drawn from strata of larger

relative size.

Second, to increase precision more elements should be drawn

from strata with larger standard deviations and fewer elements

should be drawn strata with smaller standard deviations. For

example, large retail stores might be expected to have greater

variation in the sales of some products as compared to small

stores. Hence, the number of large stores in a sample may be

disproportionately large.

Advantages


Stratified random sampling can give higher precision with the same sample

size, alternatively, the same precision with a smaller sample.

Stratified sampling can also give separate results for each stratum.

Stratified sampling can also simplifies data collection.

Disadvantages

A complete frame is needed.

Depending on the allocation principle applied, additional information, such

as knowledge of standard deviations and costs, may be needed for each

stratum.

d) Cluster Sampling: Is a probability sampling in which the population is divided into

sub-groups, each of which represents the entire population.

In cluster sampling,

First, the target population is divided into mutually exclusive and

collectively exhaustive subpopulations called clusters.

Then, a random sample of clusters is selected based on a

probability sampling technique such as simple random

sampling.

For each selected cluster, either all the elements are included in

the sample or a sample of elements is drawn probabilistically.

There are three types of cluster sampling:

One -stage sampling

Two -stage sampling

Multistage sampling

A common form of cluster sampling is area sampling.

Area sampling is a common form of cluster sampling in which the clusters

consist of geographic area such as counties, housing tracts, blocks, or

other area descriptions.

In one –stage area sampling, the researcher samples blocks and then all

the households within the selected blocks are included in the sample.


In the case of two or multi-stage are sampling, the researcher samples

blocks, and then samples households within the selected blocks.

Advantages

The major advantage of cluster sampling is that we do not need a complete

frame of the secondary sampling units. (We do not need a frame of the

clusters, however.)

Another important advantage in many kinds of cluster sampling such as

area sampling is the geographical concentration of the units to be

interviewed.

Disadvantages

If there is large variation between clusters in the variables to be examined

the method may yield poor precision.

e) Sequential Sampling: A probability sampling technique in which the population

elements are sampled sequentially, data collection and analysis are done at each

stage, and a decision is made as to whether additional elements should be sampled.

Data collection and analysis are done at each stage, and decision is made

as to whether additional population elements should be sampled.

At each stage, this rule indicates whether sampling should be continued or

whether enough information has been obtained.

Sequential sampling has been used to determine preferences for two

competing alternatives.

Ate In one study, respondents were asked which of the two alternatives

they preferred, and sampling was terminated when sufficient evidence was

accumulated to validate a preference.

It has also been used to establish the price differential between a standard

models a deluxe model of a consumer durable.

f) Double Sampling: A sampling technique in which certain population elements are

sampled twice.


In the first phase, a sample is selected and some information is collected

from all the elements in the sample.

In the second phase, a subsample is drawn from the original sample and

additional information is obtained from the elements in the sub sample.

The process may be extended to three or more phases, and the different

phase may take place simultaneously or at different times.

Double sampling can be useful when no sampling frame is readily

available for selecting final sampling units but when the elements of the

frame are known to be contained within a broader sampling frame.

2) Non-Probability Sampling Techniques

The critical difference between probability and non-probability sampling

methods is the mechanics used in the sample design.

With non-probability sampling method, selection is not based on

probability.

In other words you can’t calculate the probability of any one person in the

population being selected into the sample.

Still each non-probability sampling method strives to draw a representative

sample.

Non -Probability Sampling Methods

The non probability sampling methods include the following:

1) Convenience Sampling: A non-probability sampling technique that attempts to obtain

a sample of convenient elements. The selection of sampling units is left primarily to

the interviewer. Examples of convenience sampling includes:

Use of students , church groups , and members of social organizations.

Mall-intercept interviews without qualifying the respondents

Department stores using charge accounts lists,

Tear-out questionnaires included in a magazine, and

“People on the street” interviews.

Advantages


Convenience sampling is the least expensive and the least time

consuming of all sampling techniques.

The sampling units are accessible, easy to measure, and cooperate.

Disadvantages

Many potential sources of selection bias are present, including respondent

self-selection.

Convenience samples are not representative of any definable population.

Hence , it is not theoretically meaningful to generalize to any population

from a convenience sample, and convenience samples are not appropriate

for marketing research projects involving population inferences.

Convenience samples are not recommended for descriptive or causal

research , but they can be used in exploratory research for generating

ideas, insights, or hypothesis

Convenience samples can used for focus groups, pretesting questionnaires,

or pilot studies.

Even in these cases, caution should be exercised in interpreting the results.

Nevertheless, this technique is sometimes used even in large surveys.

2) Judgment Sampling: A form of convenience sampling in which the population

elements are purposely selected based on the judgment of the researcher. The

researcher uses his or her judgment or that of some other knowledgeable person to

identify who will be in the sample. Subjectivity enters in here, and certain members

of the population will have a smaller chance of selection than will others. The

researcher exercising common examples of judgment sampling include :judgment

or expertise , chooses the elements to be included in the sample , because he or

she believes that they are representative of the population of interest or are

otherwise appropriate.

Test markets selected to determine the potential of a new product.


Purchase engineers selected in industrial marketing research

because they are considered to be representative of the company

Bellwether precincts selected in voting behaviour research,

Department stores selected to test a new merchandising display

system.

3) Referral Samples/Snowball Samples: Respondents are asked for the names or

identities of other s like themselves who might qualify to take part in the survey. A

non probability sampling technique in which an initial group of respondents is

selected randomly. Subsequent respondents are selected based on the referrals or

information provided by the initial respondents. This process may be carried out in

waves by obtaining referrals from referrals. A major objective of snowball sampling is

to estimate characteristics that are rare in the population.

Examples include:

Users of particular government or social services, such as food stamps,

whose names can’t be revealed; special census groups, such widowed

males under 35; and members of a scattered minority population.

Snowball sampling is used in industrial buyer –seller research to identify

buyer- seller pairs.

Advantages

It substantially increases the likelihood of locating the desired characteristics

in the population.

It also results in relatively low sampling variance and costs.

4) Quota Samples: A non-probability sampling technique that is a two-stage restricted

judgmental sampling. The size of the quotas is determined by the researcher’s belief

for the relative size of each class of respondent.

The first stage consists of developing control categories or quotas of

population elements.

In the second stage, sample elements are selected based on convenience

sampling. To develop these quotas, the researcher lists relevant control


characteristics which may include sex, age and race, are identified on the

basis of judgment. A field worker is provided with screening criteria that will

classify the potential respondent into a particular quota cell.

For example, a large bank, might stipulate that the final sample be one-half

adult males and one-half adult females because in their understanding of

their market, the customer profile is about 50-50, male and female.

Often the quotas are assigned so that the proportion of the sample

elements possessing the control characteristics is the same as the

proportion of population elements with these characteristics is the same as

the proportion of population elements with these characteristics.

In other words, the quotas ensure that the composition of the sample is the

same as the composition of the population with respect to the characteristics of

interest. In the second – stage, sample elements are selected based on

convenience or judgment. Once the quotas have been assigned, there is

considerable freedom in selecting the elements to be included in the sample.

The only requirement is that the elements selected fit the control characteristics.

In certain situations, it is desirable either to under –or oversample heavy users

of a product so that their behaviour can be examined in detail. Although this type

of sample is not representative, it may nevertheless be very relevant.

Advantages

QS Attempts to obtain representative samples at a relatively low cost.

A quota system overcomes much of the non-representative ness danger

inherent in convenience samples.

It may guarantee that the researcher has sufficient sub-sample sizes for

meaningful subgroup analysis.

Under certain conditions, quota sampling obtain results close to those for

conventional probably sampling.

Strengths and Weaknesses of Basic Sampling Techniques

S/N Technique Strengths Weaknesses


I. Probability Sampling

1 Simple random sampling (SRS) Easily understood,

result projectable

Difficult to construct

sampling frame ,

expensive , lower

precision, no

assurance of

representativeness

2 Systematic sampling Can increase

representativeness ,

easier to implement

than SRS, sampling

frame not

necessary

Can decrease

representativeness

3 Stratified sampling Includes all

important

subpopulations,

precision

Difficult to select

relevant

stratification

variables , not

feasible to stratify

on many variables ,

expensive

4 Cluster sampling Easy to implement

cost effective

Imprecise, difficult to

compute and

interpret results.

II. Non- probability sampling

1 Convenience sampling Least expensive ,

least time

consuming, most

convenient

Selection bias ,

sample not

representative , not

recommended for

descriptive or

causal research

2 Judgment sampling Low cost, Does not allow


convenient, not time

consuming

generalization ,

subjective

3 Quota sampling Sample can be

controlled for

certain

characteristics

Selection bias , no

assurance of

representativeness

4 Snow ball sampling Can estimate rare

characteristics.

Time consuming

Choosing Probability versus Non - Probability sampling

S/N Factors Conditions Favoring The Use of

Probability Sampling Non-Probability

Sampling

1 Nature of research Conclusive Exploratory

2 Relative magnitude of

sampling and non

sampling errors

Sampling errors are larger Non-sampling errors

are larger

3 Versatility in the

population

Heterogeneous (high) Homogeneous (low)

4 Statistical considerations Favorable Unfavorable

5 Operational

considerations

Unfavorable Favorable

6.4 Procedures for Drawing Probability Sampling

I) Simple Random Sampling

1) Select a suitable sampling frame

2) Assign each element a number 1 to N (population size)

3) Generate n (sample size) different random numbers between 1 and N. This

can be done using a microcomputer or mainframe software package or using a

table of simple random numbers. Arbitrarily select a beginning numbers.


Select the appropriate number of digits (For Example: if N=900, select three

digits).Then proceed either up or down until n different numbers between 1

and N have been selected. Note: discard 0, duplicate numbers, and numbers

greater than N.

4) The numbers generated denote the elements that should be included in the

sample.

II) Systematic Sampling

Select a suitable sampling frame.

Assign each element a number 1 to N( population size)

Determine the sampling interval, I, i= N/n, if I is a fraction, round to the

nearest integer.

Select a random number, r, between 1 and I, as explained in simple

random sampling.

The elements with the following numbers will comprise the systematic

random sample: r, r+1, r+2i, r+3i, +4i...r+ (n-1)i.

III) Stratified Sampling

Select a suitable sampling frame.

Select the stratification variable (s) and the number of strata (H).

Divide the entire population into H strata. Based on the classification

variable, assign each element of the population to one of the H strata.

In each stratum, the number the elements from 1 to Nh (the population size

of stratum h ).

Determine the sample size of each stratum, nh, based on proportionate or

o Disproportionate ………………..

In each stratum, select a sample random sample of size nh.

IV) Cluster Sampling

We describe the procedure for selecting a simple two-stage sample, because this

represents the simpler case.

Assign a number, from 1 to N, to each element in the population.


Divide the population into C clusters, of which c will be included in the

sample.

Calculate the sampling interval, I, i= N/c. If it is a fraction, round to the

nearest integer.

Select a random number, r, between 1 and I, as explained in simple

random sampling.

Identify elements with the following numbers: r, r+1, r+2i, r+3i, ….., r+(c-1)

i.

Select the cluster that contains the identified elements.

Select sampling units within each selected cluster based on SRS or

systematic sampling. The number of sampling units selected from each

sample cluster is approximately that same and equal to n/c.

If the proportion of a cluster exceeds the sampling interval, I, that cluster is

selected with certainty. That cluster is removed from further consideration.

Calculate the new population size, N*, number of clusters to be selected,

C* (=c-1), and the new sampling interval, i*. Repeat this process until each

of the remaining clusters has a population less than the relevant sampling

interval. If b clusters have been selected with certainty, select the

remaining c-b clusters according to steps 1 through 7 . The fraction of units

to be sampled from each cluster selected with certainty is the overall

sampling fraction =n/N. Thus, for clusters selected with certainty we would

select ns =n/N (N1 +N2 + . . . + Nb) units. The units selected from clusters

selected under two-stage sampling will therefore be n*= n-ns

Developing a Sample Plan

The various aspects of sampling are logically joined together and has a definite

sequence or steps presented as follows:

Step 1: Define the relevant population.

Step 2: Obtain the “listing of the population.

Step 3: Design the sample plan (size, method).


Step 4: Access the population

Step 5: Draw the sample.

Step 6: Validate The Sample.

Step 7: Resample, If Necessary.

Method of Determining the Sample Size

The following methods include the following:

1) Arbitrary Approach:

Uses a “rule of thumb “to determine the sample size.

The researcher may determine, for instance, the sample size should be

5% of the population.

Arbitrary sample size are simple and easy to apply, but they neither

efficient not economical.

2) Conventional Approach:

It follows some “convention”, number believed somehow to be the right

sample size.

The convention might be an average of the sample sizes of similar

studies,

it might be the largest sample size of previous surveys, or

it might be equal to the sample size of a competitor’s survey.

For instance, the researcher may take the conventional sample size of

between 1000 and 1200 as determined by the industry.

Use of a conventional sample size can result in a sample that may be

too small or too large.

3) Cost Analysis

It uses cost as a basis for sample size.

In this case instead of the value of the information to be gained –from

survey being a primary consideration in the sample size, the sample

size is determined by cost factors that ignore the value.


4) Statistical Approach:

Some advanced statistical techniques require certain minimum sample

size in order to be reliable, or to safeguard the validity of the statistical

results.

Statistical analysis is used to analyze subgroups within a sample.

5) Confidence Internal Approach

To create a valid sample, the confidence interval approach applies the concepts

of:

Variability

Confidence interval

Sampling distribution , and

A standard error of a mean.

Variability: is the amount of dissimilarity in respondent’s answers to a particular

question.

Confidence interval: is a large whose end points define a certain percentage of the

responses is a question:

Standard Error: it indicates how far away from the true population value a typical

sample result is expected to fall:

There are two ways to compute a standard error:

The standard error of the mean

The slandered error of a percentage

Assume that is a driving survey a standard deviation of 3000 miles was found with a

sample of 1000 drivers. Find the standard deviation error.

SX= 3000 = 300

√100


This shows that the sample value is expected to fall 300 miles away from the true

population value.

Summary

Sampling design decisions are important aspects of research design and include both

the sampling plan to be used and the sample size that will be needed. Probability

sampling plans lend themselves to Generalizablity and non-probability sampling

designs, though not geralizable, and offer convenience and timely information. Some

probability plans are more efficient than other. Though non-probability sampling plans

have limitations in terms of Generalizablity, they are often the only designs available for

certain types of investigation, as in the case of exploratory research, or where

information is needed quickly, or is available with only certain special groups.

The sample size is determined by the level of precision and confidence desired in

estimating the population parameters, as well as the variability in the population itself.

Cost considerations could also play a part. The Generalizablity of the findings from a

study of the sample to the population is dependent on its representativeness-that is, the

sophistication of the sampling design used, and the sample size. Sample data are used

for both estimating population parameters and hypothesis testing.

Sample Data Hypothesis Testing

So far we have discussed sample data as a means of estimating the population

parameters, but sample data can also be used to test hypotheses about population

values rather than simply to estimate population values. The procedure for this testing

incorporates the same information as interval estimation, but the goals behind the two

methods are somewhat different.

Referring to the earlier example of the average dollar value purchases of customers in

a department store, instead of tying to estimate the average purchase value of the

store’s customers with a certain degree of accuracy, let us say that we now wish to

determine whether or not customers expend the same average amount in purchases at

Department Store A as in department Store B. from Chapter 5, we know that we would


first set the null hypothesis, which would state that there would be no difference in the

dollar values expended by customers hopping at the two different stores. This would be

expressed as:

Ho: µA-µB=0

The alternate hypothesis of differences would be stated non directionally (since we have

no idea whether customers buy more at Store A or Store B) as:

HA: µA-µB≠0

If we take a sample of 20 customers from each of the two stores and find that the mean

dollar value purchases of customers in store A is 105 with a standard

Figure 11.4

Illustration of the Trade-off between Precision and Confidence. (a) More Precision but

Less Confidence. (b) More Confidence but Less Precision.

.50 .99

.005 .005

.25 .25

_ x (a)

(b)


Deviation of 10, and the corresponding figures for Store B are 100 and 15, respectively,

we see that:

XA-XB= 105-100=5

Whereas our null hypothesis had postulated no difference (difference=0). Should we

then conclude that our alternate hypothesis is to be accepted? We can not say! To

determine this we must first find the probability or likelihood of the two group means

having a difference of 5 in the context of the null hypothesis of a difference of 0. This

can be done by converting the difference in the sample means to a t statistic and seeing

what the probability is of finding a t of that value. The t distribution has known

probabilities attached to it [see Table II (t-table) in the Appendix at the end of the book.].

Looking at the t distribution table, we find that with two samples of 20 each ([the

degrees pf freedom become (m1 +n2)-s=38] for the t value to be significant at the .05

level, the critical value should be around 2.021 (see t table column 6 against u 40). We

need to use the 2-tailde test since we do not know whether the difference between

Store A and Store B will be positive or negative. For even a 90% probability, it should

be at least 1.684 (see the number to the left of 2.021). The t statistic can be calculated

for testing our hypothesis as follows:

T= (_x1 – x2) – (µ1 – µ2)

Sx1 – sx2

Sx1 – sx2 = √n1s12 + n2s2

2 (1/n1 +/n2)

(n1+n2-2)

= √ (20*102) + (20/152) (1/20+1/20)

20+20-2

t= (XA – XB)-(µA- µB)

4.136


We already know that

XA-XB=5 (the difference in the means of the two stores) and

µA-µB=0 (from our null hypothesis)

Then, t=5-0=1.209

4.136

This t value of 1.209 is much below the value of 2.021 [for 40 degrees of freedom for a

two population t-test, the closest to the actual 38 df ((20+20)-21) required for the

conventional 95% probability, and even for the 90% probability, which requires a value

of 1.684. We can thus say that the difference of 5 that we found between the two stores

is not significantly different from 0. The conclusion, then, is that there is no significant

difference between how much customers buy (dollars expended) at Department Store A

and Department Store B. We will thus accept the null hypothesis and reject the

alternative.

Sample data can thus be used not only for estimating the population parameters, but

also for testing hypotheses about population value, population correlations, and so forth,

as we will see more fully.

DETERMINING THE SAMPLE SIZE

Now that we are aware of the fact that the sample size is governed by the extent of

precision and confidence desired, how do we determine the sample size required for

our research? The procedure can be illustrated through as example. Suppose a

manager wants to be 95% confident that the expected monthly withdrawals in a bank

will be within a confidence interval of +$500. let us say that a study of a sample of

clients indicates that the average withdrawals made by them have a standard deviation

of $3,500. What would be the sample size needed in this case?


We noted earlier the population mean can be estimated by using the formula:

µ=X±Kx

Since the confidence level needed here is 95%, the applicable K value is 1.96 (t-table).

The interval estimate of ± $500 will have to encompass a dispersion of (1.96*standard

error). That is,

500=1.96xSx

Sx=500/1.96=255.10

We already know that

sx= s

√n

255.10= 3500

√n

n=188

The sample size needed in the above was 188. Let us say that this bank has a total

clientele of only185. This means we cannot sample 188 clients. We can in this case

apply the correction formula and see what sample size would be needed to have the

same level of precision and confidence given the fact that we have a total of only 185

clients. The correction formula is as follows:

Sample Size Decision Model

Krejcie and Morgan (1970) greatly simplified size decision by providing a table that

ensures a good decision model. The interested researcher is advised to read Krejcie

and Morgan (1970) as well as Cohen (1969) for decisions on sample size. The table

below provides that generalized scientific guideline for sample size decisions.


Table 6.3 Sample Size for a Given Population Size

N S N S N S

10 10 220 140 1200 291

15 14 230 144 1300 297

20 19 240 148 1400 302

25 24 254 152 1500 306

30 28 260 155 1600 310

35 32 270 159 1700 313

40 36 280 162 1800 317

45 40 290 165 1900 320

50 44 300 169 2000 322

55 48 320 175 2200 327

60 52 340 181 2400 331

65 56 360 186 2600 335

70 59 380 191 2800 338

75 63 400 196 3000 341

80 66 420 201 3500 346

85 70 440 205 4000 351

90 73 460 210 4500 354


95 76 480 214 5000 357

100 80 500 217 6000 361

110 86 550 226 7000 364

120 92 600 234 8000 367

130 97 650 242 9000 368

140 103 700 248 10000 370

150 1108 750 254 15000 375

160 113 800 260 2000 377

170 118 850 265 30000 379

180 123 900 269 40000 380

190 127 950 274 50000 381

200 132 100 278 75000 382

210 136 110 285 100000 384

Importance of Sampling Design and Sample Size

It is now possible to see how both sampling design and the sample size are important to

establish the representativeness of the sample for generalize ability. If the appropriate

sampling design is not used, a large sample size will not, in itself, allow the findings to

be generalized to the population. Likewise, unless the sample size is adequate for the

desired level of precision and confidence, no sampling design, however sophisticated,

can be useful to the researcher in meeting the objectives of the study. Hence, sampling

decisions should consider both the sampling design and the sample size. Too large a


sample size, however (say, over 500) could also become a problem inasmuch as we

would be prone to committing Type II errors. That is, we would accept the findings of

our research, when in fact we should reject them. In other words, with too large a

sample size, even weak relationships (say a correlation of .10 between two variables)

might reach significance levels, and we would be inclined to believe that these

significant relationships found in the sample are indeed true of the population, when in

reality they may not be. Thus, neither too large nor too small sample sizes help

research projects.

Another point to consider, even with the appropriate sample size, is whether statistical

significance is more relevant than practical significance. For instance, a correlation of

.25 may be statistically significant, but since this explains only about 6% of the variance

(.252), how meaningful is it in terms of practical utility?

Roscoe (1975) proposes the following rules of thumb for determining sample

size:

1. Sample sizes larger than 30 and less than 500 are appropriate for most research.

2. Where samples are to be broken into sub samples; (male/females, junior/seniors,

etc), a minimum sample size of 30 for each category is necessary.

3. In multivariate research (including multiple regression analyses), the sample size

should be several times (preferably 10 times or more) as large as the number of

variables in the study.

4. For simple experimental research with tight experimental controls (matched pars,

etc), successful research is possible with samples as small as 10 to 20 in size.

Critical Discussion Questions

1. Identify the relevant population for the following research foci, and suggest the

appropriate sampling design to investigate the issues, explaining why they are

appropriate. Wherever necessary, identify the population frame as well.


A gain manufacturing firm would like to know the types of guns possessed

by various age groups in Washington DC

A hospital administrator wants to find out if the single parents working in the

hospital have a higher rate of absenteeism than parents who are not single.

A researcher would like to assess the extent of pilferage in the materials

storage warehouse of manufacturing firms on the east coast.

The director of human resources wants to investigate the relationship

between drug abuses and dysfunctional behaviour of blue-collar workers in

a particular plant.

2. Explain why cluster sampling is a probability sampling?

3. What are the advantages and disadvantages of cluster sampling?

4. Describe a situation where you would consider the use of cluster sampling.

5. Explain what precision and confidence are and how they influence sample size.

6. Discuss what is meant by the statement: there is a trade-off between precision and

confidence under certain conditions.

7. The use of a convenience sample used in organizational research is correct

because all members share the same organizational stimuli and go through almost

the same kinds of experience in their organizational life. Comment.

8. Use of a sample of 5,000 is not necessarily better than one of 500. How would you

react to this statement?

9. Non probability sampling designs ought to be preferred to probability sampling

designs in some cases. Explain with an example.

10.Because there seems to be a tradeoff between accuracy and confidence for any

given sample size, accuracy should be always considered more important than

precision. Explain with reasons why you would or would not agree?


11.For situation presented below, indicate what would be the relevant population and

the most appropriate sampling design. Make sure that you discuss the reasons for

your answers.

a. A medical inspector desires to estimate the overall average monthly

occupancy rates of the cancer wards in 80 different hospitals that re evenly

located in the northwestern , southwestern, central, and southern suburbs of

New York city.

b. The director of university women’s Professional advancement (UWPA),

appointed by the president of southern Illinois university at Carbondale to

enhance the status of women on campus some 2 years ago, was listening

to a speech made by the president of the women’s caucus. It suddenly

occurred to the director that it would be a great idea to get the option of

members of this vocal group on how effective they perceived UWPA to be in

enhancing the status of women on campus. She thought she could ask a few

quick questions as the audience left the meeting room. What should the

sampling design be to assess the tastes of this group?

c. A magazine article suggested that “ consumers 35 to 44 will soon be the

nation’s biggest spenders , so advertisers must learn how to appeal to this

over-the –thrill l crowed.” If this suggestion appeals to an appeals to apparel

manufacturer, what should be the sampling design be to assess the tastes of

this group?

d. A consultant had administered a questionnaire to some 285 employees using

a simple random sampling procedure. As she locked at the responses, she

suspected that two questions might not have been clear to the respondents.

She would like to know if her suspicion is well-founded.

12.The Macarthur Co. produces special vacuum cleaners for concurrently cleaning

the inside of cars. About a thousand of these are produced every month with

stamped serial number and stored serially in a stockroom. Once a month an

inspector does a quality control check on 50 of these. When he certifies them as to


quality, the units are selected from the stockroom for sale. The production and

sales managers, however, are not satisfied with the quality control check since,

quite often, many of the units sold are returned by customers because of various

types of defects. What would be the most useful sampling plan to test the 50 units?

Chap - 7 Qualitative Research Methods

Objective

After completing this chapter, the student will be able to:

Define Qualitative Research

Examine The Rational Behind Undertaking Qualitatitive Research

Classify Qualitative Research

7.1 Sources of Data

There are two data sources: secondary sources and primary sources.

a) Secondary Sources

Secondary data are useful not only to find information to solve our research problem,

but also to better understand and explain our research problem. In most research we

need to begin with a literature review: either study on and around our topic of research.

They include books, journal articles, and online data sources such as WebPages of

firms, governments, semi-government organizations and catalogues. The first step is to

locate these sources and then to evaluate the usefulness of the contents of each. Some

research questions can be answered only through secondary data sources, where no

further data collection is needed.


Secondary data can help researchers in the following manner:

Answering research questions or solving some or all of the research

problems;

Helping in problem formulation and /or devising more concrete and focused

research questions;

Deciding about the appropriateness of a certain research method or even

suggesting better research methods for a particular problem;

Providing benchmarking measures and other findings that can be compared

later on with the results of the study at hand.

The following secondary sources can be important for our research:

Internet sites and web pages of different companies and organizations –

www.info.com/companies.

Central and local government studies and reports , state budgets , rules on

international trade regarding imports and exports, and policies on foreign direct

investment, chamber of commerce, national trade development bureaus and

export promotion organizations.

Studies and reports of institutions and departments such as universities ,

telecommunications departments , marketing and other research institutes ,

chambers of commerce and foreign missions such as embassies , trade

centers and consulates.

Census reports on demographics, income levels and consumption patterns.

Academics as well as professional journals and newsletters relevant to the

problem area.

In many countries, different branch organizations publish journals on statistics

regarding their own industry, market share, revenues and imports and exports.

For example, local chambers of commerce, small business associations and

association of retailers.

Historical studies regarding the development of a particular discipline or

problem area. For example there are a number of “handbooks” available on


different topics, such as a handbook textbooks and other published on

international business or a handbook on qualitative research methods.

Textbooks and other published materials directly or indirectly related to the

problem area.

Commercial research companies selling data, such as: AC Nielson

(www.acnielson.com) and synovate (www.synovate.com).

International trade websites, United Nations, international trade statistics and

the world bank (www. Worldbank.org).

And last but not least, thesis’s & reports written by other students in our own

university and in other schools and universities. Many schools keep an up-to-

date record of all the theses written in different disciplines. This is perhaps the

most important secondary source at the earlier stages of our research process.

They provide us with insight not only into our problem area, but also into the

data sources mentioned above.

Advantages of Secondary Data

There are different advantages of secondary data that the researcher can obtain:

It saves an enormous amount of time and money. The researcher needs only go

to the library and locate and utilize the sources. This not only helps the

researcher to better formulate and understand the research problem, but also

broadens the base from which scientific conclusions can be drawn. In other

words, the verification process is more rapid and the reliability of the information

and conclusions greatly enhanced.

In case you need to do a longitudinal study, secondary sources provide excellent

historical data.

The secondary data can also be helpful in segmentation and sampling of your

target group.

Secondary sources facilitate cross-cultural /international research, as it is easier

to compare similar data from two or more countries. A number of international

surveys, for example the World Bank and Euromonitor, provide comparable


cross-country data that can be used as a sole data source or in combination with

some primary data collection.

They can suggest suitable methods or data to handle a particular research

problem. Moreover, they provide a comparison instrument with which we can

easily interpret and understand our primary data. Quite often some research

questions can best be answered by combining information from secondary and

primary data. In most research questions it is necessary to consult some

secondary data sources as this saves time and facilitates better handling of our

research questions.

Guide lines to get started with a search for a secondary data

Identify what you wish to know and what you already know about your topic

Develop a list of key terms and names

Search several of the general guides and directories for papers and or reports

Compile the literature you have found .rework your list of key words and

authors if necessary

Check if you have the information you wished to get.


Consult the various directory guides.

Identify authorities in the area and consult them.

Disadvantages of Secondary Data

There are some serious drawbacks in working with secondary data.

The data are collected for another study with different objectives and they may

not completely fit “our” problem. it is therefore of the utmost importance to identify

what we are studying , what we already know about the topic ,and what we want

to have as further information on it. For example, when studying the export

behaviour of smaller firms, we could use a number of studies undertaken in

different countries and could compare the results with our findings. After a closer

look, however, we might realize that “smaller firms” were defined differently. To

determine the size (small, medium or large), different measurement units were

used. Some studies defined size in terms of sales, some in terms of number of

employees, some in terms of profit and some in terms of square meters of

occupied space, as in the case of retailing firms.

Even if two studies use the same measurement units, the terms of

definition were often different. For example, in a study in Norway, firms with

200-499 employees were defined as medium sized, while in USA firms with

fewer than 500 employees were defined as smaller firms.

Using secondary data from commercial sources may be quite expensive. In this

case you have to compare the cost of collecting primary data as compared with

the price of purchasing the secondary data.


It is the responsibility of the researcher to check the whether findings presented

by another researcher are based on primary or secondary data.

Types of Secondary Data

Several types of secondary data are available, form government reports to company’s

annual reports that are always more upbeat than the reality. In business research, while

doing work for a company, a lot of information is available from internal sources,

including information on customers, suppliers, employees, marketing plans and efforts

and, sometimes, even on the competitors. The researcher cannot accept this

information at its face value, although it is free and readily available.

External sources include published books and journal articles, academic as well as

professional and popular.

b) Primary Data

There are several ways of collecting primary data, particularly in surveys. The most

important methods include:

observation method ,

interview method

through questionnaires ,

through schedules (population census, for example)

other methods which include :

Warranty Cards,

Distribute Audits/Store Audits

Pantry Audits,

Consumer Panels(transitory or continuing consumer panel)


Using Mechanical Devices(Eye camera, Pupilometric camera, psycho

galvanometer),

Projective Techniques/ Indirect interviewing method) which includes

word association test ,

sentence completion test ,

Story completion test,

Verbal projection test,

Pictorial techniques

Thematic apperception test(T.A.T)

Rozenzewig test

Holtsman inkblot Test (HIT)

Tomkins-picture arrangement test

Play technique

Quizzes tests and examinations

Sociometric

Depth Interviews, And

Content Analysis

Selection of Appropriate Method of Data Collection

There are various methods of data collection. As such the researcher must judiciously

select the methods/ methods for his own study, keeping in view of the following factors:

1. Nature ,Scope And Object Of Enquiry :


This constitutes the most important factor affecting the choice of a particular

method .The method selected should be such that it suits the type of inquiry

that is to be conducted by the researcher.

This factor is also important in deciding whether the data already available –

secondary data are to be used or the data not yet available –primary data

are to be collected.

2. Availability of Funds

Availability of funds for the research project determines to a large extent the

method to be used for the collection of the data.

When funds at the disposal of the researcher are very limited, he will have to

select a comparatively cheaper method which may not be as efficient and

effective as some other costly methods.

Finance, in fact, is a big constraint in practice and the researcher has to act

within this limitation.

3. Time Factor

Availability of time has also been taken into account in deciding a particular

method of data collection. Some methods take relatively more time, whereas

with others the data can be collected in a comparatively shorter duration.

The time at the disposal of the researcher, thus, affect the selection of the

method by which the data are to be collected.

4. Precision Required

Precision required is yet another important factor to be considered at the time

of selecting the method of collection of data.

Definition of Qualitative Research


Qualitative research: an unstructured exploratory research methodology based on

small samples that provides insights and understanding of the problem setting.

Qualitative techniques include the class of the observation methods techniques in

which the researcher relies on his or her powers of observation rather

communicating with a respondent in order to obtain information.

Rational For Qualitative Research

There are several reasons to use qualitative research.

It is not always possible, or desirable to use fully structured or formal

methods to obtain information from respondents.

People may be unwilling or unable to answer certain

questions.

People are unwilling to give truthful answers to questions

that invade their privacy , embarrass them, or have a negative

impact on their ego or status.

Examples of such sensitive questions include:

“Have you recently purchased sanitary napkins?

Drugs for nervous tensions?

Pills for anxiety?

People may be unable to provide accurate answers to questions that tap

their subconscious.

The values, emotional drives, and motivations residing at the

subconscious level are disguised from the outer world by

rationalizations and other ego defense.

For example, a person may have purchased an expensive

sports car to overcome feelings of inferiority.

However, if asked, “Why did you purchase this sports car?” he

may say, “ I got a great deal,”

“ My old car was falling apart,” or

“I need to impress my customers and clients.”


In such cases, the desired information can be best obtained

through qualitative research.

Major Classification of Qualitative Research

Qualitative research procedure is classified into either direct or indirect techniques

based on whether the true purpose of the research is known to the respondents.

1) Direct Approach (Non -Disguised)

A Direct Approach is not disguised. The purpose of the research is disclosed to

the respondents or is otherwise obvious to them from the questions asked.

One type of qualitative research in which the purpose of the project are

disclosed to the respondents or are obvious , given the nature of the interview.

The direct approach includes focus groups and depth interviews.

a) A Focus Group: Defined as an interview conducted by a trained moderator in a non

structured and natural manner with a small group of respondents. The main purpose

of focus groups is to gain insights by listening to a group of people from the

appropriate target market talk about issues of interest to the researcher. The value

of the technique lies in the unexpected findings often obtained from a free-flowing

group discussion. They are so popular that many marketing research practioners

consider this technique synonymous with qualitative research.

The key characteristics of a focus group may be described by acronym FOCUS

GROUP.

F ocused (on a particular topic).

O outline prepared for discussion.

C haracteristics of the moderator.

U nstructured.

S ize 8-12.

G roup composition: homogeneous.


R ecorded audiocassettes and videotapes.

O bservation: one way mirror.

U ndisguised.

P hysical setting: relaxed.

S everal sessions needed: 1-3 hours each.

Major Characteristics of Focus Groups

S/N Characteristics Description

1 Group size 8 to 12

2 Group composition Homogeneous, respondents prescreened

3 Physical setting Relaxed , informal atmosphere

4 Time duration 1 to 3 hours

5 Recording Use Of Audiocassettes And Videotapes’

6 Moderator Observational, Interpersonal And Communication

Skills of the Moderator.

Key Qualifications of Focus Groups Moderators

1. Kindness with firmness: The moderator must combine a disciplined

detachment with understanding empathy in order to generate the necessary

interaction.

2. Permissiveness: The moderator must be permissive yet alert to signs that

the groups cordially or purpose is disintegrating.

3. Involvement: The moderator must encourage and stimulate intense

personal involvement.

4. Incomplete understanding: The moderator must encourage respondents

to be more specific about generalized comments by exhibiting incomplete

understanding.

5. Encouragement: The moderator must encourage unresponsive members

to participate.

6. Flexibility: The moderator must be able to improve and alter the planned

outline amid the distractions of the group process.


7. Sensitivity: The moderator must be sensitive enough to guide the group

discussion at an intellectual as well as emotional level.

Procedures for Planning and Conducting Focus Groups

Determine the objectives of the marketing research project & define the

problem.

Specify the objectives of qualitative.

State the objectives /questions to be answered by focus groups.

Write a screening questionnaire.

Develop a moderator’s out line.

Conduct the focus group interview.

Review tapes & analyze the data.

Summarize the findings and plan follow-up research and action.

Other Variations in Focus Groups

Other variations include the following:

1. Two-Way Focus Groups: This allows one target group to listen to and from a

related group. In one application, physicians viewed a focus group of arthritis

patients discussing the treatment they desired. A focus group of the physicians

was then held to determine their reactions.

2. Dual –Moderator Group: This is a focus group interview conducted by two

moderators. One moderator is responsible for the smooth flow of the session,

and the other ensures that specific issues are discussed.

3. Dueling Moderator group: There are two moderators, but they deliberately

take opposite positions on how the issues to be discussed. This allows the

researcher to explore both sides of the controversial issues.

4. Respondent Moderator Groups: In this type of focus groups, the moderator

asks several participants to play the role of moderator temporarily to improve

group dynamics.


5. Client –Participant Groups: Client –personnel are identified and made part of

the discussion group. Their primary role is to offer clarifications that will make

the group process more effective.

6. Mini Groups: These groups consist of a moderator and only 4 or 5

respondents. They are used when the issues of interest require more

expensive probing than is possible in a standard group of 8 to 12.

7. Telesession Groups: Focus group sessions by phone using the conference

call techniques.

Advantages & Disadvantage of Focus Groups

Advantages

Focus groups offer several advantages over other data collection processes.

1. Synergism: putting a group of people together will produce a wider range of

information, insight and ideas than will individual responses secured privately.

2. Snowballing: a bandwagon effect often operates in a group interview, in that

one person’s comment triggers a chain reaction from the other participants.

3. Stimulation: usually after a brief introductory period, the respondents want to

express their ideas and expos e their feelings as the general level of excitement

over the topic increase in the group.

4. Security: because the participant’s feelings are similar to those of other group

members, they feel comfortable and are therefore willing to express their ideas

and feelings.

5. Spontaneity: since participants are not required to answer specific questions,

their responses can be spontaneous and unconventional and should therefore

provide an accurate idea of their views.

6. Serendipity: Ideas are most likely to arise out of the blue in a group than in an

individual interview.

7. Specialization: because a number of participants are involved simultaneously,

use of a highly trained, but expensive, interviewer is justified.


8. Scientific scrutiny: The group interview allows close scrutiny of the data

collection process, in that observers can witness the session and it can be

recorded for later analysis.

9. Structure: The group interview allows for flexibility in the topics covered and the

depth with which they are treated.

10. Speed: since a number of individuals are being interviewed at the same time,

data collection and analysis proceed relatively quickly.

Disadvantages

1. Misuse: Focus groups can be misused and abused by considering the

results as conclusive rather than exploratory.

2. Misjudge: focus group results can be more easily misjudged than the

results of other data –collection techniques. Focus Groups are particularly

susceptible to client and researcher bias.

3. Moderation: Focus groups are difficult to moderate. Moderators with all the

desirable skills are rare. The quality of the results depends heavily on the

skills of the moderator.

4. Messy: the unstructured nature of the responses makes coding, analysis,

and interpretation difficult. Focus groups data tend to be messy.

5. Misrepresentation: Focus group results are not representative of the

general population and are not projectable. Consequently, focus groups

results should not be the sole basis for decision making.

Application of Focus Groups

Focus groups can be used to address substantive issues such as:

Understanding consumer’s perceptions, preferences, and behaviour

concerning a product category.

Obtaining impressions of new product concepts.

Gathering new ideas about older products.

Developing creative concepts and copy material for advertisement.

Securing price impressions.


Obtaining preliminary consumer reaction to specific marketing programs.

The methodological applications of focus groups include:

Defining a problem more precisely.

Generating alternative courses of action.

Developing an approach to a problem.

Obtaining information helpful in structuring consumer questionnaires.

Generating hypothesis that can be tested quantitatively.

Interpreting previously obtained quantitative results.

On Line Focus Group interviews

As in the case of traditional focus groups, online focus group participation is by invitation

only. The respondents are pre recruited, generally from an online list of people who

have expressed an interest in participating. A screening questionnaire is administered

online to qualify the respondents.Thos e who qualify are invited to participate in a focus

group, they receive a time, a URL, a room name, and a password via e-mail. Before the

focus group begins, participants receive information about the focus group that covers

such things as how to express emotions when typing.

Electronic emotion indicators are produced using keyboards characters and are

standard in their use on the internet.

For example : -) and :( are examples of smiling and sad faces.

There are a wide range emotions to choose from, such as “ I am frowning, I

am laughing to myself, I am embarrassed , I am mad now. I am responding

passionately now. This is then followed by the response.

When it is time for the group they move into a web-based chat – room.

They go to the focus group location(URL) and click on the “ Enter Focus

Room Item”

To enter they must supply the room name, user name and pass word that

was e-mailed to them earlier.

In the chat room the moderator and the participants type to each other in

real time.


The general practice is for the moderator to always pose their questions

in all capital letters and the respondents are asked to use upper and

lower cases.

Advantages of On Line Focus Groups

People from all over the country or even the world can participate, and the

client can observe the group from the convenience of the home or office.

Geographical constraints are removed and time constraints are lessened.

Unlike the traditional focus groups , you have the unique opportunity to

contact group participants again at a later date , to revisit issues or

introduce them to modifications in material presented in the original focus

group

The internet enables the researchers to reach segments that are usually

hard to survey: doctors, lawyers, professionals,, working mothers, and

others who lead busy lives and are not interested in taking part in

traditional focus groups.

Disadvantages of Online Focus Groups

Only people that have and know how to use a computer can be

surveyed online.

Since the name of an individual is often private, actually verifying that a

respondent is a member of a target group is difficult.

The lack of general control over the respondent’s environment and their

potential exposure to distracting external stimuli.

Since online focus groups could potentially have respondents scattered

all over the world, the researchers and moderator(s) have no idea what

else the respondents may be doing while participating in the group.

On Line versus Traditional Focus Groups

S/N Characteristics Online Focus

Groups

Traditional, Focus Groups

1 Group size 4-6 8-12


2 Group composition Anywhere in the

world

Drawn from the local are.

3 Time duration 1-1.5 hours 1-3 hours.

4 Physical setting Researcher has

little control

Under the control of the

researcher.

5 Respondent identity Difficult to verify Can be easily verified.

6 Respondent attentiveness Respondents can

engage in other

tasks

Attentiveness can be

monitored.

7 Respondent recruiting Easier, can be

recruited online,

by e-mail, panel

or by traditional

means.

Recruited by traditional

means (telephone, mail,

mail panel)

8 Group dynamics Limited Synergistic , snowballing (

bandwagon) effect

9 Openness of the respondents Respondents are

more candid due

to lack –of-face-

to-face contact.

Respondents are candid,

except for sensitive topics.

10 Nonverbal communication Body language

cannot be

observed,

emotions

expressed by

using symbols.

Body language and

emotions observed.

11 Use of physical stimuli Limited to those

that can be

displayed on the

internet.

A variety of stimuli

(products, advertising,

demonstrations, etc.) Can

be used.

12 Transcripts Available Time consuming and


b) Depth Interview

Depth interview is an unstructured , direct , personal interview in which a single

respondent is probed by a highly skilled interviewer to uncover underlying

motivations, beliefs, attitudes , and feelings on a topic.

The main features of a depth interview may be summarized by the acronym DEPTH.

D epth of coverage.

E ach respondent individually interviewed

P robe the respondent

T alented interviewer required.

H idden motives may be uncovered

Depth- Interviewing Techniques

immediately. expensive to obtain.

13 Observer’s communication

with moderator

Observer can

communicate with

the moderator on

a split screen.

Observers can manually

send notes to the focus

group room.

14 Unique moderator skills Typing, computer

usage, familiarity

with chat room

slang.

Observational

15 Turnaround time Can be set up

and completed in

a few days.

Takes many days for setup

and completion

16 Client travel costs None. Can be expensive.

17 Basic focus group costs Much less

expensive.

More expensive due to

facility rental, food,

video/audio taping, and

transcript preparation.


There are three depth interviewing techniques that have recently gained popularity are

laddering, hidden issue questioning, and symbolic analysis.

1. Laddering: A technique for conducting depth interviews in which a line of questioning

proceeds from product characteristics to user characteristics. Laddering provides a

way to probe into consumers deep underlying psychological and emotional reasons

that affect their purchasing decisions. When determining why a person buys a

product, researchers want to know more than simply “quality” and “low price”.

Laddering requires interviewers to be trained in specific probing techniques in order

to develop a meaningful “mental map” of the consumer’s view of a target product.

The ultimate goal is to combine mental maps of consumers who are similar, which

will lead to the reasons why people purchase particular products. Probing is used to

go beyond the initial responses interview participants give to questions.

When asked why they prefer a product, responses are initially attributing –

related. Examples of these responses would include color, taste, price, size, and

brand name. Each attribute, consequence, and value of the underlying

motivators are found by “climbing the ladder” to the real reasons for purchasing

products.

The Following initial responses with “questions leads to much more useful

information for the marketer:

Answer: “I buy Maybelline cosmetics because it is good brand

name at a reasonable price.”

Question: “why are reasonably priced cosmetics so important to

you?”

Answer: “well, buying quality product that isn’t high priced makes

me feel good about myself because I am spending my money

wisely.”

2. Hidden Issue Questioning: A type of depth interview that attempts to locate personal

sore spots related to deeply felt personal concerns.

3. Symbolic Analysis: A technique for conducting depth interview in which the symbolic

meaning of objects is analyzed by comparing them with their opposites.


Advantages of Depth Interviews

Depth interviews can recover greater depth of insights than the focus

groups.

Depth interviews result in free exchange of information that may not be

possible in focus groups because there is no social pressure to conform

to group response.

Disadvantages of Depth Interviews

Skilled interviewers capable of conducting depth interviews are expensive

and difficult to find.

The lack of structure makes the results susceptible to the interviewer’s

influence, and the quality and completeness of the results depend heavily

on the interviewer’s skills.

The data obtained are difficult to analyse and interpret, and the services of

skilled psychologists are typically required for this purpose.

Focus Groups versus Depth Interviews

S/N Characteristics Focus

Groups

Depth

Interview

1 Group synergy & dynamics + -

2 Peer pressure & group dynamics - +

3 Client involvement + -

4 Generation of innovative ideas + -

5 In-depth probing of individuals - +

6 Uncovering hidden motives - +

7 Discussion of sensitive topic - +

8 Interviewing respondents who are competitors - +

9 Interviewing respondents who are professionals - +

10 Scheduling of respondents - +

11 Amount of information + -


12 Bias in Moderation and interpretation + -

13 Cost per respondent + -

Application of depth Interviews

Depth interviews can be effectively employed in special problem situations, such as

those requiring:

Detailed probing of the respondent ( automobile purchase)

Discussion of confidential , sensitive , or embarrassing topics ?( personal

finance, loose dentures)

Situations where strong social norms exist and the respondent may be easily

swayed by group response (attitude of college students toward sports.

Detailed understanding of complicated behaviour ( department stores

shopping )

Interviews with professional people ( industrial marketing research)

Interviews with competitors , who are unlikely to reveal the information in a

group setting ( travel agents perceptions of airline package travel

programs).

Situations where the product consumption experience is sensory in nature,

affecting mood states and emotions (perfumes, bath soap).

2) Indirect Approach (Disguised)

Indirect Approach disguises the true purpose of the project. It is a type of qualitative

research in which the purposes of the project are disguised from the respondents. A

projective technique is an unstructured and indirect form of questioning encourages

the respondents to project their underlying motivations, beliefs, attitudes, or feelings

regarding the issues of concern.

The main characteristics of projective techniques may be described by the

acronym PROJECTIVE.

P roject the underlying motivation, beliefs, and attitudes.

R relationship association techniques.


O vercome respondent’s unwillingness or inability to answer.

J judgment required in interpretation of responses.

E xpressive techniques.

C onstruction completion techniques.

T hematic: themes are elected.

I ndirect.

V ague situations are used as stimuli.

E exploratory in nature

In projective techniques, respondents are asked to interpret the behaviour of

others rather than describe their own behaviour. In interpreting the behaviour of

others, respondents indirectly project their own motivations, beliefs, attitudes, or

feelings into the situations. Thus, respondent’s attitudes are uncovered by

analyzing their responses to scenarios that are deliberately unstructured, vague

and ambiguous. The more ambiguous the situation, the more respondents

project their emotions , needs, motives, attitudes, and values, as demonstrated

by work in clinical psychology on which projective techniques are based. As in

Psychology, these techniques are classified as associations, completions,

constructions, and expressive.

The commonly used indirect techniques consisting of association techniques,

completion techniques, construction techniques, and expressive techniques will

be discussed.

a) Association Techniques: A type of projective techniques in which in which the

respondent is presented with a stimulus and asked to respond with the first thing that

comes to mind. Word association is a projective technique in which respondents are

presented with a list of words, one at a time. After each word, they are asked to give

the first word that comes to mind. The words of interest, called test words, are

interspersed throughout the list, which also contains some neutral, or filler words to

disguise the purpose of the study.

For example, in the department store study, some of the test words might be:

“location,” “parking,” “shopping, ““quality”, “and “price” The subject’s response to


each word is recorded verbatim and responses are timed so that respondents who

hesitate or reason out can be identified. The underlying reason assumption of these

techniques is that association allows respondents to reveal their inner feelings about

the topic of interest.

Responses are analyzed by calculating:

The frequency with which any word is given as a response ;

The amount of time that elapses before a response is given ; and

The number of respondents who do not respond at all to a test word

within a reasonable period of time.

b) Completion Techniques: A projective technique that requires the respondent to

complete an incomplete stimulus situation. Sentence completion is a projection

technique in which respondents are presented with a number of incomplete

sentence s and asked to complete them.

Sentence Completion. In the context of the store patronage study,

the following incomplete sentences may be used.

A person who shops at sears is

_________________________________________________________

_________________________________________________________

_____________________________________.

A person who receives a gift certificate good for sak’s Fifth Avenue

would be JCPenny is most liked by

_____________________________________________

_____________________________________________

_____________________________________________

_________________.

When I think of shopping in a department store, I

_____________________________________________

_____________________________________________

___________.

Story Completion


Projective techniques in which the respondents are provided with part of

a story and required to give the conclusion in their own words.

c) Construction Techniques: is a projective technique in which the respondent is

required to construct a response in the form of a story, dialogue or description.

Construction techniques are closely related to completion techniques. The two main

construction techniques are :

Picture Response: The roots of picture response techniques can be

traced to the thematic Appreciation Test (TAT), which consists of a

series of pictures of ordinary as well as unusual events; which consists

of a series of pictures of ordinary as well as unusual events. In some of

these pictures, the persons or objects are clearly depicted, while in

others they are relatively vague. The respondent is asked to tell stories

about these pictures. The respondent’s interpretation of the pictures

gives indications of the individual’s personality. For example, an

individual may be characterized as impulsive, creative, unimaginative,

and so on. The name Thematic Appreciation test is used because

themes are elicited based on the subjects perceptual interpretation of

pictures.

Cartoon characters are shown in specific situations related to the

problem. The respondents are asked to indicate the dialogue that one

cartoon character might make in response to the comments of another r

character.

d) Expressive Techniques: Projective techniques in which the respondents is

presented with a verbal or visual situation and asked to relate the feelings and

attitudes of other people to the situation. The two main expressive techniques are

role playing and third –person technique. Role Playing: respondents are asked

to assume the behaviour of someone else. Third –Person Technique: A

projective technique in which the respondent is presented with a verbal or visual

situation and asked to relate the beliefs and attitudes of a third person to the

situation.

Advantages of Projective Techniques


Projective techniques have a major advantage over unstructured direct techniques –

focus groups and depth interviews. They may elicit responses that subjects would be

unwilling or unable to give if they knew the purpose of the study. At times in direct

questioning, the respondent may intestinally or unintentially misunderstand,

misinterpret, or mislead the researcher.

In these cases, projective techniques can increase the validity of responses by

disguising the purpose. This is particularly true when the issue to be addressed is

personal, sensitive, or subject to strong social Norms. Projective techniques are also

helpful when underlying motivations, beliefs, and attitudes are operating at a

subconscious level.

Disadvantages Projective Techniques

Projective techniques suffer from many of the disadvantages of unstructured direct

techniques. These techniques generally require:

Personal interviews with highly trained interviewers.

Skilled interpreters are also required to analyze the responses. Hence,

they tend to be expensive.

Furthermore, there is a serious risk of interpretation bias.

With the exception of word association, all techniques are open and,

making the analysis and interpretation difficult and subjective.

Some projective techniques such as role playing require respondents to

engage in unusual behaviour.

In such cases the researcher may assume that respondents who agree

to participate are themselves unusual in some way.

Therefore they may be not representative of the population of interest.

Applications of Projective Techniques


The effectiveness of these techniques is enhanced when the following guidelines are

observed.

Projective techniques should be used because the required information can

not be accurately obtained by direct methods.

Projective techniques should be used for exploratory research to gain initial

insights and understanding.

Given their complexity, projective techniques should not be used naively.

As a result it is desirable to compare findings generate d by projective techniques with

the findings of the other techniques that permit a more representative sample.

A Comparison of Focus Groups, Depth Interviews, and Projective Techniques

S/N Criteria Focus groups Depth interviews Projective

techniques

1 Degree of structure Relatively high Relatively

medium

Relatively low

2 Probing of individual

respondents

Low High Medium

3 Moderator bias Relatively

medium

Relatively high Low to high

4 Interpretation bias Relatively low Relatively

medium

Relatively high

5 Uncovering subconscious

information

Low Medium to high High

6 Discovering innovative

information

High Medium Low


7 Obtaining sensitive

information

Low Medium High

8 Involve unusual

behaviour

No To a limited

extent

Yes

9 Overall usefulness Highly useful Useful Somewhat useful

Observation Methods

Observation is defined as the recording of behavioral patterns of people, objects and

events in a systematic manner to obtain information about the phenomenon of interest.

The observer does not question or communicate with the people being observed.

Information may be recorded as the events occur or from records of past events.

Conditions for the Use of Observation

Certain conditions must be met before a researcher can successfully use observation

as a research tool. These conditions are:

1. The Event Must Occur in a Short Time Interval: Short-time interval means that

the event must begin and end within a reasonable short time span. Examples

include a shopping trip in a supermarket, waiting in a teller line at a bank, or

purchasing a clothing item. Some decision-making processes can take a long time,

for example buying a home, and it will be unrealistic in terms of the time and money

required to observe the entire process. Because of this factor, observational

research is usually limited to scrutinizing activities and is therefore not


2. The Observed Behaviour Must Occur in a Public Setting: Public behaviour refers

to behaviour that occurs in a setting the researcher can readily observe. Action such

as cooking, playing with one’s children, or private worshipping are not public

activities and are therefore not suitable for observational studies such as those

described here.

3. When the possibility of faulty recall rules out collecting information by asking

the person: Occurs when actions or activities are so repetitive or automatic that the

respondent can not recall specifics about the behaviour under question.

Observation Methods

The observational methods are divided into the following:

1. Structured Vs. Unstructured Observation: Structured observation where the

researcher clearly defines the behaviors to be observed and the methods by which

they will be measured. An example would be an auditor performing inventory

analysis in a store. This reduces the potential for observer bias and enhances the

reliability of the data. Structured observation is appropriate when the marketing

research problem has been clearly defined and the information needed has been

specified. In these circumstances the details of the phenomenon to be observed can

be clearly identified. Structured observations are suitable for use in conclusive

research. In unstructured observation, the observer monitors all aspects of the

phenomenon that seem relevant to the problem at hand, for example, observing

children playing with new toys. This form of observation is appropriate when the

problem has to be yet formulated precisely and flexibility is needed in observation to

identify key components of the problem and to develop hypothesis. In an

unstructured observation, potential for observer bias is high. For this reason, the

observation findings should be treated as hypothesis to be tested, rather than as

conclusive findings. Thus, unstructured observation is most appropriate for

exploratory research.

2. Disguised Vs. Undisguised Observation: In disguised observation, the

respondent is unaware that they are being observed. Disguise enables respondents

to behave naturally, since people tend to behave differently when they know they are


being observed. Disguise may be accomplished by using one way mirrors, hidden

cameras, or in conscipious mechanical devices. Observers may be disguised as

shoppers or sales clerks or in other appropriate roles. In disguised observation, the

respondents are aware that they are under observation. For example, they may be

aware of the prescience of the observer. Researchers disagree on how much effect

the prescience of an observer has on behaviour. One viewpoint is that the observer

effect is minor and short-lived. The other position is that the observer can seriously

bias the behaviour patterns.

3. Natural vs. Contrived Observation: Natural observation involves observing

behaviour as it takes place in the environment. For example, one could observe the

behaviour of respondents eating fast food at Burger King. The advantage of natural

observation is that the observed phenomenon will more accurately reflect the

phenomenon. The disadvantages are the cost of waiting for the phenomenon to

occur and the difficulty of measuring the phenomenon in a natural setting. Contrived

observation involves to observing the behaviour in an artificial environment.

Observation Methods Classified by Mode of Administration

Observation method may be classified by mode of administration as personal

observation, mechanical observation, audit, content analysis, and trace analysis.

1. Personal Observation: an observational research strategy in which human

observers record the phenomenon being observed as it occurs. The observer does

not attempt to control or manipulate the phenomenon being observed. The observer

merely records what takes place. For example, a researcher might record traffic

counts and observe traffic flows in department store. This information could aid in

designing store layout and determining location of the individual departments, shelf

locations, and merchandise displays. Companies like Microsoft also make use of

personal observation in learning about the software needs of users.

2. Mechanical Observation: An observational research strategy in which mechanical

devices rather than human observers record the phenomenon being observed.

These devices may or may not require the respondent’s direct participation. They


are used for continuously recording ongoing behaviour for later analysis. Of the

mechanical devices that do not require respondent’s direct participation, the

ACNielsen audiometer is best known. The audiometer is attached to a television set

to continuously record what channel the set is tuned to.

Recently, people meters have been introduced. People meters attempt

to measure not only the channels to which a set is tuned but also who

are watching.

Other common examples include turnstiles that record the number of

people entering or leaving a building, and traffic counters placed

across streets to determine the number of vehicles passing certain

locations.

On-site cameras – still, motion pictures or video are increasingly used

by retailers to assess package designs, counter space, floor displays,

and traffic patterns.

Technological advances such as the Universal Product Code (UPC)

have made a major impact on mechanical observation.

The UPC system, together with optical scanners, allows for

mechanized information collection regarding consumer purchases by

product category, brand, store type, price, and quantity.

The internet can be very good source for observation and can provide

valuable information. The observations can be made in a variety of

ways. The time spent on the page can also be measured by advanced

techniques of starting the timer when the person clicks on the next

button. Further, various other links can be provided by the researcher

on the web page, and it can be observed to see which links are

accessed more often. The analysis of the links from where the

company site is being approached by the individuals will provide the

market researcher with important information regarding the

consumers related interest , and an in-depth analysis of the link sites

will provide information on advertising , competitors, consumers, and

target market demographics and psychographics.


In contrast to the internet, many mechanical observation devices do

require active respondent involvement.

These mechanical devices may be classified into five groups.

a. Eye-Tracking Monitors: Eye-tracking equipment, such as coulometer eye cameras,

or eye view minutes, records the gaze movements of the eye. These devices can be

used to determine how a respondent reads an advertisement or views a TV

commercial and for how long the respondent looks at various parts of the stimulus.

Such information is directly relevant to assessing advertising effectiveness.

b. Pupil Meters: The Pupillometer measures changes in the diameter of the pupils of

the respondent‘s eyes. Respondents are asked to look at a screen on which an

advertisement or other stimulus is projected. Image brightness and distance from

respondents eye’s are held constant. Changes in pupil size are interpreted as

changes in cognitive activity resulting from exposure to the stimulus. The underlying

assumption is that increased pupil size reflects interest and positive attitudes toward

the stimulus.

c. Psycho Galvanometers: Psycho Galvanometer an instrument that measures a

respondent’s galvanic skin response (GSR) Galvanic skin response changes in the

electrical resistance of the skin. The respondent is fitted with small electrodes that

monitor electrical resistance and is shown stimuli such as advertisements,

packages, and slogans. The theory behind this device is that physiological changes

such as increased perspiration accompany emotional reactions. Excitement leads to

increased perspiration, which increases the electrical resistance of the skin. From

the strength of the response, the researcher infers the respondent’s interest level

and attitudes toward the stimuli.

d. Voice Pitch Analyzers: Measures emotional reactions through changes in the

respondent’s voice. Changes in the relative vibration frequency of the human voice

that accompany emotional reaction are measured with audio-adapted computer

equipment.

e. Devices Measuring Response Latency: Is the time a respondent takes before

answering a question. It is used as a measure of the relative preference for various


alternatives. Response time is thought to be directly related to uncertainty.

Therefore, the longer a respondent takes to choose between two alternatives, the

closer the alternatives are in terms of preference. On the other hand, if the

respondent makes a quick decision, one alternative is clearly preferred. With the

increased popularity of computer-assisted data collection, response latency can be

recording accurately and without the respondent’s awareness.

3. Audit: The researcher collects data by examining physical records or performing

inventory analysis. Audits have two distinguishing features. First, data are collected

personally by the researcher. Retail and wholesale audits conducted by marketing

research suppliers were discussed in the context of syndicated data. In pantry audit,

the researcher takes an inventory of brands, quantities, and package sizes in a

consumer’s home, perhaps in the course of a personal interview. Pantry audits

greatly reduce the problem of untruthfulness or other forms of response bias.

However, obtaining permission to examine consumer’s pantries can be difficult, and

the fieldwork is expensive. Furthermore, the brands in the pantry may not reflect the

most preferred brands or the brands purchased most often.

4. Content Analysis: Content analysis: is an appropriate method when the

phenomenon to be observed is communication, rather than behaviour or physical

objects. It is defined as the objective, systematic, and quantitative description of the

manifest content of a communication. It includes observation as well as analysis.

The unit of analysis may be words –different types of words or types of words in the

message, character - individuals or objects, themes- propositions, space and time

measures –length or duration of the message, or topics –subject of the message.

Analytical categories for classifying the units are developed and the communication

is broken down according to prescribed rules. Marketing research applications

involve observing and analyzing the content or message of advertisements,

newspaper articles, television and radio programs, and the like. For example, the

frequency of appearance of blacks, women, and members of other minority groups

in mass media has been studied using content analysis. Suppose we wanted to

examine how the portrayal of women in U.S magazine advertising has changed,

positively or negatively, over 10- year period from 1996 to 2006. We consider select


a sample of 100 magazines that were in circulation in 1996 as well as in 2006. We

could select 10 advertisements featuring women for each of these magazines, from

different issues of each magazine, for 1996 as well as 2006. This will give us a

sample of 1,000 advertisements for each year. We could then develop positive as

well as negative categories for classifying the advertisements based on

5. Trace Analysis: An observation approach in which data collection is based on

physical traces, or evidence, of past behaviour. These traces may be left

intentionally or unintentionally by the respondents. Several other innovative

applications of trace analysis have been made in marketing research. The selective

erosion of tiles in a museum indexed by the replacement rate was used to determine

the relative popularity of exhibits. The number of different fingerprints on a page the

S/N Criteria Personal

observation

Mechanical

observation

Audit Content analysis Trace

analysis

1 Degree of

structure

Low Low to high High High Medium

2 Degree of

disguise

Medium Low to high Low High High

3 Ability to

observe in

natural

setting

High Low to high High Medium Low

4 Observation

bias

High Low Low Medium Medium

5 Analysis

bias

High Low to

medium

Low Low Medium

6 General

remarks

Most

Flexible

Can be

intrusive

Expensive Limited to

communications

Method

of last

resort


readership of various advertisements in a magazine. The position of the radio dials

in cars brought in for service was used to estimate share of listening audience of

various radio stations. Advertisers used the estimates to decide on which stations to

advertise. The age and conditions of cars in a parking lot were used to assess the

affluence of customers. The magazines people donated to charity were used to

determine people’s favorite magazines. Internet visitors leave traces that can be

analyzed to examine browsing and usage behaviour by using cookies.

A Comparative Evaluation of Observations Methods

Relative Advantages of Observations

They permit measurement of actual behaviour rather than reports of intended or

preferred behaviour. There is no reporting bias, and potential bias caused by the

interviewer and the interviewing process is eliminated or reduced. Certain types of data

can be collected only by observations.

These include behaviour patterns that the respondent is unaware of or

unable to communicate.

For example, information on babies’ toy preferences is best obtained by

observing babies at play.

Since they are unable to express themselves adequately.

Moreover, if the observed phenomenon occurs frequently or is of short

duration, observational methods may be cheaper and faster than survey

methods.

Relative Disadvantages of Observations

The observed behaviour may not be determined because little is known about the

underlying motives, beliefs, attitudes, and preferences.

For example, people observed buying a brand of cereal may or may not

like it themselves.

They may be purchasing that brand for someone else in the household.

Selective perception-bias in the researcher’s perception can bias the

data.


Observation is often is time consuming and expensive, and it is difficult to observe

certain forms of behaviour such as personal activities. In some case the use of

observation methods may be unethical, as in monitoring the behaviour of people without

their knowledge or consent. To recap, a limitation of observation is that motivations,

attitudes, and other “internal” conditions can not be observed.

Analysis of Qualitative Data

Compared to quantitative research , where numbers and what they stand for are the

unit of analysis , qualitative data analysis use swords as the units of analysis and is

guided by fewer universal rules and standard procedures.

The goal of the qualitative research is to decipher, examine, and interpret

meaningful patterns or themes that emerge out of the data.

The “meaningfulness” of patterns and themes is determined by the

research question at hand.

There are three general steps that should be followed when analyzing qualitative

data:

Data Reduction: In this step the researcher chooses which aspects of the

data are emphasized, minimized, or set aside for the project at hand.

Data Display: in this step the researcher develops visual interpretations of

the data with the use of such tools as a diagram, chart or matrix. The

display helps to illustrate patterns and interrelationships in the data.

Conclusion Drawing and Verification: In this step, the researcher considers

the meaning of analyzed data and assesses its implication for the research

questions at hand.

Review Questions

1. Define qualitative research methods

2. Identify and explain qualitative research Methods

3. Is qualitative method inferior as compared to Quantative method?


Chap-8 Quantitative Research Methods

Chapter objective


Define a survey method

Discuss the benefits and demerits of survey methods

Identify and examine various survey methods

7.1 Survey methods


A structured questionnaire given to a sample of a population and designed to elicit

specific information from respondents.

Structured data collection is the use of a formal questionnaire that presents questions

in a prearranged order.

Fixed –alternative questions are questions that require respondents to choose from a

set of predetermined answers.

Advantages of Surveys

Compared to observation or other qualitative methods, survey methods allow the

collection of significant amounts of data in an economical and efficient manner; and they

typically allow for much larger sample sizes.

There are five advantages of using survey methods:

1) Standardization:

Questions are preset and organized in a particular arrangement on a

questionnaire

Survey methods ensure that all respondents are asked the same

questions and are expose d to the same response options for each

question.

2) Ease of administration

Survey modes are easy for administration as the respondent may fill out

the questionnaire unattended.

The administration aspects of survey are much simpler than, for instance,

conducting a focus group or utilizing depth interviews.

3) Ability to tap the “ unseen”

The four question of what, why, how, and who help uncover “unseen” data.

For instance we can ask customers their motives a particular brand, their

attitude toward a particular product or the company, etc.

Much information is unobservable and requires direct questions.

4) Suitability to tabulation and statistical analysis


The commercial researcher ultimately must interpret the patterns or

uncommon themes sometimes hidden in the raw data he or she collects.

Statistical analysis, both simple and complex, is the preferred means of

achieving this goal , and large cross sectional surveys perfectly

complement these procedures.

Increasingly, questionnaire design software includes the ability too

perform simple statistical analysis , such as tabulations of the answers to

each question, as well as the ability to create color graphs summarizing

these tabulations.

5) Sensitivity To Subgroup Differences

Surveys involve large numbers of respondents.

It is relatively easy to divide the sample into demographic groups or other

subgroups and then to compare them for market segmentation

implications.

In fact, the survey sample design may be drawn up to specifically include

important subgroups as a means of looking at market segment differences.

In any case, the large sample sizes that characterize surveys facilitate

subgroup analysis and comparisons of various groups existing in the

sample.

7.2 Types of Survey Methods / Data Collection Modes

There are three major methods of collecting information from respondents:

I) Person –administered Surveys (PAS)

A person –administered surveys is one in which an interviewer reads questions

to the respondent and records his or her answer. It was the primary

administration method for many years. However, its popularity has fallen of as

communications systems have developed and technology has advanced.

Nevertheless, person-administered surveys are still used.

Advantages


Person administered surveys have four unique advantages:

1. Feed back

Interviewers often must respond to direct questions from respondents during an

interview. Sometimes respondents do not understand the instructions, or they

may not hear the question clearly, or they might become distracted by some

outside factor during the interview. A human interviewer may be allowed to adjust

his or her questions according to verbal or nonverbal cues and provide

clarifications whenever a need arise.

2. Rapport

It is often helpful to have another human being present to develop some rapport

with the respondent early on in the questioning process. Once a bridge of trust

and understanding has been established, most respondents will become visibly

more relaxed with the interview and will open up more to the various questions

being posed.

3. Quality control

An interview sometimes must select certain types of respondents based on sex,

age, or some other distinguishing characteristics. Personal interviewer may be

used to ensure respondents are selected correctly. Alternatively, some

researchers feel that respondents are more likely to be truthful when they

respond face to face.

4. Adaptability

Personal interviewers can adapt to respondent differences. It is not unusual , for

instance , to find an elderly person who must be initially helped step-by-step

through the answering process in order to understand how to respond to

question asking if the respondent “ strongly agrees,” “ somewhat agrees,” “

somewhat disagrees, “ or “ strongly disagrees”

Disadvantages of Personal Administered Surveys

Person administered surveys have four unique disadvantages:

Slower


Prone to errors; and

More expensive than interviewing on the telephone or mailing the

questionnaire to respondents.

Interviewing Techniques in Personal Administered Surveys

1) In-Home Interview

Two important factors justify the use of in-home interviews.

The researcher must believe that personal contact is essential to the

success of the interview.

The survey may incorporate a set of advertisements the

researcher wants viewed.

It might require the respondent to see and touch a product,

the respondent may have to perform a complicated task

such as sorting cards with brand names on them into piles,

or it might be vital that the interviewer make visual

confirmations of the respondent’s qualifying characteristics

or nonverbal causes.

The researcher must be convinced that the in-home environment is

conducive to the questioning process.

It is often believed that conducting an interview in the home

greatly improves the quality of responses and facilitates the

rapport between interviewer and interviewee.

When a respondent is a secure, comfortable environment,

the likelihood of distraction is reduce, and it is believed that

respondents take more care in responding to various

questions.

2) Mall-Intercept Interview

The expense of in-home interviewer travel is high, even for local surveys,

the mall-intercept interview is one in which the respondent is encountered

and while he or she is visiting a shopping mall.


Advantages

Mall intercept interviewing has acquired a major role as a survey

method due to its ease of implementation and low cost.

The presences of an interviewer who can interact with the

respondent.

Disadvantages

1. Sample representativeness is an issue, for most malls draw from a relatively

small area in close proximity to their location.

Some people shop at malls more frequently than other and

therefore have a greater chance of being interviewed.

2. Shortcomings of small intercept interviewing are that a shopping mall does

not have a comfortable home environment that is conducive to rapport and

close attention to details.

The respondent may feel uncomfortable as they may be

pressed for time or otherwise preoccupied by various

distractions outside the researcher’s control.

These factors may adversely affect the quality of the interview.

3) In-Office Interview

Business research conducted in the business –to-business or

organizational market typically requires interviews with business

executive, purchasing agents, engineer, or other managers.

Normally, in-office interviews take place in person while the respondent is

in his or her office.

In office personal interviews incur relatively high costs.

4) Telephone Interviews (“Traditional “Telephone Interview & Central Location

Telephone Interview)


Telephone interviewing is an attractive option.

Advantages

Telephone survey has the potential to yield a very high quality sample.

If the researcher employs random dialing procedures and proper

call-back measures, the telephone approach may produce a better

sample than any other survey procedure.

Telephone surveys have very quick turnaround times. Most telephone interview

are of short duration anyway, but a good interviewer may complete several

interviews per hour.

Disadvantages

The respondent cannot be shown anything. This shortcoming ordinarily

eliminates the telephone survey as an alternative institutions requiring that the

respondent view product prototypes, advertisements, package s, or anything

else.

The telephone interview does not permit the interviewer to make the various

judgments and evaluations that can be made by the face-to- face interviewer.

Questions on alcohol consumption, contraceptive methods, racial

issues, or income tax reporting will probably generate more valid

responses when asked in the relative anonymity of the telephone

than when administered face-to-face.

The researcher is more limited in the quantity and types of information he or she

can obtain.

Very long interviews are inappropriate for the telephone, as are

questions with lengthy lists of response options that respondents

will have difficulty remembering when they are read over the

telephone.

A last problem with telephone interviews is the growing use of

answering machines and caller recognition devices being adopted

by consumers.


II)Computer Administered Surveys(CAS)

Computer technology represents a viable option with respect to survey

mode.

Computer assisted surveys are in an evolutionary state, and they are

spreading to other types.

Basically, a computer administered survey is one in which computer

technology plays an essential role in the interview work.

Here, either the computer assists an interview, or it interacts directly with

respondent.

In the case of internet questionnaires, the computer acts as the medium by

which potential respondents are approached, and it is the means by which

respondents return their completed questionnaire.

Advantages of Computer Administered Surveys (CAS)

Computer Administered Surveys have five advantages:

1) Speed

The compute administered approach is much faster than the human interview

approach.

The speed factor translates into cost savings, and there is a claim that

internet surveys are about one-half the cost of mail or phone surveys.

2) Error-Free Interviews

Properly programmed , the computer administered approach guarantees

zero interviewer errors such as:

Inadvertently skipping questions,

Asking inappropriate questions based on previous

responses,

Misunderstanding how to pose questions,

Recording the wrong answer, and

The computer neither becomes fatigued nor cheats.

3) Use of Pictures , Videos, And Graphics


Computer graphics can be integrated into questions as they are viewed on a

computer screen.

CD-ROM disk capabilities allow high quality video windows to appear so the

respondent can see the product in use or can be shown a wide range of

visual displays.

4) Real-Time Capture Of Data

Respondents are interacting with the computer and not a human who is

recording their answers on a questionnaire, the information is directly entered

into a computer‘s data storage system and can be accessed for tabulation or

other analyses at completed in a matter of minutes.

This feature is so beneficial that some interview companies have telephone

interviewers directly linked to computer input when they conduct their

interviews.

5) Reduction of “ Interview Evaluation” Concern In Respondent

Some people, when they are involved in responding to questions in a survey,

become anxious about the possible reaction of the interviewer to their

answers.

This is known as interviewer evaluation apprehension.

Questions about personal hygiene, political opinions , financial matters , and

even age are considered to be “ personal” by many people , and the

presence of a human interviewer may deter them from answering , or they

may give false answers that they believe the interviewer will accept.

On the other hand, some respondents try to please the interviewer by saying

what they think the interviewer wants to hear.

In any case, some researchers believe that respondents will provide more

truthful answers to potentially sensitive topics when interacting with a

computer.

Dis Advantages of Computer Administered Surveys (CAS)

Computer Administered Surveys have five dis advantages:


Although computers are relatively inexpensive at present, there are significant

costs involved in computer design, programming, and set-up, which must be

incurred with each survey.

These costs, including the time factor associated with them, often render

computer administered delivery systems for surveys unattractive relative to other

data collection options.

However, set up costs are falling rapidly with user friendly programs such as

decisive survey.

Interviewing Techniques in Computer Assisted Surveys

The most important computer –assisted interview methods includes the following:

1) Computer assisted Telephone Interview ( CATI )

The most advanced companies have computerized the central location

telephone interviewing process; such systems are called computer assisted

telephone interviews (CATI).

Although each system is unique, and new development occur almost daily; we

can describe a typical situation.

The interviewer reads the question to the respondent, enters the response

code, and the computer moves on the next appropriate question.

The human interviewer is just the “voice “of the computer.

Advantages

Computer assisted telephone interviewing options are very attractive to business

researchers because of :

The Cost savings, quality control, and time savings over the paper-and-

pencil method.

With computer prices falling and questionnaire design software becoming

available, more companies are adopting the high-technology approach.


The prospect of CATI is very good, and marketing researchers can be assured

that when they consider telephone interviews in the future, the computer assisted

option will be an attractive option available to them.

2) Fully computerized interview

Some companies have developed fully computerized interviews, in which the

survey is administered completely by a computer.

In the research industry, this approach is known as CATS, completely

automated telephone survey.

CATS have been successfully employed for customer satisfaction studies,

service quality monitoring, product/warranty registration, and even in-home

product tests with consumers who have been given a prototype of a new

product.

III) Self-Administered Surveys (SAS)

A self –administered survey is one in which the respondent completes the

survey on his or her own.

It is different from other survey methods in that there is no agent human or

computer administering the interview.

So, the respondent reads the questions and responds directly on the

questionnaire.

Normally, the respondent goes at his or her own pace, and in most instances

he or she selects the place and time to complete the interview.

He or she also may decide when the questionnaire will be returned.

As with other survey methods, those that are self –administered have their

advantages and disadvantages.

Advantages of Self –Administered Surveys (SAS)

Self –administered surveys have three important advantages:

1) Reduced Cost


By eliminating the need for an interviewer or an interviewer or an

interviewing device such as a computer program, there can be significant

savings in cost.

2) Respondent control

Respondents can control the pace at which they respond, so they may

not feel rushed.

Ideally, a respondent should be relaxed while responding and a self-

administered survey any effect this state.

3) No interviewer evaluation apprehension

As we just noted, some respondents feel apprehensive when answering

questions.

The self-administered approach takes the administrator, whether human

or computer, out of the picture, and respondents may feel more at ease.

Disadvantages of Self –Administered Surveys (SAS)

Self administration places control of the survey in the hands of the prospective

respondent. Hence, this type of survey is subject to the possibilities that:

Respondents may not complete the survey

Respondents may answer questions erroneously,

Respondents may not respond in a timely manner, or

Respondents may refuse to return the survey at all.

The major reason for these drawbacks is that no opportunity exists to monitor or interact

with the respondent during the course of the interview. Due to the absence of the

interviewer, the burden of respondent understanding falls on the questionnaire itself. It

must have very clear instructions, examples and reminders throughout.

Interviewing Techniques in self-Assisted Surveys


Self administered Survey includes:

1) Group Self-Administered Surveys

Basically, a group self-administered survey entails administering a

questionnaire to respondents in groups, rather than individually, for

convenience or to gain certain economies.

One way to be more economical is to have respondents self –administer the

questions.

As you would suspect , it is handled in a group context primarily to reduce

costs and to provide the ability to interview a large number of people in a

short time.

Variations for group self-administered surveys are limitless.

Students can be administered surveys in their classes: church groups can be

administered surveys during meetings; social clubs and organizations,

company employees , movie theater patrons, and any other group can be

administered surveys during meetings , work, or leisure time.

2) Drop-Off Surveys

Another variation of the self administered survey is the drop-off survey , in which

the survey representative approaches a prospective respondent, introduces the

general purpose of the survey to the prospect , and leaves it with the

respondent to fill out in his or her own .

Essentially, the objective is to gain the prospective respondents cooperation.

The respondent is told the questionnaire is self-explanatory, and it will be left with

him or her to fill out leisure.

Perhaps the representative will return to pick up the questionnaire at a certain

time, or the respondent may be instructed to complete and return it by prepaid

mail.

3) Mail Survey

A mail survey is one in which the questions are mailed to prospective respondents who

are asked to fill them out and return them to the researcher by mail. Part of its

attractiveness stems from its self administered aspect. There are no interviewers to


recruit, train, monitor, and compensate. Similarly, mailing lists are readily available from

companies that specialize in this business, and it is possible to access very specific

groups of target respondents. Besides, on a pre-mailed respondent basis, mail surveys

are very inexpensive. In fact, they are almost always the least expensive survey method

in this regard. But mail surveys incur all of the problems associated with not having an

interviewer present. The mail survey is plagued by two major problems.

Non-response-which refers to questionnaires that are not returned.

Self-selection bias-which means that those who do respond are

probably different from those who do not fill out the questionnaires

and return it , and therefore that sample gained through this method

is non-representative of the general population.

Factors Determining the Choice of a Particular Survey Method

When deciding on a survey data collection method, a researcher should take most of

the following factors into consideration:

1) Researcher ‘S Resources And Objective

In the case of data collection, the driving objectives are translated into a single concern:

the quality of information desired.

Balanced this objective is two critical constraints namely:

a) The Survey data collection time horizon

Regardless of its nature , every management decision has a

deadline when it must be resolved , an this dead line directly

impacts the amount of time allowed , or the time horizon, for a

particular commercial research survey.

In some instances, the deadline is distant, and the researcher is

allowed the luxury of choosing his or her data collection methods.

Frequently, however, the time period is compressed , and the

researcher is forced into choosing a data collection method that


may not be his or her first choice but is one that does a reasonable

job within the permissible time horizon..

Some approaches , such as mail surveys , door-to-door , and

personal interviews , require long time periods; whereas other ,

principally telephone studies and mall intercept surveys , typically

take much less time.

b) Survey data collection budget

The commercial researcher frequently encounters in which the budget

available for a study greatly influences the survey method used.

In truth, the budget alone does not dictate the choice of a survey

interviewing method; rather, the budget constraint in combination with

other considerations influences the survey mode choice.

c) Desired Quality Of Data

Some researchers claim that quality, not quantity, of data is the main

issues in deciding on which data collection method to use.

For instance, you are aware that mail surveys suffer from low response

and self-selection problems.

Obviously, if the researcher desires a comprehensive sample and an

extremely accurate sample, a mail survey is unlikely to achieve this

objective because of non-response.

Similarly, for sensitive topics, the face-to-face alternatives may create

embarrassment for the respondent, who will then give socially desirable

answers rather than the truth, thus compromising the results.

The two aspects of data quality includes:

i. Generalizablity

Generalizablity is the confidence that the researcher

has that the data collected from a particular method

ensure respondents who accurately describe the

population under study.

Mall-intercept interviews have Generalizablity

shortcomings because they are conducted only with


mall shopper , whereas telephone interviews cover

the population better and are therefore more

generelizable if the survey is aimed at all types of

shoppers.

ii. Completeness

Which is defined as the depth and breadth of

information obtained from each person?

Mail surveys can garner completeness, but they suffer

on the Generalizablity criterion because of non

response and self selection problem which eliminates

certain types of potential respondents.

2) Respondent characteristics

At least four characteristics of the targeted group must be considered in the choice

of a data collection mode:

a) Incidence rate

The incidence rate is the percentage of people in the general population

that fits the qualifications of those people the researcher desires to have

interviewed.

Telephone interviewing offers an efficient device to screen the population

and identify low-incidence prospective respondents.

In fact, it is common to us telephone screening as a qualifying phase.

The individuals who satisfy the criteria for participating in the survey are

subsequently interviewed by whatever other method is most suitable.

b) Willingness to participate

Respondent’s willingness is a serious consideration in the survey.

High refusal rates always alert the researcher to question the

representativeness of the results.


Two types of unwillingness will be encountered in any survey.

A generalized suspicion or desire for privacy in many people:

they simply do not like to participate in surveys.

Pertains to topic –specific tendencies. That is, some people

will not participate because the topic is something they do not

want to discuss.

In any case, self-selection takes place.

Willingness can be affected by the survey method.

For example people find it more difficult to refuse a face-to-face request to

participate than to decline a telephone request or a mailed questionnaire.

c) Diversity of respondents

Nothing is more frustrating to an interviewer than a prospective

respondent qualified and willing to participate in the survey but unable to

do so at that time.

A prospective mail respondent puts the questionnaire in a drawer and

then forgets about it , or a telephone interviewer encounters a

respondent whose favorite television show is about to begin.

As a general rule, personal contact should increase the ability of

prospective respondents to participate because respondents will make time

available more readily when approached in person.

A mail survey, on the other hand, has no such social facilitator.

With telephone surveys, lack of participation can be lessened through a

rescheduling of the interview or a call-back policy for non respondents.

3) Characteristics of Questions Asked By Researcher

Because all interviews concern questions, it is necessary to look at:

a) Complexity of tasks


Some commercial research studies have fairly complicated preparation and

administration procedures.

Taste tests or TV ad tests fall into this category.

Taste tests, for example, typically require food presentation done under

controlled conditions so that the researcher can be certain that each person

interviewed responded to the same stimulus.

In general, the more complex the tasks and preparation activities in the

interview, the greater the need for personal administration.

b) Amount of Information Required Per Respondent

The amount of information sought from each respondent varies greatly from

study to study.

Traditionally, phone surveys are short, whereas in-home personal interviews

are long and other data collection modes fall in between.

Strategies range from scheduling a series of interviews to combining

different data collection methods, such as merging a telephone interview

and a follow up mail survey.

Monetary or other incentives have also been used to entice respondents to

“stay with” the interview.

If the researcher must collect a great deal of information from each

respondent in one interview , then two option exist:

To use a personal interviewer whose physical; prescience

compels the respondent to complete the interview.

To allow the respondent to complete the survey at his or her

own pace and without interviewer prescence.

Here, the mail survey or drop-off method would fit, probably

augmented with sizeable incentives to reduce non response

bias.

c) Topic Sensitivity

The sensitivity of the topic is rarely a consideration for the vast majority of

commercial research studies.


With the exception of a question on income level, which most studies

include, the remaining questions on brands purchased, stores patronized,

satisfaction with present products, or buying intentions, for example, are

typically very low in sensitivity.

Occasionally, however, a researcher finds himself or herself working with

topics that are inherently sensitive.

Such topics include medication abuse, financial matters, blood donation,

retail issues, and personal hygiene.

Industry wisdom suggests that for sensitive issues , face-to –face

interviews are less appropriate than alternatives such as telephone

interviews or computer-administered interviews in which the respondent

will not feel ill at ease when divulging some personal habit or sensitive

answer to a machine.

Review Questions

1. List the major advantages of survey research methods over qualitative

methods and identify the major drawbacks of survey.

2. What are the advantages of person-administered surveys over computer-

administered ones?

3. Indicate the difference between: (a) in-home interviews, (b) mall-intercept

interviews, and (c) in-office interviews. What do they share in common?

4. Indicate the pros and cons of self-administered surveys.

5. How does a drop-off survey differ from a mail survey?

6. What are the three aspects of the researcher’s resources and objectives that

have much influence in the determination of which survey mode will be

used? How does each one affect he decision?

7. Differentiate “incidence rate” from “diversity of respondents”/

8. Is a telephone interview inappropriate for a survey that has as one of its

objectives a complete listing of all possible advertising media a person was

exposed to in the last week? Why or why not?


Chap - 9 Measurement in Research

Chapter Objectives



Define measurement

Explain the different types of scales

Describe international diamensions of operational definition

8.1 Definition of Measurement in Research

Measurement is the assignment of numbers or other symbols to characteristics of

objects according to certain prespecified rules.

A measurement is a number that reflects some characteristics of an object

Note that what we measure is not the object, but some characteristics of it.

Thus, we do not measure consumers- only their perceptions, attitudes,

preferences, or other relevant characteristics.

In marketing research, numbers are usually assigned for one of two reasons.

First, numbers permit statistical analysis of the resulting data.

Second, numbers facilitate the communication of measurement rules and

results.

8.2 Definition of Scaling

Scaling: is the generation of a continuum upon which measured objects are located.

To illustrate consider a scale from 1 to 100 for locating consumers according to the

characteristics “attitude toward department stores.” Each respondents is assigned a

number from 1 to 100 indicating the degree (un)favorableness, with 1 = extremely

unfavorable, and 100 = extremely favorable. Measurement is the actual assignment of a

number from 1 to 100 to each respondent. Scaling is the process of placing the

respondents on a continuum with respect to their attitude toward department stores. In

the opening example of most admired companies, the assignment of numbers to reflect

the annual revenue was an example of measurement. The placement of individual

companies on the annual revenue continuum was scaling.

8.2 Primary Scales of Measurement


There are four primary scales of measurement: nominal, ordinal, interval, and ratio.

The four primary types of scales may be described by the Acronym FOUR.

F figurative: Nominal

O rdinal scale

U unconstructed zero point: interval scale

R atio scale

The different comparative and non comparative scales may be represented by the

acronym SCALES

S semantic differential scale

C nstant Sum scale

A rranged in order: rank order scale

L ikert scale

E nagaged : paired comparison scale

S taple scale

The four scales briefly discussed here in under.

1) Nominal Scales: is defined as a scale whose numbers serve only as labels or tags

for identifying and classifying objects with a strict one-to-one correspondence

between the numbers and the objects. For example, the numbers assigned to the

respondents in a study constitute a nominal scale is used for the purpose of

identification, there is a strict one-to-one object and each object has only one

number and the objects. Each number is assigned to only one object and each

object has only one number assigned to it. Common examples include social

security numbers and numbers assigned to football players.


For example, In marketing research, nominal scales are used for identifying

respondents, brands, attributes, stores, and other objects. When used for

classifying purposes, the nominally scaled numbers serve as labels for classes or

categories . For example, you might classify the control group 1 and the

experimental group as group 2. The classes are mutually exclusive and

collectively exhaustive. The objects in each class are viewed as equivalent with

respect to the characteristics represented by the nominal number.

All objects in the same class have the same number and no two classes have the

same number. The numbers in a nominal scale do not reflect the amount of the

characteristics possessed by the objects.For example, a high school security

number does not imply that the person is in some way superior to those with

lower social security or vice versa.

Sample Nominal –Scaled Questions

1) Please indicate your gender. Male Female

2) Check all the brands you would consider purchasing.

____________ Sony

____________Jvc

____________Panasonic

___________Philips

2) Ordinal Scales : is defined as a ranking scale in which numbers are assigned to

objects to indicate the relative extent to which some characteristics is possessed.

Thus, it is possible to determine whether an object has more or less of

characteristics than some other object. The object ranked first has more of the

characteristics as compared to the object ranked second, but whether the object

ranked second is a close second or a poor second is knot known. Common

examples of ordinal scale include quality rankings, ranking of teams in a tournament,

socioeconomic class, and occupational status. In marketing research, ordinal scale

is used to measure relative attitudes, opinions, perceptions, and preferences.

Sample Ordinal –Scaled Questions


1. Please rank each brand in terms of your preference. Place a “I” by your first

choice, a “2” by your second choice, and so on.

____________ Pepsi

___________ Seven Up

____________Coca Cola

2. For each pair of supermarkets, circle the one you would be more likely to

patronize.

Loyal Versus Hadya

Bambis versus Tana

3. In your opinion , would you say the prices at Bambis are

_____________higher than Hadya

_____________about the same as she Hadya

_______________Lower than Loyal

3. Interval Scales: is defined as a scale in which the numbers are used to rate objects

such that numerically equal distances on the scale represent equal distances in the

characteristics being measured. The difference between any two adjacent scale

values is identical to the difference between any other two adjacent values of an

interval scale. There is a constant or equal interval between scale values.

The difference between 1 and 2 is the same as the difference between 2 and 3, which is

a temperature scale. In marketing research, attitudinal data obtained from ratings scales

are often treated as interval data. In the interval scale, the location of the zero point is

not fixed. Both the zero point and the units of measurement are arbitrary. Hence, any

positive linear transformation of the form y= a +bx preserve the properties of the scale.

Here, x is thus the original scale value, y is the transformed scale value, b is a positive

constant, and is any constant.


Therefore, two interval scales that rate objects A, B, C, and D as 1, 2, 3, and 4, or

as 22, 24, 26, and 28, are equivalent. Note that the latter scale can be derived from the

former by using a=20 and b=2 in the transforming equation. Because the zero point is

not fixed, it is not meaningful to take ratios of scale values. As can be seen, the ratio of

D to B values changes from 2: 1 to become 7:6 when the scale is transformed. Yet

ratios of differences between scale values are permissible.

In this process, the constants a and b in the transforming equation drop out in the

computations. The ratio of the difference between D and B to the difference between C

and B is 2: 1 in both the scales.

Sample Interval –Scaled Questions

1. Please rate each brand in terms of its overall performance.

Very poor Very good

Lacoste 1 2 3 4 5

Parker 1 2 3 4 5

Sony 1 2 3 4 5

2. Indicate your degree of agreement with the following statements by

encircling the appropriate number.

Statement Strongly Disagree Strongly Agree

a. I always look for bargains 1 2 3 4 5

b. I enjoy being outdoors 1 2 3 4 5

c. I love to cook. 1 2 3 4 5

4. Ratio: is defined as the highest scale. It allows the researcher to identify or classify

objects, rank order the objects, and compare intervals or differences. It is also

meaningful to compute ratios of scale values. Not only is the difference between 2 and 5

the same as the difference between 14 and 17, but also 14 is seven times as large as


two in an absolute sense. Common examples of ratio scales include height, weight, and

money.

In marketing, sales, costs, market share, and number of customers are variables

measured on a ratio scale. Ratio scales allow only proportionate transformations of the

form Y= bx, where b is a positive constant. One can not add arbitrary constant, as in the

case of an interval scale.

An example of this transformation is provided by the conversion of yards to feet

(b=3).The comparison between the objects is identical whether made in yards or feet.

All statistical techniques can be applied to ratio data. These include specialized

statistics such as geometric Mean, harmonic mean, and coefficient of variation.

Sample Ratio –Scaled Questions

1. Please indicate your age.___________years.

2. Approximately how many times in the last week have you purchased anything

over birr 5 in value at Tele Bar?

0 1 2 3 4 5 more (specify__________)

3. How much do you think a typical purchase of a birr 100, 000 term life insurance

policy pays per year for that policy? Birr__________

4. What is the probability that you will use a lawyer‘s services when you are ready to

make a will? _______________percent.

8.3 Comparative vs. Non Comparative Scaling Techniques

8.3.1 A Comparative Scaling Technique (CST)

Comparative scale s is one of the two types of scaling techniques in which there is

direct comparison of stimulus objects with one another. For example, respondents might

be asked whether they prefer Coke or Pepsi. Comparative scale data must be

interpreted in relative terms and have only ordinal or rank order properties.


The major benefit of comparative scaling is that small differences between stimulus

objects can be detected. As they compare the stimulus objects, respondents are forced

to choose between them.

The major disadvantages of comparative scaling include the ordinal nature of the data

and the inability to generalize beyond the stimulus objects scaled. For example to

compare RC cola to coke and Pepsi, the researcher would have to do a new study. The

previous study comparing coke and Pepsi would not be of much use. These

disadvantages are substantially overcome by non- comparative scaling techniques.

Comparative scales can be divided into the following:

1) Paired Comparison: is defined as comparative scaling techniques in which a

respondent is presented with two objects at a time and asked to select one object in

the pair according to some criterion. The data obtained are ordinal in nature. Paired

comparison scales are frequently used when the stimulus objects are physical

products. Coca-Cola is reported to have conducted more than 190,000 paired

comparison s before introducing New Coke. Comparative comparison scaling is the

most widely used comparative scaling technique. The most common method of taste

testing is paired comparison.

The consumer is asked to sample two different products and select the one with the

most appealing taste. Transitivity of preference: is defined as an assumption

made in order to convert paired comparison data to rank order data. It implies that if

brand A is preferred to brand B and brand B is preferred to brand C, then brand A is

preferred to brand C.

2) Rank Order: Is defined as a comparative scaling technique in which respondents

are presented with several objects simultaneously and asked to order or rank them

according to some criterion. For example, respondents may be asked to rank brands

of toothpaste according to overall preference. These rankings are typically obtained

by asking the respondents to assign a rank of 1 to the most preferred brand, 2 to the

second most preferred brand, and so on, until a rank of n is assigned to the least

preferred brand. Rank order scaling is commonly used to measure preference for

brands as well as attributes. Rank order data are frequently obtained from


respondents in conjoint analysis , because rank order scaling forces the

respondents to discriminate among the stimulus objects.

Advantages

It takes less time and process eliminates intransitive responses.

If there are n stimulus objects, only (n-1) scaling decisions need be made

in rank order scaling.

However, in paired comparison scaling, [n (n-1)/2] decisions would be

required.

Most respondents easily understand the instructions for ranking.

Disadvantages

This technique produces only ordinal data.

Preference for Toothpaste Brands Using Rank and Order Scaling

Rank the various brands of toothpaste in order of preference. Begin by picking out the

one brand that you like most and assign it a number 1.

Then, find the second most preferred brand and assign it a number 2. Continue this

procedure until you have ranked all the brands of toothpaste in order of preference. The

least preferred brand should be assigned a rank of 10.

No two brands should receive the same rank number.

The criterion of preference is entirely up to you. There is no right or wrong answers. Just

try to be consistent.

S/N Brand Rank Order

1 crest

2 Colgate

3 aim

4 gleam

5 sensodyne


6 ultra brite

7 Close up

8 pepsiodent

9 plus white

10 stripe

3) Constant Sum Scaling: is defined as comparative scaling techniques in which

respondents are required to allocate a constant sum of units such as points, dollars,

chits, stickers, or chips among a set of stimulus objects with respect to some

criterion. Respondents may be asked to allocate 100 points to attributes of toilet

soap in a way that reflects the importance they attach to each attribute.

If an attribute is unimportant, the respondent assigns it zero points. If an attribute is

twice as important as some other attribute, it receives twice as many points. The

sum of all the points is 100. Hence, the name of the scale. The attributes are scaled

by counting the points assigned to each one by all the respondents and dividing by

the number of respondents.

Importance of toilet soap attributes using a constant sum scale.

Instructions

Below are eight attributes of toilet soaps. Please allocate 100 points among the

attributes so that your allocations reflect the relative importance you attach to each

other. The more points an attribute receives, the more important that attribute is.

If an attribute is not at all important, assign it zero points. If an attribute is twice as

important as some other attributes, it should receive twice as many points.

S/N Average Responses of Three Segments

Attributes Segment I Segment II Segment III

1 Mildness 8 2 4

2 Lather 2 4 17

3 Shrinkage 3 9 7


4 Price 53 17 9

5 Fragrance 9 0 19

6 Packaging 7 5 9

7 Moisturizing 5 3 20

8 Cleaning power 13 60 15

sum ……………………. 100 100 100

Analysis :

Segment I attaches overwhelming importance to price.

Segment II considers basic cleaning power to be of a prime importance

Segment III values lather, fragrance, moisturizing, and cleaning power.

Such information cannot be obtained from rank order data unless they are transformed

into interval data. Note that the constant sum also has an absolute zero-10 points are

twice as many as 5 points, and the difference between 5 and 2 points is the same as

the difference between 57 and 54 points. For this reason, constant sum scale data are

sometimes treated as metric. Although this may be appropriate in the limited context of

this stimuli scaled, these results are not generelizable to other stimuli not included in the

study. Hence strictly speaking, the constant sum should be considered an ordinal scale

because of its comparative nature and the resulting lack of generizablity.

Advantages

It allows for fine discrimination among stimulus objects without requiring too

much time.

Disadvantages

Respondents may allocate more or fewer units than those specified.

For example a respondent may allocate 108 or 94 points.

The researcher must modify such data in some way or eliminate

this respondent from analysis.

Rounding error, if too few units are used.


The use of a large number of units may be too taxing on the

respondent and cause confusion and fatigue.

4) Q-Sort and Other Procedures: is defined as a comparative scaling techniques that

uses rank order procedure to sort objects based on similarity with respect to

some criterion. This technique uses a rank order procedure in which objects are

sorted into piles based on similarity with respect to some criterion. For example,

respondents are given 100 attitude statements on individual cards and asked

to place them into 11 piles , ranging from “ most highly agreed with “ to “ least

highly agreed with.”

The number of objects to be sorted should not be less than 60 nor more than 140;

60 to 90 objects is a reasonable range. The number of objects to be placed in each

pile is prespecified, often to result in a roughly normal distribution of objects over the

whole set. For example , respondents may be asked to indicate whether they

agree or disagree with each of a series of statements measuring attitude toward

department stores.

Then, they assign a number between 0 to 100 to each statement to indicate the

intensity of their agreement or disagreement. Providing this type of number imposes

a cognitive burden on the respondents. Finally, mention must be made of Guttmann

scaling, or scalogram analysis, which is a procedure for determining, whether a set

of objects can be ordered into an internally consistent, uni dimensional scale.

8.3.2 Non Comparative Scale (NCST)

NCST - is defined as one of two types of scaling techniques in which each stimulus

object is scaled independently of the other objects in the stimulus set. The resulting data

are generally assumed to be interval or ratio scaled. For example, respondents may be

asked to evaluate coke on a 1-to- 6 preference scale (1=not at all preferred, 6 =greatly

preferred). Similar evaluations would be obtained for Pepsi and RC cola. Non -


comparative scales can be further classified as likert, semantic differential or staple

scales. Non - comparative scaling is the most widely used technique in marketing

research.

Non comparative techniques consist of continuous and itemized rating scales.

i) Continuous Rating Scale : is referred to as a graphic rating scale , this

measurement scale has the respondents rate the object by placing a mark at the

appropriate position on a line that runs from one extreme of the criterion variables to

the other. Thus, the respondents are not restricted to selecting form marks previously

set by the researcher. The form of the continuous scale may vary considerably. For

example, the line may be vertical or horizontal; scale points, in the form of numbers or

brief descriptions, may be provided; and, if provided, the scale points may be few or

many.

Three version of a continuous rating scale are illustrated below.

How would you rate Tana Department store?

Version 1

Probably the Worst…………I………………………………………..Probably the Best

Version 2

Probably the Worst…………I………………………………………..Probably the Best

0 10 20 30 40 50 60 70 80 90 100

Version 3

Probably the Worst……………………I……………………......Probably the Best

0 10 20 30 40 50 60 70 80 90 100


Once the respondent has provided the ratings, the researcher divides the line into as

many categories as desired and assigns scores based on the categories into which the

ratings fall.

Advantages

They are easy to construct.

Disadvantages

Scoring is cumbersome and unreliable.

Continuous scales provide little new information.

Hence, their use in marketing research has been limited.

ii) Itemized Rating Scale (IRS): is a measurement scale having numbers and/ or brief

descriptions associated with each category. The categories are ordered in terms of

scale position and the respondents are required to select the specified category that

best describes the object being rated. Itemized rating scales are widely used in

marketing research and form the basic components of more complex scales , such as

multi-item ratings scales.

The most commonly used rating scales are the likert, semantic differential and staple

scales, and then examine the major issues surrounding the use of these scales.

1) Likert Scale (LS): is a measurement scale with five response categories ranging

from “ strongly disagree” to “ strongly agree” , which requires the respondents to

indicate a degree of agreement or disagreement with each of a series of

statements related to the stimulus objects. A likert scale can be used for evaluating

attitudes towards Tana department store.

Project Research Using Likert Scale

Instructions

Listed below are different opinions about Tana department stores. Please indicate how

strongly you agree or disagree with each by using the following scale:


1= strongly disagree

2= Disagree

3= neither agree nor disagree

4= agree

5= strongly agree

S/N Attributes

Strongly

Disagre

e

Disagree Neither

agree nor

disagree

Agre

e

Strongly

agree

1 Tana sells high quality merchandise

1 2 3 4 5

2 Tana has poor in-store service 1 2 3 4 5

3 I like to shop at Tana 1 2 3 4 5

4 Tana does not offer good mix

different brands within a product

category.

1 2 3 4 5

5 The credit policies at tana are

terrible.

1 2 3 4 5

6 Tana is where Ethiopia shops. 1 2 3 4 5

7 I do not like the advertising done by

Tana.

1 2 3 4 5

8 Tana sells a wide variety of

Merchandise.

1 2 3 4 5

9 Tana charges fair prices. 1 2 3 4 5

Analysis

To conduct the analysis, each statement is assigned a numerical score, ranging either

from -2 to +2 or 1 to 5. The analysis can be conducted on an item-by-item basis (profile


analysis), or a total (summated) score can be calculated for each respondent by

summing across items. LS is also called the summated scale. Note that for a negative

statement, an agreement reflects an unfavorable response, whereas for a positive

statement, agreement represents a favorable response.

Accordingly, a “strongly agree” response to a favorable statement and a “strongly

disagree” response to an unfavorable statement would both receive scores of 5. In the

scale shown above, if a higher score is to denote a more favorable attitude, the scoring

of items 2, 4, 5, and 7 will be reversed. Thus, the respondent in the department store

project example has an attitude score of 22. Each respondent’s total score for each

store is calculated. A respondent will have the most favorable attitude toward the store

with the highest score.

Advantages

It is easy to construct and administer.

Respondents readily understand how to use the scale, making it suitable

for mail, telephone, or personal interviews.

Disadvantages

It takes longer to complete than other itemized rating scales because

respondents have to read each statement.

2) Semantic Differential (SD): is a 7 –point rating scale with endpoints associated

with bipolar labels that have semantic meaning. In a typical application, respondents

rate objects on a number of itemized, 7-point scales bounded at each end by one of

two bipolar adjectives, such as “cold” and “warm.”

Project Research on Semantic Differential Scale

Instructions

This part of the study measures what certain department stores mean to you by having

you judge them on a series of descriptive scales bounded at each end by one of two

bipolar adjectives.


Please Mark (X) the blank that best indicates how accurately one or the other adjectives

describes what the store means to you. Please be sure to mark every scale; do not omit

any scale.

Form

Tana is:

Powerful ____/_____/_____ /_____/__X___/_________/_______/ Weak

Unreliable ____/_____/_____ /_____/__X___/_________/_______/ Reliable

Modern ____/_____/_____ /_____/_____/_________/___X____/ Old-fashioned

Cold ____/_____/_____ /_____/_____/_____X____/_______/ Warm

Careful ____/__X__/_____ /_____/__ __/_________/_______/ Careless

A semantic differential Scale for measuring self-concepts, person concepts, and product

concepts.

1. Rugged /___/____/____/____/_____/_____/_____/ Delicate

2. Excitable /___/____/____/____/_____/_____/_____/ Calm

3. Uncomfortable /___/____/____/____/_____/_____/_____/ comfortable

4. Dominating /___/____/____/____/_____/_____/_____/ submissive

5. Thrifty /___/____/____/____/_____/_____/_____/ Indulgent

6. Pleasant /___/____/____/____/_____/_____/_____/ unpleasant

7. Contemporary /___/____/____/____/_____/_____/_____/ Non contemporary

8. Organized /___/____/____/____/_____/_____/_____/ Unorganized

9. Rational /___/____/____/____/_____/_____/_____/ Emotional

10.Youthful /___/____/____/____/_____/_____/_____/ Mature

11.Formal /___/____/____/____/_____/_____/_____/Informal

12.Orthodox /___/____/____/____/_____/_____/_____/ liberal

13.Complex /___/____/____/____/_____/_____/_____/ simple

14.Colorless /___/____/____/____/_____/_____/_____/ colorful

15.Modest /___/____/____/____/_____/_____/_____/ Vain


Analysis

Individual items on a semantic differential scale may be scored on a -3 to + 3 or a 1-to-

7 scale. The resulting data are commonly analyzed through profile analysis. In profile

analysis, means or median values on each rating scale are calculated and compared by

plotting or statistical analysis. This helps determine the overall differences and

similarities among the objects.

To assess differences across segments of respondents, the researcher can compare

mean responses of different segments. Although the mean is most often used as a

summary statistics, there is some controversy as to whether the data obtained should

be treated as an interval scale. On the other hand, in cases when the researcher

requires an overall comparison of objects, such as to determine store preference, the

individual item scores are summed to arrive at a total score.

Its versatility makes the semantic differential a popular rating scale in marketing

research. It has been widely used in comparing brand, product, and company images. It

has also been used to develop advertising and promotion strategies and in new product

development studies. Several modifications of the basic scale have been proposed.

3) Staple Scales (SS): is a scale for measuring attitudes that consists of single

adjectives in the middle of an even –numbered range of values, from -5 to +, without

a neutral point (Zero). This scale is usually presented vertically. Respondents are

asked to indicate how accurately or inaccurately each term describes the object by

selecting an appropriate numerical response category. The higher the number, the

more accurately the term describes the object, as shown in the department store

project. Several modifications of the basic scale have been proposed.

Project Research on Staple Scale

Instructions

Please evaluate how accurately each word or phrase describes each of the department

stores. Select a plus number for the phrases you think describe the store accurately.

The more accurately you think the phrase describes the store, the larger the plus


number you should choose. You should select a minus number for phrases you think do

not describe it accurately. The less accurately you think the phrase describes the store,

the larger the minus number you should choose. You can select any number, from +

5 for

phrases you think are very accurate, to – 5 for phrases you think are very

inaccurate.

High Quality Poor Service

-1 -1

-2 -2

-3 -3

-4 -4

-5 -5

+5 +5

+4 +4

+3 +3

+2 +2

+1 +1


Non Comparative Itemized Rating Scale Decisions

The researcher must be made six major decisions when constructing any of these

scales.

1. The Number of Scales Categories to Use.

Two conflicting considerations are involved in deciding the number of scales categories.

On the other hand, most respondent can not handle more than a few categories.

Traditional guidelines suggest that appropriate number of categories should be seven

plus or minus two: between five and nine. Yes there is single optimal number of

categories. Several factors should be taken into account in deciding on the number of

categories. If the respondents are interested in the scaling task and are knowledgeable

about the objects, a larger number of categories may be employed.

On the other hand, if the respondents are not very knowledgeable or involved with the

task, fewer categories should be used. Likewise, the nature of the objects is also

relevant. Some objects do not lend themselves to fine discrimination, so a small number

of categories is sufficient. Another important factor is the mode of data collection.

If telephone interviews are involved, many categories may confuse the respondents.

Likewise, space limitations may restrict the number of categories in mail questionnaires.

How the data are to be analyzed and used should also influence the number of

categories. In situations where several scale items are added together to produce a

single score for each respondent, five categories are sufficient. The same is true if the

researcher wishes to make broad generalizations or group comparisons. The same is

true if the researcher wishes to make broad generalizations or group comparisons.

2. Balanced Versus Unbalanced Scale: Balanced scale is a scale with an equal

number of favorable and unfavorable categories. Examples of balanced and

unbalanced scales are given below:

Balanced and Unbalanced Scales

Balanced Scale Unbalanced Scales

Musk For Men is : Musk for men is :

Extremely Good ______________ Extremely ________________


Good

Very Good ________________ Very Good __________________

Good ________________ Good ________________

Bad _____________________

Very Bad ___________________ Very Bad _____________________

Extremely Bad _____________________ Extremely Bad ____________________

In general the scale should be balanced in order to obtain objective data. However, if

the distribution of responses is likely to be skewed, either positively or negatively, an

unbalanced scale with more categories in the direction of skewness may be

appropriate. If an unbalanced scale is used, the nature and degree of unbalance in the

scale should be taken into account in data analysis.

3. Odd or Even Number Categories: With an odd number of categories, the middle

scale position is generally designated as neutral or impartial. The presence, position,

and labeling of a neutral category can have a significant influence on the response.

The likert scale is balanced rating scale in the scale should be taken into account in

data analysis.

The decision to use an odd or even number of categories depends on whether some

of the respondents may be neutral on the response being measured. If, on the other

hand , the researcher wants to force a response or believes that no neutral or

indifferent responses exists , a rating scale with an even number of categories no

neutral or indifferent response exists , a rating scale with an even number of

categories should be used. A related issue is whether the scale should be forced or

none forced.

4. Forced Versus Non Forced Choice: A forced scale is a rating scale that forces the

respondents to express an opinion because “no opinion” or “no knowledge “option is

not produced. In such a case, respondents without an opinion may mark the middle

scale position. If a sufficient proportion of the respondents do not have opinions

on the topic , marking the middle position will distort measures of central tendency


and variance. In institutions where the respondents are expected to have no

opinions, as opposed to simply being reluctant to disclose it, the accuracy of data

may be improved by a non-forced scale that includes a “no opinion” category.

5. The Nature and Degree of the Verbal Description: The nature and degree of

verbal description associated with scale categories varies considerably and can

have an effect on the responses. Scale categories may have verbal, numerical, or

even pictorial descriptions. Furthermore, the researcher must decide whether to

label every scale category, some scale categories, or only extreme scale categories.

Surprisingly, providing a verbal description for each category may not improve the

accuracy or reliability of the data. Yet an arrangement can be made for labeling all or

many scale categories to reduce scale ambiguity.

The category descriptions should be located as close to the response categories as

possible. The strength of adjectives used to anchor the scale may influence the

distribution of the responses. With strong anchors (1= completely disagree, 7 =

completely agree), respondents are less likely to use the extreme scale categories.

This results in less variable and more peaked response distributions. Weak anchors

(1= generally disagree, 7= generally agree), in contrast, produce uniform or flat

distributions. Procedures have been developed to assign values to category

descriptors so as to result in balanced or equal –interval scales.

6. The Physical Form of the Scale: A number of options are available with respect to

scale form or configuration. Scales can be presented vertically or horizontally.

Categories can be expressed by boxes, discrete lines, or units on a continuum and

may or may not have numbers assigned to them. If numerical values are used, they

may be positive, negative, or both.

A variety of scale configurations may be employed to measure the gentleness of cheer

detergent. Some examples include:

Cheer detergent is :

Rating Scales Configurations

1. Very harsh ___ _____ _____ _______ ______ ____ _____ Very gentle

1 2 3 4 5 6 7


2. Very harsh 1 2 3 4 5 6 7 Very gentle

3.

Very good

Neither harsh Nor gentle

4. _________ ____ _____ ____________ _________

________ ___________ ______________

Very harsh Harish Somewhat harsh Neither harsh somewhat

Gentle gentle Very gentle

nor gentle

5.

Very Harish Neither harsh Very gentle

nor gentle

The following table summarizes the six decisions in designing rating scales.

Summary of itemized Rating scales Decisions

1 Number of categories although there is no single , optimal number

traditional guidelines suggest that there should be

-3 -2 -1 0 +1 +2 +3


between five and nine categories.

2 balanced versus unbalanced In general, the scale should be balanced to obtain

objective data.

3 odd or even number categories If a neutral or indifferent scale response is

response is possible from at least some of the

respondents, an odd number of categories should

be used.

4 forced versus nonforced In situations where the respondents are expected

to have opinion, the accuracy of data may be

improved by a non-forced scale.

5 verbal description An argument can be made for labeling all or many

scale categories. The category descriptions

should be located as close to the response

categories as possible.

6 Physical form A number of options should be tried and the best

one selected.

The following table presents some commonly used scales. These scales as having five

categories, the number of categories can be varied depending upon the judgment of the

researcher.

Some Commonly Used Scales in Marketing

Construct Scale Descriptors

Attitude Very Bad Bad Neither Bad

Nor Good

Good Very Good

Importance Not At All

Important

Not Important Neutral Important Very

Important


Satisfaction Very

Dissatisfied

Dissatisfied Neither

Dissatisfied

Nor Satisfied

Satisfied Very

Satisfied

Purchase

Intent

Definitely Will

Not Buy

Probably Not

Buy

Might Or

Might Not

Buy

Probably Will

Buy

Definitely Will

Buy

Purchase

Frequency

Never Rarely Sometimes Often Very Often

Multi-Item Scale

The development of multi-item scales requires considerable technical expertise. The

figure is a paradigm for constructing multi-item scales.

Development of a multi item scale

Develop a Theory

Generate An Initial Pool Of Items:

Theory, Secondary Data, and Qualitative Research

Select A Reduced Set Of Items Based On Qualitative Judgment

Collect Data from a Large Pretest Sample


Perform Statistical Analysis

Develop a Purified Scale

Collect More Data from A Different Sample

Evaluate Scale Reliability, Validity, and Generalizablity

Prepare the Final Scale

The characteristics to be measured are frequently called a construct. Scale

development begins with an underlying theory of the construct being measured. A

theory is necessary not only for constructing the scale but also for interpreting the

resulting scores. The next step is to generate an initial pool of scale items. Typically,

this is done based on theory, analysis of secondary data, and qualitative research.

From this pool, a reduced set of potential scale items is generated by the judgment of

the researcher and other knowledgeable individuals. Some qualitative criterion is

adopted to aid their judgment.

The reduced set of items is still too qualitative criterion is adopted to aid their judgment.

The reduced set of items is large to constitute a scale. Thus, further reduction is

achieved in a quantitative manner. Data are collected on the reduced set of potential

scale items from a large pretest sample of respondents.

The data are analyzed using techniques such as correlations, factor analysis, cluster

analysis, discriminant analysis. As a result of this statistical analysis, several more items

are eliminated, resulting in a purified scale. The purified scale is evaluated for reliability


and validity by collecting more data from a different sample. One the basis of these

assessments, a final set of scale items is selected.

Measuring technical sophistication with a technically sophisticated scale.

The following multi-item scale measures the technical sophistication of a product line.

1 Technical 1 2 3 4 5 6 7

2 Low Engineering

Content

1 2 3 4 5 6 7

3 Fast Changing 1 2 3 4 5 6 7

4 Unsophisticated 1 2 3 4 5 6 7

5 Commodity 1 2 3 4 5 6 7

6 Unique 1 2 3 4 5 6 7

7 Complex 1 2 3 4 5 6 7

Item 1, 3, 6, and 7 are reversed when scoring. This scale can be used in industrial

marketing to measure the technical sophistication of a customer‘s product line and

suggest changes to improve technical quality.

Critical Thinking Discussion Questions

1. Briefly describe the difference between attitude rating scales and ranking scales

and indicate when the two are used.

2. Why is it important to establish the “goodness” of measures and how is this done?

3. Construct a semantic differential scale to assess the properties of a particular

brand of coffee or tea.

4. Whenever possible, it is advisable to use instruments that have already been

developed and repeatedly used in published studies, rather than develop our own

instruments for our studies. Do you agree? Discuss the reasons for your answer.

5. “A valid instrument is always reliable, but a reliable instrument may not always be

“valid “. Comment on this statement.


Chap - 10 Designing Data Collection Forms

Chapter objectives:


Define what a questionnaire is?

Describe Objectives of Questionnaires

Examine types of questionnaires

Explore the questionnaire designing process

9.1 Definition of a Questionnaire

A Questionnaire is a structured technique for data collection that consists of a series of

questions, written or verbal, that a respondent answers.

Typically, a questionnaire is only one element of a data collection package that might

also include:

Fieldwork procedures, such as instructions for selecting, approaching,

and questioning respondents.

Some reward, gift, or payment offered to respondents; and

Communication aids, such as maps, pictures, advertisements, and

products as in personal interviews and return envelops in mail surveys.

9.2 Objectives of Questionnaires

Any questionnaires have three specific objectives:

It must translate the information needed into a set of specific questions

that the respondents can and will answer.

Developing questions that respondents can and will answer yield

the desired information is difficult.

Two apparently similar ways of posing a question may yield

different information.


Hence, this objective is a challenge.

A questionnaire must uplift, motivate, and encourage the respondent to

become involved in the interview, to cooperate, and to complete the

interview.

In designing a questionnaire, the researcher should strive to

minimize respondent fatigue, boredom, incompleteness, and

nonresponsive.

A well-designed questionnaire can motivate the respondents and

increase the response rate.

A questionnaire should minimize response error.

A questionnaire can be a major source of response error.

Minimizing this error is an important objective of questionnaire

design.

9.3 Questionnaire Design Process

Specify the information needed

Specify the type of interviewing method

Determine the content of individual question

Design the questions to overcome the respondent’s inability and unwillingness to

answer

Decide on the question structure.


Determine the question wording

Arrange the questions in proper order

Identify the form and layout

Reproduce the questionnaire.

Eliminating bugs by pretesting.

1. Specify Information Needed: The information needed becomes more and more

clearly defined. It is helpful to review components of the problem and the approach,

particularly the research questions, hypotheses and the information needed. To

further ensure that the information obtained fully addresses all the components of

the problem, the researcher should prepare a set of dummy tables. It is also

important to have a clear idea of the target population. The characteristics of the

respondent group have a great influence on questionnaire design. Questions that

are appropriate for college students may not appropriate for housewives.

Understanding is related to respondent socioeconomic characteristics. Furthermore,

poor understanding is associated with a high incidence of uncertain or no-opinion

responses. The more diversified the respondent group, the more difficult it is to

design a single questionnaire that is appropriate for the entire group.

2. Type of Interviewing Method: An appreciation of how the type of interviewing

method influences questionnaire design can be obtained by considering how the

questionnaire is administered under each method. In personal interviews,

respondents see the questionnaire and interact face to face with the interviewer.

Thus, lengthy, complex, and varied questions can be asked. In telephone interviews,


the respondents interact with the interviewer, but they do not see the telephone

interviewers, the respondents interact with the interviewer, but they do not see the

questionnaire. This limits the type of questions that can be asked to short and simple

ones. Mail questionnaires are self-administered, so the questions must be simple

and detailed instruction must be provided. In computer-assisted interviewing (CAPI

and CATI), complex skip patterns and randomization of questions to eliminate order

bias can be easily accommodated. Internet questionnaires share many of the

characteristics of CAPI, but e-mail questionnaires have to be simpler.

Questionnaires designed for personal and telephone interviews, should be written in

a conversational style.

3. Individual Question Content: Once the information needed is specified and the

type of interviewing method decided, the next step is to determine individual

question content: what to include in individual questions.

4. Is the Question Necessary? Every question in a questionnaire should contribute to

the information needed or serve some specific purpose. If there is no satisfactory

use for the data resulting from a question, that question should be eliminated. In

certain situations, however, questions may be asked that are not directly related to

the information that is needed. It is useful to ask some neutral questions at the

beginning of the questionnaire is sensitive or controversial. Sometimes filler

questions are asked to disguise the purpose or sponsorship of the project. Rather

than limiting the questions to the brand of interest, questions about competing

brands may also be included to disguise the sponsorship. For example, a survey on

personal computers sponsored by IBM may also include filler questions related to

Dell and Apple. Questions unrelated to the immediate problem may sometimes be

included to generate client support for the project. At times, certain questions may

be duplicated for the purpose of assessing reliability or validity?

5. Are Several Questions Needed Instead of One? Once we have ascertained that a

question is necessary, we must make sure that it is sufficient to get the desired

information. Sometimes, several questions are needed to obtain the required

information in an unambiguous manner. “Do you think Coca-Cola is a tasty and


refreshing soft drink?” (Incorrect) A “yes” answer will presumably be clear, but

what if the answer is “no”? Does this mean that the respondent thinks that Coca-

Cola is not tasty, that it is not refreshing, or that it is neither tasty nor refreshing?

Such a question is called double –barreled questions, because two or more

questions are combined into one. To obtain the required information, two distinct

questions should be asked: Do you think Coca-Cola is a tasty soft drink?” Do

you think Coca-Cola is a refreshing soft drink?”

6. Overcoming Inability to Answer: Researchers are not assuming that respondents

can provide accurate or reasonable answers to all questions. The researcher should

attempt to overcome the respondent’s inability to answer. Certain facts limit the

respondent’s ability to provide the desired information. The respondents may not be

informed, may not remember, or may be unable to articulate certain types of

responses.

7. Is the respondent informed? Respondents are often asked about topics on which

they are not informed. A husband may not be informed about the expenses for

groceries and department store purchases, if it is the wife who makes these

purchases, or vice versa. Research has shown that respondents will often answer

questions even though they are uninformed. In situations, where not all respondents

are likely to be informed about the topic of interest, filter questions that measure

familiarity, product use, and past experience should be asked before questions,

about the topics themselves. Filter questions enable the researcher to filter out

respondents who are not adequately informed.

8. Can The Respondent Remember? Many things that we might expect everyone to

know are remembered by only a few. Test this out on yourself.

Can you answer the following?

What is the brand name of the shirt you were wearing two weeks ago?

What did you have for lunch a week ago?

What were you doing a month ago at noon?


How many gallons of soft drinks did you consume during that last 4

weeks?

These questions are incorrect because they exceed the ability of the respondents

to remember.

Evidence indicates that consumers are particularly poor at remembering

quantities of products consumed.

In situations, where factual data were available for comparison, it was

found that consumer reports of product usage exceeded actual usage

by 100 % or more.

Thus , soft drink consumption may be obtained by asking:

How often do you consume soft drinks in a typical week?

_____________less than once a week?

_____________1 to 3 times per week

_____________4 to 6 times per week

____________7 or more times per week.

The inability to remember leads to errors of omissions, telescoping, and creation.

Omissions are the inability to recall an event that actually took place.

Telescoping takes place when an individual telescopes or compresses time

by remembering an event as occurring more recently than it actually

occurred.

For example, a respondent reports three tips to supermarket in the last two

weeks when, in fact, one of these trips was made 18 days ago.

Creation error takes place when a respondent “remembers “an event that

did not actually occur.

The ability to remember an event is influenced by:

The event itself,

The time elapsed since the event, and


The presence or absence of events that would aid memory.

9. Can The Respondents Articulate? Respondents may be unable to articulate

certain types of responses. For example, if asked to describe the atmosphere of the

department store they would prefer to patronize, most respondents may be unable to

phrase their answers. On the other hand, if the respondents are provided with

alternative descriptions of store atmosphere, they will be able to indicate the one

they like the best. If the respondents are unable to articulate their responses to a

question, they are likely to ignore that question and may refuse to respond to the

rest of the questionnaire. Thus, the respondents should be given aids such as

pictures, maps, and descriptions to help them articulate their responses.

10.Overcoming Unwillingness to Answer

Even if respondents are able to answer a particular question, they may be unwilling to

do so , either because too much effort is required , the situation or context may not

seem appropriate for disclosure , no legitimate purpose or need for the information

requested is apparent , or the information requested is sensitive.

Context: some questions may seem appropriate in certain contexts but not in

others.

For example, questions about personal hygiene habits.

Legitimate purpose: respondents are also unwilling to divulge information

that they do not see as serving a legitimate purpose.

Why should firm marketing cereals want to know their age, income,

and occupation?

Sensitive information: respondents are unwilling to disclose, at least

accurately, sensitive information because this may cause embarrassment or

threaten the respondent’s prestige or self –image.

If pressed for the answer, respondents may give biased responses,

especially during personal interviews.


Sensitive topics include money, family life, political and religious beliefs, and

involvement in accidents or crimes.

Increasing the Willingness of Respondents

Respondents may be encouraged to provide information that they are unwilling to give

by the following techniques:

1) Place sensitive topics at the end of the questionnaire. By then, initial mistrust has

been overcome, rapport has been created, the legitimacy of the project has been

established, and respondents are more willing to give information.

2) Preface the question with a statement that the behaviour of interest is common. For

example, before requesting information on credit, say, “recent studies show that

most Americans are in debt.” This technique, called the use of counter biasing

statements.

Choosing Questions Structures

A question may be unstructured or structured.

Unstructured questions (UQ)

UQ are open –ended questions that respondents answer in their own words.

They are also referred to as free-response or free –answer questions.

The following are some examples:

What is your occupation?

What do you think of people who patronize discount department stores?

Who is your favorite political figure?

Structured Questions (SQ)

SQ is questions that prespecifiy the set of response alternatives and the

response format.

A structured question could be multiple choices, dichotomous, or a scale.


Multiple Choices

In multiple choices –questions, the researcher provides a choice of answers and

respondents are asked to select one or more of the alternatives given.

Do you intend to buy a new car within the next six months?

______ definitely will not buy.

________Probably will not buy.

______ Undecided

______ probably will buy.

_______Definitely will buy.

_______Other (please specify).

Advantages

Overcome the disadvantages of open-ended questions, because

interviewer bias is reduced and these questions are administered quickly.

Also, coding and processing of data are much less costly and time

consuming.

In self-administered questionnaires, respondent cooperation’s is improved

if the majority of the questions are structured.

Disadvantages

Considerable time is required to design effective multiple-choice questions.

Exploratory research using open-ended questions may be required to

determine the appropriate response alternatives.

Even if an “other (please specify)”category is included, respondents the list

of possible answers produces biased responses.


There is also the potential for order bias. (Order or position bias is a

respondent’s tendency to check an alternative merely because it

occupies a certain position or is listed in a certain order.)

Dichotomous Questions (DQ)

A DQ is a structured question with only two response alternatives, such as

yes and no; agree or disagree, and so on.

Often the two alternatives are supplemented by a neutral alternative,

such as “no opinion”, “don’t know,” “both” or “none”.

Do you intend to buy a new car within the next six months?

_______Yes

_______No

_______Don’t Know

Scale (See Chapter on Measurement)

Scale is the generation of a commitment upon which measured objects are located.

Do you want to buy a new car with in the next six months?

Definitely

Won’t Buy

Probably

Won’t Buy

Undecided Probably Will

Buy

Definitely Will

Buy

1 2 3 4 5

Choosing Questions Wording

Question wording is the translation of the desired question content and structure into

words that respondents can clearly and easily understand.

To avoid these problems, we offer the following guidelines:

1) Define the issue,

2) Use ordinary words,

3) Avoid implicit assumptions,


4) Avoid leading questions,

5) Avoid implicit alternatives,

6) Avoid implicit assumptions,

7) Avoid generalizations and estimates, and

8) Use positive and negative statements

1) Define the issue

A question should clearly define the issue being addressed.

For example:

Which brand of shampoo do you use? …………………….Incorrect.

Which brand or brands of shampoo have you personally used at home

during the last month? In case of more than one brand, please list all the

brands that apply? ……………………………………Correct.

2) Use Ordinary Words

Ordinary words should be used in a questionnaire and they should match

the vocabulary level of respondents.

When choosing words, keep in mind that the average in the USA has a high

school level, not a college, education.

For certain respondent groups, education level is even lower.

Most respondents do not understand technical marketing words.

For example, instead of asking:

“Do you think the distribution of soft drinks is

adequate?”…………….incorrect.

“Do you think soft drinks are readily available when you want

to buy them?................correct.

3) Avoid Implicit Assumptions

The words used in questionnaires should have a single meaning that is

known to the respondents.

A number of words that appear to be unambiguous have different meanings

to different people.


These includes “ usually”, “ normally”, “ frequently,” “ often, “regularly,”

occasionally,” and “ sometimes”.

Consider the following question:

In a typical month, how often do you shop in department stores?

____________Never.

____________Occasionally.

____________ Sometimes.

____________ 3 or 4 times.

___________ More than 4 times.

In deciding on the choice of words, researchers should consult a dictionary and

thesaurus and ask the following questions of each word used:

Does it mean what we intended?

Does it have any other meanings?

If so, does the context make the intended meaning clear?

Does the word have more than one pronunciation?

Is there any word of similar pronunciation that might be confused with this

word?

Is a supplier word or phrase suggested?

4) Avoid Leading Questions

A leading question that gives the respondent a clue as to what answer is

desired or leads the respondent to answer in a certain way.

Do you think that patriotic Ethiopians should buy imported automobiles

when that would put Ethiopian labor out of work?

____________yes

___________No

___________ Don’t Know.

This question would lead respondents to a “No” answer.


A better question would be :

Do you think Ethiopians should buy imported automobiles?

____________Yes

___________ No

___________ Don’t Know.

Bias may also arise when respondents are given clues about the

sponsor of the project. Respondents tend to respond favorably toward the

sponsor.

The question,

“Is collegiate your favorite toothpaste?” is likely to bias

the responses in favor of a colligate.

A more unbiased way of obtaining this information would be to ask,

“What is your favorite toothpaste brand?” .

Likewise, the mention of a prestigious or non prestigious name can

bias the responses, as in,

“Do you agree with Ethiopian Dental association that

colligate is effective in preventing cavities?”

An unbiased question would be to ask,

“Is colligate effective in preventing cavities?”

5) Avoid Implicit Alternatives

Questions should not be worded so that the answer is dependent upon

implicit assumptions about what will happen as consequences.

Implicit assumptions are assumptions that are not stated in the questions,

as in the following example.


1) Do you like to fly when travelling short

distances:……………….incorrect

2) Do you like to fly when traveling short distances, or would you

rather drives?.........correct .

In the first question, the alternative of driving is only implicit, but in the

second question, it is explicit.

The first question is likely to yield a greater preference for flying than the

second question.

6) Avoid Implicit Assumptions

Questions should not be worded so that the answer is dependent upon

implicit assumptions about what will happen as a consequence.

Implicit assumptions are assumptions that are not stated in the question, as in

the following example.

1) Are you in favor of a balanced budget?

…………..incorrect.

2) Are you in favor of a balanced budget if it would result in an

increase in the personal income tax? ..........correct.

Implicit in question 1 are the consequences that will arise as a result of a

balanced budget.

There might be a cut in defense expenditures, increase in personal income

tax, cut in social programs, and so on.

Question 2 is a better way to word this question.

Questions 1 ‘s failure to make its assumptions explicit would result in

overestimating the respondents support for a balanced budget.

7) Avoid Generalizations and Estimates

Questions should be specific, not general.

Moreover, questions should be worded so that the respondent does not have to

make generalization or compute estimates.

Suppose we were interested in households annual per capit expenditure on

groceries.

If we asked respondents:


“What is the annual per capit expenditure on groceries in your

household?”……………..incorrect.

A better way of obtaining the required information would be to ask the

respondents two simple questions:

“What is the monthly or weekly expenditure on groceries in

your household?”

“How many members can then perform the necessary

calculations?”………………….correct.

8) Dual statements : Positive and Negative Statements

Many questions, particularly those measuring attitude and lifestyles, are

worded as statements to which respondents indicate their degree of

agreement or disagreement.

Evidence indicates s that the response obtained is influenced by the

directionality of the statements: whether they are stated positively or

negatively.

In these cases it is better to use dual statements, some of which are

positive and the others negative.

Two different questions could be prepared.

One questionnaire would contain half negative and half positive

statements in an interspersed way.

The direction of these statements would be reversed in the other

questionnaire. (See summated likert scale).

Determining the Order of Questions

Opening questions

The opening questions can be crucial in gaining the confidence and

cooperation of respondents.

The opening questions should be interesting, simple, and

nonthreatening.


Questions that ask respondents for their options can be good opening

questions, because most people like to express their opinions.

“Opening opinion question opens the door to cooperation.”

To begin with, I‘d like to know much do you like living in this (town/city)?

Would you say you like it a lot, a little, or not too much?”

A lot _____________

A little ___________

Not too much ________

Don’t Know ___________

9.4 Type of Information in a Questionnaire

The type classification information obtained in a questionnaire may be classified as:

1. Basic information: relates directly to the research problem.

2. Classification information: consisting of socioeconomic and demographic

characteristics, is used to classify the respondents and understand the result.

3. Identification information: a type of information obtained in a questionnaire that

includes name, address, e-mail address, and phone numbers.

Difficult Questions

Difficult questions or questions that are sensitive, embarrassing, complex,

or dull should be placed late in the sequence.

After rapport has been established and the respondents become

involved, they are less likely to object to these questions.

Thus, in the department store project, information about credit card and

debt was asked at the end of the section on basic information.

Likewise, income should be the last question in the classification section,

and telephone number the final item in the identification section.

Effect of Subsequent Questions


Questions asked early in a sequence can influence the responses to

subsequent questions.

As a rule of thumb, general questions should precede specific questions.

Thus, in the department store project, information about credit card debt

was asked at the end of the section on basic information.

Likewise, income should be the last question in the classification section,

and telephone number the final item in the identification section.

Effect on Subsequent Questions

Questions asked early in a sequence can influence the response to

subsequent questions.

As a rule of thumb, general questions should precede specific questions.

This prevents specific questions from biasing responses to general

questions.

Consider the following sequence of questions:

Q1: “What considerations are important to you in selecting a department

store?””

Q2: in selecting a department store, how important is convenience of location?”

Note that the first question is general, whereas the second is specific.

This is the correct sequence.

If the questions were asked in the reverse order , respondents would be

clued about convenience of location and would likely to give this

response to the general question.

Funnel approach is a synergy for ordering questions in a questionnaire in

which the sequence starts with general questions that are followed by

progressively specific questions in order to prevent specific questions from

biasing general question.

Pretesting


Refers to the testing of the questionnaire on a small sample of

respondents to identify and eliminate potential problems.

Even the best questionnaire can be used in the field of survey without

adequate pretesting.

As a general rule, a questionnaire should not be used in the field survey

without adequate pretesting.

A pretest should be extensive.

All aspects of the questionnaire should be twisted, including question

content, wording, sequences, form and layout, question difficulty, and

instructions.

The respondents in the pretest should be similar to those who will be

included in the actual survey in terms of background characteristics,

familiarity with the topic, and attitudes and behaviors of interest.

In other words, respondents for the pretest and for the actual survey

should be drawn from the same populations.

Pretests are best done by personal interviews, even if the actual survey is

to be conducted by mail, telephone, or electronic means, because

interviewers can observe respondents reactions and attitudes.

After the necessary changes have been made, another r pretest could be

conducted by mail, telephone, or electronic means if those methods are to

be used in the actual survey.

The latter pretests should reveal problems peculiar to the interviewing

method.

To the extent possible, a pretest should involve administering the

questionnaire in an environment and context similar to that of the actual

survey.

9.5 Questionnaire Design Check List

Step 1: Specify the information needed.


Ensure that the information obtained fully address all the components of

the problem. Review components of the problem and the approach,

particularly the research question, hypothesis, and the information needed.

Prepare a set of dummy tables.

Have a clear idea of the target populations.

Step 2: specify the type of interviewing method.

Review the type of interviewing determined based on considerations.

Step 3: Determine the content of individual questions.

Is the question necessary?

Are several questions needed instead one to obtain the required

information in an unambiguous manner?

Do not double –barreled questions.

Step 4: design the questions to overcome the respondent’s inability and

unwillingness to answer.

Is the respondent informed?

If respondents are not likely to be informed, filter questions that measure

familiarity, product use, and past experience should be asked before

questions about the topics themselves.

Can the respondent remember?

Avoid errors of omissions, telescoping, and creation.

Questions that do not provide the respondent with cues can underestimate

the actual occurrence of an event.

Can the respondent articulate?

Minimize the effort required of the respondents.

Is the context in which the questions are asked appropriate?

Make the request for information seem legitimate.

If the information is sensitive.

Place sensitive topics at the and of the questionnaire.


Preface the question with a statement that the behaviour of

interest is common.

Ask the question using the 3rd person technique.

Hide the question is a group of other questions that

respondents are willing to answer.

Provide response categories rather than asking for specific

figures.

Use randomized techniques, if appropriate.

Step 5: Decide on the Question Structure

Open-ended questions are useful in exploratory research and as

opening questions.

Use structured questions wherever possible.

In multiple –choice questions, the response alternatives should

include the set of all possible choices and should be manually

exclusive.

In a dicthomus question, if a substantial proportion of the respondents

can be expected to be neutral, include a neutral alternative.

Consider the use of the split ballot technique to reduce order bias in

dichotomous and multiple-choice questions.

If the response alternative is numerous, consider using more than one

question to reduce the information processing demands on the

respondents.

Use positive and negative statements.

Step 7: Arrange The Question In Proper Order.

The opening questions should be interesting, simple, and nonthreatening.

Qualifying questions should serve as the opening questions.

Basic information’s should be obtained first, followed by classifications, and,

finally, identification information.


Difficult, sensitive, or complex questions should be placed late in the

sequences.

General questions should precede the specific question.

Questions should be asked in a logical order.

Branching questions should be designed carefully to cover all possible

contingencies.

The question being branched should be place d as close as possible to the

questions causing the branching , and the branching questions should be

ordered so that the respondents can not anticipate what additional will be

required.

Step 8: Identify the Form and Lay Out

Divide a questionnaire into several parts.

Question in each part should be precoded.

The questionnaires themselves should be numbered serially.

Step 9: Reproduce The Questionnaire.

The questionnaire should have a professional appearance.

Booklet format should be used for long questionnaire.

Each question should be reproduced on a single page or double -page

spread.

Vertical response columns should be used.

Grids are useful when there are a number of related questions that use the

same set of response categories.

The tendency to crowed questions to make the questionnaires look shorter

should be avoided.

Directions or instructions for individual questions should be placed as close to

the questions as possible.

Step 10: Eliminate Bugs to Pretesting

Pretesting should be done always.


All aspects of the questionnaire should be tested, including question

content, wording, sequence, form and layout, question difficulty and

instructions.

The respondents in the pretest should be similar to those who will be

included in the actual survey.

Begin the pretest by using personal interviews.

Pretest should also be conducted by mail or telephone if those methods are

to be used in the actual survey.

A variety of interviewers should be used for pretests.

The pretest of interview sample size is small varying from 15 to 30

respondents for the initial testing.

Use protocol analysis and debriefing to identify problems.

After each significant revision of the questionnaire, another pretest should

be conducted, using a different sample of respondents.

The responses obtained from the pretest should be coded and analyzed.

Observational Forms

Forms for recording observational data are easier to construct than the

questionnaires.

The researcher need not be concerned with the psychological impact of the

questions and the way they are asked.

The researcher need only develop a form that identifies the required

information clearly , makes it easy for the field worker to record the information

accurately , and simplifies the coding , entry, and analysis of data.

Observational forms should specify the who, what, when, where, why, and way of

behaviour to be observed.

In the department store project, an observational form for the study of purchase

would include space for all of the following information.

Project Research

Observations


Who: purchases, browsers, males, females, parents with children, children alone.

What: products /brands considered, product/brands purchased, size, price of package

inspected, influence of children or other family members.

When: day, hour, date of observation.

Where: inside the store, checkout counter, or type of department within the store.

Why: influence of price, brand name, package size, promotion, or family members on

the purchase.

Way: personal observer disguised as sales clerk, undisguised personal observer,

hidden camera, or obtrusive mechanical device.

The form and layout as well as the reproduction of observational forms

should follow the same guidelines discussed for questionnaires.

A well designed form permits field workers to record individual

observations, but not to summarize observations as that could lead to

error.

Finally, like questionnaires, observational forms also require adequate

pretesting.

9.5Critical Thinking Discussion Questions

1. What is meant by question bias? Write two biased questions.

2. Explain the client’s role during questionnaire development.

3. indicate the function of:

a) screening question

b) warm-ups

c) transitions

d) “skip” question and


e) classification questions

4. Differentiate the funnel approach from the work approach to questionnaire

design.

5. Using the quality of service provided by the library of the College as the topic of a

survey, define and give examples of questions that:

a) Are loaded

b) Are leading

c) Are double barreled, and

d) Use assumed criteria

6. What is meant when questions:

a) Overstate the condition,

b) Require the respondent to guess a generalization

c) Use a specific example to represent a general class, and

d) Use a complex sentence.

7. Describe pre-coding and give examples of the pre-coding for a closed-ended

question and scaled-response questions.

8. Why should the researcher expend so much effort on the cover letter or opening

comments of a questionnaire?

9. Distinguish anonymity from confidentiality.


Chap- 11 The Data –Preparation Process

Chapter objectives:

After completing this chapter, the student will be able to:

Prepare a preliminary plan for data Analysis

Develop a Questionnaire checking

Edit the questionnaire for consistency

Code unstructured and un-coded questionnaires

Manipulate of scale values to ensure comparability with other scales or

otherwise make the data suitable for analysis.

Perform proper Data cleaning activities

Select a data analysis strategy

10.1 Major Steps in Data Preparation

The Data –Preparation Process is shown in the figure:

Preparing Preliminary Plan of Data Analysis

Questionnaire Checking

Editing

Coding


Transcribing

Data Cleaning

Statistically Adjusting the Data

Selecting a Data Analysis Strategy

Step 1: Questionnaire Checking

Questionnaire checking involves a check of all questionnaires for

completeness and interviewing quality.

Often these checks are made while fieldwork is still underway.

If the field work was contracted to a data –collection agency, the researcher

should make an independent check after it is over.

A questionnaire returned from the field may be unacceptable for several reasons.

Parts of the questionnaire may be incomplete.

The pattern of responses may indicate that the respondent did not understand or

follow the instructions. For example, skip patterns may not have been followed.

The responses show little variance. For example, a respondent has checked only

4s on a series of 7-point rating scales.

The returned questionnaires are physically incomplete: one or more pages are

missing.

The questionnaire is received after the pre-established cutoff date.


The questionnaires are answered by someone who does not qualify for

participation.

If quotas or cell group’s sizes have been imposed, the acceptable

questionnaires should be classified and counted accordingly.

Any problems in meeting the sampling requirements should be

identified and corrective action taken , such as conducting

additional interviews in the under presented cells , before the data

are edited .

Step 2: Editing: Editing is the review of the questionnaires with the objectives of

increasing accuracy and precision. It consists of screening questionnaires to identify

illegible, incomplete, inconsistent, or ambiguous responses.

Responses may be illegible if they have been poorly recorded.

This is particularly common in questionnaires with a large number of

unstructured questions.

The data must be legible if they are to be properly coded.

Likewise questionnaires may be incomplete to varying degrees.

For example , a respondent reports an annual income of less than $20,000, yet

indicates frequent shopping at prestigious departments stores such as Tana

Department stores.

Treatment of Unsatisfactory Responses

Unsatisfactory responses are commonly handled by returning to the field to get better

data, assigning missing values, or discarding unsatisfactory respondents.

i) Returning To The Field: The questionnaires with unsatisfactory responses

may be returned to the field, where the interviewers re contact the

respondents. This approach is particularly attractive for business and

industrial marketing surveys, where the sample sizes are small and the

respondents are easily identifiable. However, the data obtained the second

time may be different from those obtained during the original survey. These


differences may be attributed to changes over time or differences in the mode

of questionnaires administration (telephone vs. in-person interview).

ii) Assigning Missing Values: If returning the questionnaires to the field is not

feasible, the editor may assign missing values to unsatisfactory responses.

This approach may be undesirable if:

The number of respondents with unsatisfactory responses is very

small.

The proportion of satisfactory responses for each of these

respondents is small, or

The variables with unsatisfactory responses are not the key

variables.

iii) Discarding Unsatisfactory Respondents: In this approach, the

respondents with unsatisfactory responses are simply discarded. This

approach may have merit when

The proportion of unsatisfactory respondents is small-less than

10%,

The sample size is large.

The unsatisfactory respondents do not differ from satisfactory

respondents in obvious ways such as demographics, product

usage characteristics,

The proportion of unsatisfactory responses for each of these

respondents is large, or ,

Responses on key variables are missing.

However, unsatisfactory respondents may differ from satisfactory respondents in

systematic ways, and the decision to designate a respondent as unsatisfactory may be

subjective. Both these factors bias the results. If the researcher decides to discard

unsatisfactory respondents, the procedure adopted to identify these respondents and

their number should be reported.

Step 3: Coding: assigning a code, usually a number, to each possible response to

each question. The code includes an indication of the column position or field and data

record it will occupy. For example, sex of respondents may be coded as 1 for Females


and 2 for males. A field represents a single item of data, such as sex of the

respondent. A record consists of related fields, such as sex, marital status, age,

household size, occupation, and so forth.

Often all the data for a respondent will be contained in a single record,

although a number of records may be used for each respondent.

If the questionnaire contains only structured questions or very few

unstructured questions, it is precoded.

This means that codes are assigned before fieldwork is conducted.

If the questionnaire contains unstructured questions, codes are assigned

after the questionnaires have been returned from the field -post coding.

Pre-coding refers to assigning a code to every conceivable response before data

collection.

Coding questions: the respondent code and the record number should appear on each

record in the data. However, the record code can be dispensed if there is only one

record for each respondent. Coding of structured questions is relatively simple, because

the response options are predetermined. The researcher assigns a code for each

response to each question and specifies the appropriate record and columns in which

the response codes are to appear.

For example: Do you have a currently valid passport?

1. Yes 2. No (1/54) : For this question, a “Yes “response is coded 1 and a “No”

response 2.

The numbers in the parenthesis indicate that the code assigned will appear on the first

record for this respondent in column 54. Because only one response is allowed and

there are only two possible responses (1 or 2), a single column is sufficient. In general,

a single column is sufficient to code a structured question with a single response if there

are less than nine possible responses. The coding of unstructured or open-ended

questions is more complex.

Respondent’s verbatim responses are recorded on the questionnaire.

Codes are then developed and assigned to these responses.

Sometimes, based on previous projects or theoretical considerations, the

researcher can develop the codes before beginning fieldwork.


Usually this must wait until the completed questionnaires are received.

Then, the researcher lists 50 to 100 responses to unstructured questions

to identify the categories suitable for coding.

Once codes are developed, the coders should be trained to assign the

correct codes to the verbatim responses.

Guidelines suggested for coding unstructured questionnaires and questionnaires

in general:

Category codes should be mutually exclusive and collectively exhaustive.

Categories are mutually exclusive, if each response fits into one and only

one category code.

Categories should not overlap.

Categories are collectively exhaustive if every response fits into one of the

assigned category codes.

This can be achieved by adding an additional category code of “other” or

“none of the above”.

However, only a few 10% or less of the responses should fall into this

category.

The vast majority of the responses should be classified into meaningful

categories.

Codebook

A code book contains coding instructions and necessary information about variables

in the data set. A codebook guides the coders in their work and helps the researcher to

properly identify and locate the variables. Even if the questionnaire has been precoded,

it is helpful to prepare a formal codebook. A Codebook generally contains the following

information:

Column number ,

Record number,


Variable number,

Variable name ,

Question number, and

Instructions for coding.

Step 4: Transcribing: Transcribing data involves transferring the coded data from the

questionnaire or coding sheets onto the disks or magnetic tapes or directly into

computers by keypunching. If the data have been collected via CATI or CAPI, this step

is unnecessary because the data are entered directly into the computer as they are

collected. Besides keypunching, the data can be transferred by using mark sense

forms, optical scanning, or computerized sensory analysis.

Step 5: Data Cleaning: Data cleaning includes consistency checks and treatment of

missing responses. Although preliminary consistency, checks have been made during

editing, the checks at this stage are more thorough and extensive, because they are

made by computer.

Consistency Checks:

is a part of the data-cleaning process that identifies data that are out of range,

logically inconsistent, or have extreme values.

Data with values not defined by the coding scheme are inadmissible. For

example a respondent reports both unfamiliarity with, and frequent usage of,

same product.

The necessary information –respondent code, variable code , variable name,

record number, column number, and inconsistent values can be printed to

locate these responses and take corrective action.

Finally extreme values should be closely examined.

Not all extreme values result from errors, but they may point to problems with

the data.

For example, extreme values should be closely examined. Not all extreme

values result from errors, but they may point to problems with the data.


For example, an extremely low evaluation of a brand may be the result of the

respondent indiscriminately circling 1s (on a 1 to -7 rating scale) on all

attributes of this brand.

Step 6: Treatment of Missing Responses: Missing responses represent values of a

variable that are unknown, either because respondents provided ambiguous answers or

their answers were not properly recorded. Treatment of missing responses poses

problems, particularly if the proportion of missing responses is more than 10%.

The following options are available for the treatment of missing responses.

Substitute a Neutral Value: a neutral value, typically the mean response to

the variable, is substituted for the missing responses. Thus, the mean of the

variable remains unchanged and other statistics, such as correlations, are

not affected much. Although this approach has some merit, the logic of

substituting a mean value say ( 4 ) for respondents who, if they had

answered, might have used either high ratings (6 or 7 ) or low ratings ( 1 or

2) is questionable.

Substitute an Imputed Response: the respondents pattern of responses

to other questions are used to impute or calculate a suitable response to

the missing questions. The researcher attempts to infer from the available

data the responses the individuals would have given if they had answered

the questions. This can be done statistically by determining the relationship

of the variable in question to other variables, based on the available data.

For example, product usage could be related to household size for

respondents who have provided data on both variables. The missing

product usage response for a respondent could then be calculated, given

that the respondent’s household size. However, this approach requires

considerable effort and can introduce serious bias. Sophisticated statistical

procedures have been developed to calculate imputed values for missing

responses.


Step 7: Statistically Adjusting the Data: Procedures for statistically adjusting the

data consist of Weighting, Variable Re Specification, and Scale Transformation.

These adjustments are not always necessary but can enhance the quality of data

analysis.

i) Weighting: is a statistical adjustment to the data in which each case or respondent in

the data base is assigned a weight to reflect its importance relative to other cases or

respondents.

The effect of weighting is to increase or decrease the number of cases in

the sample that possess certain characteristics.

Weighting is most widely used to make the sample data more

representative of a target population on specific characteristics.

For example, it may used to give greater importance to cases or

respondents with higher quality data.

Yet another use of weighting is to adjust the sample so that greater

importance is attached to respondents with certain characteristics.

If a study is conducted to determine what modifications should be

made to an existing product, the researcher might want to attach

greater weight to the opinions of heavy users of the product. This

could be accomplished by assigning weights of 3.0 to heavy users,

2.0 to medium users and 1.0 to light users and nonusers. Weighting

should be applied with caution because it destroys the self-

weighting nature of the sample design.

ii) Variable Respecification (VR) : VR involves the transformation of data to create

new variables or the modification of existing variables so that they are more consistent

with the objectives of the study. The purpose of respecification is to create variables that

are consistent with the objectives of the study.


For example, suppose the original variable was product usage, with 10 response

categories. These might be collapsed into 4 categories, heavy, medium, light, and

nonuser. Or the researcher may create an index of information search (IIS), which

is the sum of information customers seek from dealers, promotional materials,

the internet, and other independent sources.

An important respecification procedure involves the use of dummy

variables for re specifying categorical variables.

Dummy variables are also called binary, dichotomous, instrumental,

qualitative variables.

They are variables that may take on only two values as 0 or 1.

The general rule is that to re specify categorical variables with K

categories, k-1 dummy variables are needed.

The reason for having K-1, rather than K, dummy variables is that only

K-1 categories are independent.

Given the sample data, information about the Kth category can be

derived from information about the other K-1 categories.

Consider sex, a variable is needed.

Information on the number or percentage of males in the sample can be

readily derived from the number or percentage of females.

i) Scale Transformation (ST): ST is a manipulation of scale values to ensure

comparability with other scales or otherwise make the data suitable for analysis.

Frequently, different scales are employed for measuring different variables.

For example, image variables may be measured on a 7-point semantic

differential scale, attitude variables on a continuous rating scale, and

lifestyles variables on a 5-ponit likert scale.

Therefore, it would not be meaningful to make comparisons across the

measurement scales for any respondent.

To compare attitudinal scores with lifestyles or image scores, it would

be necessary to transform the various scales.


Even if the same scale is employed for all the variables, different

respondents may use the scale differently.

For example, some respondents consistently use the upper end of a

rating scale, whereas, others consistently use the lower end.

These differences can be corrected by appropriately transforming the

data.

Step 8- Selecting a Data Analysis Strategy: The selection of a data analysis strategy

should be based on the earlier steps of the marketing research process , known

characteristics of the data , properties of statistical techniques , and the background

and philosophy of the researcher.

Data analysis is not an end in itself. Its purpose is to produce information that will

help address the problem at hand.

The selection of a data analysis strategy must begin with a consideration of

the earlier steps in the process problem definition ( step I), development of an

approach ( Step II), and research design ( step III).

The preliminary plan of data analysis prepared as part of the research design

should be used as a spring board.

Changes may be necessary in light of additional information generated in

subsequent stages of the research process.

The next step is to consider the known characteristics of the data. The

measurement scales used exert a strong influence on the choice of statistical

techniques.

In addition, the research design may favor certain techniques. For example, analysis

of variance is suited for analyzing experimental data from causal designs.

>>> Selecting a Data Analysis Strategy >>>


The insights into the data obtained during data preparation can be valuable for selecting

a strategy for analysis. It is also important to take into account the properties of the

statistical techniques, particularly their purpose and underlying assumptions.

Earlier steps (I, II, and III)

Of the marketing Research Process

Known Characteristics of the Data

Properties of statistical Techniques

Background and Philosophy of the researcher

Data Analysis


Some statistical techniques are appropriate for examining differences in variables,

others for making predictions. The techniques also involve different assumptions, and

some techniques can withstand violations of the underlying assumptions better than

others.

Finally, the researcher‘s background and philosophy affect the choice of a data analysis

strategy. The experienced, statistically trained researcher will employ a range of

techniques, including advanced statistical methods. Researchers differ in their

willingness to make assumptions about the variables and their underlying populations.

Researchers who are conservative about making assumptions will limit their choice of

techniques to distribution-free methods. In general, several techniques may be

appropriate for analysis the data from a given project.

10.2 Computer Programs for Data Preparation

SPSS

Out- of- range values can be selected using the SELECT IF or PROCESS if

value statements.

These cases, with the identifying information (subject ID, record No; variable

name, variable value), can then be printed using the PRINT or WRITE

commands.

As a further check, the LIST command can be used to display the values of

variables for each case. SPSS data Entry simplifies the process of entering new

data files.

It facilitates data cleaning and checking for logical inconsistencies.

SAS

The IF, IF-THEN, and IF-THEN/ELSE statements can be used to select cases

with missing or out-of –range values.

The SELECT statement is useful for printing suspicious input lines.


The LOSTCARD statement can be used to identify missing records in the

data.

The PRINT and PRINTTO procedures can be used to identify cases and print

variables values.

In addition, the OUTPUT and PUT statements can be used to write the values

of variables.

MINTAB

There are control statements that permit the control of the order of

commands in a macro.

The IF commands allows implementation of different blocks of commands.

This includes IF, ELSEIF, and ENDIF.

EXCEL

The IF statement can be used to make logical checks out-of-range values.

The IF statements can be accessed under INSERT> FUNCTION >IF.

Chap-12 Data Analysis & Interpretation

Chapter objectives

After successful completion of this chapter the student will be able to:

Describe objectives of data analysis

Examine descriptive statistics

Examine inferential statistics

Guidelines how to process data using SPSS


12. 1 Basic Objectives of Data Analysis

There are three objectives in data analysis:

1. Getting a Feel for the Data: The feel for the data will give preliminary ideas of

how good the scale are , how well the coding and entering of data have been done , and

so on.

Suppose an item tapped on a 7-point scale has been improperly coded and

/or entered as 8; this will be highlighted by the maximum values on the

descriptive statistics and the error can be rectified.

The mean, the range, the standard deviation, and the variance in the data

will give the researcher a good idea of how the respondents have reacted to

the items in the questionnaires and how good the items and measures are.

If the response to each individual item can in a scale does not have a good

spread or range and shows very little variability, then the researcher would

suspect that the particular question was probably not properly worded and

respondents did not quite understand the intent of the question.

Biases, if any, could also be detected if the respondents have tended to

respond similarly to all items-that is, stuck to only certain points on the

scale.

The maximum and minimum scores, mean, standard deviation, variance,

and other statistics can be easily obtained, and these will indicate whether

the responses range satisfactorily over the scale.

Remember that if there is no variability in the data, then no variance can be

explained.

Researchers go to great lengths obtaining the central tendency, the range,

the dispersion, and other statistics for every single item measuring the

dependent and independent variables, especially when the measures for a

concept are newly developed.

It is always prudent to obtain:

The frequency distributions for the demographic variables,


The mean, standard deviation, range, and variance on the other

dependent & independent variables , and

An inter correlation matrix of the variables, irrespective of whether or not

the hypotheses are directly related to these analyses.

These statistics give a feel for the data.

In other words, examination of the measure of central tendency, and how

clustered or dispersed the variables are, gives a good idea of how well the

questions were framed for tapping the concept.

The correlation matrix will give an indication of closely related or unrelated

the variables under investigation are.

If the correlation between two variables happens to be high –say, over .75

–we would start to wonder whether they are really two different concepts,

whether they are measuring the same concept.

2. Testing the Goodness of the Data: Testing the goodness of data can be

accomplished by submitting the data for factor analysis, obtaining the Cronbach’s

alpha or the split-half reliability of the measures, and so on. Establishing the

goodness of data lends credibility to all subsequent analysis and findings. Hence,

getting a feel for the data becomes the necessary first step in all data analysis.

Based on this initial feel further detailed analysis may be done to test the goodness

of the data.

The reliability and validity of the measures can now be tested.

Reliability: the reliability of a measure is established by testing for both consistency

and stability. Consistency indicates how well the items measuring a concept hang

together as a set. Cronbach’s alpha is a reliability coefficient that indicates how well

the items in a set are positively correlated to one another. Cronbach’s alpha is

computed in terms of the average intercorrelations among the items measuring the

concept.

The closer cronbache’s alpha is to 1, the higher the internal consistency reliability. Split-

half reliability coefficient reflects the correlations between two halves of a set of


items, the coefficients obtained will vary depending on how the scale is split. Sometimes

split-half reliability is obtained to test for consistency when more than one scale,

diamensions, or factor, is assessed. The items across each of the diamensions or

factors are split, based on some predetermined logic. In almost every case, Cronbach’s

alpha is an adequate test of internal consistency reliability.

Validity: factorial validity can be established by submitting the data for factor analysis.

Criterion – related validity: can be established by testing for the power of

the measure to differentiate individuals who are known to be different.

Convergent-validity: can be established when there is high degree of

correlation between two different sources responding to the same measure.

For example: both supervisions and subordinates respond similarly to a

perceived reward system measure administered to them.

Discriminant validity: can be established when two distinctly different

concepts are not correlated to each other as for example, courage and

honesty; leadership and motivation; attitudes and behaviors.

3. Testing the Hypothesis Developed for The Research: Hypothesis testing is

achieved by choosing the appropriate menus of the software programs, to test each

of the hypotheses using the relevant statistical test.

The results of these tests will determine whether or not the hypothesis is

substantiated.

Once the data are ready for analysis that is, out- of -range /missing

responses, etc, are cleared up, and the goodness of the measures is

established, the researcher is ready to test the hypothesis already

developed for the study.

12.2 Descriptive Statistics: Measures of Central Tendencies and Dispersion

Descriptive statistics such as frequencies, the mean, and the standard deviation,

which provide descriptive information of a set of data. Descriptive statistics involve

transformation of raw data into a form that would provide information to describe a set of

factors in a situation.


This is done through ordering and manipulation of the raw data collected. Descriptive

statistics are provided by frequencies, measures of central tendency, and dispersion.

Frequencies: simply refers to the number of times various subcategories of a certain

phenomenon occurs, from which the percentage and the cumulative percentage of their

occurrence can be easily calculated.

Example:

An MNC president wants to know how many African Americans, Hispanics, Asians,

whites, and “others” are on its payroll.

A marketing manager wants to know many units of each brand of coffee are sold

in a particular region during a given period

A tax consultant desires to keep count of the number of times different sizes of firms-

small, medium and large are audited.

In all the foregoing cases, it may be noted that we desire to obtain the

frequencies on a nominally scaled variable.

Measures of Central Tendency

The Mean: the mean or average is a measure of central tendency that offers a general

picture of the data without unnecessarily inundating one with each of the observations in

the data set. Example: the production department might keep detailed records on how

many units of a product are being produced each day. However, to estimate the raw

materials inventory, all that the manager might want to know is how many units per

month, on an average, the department has been producing over the past 6 months.

The Median: is a set of observation would not lend itself to a meaningful representation

through either the mean or the median, but can be signified by the most frequently

occurring phenomenon.


Example: in a department where there are 10 white women, 24 white men, 3 African

American women, and 2 Asian women , the most frequently occurring group is the

mode…….the Whiteman.

Measures of Dispersion

Dispersion is the variability that exists between in a set of observation.

Two sets of data might have the same mean, but the dispersions could be different. For

example, if company A sold 30, 40, and 50 units of a product during the months of April,

May, and June respectively, and the company B sold 10, 40, and 70 units during the

same period, the average units sold per month by both companies is the same-40 units

–but the variability or the dispersion in the later company is larger.

The three measurements of dispersion connected with the mean are the range, the

variance, and the standard deviation, which are explained below.

Range: refers to the extreme values in a set of observations. The range is between 30

and 50 for company A (a dispersion of 20 units), while the range is between 10 and 70

units (a dispersion of 60 units) for company B.

Variance: the variance is calculated by subtracting the mean from each of the

observations in the data set, taking the square of this difference, and dividing the total of

these by the number of observations. In the above example, the variance for each of the

two companies is:

Variance for Company A= (30-40)2 + (40-40)2 + (50-40)2 = 66.67

3

Variance for Company B= (10-40)2 + (40-40)2 + (70-40)2 = 600

3

As we can see, the variance is much larger in company B than company A. It makes it

more difficult for the manager of company B to estimate how much goods to stock than


it is for the manager of company A. Thus, variance gives an indication of how dispersed

the data in a data set are.

Standard Deviation: another measure of dispersion for interval and ratio scaled data

,offers an index of the spread of a distribution or the variability in the data .It is very

commonly used to measure of dispersion, and is simply the square root of the variance.

In the case of the above two companies, the standard deviation for companies A & B

would be 66.67 and 600 or 8.67 and 24.495, respectively.

The mean and the standard deviation are the most common descriptive statistics. The

standard deviation, in conjunction with the mean, is very useful tool because of the

following statistical rules, in a normal distribution.

Practically all observation falls within three slandered deviation of the average

or the mean.

More than 90% of the observation is within two standard deviations of the

mean.

More than half of the observation is within one standard deviation of the

mean.

Other Measures of Dispersion

When the median is the measure of central tendency, percentiles, deciles, and quartiles

become meaningful. Just as the median divides the total realm of observations into two

equal halves, the quartile divides it into four equal parts, the decile into 10, and the

percentile to 100 equal parts.

The percentile is useful when huge masses of data, such as the GRE or GMAT scores,

are handled. When the area of observations is divided into 100 equal parts, there are 99

percentile points.

Any given score has a probability of .01 that it will fall in any of those points. If John‘s

score is in the 16th percentile, it indicates that 84% of those who took the exam scored

better than he did, while 15% did worse.


Often times we are interested in knowing where we stand in comparison to others-are

we in the middle, in the upper 10 or 25% or in the lower 20 or 25%, or where? For

instance, if in a company –administered test, Mr Tesfaye scores 78 out of a total of

100 points , he would be unhappy if he were in the bottom 10% among his colleges-test

takers , but he would be reasonably pleased if he were in the top 10%, despite the fact

that his scores remains the same. His standing in relation to others can be determined

by the central tendency median and the percentile he falls in

Inter Quartile Range

The measure of dispersion for the median, interquartile range consists of the middle

50% of the observations-observations excluding the bottom and top 25% quartiles. The

interquartile range could be very useful when comparisons are made among several

groups. For instance, telephone companies can compare long-distance charges of

customers in several areas by taking samples of customer bills from each of the cities to

be compared. By plotting the first and third quartiles and comparing the median and the

spread, they can get a good idea of where billings tend to be highest, to what extent

customers vary in the frequency of use of long –distance calls, and so on. This is done

by the box-and –whisker plot for each area.

12.3 Inferential Statistics

Inferential Statistics is statistics that help to establish relationships among variables

and draw conclusion there from. We might be interested to know or infer from the data

through analysis.

The relationship between two variables for example between

advertisements and sales –between advertisement and sales.

Differences in a variable among different subgroups –whether women or

men buy more of a product.

How several independent variables might explain the variance in a

dependent variable –how investments in the stock market are influenced by


the level of unemployment , perceptions of the economy, disposable income

and dividend expectations .

Pearson Correlation

Many times, however, we would be interested in inferential statistics. That is, we might

be interested to know or infer from the data through analysis

the relationship between two variables (e.g., between advertisement and

sales),

differences in a variable among different subgroups (e.g., whether women or

men buy more of a product),

how several independent variables might explain the variance in a

dependent variable (e.g. how investments in the stock market are influenced

by the level of unemployment, perception of the economy, disposable

incomes, and dividend expectations).

(e.g., how investments in the stock market are influenced by the level of unemployment,

perceptions of the economy, disposable incomes, and dividend expectations). We will

now discuss some of these inferential statistics.

Figure 12.1 Boxes and Whisker Plot

a. Comparison of telephone bills in three cities.

Los Angeles


________________________

__________________________________________

X (1) Q1 Q2 Q3 x(n) 2 4 6 8 10 12 14 16 18 20 22 24

26

(a) (b)

Correlations

In a research project that includes several variables, beyond knowing the means and

standard deviations of the dependent and independent variables, we would often like to

know how one variable is related to another. That is, we would like to see the nature,

direction, and significance of the bivariate relationships of the variables used in the

study (that is, the relationship between any two variables among the variables tapped

among the variables tapped in the study). A Pearson correlation matrix will provide this

information, that is, it will indicate the direction, strength, and significance of the

bivariate relationships of all the variables in the study.

The correlation is derived by assessing the variations in one variable as another

variable also varies. For the sake of simplicity, let us say awe have collected data on

two variables-price and sales-for two different products. The volume of sales at every

price level can be plotted for each product, as shown in the scatter diagrams if Figure M

2a and M 2b.

Figure 12.2b indicates a discernible pattern of how the two factors vary simultaneously

(the trend of the scatter is that of a downward straight line), whereas figure 12.2a does

……


not. Looking at the scatter diagram in Figure 12.2b, it would seem there is a direct

negative correlation between price and sales for this product. That is, as the price

increases, the sale of the product drops consistently. Figure 12.2a suggests no

interpretable pattern for the other product. A correlation Coefficient that indicates the

strength and direction of the relationship can be computed by applying a formula that

takes into consideration the two sets of figure-in this case, different sales volume at

different prices.

Figure 12.2

(a) Scatter diagram with no discernible patter.

(b) Scatter diagram indicating a downward or negative slope.

Sales

Price Price

(a) (b)

Theoretically, there could be range between-1.0 and 1.0, we need to know if any

correlation found range between two variables is significant or not (i.e. if it has occurred

solely by chance or if there is a high probability of its actual existence). As we know, a

significance of p=.05 is the generally accepted conventional level in social science

research. This indicates that 95 times out of 100, we can be sure that there is a true or

significant correlation between the two variables, and there is only a 5% chance that the

relationship does not truly exist. If there is a correlation of .56(denoted as r=.56)


between two variables A and B, with p<.01 then we know that there is a positive

relationship between the two variables and the probability of this not being true is 1% or

less. That is, over 99% of the time we would expect this correlation to exist. The

correlation of .56 also indicates that the variables would explain the variance in one

another (to the extent of 31.4% (.56).

We would not know which variable causes which, but we would know that the two

variables are associated with each other. Thus, a hypothesis that postulates a

significant positive (or negative) relationship between two variables can be tested by

examining the correlation between the two. A bivariate correlation analysis, which

indicates the strength of relationship between the two variables, can be generated for

variables measured on an interval or ration scale.

Nonparametric tests are also available to assess the relationship between variables not

measured on an interval or ratio scale. Spearman’s rank correlation and Kendall’s rank

correlation are used to examine relationships between two ordinals Variables.

Table 12.1: Contingency Table of Skin Color and Job Type

Job Type

Skin Color White Collar Blue Collar Total

White 30 5 35

None white 2 18 20

Total 32 23 55

See Table 12.3 which shows the different types of statistical analyses that data can be

subjected to, when the independent and the dependent variables are measured on

different scales.

Relationship between Two Nominal Variables: X2 Test


We might sometimes want to know if there is a relationship between two nominal

variables of whether they are independent of each other.

As examples:

1. Is viewing television advertisement of a product (yes/no) related to buying that

product by individuals (buy/don’t buy)?

2. Is the type of job done by individuals (white-color jobs vs. blue-collar jobs) a

function of the color of their skin (white vs. non white)? Such comparisons are

possible by organizing data by groups or categories and seeing if there are any

statistically significant relationships. For example, we might collect data from a

sample of 55 individuals whose color of skin and nature of jobs, culled from a

frequency count, might be illustrated as in Table 12.2 in a two-by-two

contingency table.

Just by looking at Table 12.2, a clear pattern seems to emerge that those who are

White hold white- collar jobs. Only a few of the nonwhites hold white-collar jobs.

Thus, there does seem to be a relationship between the color of the skin and the

type of job handled; the two do not seem to be independent. This can be statistically

confirmed by the chi-square (x2) test- a nonparametric test-which would indicate

whether or not the observed pattern is due to chance. As we know, nonparametric

tests are used when normality of distributions cannot be assumed as in nominal or

ordinal data.

The x2 test compares the expected frequency (based on probability) and the

observed frequency, and the x2 statistics is obtained by the formula:

X2=∑ (0i-E)2

Ei


Where x2 is the chi-square statistic; 0i is the observed frequency of the ith cell; and Ei is

the expected frequency. The x2 statistic with its level of significance can be obtained for

any set of nominal data through computer analysis.

Thus, in testing for differences in relationships among nominally scaled variables, the x2

(chi-square) statistic comes in handy. The null hypothesis would be set to state that

there is no significant relationship between two variables (color of skin and

nature of the job, in the above example), and the alternative hypothesis would be

that there would be a significant relationship.

The chi-square statistic is associated with the degrees of freedom (df), which denotes

whether or not a significant relationship exists between two nominal variables. Degrees

of freedom is 1 (one) less than the number of cells in the columns and rows. If there are

four cells (two in a column and two in a row), then the degree of freedom would be ((2-

1) x (-1)).

X2 statistic can also be used for multiple levels of two nominal variables. For instance,

one might be interested to know if four groups of employees-production. Sales,

marketing, and R&D personnel-react to a policy in four different ways (i.e., with no

interest at all, with mild interest, moderate interest, and intense interest). Here the X2

value for the test of independence will be generated by cross-tabulating the data in 16

cells-that is, classifying the data in terms of the four groups of employees and the four

categories of interest. The degrees of freedom here will be 9 ((4-1) (4-1)).

The x2 test of significance thus helps us to see whether or not two nominal variables

are related. Besides the X2 test, other tests, such as the Fisher exact probability test

and the Cochran Q test are used to determine the relationship between two nominally

scaled variables.

Significant Mean Differences between Two Groups: The-Test

There are many instances when we would be interested to know whether two groups

are different from each other on a particular interval-scaled or ratio-scaled variable of

interest. For example,


Would men or women press their case for the introduction of flextime at the

workplace to the same extent, or would their needs be different?

Do MBAs perform better in organizational settings than business students

with only a bachelor’s degree?

Do individuals in urban areas have a different investment pattern of their

savings than those in semi-urban area?

Do CPAs perform better than non-CPAs in accounting tasks? To find answers

to such questions, a t-test is done to see if there are any significant

differences in the means for two groups in the variable of interest.

That is, a nominal variable that is split into two subgroups (as for example, smokers

and nonsmokers, employees in the marketing department and those in the accounting

department; younger and older employees) is tested to see if there is a significant mean

difference between the two split groups on a dependent variable, which is measured on

an interval or ratio scale (as for instance, extent of well-being; pay; or comprehension

level).

The t-test takes into consideration the means and standard deviations of the two groups

on the variable and examines if the numerical difference in the means is significantly

different from o (zero) as postulated in our null hypothesis. We examines if the

numerical difference in the means is significantly different from 0(zero) as postulated in

our null hypothesis

When we compare the mean differences between two different groups on a variable, we

do a t-test on two independent samples. We can also do a t-test to examine the

differences in the same group before and after a treatment.

For example, would a group of employees perform better after undergoing training than

they did before? In this case, the formula for the t-test is adjusted to take into account

correlation between the two scores, if any. In other word, the adjusted t-test for the

matched sample or other type of dependent samples reflects the true mean differences.


The Mann-Whitney U test is a nonparametric test for examining significant differences

when the dependent variable is measured on an ordinal scale and the independent

variable on a nominal scale.

Significant Mean Differences: Among Multiple Groups: ANOVA

Whereas the t-test would indicate whether or not there is a significant mean difference

in a dependent variable between twp grips, an analysis of variance (ANOVA) helps to

examine the significant mean differences among more than two groups on an interval or

ratio-scaled dependent variable.

For example, would there be a significant difference in the amount of sales by

the following four groups of salespersons:

Those who are sent to training schools, those who are given on-the-job

training during field trips, those who have been tutored by sales manager,

and those who have had none of the above? Or

Would the rate of promotion be significantly different for those who have

assigned mentors, choose their own mentors, and have no mentors in the

organizational system?

The results of ANOVA show whether or not the means of the various groups are

significantly different from one another, as indicated by the F statistic. The F statistic

shows whether two sample variances differ from each other or are from, the same

population. The F distribution is a probability distribution of sample variances and the

family of distributions changes with the changes in the sample size. Details of the F

statistic may be seen in Table IV at the end of the book.

When significant mean differences among the groups are indicated by the F

statistic, there is no way of knowing from the ANOV results alone as to where they lie,

that is, whether the significant difference is between Groups A and B, or between B and

C, or A and C, and so on. It would therefore be unwise to use multiple t-tests, taking

two groups at a time, because the greater the number of t-tests done, the lower the


confidence we can place on results. For example, three t-tests done simultaneously

decrease the confidence level from 95% to 86% (.95).

However, several tests, such as Scheffe’s test, Duncan Multiple Range test, Tukey’s

test, and Student-Newman-Keul’s test are available and can be used, as appropriate, to

detect where exactly the mean differences lie.

The Kruskal-Wallis one-way analysis of variance is the nonparametric test used when

the dependent variable is on an ordinal scale, and the independent variable is nominally

scaled.

Tables 12.3 and 12.4 tests provide information on the statistical techniques and tests

used, as well the different nonparametric tests.

Table 12.3 Statistical Techniques and Classified According To Type, Number and

Measurement Scale Of Variables

Criterion Variables

One Two or More

Nominal Ordinal Interval Nominal

Ordinal

Interval


Va

ria

nts

On

e

No

min

al

Chi-square

Test for

independen

ce

Cohran Q

test

Fisher exact

probability

Sign test

Median test

Mann-Whitney

U test

Kruskal- Wallis

one-way

analysis of

variance

Analysis of

variance

Multiple

discriminate

analysis

Ord

ina

l

Spearman’s

rank

correlation

Kendall’s rank

correlation

Analysis of

variance with

trend

analysis

Tw

o o

r M

ore

Inte

rva

l

Analysis of

variance

Regression

analysis

Analysis of

variance

Multiple

regression

analysis

No

min

al

Friedman two-

way analysis

of variance

Analysis of

variance

(factorial

design)

Analysis of

variance

Ord

ina

l


Inte

rva

l

Multiple

discriminate

analysis

Multiple

regression

analysis

Multiple

discrimi

nate

analysis

Canonical

correlation

“Adapted from R.L.Baker & R.E Schulz (Eds).Instructional product research. New York:

Van Nostrand Co., 1972, p. 110.

Multiple Regression Analysis

Whereas the correlation coefficient r indicates the strength of relationship between two

variables, it gives us no idea of how much of the variance in dependent or criterion variable

will be explained when several independent variables are theorized to simultaneously

influence it. For example, when the variance in a dependent variable X (say performance) is

expected to be explained by four independent variables, A,B,C and D (say, pay, task

difficulty, supervisory support, and organizational culture), it should be note that

Not only are the four independent or predictor variables correlated to the dependent variable

in varying degrees, but they might also be inter correlated (i.e., among themselves).

For example, task difficulty is likely to be related to supervisory support, pay might be

correlated to task difficulty, and all three task difficulties, supervisory support, and

pay-might influence the organizational culture. When these variables are jointly

regressed against the dependent variable in an effort to explain the variance in it, the

individual correlation collapse into what is called a multiple r or multiple correlation.

The square of multiple r, R-square or R2 as it is commonly know, is the amount of variance

explained in the dependent variable by the predictors. Such analysis, where more than one

predictor is jointly regressed against the criterion variable, is known as multiple regression

analysis. When the R-square vale, the F statistic, and its significant level are known, we can

interpret the results.


For example, if the R2 is .63 with an F value of say, 25.56, and a significance level of p<.001,

then we can say that 63% of the variance has been significantly explained by the set of

predictors. There is less than.001% chance or this not holding true.

Table 12.4: Use of Some Nonparametric Tests

Test When Used Function

Chi-square With nominal data for one Test for independence of

variable

Sample or two or more variables.

Independent samples Help when data fall into two

Cochran Q With more than two related natural categories.

Samples measured on nominal

Scale.

Fisher exact with two independent sample More useful than x2

when

Probability measured on nominal scale. expected frequencies are

small.

Sign test With two related samples A good test for ranked data.

Measured on ordinal scale.

Media test With one sample, to see if In a symmetric distribution,

randomly drawn measurement the mean and median will

be

are from a population with a the same.

specified median.


Mann-Whitney U With two independent samples Analogue of the two

Tests on ordinal data. independent sample t-

tests.

Kruskal-Wallis with more than two independent An alternative to one-way

One-way ANOVA samples on an ordinal scale. ANOVA where normality

of distributions cannot

be

assumed.

Friedman two-way with more than two related A good alternative to two way

ANOVA sample on ordinal data. ANOVA where normality

cannot assumed.

Kolmogorov-Smirnov With one sample or Is a more powerful test than X2

two independent samples or Mann-Whitney U.

measured on an ordinal

scale.

In sum, multiple regression analysis is done to

Examine the simultaneous effects of several independent variables on a dependent

variable that is interval scaled.

In other word, multiple regression analysis aids in understanding how much of the

variance in the dependent variable is explained by a set of predictors.


If we want to know which among the set of predictors is the most important in

explaining the variance, which the next, and so on, a stepwise multiple regression

analysis can be done.

If we want to know whether a set of job-related variables (e.g., job challenge, job

variety, and job stress) would significantly add to the variance explained in the

dependent variable (say, job satisfaction), over and above that explained by a set of

organizational factors (e.g., participation in decision making, communication,

supervisory relationship), a hierarchical regression analysis van be done.

Multiple regressions are also done to trace the sequential antecedents that cause

the dependent variable through what is known as path analysis.

As stated in an earlier chapter, this tracing of sequential antecedents is possible

even in cross-sectional data.

Other Multivariate Tests and Analyses

Multivariate analyses examine several variables and their relationship simultaneously, in

contrast to bivariate analyses where one variable at a time is examined for

generalization form the sample to the population.

The multivariate techniques for generalization from the sample to the population. The

multivariate techniques are now presented superficially to enable you to have some

idea of their use.

Multivariate analysis of variance (MANOVOA),

discriminate analysis,

canonical correlations;

factor analysis,

cluster analysis, and

Multidimensional scaling.


MANOVA

is similar to ANOVA,with the difference that ANOVA tests the mean differences of

more than two groups on one dependent variable, whereas MANOVA tests mean

differences among groups across several dependent variables simultaneously, by

using sums of squares and cross-product matrices.

Just as multiple t-tests would bias the results (as explained earlier), multiple

ANOVA tests, and using one dependent variable at a time, would also bias the

results, since the dependent variables are likely to be interrelated.

MANOVA circumvents this bias by simultaneously testing all the dependent

variables, canceling out the effect of any inter correlations among them.

In MANOVA tests, the independent variable is measured on a nominal scale and

the dependent variables on an interval or ratio scale.

The null hypothesis tested by MANOVA is: µ1= µ2= µ3=……… µn,

The alternate hypothesis is: µ1= µ2= µ3=………. µn.

Discriminant Analysis

Helps to identify the independent variables that dis-criminate a nominally scaled

dependent variable of interest-say those who are high on a variable from those who

are low on it.

The linear combination of independent variables indicates the discriminating

function showing the large difference that exists in the two group means.


In other words, the independent variables measured on an interval or ratio scale

discriminate the groups of interest to the study.

Canonical Correlation

Examines the relationship between two or more dependent variables and several

independent variables; for example, the correlation between a set of job behaviors

(such as engrossment in work, timely completion of work and number of absences)

and their influence on a set of performance factors (such as quality of work, the

output, and rate of rejects).

The focus here is on delineating the job behavior profiles associated with

performance that results in high-quality production.

Other types of statistical analyses such as factor analysis, cluster analysis, and

multidimensional scaling help us to understand how the variables under study form a

pattern or structure, in contrast to focusing on predicting the dependent variable or

tracing relationships.

Factor Analysis

Helps to reduce a vast number of variables (for example, all the questions tapping

several variables of interest in a questionnaire) to a meaningful, interpretable, and

manageable set of factors.

A principal-component analysis transforms all the variables into a set of composites

variables that are not correlated to one another.

Suppose we have measured in a questionnaire the four concepts of mental health,

job satisfaction, life satisfaction, and job involvement, with 7 questions tapping each.

When we factor analyze theses 28 items, we should find four factors with the right

variables loading of each factor, confirming that we have measured the concepts

correctly.

Cluster Analysis


CA is used to classify objects or individual into mutually exclusive and collectively

exhaustive groups with high homogeneity within clusters and low homogeneity

between clusters.

In other words, cluster analysis helps to identify objects that are similar to one

another, based on some specified criterion.

For instance, if our sample consists of a mix of respondents with different brand

preferences for a product, cluster analysis will cluster individuals by their

preferences for each of the different brands.

Multidimensional Scaling

Groups objects in multidimensional space. Objects that are perceived by

respondents to be different are distanced, and the greater the perceptual

differences, the greater the distance between the objects in the multidimensional

space.

In other words, multidimensional scaling provides a spatial portrayal of

respondents’ perception of products, services, or other items of interest, and

highlights the perceived similarities and differences.

In sum, multivariate techniques such as MANOVA, Discriminant analysis, and

canonical correlation help us to analyze the influence of independent variables

on the dependent variable in different ways.

Other multivariate techniques such as factor analysis, cluster analysis, and

multidimensional scaling offer meaningful insights into the data set by forming

patterns of the data in one form or the other.

It is advantageous that several univariate, bivariate, and multivariate techniques

are available to analyze sample data, so we can generalize the results obtained

from the sample to the population at large.


It is, however, important to pay attention to what each hypothesis is, and use the

correct statistical technique to test it, rather than apply advanced inappropriate

techniques.

Analysis of Qualitative Data

As we know, qualitative data can be obtained through many sources, prime among

them being in- depth interviews, participant or non participant observations, films and

videotapes, projective tests, case studies, and documents and archival data.

Description of the matter under study is the main essence of qualitative research and a

range of interpretive techniques can be used to decode, translate, decipher patterns,

and discover the meaning of phenomena that occur. The data culled can be categorized

and coded according to some meaningful classification scheme. Frequency counts can

thereafter be taken, and X2 or other appropriate non parametric tests done.

As an example, a manager might be concerned about the productivity at the workplace,

especially since a diverse group of individuals work together. The researcher who

assists the manager might want to know how people of different ethnic origins might

perceive their White bosses. Would these individuals feel they are respected and

treated well at the workplace, that they are not being stereotyped in different ways, and

that the differences in ethnicity are valued rather than devalued? In addition, the

researcher might want to know if they express any special needs based on their

parenting status. For instance, women with infants and young children might like

flextime or flexible workplace or part time jobs, which might improve their productivity.

The researcher might talk to several employees about these issues, and make a note of

the ethnic origin, parenthood status, and responses to a number of open ended

questions. These interview data may be later tabulated and coded, and then entered in

the computer for non parametric analysis. For example, if a Hispanic woman feels that

communication with the boss is a problem for her, this could be coded as a

communication concern.


If an African American says that he is being discriminated against in the nature of the

tasks assigned to him then it could be coded under “discrimination/task,” and so on, can

then be submitted for cross tabulation and a X2 test.

Table 12.5 illustrates a tabulation of the responses obtained from 25 employees to the

open ended, unstructured interview, which posed the question: “What is your reaction to

the computerized production system recently introduced?” Frequency counts can be

obtained based on these tabulations and the applicable nonparametric tests used to

interpret data.

When information is gathered from such sources as newspapers, journal articles, and

audio and videotapes, it can be classified, tabulated, and analyzed to understand

phenomena, explain them, or foretell future trends. Naisbitt’s well known Forecast of the

future (see Megatrends) could have been the result of engrossing himself in such an

exercise. In analyzing qualitative information, the researcher engages in an in depth

probe and subjectively interprets the data in an effort to account for much of the

variation in the phenomenon of interest.

Table 12.5: Tabulation of qualitative Data obtained from 25 interviewees

Nature

of

Responses

Physical Psychological Substantive

Favorable # Relieves physical

exhaustion

# Will reduce muscular

pains.

### Can sleep better

at night.

@ Don’t have to

worry about

personal

responsibility for

meeting targets

@@ Mind is free

to think about

other things as

* Production will increase.

** Just in time systems can be

easily incorporated.

+ Less supervision needed.


well.

Unfavorable $ Sedentary life not

good for physical

fitness.

= Muscles will get

flabby

X Sooner or later

there will be

layoffs.

Xx I am not sure

what I am now!

Xxx Do I have to

learn new things

now? At my age

will I be able to?

& will the heavy investment

really pay off in the long run?

^ Maintenance costs can be

very high.

% Too much time will be spent

on training mean time,

production will suffer.

Respondents Respondents Respondents

#6, 8, 15, 22

## 8, 15, 19

### 6, 8, 15, 19

X 1, 9, 21

Xx 2, 3, 22, 24

Xxx 9, 23

@1, 11, 16, 17

@@ 3, 8, 9, 13,

16

$ 2, 20, 24

=9

&10, 18, 25

^4, 12, 18

% 7, 12, 14

* 4, 7, 10

** 7, 12, 18

+ 10, 12, 14, 5

Top Managers: 4, 7, 10

Middle Managers: 12, 14, 18, 25


Events, objects, persons, words, and syntax used in the various sources are frequently

carefully analyzed for their content, from which several inferences are drawn and

projections made for the future. Solutions for the rectification for some types of

problems faced in organizations are also possible through content analysis of case

studies, audio and videotape reviews, and such others.

Content analysis involves the quantification of the qualitative information obtained

through a systematic analysis of the relevant information, thus providing a means for

submitting it to statistical analysis. For example, a content analysis of the feedback on

the effectiveness of different types of carefully manipulated media advertisements on

TV, radio, newspaper, and web sites can be examined as to its efficacy on recall and

subsequent purchase of a product. It is also possible to convert the qualitative data into

interval like data by developing some justifiable rational scheme, as per the following

illustration.

Illustrative Example: Let us suppose that five open ended questions are asked to

understand how members feel about the organizational climate and we want to set up a

numerical scale for the responses received for this variable. We may adopt the following

categorization and coding scheme for the purpose.

1. if only one response is favorable, or if all responses are unfavorable, the variable

might be categorized as the “organizational climate is experienced to be very

unsatisfactory,” and assigned code1;

2. if two of the five responses are favorable, the answer might be categorized as “the

organizational climate is experienced as unsatisfactory” and assigned code 2;

3. if three responses are favorable, the response might be categorized as “the

organizational climate is experienced as neither satisfactory nor unsatisfactory,” and

assigned code 3;

4. if four responses are favorable, the variable can be categorized as “the

organizational climate is experienced as satisfactory” and coded as 4; and


5. If all five responses are favorable, the response can be categorized as “the

experienced organizational climate is very satisfactory” and the response coded as

5.

These interval like data can then be submitted for different kinds of parametric statistical analysis,

as warranted, to find answers to the issues relating to the research. In sum, while analyzing

qualitative data, the notes transcribed are integrated and categorized under appropriate themes,

the response categories then transformed into numbers, and subjected to appropriate data

analysis.

By using multiple methods such as interviewing, observing, and referring to information

available with company, the researcher establishes convergent validity and a sense of reliability

for the data.

The Pearson correlation coefficients are appropriate for interval –and –ratio scaled

variables, and the spearman rank or the Kendall’s Tau coefficients are appropriate

when variables are measured on an ordinal scale.

Any Bivariate correlation can be obtained by clicking the relevant menu, identifying

the variables, and seeking the appropriate parametric or nonparametric statistics.

MANAGERIAL RELEVANCE

Managers make decisions every day, some of which are routine and some very critical for the

organization. The ability to understand the different types of analysis as well as the probabilities

associated with each projected outcome helps managers to take calculated risks (or avoid them),

based on their own natural inclinations as well as the gravity of the problem situation. If, for

instance, the manager decides that significance level of 90 (or even .80) in the data analytic results

is acceptable, then he or she is aware that the probability of making a wrong decision is 10% (or

20%). Such knowledge is extremely crucial for decision making on various matters of differing

complexity and consequence.

Knowledge of analysis such as multiple regression reminds the manager that multiple factors

influence outcomes and attention has to be paid to all the critical variables indicated by the results

of the analysis. The manager also gains a new appreciation of a scientific; data based information

system that would lend itself to different types of analyses to solve problems in a sophisticated and


reliable manner. More advanced multivariate analyses, when comprehended, offer managers

valuable insights into developing strategies for organizational growth.

Guidelines to Process Data’s Using SPSSS

Checking the Reliability of Measures: Cronbache’s Alpha

The closer the reliability coefficient gets to 1.0, the better.

In general, reliabilities less than .60 are considered to be poor, those in the

.70, range, acceptable, and those over .80 good.

It is important to note that all negatively worded items in the questionnaires

should first be reversed before the items are submitted for reliability test.

Unless all the items measuring a variable are in the same direction, the

reliabilities obtained will be incorrect.

Reliability Analysis

1. From the menu, choose

Analyse

Scale

Reliability analysis

2. Select the variables constituting the scale

3. Choose model alpha.

Obtaining Descriptive Statistics: Frequency Distributions

Frequency distribution was obtained for all the personal data or classification

variables.

Frequencies

From the menus, choose

Analyze


Descriptive statistics

Frequencies …..

(Select the relevant variable)

Choose needed:

Statistics

Charts….

Format (for the order in which the results are to be displayed)

The frequencies can also be visually displayed as bar charts, histograms, or pie charts

by clicking on statistics in the menu then summarize, then Frequencies, and charts in

the frequencies dialog box and then selecting the needed chart.

Pearson Correlation Matrix

From the menus, choose:

Analyze

Correlate

Bivariate ……

Option………

Select:

Type of correlation coefficient: select relevant one. For example

Pearson, Kendall’s, Tau, Spearman.

Test of sigiinificance –two tailed, one –tailed.

T-Test for Difference between Two Groups

(Independent Sample Test)


Choose

Analyse

Compare means

Independent- samples t- test

Select a single grouping variable and click Define groups to specify the

two codes to be compared.

Options…………

(Specify confidence level required - .05, .01, etc.)

Analysis over Variance (ANOVA)

Choose

Analyze

Compare means

One-way ANOVA

(Select the dependent variable‘s and one independent factor

variable)

For post hoc test to determine in which of the multiple groups the differences lie click

on:

Post hoc ………

(Select from among the many tests such as Bobferoni, scheffe, Tukey, Duncan, as

appropriate)

Chi-Square Test

Choose:


Analyze

Descriptive Statistics

Crosstabs………..

(Enter variables in the rows and columns boxes)

Statistics ……………..

Select Chi-Square

Multiple Regression Analysis

Choose

Analyze

Regression

Linear …………

(Enter dependent and independent variables)

SUMMARY

We examined the use of descriptive statistics like the mean, median, and mode as

measures of central tendency and range, standard devastation, variance, and

interquartile range as measures of dispersion. These descriptive statistics help

managers to understand and describe the nature of the phenomena encountered in a

situation, whether they relate to people, stocks, production, events, or any other feature

of interest.

We also saw the need for generating frequency distributions in the case of some

nominal demographic data, like educational level and number of organizations in which

individuals have worked. Organizations might also like frequency distributions for certain

types of occurrences such as machine breakdowns due to different specific reasons, or

types of accounting errors made, or the investment portfolio during a given period.


While discussing inferential statistics, we examined:

1. how correlational analysis can be done to test the relationship between two

variables,

2. how X2 tests can detect whether two nominal variables are dependent or

independent,

3. how to trace significant differences between two groups on a dependent

variable, using the t-test,

4. how to trace differences among several groups, using ANOVA, and

5. How to explain and predict the variance in the dependent variable when

multiple independent variables are theorized to influence it, using multiple

regression analysis.

We noted the roles of the X2, t, and f statistic as tests of significance for different types

of data analyses. We also briefly described the use of multivariate analysis such as

MANOVA, Discriminant analysis, and canonical correlation. In addition, we saw that

techniques such as factor analysis, cluster analysis, and multidimensional scaling help

to detect patterns in the collected data.

We also examined how qualitative data are handled. A sophisticated categorization of

the content under different heads for example, under male and female responses as to

how household finance is handled, which members of the family usually buy the

necessities and luxury items, and so on can open up a vista of ideas to financial

institutions, retailers, service agencies, and others, to develop strategies. We also saw

that development and adoption of a good and useful coding system using creative

classification schemes can lead to valuable information that can foster further research.

Having recapitulated some statistical terms and tests, and refreshed our memory of

these here, discussions on how the data are actually analyzed will become clearer and

more easily understood


We observed the various statistical analysis and tests used to examine different

hypothesis to answer the research question. We also learned how the computer

results are interpreted .an important point to note is that data analysis should be based

on testing hypothesis that have been already formulated. It would be incorrect to

change our original hypothesis to suit the results of data analysis. It is however,

acceptable to develop inductive hypothesis and later test them through further research.

We also looked at some of the emerging software programs that help with questionnaire

design and administration, data gathering and analysis.

We examined descriptive statistics which consists of the mean, median, and the mode

as measures of central tendency and the range, slandered deviation, variance, and

interquartile range as measures of dispersion. These descriptive statistics help

managers to understand and describe the nature of the phenomena encountered in a

situation, whether they relate to people, stocks, production, events, or any other feature

of interest.

In inferential statistics, we examined: how exceptional analysis can be done to test the

relationship between two variables; how X2 tests can detect whether two nominal

variables are dependent or independent; how to trace significant differences between

groups on a dependent variable, using the t-test; how to trace differences among the

groups, using ANOVA, and how to explain and predict the variance in the dependent

variable when multiple independent variables are theorized to influence it, using

multiple regression analysis.

Critical Thinking Discussion for Questions

1. What kinds of biases do you think could be minimized or avoided during the data

analysis stage of research?

2. When we collect data on the effects of treatment in experimental designs, which

statistical test would be most appropriate to test the treatment effects?

3. A tax collector wonders whether he should be more selective about the class of

clients he serves so as to maximize his income. He usually deals with four


categories of clients: the very rich, rich, upper class, and middle class. He has

records of each and every client served, the taxes paid by them, and how much he

has charged them. Since many particulars in respect of the clients vary……number

of dependents , business deductibles etc irrespective of the category they belong

to, he would like an appropriate analysis to be done to see which among the four

categories of clientele be should choose to continue to serve in the future. What

kind of analysis should be done in the above case and why?

4. Which measures of central tendency and dispersion are appropriate in the following

cases, and why?

a. The ages of individuals who are grouped as follows:

Under 25 ………3

25-35…………120

36-55………....80

Over 55 ……..22

b. The performance ratings on a 100 –point scale given by the head of the

department to the top six performers:

Top scorer ……87%

Second………...82

Third……………81

Fourth…………76

Fifth…………….74

Sixth…………….68%

c. The weights of eight boxes of raw materials purchased:

275, 263, 298, 197, 275, 287, 263, and 243 pounds.


5. What is the chi-square test? State a research hypothesis that would call for a X2 test.

6. If you want to know whether three groups of employees –those who have served

the organization from 4-6 years, 7 to 9 years, and 10 to 12 years –are to be

classified as different in the number of trips they have taken outside the city on

business work, what statistical test would you use and why.

7. Explain in your own words what multiple regression analysis is. Give an

organizational situation that statistical test would you use and why?

8. The vice president of Lucas International was perplexed by the rate of turnover in

the company during the past 18 months or so. She suspected that tree possible

factors contributed to this the lower salaries paid to staff compared to industry

average, the location of the company, and the extent of bureaucracy that pervaded

the system. She was not sure if there were any differences among the four

categories of employee’s managers, clerical staff, machine operators, and

secretarial staff in their intentions to quit in the next 6 months. “It would be helpful to

know how many of each category of staff are currently in the organization, and to

have a profile of their ages, educational qualifications, and experience with the

organization, “she thought. Furthermore, she wanted to know which of the

employees were more disgruntled the older or the younger. She put her assistant

who had taken a course on research methods to work, to gather the necessary data

and give her the needed information. Indicate the variables on which the assistant

would gather data, and what kinds of analyses he will be submitting the data to, to

submit a report to the VP. (It would help you to first list the information that the VP

wants and then proceed with the exercise.)

9. What is the chi-square test? State a research hypothesis (not in the examples given

in the book) that would call for a X2 test.

10. If you want to know whether three groups of employees those who have served the

organization from 4 to 6 years, 7 to 9 years, and 10 to 12 years are to be classified


as different in the number of trips they have taken outside of the city on business

work, what statistical test would you use and why?

11.Explain in your own words what multiple regression analysis is. Give an

organizational situation that would call for the use of multiple regression analysis.

Chap-13 Report Preparation and Presentation

Objectives

After completing this unit the student will be able to:

Comprehend different report writing formats

Prepare a research report keeping scientific standards.

Examine guidelines for presentation.


12.1 Standardized Reporting Formats

Researchers differ in the ways they prepare a research report. The personality,

background, expertise, and responsibility of the researcher, along with the decision

maker (DM) to whom the report is addressed, interact to give each report a unique

character. Yet there are guidelines for formatting and writing reports and designing

tables and graphs.

The standardized reporting format has two major parts: Prefatory Parts /Preliminary

Pages & Main Bodies / main text

Prefatory Parts /Preliminary Pages

In the preliminary pages the report should carry a title and date, followed by

acknowledgements in the form of “preface “or “foreword”. Then, there should be a table

of contents followed by list of tables and illustrations so that the decision maker or

anybody interested in reading the report can easily locate the required information in the

report.

I) Title page

II) Title fly page

III) Letter of transmittal

IV) Letter of authorization

V) Table of contents

VI) List of tables

VII) List of graphs

VIII) List of appendices

IX)List of exhibits

X) Executive summary

Objectives

Major findings

Conclusions

Recommendations

Report Parts / Main Body /Main Text


The main text provides the complete outline of the research report along with all details.

Title of the research study is repeated at the top of the first page of the main text and

then follows the other details on pages numbered consecutively, beginning with the

second page. Each main section of the report should begin on a new page. The main

text of the report should have the following sections.

XI) Introduction / Back Ground Of The Study

Background to the problem/ study

Statement of the problem

XII) Research Design/Research Methodology

Type of research design

Information needs

Data collection from secondary sources

Data collection from primary sources

Scaling techniques

Questionnaire development and pretesting

Sampling techniques

Fieldwork

XIII) Analysis of data presentation of finding:

This section constitutes the main body of the report wherein

the results of the study are presented in clear and non-

technical terms with liberal use of all sorts of illustrations

such as charts, diagrams and the like ones.

XIV) Limitation and Caveats

Sample size limitation

Sample selection limitation

Other limitations ( frame error, timing, analysis , etc )

XV) Conclusions and Recommendations

Conclusion- detailed summary of the findings and the policy

implications drawn from the results be explained.

Recommendation


XVI) Bibliography /references various sources consulted be prepared and

attached.

XVII) Appendices

Questionnaires and forms

Technical appendices – detailed explanation of a statistical tool

such as conjoint analysis /Statistical output

Other appendices as necessary such as a map of the area in

which the survey took place

12.2 The Research Report Writing Process

Organization

Rewriting

Gather materials and data

Consider over all Format

Make detailed Outline

Improve Readability

Correct Grammar and spelling

Evaluate appropriateness

Evaluate content

Writing First Draft


12.3 Research Presentation

Materials for Oral Presentation

Presentation outline

Visuals

Management or executive summary

Copies of the final report

Question To Be Kept In Mind by the Presenter

What do the data really mean?

What impact do they have?

What do we need to do, given the information we now have?

How can future studies of this nature be enhanced?

What can make information such as this more useful?

Ways or Methods to Communicate Better

Prepare yourself by organizing primary and secondary data for your

presentation

Get to know your audience members and their interests before you make

a presentation.

Know your objectives. Ask yourself: why am I making the presentation?

Develop a communication strategy of how to combine the interest of your

audience with your own goal. Practice the communication concept.

Use notes. Memorize the opening if you must.

Just be yourself. Nobody expects you to be Jay Leno, Bob Hope or Billy

Graham.

Start by outlining the entire presentation in the form of four or five key

topics on the screen. It helps your audience see where things fit together

and where the presentation is going.

Final Word Processing Circulating Or Publishing


Begin with your most dramatic and convincing point or quickly build up to

a climax so that you receive the full attention of the audience from the

start.

Think of your presentation as a service. Look at it from your audience

members viewpoint s. continuously asks yourself: how does the

presentation help the audience? The organization? My own operation?

What is in it for all? Establish mutual benefits. Think of the plus-plus

relationship in transactional analysis.

Keep it simple and easy to understand. Use short sentences: subject verb,

object.

Do not discuss too many points and details: the main mission of your

presentation may get lost.

Anticipate objections. Recognize them if you cannot fight them. Starting an

objection calmly takes the emotion out of it. You also see how that you

are aware of it.

End with a positive note. Summarize what the listeners might lose and

what they will gain.

Remember, a presentation is not sermon. It is sharing ideas with your

audience –ideas that may affect their jobs, their future.

Don’t talk down to your audience. Use “we “instead of “I”.

Talk directly to your audience. Don’t lose personal contact by looking out

of the window or at your shoes. Look people straight in the eyes.

Use charts and figures. Don’t overload them. Keep them simple. A chart

or figure should not say all there is to say. It can later be distributed.

When you use charts or figures, talk to the audience, not to the chart or

figure.

Look at your watch. Finish your presentation on time.

Critical Thinking Questions for Discussion

1. Identify basic component f a research report?


2. What is the difference in writing a company report and a thesis?

3. What are the most important issues to be considered while writing the final

report for a research project?

4. Is writing for publication different from writing up the thesis? How?

5. How do you divide your time a 30- minute presentation? suggest how to

divide your time

Presenting Objectives And Research Questions

Presenting How You Carried Out The Study

Presenting Findings And Conclusions

Questions And Answers Sessios

Chap-14 Code of Ethics of the American Marketing Association

The American marketing association , in furtherance of its central objectives of the

advancement of science in marketing and in recognition of its obligation to the public

,has established these principles of ethical practice of marketing research for the

guidance of its members. In an increasingly complex society, marketing research is

more and more dependent upon marketing information intelligently and systematically

obtained. The consumer is the source of much of this information .Seeking the

cooperation of the consumer in the development of information, marketing management

must acknowledge its obligation to protect the public from misrepresentation and

exploitation under the guise of research.

Similarly the research practioners has an obligation to the discipline he practices and

to those who provide support for his practice –an obligation to adhere to basic and

commonly accepted standards of scientific investigation as they apply to the domain

of marketing research.

It is the intent of this code to define ethical standards required of marketing research in

satisfying this obligation.

Adherence to this code will assure the user of marketing research that the research that

the research that the research was done in accordance with acceptable ethical


practices. Those engaged in research will find in this code an affirmation of sound and

honest basic principles which have developed over the years as the profession has

grown. The field interviewers who are the point of contact between the profession and

the consumer will also find guidance in fulfilling their vitally important role.

For Research Users, Practioners and Interviewers

1. No individual or organization will undertake any activity which is directly or

indirectly represented to be marketing research , but which has as its real

purpose the attempted sales of merchandise or services to some or all of the

respondents interviewed in the course of the research.

2. it is a respondent has been led to believe , directly or indirectly , that he is

participating in a marketing research survey and that his anonymity will be

protected, his name shall not be made known to anyone outside the research

organization or research department , or used for other than research

purposes.

For Research Practioners

1. There will be no intentional or deliberate misrepresentations of research

methods or results. An adequate description of methods employed will be

made available upon request, be made available to buyers of research.

2. The identity of the survey sponsor and /or the ultimate client for whom a

survey is being done will be held in confidence at all times, unless this identity

is to be revealed as part of the research design. Research information shall

be held in confidence by the research organization or department and not

used for personal gain or made available to any outside party unless the

client specifically authorizes such releases.

3. A research organization shall not undertake studies for competitive clients

when such studies would jeopardize the confidential nature of client-agency

relationships.

For Users of Marketing Research


1. A user of research shall not knowingly disseminates conclusions from a

given research project or service that are inconsistent with or not warranted

by the data.

2. To the extent that there is involved in a research project a unique design

involving techniques, approaches or concepts not commonly available to

research practioners , the prospective user of a research shall not solicit

such a design from one practioners and deliver it to another fro execution

without the approval of the design originator.

For Field Interviewers

1. Research assignments and materials received, as well as information

obtained from respondents, shall be held in confidence by the interviewer and

revealed to no one except the research organization conducting the

marketing study.

2. No information gained through a marketing research activity shall be used,

directly or indirectly, for the personal gain or advantage of the interviewer.

3. Interviews shall be conducted in strict accordance with specifications and

instructions received.

4. An interviewer shall not carry out two or more interviewing assignments

simultaneously unless authorized by all contractors or employees concerned.

5. Members of the American marketing association will be expected to conduct

themselves in accordance with provisions of this code in all of their marketing

research activities.

Code of Ethics

The code of professional ethics and practice of the

Marketing research association, INC. is subscribed to as follows

1. To maintain high standards of competence and integrity in marketing and

survey research.


2. To exercise all reasonable care and to observe the best standards of

objectivity and accuracy in the development, collection, processing and

reporting of marketing and survey research information.

3. To protect the anonymity of respondents and hold all information’s concerning

an individual respondent privileged ,such that this information is used only

within the content of the particular study.

4. To thoroughly instruct and supervise all persons for whose work I am

responsible in accordance with study specifications and general research

techniques.

5. To observe the rights of ownership of all materials received from and /or

developed for clients, and to keep in confidence all research techniques, data

and other information considered confidential by their owners.

6. To make available to clients such details on the research methods and

techniques of an assignment as may be reasonably required for proper

interpretation of the data providing this reporting does not violate the

confidence of respondents or clients.

7. To promote the trust of the public for marketing and survey research activities

and to avoid any procedure which misrepresents the activities of a respondent,

the rewards of cooperation or the uses of the data?

8. To refrain from referring to membership in this organization as proof of

competence, since the organization does not so certify any person or

organization.

9. To encourage the observance of the principles of this code among all the

people engaged in marketing and survey research.

Code of Ethics in Marketing Research

Code of Ethics is a set of guidelines that states the standards and operating procedures

for ethical practices by researchers.


Rights and Obligations of the Respondent

The obligation to be truthful

Privacy

Deceptions

The right to be informed

Rights and Obligations of the Respondent

The purpose of research is research

Objectivity

Avoid misrepresenting research

Protect the rights to confidentiality of both subjects & clients

Avoid dissemination of faulty conclusion

Competitive research proposals.

Rights and Obligations of the client (sponsor)

Ethical behavior between buyer and seller

An open relationship with research suppliers

References

Aeker, Kumar, Day, Marketing Research: 6th ed, John Wiley and Sons, Inc., 1999.

Alley, Michael (2008) The Craft of Scientific Presentations: Critical steps to succeed

and critical errors to avoid. New York, Springer Verlag,LLC.

Alley, Michael (2008) The craft of scientific writing, 3rd Ed. New York, Springer-

Verlag, LLC.


Bell J. (1993) Doing Your Research Project. A Guide for First-Time Researchers in

Education and Social Science, Buckingham: Open University Press. See Part

two: Selecting methods of data collection pp. 61-122

Berg, Bruce L. (1995). Qualitative Research Methods for the Social Sciences. 2nd

ed. Boston: Allyn & Bacon.

Boyd, Westfall, Stasch, Marketing research: 7th ed, Richard D. Irwin, Inc., Homewood,

Illinois, 1999.

Burns & Bush, Marketing research: 2nd ed., Prentice Hall Inc., New Jersey, 1998.

De Marrais, K & Lapan, S.D. (eds.) (2004). Foundations for Research: Methods of

Inquiry in Education & the Social Sciences. Mahwah: Lawrence Erlbaum

Associates.

Fisher R A (1959). Statistical Methods & Scientific Inference. New York: Hafner

Publishing.

Gates, Nandan, Barringer Marketing research essentials: 2nd ed, 1998.colin fisher

and Etal (2007), Researching & Writing A Dissertation: A Guide Book For

Business Students, Prentice Hall, 2nd ed.

Gay, L.R.; Mills, G.E.; & Airasian, P. (2009). Educational Research: Competencies

for Analysis and Applications (9th ed.). New Jersey: Pearson.

Jackson, Sherri (2007) Research Methods: A Modular Approach Cengage Learning.

Krueger R (1994) Focus Groups. A Practical Guide for Applied Research, 2nd

edition, Sage: London.

Marczyk, G.; DeMatteo, D.; and Festinger, D. (2005). Essentials of Research Design

and Methodology. New Jersey John Wiley & Sons, Inc.

Mitchell, M.L. & Jolley, J.M. (2004). Research Design (5th ed.). Wadsworth: Thomson.

Naresh K. Malhotra, Marketing Research: 2nd ed, 1999.


Pervez G & Kjell G. ( 2005 ), Research Methods In Business Studies : A Practical

Guide , prentice Hall , 3rd ed.

Stewart, D. and Shamdasani, P. (1990) Focus Groups: Theory and Practice,

Newbury Park: Sage

Sudman, S. (1982) Asking Questions: A Practical Guide to Questionnaire Design,

San Fransisco: Jossey-Bass.

Tull Donald S. & Hawkins Del I., Marketing research – Measurement and Method: 6th

ed., McMillan Publishing Co, 1999.

Tull, Albaum, Green, Research for Marketing Decision: 5th ed.

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