Chapter 1: The Nature and Scope of Marketing Research 1.1 ... of FBE/Marketing...Marketing Research...

Marketing Research handout 2012/2020

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Chapter 1: The Nature and Scope of Marketing Research

1.1. Meaning and Definition of Research: Research in common parlance refers to a search for

knowledge. Once can also define research as a scientific and systematic search for pertinent

information on a specific topic. In fact, research is an art of scientific investigation. The

Advanced Learner‟s Dictionary of Current English lays down the meaning of research as “a

careful investigation or inquiry especially through search for new facts in any branch of

knowledge.” Redman and Mory define research as a “systematized effort to gain new

knowledge.”

Word „Research‟ is comprises of two words = Re+Search. It means to search again. So research

means a systematic investigation or activity to gain new knowledge of the already existing facts.

Research is an academic activity and as such the term should be used in a technical sense.

According to Clifford Woody research comprises defining and redefining problems,

formulating hypothesis or suggested solutions; collecting, organizing and evaluating data;

making deductions and reaching conclusions; and at last carefully testing the conclusions to

determine whether they fit the formulating hypothesis. D. Slesinger and M. Stephenson in the

Encyclopedia of Social Sciences define research as “the manipulation of things, concepts or

symbols for the purpose of generalizing to extend, correct or verify knowledge, whether that

knowledge aids in construction of theory or in the practice of an art.” Research is, thus, an

original contribution to the existing stock of knowledge making for its advancement. It is the

pursuit of truth with the help of study, observation, comparison and experiment. In short, the

search for knowledge through objective and systematic method of finding solution to a problem

is research. The systematic approach concerning generalization and the formulation of a theory is

also research. As such the term „research‟ refers to the systematic method consisting of

enunciating the problem, formulating a hypothesis, collecting the facts or data, analyzing the

facts and reaching certain conclusions either in the form of solutions(s) towards the concerned

problem or in certain generalizations for some theoretical formulation.

Marketing Research Concept: Do customers like our product? Will they buy it again? What do

they not like about our stores? Which magazine is the best for our advertising? How do we

decide on which media to use to promote our products? What are the influences on our

customer‟s decision-making process? For the organization, many questions need to be answered

in order to make the best decisions. Decision making, as we shall see, is an important component


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of the management of an organization. When making decisions in a dynamic environment,

having information about all internal and external environmental factors reduces risk. Much data

is available, but it is not necessarily appropriate or in the correct format for the specific decision.

This data must be managed so that the correct information can be made available to management

as the need arises. Marketing research is a scientific process of gathering and processing

information to help marketing management make decisions. It is only used when specific

information is needed. Many organizations use a marketing information system to gather, sift,

analyze, store and make data available to marketing management. Small businesses require only

a simple system, while large organizations will use a more comprehensive one.

The necessity for marketing information: „Knowledge is power‟ is a common phrase often

quoted in the business world. Knowledge comes from information, which comes from data,

which comes from marketing research. The world is becoming a very complex place, with

changes occurring at an increasingly rapid rate, and with the growth of information technology,

consumers are exposed to more and more information on which to base their decisions.

Marketing information is therefore crucial for the organization if it is to develop products and

strategies that keep pace with the changing environment. Marketing research serves two

important purposes:

It provides the organization with information about customer demand for products and

services.

It gives customers the opportunity to express their views and needs, and thus influence

decision making to ensure they get the products and services that they want.

Marketing Research Definition

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

problem (Emboy &Cooper). Research is an organized and systematic way of finding answers

to questions.

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.

Marketing research is the function that links an organization to its market through the

gathering of information. This information allows for the identification and definition of

market-driven opportunities and problems and generation, refinement and evaluation of

marketing actions. It allows for the monitoring of marketing performance and improved

understanding of marketing as a business process (AMA).

Marketing research is a formalized means of designing, gathering, analyzing and reporting

information that may be used to solve a specific marketing problem (Burns and Bush).

1.2. The Role of Research: Why do we need to do research? Research is conducted with a

problem and policy goal in mind and its sole justification is to uncover till now unavailable

information. To be specific, the main objectives of a research are to:


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Discover new knowledge

Describe and explain phenomenon (happening)

Enable prediction and

Develop a theory

The key roles of marketing research: With so much pressure around the need for marketing

information, it is important from the outset to understand what marketing research can deliver to

the marketing system. Marketing research performs three key roles:

Descriptive research, which depicts a current situation. The focus is on providing the

particular company with historical and current data about the consumer, industry, and

environment and the impact or relevance of this data to the company in question.

Diagnostic research, which diagnoses the effects of a certain event in a given situation. This

could be the cause and effect of introducing a new strategy; or something as small as how a

slight price change will affect a product‟s sales.

Predictive research, which is used to predict or forecast the outcomes of new strategies being

developed. The focus is on looking towards the future and identifying new opportunities to

be taken advantage of.

1.3. Scientific Method/Scientific Research process: Scientific method is a philosophy common

to all research methods and techniques. It refers to the procedures that could be followed in

research whether it is a social or natural science even though they could vary in the material they

research; material for social science is human being. In general, scientific method is a means

whereby approaching into undiscovered truth is sought by problem identification, formulating

hypothesis, collecting data and testing the hypothesis. Scientific research is a pursuit of truth as

determined by logical considerations; the idea of science is to achieve systematic relation of

facts. So, scientific method attempts to achieve systematic relation of facts by experimentation,

observation and logical argument. The basic features of scientific research concerned with

observed facts systematically classified, and include 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.

Characteristics of Scientific Methods: Two general traits characterize the scientific method:

Validity: is the characteristic 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 research and confirm the results.


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Distinction b/n 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.

1.4. Criteria of Good Research: Whatever may be the types of research works and studies, one

thing that is important is that they all meet on the common ground of scientific method employed

by them. One expects scientific research to satisfy the following criteria:

1. The purpose of the research should be clearly defined and common concepts be used.

2. The research procedure used should be described in sufficient detail to permit another

researcher to repeat the research for further advancement, keeping the continuity of what has

already been attained.

3. The procedural design of the research should be carefully planned to yield results that are

as objective as possible.

4. The researcher should report with complete frankness, flaws in procedural design and

estimate their effects upon the findings.

5. The analysis of data should be sufficiently adequate to reveal its significance and the

methods of analysis used should be appropriate. The validity and reliability of the data

should be checked carefully.

6. Conclusions should be confined to those justified by the data of the research and limited to

those for which the data provide an adequate basis.

7. Greater confidence in research is warranted if the researcher is experienced, has a good

reputation in research and is a person of integrity.


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Chapter Two: The Research Process

2.1. Establish the Need for Research: Research may not be needed: Management should not

automatically commission a research study each and every time a decision must be made. There are

several situations in which management should not consider a research project. Here are four situations

in which research may be inappropriate.

Information is already available: If management knows its markets, competition, and the products/services, they may have the necessary information to make an informed decision without commissioning a research study. Contemporary managers have access to much information about their business. One of the problems in the past was that this information was not readily available, and a research project would have to be undertaken just to find the information and produce it in the proper form. Today computer technology has provided management with the ability to record, store, and retrieve much information, costs, and profitability available by product, customer, region, salesperson, and so on, at the touch of a key this situation is likely to increase as more and more businesses invest in information processing technology that makes more of the right information available to the right decision makers at the right time. In the past, many research studies were undertaken simply to correct inadequacies in information processing capabilities.

There is insufficient time for research: Occasionally, a problem is discovered that requires an immediate response on the part of management. Unfortunately, although some research can be performed in a relatively short time frame, much of it requires weeks or months to complete. When competitive pressures or customer shifts demand quick management action, there may not be enough time to carry out a properly conducted research deemed imperative, and although research would be helpful, circumstances argue strongly against performing it.

Resources are not available: Oftentimes, resources are not available for research If conducted I house, research requires a commitment of personnel, facilities, and budget. If conducted by an outside research 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. Of courses, management always runs the risk of discovering that it invested resources in a strategy that research would have identified as being inferior to alternative strategies. The firms that are strapped for cash, and thus feel they cannot afford to spend dollars on research, are usually the firms that could probably benefit the most by performing the research to help them make the best decisions. Nevertheless, resources may simply not be available for research.

Cost outweighs the value of the research: Even when fund and other resources are available to conduct research management must always weigh 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.


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Step 2: Define the problem: Defining the problem is the single most important step in the research

process. A clear, concise statement of the problem is a key to good research. There is much truth to the

saying, “A problem well defined is half solved”. Unfortunately, this is much easier to say than to do.

Often, clients themselves do not know what the problem is. They know that sales are falling and that

market share is shrinking, but they do not know the cause of these symptoms. Part of the researcher’s

job is to work closely with the client to correctly determine the problem. All too often, research studies

are commissioned without a clear understanding of the problem the research should address. Take the

following situation. The president was concerned about declining profits during five consecutive

quarters of business activity. He hired a consultant who, after one visit to the firm, returned with a

proposal to conduct tests of several advertising copy alternatives to be used in the firm’s radio

advertising program. Do you see anything wrong with this situation? How does the firm know that

profits are declining as a result of problems with advertising copy? They do not know, and neither does

the consulting firm. This is a classic situation where research is proposed that does not address the real

problem. A firm may spend literally hundreds, of thousands of dollars doing research but, if they have

not correctly identified the problem, those dollars will have been wasted. To avoid this scenario, care

must be taken to explore all possible causes of the symptom. This requires time and a great deal of

communication between the researcher and the client. Often, a form of research, called exploratory

research, is needed to clearly define the problem so that the proper research may be conducted.

Step 3: Establish Research Objective: Research objectives, although relate to and determined by the

problem definition, are set so that, when achieved, they provide the necessary information to solve the

problem. A good way of setting research objectives is to ask, “What information is needed in order to

solve the problem?” You should notice that the research objectives are different from the defined

problem. Yet, when the information is gathered as a result of carrying out the research objectives, the

problem, is solved. A key aspect of the research objective 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.

Step 4: Determine Research Design: As stated earlier, almost every research project is different. Still,

there are enough commonalties among research projects to enable us to categorize them by the

research methods and procedures used to collect and analyze data. There are three types of research

design:

1. Exploratory research, 2. Descriptive research, and 3. Causal research.

Exploratory Research Design: Exploratory research is defined as collecting information in an unstructured and informal manner. A manager reading periodicals about the status of his or her industry, for example, could be viewed as conducting exploratory research. For instance an owner of a successful restaurant, often eat out at competitor’s restaurants in order to gather information about menu selections, prices, and service quality. Exploratory research is often used to define the


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problem. This might involve examining not only company sales and profits but industry sales and profits as well. It may be determined that, whereas company sales had been declining, industry sales had fallen even more and the company’s own market share had actually increased. This observation would certainly have an impact on defining the problem or even on the decision as to whether or not to conduct any further research. Exploratory research is very intuitive, and it is practiced as second nature by managers who constantly watch the many factors that influence their markets. However, exploratory research can be applied formally by researchers. That is marketers can use procedures that help mangers define the problem or perhaps make symptoms more visible. An example of exploratory research is the ongoing research some companies conduct in order to spot opportunities in the marketplace. For example, some companies employ clipping services whose employees scour the printed news media and other periodicals, and when they encounter and article or comment dealing with the client’s markets, they copy it and send it to the client. With this system the client may become aware of a competitor’s problems with inventory, for instance, or dissatisfaction with some aspect of customer service, and the client company may sense and seize a competitive opportunity based on this early warning system.

Descriptive Research Design: Descriptive research designs refer to a set of methods and procedure that describe research variables. Descriptive studies portray these variables by answering who, what, why, and how questions. These types of research studies may describe such things as consumers’ attitudes, intentions, and behaviors, the number of competitors and their strategies. For instance, several banking firms conduct annual studies wherein consumers’ attitudes toward their own banks, as well as competitors’ banks, are described. These studies, called ‘image analysis surveys,” essentially describe how consumers rate banks’ services, availability of loans, convenience of locations and so on. Occasionally, a firm will use an observation research, during which the consumer’s behavior is observed and described in such a way as to answer the research problem. Descriptive studies are very common in research and make up a large part of the studies that are conducted by either in- house research departments or commissioned to outside research companies. They are the mainstays of research because they generally allow the manager to draw inferences about his or her customers target markets, competitors, or other areas of concern. Therefore, it is important that the researcher must have some underlying logic by which a research variable should be included in a descriptive research. Little may be gained by a simple description of whimsically selected variables.

Causal Research Design: Causal research designs allow us to isolate causes and effects. Causal research is conducted by controlling various factors to determine which factor is causing the problem. By changing one factor, say price, we can monitor its effects on a key consequence, such as sales. In other words, causal designs allow us to determine causality, or which variable is causing another variable to change. We refer to the variables causing the change as independent variables, and the variables that are affected by these factors are referred to as the dependent variables. Causal research allows for the highest level of understanding that you can achieve regarding any type of phenomenon. It gives the manager the ability to make if/then statements For example; a factory outlet owner might be able to say, “If I increase my advertising by 10 percent, then our sales will increase by 20 percent.” Essentially, causal designs involve experiments that allow the observer to measure the change in another variable, say advertising (independent), while holding all other variables constant. Although causal research designs give you a high level of understanding of the variables you are researching, the designs often require experiments that are complex and expensive. Even with inexpensive experiments, ability to control or at least measure all of the


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factors that might be causing the phenomenon under research may be uncertain. For this reason, causal research designs are relatively few in the research industry.

Steps 5: Identify Information Types and Sources

Secondary data: Secondary data, as its name implies, refers to information that has been collected for

some other purpose. That is, it is being used for a purpose that is secondary to its original function.

Sources of secondary data can be external, such as census data and other publication. Or sources can be

internal, arising from sources inside the firm, such as sales record of the firm. Many marketing problems

may be resolved with the use of secondary data. A marketer, for example, could easily allocate regional

TV advertising budgets based on information contained in sales Marketing management’s Survey of

Buying Power. Secondary data should almost always be used in any research project because it may be

collected quickly and it is relatively inexpensive. Secondary data is typically used to carry out exploratory

research which, as we stated earlier, is used in many cases to define the problem. Some problems arise,

however, with the use of secondary data. For example, it may be outdated, the information may not be

in a form that is usable for the problem at hand, and questions about the integrity of the organization

that collected and reported the data may come into play.

Primary data: The second type of information, primary data, refers to information that has been

gathered specifically to serve the research objectives at hand. For many types of problems, secondary

data simply will not suffice. For example, we mentioned that many banks conduct image analysis

studies. Although a bank may have its image analysis studies from years gone by, there simply is no

secondary information that will provide the bank with current information. Therefore, if a bank wants

this information, it must collect primary data.

Sept 6: Determine methods of accessing data: Once the researcher has determined which type of types

of information is needed, he or she must determine methods of accessing data. How does he or she

accomplish this? It depends on the type of data needed. Compared to primary data, accessing secondary

data is relatively easy. If the data are internal, the manager may gather the information from company

records, salespersons, other company executives, management information systems which offer a

wealth of information. Methods of accessing external secondary data have greatly improved over the

last few years. Not only has the quantity of information available increased but, perhaps more

significantly, information-processing technology (on-line computerized search, CD-ROM databases, and

so on) has vastly improved our ability to easily and quickly retrieve the information. Most libraries offer

these services at nominal fees, and there are many commercial sources of industry and even brand-

specific data.


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There are several different methods of collecting primary data. These methods include telephone

surveys, mail surveys, door – to – door personal interviews, and mall intercept studies. Technology has

also impacted access to primary data. New companies are being developed that apply new data

collection technologies in unique ways. Fr example, there are systems that survey TV viewers and radio

listeners using computer generated questionnaires to record respondents’ audio responses. The

computerized questionnaire program customized each question depending on the respondent’s verbal

response to the previous question. Some companies are developing virtual reality programs that let

consumers “walk” through supermarkets and select their items for purchase. Computers record their

purchases and measure their reactions to research variables such as pricing, packaging shape and color,

and in store promotional displays.

Step 7: Design Data Collection Forms: Because research projects gather information, the actual design

of the data collection form that is used to ask and record this information is critical to the success of the

project Even when the correct problem has been defined and the most appropriate research design

planned, asking the wrong questions, or asking the right questions in the wrong order, will destroy the

usefulness of the research effort. There are two basic methods by which researchers collect information:

By asking questions or By observing.

Both information collecting formats use standardized forms, called questionnaires, which record the

information communicated by respondents or the respondents’ behavior as observed by the researcher.

There are two types of forms: structured and unstructured. Structured questionnaires list questions that

have pre-specified answer choices. Unstructured questionnaires have open ended questions and/or

questions that are asked based on a prior response. Both forms can b disguised. Which means that the

true object of the research is not identified or undisguised, in which case the respondent is made full

aware of the purpose and/or sponsor of the survey? A questionnaire’s apparent simplicity (writing a list

of questions) is very deceptive. Care must be taken to design a questionnaire that will cooperatively

elicit objective information from the respondents. This means avoiding both ambiguous and leading

questions.

Step 8: Determine Sample plan and Size: It is important to determine a sample plan and its size. Yet,

how is this done? Typically, it begins when a researcher wants to know something about a “group,” such

as the brand manager may want to know the brand preferences among teenagers, aged 13 to 16, for

five brands of snacks. Or, a marketing manager may want to know sales of his or her company’s

products through different types of retail stores by region in the country. The group that the researcher

is interested in knowing something about is referred to as the population, on occasion, a population is

small enough that a manager may research all of the members of the population in order to learn

whatever it is they are interested in knowing. However, this is researchers’ research extremely rare.

Consequently, researchers research subsets of the population, called samples, in order to gather the

necessary information in an efficient manner. Care must be taken in designing and drawing the sample.

It is important to note that, because a sample represents something less than the population being


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studied, the characteristics of the sample will not likely exactly match the characteristics of the

population. The difference between sample data and there true population data, is referred t as

sampling error. Fortunately, as you will see, you can measure the amount of sampling error that exists in

a given sample’s estimate of some variable of the pupation (parameter).

Step 9: Collect Data: Data collection is usually done by trained interviewers who are employed by field

data collection companies to collect primary data. Many possible errors, called non- sampling errors

because they are attributable to factors other than sampling errors, may occur during data collection.

Such errors include:

Selecting the wrong sample elements to interview, Securing subjects who refuse to participate or are simply not at home when the interviewer calls, Interviewing subjects who intentionally give out the wrong information, or Hiring interviewers who cheat and fill out fictitious survey questionnaires. Even interviewers who honestly complete their interviews may make inadvertent no sampling errors

by copying down the wrong information on their survey form.

Step 10: Analyze Data: Some type of data analysis is needed to give the raw data any meaning. Data

analysis involves:

The first step in data analysis is data cleaning, which is the process by which the raw data is checked to verify it is correct and entered where it should be on the data collection form. Typically, data analysis is conducted with the assistance of a computerized data analysis program that, in most cases, allows only numerical entries.

Coding is the next step after data cleaning Coding is the process of assigning all response categories a numerical value; that is, males, = 1, females = 2, and so on.

An important step in data analysis is performed by tabulation, which refers to the actual counting of the number of observations that fall into each possible response category. As you will see, there are several uses of tabulation, some devoted to data cleaning, and other allow the researcher to understand what the collected data means. Examining two or more response categories at the same time, called cross- tabulation, is another form of data analysis. Finally, a variety of statistical tests, which include means, frequencies, correlation’s trend analysis, and regression, are also used to analyze data.

Step 11: Prepare and present the Final Research Report: The last step in the research process is to

prepare and present the final research report one of the most important phases of research. Its


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importance cannot be overstated because it is the report, or its presentation that properly

communicates the research results to the client. Every researcher should confer with the client to

determine exactly what is wanted in the research report. However, there are widely accepted

standardized sections of a research report, such as the introduction, the methodology, the results, and

so on. Computer graphics may be included to present information visually; Graphs are invaluable as

communication vehicles when researchers want to summarize meaningful patterns notable findings for

the marketing managers who will be making decisions on the research results. Sometimes researchers

not only turn in a written research report but they also make an oral presentation of the research

methods used to conduct the research as well as the research findings to their client. Typically, the

presentation entails overhead transparencies or even color slides with outlines, bulleted lists, and

graphs.

Chapter Three: Research Proposal

3.1 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, list, statement of plan or a 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.

Value/ 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.

3.2 Types of Research Proposals: Proposals can be internal or external.

I) Internal Proposal: is more short and snappy than external ones.

is done for the corporation by staff specialist or by the research department of the firm.

is a memo from the researcher to management outlining the problem statement, study objectives, research design, and schedule enough to start an exploratory study.

is concerned with how to solve a particular problem, make a decision, or improve an aspect of their business.

II) External proposal: Is prepared by outsiders Is invited others to compete among different organization is either solicited or unsolicited.


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is likely competing against others for a contract or grant. The writer of the unsolicited proposal must decide to whom the document should be sent.

1.3. Structure of the research proposal /Components of a Research Proposal:

Title/Topic page

Summary/Abstract

Table of contents

1. Introduction

1.1. Background of the study 1.2. Statement of the problem 1.3. Objective/Aim of the study 1.4. Hypotheses /Questions 1.5. Significance of the study 1.6. Scope of the study 1.7. Limitation of the study 1.8. Organization of the study

2. Literature review

2.1. Conceptual framework (if any)

3. Research Methodology

3.1Study area

3.2Study design

3.3. Study subjects/population

3.4. Sample size

3.5. Sampling methods

3.6. Data sources and type

3.7. Data collection and instruments

3.8.Data analysis

10. Work plan

11. Budget

12. References

13. Appendices/Annexes

The major components of proposal include:

1. Title Page: Before an attempt is made to start with a research project, a research proposal should be compiled. For the beginner researcher, this is usually among the most difficult parts. It is, however, the most important aspect of the research project and should be considered carefully by the researcher. This does not only require subject knowledge, but also insight into the problem that is going to be investigated, so as to give logic and structure to the research envisaged. Defining the


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problem is the first step and one of the most difficult in research undertaking. There is a tendency for the beginner in research to ask questions that are usually diffuse or vague. Each topic that is proposed for research has to be judged according to certain guidelines or criteria. Before deciding on a research topic, each proposed topic must be compared with all other options.

Criteria for selecting a research topic:

Relevance/Significance

Urgency of data needed (timeliness)

Feasibility of study

Check duplication

Applicability of results

Interest to the researcher

Ethical acceptability Accessibility: 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.

Enough Resources :Such as 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

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.

2. Summary/Abstract

The abstract is a one page brief summary of the research proposal. It needs to show a reasonably informed reader why a particular topic is important to address & 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.


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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:

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. 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 summarizes the question you are trying to answer. Effective problem

statements answer the question “Why does this research need to be conducted.”

5. Objective/Aim of the Study

The objectives of a research explain the ends/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.

6. Hypotheses /Questions: The word hypothesis consists of two words: Hypo + thesis = Hypothesis

‘Hypo’ means tentative or subject to the verification and ‘Thesis’ means statement about solution of a

problem. The word meaning of the term hypothesis is a tentative statement about the solution of the

problem. Hypothesis offers a solution of the problem that is to be verified empirically and based on

some rationale. Another meaning of the word hypothesis which is composed of two words: ‘Hypo’

means composition of two or more variables which is to be verified. ‘Thesis’ means position of these

variables in the specific frame of reference. This is the operational meaning of the term hypothesis.

Hypothesis is the composition of some variables which have some specific position or role of the

variables i.e. to be verified empirically. It is a proposition about the factual and conceptual elements.


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Hypothesis is called a leap into the dark. It is a brilliant guess about the solution of a problem. A

tentative generalization or theory formulated about the character of phenomena under observation are

called hypothesis. It is a statement temporarily accepted as true in the light of what is known at the time

about the phenomena. It is the basis for planning and action- in the research for new truth. Hypotheses

vary in form and some extent, form is determined by some function. Thus a working hypothesis or a

tentative hypothesis is described as the best guess or statement derivable from known or available

evidence. The amount of evidence and the certainty or quality of it determines other forms of

hypotheses. Hypotheses can be: (a) Question or (b) Declaration Statement

Question form of Hypotheses: Some writers assert that a hypothesis may be stated as a question. At

best, it represents the simplest level of empirical observation. There are cases of simple investigation

and search which can be adequately implemented by raising a question, rather than dichotomize

hypothesis forms into acceptable/ rejectable categories. Example:

Is there a significant difference between customers‟ expectations and perceptions of service quality

performance of commercial banks or not?

Declarative Statement: A hypothesis may be developed as a declarative which provides an anticipated

relationship or difference between variables. The anticipation of a difference between variables would

imply that the hypothesis developer has examined existing evidence which led him to believe a

difference may be anticipated as processes additional evidence. The following is an example of this form

of hypothesis:

H: There is a significant difference between customers’ expectations and perceptions of service quality

performance of commercial banks.

Hypotheses and questions are linked to the speculative proposition of the problem statement.

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.

Example: Basic Research Questions

The study attempted to answer the following basic research questions:

1. Is there a significant difference between customers‟ expectations and perceptions of service quality

performance of commercial banks or not?

2. Which of the three bank customers‟ perceptions most matches with their expectations?

3. Which of the five banks' service quality dimension(s) do customers favor most?


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7. Significance /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.

8. Delimitation/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;

11. Literature Review

Examines recent research studies, company data, or industry reports that acts as a basis for the proposed study.

Discussion should move from a comprehensive perspective to more specific studies that linked with your problem.

If the problem has a historical background, begin with the earliest references. Avoid unrelated details of the literature, do a brief review of the information, not a comprehensive

report. Always refer to the original source. 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 it 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.

Relevant materials for literature reviews:

books (monographs, text books, reference books);

articles from journals, whether print or electronic (but make sure electronic journals have been

subject to the peer review process);

newspaper articles;

historical records;


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commercial reports and statistical information;

government reports and statistical information;

theses and dissertations;

other types of information

12. 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 etc. Limitations, assumptions, and range of validity. Description of your analytical methods, including reference to any specialized statistical software.

The proposal should describe in detail the general research plan (May not necessarily be true for all types

of research)

1. Description of study area (where the study will be conducted?)

2. Description of study design (exploratory, descriptive and casual)

3. Source of data( primary vs secondary)

4. Description of study participants( which will asked or investigated)

5. Eligibility criteria ( if any) if there is some restriction such as judgment, quota etc

6. Determination of sample size (if any) (what amount of sample will be selected from the whole

population?)

7. Description of selection process (sampling method) (who to communicate/ contact the sample?)

8. Methods of data collection( by what instrument will be collected questionnaire, observation,

interview etc)

9. Description of variables (if any) (What cause the problem and which affected?)

10. How data quality is ensured (is the instrument/questionnaire reliable or valid?)

11. Operational definition (what will be really to be measured?)

12. Presentation of the data analysis methods (how to get the facts from raw data?)

13. Time Table/ Work plan: This section describes the sequence of activities necessary to conduct the

research. It will include the time necessary to complete each activity. In short it is all about “activity

against time”. 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.


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The GANTT chart indicates: the tasks to be performed; who is responsible for each task; and the time each task is expected to take.

S. No Work plan March 25 April 25 May 20 June 10

1 Finalized Proposal and Instruments

2 Data Collection

3 First Draft

4 Final Draft

14. Budget and Funding

This section of a research proposal answer question like; what are the associated with the study? Budget

for research can include costs of salary for research personnel and all expected expenses; supplies,

travel, telephone cost, coping etc. It is important to ensure in this section that the budget is sufficient to

achieve the objective of the study, is justifiable, and demonstrates accountability. Cost can be:

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

Example:

No Item Description Quantity Unit price Total

2 Storage media Flash & CD-RW 1 200 200

3 Computer Paper Reams 2 80 160

4 Note Book For short notes 2 15 30

5 Pen packet 1 50 50

6 Transportation Vehicle 1500 2000

7 Secretarial Services For writing 800 800

8 Literature Photocopies For literature 4 200 800

9 Books For literature 2 500 1000

10 Binding paper 4 15 60


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11 Printing Questionnaire and paper 200 1 200

12 Contingency 500

Total 5800

15. Reference/ Bibliography: The Reference list at the end of the proposal should include all works cited

in the proposal; conversely, all items listed as a reference must have been cited in the text of the

proposal. Only references cited in the text of the proposal are including in the reference list. However,

exceptions can be found to this rule. For example, if your reader require evidence that you are familiar

with a broader spectrum of literature then that immediately relevant to your research. In such

instances, the reference list may be called a bibliography. The bibliography includes a list of full

reference documentation for all articles and texts relevant to the study and mentioned in the proposal.

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.

Eg. Ary, Donald, (1972), Introduction to Research in Education 2nd Ed, New York, Holt Rinehart and

Winston Inc., 308 pp.

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.

16. Appendices/Annexes

The need for complete documentation generally dictates the inclusion of appropriate appendices in the

proposal. Appendices of a proposal should include data collection tools, such as consent forms, letter of

introduction to the subject, questionnaire, Interview protocols, and the like. The appendices section

should begin with its own cover page, followed by its own table of contents page.


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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)

Chapter 4: Defining Research Problem & Objectives

4.1 Define Manager’s Problems

Research may not be needed when information is already available, there is insufficient time for research, resources are not available and costs outweigh the value of the research. The right answer to the wrong question may be absolutely worthless indeed, it may even be harmful.

Problems mean gaps which occur when there are a d/c b/n the current conditions & a more preferable set of conditions. In other words, a gap exists b/n the way things are now and a way that things could be better. A problem well defined is a problem half solved. A good definition is necessary if research is to contribute to the solution of the problem. A bad definition damages the entire project from the start and guarantees that subsequent marketing and marketing research efforts will prove useless.

A problem is something wrong & needs attention. A problem is the result of an ineffective strategy, a change in situational factors, /a combination of the two. Like a medical doctor, a researcher must examine all the symptoms (presented to him or observed by him) concerning a problem before he can diagnose correctly.

Problem refers to some difficulty which a researcher experiences in the context of either a theoretical or practical situation and wants to obtain a solution for some event.

The first step is to identify and define the nature and scope of the research problem.

Decision makers frequently make the mistake of concentrating on the right answer instead of asking the right questions.

Mgt problem is the problem confronting the marketing/ decision-maker, which asks what the decision-maker needs to do.

Research problem is a problem that entails determining what information is needed and how it can be obtained in the most feasible way.

The mgt problem is action oriented. It is concerned with the possible actions the manager can take. A decision is present in situations that involve problems and opportunities, where the manager faces a choice of alternative courses of action and uncertainty exists about the outcome of the decision.

A decision-making situation is characterized first by a symptom, and so the underlying problem or opportunity that gave rise to the symptom must be defined.

A symptom is a particular condition that indicates the presence of a problem or an opportunity. In other words, it is a sign to the decision maker that a problem or opportunity requiring decisions is about to arise. For example, a decline in sales is not in itself a problem but a symptom of an underlying problem about which the decision maker must make decisions.


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Examples of Well-Defined Management Problems (Topics of the Research)

How has the new packaging affected the sales of the product? Has the new advertising message resulted in enhanced recall? How should the loss of market share be arrested? Should the market be segmented differently? Should a new product be introduced? Should the promotional budget be increased?

4.2. Define Research Problems: Identifying a marketing/mgt problem or opportunity does not mean

that the research problem has been defined. In fact, in research terms the research problem is a

redefinition of the marketing problem (mgt problem). In contrast, the research problem is information

oriented. It involves determining what information is needed and how that information can be obtained

effectively and efficiently.

Consider, for example, the loss of market share for a particular product line. The mgt’s problem is how

to recover this loss. Alternative courses of action can include:

Modifying existing products, Introducing new products, Segmenting the market. Hiring other employees/personal selling. Expanding business

Suppose that the decision-maker & the researcher believe that the problem is caused by inappropriate segmentation of the market and wanted research to provide information on this issue.

The research problem would then become the identification and evaluation of an alternative basis for segmenting the market. This would be done with respect to factors that influence a company in its choice in any case to handle its transactions. The ff example further distinguishes b/n the decision problem and the research problem.


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The decision maker is only aware that something is wrong with the business activities of the enterprise.

Decision makers usually become aware of a problem or opportunity when conflicting fragments of

information, reports, opinions and symptoms come to their attention but researcher finds the solution.

Problem Formulation: Defining a problem involves the task of laying down boundaries within which a

researcher shall study the problem with a pre-determined objective in view. How to define a research

problem is certainly an extra special task.

1. Statement of the problem in a general way: First of all the problem should be stated in a broad or general way, keeping in view either some practical concern or some scientific or intellectual interest. For this purpose, the researcher must immerse himself carefully in the subject matter concerning which he wishes to pose a problem. The problem stated in a broad general way may contain various ambiguities which must be resolved by cool thinking and rethinking over the problem.

2. Understanding the nature of the problem: The best way of understanding the problem is to discuss it with those who first raised it in order to find out how the problem originally came about & with what objectives in view. If the researcher has stated the problem himself, he should consider once again all those points that induced him to make a general statement concerning the problem.

3. Surveying the available literature: All available literature concerning the problem at hand must necessarily be surveyed and examined before a definition of the research problem is given. This means that the researcher must be well-conversant with relevant theories in the field, reports and records as also all other relevant literature


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4. Developing the ideas through discussions: Discussion concerning a problem often produces useful information. Various new ideas can be developed through such an exercise. Hence, a researcher must discuss his problem with his colleagues & others who have enough experience in the same area or in working on similar problems. This is quite often known as an experience survey.

5. Rephrasing the research problem (Problem Statement): Finally, the researcher must sit to rephrase the research problem into a working proposition. Once the nature of the problem has been clearly understood, the environment (within which the problem has got to be studied) has been defined, discussions over the problem have taken place and the available literature has been surveyed and examined, rephrasing the problem into analytical or operational terms is not a difficult task.

Evaluating the research Problem

1. Is the problem researchable? a. Do the data exist upon which a solution may be based? b. What about religion Christian or Muslim which is better? c. Is God is good?

2. Is the problem significant? 3. Is the problem a new one? 4. Is the answer already available? 5. Is the problem feasible for a particular researcher? 6. While the problem may be a good one? Some of the questions that should be raised are:

a. Do I have the necessary competence to plan and carry out a study of this type? b. Will I have the necessary financial resources to carry on this study? c. Will I have enough time to complete the project?

4.3. Developing the Research Objectives: The research objectives indicate broadly what the research

hopes to accomplish. It informs what the researcher wants to attain through the study. An answer must

be found to the question: ‘What is the purpose of the investigation?’ Research objectives address

information gaps that must be closed in order for the manager to go about resolving the current

problem. Each research objectives must be precise, detailed, clear and operational. With regard to

secondary objectives:

a. They should be numbered. b. The wording should be clear, complete and specific. c. They should communicate the intention of the researcher. d. Each objective should contain focus on aspect of the project. e. Action-oriented words or verbs should be used in the formulation of the objectives. f. Each objective should start with phrases such as:

‘to determine’ ‘to examine’ ‘to find out’ ‘to ascertain’ ‘to measure’ ‘To explore’, etc.

The wording of objectives determines the type of research and the type of research design that the

researcher needs to adopt to achieve them (exploratory, descriptive and causal).


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Research Objective Can Be General and Specific

Example if your title is “Determinants of Micro and Small Enterprises Growth in Selected Towns of West

Shoa Zone, Oromia regional state, Ethiopia” the objectives can be written as:

General objective

The general objective of the study is to examine the factors determining the growth of Micro and

Small enterprise at West Shoa Zone In the case of Ambo, Ginchi and Guder Towns.

Specific objectives:

To identify whether entrepreneurial competency and managerial skills affects the growth of MSEs.

To examine how access to finance and access markets affects the growth of MSEs.

To assess whether initial investment and sector affects the growth of MSEs.

To identify whether innovation engagement affects the growth of MSEs

To examine which factor critically influencing the growth of MSEs.

Research Questions: 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 Questions is refined statements of the specific components of the problem.

Hypothesis is an unproven statement or proposition about a factor or phenomenon that is of interest to

the researcher. Research questions express the research objectives in terms of questions that can be


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addressed by research. Hypotheses are more specific than research questions. One key distinction b/n

research questions and hypotheses is that hypotheses can generally specify the direction of a

relationship. The research problem indicates the type of phenomenon (individual, group, organization,

social interaction or social object) & the aspects, characteristics or dimensions of the phenomenon that

have to be researched.

Specific Research Questions:

Is entrepreneurial competency and managerial skills affects the growth of MSEs?


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Chapter 5: Research Design

5.1 The Significance of Research Design

“A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure.”

The research design is the conceptual structure within which research is conducted; it constitutes the blueprint for the collection, measurement and analysis of data.

Design includes an outline of what the researcher will do from writing the hypothesis and its operational implications to the final analysis of data.

The R design serves as a master plan of the methods and procedures that should be used to collect and analyze the data needed by the decision maker.

Decisions regarding what, where, when, how much, by what means concerning an inquiry or a research study constitute a research design.

The significance of research design:

Research design is needed because it facilitates the smooth sailing of the various research operations. Just as for better, economical and attractive construction of a house, we need a blueprint (or what is

commonly called the map of the house) well thought out and prepared by an expert architect, similarly we need a research design or a plan in advance of data collection and analysis for our research project.

Research design stands for advance planning of the methods to be adopted for collecting the relevant data and the techniques to be used in their analysis, keeping in view the objective of the research and the availability of staff, time and money.

Research design provides the researcher opportunity to undertake various research operations smoothly.

It makes research as efficient as possible generating maximum information with minimum effort, time and money.

It is like blueprint which we need in advance to plan the methods to be adopted for collecting the relevant data and techniques to be used in its analysis for preparation of research project.

The reliability of result, which a researcher is looking, is directly related with research design which constitutes a firm foundation of entire body of research work.

Features of a Good Research Design Simplicity: It should be simple and understandable

Economical: It must be economical. The technique selected must be cost effective and less time-

consuming

Reliability: It should give the smallest experimental error. This should have the minimum bias and

have the reliability of data collected and analyzed.

Workability: It must be workable. It should be pragmatic and practicable.

Flexibility: It must be flexible enough to permit the consideration of many different aspects of a

phenomenon.

Accuracy: It must lead to accurate results

Research Methodology, Methods and Techniques:


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R methodology involves the systematic procedures by which the Researcher starts from the initial identification of the problem to its final conclusions and consists of procedures and techniques for conducting a study.

Also it involves general activities as identifying problems, review of the literature, formulating hypotheses, procedure for testing hypotheses, measurement, data collection analysis of data, interpreting results and drawing conclusions.

R methodology is a way to systematically solve the R problem. It may be understood as a science of studying how R is done scientifically.

R methods refer to the methods the Researchers use in performing R operations. R techniques refer to the behavior and instruments we use in performing R operations such as making

observations, recording data, techniques of processing data and the like.

Types of RD: All R approaches can be classified into one of three general categories of RD: Exploratory R

Descriptive R and Causal R. The three basic categorical types of R design (exploratory, descriptive, and

causal) used to collect primary data and create data structures and information (Hair et.al Marketing R 2nd

Ed p.210)

1. Exploratory RD: Exploratory research design is undertaken when the research objectives focus on gaining background

information and clarifying management’s and/or the redefined research problems to create hypotheses, define terms, and/or establish research priorities.

Exploratory R is most commonly unstructured, informal R that is undertaken to gain background information about the general nature of the R problem.

By unstructured, we mean that exploratory R does not have a formalized set of objectives, sample plan, or questionnaire, It is usually conducted when the Researcher does not know much about the problem and needs additional information or desires new or more recent information.

Because exploratory R is aimed at gaining additional information about a topic and generating possible hypotheses to test, it is described as informal.

Exploratory R 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. In the following sections, we discuss the specific uses of exploratory R as well as the different methods of conducting exploratory R.

Exploratory R focuses on collecting either secondary or primary data and using unstructured formal or informal procedures to interpret them.

Among the three types of R designs, exploratory R incorporates the fewest characteristics or principles of the scientific method. It is often used simply to classify the problems or opportunities and it is not intended to provide conclusive information from which a particular course of action can be determined.

Some examples of exploratory R techniques are focus- group interviews, experience surveys, and pilot studies. Exploratory R may also use some forms of secondary data (e.g., online databases).

Exploratory R can be somewhat intuitive and is used by many decision makers who monitor market performance measures pertinent to their company or industry.

An exploratory R design is undertaken when the R objectives focus on gaining background information and clarifying management’s and/or the redefined R problems to create hypotheses; define terms, and/or establish R priorities.


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Uses of Exploratory R:

Gain Background Information: When very little is known about the problem or when the problem has not been clearly formulated, exploratory R may be used to gain much-needed background. This is easily accomplished 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 r are that some exploratory R is not undertaken to gain current, relevant background information. There is far too much to be gained to ignore exploratory information.

Define Terms: Exploratory R helps to define terms and concepts. By conducting exploratory R 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. Not only would exploratory R identify the components of bank image but it could also demonstrate how these components may be measured.

Clarify Problems and Hypotheses: Exploratory R allows the Researcher to define the problem more precisely and to generate hypotheses for the upcoming R. For example, exploratory R on measuring bank image reveals the issue of different groups of bank customers. Banks have three types of customers: retail customers, commercial customers, and other banks for which services are performed for fees. This information is useful in clarifying the problem of the measurement of bank image because it raises the issue of which customer group bank image should be measured on. Exploratory R can also be beneficial in the formulation of hypotheses, which are statements describing the speculated relationships among two or more variables. Formally stating hypotheses prior to conducting R is very important to ensure that the proper variables are measured. Once a R has been completed, it may be too late to state which hypotheses are desirable to test.

Establish R Priorities: Exploratory R can help a firm prioritize R topics in order of importance, especially when it is faced with conducting several R studies. A review of customer complaint letters for example, may indicate which product or services are most in need of management’s attention. For example, one furniture store chain owner decided to conduct R on the feasibility of carrying office furniture after some or exploratory interviews with salespeople revealed that their customers often asked for directions to stores carrying office furniture.

2. Descriptive RD: Descriptive research design is considered when the research objectives emphasize describing and measuring marketing phenomena at a particular point in time. The data and information from a descriptive design tend to provide answers to information research questions framed in who, what, where, when, how many, how much, how often formats as they relate to management’s initial decision problem or opportunity situation. When we wish to know how many customers we have, what brands they buy and in what quantities, which advertisements, they recall, and what their attitudes are toward our company and our competitors. 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. Consider the following examples. 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


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may be defined as the places the customers are buying these products. When refers to the time or the

frequency with which purchases are made. How may mean the ways in which customers are using the

products. Note that we cannot conclusively answer the questions such as why sales increase or decrease if

we increase or decrease advertising or why one ad gains greater attention than another are questions that

must be answered through causal R designs.

Descriptive R uses a set of scientific methods and procedures to collect raw data and create data structures

that describe the existing characteristics (e.g., attitudes, intentions, preferences, purchase behaviors,

evaluations of current marketing mix strategies) of a defined target population or market structure.

Descriptive R designs are appropriate when the R objectives include determination of the degree to which

marketing (or decision) variables are related to actual market phenomena.

Descriptive studies generally allow decision makers to draw inferences about their customers, competitors,

target markets, environmental factors, or other phenomena of concern. For example, there is a growing

trend among today’s major chain restaurants to conduct annual studies designed to identify and describe

consumers’ attitudes, feelings, and patronage behavior toward their own restaurants as well as toward

those of their main competitors. These studies, referred to as either image assessment surveys or customer

service satisfaction surveys, partially describe how consumers rate different restaurants’ customer service,

convenience of location, food quality, overall quality, and so on. However, descriptive designs are not

capable of addressing any of the why questions associated with a given R problem. Still, the data and

information generated through descriptive designs can provide decision makers with evidence that can lead

to a course of action.

Descriptive R 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 behavior. 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)?

3. Causal RD: Data and information for addressing most why questions would be gained through the employment of a causal research design. Causality may be thought of as understanding a phenomenon in terms of conditional statements of the form “if x, then y,” These “if-then”


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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. Unfortunately, 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. Consumers are bombarded on a daily and sometimes even hourly basis by a vast multitude of factors, all of which could cause them to act in one way or another. Nevertheless, there is a high “reward” in the marketplace for even partially understanding. How are causal relationships determined?

Causal R 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.

Differences between Exploratory and Conclusive R

S/n Factors Exploratory Causal

1. Objective to provide insights and understanding to test specific hypothesis and examine relationship

2. Characteris

tics

Information needed is defined only loosely.

R process is flexible and unstructured. Sample is small and non representative. analysis of primary data is qualitative

Information needed is clearly defined.

R process is formal and structured. sample is large and representative Data analysis is quantitative.

3. Findings/R

esults

Tentative conclusive

4. Outcomes Generally followed by further exploratory or conclusive R.

findings used as input into decision making

A Comparison of Basic R Design

S/n Factors Exploratory Descriptive Causal

1. Objective discover ideas and insights

describe market characteristics or functions

determine cause and effect relationships

2. Characteristics Flexible Versatile often the front end

of total R design

Marked by the prior formation of specific hypothesis.

preplanned and structured design

Manipulating of one or more independent variables.

control of other


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mediating variables

3. Methods expert surveys pilot surveys secondary

data(analyzed quantitatively)

qualitative R

secondary data (analyzed quantitatively)

surveys panels observational and other

data

experiments

CHAPTER 6: QUALITATIVE RESEARCH METHODS

6.0. Introduction: Data can be classified as primary & secondary. Primary data can be collected through

Qualitative & Quantitative R. Qualitative research is an unstructured, primarily exploratory design

based on small samples, intended to provide insight and understanding. Quantitative research is

research techniques that seek to quantify data &, typically, apply some form of statistical analysis.

6.1. Qualitative Research:

Qualitative research involves an interpretive, naturalistic approach to the world. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or to interpret, phenomena in terms of the meanings people bring to them.

Qualitative research is a method such using participant observation or case studies which result in a narrative, descriptive account of a setting or practice.

Qualitative research helps the marketer to understand the richness, depth and complexity of consumers.

Researchers may undertake qualitative research to help define a research problem, to support quantitative, descriptive or causal research designs or as a design in its own right.

Qualitative Research is primarily exploratory research. It is used to gain an understanding of underlying reasons, opinions, and motivations.

It provides insights into the problem or helps to develop ideas or hypotheses for potential quantitative research.

The strength of qualitative research is its ability to provide complex textual descriptions of how people experience a given research issue.

It provides information about the “human” side of an issue – that is, the often contradictory behaviors, beliefs, opinions, emotions, and relationships of individuals.

Qualitative methods are also effective in identifying intangible factors, such as social norms, socioeconomic status, gender roles, ethnicity, and religion, whose role in the research issue may not be readily apparent.

When used along with quantitative methods, qualitative research can help us to interpret and better understand the complex reality of a given situation and the implications of quantitative data.

Characteristics of Qualitative R Methods:


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It is used more in exploratory R designs. Its objective is to gain preliminary insights into decision problems and opportunities. It incorporates SOME SCIENTIFIC elements but normally lack the critical elements of true reliability. It tends to collect detailed primary data from small samples of subjects by asking questions /observing

behavior Researchers must be well trained in interpersonal communication and interpretive skills to use either

open-ended questions that allow for in-depth probing of the subjects’ initial responses/scientific observational techniques that allow for analysis of behavior.

Qualitative data can be collected within short periods of time, but it is difficult summarize/ quantify the data structures into meaningful forms or numbers.

It involves collecting, analyzing, and interpreting data by observing what people do and say.

Guidelines for Qualitative R Method:

Identifying a business problem or opportunity situation or establishing information requirements. To obtain preliminary insights about motivation, emotional, attitudinal, & personality factors in the

marketplace. In building theories and models to explain marketplace behaviors /relationships between two/ more

marketing constructs. Attempting to develop reliable & valid scale measurements for investigating specific market factors,

consumer qualities (e.g., attitudes, emotional feelings, preferences, beliefs, and perceptions) and behavioral outcomes.

Trying to determine the preliminary effectiveness of their marketing strategies on actual marketplace behaviors.

Interested in new-product or service development or repositioning current product or service images.

Merit and Demerit of Qualitative Research Methods

6.2. Data Collection Forms in Qualitative Research:

1. Observation: Observation refers to the systematic activities of witnessing and recording the

behavioral patterns of objects, people, events, and other phenomena without directly communicating

with them. The main reason for using observational techniques is to generate primary data structures

as events or marketing phenomena occur or to compile evidence of behaviors from records of past

events. Recording the behavioral patterns of objects, people, or marketing phenomena can be achieved


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by using trained human observers/such devices as videotapes, movie cameras, audiotapes, computers,

handwritten notes and logs/some other tangible recording mechanism.

Types of Observation: There are four general ways of organizing observations.

1. Direct versus indirect: Observing behavior as it occurs is called direct observation. For example, if we are interested in finding out how much shoppers grasp tomatoes to assess their freshness, we can observe people actually picking up the tomatoes. In order to observe types of hidden behavior, such as past behavior, we must rely on indirect observation. With indirect observation, the researcher observes the effects or results of the behavior rather than the behavior itself.

2. Disguised Versus Undisguised: With disguised observations, the subject is unaware that he/ she is being observed. An example of this method might be a “secret shopper” that is used by a retail store chain to record and report on sale clerks’ assistance and courtesy. One-way mirrors and bidden cameras are a few of the other ways that are used to prevent subjects from becoming aware that they are being observed. This disguise is important because if the observes were aware of the observation, it is possible that they would change their behavior, resulting in observations of atypical behavior. With undisguised observation, the subject is aware that he/she is the subject of observation.

3. Structured Versus Unstructured: When using structured observation techniques, the R identifies beforehand which behaviors are to be observed. All other behaviors are “ignored.” Often, checklist/ a standardized observation form are used to isolate the observer’s attention to specific factors. These highly structured observations topically require a minimum of effort on the part of the observer. Unstructured observation places no restriction on what the observer would note. The observer just watches the situation and records what he or she deems interesting or relevant. This type of observation is often used in exploratory R.

4. Human versus Mechanical: With human observation, the observer is a person hired by the Researcher, or, perhaps, the observer is the Researcher. However, it is sometimes possible (or desirable) to replace the human observer with some form of observing device, as in mechanical observation.

2. Focus Groups: A FG is a small group of people brought and guided by a moderator thorough an

unstructured, spontaneous discussion about some topic. The goal of a FG is to draw out ideas, feelings,

and experiences about a certain issue that would be obscured or stifled by more structured methods of

data collection. The use of a small group allows the operation of group dynamics and aids in making the

participates feel comfortable in a strange environment. It is called a “focus” group because the

moderator serves to focus the discussion on the topic and does not let the group move off onto

tangents or irrelevant points.


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General Guidelines for Answering FG Questions

1. What should be the Size of a FG? According to industry wisdom, the optimal size of a FG is 8 to 12 people. A small group (fewer than eight participants) is not likely to generate the energy and group dynamic necessary for a truly beneficial FG session. With fewer participants, it is common that one or two of the participants do most of the talking in spite of the moderator’s efforts. At the same time, a small group will often result in awkward silences and force the moderator to take too active a role in the discussion just to keep the discussion alive.

2. Who should be in the FG? It is generally believed that the best FGs are ones in which the participants share homogenous characteristics. This requirement is sometimes automatically satisfied by the Researcher’s need to have particular types of people in the FG. The need/ similar demographic/ other relevant characteristics in the FG members is so required by the fact that the FG participants are typically strangers. In most cases, they are not friends/ even casual acquaintances, and many people feel intimidated or at least hesitant to voice their opinions and suggestions to a group of strangers.

3. How Should FG Participants be Recruited and Selected?: The selection of FG participants is determined largely by the purpose of the FG, For instance, if the purpose is to generate new ideas on product packaging, the participants must be consumers who have used the brand. If the FG is intended to elicit building contractors, reactions to a new type of central air conditioning unit, it will be necessary to recruit building contractors.

4. Where Should a FG Meet? : It is important that the physical arrangement of the group be comfortable and conducive to group discussion. So FGs ideally are conducted in large rooms set up in a round table format. Aside from a circular seating arrangement where participants can sell one another, the second critical requirements in selecting a meeting place is find one quiet enough to permit an intelligible audio taping of the sessions.

Ten Traits of a Good FG Moderator

1. Be experienced in FG R 2. Provide sufficient help in conceptualizing the FG R design. 3. Prepare a detailed moderator guide well in advance of the FG 4. Engage in advance preparation to improve overall knowledge of the area being discussed. 5. Proved some “added value” to the project beyond simply doing an effective job of conducting the

session. 6. Maintain control of the group without leading or influencing the participants 7. Be open to modern techniques such as visual stimulation, attitude scaling/ role-playing in the minds

of participants. 8. Take personal responsibility for the amount of time allowed for the recruitment, screening, and

selecting of participants 9. Share in the feeling of urgency to complete the FG while desiring to active an excellent total R

project. 10. Demonstrate the enthusiasm and exhibit the energy necessary to keep the group interested when

the hour is running late.


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3. 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 & feelings on a topic.

4. Projective technique (Indirect Approach or 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. In projective techniques, respondents are asked to interpret the behavior of others

rather than describe their own behavior. In interpreting the behavior of others, respondents indirectly

project their own motivations, beliefs, attitudes, or feelings into the situations.

A Comparison of FGs, Depth Interviews, and Projective Techniques

S/N Criteria FGs 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 behavior No To a limited extent Yes

9 Overall usefulness Highly useful Useful Somewhat useful


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CHAPTER 7: QUANTITATIVE RESEARCH METHOD

7.1. Alternative Data Collection Modes: In this chapter, we focus on the major techniques employed in

descriptive research designs: surveys and quantitative observation. Descriptive research has as its

prime objective the description of something, usually consumer or market characteristics. Survey and

quantitative observation techniques are vital techniques in descriptive research designs. Survey

techniques may be classified by mode of administration as traditional telephone interviews, computer-

assisted telephone interviews, personal in-home or office interviews, street interviews, computer-

assisted personal interviews, postal surveys, electronic surveys and mail panels.

The major observational techniques: personal observation including mystery shopping research,

electronic observation, audit, content analysis and trace analysis. Survey techniques are based upon the

use of structured questionnaires given to a sample of a population. Respondents may be asked a variety

of questions regarding their behavior, intentions, attitudes, awareness, motivations, and demographic

and lifestyle characteristics. These questions may be asked verbally, in writing or via a computer, and

the responses may be obtained in any of these forms. ‘Structured’ here refers to the degree of

standardization imposed on the data collection process. In structured data collection, a formal

questionnaire is prepared and the questions are asked in a prearranged order; thus, the process is also

direct. Whether research is classified as direct or indirect is based on whether the true purpose is known

to the respondents.

The survey method has several advantages. First, the questionnaire is simple to administer. Second, the

data obtained are consistent because the responses are limited to the alternatives stated. The use of

fixed-response questions reduces the variability in the results that may be caused by differences in

interviewers. Finally, coding, analysis and interpretation of data are relatively simple. Disadvantages are

that respondents may be unable or unwilling to provide the desired information.


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Differences between Qualitative and Quantitative R Methods

Advantages of Surveys: 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 FG 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.


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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 to 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 / Three Alternative Data Collection Modes: There are three major ways to collect

information from respondents:

1. Person-administered surveys (PAS) 2. Computer-administered surveys(CAS) 3. Self-administered surveys(SAS)

1. Data Collection Modes In case of PAS: A PAS is one in which an interviewer reads questions to the respondent and records his or her answers. It was the primary administration method for many years. However, its popularity has fallen off as communications systems have developed and technology has advanced.

In-home interviews: Just as the name implies, an in home interview is conducted in the home of the respondent.

Mall intercepts interviews: The mall-intercept interview is one in which the respondent is encountered and questioned while he or she is visiting a shopping mall (large departmental stores).Mall intercept interviewing has acquired a major role as a survey method due to its ease of implementation and low cost.

In-office interviews: Business R conducted in the business-to-business or organizational market typically requires with business executive, purchasing agents, engineer, or other managers..

Telephone interview: If physical contract is not necessary, telephone interviewing is an attractive option. There are a number of advantages as well as disadvantages associated with telephone interviewing.

2. Data Collection Modes In case of 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 survey types. Basically, a CAS is one in which computer technology plays an essential role in the interview work.

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).


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Computer-Administered Telephone Interviewing: options are very attractive to business Researchers because of the advantages of 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 this high-technology approach. The prospect for 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.

Fully Computerized interview: Some companies have developed fully computerized interviews, in which the survey is administered completely by a commuter. In the R 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.

3. Data Collection Modes In case of SAS: A SAS 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.

Group Self-Administered Survey: Basically, a group self-administered survey entails 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. 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.

Drop off Survey : 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 his or her own. Essentially, the objective is to

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.

Summary of the Advantages & Disadvantages of Data Collection Methods

Method Key Advantages Key Disadvantages Comment

In-home

interview

Conducted in privacy of

the home, which

facilitates interviewer

respondent rapport

Cost per interview can be high;

interviewers must travel to

respondent’s home

Often much information per

interview is gathered

Mail-Intercept Fast and convenient Only mall patrons are mail

intercept

Mail intercept company

Interview Data collection method Interviewed; respondents may

feel uncomfortable answering

questions in the mall

Often has exclusive interview

rights for that mall


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In office

interview

Useful for interviewing

buys executives

Relatively high cost per

interview; gaining access is

sometimes difficult

Useful when respondents

must examine prototypes or

samples of products

Central Location

Telephone

Interview

Fast turnaround; good

quality control;

reasonable cost

Restricted to telephone

communication

Long distance calling is not a

problem

CATI Computer eliminates

human interviewer error;

simultaneous data input

to computer file; good

quality control

Set-up costs can be high Will advance with new

computer and

telecommunications

technology

Computer-

assisted

interview types

Respondent responds at

his/her own pace;

computer data file results

Respondent must have access to

a computer or be computer

literate

Many variations and an

emerging data collection

method with exciting

prospects

Group self-

administered

survey

Cost of interviewer

eliminated; economical

for assemble groups of

respondents

Must find groups and secure

permission to conduct the

survey

Prone to errors of self-

administered surveys; good

for pretests or pilot tests.

Drop-off survey Cost of interviewer

eliminated, appropriate

for local market surveys

Generally not appropriate for

large scale national surveys

Many variations exist with

respect to logistics and

applications

Mall survey Economical method;

good listing companies

exist

Low response rates; self-

selection bias; slow

Many response rate increase

strategies exist

CHAPTER 8: MEASUREMENTS IN RESEARCH


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8.1. Basic Concepts of Measurement in Research: ‘When you can measure what you are speaking

about & express it in numbers, you know something about it’ (Lord Kelvin). Measurement means

assigning numbers or other symbols to characteristics of objects according to certain pre-specified rules.

We do not measure the object but some characteristic 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.

a) First, numbers permit statistical analysis of the resulting data. b) Second, numbers facilitate universal communication of measurement rules & results.

The most important aspect of measurement is the specification of rules for assigning numbers to the

characteristics. Objects include consumers, brands, stores advertisements, or whatever constructs is of

interest to the researcher working with a particular manager. Properties are the specific features or

characteristics of an object that can be used to distinguish it from another object. For example, assume

the object we want to research is a consumer. The properties of interest to a manage who is trying to

define who buys a specific product are a combination of demographics such as age; income level;

gender; & buyer behavior, which includes such things as the buyer’s impressions or perceptions of

various brands.

Scaling involves creating a continuum upon which measured objects are located. To illustrate, consider a

scale for locating consumers according to the characteristic „attitude towards banks’. Each respondent is

assigned a number indicating an unfavorable attitude (measured as 1), a neutral attitude (measured

as 2) or a favorable attitude (measured as 3). Measurement is the actual assignment of 1, 2 or 3 to each

respondent. Scaling is the process of placing the respondents on a continuum with respect to their attitude

towards banks. In our example, scaling is the process by which respondents would be classified as having

an unfavorable, neutral or positive attitude.

8.2. Levels of Measurement of Scales: In the scales of measurement, data collection requires one of the

following scales of measurement: nominal, ordinal, interval, or ratio. When the data for a variable

consist of labels or names used to identify an attribute of the element, the scale of measurement is

considered a nominal scale.

Data can also be classified as either qualitative or quantitative. Qualitative data include labels or names

used to identify an attribute of each element. Qualitative data use either the nominal or ordinal scale

of measurement & may be nonnumeric or numeric. Quantitative data require numeric values that

indicate how much or how many. Quantitative data are obtained using either the interval or ratio scale

of measurement. A qualitative variable is a variable with qualitative data, & a quantitative variable is a

variable with quantitative data.


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1. Nominal Scale

The numbers serve only as labels or tags for identifying & classifying objects. The numbers do not reflect the amount of the characteristic possessed by the objects. The only permissible operation on the numbers in a nominal scale is counting. Only a limited number of statistics, such as frequency counts, e.g., percentages, & mode. Provide data that cannot be arranged in a “greater than/less than”.

Some examples are:

1. The use of numbers (9, 4, 16, 8, 8, etc.) to identify the players on a basketball team so that the scorekeeper can correctly record points scored or fouls committed;

2. YES & NO responses to the question “Are you going to purchase a new automobile within the next six months?”

3. The Use Of Colors (red, green, blue, pink, etc.) to identify the types of cars, clothes, or bathroom towels that consumers buy for personal consumption; &

4. The use of size indicators (large, medium, small, etc.) to identify the quantity of soft drinks, or fitting of clothes, or amount of pizza that people might order from a retail store.

Example of 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 Scale

Numbers are assigned to objects to indicate the relative extent to which the objects possess some characteristic.

Can determine object has more or less of a characteristic than some other object. Any series of numbers can be assigned that preserves the ordered relationships between the

objects. Permit the use of statistics based on e.g., percentile, quartile, & median. The structure of an ordinal scale activates both the assignment & order scaling properties. Allows respondents to express relative magnitude between the answers to a question. The researcher can rank-order the raw responses into a hierarchical pattern. As such, it is easy to

determine “greater than/less than,” “higher than/lower than,” “more often/less often,” “more important/less important” /“less agreement/more agreement” types of relationships between the responses.

Example of Sample Ordinal–Scaled Questions: Please rank each brand in terms of your preference.

Place “1” by your first choice, “2” by your second choice, & so on.

Pepsi__________


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Seven Up______ Coca Cola______

3. Interval scale

Interval scale a scale in which the numbers are used to rank objects such that numerically equal distances on the scale represent equal distances in the characteristic being measured.

It permits comparison of the differences between objects. Statistical techniques that may be used include all of those that can be applied to nominal & ordinal

data & in addition the arithmetic mean, standard deviation, & other statistics commonly used in marketing research.

In reality, an interval scale is nothing more than a collapsed set of ratio-based responses. When the researcher employs the distance property, it allows for more powerful statistical

techniques to be used to analyze the raw data into meaningful data structures.

Example: Sample Interval –Scaled Questions

1. Indicate your degree of agreement with the following statements by encircling the appropriate number. Statement Strongly Disagree Strongly Agree

1. I always look for bargains 1 2 3 4 5 2. I enjoy being outdoors 1 2 3 4 5 3. I love to cook. 1 2 3 4 5

4. Ratio scale

Possesses all the properties of the nominal, ordinal, & interval scales. It has an absolute zero point. It is meaningful to compute ratios of scale values. All statistical techniques can be applied to ratio data. Is the only level of scale that simultaneously activates all four scaling properties.

Example of Sample Ratio –Scaled Questions

1. Please indicate your age________. 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.


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8.3. Scaled Response Question Forms/Classification of Scaling Techniques: The scaling techniques

commonly employed in marketing research can be classified into comparative & non-comparative

scales.

1) Comparative scaling: Comparative scales involve the direct comparison of stimulus objects. For example, respondents may be asked whether they prefer Coke or Pepsi. Comparative scale data must be interpreted in relative terms & have only ordinal or rank order properties. For this reason, comparative scaling is also referred to as non-metric scaling. Non-metric scale is a scale that is either nominal or ordinal in nature.

1. Paired Comparison Scaling: A comparative scaling technique in which a respondent is presented with two objects at a time & asked to select one object in the pair according to some criterion. The data obtained are ordinal in nature. A respondent may state that he or she prefers coffee to tea, or likes Adidas more than Nike. Paired comparison scales are frequently used when the stimulus objects are physical products. Paired comparison scaling is the most widely used comparative scaling technique.

Item prefer

Coffee


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Tea

2. Rank order scaling: In rank order scaling respondents are presented with several objects simultaneously & asked to order or rank them according to some criterion. For example, respondents may be asked to rank brands of cars according to overall preference. Furthermore, rank order scaling also results in ordinal data. Rank order scaling is commonly used to measure attributes of products & services as well as preferences for brands.

2) Non-comparative: Non-comparative scales, also referred to as monadic or metric scales, each object

is scaled independently of the others in the stimulus set. The resulting data are generally assumed to be

interval or ratio scaled.

1. In a continuous rating scale, also referred to as a graphic rating scale, respondents rate the objects by placing a mark at the appropriate position on a line that runs from one extreme of the criterion variable to the other. Thus, the respondents are not restricted to selecting from 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; & if provided, the scale points may be few or many.


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2. Itemized rating scale: In an itemized rating scale, respondents are provided with a scale that has a

number or brief description associated with each category. The commonly used itemized rating scales

the Likert, & semantic differential.

a. Likert scale: A measurement scale with five response categories ranging from ‘strongly disagree’ to ‘strongly agree’ that requires respondents to indicate a degree of agreement or disagreement with each of a series of statements related to the stimulus objects. Named after its developer, Rensis Likert, the Likert scale is a widely used rating scale that requires the respondents to indicate a degree of agreement or disagreement with each of a series of statements about the stimulus objects.

Example: Sample Interval –Scaled Questions

Indicate your degree of agreement with the following statements by encircling the appropriate number.

Statement SD D N A SA

1. I always look for bargains 1 2 3 4 5

2. I enjoy being outdoors 1 2 3 4 5

3. I love to cook. 1 2 3 4 5

b. Semantic differential scale: The semantic differential is a seven-point rating scale with end points associated with bipolar labels that have semantic meaning. In a typical application, respondents rate objects on a number of itemized, seven-point rating scales bounded at each end by one of two bipolar adjectives, such as ‘cold’ & ‘warm’. Weak, reliable, very old fashioned, low-tech & careful. The negative adjective or phrase sometimes appears at the left side of the scale & sometimes at the right.

Example: What does Dresdner Bank mean to you? The following descriptive scales, bounded at each end by

bipolar adjectives, summarize characteristics of the bank. Please mark ✘ the blank that best indicates what the

Dresdner Bank means to you. Form Dresdner Bank is:


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CHAPTER 9: DESIGNING QUESTIONNAIRE

9.1. Questionnaires: Survey & observation are the two sets of techniques for obtaining quantitative

primary data in descriptive research. Both methods require some procedure for standardizing the data

collection process so that the data obtained are internally consistent & can be analyzed in a uniform &

coherent manner.

Questionnaire is a structured technique for data collection consisting of a series of questions, written or verbal, that a respondent answers.

A questionnaire is a formalized set of questions for obtaining information from respondents. A questionnaire is a self-report data collection instrument/ method that are filled out by research

participants. Questionnaires are sometimes called survey instruments, which are fine. The word survey refers to

the process of using questionnaire or interview protocol to collect data. In short, questionnaire is a data collection method, which contain a series of questions, which will be filled by the respondent/ participant themselves without any direct oral explanation/interpretation from the investigator.

The functions of a questionnaire:

It translates the research objective into specific questions that are asked of the respondents. It standardizes those questions & the response categories so every participant responds to identical

stimuli. It fosters cooperation & keeps respondents motivated throughout the interview. Questionnaires serve as permanent records of the research They speed up the process of data analysis They contain the information upon which reliability assessments such as test-retest or equivalent-

form questions may be made, & they are used in follow-up validation of respondents, participations in the survey.

The Five “Should” Of Question Wording:

1. The question should be focused on a single issue or topic: The researcher must stay focused on the specific issue or topic.

2. The question should be brief: Unnecessary & redundant words should always be eliminated, regardless of the data collection administered verbally, such as over the telephone.

3. The question should be interpreted the same way by all respondents: All respondents should “see” the question identically.

4. The question should use the respondent’s core vocabulary: The core vocabulary is he everyday language respondents use to converse with others like themselves, but it does not include slang or jargon.


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5. The question should be a grammatically simple sentence if possible: A simple sentence is preferred because it has only a single subject & predicate, whereas compound & complex sentences are busy with multiple subjects, predicates objects, & complements.

The 10 “Should Nots” Of Question Wording

1. The question should not assume criteria that are not obvious: Questions frequently require respondents to make judgments, & judgment assumes that certain criteria are being applied.

2. The question should not be beyond the respondent’s ability of experience: Questions should not transcend the respondent’s experience, For example, it makes little sense to ask teenagers what type of family automobile they will purchase when they are mare married.

3. The question should not use a specific example to represent a general case: The danger in using a specific example to measure a broader situation lies in the possibility that the respondent will concentrate only on that example.

4. The question should not ask the respondent to recall specifics when only generalities will be remembered: sometimes a question designer forgets that people do not have perfect memory, & the detail requested in the question is beyond the respondent’s abilities to reconstruct what actually happened.

5. The question should not require the respondent to guess a generalization: When asked to respond to a question involving a generality, respondents may be inclined to respond with what they think “must” have happened or what “should” happen.

6. The question should not ask for details that cannot be related: Business men sometimes ask for information that is impossible to remember, forgetting that consumers have more important things to worry about than consumption.

7. The question should not use words that overstate the condition: Avoid using words that overstate condition. It is better to present the question in a neutral tone rather than in a positive or a negative tone.

8. The question should not have ambiguous wording: Ambiguity in wording allows respondents to apply their own situations, experiences, or interpretations to them.

9. The question should not be “double-barreled”: A “double-barreled” question is really two different questions posed in one question. With two question posed together, it is difficult for a respondent to answer either one directly.

10. The question should not lead the respondent to a particular answer: A leading question is worded in such a way as to give the respondent a clue as to how to answer.

9.2. The Questionnaire Development/Design Process

1. Specify the information needed 2. Specify the type of interviewing method 3. Determine the content of individual questions 4. Overcome the respondent’s inability & unwillingness to answer 5. Choose question structure 6. Choose question wording 7. Arrange the questions in proper order 8. Identify the form & layout 9. Reproduce the questionnaire 10. Eliminate problems by pilot-testing


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Step 1: Specify the Information Needed

Ensure that the information obtained fully addresses all the components of the problem. Review components of the problem & the approach, particularly the research questions, hypotheses & characteristics that influence the research design.

Have a clear idea of characteristics & motivations of the target population.

Step 2: Specify the Type of Interviewing Method

Review the type of interviewing method determined. Mail questionnaires, self-administered, &

computer-assisted interviewing (CAPI can be applied. Questionnaires designed for personal & telephone

interviews, should be written in a conversational style.

Step 3: Determine the Content of Individual Questions

Is the question necessary? Are several questions needed instead of one to obtain the required information in an

unambiguous manner? Do not use double-barreled questions.

Step 4: Overcome the Respondent’s Inability & Unwillingness to Answer

Is the respondent informed? If the respondent is not likely to be informed, filter questions that measure familiarity, product use

& past experience should be asked before questions about the topics themselves. Can the respondent remember? Avoid errors of omission, telescoping & 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 respondent. Is the context in which the questions are asked appropriate? Make the request for information seem legitimate. If the information is sensitive:

o Place sensitive topics at the end of the questionnaire. o Preface the question with a statement that the behavior of interest is common. o Ask the question using the third-person technique. o Hide the question in a group of other questions that respondents are willing to answer. o Provide response categories rather than asking for specific figures. o Use randomized techniques, if appropriate.

Step 5: Choose Question Structure

Open-ended questions are useful in exploratory research & as opening questions.


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Use structured questions whenever possible. In multiple-choice questions, the response alternatives should include the set of all possible choices

& should be mutually exclusive. In a dichotomous 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 & multiple-choice

questions. If the response alternatives are numerous, consider using more than one question to reduce the

information processing demands on the respondents.

Step 6: Choose Question Wording

Define the issue in terms of ‘who’, ‘what’, ‘when’ & ‘where’. Use ordinary words. Words should match the vocabulary level of the respondents. Avoid ambiguous words: usually, normally, frequently, often, regularly, occasionally, sometimes, etc. Avoid leading or biasing questions that cue the respondent to what the answer should be. Avoid implicit alternatives that are not explicitly expressed in the options. Avoid implicit assumptions. Respondent should not have to make generalizations or compute estimates. Use positive & negative statements.

Step 7: Arrange the Questions in the Proper Order

The opening questions should be interesting, simple & non-threatening. Qualifying questions should serve as the opening questions. Basic information should be obtained first, followed by classification & finally identification

information. Difficult, sensitive or complex questions should be placed late in the sequence. General questions should precede specific questions. 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 placed as close as possible to the question causing the

branching, & the branching questions should be ordered so that the respondents cannot anticipate what additional information will be required.

Step 8: Identify the Form & Layout

Divide a questionnaire into several parts. Questions in each part should be numbered. The questionnaire should be pre-coded. The questionnaires themselves should be numbered serially.

Step 9: Reproduce the Questionnaire

The questionnaire should have a professional appearance.


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A booklet format should be used for long questionnaires. Each question should be reproduced on a single page (or double-page spread). Vertical response columns should be used. The tendency to crowd questions to make the questionnaire look shorter should be avoided. Directions or instructions for individual questions should be placed as close to the questions as

possible.

Step 10: Eliminate Problems by Pilot-testing

Pilot-testing should always be done. All aspects of the questionnaire should be tested, including question content, wording, sequence,

form & layout, question difficulty & instructions. The respondents in the pilot-test should be similar to those who will be included in the actual

survey. Begin the pilot-test by using personal interviews. The pilot-test 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 pilot-tests. The pilot-test sample size should be small, varying from 15 to 30 respondents for the initial testing. Use protocol analysis & debriefing to identify problems. After each significant revision of the questionnaire, another pilot-test should be conducted, using a

different sample of respondents. The responses obtained from the pilot-test should be coded & analyzed.

CHAPTER 10: SAMPLING DESIGN

10.1. Sampling Definition: Sampling involves selecting a relatively small number of elements from a larger defined group of elements & expecting that the information gathered from the small group will allow judgments to be made about the larger group or Sampling refers to drawing a sample (subset) from a population (the fullest). A complete enumeration of all items in the „population‟ is known as a census inquiry. But in practice this may not be true. However, it needs to be emphasized that when the universe is a small one, it is no use resorting to a sample survey. The selected respondents constitute what is technically called a „sample‟ & the selection process is called „sampling technique.‟ The survey so conducted is known as „sample survey’. Algebraically, let the population size be N & if a part of size n (which is n < N) of this population is selected according to some rule for studying some characteristic of the population, the group consisting of these n units is known as „sample‟. Researcher must prepare a sample design for his study i.e., he must plan how a sample should be selected & of what size such a sample would be.


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10.2. Sampling Terminology

1. Universe/Population: The term „Universe‟ refers to the total of the items or units in any field of inquiry, whereas the term „population‟ refers to the total of items about which information is desired. The aggregate of such units is generally described as population. The population or universe can be finite or infinite. The population is said to be finite if it consists of a fixed number of elements so that it is possible to enumerate it in its totality. For instance, the population of an Ambo Town, the number of workers in Ambo University is examples of finite populations. An

infinite population is that population in which it is theoretically impossible to observe all the elements. Thus, in an infinite population the number of items is infinite i.e., we cannot have any idea about the total number of items. The number of stars in a sky, possible rolls of a pair of dice are examples of infinite population.

2. A sample: A set of element taken from a population (i.e. a finite subset of individuals defined in a population.)

3. Sampling unit: The constituents of a population, which are individuals have to be sampled from the population & cannot be further subdivided for the purpose of sampling at a time.

4. Sampling frame: The elementary units or the group or cluster of such units may form the basis of sampling process in which case they are called as sampling units. A list containing all such sampling units is known as sampling frame. Thus sampling frame consists of a list of items from which the sample is to be drawn. If the population is finite & the time frame is in the present or past, then it is possible for the frame to be identical with the population.

5. Sampling design: A sample design is a definite plan for obtaining a sample from the sampling frame. It refers to the technique or the procedure the researcher would adopt in selecting some sampling units from which inferences about the population is drawn. Sampling design is determined before any data are collected. This includes probability& non-probability.

6. Statistic(s) & parameter(s): A statistic is a characteristic of a sample, whereas a parameter is a characteristic of a population. Thus, when we work out certain measures such as mean, median, mode or the like ones from samples, then they are called statistic(s) for they describe the characteristics of a sample.

7. Sampling error & non-sampling error: Sample surveys do imply the study of a small portion of the population & as such there would naturally be a certain amount of inaccuracy in the information collected. This inaccuracy may be termed as sampling error or error variance. In other words, sampling errors are those errors which arise on account of sampling & they generally happen to be random variations in the sample estimates around the true population values. Sampling error: Although the sample is the part of the population it cannot be expected generally to supply full information about the population. So there may be in most cases difference between statistics & parameters. The discrepancy between a parameter & its estimate (statistics) due to sampling process is known as sampling error (i.e., the difference between the value of a sample statistics (such as the sample mean) & the true value of the population parameter (such as the population mean). Non-sampling error: In all research /survey some errors may occur during collection of actual information. These errors are called non-sampling error. Sampling error is inversely related to the size of the sample i.e., sampling error decreases as the sample size increases & vice-versa. A measure of the random sampling error can be calculated for a given sample design & size & this measure is often called the precision of the sampling plan.

8. Precision: Precision is the range within which the population average (or other parameter) will lie in accordance with the reliability specified in the confidence level as a percentage of the estimate or as a numerical quantity. For instance, if the estimate is ETB 4000 & the precision desired is 4%, then the true value will be no less than ETB3840 & no more than Rs 4160. This is the range (ETB3840 to ETB 4160) within which the true answer should lie. But if we desire that the estimate should not deviate from the actual value by more than ETB 200 in either direction, in that case the range would be ETB 3800 to ETB4200.


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9. Confidence level & significance level: The confidence level or reliability is the expected percentage of times that the actual value will fall within the stated precision limits. Thus, if we take a confidence level of 95%, then we mean that there are 95 chances in 100 (or .95 in 1) that the sample results represent the true condition of the population within a specified precision range against 5 chances in 100 (or .05 in 1) that it does not. Precision is the range within which the answer may vary & still be acceptable; confidence level indicates the likelihood that the answer will fall within that range, & the significance level indicates the likelihood that the answer will fall outside that range. We can always remember that if the confidence level is 95%, then the significance level will be (100 – 95) i.e., 5%; if the confidence level is 99%, the significance level is (100 – 99) i.e., 1%, & so on. We should also remember that the area of normal curve within precision limits for the specified confidence level constitutes the acceptance region & the area of the curve outside these limits in either direction constitutes the rejection regions.

10.3. The Need for Sampling

1. Less time: since the sample is the study of part of the population; considerable time & labor are saved when a sample survey is carried out.

2. Less Cost: the amount of effort & expenses involved in collecting information is not always greater in sampling than a complete census. The total financial burden of a sample survey is generally less than that of a complete enumeration.

3. More reliable results: There are several reasons for this justification. First, it is always possible to determine the extent of sampling error. Secondly, other types of errors to which a survey is subjected such as inaccuracy of information incompleteness of returns, etc. are likely to be more serious in a complete census than in a sample survey.

4. Sampling provides mush quicker results than does a census: when the time between the recognition of the need of information & the availability of that information is short, sampling helps not to miss the information.

5. Sampling is the only process possible if the population is infinite: If the population members are not exhaustively counted, we cannot carry out census because in census each & every element of the population should be considered. In such a case, sampling is the only way of getting data.

10.4. Steps in Sample Design: A sample design is a definite plan for obtaining a sample from a given population. It refers to the technique or the procedure the researcher would adopt in selecting items for the sample. Sample design may as well lay down the number of items to be included in the sample i.e., the size of the sample. Sample design is determined before data are collected. There are many sample designs from which a researcher can choose. While developing a sampling design, the researcher must pay attention to the following points:

1. Type of universe: The first step in developing any sample design is to clearly define the set of objects, technically called the Universe, to be studied. The universe can be finite or infinite. In finite universe the number of items is certain, but in case of an infinite universe the number of items is infinite, i.e., we cannot have any idea about the total number of items.

2. Sampling unit: A decision has to be taken concerning a sampling unit before selecting sample. Sampling unit may be a geographical one such as state, district, village, etc., or a construction unit such as house, flat, etc., or it may be a social unit such as family, club, school, etc., or it may be an individual. The researcher will have to decide one or more of such units that he has to select for his study.

3. Source list: It is also known as „sampling frame‟ from which sample is to be drawn. It contains the names of all items of a universe (in case of finite universe only). If source list is not available,


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researcher has to prepare it. Such a list should be comprehensive, correct, reliable & appropriate. It is extremely important for the source list to be as representative of the population as possible.

4. Size of sample: This refers to the number of items to be selected from the universe to constitute a sample. This major problem before a researcher.

5. Parameters of interest: In determining the sample design, one must consider the question of the

specific population parameters which are of interest. 6. Budgetary constraint: Cost considerations, from practical point of view, have a major impact

upon decisions relating to not only the size of the sample but also to the type of sample. This fact can even lead to the use of a non-probability sample.

7. Sampling procedure: Finally, the researcher must decide the type of sample he will use i.e., he must decide about the technique to be used in selecting the items for the sample.

10.5. Sample size determination: AS Kothari (2004) the desired sample size can be calculated by using a formula for a single population proportion p, where p is assumed that 50% of customers satisfied with SQ, & 5% margin of error at 95% confidence level. The Sample size can be determined by using the following formula in case of finite population.

For FINITE

0r we can use INFINITE case

10.6. Sampling design: Overall, there are two basic sampling designs: probability & non-probability.

1. Probability sampling: In probability sampling, each sampling unit in the defined target population

has a known, nonzero probability of being selected for the sample. There are 4 ways of probability sampling.


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1. Simple Random Sampling (SRS): Researchers use a table of random numbers, random digit

dialing, or some other random selection procedure that ensures that each sampling unit making up the defined target population has a known, equal, nonzero chance of being selected into the sample. For example, let‟s say an instructor decided to draw a sample of 10 students (n = 10) from among all the students in a marketing research class that consisted of 30 students (N = 30). The instructor could write each student‟s name on a separate, identical piece of paper & place all of the names in a jar. Each student would have an equal, known probability of selection for a sample of a given size that could be expressed by the following formula:

2. Systematic Random Sampling (SYMRS): Using some form of an ordered list of the members of

the defined target population, researchers select a random starting point for the first sampled member. After determining what the constant “skip interval” value needs to be to ensure representativeness, the skip interval is applied to select every nth member from the random starting point until the necessary sample is drawn. This sampling method is used such that the entire list is covered, no matter of the starting point. This method accomplishes the same end goal of the SRS method, & is more efficient. In Systematic Random sampling each unit in the population is identified & each unit has an equal chance of being in the sample. It is also called Quasi- random sampling. Selection procedures: Systematic sampling involves three steps:

a. Determine the sampling interval, which is symbolized by “K” (i.e., it is the population size divided by the desired sample size 100/10=10).

b. Randomly select a number between 1 & k & include that person/unit in your sample.

c. Include all kth elements in your sample. For example if K is10 & your random selected number between 1 & 10, for instance 5, then you will select persons 5,15,25----etc. When you get to the end of your sampling frame you will have all the people to be included in your sample.

3. Stratified Random Sampling (STRS): When the defined target population is believed to have

a non-normal (or skewed) distribution for one or more of its distinguishing characteristics (e.g., age, income, & product ownership), researchers must identify subpopulations, referred to as strata. After the strata are segmented, a simple random sample is drawn for each stratum. Proportional & disproportional weighting factors may be applied to estimate overall population values. All of the methods of sampling the procedure commonly used in surveys are stratified sampling. This technique is mainly used to reduce the population. Stratification means division in to groups. In this method the population is divided in to a number of sub-groups/strata.

Example: A sample of 50 students is to be drawn from a population consisting of 500 students belonging to two institutions A & B. The number of student in the institution A is 200 & the institution B is 300. How will you draw the sample using proportional allocation? There are two strata in this case with sizes N1=200 & N2 =300 & the total population N= N1 + N2 = 500. The sample size is 50. If n1 & n2 are the sample size:

n1= n x N1 = 50 x 200 =20

N 500

n2 = n x N2 = 50 x 300 = 30


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N 500

The sample size is 20 from A & 30 from B. Then the unit from each institution is to be selected by simple random sampling.

4. Cluster Sampling: This method requires that the defined target population be segmented into

geographic areas, each of which is considered to be very similar to the others. Researchers randomly select a few areas, & then conduct a census of the elements in each area. As an alternative, researchers can select more areas & take samples from each of those areas. This sampling method is appealing when researchers can easily identify the highly similar areas.

2. Non-probability sampling: In non-probability sampling, the probability of selection of each

sampling unit is not known. Therefore, potential sampling error cannot be accurately known either. The selection of sampling units is based on some type of intuitive judgment, desire, or knowledge of the researcher. A researcher should not even attempt to generalize the survey‟s data results or make inferences about those people in a non-probability sample who for some reason demonstrated an unwillingness to participate.

1. Convenience Sampling: also called accidental /Haphazard /man in the street sampling. The

researcher selects units that are convenient, close at hand, & easy to reach. A method in which samples are drawn at the convenience of the researcher or interviewer, often as the study is being conducted.

2. Judgment Sampling: Participants are selected according to the researcher‟s or some other

experienced individual‟s belief that they will meet the requirements of the study. The underlying assumption is the researcher‟s subjective belief that the opinions of a group of perceived experts on the topic of interest are representative of the entire defined target population.

3. Quota Sampling: This method involves the selection of prospective participants according to

pre-specified quotas regarding demographic characteristics (e.g., age, race, sex, & income), specific attitudes (e.g., satisfied/dissatisfied, liking/disliking, great/marginal/no-quality), or specific-behaviors (e.g., regular/occasional/rare shopper, product user/nonuser, heavy user/light user).

4. Snowball Sampling: A method that involves the practice of subjectively identifying &

qualifying a set of initial prospective respondents who can, in turn, help the researcher identify additional people to be included in the study.


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Chapter 11: Data Processing & Analysis

11.1. Data Processing: Data Processing, implies editing, coding, classification & tabulation of collected

data so that they are amenable to analysis. The preparation normally follows the ff steps:

1. Editing: It is the review of the questionnaires with the objectives of increasing accuracy & 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.

2. Coding: Assigning a code, usually a number, to each possible response to each question. If the questionnaire contains only structured questions or very few unstructured questions, it is preceded. This means that codes are assigned before fieldwork is conducted. For example: “Yes “response is coded 1 & “No” response 2.

3. Classification: Data having a common characteristic are placed in one class & in this way the entire data get divided into a number of groups or classes. Classification can be one of the ff two types, depending upon the nature of the phenomenon involved:

a. Classification according to attributes: Data are classified on the basis of common characteristics which can either be descriptive (such as literacy, sex, honesty, etc.) or numerical (such as weight, height, income, etc.). Qualitative phenomenon which cannot be measured quantitatively; only their presence or absence in an individual item can be noticed.

b. Classification according to class-intervals: Data relating to income, production, age, weight, etc. come under this category. Such data are known as statistics of variables & are classified on the basis of class intervals.

4. Data entry: Data entry includes tasks involved with the direct input of the coded data into some specified software package that will ultimately allow the research analyst to manipulate & transform the raw data into useful information.

5. Data Tabulation: Tabulation is a simple process of counting the number of observations (cases) that are classified into certain categories.

11.2. Types of Statistical Analyses Used in Research: Data can be analyzed using descriptive &

inferential statistics. The terms "statistics" & "data analysis" are more similar. The word statistics refers

to the methods of dealing with numerical facts or statistics is a science of collecting, classifying,

summarizing, analyzing &, interpreting of numerical facts & also refers to the summarized figures of

numerical facts such as percentage, averages, means, medians, modes, standard deviations etc. The

term analysis refers to the computation of certain measures along with searching for patterns of

relationship that exist among data-groups.

11.2.1. Descriptive Statistics: Descriptive statistics are provided by frequencies, measures of central

tendency, & dispersion. It includes: It is covered under statistics courses. Try to refer this part.

a. Measure of counts/frequencies.


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b. Measures of location/ central tendency (mean, median, mode & percentiles quartiles). c. Measures of variability/dispersion (range, variance, standard deviation, & coefficient of variation). d. Measures of relative location & distribution shape (z-score).

2.2.2. Inferential Statistics: It is statistics that help to establish relationships among variables & draw

conclusion from. Inferential analysis is concerned with the various tests of significance for testing

hypotheses in order to determine with what validity data can be said to indicate some conclusion or

conclusions.

What is Hypothesis Testing? This part is also covered in statistics courses. Hypothesis is an empirically testable

though yet unproven statement developed in order to explain phenomena. Frequency distributions & measures of

central tendency are very useful to marketing researchers. In most cases, however, the researcher will have some

preconceived notion of the relationships the data should present. The preconception should be based on

marketing theory/ previous research. The preconception is called a hypothesis.

Types of Hypotheses:

a. Null hypothesis: A statement that asserts the status quo; that is, that any change from what has been thought to be true is due to random sampling error. In hypothesis development, the NH states that there is no d/ce b/n the group means in the comparison. In this case, the NH states there is no d/ce b/n the two means/groups. The NH is the one that is always tested by statisticians & market researchers which will be proved by data collections & analysis.

b. Alternative hypothesis: AH is a statement that is the opposite of the null hypothesis, that is, that the d/ce exists in reality & is not simply due to random error. If the NH is accepted, there is no change to the status quo. If the NH is rejected, we automatically accept the AH & conclude that there has been a change in opinions or actions. A NH refers to a population parameter, not a sample statistic.

Types of Error in Hypothesis Testing:

1. Type I Error: Type I error made by rejecting the NH when it is true; the probability of alpha (True to False). This type of error, frequently called alpha ( ), occurs when the sample data lead to rejection of a NH that is in fact true. The probability of such an error is termed the level of significance. Usually, researchers accept a level of significance of .10, .05, or .01, depending on the research objectives. This means that the researcher is willing to accept some risk of incorrect rejection of the null hypothesis, but that level of risk is prespecified.

2. Type II Error: It is the error that occurs when the sample data produce results that fail to reject the NH when in fact the NH is false & should be rejected (False to True). Type II error is frequently called Beta (ß). Type II error is the error of failing to reject the NH when the alternative hypothesis is true; the probability of beta. Unlike , which is specified by the researcher, ß depends on the actual population parameter. Sample size can help control Type I & Type II errors.

2.2.2.2. D/tial statistics (ANOVA): The ff sections describe important hypothesis testing tools: the t-test

(to compare two means) & analysis of variance (ANOVA, F-test) which is to compare three or more

group means.


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1. T-test: This test is used frequently in situations where the sample size is small (less than 30) & the population is unknown. Although an alternative test statistic, the z-test, is also used (when sample sizes are larger, i.e., more than 30), in practice, the t-test is more frequently utilized.

Example:

2. Analysis of variance (ANOVA, F-test): Analysis of variance (ANOVA) is used to determine the statistical d/ce b/n three or more means. The total variance in a data set is divisible into b/n & within components, if there is more variance explained or accounted for by considering d/ces b/n groups than there is within groups, & then the independent variable probably has a significant impact on the dependent variable. The larger the F-ratio, the more likely it is that the NH will be rejected. Example:


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2.2.2.3. Associative Statistics (Regression Analysis, & Correlation Analysis)

1. Chi-square (Chi-X2) Analysis: It permits us to test for significance b/n the frequency distributions of two or more groups, say, males versus females. Categorical data from questions about sex, education, or other nominal variables can be examined to provide tests of hypotheses of interest. It also compares the observed frequencies of the responses with the expected frequencies, which are based on our ideas about the population distribution or our predicted proportions. It involves using sample data to test for the independence of two variables. The sample data are given in to a two way table called a contingency table. The test is based on the ff Chi-square test statistic.

ij

ij

e

eijO2

2Or

e

eo

f

ff2

2

Where: Oij (fo) = observed frequency for contingency table category in row i & column j.

Eij (fe) = expected frequency for contingency table in row i & column j.

Example1: A company planning a TV advertising campaign wants to determine which TV shows its target

audience watches & thereby to know whether the choice of TV program an individual watches is

independent of the individuals income. The table supporting this is shown below. Use a 5% level of

significance & the null hypothesis.


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Income Type of Show

Basketball Movie News Total

Low 143 70 37 250

Medium 90 67 43 200

High 17 13 20 50

Total 250 150 100 500

Solution

1: Ho: Choice of TV program an individual watches is independent of the individual’s income.

Ha: Income & Choice of TV program are not independent.

2. Decision rule: = 0.05 DF = (R-1) (C-1) = (3-1) (3-1) = 4 X2, df = X2

0.05, 4 = 9.49

R=raw, C=Column, Reject Ho if X2 calculated is greater than 9.49

e11 125 e21 = 100 e31 =25

e12= 75 e22 =60 e32 =15

e13 =50 e23 =40 e33 =10

Eg. e11 = 250x250/500 = 125

15

1513

25

2517

60

6067

100

10090

50

5037

75

7570

125

1251432222222

2

174.21

40

4043

10

102022

3. Decision. 21.174 is greater than 9.49 then reject the Ho that choice of TV program is independent from income

level.

2. Correlation: Correlation is a statistical method used to determine whether a relationship b/n

variables exists. Correlation is a statistical measure used to determine the degree of association

(relationship) b/n two or more variables. It is represented by xyr.

Properties of Pearsonian coefficient of correlation

The value of xyr is always b/n -1 & 1. That is 11 xyr

If we say there is a positive correlation b/n the variables.

If we say there is a negative correlation b/n the variables.

If xyr =1 we say there is a perfect positive correlation b/n the variables.

If xyr =0 we say there is no linear correlation b/n the variables.

If xyr =-1 we say there is a perfect negative correlation b/n the variables.

The Pearson coefficient of correlation denoted by xyr is given by:


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2222

iiii

iiii

xy

YYnXXn

YXYXnr

Example: Compute the value of the correlation coefficient for the data obtained in the study of the number of

absences and the final grade of the seven students in the statistics class given in below

.

The value of r suggests a strong negative relationship between a student‟s final grade and the number of

absences a student has. That is, the more absences a student has, the lower is his or her grade.

3. Regression: Regression is the estimation or prediction of values of one variable from known values of

one or more variables. Simple linear regression is a regression that involving only two variables &


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having a linear relationship, but if it is greater than two multiple regression. Regression equation is a

mathematical equation that defines the relationship b/n two variables. Let X & Y, be two variables of

interest such that X is the independent variable & Y is the dependent variable. Suppose we have data of

n-observations for each of the two variables.

Or

:

XbYa

XXn

YXYXn

XX

YYXXb

OLSsquareleasttheasknownismethodThis

ii

iiii

i

ii

222

Example: Car Rental Companies: Find the equation of the regression line for the data in Example

below.

The regression slope (b): The regression slope (b) also called the regression coefficient indicates the

value by which the variable Y changes for a unit change in X.

If b is positive, we say there is a direct relationship b/n the two variables.


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If b is negative, we say there is an inverse relationship b/n the two variables. If b is zero, we say there is no linear relationship b/n the two variables.

Examples 2: The systolic blood pressures of 10 men of various ages are tabulated as follows:

Age(X) 37 35 41 43 42 50 49 54 60 65

Systolic Blood pressure (Y) 110 117 125 130 138 146 148 150 154 160

a) Find the regression equation of Y on X b) Find the value of systolic blood pressure for an age of 55 c) Find the correlation coefficient d) Find the covariance e) Find the coefficient of determination

Solution:

Xi Yi Xi2 XiYi Yi

2

37 110 1369 4070 12100

35 117 1225 4095 13689

41 125 1681 5125 15625

43 130 1849 5590 16900

42 138 1764 5796 19044

50 146 2500 7300 21316

49 148 2401 7252 21904

54 150 2916 8100 22500

60 154 3600 9240 23716

65 160 4225 10400 25600

iX =476 i

Y =1378 2

iX =23530 iiYX =66968 2

iY =192394

X =476/10=47.6

X =137.8


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a) Regression of Y on X

.

576.1766.62ˆ:

766.62)6.47(567.18.137

567.18724

13752

476)23530(10

1378476)66968(10222

pressurebloodsystolicandagebetweeniprelationshdirectaistherepositiveisbSince

XYbygivenisXonYoflineregressiontheTherefore

XbYa

XXn

YXYXnb

ii

iiii

b) X=55 149951.148)55(567.1766.62 Y . Therefore the value of systolic blood pressure

that corresponds to age of 55 is 149.

c) Correlation coefficient ( xyr )

pressurebloodsystolicandagebetweenncorrelatiopositiveaisthererSince

YYnXXn

YXYXnr

xy

iiii

iiii

xy

10

93.03.1584.93

13752

250568724

13752

2222

d) Covariance

8.152

910

13752

1

nn

YXYXnS

iiii

xy

e) R2= %1002xyr = 49.86%10093.0 2 .

Interpretation: 86.49% of the variation in Y is explained by the regression equation.

Example 2: The ff summary data gives the score of 12 students in mid-exam (X) & final exam (Y).

iX =687 i

Y =741 2

iX =45591 iiYX =48407 2

iY =52525

a) Find the regression equation of Y on X b) Predict the score of final exam if the score of final exam is 85 c) Find the correlation coefficient d) Determine the proportion of the total variation in Y which is explained by the regression

equation Solution:

a) Regression of Y on X

.

956.0194.70ˆ:

194.70)25.57(956.075.61

956.0687)45591(12

741687)48407(12222

scoresexamfinalandexammidbetweeniprelationshdirectaistherepositiveisbSince

XYbygivenisXonYoflineregressiontheTherefore

XbYa

XXn

YXYXnb

ii

iiii

b) X=85 28.88)85(956.0194.70 Y . Therefore final exam score that corresponds to mid-

exam score of 85 is 88.28.


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c) Correlation coefficient ( xyr )

scoreexamfinalandscoreexammidbetweenncorrelatiopositiveaisthererSince

YYnXXn

YXYXnr

xy

iiii

iiii

xy

10

9149.0741)52525(12687)45591(12

)741(687)48407(12

222222

d) R2= %1002xyr = 7.83%1009149.0 2 . Interpretation: 83.7% of the variation in Y is explained

by the regression equation.

Chapter: 12

Selected Application of Marketing Research

12. 1. Introduction: As ESOMAR Marketing research is a key element within the total field of marketing

information. It links the consumer, customer and public to the marketer through information which is used to

identify and define marketing opportunities and problems; to generate, refine and evaluate marketing actions; and to

improve understanding of marketing as a process and of the ways in which specific marketing activities can be made

more effective. This definition encapsulates two key reasons for undertaking marketing research: (1) to

identify opportunities and problems, and (2) to generate and refine marketing actions. This distinction

serves as a basis for classifying marketing research into problem identification research and problem-

solving research. Linking this classification to the basic marketing paradigm as shown in figure below

Problem identification research can be linked to the description of the nature and scope of customer

groups, understanding the nature of forces that shape customer groups, and understanding the nature

of forces that shape the marketer’s ability to satisfy targeted customer groups. Problem identification

research is undertaken to help identify problems that are, perhaps, not apparent on the surface and yet

exist or are likely to arise in the future. Examples of problem identification research include market

potential, market share, brand or company image, market characteristics, sales analysis, short-range

forecasting, long-range forecasting and business trends research.

Research of this type provides information about the marketing environment and helps diagnose a

problem. For example, a declining market potential indicates that the firm is likely to have a problem

achieving its growth targets. Similarly, a problem exists if the market potential is increasing but the firm

is losing market share. The recognition of economic, social or cultural trends, such as changes in

consumer behavior, may point to underlying problems or opportunities.

Problem-solving research can be linked to: test individual and interactive marketing mix variables, and

to monitor and reflect upon past successes and failures in marketing decisions. Once a problem or


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opportunity has been identified, problem-solving research may be undertaken to help develop a

solution. The findings of problem-solving research are used to support decisions that tackle specific

marketing problems. Problem identification research and problem-solving research go hand in hand and

a given marketing research project may combine both types of research.

A marketing research project for a European beer manufacturer that sees its market share diminish may

determine through image research that its brand is perceived in a most positive manner. This may

indicate that the brand should be extended into other types of beer or even into clothes and fashion

accessories. Appropriate target markets may be selected, with detailed profiles of potential customers

and an associated media and product image. These decisions can clearly be supported with problem

solving research. Whether the focus is upon problem identification or problem-solving research, it is

vital that the process of marketing research is conducted in a systemic and rigorous manner.


70 | P a g e

In general marketing research can be focus on the ff areas:

1. Product or Service Research: Product means the goods and services which are sold to the consumers. It includes consumer products and industrial products. Product research studies the individual product. It studies the making and marketing of the product. It studies the color, size, shape, quality, packaging, brand name, and price of the product. It also deals with product modification, product innovation, product life cycle, etc. The product is modified (changed) as per the needs and wants of the consumers. Therefore, the product will not fail in the market.

2. Consumer behavior Research: Consumer is the person who purchases the goods and services. The consumer is the king in the market. Consumer research studies consumer behavior. It studies the consumers needs, wants, likes, dislikes, attitude, age, sex, income, location; buying motives, etc. This data is used to take decisions about the product, its price, place, and promotion.


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3. Packaging Research: Packaging research is a part of product research. It studies the package of the product. It improves the quality of the package. It makes the package more attractive. It makes the package more convenient for the consumers. It reduces the cost of packaging. It selects a suitable method for packaging. It also selects suitable packaging material.

4. Pricing Research: Pricing Research studies the pricing of the product. It selects a suitable method of pricing. It fixes the price for the product. It compares the companies’ price with the competitor's price. It also fixes the discount and commission which are given to middlemen. It studies the market price trends. It also studies the future price trends.

5. Advertising Research: Advertising research studies the advertising of the product. It fixes the advertising objectives. It also fixes the advertising budget. It decides about the advertising message, layout, copy, slogan, headline, etc. It selects a suitable media for advertising. It also evaluates the effectiveness of advertising and other sales promotion techniques. Media research studies various advertising media. The d/t advertising media are television (TV), radio, newspapers, magazines, the internet, etc. Media research studies the merits and demerits of each media. It selects a suitable media for advertising. It does media planning. It also studies media cost. It helps in sales promotion and to avoid wastage in advertising.

6. Sales Research: Sales research studies the selling activities of the company. It studies the sales outlets, sales territories, sales forecasting, sales trends, sales methods, effectiveness of the sales force, etc.

7. Distribution Research: Distribution research studies the channels of distribution. It selects a suitable channel for the product. It fixes the channel objectives. It identifies the channel functions like storage, grading, etc. It evaluates the competitor's channel.

8. Policy Research: Policy research studies the company's policies. It evaluates the effectiveness of the marketing policies, sales policies, distribution policies, pricing policies, inventory policies, etc. Necessary changes, if any, are made in these policies.

9. International Marketing Research: International marketing research studies the foreign market. It collects data about consumers from foreign countries. It collects data about the economic and political situation of d/t countries. It also collects data about the foreign competitors. This data is very useful for the exporters.

10. Motivation Research: Motivation research studies consumers' buying motives. It studies those factors that motivate consumers to buy a product. It mainly finds out, why the consumers buy the product? It also finds out the causes of consumer behavior in the market.

11. Market Research: Market research studies the markets, market competition, market trends, etc. It also does sales forecasting. It estimates the demand for new products. It fixes the sales territories and sales quotas.

http://kalyan-city.blogspot.com/2010/07/5-ms-of-advertising-advertising.html

http://kalyan-city.blogspot.com/2010/06/motivation-motivational-factors.html


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Chapter13: Research Report Writing & Presentation

13.1. Importance of the research report: A research report deals with results of completed research

work. After completing a research work, it is generally produced in the written ‘form, & is called

research report or thesis.

A research report is needed due to the ff reasons:

The research must be reported in full & its results should be subjected to a criticism & verification. The research work is done for the benefit of human being; therefore, it must be communicable to

the general public for the practical use. The research should be considered to be the culminating act for reflective thinking. It encourages

other persons to take up some problem for further investigation. The research report requires the creative thinking of a researcher to review the related studies &

discussing the result of the study & also suggest some new problems for further studies. The research report is also necessary for giving shape & form to the investigation & solidifying it. It is needed for providing a clear picture of research method, sample, & techniques used in

conducting the research work.

13.2. Format of Research Report: A written format of a research work is known as thesis or research

report. All such works may differ considerably in scope of treatment & details of presentation. Even then

all types of research reports are expected to follow a general uniform, common pattern of format, style,

& structure. The general format of research report is evolved & it has become a tradition in academic

area. A research report or thesis is an organized format of research work done. It is viewed in three

major categories: Preliminaries, Textual Body, & References.

A. Preliminary Pages: In the preliminary pages the report should carry a title & date, followed by

acknowledgements in the form of “preface “or “foreword”. Then, there should be a table of contents

followed by list of tables & illustrations so that the decision maker or anybody interested in reading the

report can easily locate the required information in the report.

1. Title page 2. The fly page 3. Letter of authorization/ Approval 4. Declaration 5. Acknowledgment 6. Table of contents 7. List of tables 8. List of graphs /figures 9. Acronym/Abbreviations


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10. Summary( objectives, findings, conclusion & recommendations)

A. Preliminary Section: As the preliminaries form a significant part of the whole thesis report, due care

should be taken in preparing them. If the specifications are already laid down by some colleges or

universities they should be observed. However, a general standard pattern suggested here in each case

will be helpful for a researcher.

1. Title Page: This is the first page of a thesis or a dissertation. It includes:

Title of thesis. Name of the candidate. Purpose or relationship of the thesis to the course or degree requirement. College &/or department in which the candidate has been admitted for the degree. Name of the university to which it is submitted. Month & year of submission or acceptance.

2. Acknowledgement: It the acknowledgement to the persons & sources that have been helpful to the

investigator.

3. Table of Contents: This section lists all the main chapter headings & the essential sub-heading in each

with the appropriate page numbers against each. The listing of main chapters is generally preceded by

some preliminaries like preface or acknowledgement, list of tables, list of figures, abstract or synopsis &

their respective pages in small Roman numbers & followed at the end by appendices, & Indexes.

4. List of Tables: The table of contents is followed by the list of tables on a separate page.

5. List of Figures & Illustrations: If any charts graphs or any other illustrations are used in the thesis, a

list of figures on a separate page is prepared in the same form as the list of tables except that they are

numbered with Arabic numbers. An example has been given here for this purpose.

6. Executive Summary/Abstract: The executive summary is the most important part of the report. Many

consider it the soul of the report, insofar as many executives will only read the report summary. The

executive summary must present the major points of the report; it must be complete enough to provide

a true representation of the entire document but in summary form.

B. Main Body

Chapter 1: Introduction: The main purpose of this chapter is to indicate the need & scope of the study.

It consists essentially of the statement of research inquiry. It is reported in past tense form of work

completed. The problem objectives, hypotheses, assumptions, & delimitations of the study are reported

precisely. If an introduction is required, the researcher should make certain that it is an introduction

that generates an interest & appropriate mental set which introductions are regarded as capable of


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producing. It must be long enough to do its jobs & nothing more. The introduction contains background

information necessary for a complete understanding of the report.

Chapter 2: Review of Related Literature: This chapter is essential in most of the research studies. It

presents the comprehensive development of the problem background. It indicates what has already

been studied by others, which has a bearing upon the present study. The review of literature stresses

two aspects: the first is the consideration of the subject-matter & it is likely more important than the

other. The second is related to methodology & design.

Chapter 3: Methodology of Research: This chapter indicates the line of approach of the study. The first

aspect deals with the method, population & sample of the study & second part provides the tools &

techniques employed in the research. It also presents the procedure of the study. The whole plan of the

study s discussed in detail under this chapter. Administration of tools & scoring procedure are reported

systematically. The data organization & presentation should be given in this section. It may be reported

in a separate chapter of the report.

Chapter 4: Analysis & Interpretation of the Data: In this chapter analysis & results are reported so as to

draw the inferences of the study. The analyses of data are presented in tabular form & in figures or

pictorial presentation. The results are interpreted at length. This chapter provides the original work or

contribution by the researcher. The communicative accuracy is required in this chapter. The text must

be developed to ensure an effective ordering of the evidences.

Chapter 5: Summary, Conclusions & Recommendation: This is most important chapter of the report. It

requires the creative & reflective aspect of the researcher. This is the final chapter of a report, findings

of the study are summarized, & suggestions for the further studies are also given. Conclusions &

recommendations are derived specifically from the findings. Conclusions can be considered broad

generalizations that focus on answering questions related to the research objectives.

C. Reference: The ffs are the examples of writing references:

Marlow, S. and Carter, S. (2006) “If you don’t ask you don’t get! Women, self -employment and finance”, paper presented to Warwick Business School Small Firms Finance Conference, May, Coventry

McCartan -Quinn, D. and Carson, D. (2003), ‘Issues which impact upon marketing in the small firm’, Small Business Economics, Vol.21, No.2, pp.201-213

2. Appendix: An appendix is the important reference materials category. It includes the material which

cannot be logically included in the main body or textual body of the research report or the relevant


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materials too unwieldy to include in the main body. The appendix usually includes tools of research,

statistical tables, & sometime raw-data (when data were processed through computer).

13.3. Guidelines for Presentations: Traditional presentation methods include chalkboards, whiteboards

(dry-erase boards), & overhead projectors with handmade transparencies. These tried-&-true methods

may be preferable in situations where simple concepts & ideas are to be communicated to relatively

small audiences. Currently, there are many d/t computer presentation software packages, & more are

being introduced every day. One of the most widely used presentation software packages is Microsoft’s

Power Point. PowerPoint can be used to develop transparencies, 35-mm slides, & on-screen electronic

presentations; it can also be used to develop notes, audience handouts, & outlines, all from the same

information.

Marketing Research Handout 2012/2020

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