Business Research Methodology

DBA 1657 RESEARCH METHODS IN BUSINESS

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Anna University Chennai

UNIT -1

Introduction to ResearchUnit structure:

1.1 Introduction1.2 Learning objectives1.3 Definition of research1.4 Importance of research1.5 Hallmarks of scientific research1.6 The Building Blocks of Science in research1.7 Research process: An Overview

1.7.1. Defining the research problem1.7.2. Establishing research objectives1.7.3 Developing the research design1.7.4 Preparing a research proposal1.7.5 Data Collection1.7.6 Data Analysis and Interpretation1.7.7 Research report

1.8 Theoretical Framework1.8.1 Types of Variables1.8.2 Theoretical framework : The need and features

1.9 Hypotheses: Types and testing1.10 Research Design

1.10.1 The need for research design1.10.2 Classification of research designs

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1.1 INTRODUCTION

Managers are mostly involved in studying and analyzing issues that lead todecision making. They are involved in some form of research for making an appropriatedecision. Decision making today is complicated and complex. There is a myriad flow ofinformation enabled by data mining and warehousing which provides a vital input fordecision making. The success or failure of a business decision depends on the dataassociated with the decision. The decisions can be made in an objective or subjectivemanner. Objective decision making is rationale and scientific. To arrive at objective


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decision making the business managers often involve themselves in some form ofresearch.

Research is simply the process of finding solutions to a problem after a thoroughstudy and analysis of the situational and other related factors. Business research is asystematic and organized effort to investigate a specific problem encountered in thework setting that needs a solution. It comprises of a series of steps designed and executedwith the goal of finding answers to the issues that are of concern to the manager.

This unit provides a basic understanding of research, the process involved andthe steps involved in the development and testing of hypothesis. Further, the need andthe major types of research design is dealt in detail.

1.2 LEARNING OBJECTIVES:

After reading this unit you should be able to:

Define research and understand the advantages of the knowledge of research

Highlight the distinctive characteristic features of research

Describe the building blocks of Science in research

Understand the steps in the research process

Develop a research design

Understand the need and basic features of the theoretical framework

Describe the steps in hypotheses development and testing

Understand the need and the major types of research design

1.3 DEFINITION OF RESEARCH

Research refers to search for knowledge. It is an art of scientific investigation.Redman and Mory define research as “Systematized effort to gain new knowledge”.Research is an original contribution to the existing stock of knowledge. D.S. Lesingerand M.Stephenson in the Encyclopedia of Social Sciences, define research as “themanipulation of things, concepts or symbols for the purpose of generalizing to extend,correct or verify knowledge, whether that knowledge aids in construction of theory orin the practice of an art”.

According to Clifford Woody “research comprises defining and redefiningproblems, formulating hypothesis or suggested solutions, collecting, organizing andevaluating data; making deductions and reaching conclusions, and at last carefully testingthe conclusions to determine whether they fit the formulated hypothesis”. Businessresearch is an organized, systematic, data-based, critical, objective, scientific inquiry or


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investigation into a specific problem undertaken with the purpose of finding solutions toit. Research provides the needed information that guides managers to make informeddecisions to successfully deal with problems.

1.4 IMPORTANCE OF KNOWLEDGE OF RESEARCH IN BUSINESS SETTINGS:

The knowledge of research is important on account of the following reasons:

The business world today is more complicated and complex. In this contextthe research enables the manager to face the competitive global market withgreater confidence. Research enables to consider the available information in asophisticated and creative way.

Research enables the managers to identify critical issues, gather relevantinformation, analyse the data and implement the right course of action.

Managers need to understand, predict and control events that are dysfunctionalto the organization. Research enables to understand, predict and control theenvironment.

Research enables to sense, spot and deal with problems before they go out ofhand.

The organizations may not be able to solve all the problems encountered in-house. Consultants may be engaged for expert advice. The manager needs tohave knowledge of research to interact with the research consultants effectivelyand to get the maximum benefit out of them

All the research findings published cannot be accepted as such. The soundnessof findings should be evaluated before making decisions on the basis of researchfindings. The managers needs to know about the research so as to evaluate anddiscriminate the research findings based on the soundness of methodology etc.,

The knowledge of research and research methods sensitize the mangers to thevarious variables operating in a situation and remind them of the multicausalityand multifinality of the situations and thereby avoiding inappropriate, simplisticnotions of one variable causing another.

It enables the managers to understand the research reports prepared byprofessionals so as to take intelligent, calculated risks with known probabilitiesattached to the success or failure of their decision.

Knowledge about the scientific investigation will enable the managers to eliminateor avoid making decisions on subjective or biased manner.


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Knowledge about research helps the manager to understand the need for andshare the pertinent information with the research consultants.

1.5 HALLMARKS OF SCIENTIFIC RESEARCH

Successful managerial decisions are seldom made on hunches or on trail anderror method. The sound and effective decisions are always made on the basis ofscientific research. Scientific research focuses on solving problems in a step-by-steplogical, organized and rigorous manner in each step of research viz., identifying problem,gathering data, analyzing it and in arriving at a valid conclusion. Organizations may notalways be involved in the scientific research due to various reasons like- simple problemswhich can be solved with previous experience, time contingency, lack of knowledge,resource constraints etc., However, the scientific research performed in a rigorous andsystematic way leads to repeatable and comparable research findings. It also enablesthe researchers to arrive at accurate, dependable and subjective findings. The hallmarksor distinguishing characteristic features of scientific research are as follows:

1.5.1 Purposiveness

The research is conducted with a purpose. It has a focus. The purpose of theresearch should be clearly mentioned in an understandable and unambiguous manner.The statement of the decision problem should include its scope, its limitations and theprecise meaning of all words and terms significant to the research. Failure to mentionthe purpose clearly will raise doubts in the minds of stakeholders of the research as towhether the researcher has sufficient understanding of the problem.

1.5.2. Rigor

Rigor means carefulness, scrupulousness and the degree of exactness in researchinvestigation. In order to make a meaningful and worthwhile contribution to the field ofknowledge, research must be carried out rigorously. Conducting a rigorous researchrequires a good theoretical knowledge and a clearly laid out methodology. This willeliminate the bias, facilitate proper data collection and analysis, which in turn wouldlead to sound and reliable research findings.

1.5.3. Testability

Research should be based on testable assumptions/hypotheses developed aftera careful study of the problems involved. The scientific research should enable thetesting of logically developed hypotheses to see whether or not the data collected supportthe hypotheses developed.


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1.5.4. Replicability

Research findings would command more faith and credence if the same resultsare evolved on different set of data. The results of the test hypothesis should be supportedagain and again when the same type of research is repeated in other similar circumstances.This will ensure the scientific nature of the research conducted and more confidencecould be placed in the research findings. It also eliminates the doubt that the hypothesesare supported by chance and ensures that the findings reflect the true state of affairs.

1.5.5. Precision and Confidence

In management research the findings are seldom definitive due to the fact thatthe universe of items, events or population are not taken as such but based on samplesdrawn from universe. There is a probability that the sample may not reflect the universe.Measurement errors and other problems are bound to introduce an element of error inthe findings. However, the research design should ensure that the findings are as closeto the reality as possible so that one can have confidence in the findings .

Precision refers to the closeness of the finding to ‘reality’ based on sample. Itreflects the degree of accuracy or exactitude of the results on the basis of the sample towhat exactly is in the universe. The confidence interval in statistics is referred here asprecision.

Confidence refers to the probability that the estimation made in the researchfindings are correct. It is not enough if the results are precise but it is also important toclaim that 95% of the time the results would be true and there is only a 5% chance ofthe results being wrong. This is known as confidence level.

If the precision and confidence level of the research findings are higher than thefindings of the research study would be more scientific and useful. Precision andconfidence can be attained through appropriate scientific sampling design.

1.5.6. Objectivity

Research findings should be factual, data-based and free from bias. Theconclusion drawn should be based on the facts of the findings derived form the actualdata and not on the basis of subjective or emotional values. Business organizations willsuffer a greater extent of damage if non-data-based or misleading conclusions drawnfrom the research is implemented. Scientific approach ensures objectivity of research.

1.5.7. Generalizability

It refers to the scope of applying the research findings of one organizationalsetting to other settings of almost similar nature. The research will be more useful if thesolutions are applicable to a wider range. The more generlizable the research, the greater


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will be its usefulness and value. However it is not always possible to generalize theresearch findings to all other settings, situations or organizations. For achievinggenaralizability the sampling design has to be logically developed and data collectionmethod needs to be very sound. This may increase the cost of conducting the research.In most of the cases though the research findings would be based on scientific methodsit is applicable only to a particular organization, settings or situations.

1.5.8. Parsimony

Research needs to be conducted in a parsimonious ie simple and economicalmanner. Simplicity in explaining the problems and generalizing solutions for the problemsis preferred to a complex research framework. Economy in research models can beachieved by way of considering less number of variables leading to greater variancerather than considering more number of variables leading to less variance. Clearunderstanding regarding the problem and the factors influencing the same will lead toparsimony in research activities. Sound understanding can be achieved through structuredand unstructured interview with the concerned people and by undertaking a study ofrelated literature in the problem area.

The scientific research in management area cannot fulfill all the above discussedhallmarks to the fullest extent. In management research it is not always possible toconduct investigations that are 100% scientific like in physical science as it is difficult tocollect and measure the data regarding the feelings, emotions, attitudes and perception.It is also difficult to obtain representative sample. These aspects restrict the generlizabilityof the findings. Though it is not possible to meet all the above said characteristics of thescientific research, to the possible extent the research activities should be pursued inthe scientific manner.

1.6 THE BUILDING BLOCKS OF SCIENCE IN RESEARCH

The essential tenets of scientific research are: direct observation of phenomena,clearly defined variables, methods and procedures, empirically testable hypotheses,ability to rule out rival hypotheses, statistical justification of conclusions and self correctingprocess. One of the primary methods of scientific investigation is the hypothetico-deductive methods. The method of starting with a theoretical framework, formulatinghypotheses and logically deducing from the results of the study is known as hypothetico-deductive method. The deduction and induction are two important aspects of the scientificresearch through which the answers to a research question can be arrived at. Furtherdetails on deduction and induction are dealt below:

Deduction

Deduction is a process by which the researcher arrive at a reasoned conclusionby logical generalization of a known fact. Deduction leads to conclusions which should


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be necessarily based on reasons. The reasons are said to imply the conclusions andrepresent a proof. The bond between the reasons and conclusions is much strongerthan in the case of induction. To be correct, a deduction should be both valid and true.True in the sense that the reasons given for the conclusions must agree with the realworld. Valid means the conclusion must necessarily be arrived from the reasons.

Researchers often use deduction to reason out the implication of various actsand conditions. For example, in a survey a researcher may reason as follows:

Surveying households in urban area is difficult and expensive (Reason 1)

The study involves interview with households in urban area (Reason 2)

The interview in this survey will be difficult and expensive (Conclusion)

Induction

Induction is a process where certain phenomenon is observed on the basis ofwhich conclusions are arrived at. The conclusions are drawn from one or more facts orpieces of evidence. The conclusions in induction results in hypotheses. Induction leadsto establish a general proposition based on observed facts. For example, the researcherunderstands that production processes is the prime feature of factories. It is thereforeconcluded that factories exist for production purposes.

Research is based on both deduction and induction. It helps us to understand,explain and predict business phenomena.

The building blocks of scientific inquiry include the following sequences:

1. Observing a phenomena

2. Identifying a problem

3. Constructing a theory

4. Developing hypotheses

5. Developing research design

6. Collecting data

7. Analyzing data and

8. Interpreting results

Observation a phenomena may be casual or purposeful. A casual scanning ofthe environment may lead us to the knowledge of interesting facts. This observationmay lead to identifying the problem in the concerned area. The problem identificationneeds gathering of primary data from the customers or from the employees ormanagement concerned with the particular problem. Further insights may be obtained


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to refine the problem in a more specific manner. The next step is to build a conceptualmodel or theoretical framework taking into consideration all the factors contributing tothe problem. The framework enables to integrate all the information collected in ameaningful manner. From this theoretical framework several hypotheses can be generatedand tested to support the concept. A research design provides the blue print of themechanism or insight regarding the methods of collecting data, analyzing the same andinterpreting them in order to solve the problem.

The building blocks of science discussed above provide the genesis for thehypothetico-deductive method of scientific methods. The steps are discussed below:

1. Observation

Observation is the first stage in scientific investigation. In this process, theresearcher takes into account the changes that are occurring in the environment. Toproceed further the changes observed in the environment should have importantconsequences. The changes may be in the form of sudden drop in the sales, increase inthe employee turnover, decrease in the number of customer and the like.

2. Preliminary information gathering

This involves seeking in depth information regarding the facts being observed.The information may be gathered through formal questionnaires, interview schedules orthrough informal or causal talk with the concerned people. Desk research may also beconducted to enrich the information gathered. The next step is to make sense out of thefactors identified in the information gathering stage by assembling them together in ameaningful manner.

3. Formulation of theory

Theory formulation enables to integrate all the information in a logical mannerso as to conceptualize and test the factors responsible for the problem. The criticalvariables contributing to the problem are examined. The association or relationshipamong the variables contributing to the problem is studied in order to formulate thetheory.

4. Developing Hypotheses

The next logical step leads to framing of testable hypotheses. Hypotheses testingare called deductive research. Sometimes it may so happen that the hypotheses whichare not originally formulated get generated through the process of induction. After thecollection of data an insight may occur based on which new hypotheses can be formulated.Thus hypotheses testing through deductive research and hypotheses generation throughinduction are both common.


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5. Scientific Data collection

After the hypothesis is developed, the data with respect to each variable in thehypotheses needs to be obtained in a scientific manner so as to test the hypotheses. Theprimary and secondary sources can both be explored in order to collect the data. Dataon every variable in the theoretical framework from which the hypothesis is generatedshould be collected.

6. Data Analysis

The data gathered are to be statistically analyzed to validate the hypothesispostulated. Both qualitative and quantitative data needs to be analyzed. Qualitativedata refer to information gathered through interviews and observations. Through scalingtechniques the qualitative data can be converted into quantifiable form and subjected toanalysis. Appropriate statistical tools should be used to analyze the data.

7. Deduction

Deduction is the process of arriving at conclusions by interpreting the meaningof results of the data analysis. Based on the deduction, recommendations can be madeto solve the problem encountered.

1.7. RESEARCH PROCESS : AN OVERVIEW

Research process involves execution of a series of phases towardsaccomplishment of the objectives of research. Each phase in the research process neednot be carried out in a sequential process. Some the phases can be carried outsimultaneously. However, the idea of sequence will be useful for developing and carryingout a research study in a systematic manner. The research process consists of thefollowing distinctive interrelated phases: (1) Defining the research problem (2) EstablishingResearch Objectives (3) Developing the research design (4) Preparing a researchproposal (5) Data Collection (6) Data Analysis and Interpretation and (7) Researchreporting.

1.7.1. Defining the Research Problem:

A problem need not necessarily mean that something is wrong in the currentsituation which needs to be rectified immediately. It simply indicates an issue for whichfinding a solution could help to improve an existing situation. Problem can be defined asany situation where a gap exists between the actual and the desired state. Problemstatement or problem definition refers to a clear, precise and succinct statement ofquestion or issue that is to be investigated with the goal of finding an answer or solution.

Components of research problem

The components of research problem are as suggested by R.L.Ackoff in theDesign of Social Research is elaborated below:


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There must be an individual or a group which has some difficulty with problem

There must be some objective(s) to be attained at.

There must be alternative means or course of action for obtaining the objectives

There must be some doubt in the minds of a researcher with regard to theselection of alternatives.

There must be some environment to which the difficulty pertains.

Criteria for selecting the research problem

The following criteria can be kept in the minds of researcher in selecting theresearch problem.

Subjects on which the research is carried on amply should not be normallychosen as there will not be new dimension to reveal

Too narrow or too vague problems should be avoided

The researcher should be familiar with the subject chosen for research. Theresearcher should have enough knowledge, qualification and training in theselected problem area. The resources needed to solve the problem in terms oftime, money, efforts, manpower requirement should be taken into account beforeembarking on a problem.

The subject of research should be familiar and feasible so that related researchmaterial or sources of research can be obtained easily.

The selection of a problem must be preceded by a preliminary study.

Research problems trigger the research process. Defining the research problemis a critical activity. A thorough understanding of research problem is a must for achievingsuccess in the research endeavor. Defining the research problem begins with identifyingthe basic dilemma that prompts the research. It can be further developed by progressivelybreaking down the original dilemma into more specific and focus oriented objectives.Five steps could be envisaged (1) Identifying the broad problem area(2) Literaturereview (3) Identifying the research question (4) Refining the research question (5)Developing investigative questions. They are discussed below:

1.7.1.1 Identifying the broad problem area

The process begins with specifying the problem at the most general level eg.,declining sales, increased cost, increased employee turnover etc. From this generalspecification of problem the next step is to move towards the question. The questionreinstates the general problem. For eg., What is the reason for declining sales?. Thequestions that can be raised can be grouped into three categories;(1) Choice of purposes


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or objectives where the question focuses on what objectives the researcher wishes toachieve by conducting the research (2) Generation and evaluation of solutions wherethe question focuses on the alternatives available to solve a problem in hand (3) Troubleshooting or control situation where the query focuses on monitoring and diagnosing,why an organization is not achieving the established goals.

The researcher can identify the problem through the following sources:

Own experience as well as observation of others experience and situationsmay give raise to researchable problem

Detailed discussion with various authorities concerned with the problem

Focus group interviews

Scrutinizing published data

Review of literature enables to identify problems which are researched andquestioned in other studies. The same can be simulated by the researcher.

The above techniques would enable the researcher to understand the problemin a better manner and also to outline the possible variables that might exert an influence.The nature of information needed by the researcher could be broadly classified underthree headings:

1. Background information of the organization for which research isconducted viz, the origin and history of the company, its assets, numberof employees, location etc., The information can be obtained fromcompany records, published data, Census of Business and Industryand the web.

2. Information regarding managerial philosophy, company policies andother structural aspects can be collected by asking direct question fromthe management

3. Information regarding the perception, attitudes and behavioural aspectof employees could be obtained by way of observations, interviewsand questionnaires.

1.7.1.2 Literature survey

Literature survey is the review of published and unpublished work fromsecondary source in the area of interest to the researcher. The purpose of conductingliterature survey at this stage is:

To document the studies relevant to the problem identified for research


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To ensure that no variable that has been taken up in the past related studies isignored.

To avoid conducting similar type of study and thereby stop the researcher frominvesting his resources in terms of time and effort in an research venture whichis already solved.

To provide a good frame work and a solid foundation to proceed further in theinvestigation.

To have a comprehensive theoretical framework from which hypothesis can bedeveloped for testing.

To enable to develop the problem statement in a precise and clear manner

To enhance the testability and replicability of the findings of the current research.

To understand the research gap

To stimulate researcher to carry out the work

To confirm the appropriateness of procedure by referring to similar studiesconducted in the past

To trace inconsistencies, contradictions and consistencies

To clear conceptualization

To familiarize with methodology, research tools and statistical analysis

The literature review needs to be performed on the variables identified throughthe interview process. It comprises of three steps viz., (i) Identifying the sources (ii)Gathering relevant information (iii) Writing up the Literature review.

i. Identifying the sources

The data can be obtained from library by going through books, journals,newspapers, magazines, conference proceedings, doctoral dissertation, thesis,government publication and other reports. The development of information technologyhas led to many online databases like Prowess, EBSCO etc and the interlinking oflibraries has led to a myriad of information in the hands of the researcher with the clickof the mouse. Computerized databases include bibliographies, abstract and full text ofarticles. Bibliographic databases display only the bibliographic citations i.e., the nameof the author, the title of the article / journal, source of publication, year, volume andpage numbers. The abstract databases in addition to the above said information providesan abstract or summary of the article. The full-text databases as the name suggestenables to download the full text of the article.


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ii. Gathering relevant information.

The articles gathered either from books, journal or on line sources could assuch act as a reservoir of information. These sources could lead to further informationthrough the citation and references used. The list of journal and references referred inthe articles could lead us further to the source of information. Also, during the course ofreading the articles, the researcher can get insight into new variables or new avenueshitherto unexplored.

iii. Presenting the Literature review

. The literature should be presented in a clear and logical manner citing theauthor, year of study, objectives of the research, major findings and implications. Theresearcher should present the literature in a chronological order and in a coherentmanner. There are several methods of citing references in the literature. The publicationManual of American Psychological Association (2001) offers detailed informationregarding citations, quotations, references and so on. The Chicago Manual of Stylealso prescribes the format.

1.7.1.3 Identifying the research problem/question

The next step is converting the broad problem into a research question. Theresearch question is fact-oriented and requires gathering information. A research questionstates the objective of the research study. It is a more specific question that must beanswered. It can be more than one question or just only one.

1.7.1.4 Refining the research question

The refined research question will have better focus and will enable to conductresearch with more clarity than the initially formulated questions. In addition to fine-tuning the original question, other research question related activities should be addressedin this phase to enhance the quality of research work viz.,

1. Examine the concepts and constructs used in the study.

2. Review the research question and break them down to second and third levelquestions.

3. Whether hypotheses are postulated in a proper and standard manner?

4. What is not included in the scope of the research questions?

If the research questions are well defined, the subquestions can be easily arrived at.However, if the research question is poorly defined the researcher will need further


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exploration and question revision to refine the original question and generate the materialfor constructing the investigative questions.

1.7.1.5 Developing investigative questions

Investigative questions are questions that the researcher must answersatisfactorily to arrive at a conclusion about the research question. To formulate them,the researcher should break down the research question into more specific questionsfor which the data is to be gathered. This fractioning process can be continued down toseveral levels with increasing specificity. The investigative questions guides to develop asuitable research design. They are the foundations for creating the research data collectioninstrument. In developing the investigative questions performance considerations,attitudinal issues and behavioral issues can be included, depending on the researchproblem.

The problems in defining research questions

There might be some problems in defining the research questions which arediscussed below:

The researcher may recast the management question so that it is amenable tofavorite methodology.

The existence of a pool of information or a database may distract the researcherto reduce the need for other research.

All management questions are not researchable. To be researchable, a questionmust be one for which observation or other data collection can provide theanswer.

Some problems are complex, value-laden and bound by constraints. These ill-defined questions have characteristics that are virtually the opposite of those ofwell-defined problems. These problems require a thorough exploratory studybefore proceeding.

1.7.2 Establishing Research Objectives

The research objectives should be set once the research problem is finalized.Research objectives provide the guidelines for determining the other steps to beundertaken in the research process. If the objectives are achieved the decision makerwill have the information needed to solve the problem. Research objectives justify theneed for undertaking the research work. It provides a purpose and direction for theresearch.


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1.7.3 Developing the research design

A research design is the specification of methods and procedures for acquiringthe information needed to structure or to solve problems. It is an overall operationalpattern or framework of the project that stipulates the information to be collected, thesources from which information can be collected and the procedures for collection ofinformation. In other words the researcher should consider (1)the design technique,(2)the type of data, (3)the sampling methodology and procedures, (4)the schedule andthe budget. A good research design ensures that the information obtained is relevant tothe research problem in an objective and economical manner. The research design canbe described as a master plan or model or blueprint for the conduct of investigation.

1.7.3.1The type of research design

Most of the research objectives could be met by using any one of the threetypes of research designs; exploratory, descriptive and casual research designs.Exploratory research focuses on collecting data using an unstructured format or informalprocedure to capture data and to interpret them. It is often used to classify the problemsor opportunities and it is not intended to provide conclusive information from which aparticular course of action can be determined.

Descriptive research uses a set of scientific methods and procedures to collectraw data and create data structures that describe the existing characteristics of a definedtarget population. For eg., the profile of the consumers, pattern of purchase behaviouretc. In descriptive research design the researcher looks for answers to the how, who,what, when and where questions concerning the different components of a marketstructure. The data and information generated through the descriptive designs can providethe decision makers with evidence that can lead to a course of action.

Casual research design deals with collecting raw data, creating data structuresand information that will allow the decision maker or researcher to model cause-effectrelationships between two or more market variables. The casual research design enablesto identify, determine and explain the critical factors that affect the decision making.However, the research process is more complex, expensive and time-consuming.

1.7.3.2 The type of data

The data can be grouped into two broad categories viz., primary and secondary.Primary data represents the first hand raw data that have been specifically collected forthe current research problem. Primary data are raw, unprocessed and yet to receiveany type of meaningful interpretation. Sources of primary data tend to be the output ofconducting some type of exploratory, descriptive or casual research.


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The secondary data is the historical data previously collected and assembledfor some other research problem. Secondary data can be usually gathered at faster andeconomical manner than the primary data. However, the data may not fit in theresearcher’s information need. The secondary data can be obtained from the libraries,website, published as well as unpublished documents etc.,

1.7.3.3 Sampling methodology and procedure

Sampling refers to randomly selected subgroup of people or objects from theoverall membership pool of defined target population. Sampling plans can be broadlyclassified into probability and non probability sampling. In a probability sampling plan,each member of the defined target population is a known and has an equal chance ofbeing drawn into the sample group. Probability sampling gives the researcher theopportunity to assess the sampling error. In the case of non probability sampling theresearch finding cannot be generalized and the sampling error cannot be assessed. Thefindings are limited to the sample which provided the original raw data. However nonprobability sampling may be the only choice in case where the population cannot beascertained. (A more detailed discussion on sampling is dealt in Unit 3 )

1.7.3.4The time schedule and the budget

The time schedule for completing the research along with the break up of timerequired for each task has to be ascertained. Scheduling will enable the completion ofthe project in time. A budget displays the sources and application of funds for theresearch. The budget may require less attention in case of a in-house project or one whichis sourced by the researcher . However, a budget which is prepared for financial grantsneeds to be prepared very systematically supported with proper documentation. Thebudget may be prepared on various basis for eg., the rule-of- thumb budgeting wherea fixed percentage is arrived on some criterion like a percentage of sales or previousyears research budget.

Task budgeting selects specific research projects to support on an adhoc basis.

1.7.4. Preparing a research proposal

The research proposal is an oral or written activity that incorporates decisionsmade regarding the research work. It includes the choices the researcher made in thepreliminary steps. A written proposal is often made when the study is suggested. Itensures the project purpose, methodology, time and budget. The length and complexityof the proposal varies according to the needs and desires of the researcher. Irrespectiveof the length of the proposal it should have two basic sections; statement of the researchproblem and the research methodology.


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1.7.5 Data collection

The data gathering phase begins with the pilot testing. It is done to detect theweakness in the research design, questionnaire/interview schedule and provides proxydata for selection of probability sample. The pilot testing should stimulate the procedureand protocols designed for data collection. If the study is to be conducted by email thenthe pilot questionnaire should be emailed. The size of the pilot group may range normallyfrom 25 to 100 respondents who need not be statistically selected. There are a numberof variations of pilot testing. Some of them may be restricted to data collection only.One form is ‘pretesting’ where the responses are collected from colleagues, respondentssurrogates or actual respondents for the main purpose of refining the questionnaire.Based on the pilot testing the questionnaire may be redesigned, rephrased and improved.Pretesting may be repeated many times to refine questions or procedures.

Data is the facts presented to the researcher from the study environment. Datacan be gathered from a singe location or from all over the world based on the researchobjectives and the resource allocation. The data collection method ranges fromobservation, questionnaires, laboratory notes and other modern instruments and devices.Data can be characterized by their abstractness, verifiability, elusiveness and closenessto the phenomenon. As abstractions, data are more metaphorical than real. When sensoryexperiences consistently produce the same result then the data is said to be trustworthyas they are verified. Data capturing is elusive, complicated by the speed at which eventsoccur and the time-bound nature of observation. Data reflect their truthfulness measuredby the degree of closeness to the phenomena. Secondary data has at least one level ofinterpretation inserted between the event and its recording. Primary data are close tothe truth.

Data collected need to be edited for ensuring consistency and to locateomissions. In case of survey method editing reduces errors in the recording, improveslegibility and clarifies unclear and inappropriate responses. Edited data are then convertedinto analyzable form. Computers can be used to find missing data, validate data, editand code so that further analysis can be carried out in a valid manner.

1.7.6 Data Analysis and Interpretation

Research is conducted for the purpose of acquiring information. Raw data assuch does not provide information. Further analyzes needs to be done to crunchinformation out of data. Data analysis involves application of statistical techniques forreducing accumulated data to a manageable size leading to summaries. Responsesacquired by way of administering questionnaires should be subjected to analysis so asto ascertain the behaviour of variable, the relationship between variables etc. Analysisshould be focused to find answers to the research questions / hypothesis.


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Various statistical softwares are available to make the job of data analysis easierand scientifical. However, the interpretation needs to be made with expertise as therecommendations are made on the basis on them.

1.7.7 Research Report

It is only through reports the researcher communicates about the research work,findings and recommendations to the outside world. The report has to be prepared inthe style that will be understood by the target audience. The reports may becommunicated by way of written documents or in an oral manner, through letters orthrough telephone calls or a combination of all. The type of report varies depending onthe type of research, length of report and the purpose.

The researcher should take care to see that the report addresses all the objectivesof research in a lucid manner. The report should be adapted to the needs of the targetaudience and care must be taken to use appropriate words in projecting the interpretation,recommendations and conclusion.

A report should contain an executive summary consisting of synopsis of problem,findings and recommendations. It should speak about the background of the study, thestatement of problem, literature summary, methods and procedures, findings,recommendations and conclusion. A detailed discussion on report writing follows inUnit 5.

1.8 THEORETICAL FRAMEWORK

A theoretical framework is a conceptual model of how one theorizes or makeslogical sense of the relationship among the several factors that have been identified asimportant to the research problem concerned. To put it simply a theoretical frameworkinvolves identifying the network of relationship among the variables considered importantto the study. It provides the conceptual foundation to proceed further with the research.The theory is developed based on the documentation of previous research studiesundertaken in the relevant study area or similar problems.

Understanding the conceptual framework enables to postulate hypotheses andtest the relationships. A testable hypothesis can be developed to examine whether thetheory formulated is valid or not. The hypothesized relationship can be tested by meansof suitable statistical techniques. In case of applied research, testable hypothesis neednot be evolved from the theoretical framework but still it is important as it provides abackground for understanding the problem researched. Thus, the entire research processrests on the soundness of the theoretical framework undertaken.

Having a background knowledge of variables is absolutely necessary tounderstand the relationship so as to formulate testable hypotheses. A variable, as the


19

NOTES


name suggest takes varied values. The values may be different at various time for thesame object or person or at the same time for different objects or persons. For eg.,Age is a variable, as it can be different for different consumers and also for a singleconsumer it varies as time evolves.

1.8.1 Types of variables

There are many types of variables like the dependent, independent, moderating,intervening, discrete, continuous, extraneous etc.,

1.8.1.1 Dependent variable

As the name suggests the value of a dependent variable is influenced by othervariables. It is the main variable of interest to the researcher. Understanding the variablesthat influence the dependent variables will lead to finding solutions to the problem. Forthis purpose the researcher will be interested in quantifying and measuring the dependentvariable as well as the other variables that influences the dependent variables. For eg.sales of an organization is a dependent variable. The sales value depends on the demand,price fixed, environmental factors etc. The sales also vary from time to time. Hence itcan be called as dependent variable. There can be more than one dependent variablein a study. In this case the researcher may be interested to know factors that influenceall the dependent variables and the difference in the degree of variance among thedifferent dependent variables.

1.8.1.2 Independent variables

An independent variable influences the value of dependent variable either in apositive or in a negative way. The variance in the dependent variable is accounted forby the independent variable. To manipulate the dependent variable the independentvariable can be used. With each unit of increase in the dependent variable the independentvariable may increase or decrease. The variance in the dependent variable is causedby the independent variables. To establish the casual relationship the independent variableis manipulated. For example age of a customer may influence the choice of a product.Here age is the independent variable and the choice of the product is a dependentvariable.

Independent variable Dependent variable

1.8.1.3 Moderating variable

The variable that moderates the relationship between dependent andindependent variables is called as a moderating variable. The moderating variable has a

Age

Choice of product


20

NOTES


strong contingent effect on the relationship between the independent and the dependentvariable. The presence of a third variable modifies the original relationship between thedependent and independent variables. In the example discussed above the price of theproduct is a moderating variable. Though the age influences the price may moderate thechoice of the product.

1.8.1.4 Intervening variables

An intervening variable is one that surfaces between the time, the independentvariable start operating to influence the dependent variable and the time the impact isfelt on it. The intervening variable surfaces as a function of the independent variablesoperating in any situation and helps to conceptualize and explain the influences of theindependent variables on the dependent variables.

Age Attitude Choice of product Independent Intervening Dependent variable

1.8.2 Theoretical Framework: The need and features

The theoretical framework is the foundation on which the entire research iscarried out. It is logically developed, described and an elaborated network of associationsamong the variables, deemed to be relevant to the problem situation and an identifiedthrough such processes as interviews, observations and literature survey. Experienceand intuition can also be taken up in developing the theoretical framework.

To arrive at good solutions to the problem, correct identification of the problemand the variables contributing to the same is a must. After identifying the variables, thenext step is to elaborate the network of associations among the variables. This willenable formulation of hypotheses which can be subsequently tested.

The literature survey provides a solid foundation to develop the theoreticalframework. Through literature survey the variables that are important are identifiedthrough previous research findings. This forms the basis for a theoretical model. Thetheoretical framework elaborates the relationship among variables, explains the theory

Age Choice of product

Price


21

NOTES


underlying the relations and describes the nature and directions of the relationship. Thetheoretical foundations provide the basis for developing testable hypotheses.

The following are the basic features of a theoretical framework:

The variables influencing the research problem should be clearly identified, definedand discussed.

The discussion should also highlight the relationship between the variables soidentified.

The type of relationship for eg., Positive or negative should be highlighted.

The reason for assuming the type of relationship should be mentioned drawingon the previous research studies identified through the literature review.

A model showing the relationship among the variables can be given so that theconcepts can be visualized and understood clearly by the reader.

1.9 HYPOTHESIS : TYPES AND TESTING PROCEDURE

A hypothesis can be defined as a logically conjectured relationship betweentwo or more variables expressed in the form of a testable statement. Relationships areassumed on the basis of the network of associations established in the theoreticalframework. Formulating such testable statement is called hypothesis development. Thehypothesis can be grouped on the following basis:

1. Statement of hypotheses

Hypothesis can be expressed either as propositions or in the form of if-thenstatements.

Example:

Aged customers will be inclined to take insurance policy

If customers are aged, then they will be inclined to take insurance policy

2. Directional and Non directional Hypotheses

The hypothesis which indicates the type or direction of relationship betweenvariables is called as directional hypothesis. In specifying the relationship betweenvariables the terms such as positive, negative, more than, less than and the like are usedin these hypotheses.

Eg., High income consumers spend more on consumer durables.

Non directional hypotheses postulate relationship but does not offer indicationof the direction of the relationship.


22

NOTES


Eg., Education of the respondent does not have an influence on the importancegiven to the information source.

Non directional hypotheses are formulated in the case where previous studieshave not explored the direction of relationship or there is no evidence to assume thedirection of the relationship among the variables. The previous research studies maygive rise to conflicting findings which will also be the reason for nondirectional hypothesis.

3 Null and alternative hypotheses

Null hypotheses states that there is no significant relationship between thevariables. Null hypotheses also state that there is no difference between what we mightfind in the population characteristics and the sample that is being studied. It is impliedthrough null hypotheses that the difference, if any between the two samples groups orany relationship between two variables based on our sample is simply due to randomsampling fluctuations and not due to any ‘true’ differences between the two populationgroups. The null hypotheses so formulated are tested for possible rejection. It maystate that the population correlation between two variables is equal to zero or that thedifference in the means of two groups in the population is equal to zero.

The hypotheses generation and testing can be done through both induction anddeduction. In deduction, the theoretical model is first developed, testable hypothesesare formulated on the basis of the theoretical framework, data collected and then thehypotheses are tested. In the inductive process, new hypotheses are formulated basedon the known facts collected already which are subjected to test. The findings wouldadd to the knowledge and help to build a theoretical framework.

1.9.1 Hypothesis testing: Meaning and Approaches

The purpose of hypothesis testing is to determine the accuracy of the hypothesesframed due to the fact that the data is collected from sample and not from the entirepopulation. The accuracy of hypotheses is evaluated by determining the statisticallikelihood that the data reveal true differences and not the random sampling error.

There are two approaches to hypothesis testing; classical or sampling theoryand the Bayesian approach. Classical approach is mostly used in research application.This approach represents an objective view of probability and the decision making ismade totally on an analysis of available sampling data. A hypothesis is accepted orrejected based on the sample data collected. The sample drawn may vary at least to asmaller extent from the population and hence it is a must to know whether the differencesare statistically significant or insignificant. A difference is statistically significant if there isa good reason to believe that the difference does not represent the random samplingfluctuations only.


23

NOTES


Bayesian statistics also use sampling data for decision but go beyond them andconsiders all other available information. The additional information consists of subjectiveprobability estimates stated in terms of degrees of belief. The subjective estimates arebased on general experience rather than on specific data collected . They are expressedas a prior distribution that can be revised after sample information is gathered. Therevised estimate known as posterior distribution can be further revised by additionalinformation and so on. Various decision rules are established, cost and other estimatescan be introduced and the expected outcome of the combination of these elements areused to judge the decision alternatives

1.9.2 Statistical testing procedure

The sequence for testing a hypothesis is discussed below:

State the null hypothesis

The null hypothesis is mostly used for statistical purposes. It is used in the casewhere the researcher is interested in testing a hypothesis of change or difference.

Choose the statistical test

Appropriate statistical test must be chosen to test the hypothesis. Criteria is tobe employed in selecting the appropriate statistical test. The criteria may include thetype of sample used, the nature of population, type of measurement scale used etc.

Select the desired level of significance

The level of significance must be decided before the data collection. Commonlyused level is .05 however .01 is also widely used. The other significance levels are.10,.025and .001. The level of significance is determined on the basis of extent of risk theresearcher is willing to accept and the effect of the choice on the risk. The larger thelevel of significance, the lower will be the risk.

Compare the calculated difference value

Once the data is collected then a selected statistical formula is used to obtainthe calculated value Obtain the critical test value

After obtaining the calculated value, the critical value is to be obtained from theappropriate statistical table. The critical value is the criteria that defines the region ofrejection from the region of acceptance of the null hypothesis

Interpret the test

If the calculated value is larger than that of the critical value the null hypothesisis rejected and it is concluded that the alternative hypothesis is accepted. If the criticalvalue is larger the null hypothesis is accepted.


24

NOTES


1.9.3 Parametric and Non parametric test

There are two general classes of significance tests: parametric and nonparametric.

In the case of Parametric test the data are derived from interval and ratio measurements.Non parametric tests are used to test the hypotheses with nominal and ordinal data.

The following assumptions are made in case of parametric tests:

The observation must be independent i.e., the selection of one case should notaffect the chances of any other cases to be included in the sample

The observations should be drawn from normally distributed population

The population should have equal variances

The measurement scales should be at least intervals so that arithmetic operationscan be used with them

Non parametric tests have few assumptions. They are easy to understand andsimple to use. They do not specify normally distributed population or homogeneity ofvariance. Some tests require independence of cases and others are designed for relatedcases. Non parametric tests are the only ones usable with nominal data; they are theonly technically correct test to be used with ordinal data. Non parametric test can alsobe used in the case of interval and ratio data. However, it will result in waste of someinformation available. The non parametric tests are highly efficient as compared toparametric tests. Non parametric test with the sample of 100 will provide the samestatistical testing power as a parametric test with a sample of 95.

1.9.4 Types of test

Five different types of tests can be applied to test the hypotheses viz., One-sample test, two independent sample test, two related sample test, K Independentsample test and K related sample test. In order to test the hypothesis a particular testhas to be selected based on the following criterion:

The samples involved viz., one sample, two samples or k samples.

In case of two samples it has to be identified whether the individual cases areindependent or related.

The type of scale used i.e., nominal, ordinal, interval or ratio

The following section explores the type of test to be used based on the threecriteria discussed above:


25

NOTES


I. One-sample Tests

One-sample tests are used when a single sample is taken and test is undertakento know whether the sample comes from a specified population.

Parametric tests

The parametric tests Z or t-test can be used to determine the statisticalsignificance between a sample distribution mean and a parameter. When sample sizesare beyond 120 then the t and z distributions are virtually identical.

Non-parametric tests

Different types of non parametric tests may be used in the case of one-sampletest depending on the measurement scale used and other conditions. If measurementscale is nominal, binomial or chi-square test can be used. The binomial test is appropriatewhen the population is viewed as only two classes such as male and female, buyers andnon-buyers and all observations fall into one of these categories. The binomial test isuseful when the size of sample is very small and the chi-square test cannot be used

Chi-square test is the most widely used non parametric test of significance. It isparticularly useful in those tests involving nominal data but can also be used for higherscales. Using this technique, the significant differences between the observed distributionof data among categories and the expected distribution are tested on the null hypothesis.This test can be used in one sample, two independent samples or k independent samples.It must be calculated with actual counts rather than percentages. The formula for thechi-square(

2x

) test is( )∑

=

−=

k

t i

i

EEO

x1

212

Oi = observed number of cases categorized in the ith category

Ei = Expected number of cases in the ith category under Ho

K = the number of categories

II. Two-independent samples tests

The need for two independent samples tests is often encountered in research.One can compare the purchasing predisposition of a sample of subscribers from twomagazines to discover if they are from the same population.

Parametric tests

The z and t-tests are frequently used parametric tests for independent samples,however F test can also be used


26

NOTES


The z test is used with sample sizes exceeding 30 for both independent samplesor with smaller samples when the data are normally distributed and population variancesare known. The formula for the z test is

( ) ( )

2

22

1

21

02121

nS

nS

Xxxz

+

−−−=

µµ

In the case of small sample sizes, normally distributed populations and assuming equalpopulation variances, the t-test is appropriate:

( ) ( )

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−−−=

21

2

02121

11nn

S

Xxxt

p

µµ

( )21 µµ − is the difference between the two population means is associated with thepooled variable estimate:

( ) ( )2

11

21

222

2112

−+−+−

=nn

SnSnSp

Non-parametric tests

The chi-square test is appropriate for situations in which a test for differencesbetween samples is required. It is especially valuable for nominal data, however it canbe used with ordinal measurements also. The formula slightly differs from earlier oneand it is as below:

( )ij

ijijji E

EOx

22 −

∑∑=

Oij = Observed number of cases categorized in the ijth cell

Eij = Expected number of cases under Ho to be categorized in the ijth cell.

III Two Related Samples Test

The two related samples tests are used in situations in which persons, objectsor events are closely matched or the phenomena is measured twice. The efficiency ofworkers before and after training can be measured.


27

NOTES


Parametric Tests

The t-test for independent samples is inappropriate because one of theassumptions is that observations are independent. This problem is solved by a formulawhere the difference is found between each matched pair of observations, therebyreducing the two samples to the equivalent of one-sample case.

In the following formula , the average difference, D corresponds to the normaldistribution when the (alpha) difference is known and the sample size is sufficient. Thestatistic t with (n – 1) degrees of freedom is defined as

nSDt

D

= where nDD ∑

= ( )

( )1

22

−

∑−∑

=n

nDD

SD

Nonparametric Tests

The McNemar test may be used with either nominal or ordinal data and isespecially useful with before-after measurement of the same subjects.

IV. K Independent Samples Tests

K independent samples tests are normally used in management and economicresearch when three or more samples are involved. The test is concerned with whetherthe samples might come from the same or identical population. When the data aremeasured on an interval-ratio scale and the necessary assumptions are met then theAnalysis of Variance and F test are used. If the assumptions cannot be met or if thedata are measured on ordinal and nominal scale then the non parametric test can beselected. The samples are assumed to be independent.

Parametric Tests

Analysis of Variance(ANOVA) is a statistical method of testing the nullhypothesis that the means of several populations are equal. To use ANOVA certainconditions must be met.

1 The samples must be randomly selected from normal population

2 Populations should have equal variances

3 The distance from one value to its group’s mean should be independent of thedistance of other values to that mean

ANOVA breaks down or partitions total variabaility into component parts. Ituses squared deviations of the variance so computation of distances of the individualdata points from their own mean or from the grand mean can be summed up. In ANOVA


28

NOTES


model each group has its own mean and values that deviate from that mean. Similarly,all the data points form all of the groups produce an overall grand mean. The totaldeviation is the sum of the squared differences between each data point and the overallgrand mean.

The total deviation of any particular data point may be portioned into betweengroups variance and within-group variance. The between-groups variance representsthe effect of the treatment or factor. The differences between-groups means imply thateach group was treated differently and the treatment will appear as deviations of thesample means from the grand mean. The within-groups variance describes the deviationsof the data points within each group from the sample mean. This results from variabilityamong subjects and from random variation. This is often called error. When the variabilityattributable to the treatment exceeds the variability arising from error and randomfluctuations, the viability of the null hypothesis begins to diminish.

The test statistic for ANOVA is the F ratio. It compares the variance betweentwo sources:

F = Between-groups variance Mean square between

————————————— = —————————————

Within-groups variance Mean square within

Mean square between = Sum of squares between

——————————————-

Degrees of freedom between

Mean square within = Sum of squares within

——————————————-

Degrees of freedom within

IV. K Related Sample Case

Parametric test

A k related sample test is required in the following situations:

The grouping factor has more than two levels

The observations or subjects are matched or the same subject is measuredmore than once

The data are atleast interval


29

NOTES


In this method it is often necessary to measure subjects several times. These repeatedmeasurements are called trials. The repeated-measures ANOVA is a special type of n-way analysis of variance.

Non parametric test

In case of k related samples to be measured on a nominal scale the Cochran Qtest is appropriate. The test extends McNemar test. It tests the hypothesis that theproportion of cases in a category is equal for several related categories. When the datais ordinal, the Friedman two-way analysis of variance is appropriate. It tests matchedsamples, ranking each case and calculating the mean rank for each variable across allcases. It uses ranks to compute a test statistic.

The basis aspects of a research design

The need and the major types of research design

1.10 Research Design

The research design is a blue print of action. It involves a series of rationaldecision making choices regarding the purpose of the study, its scope, its location, thetype of investigation, the extent to which its is controlled and manipulated by theresearcher, the time aspects, the collection, measurement and analysis of data. It is aplan and structure to obtain answer to the research questions. It aids the researcher inthe allocation of the resources in a well-defined manner. The more sophisticated andrigorous the research design is, the greater outcome of the research.

The essentials of research design:

It is an activity and time based plan

The design is based on the research questions

The design guides the selection of sources and types of information.

It is a framework for specifying the relationship among the study’s variable

It outlines the procedures for every research activity

The overall research design can be split into the following parts:

The sampling design which deals with the method of selecting the samples forthe purpose of conducting the study

The observational design which deals with the conditions under which theobservation is made

The statistical design which is concerned with the number of samples to beobserved and the how the data gathered is to be analyzed


30

NOTES


The operational design which relates to the techniques by which the proceduresspecified in the sampling, statistical and observational designs can be carriedout.

In a nut shell, the research design should contain the following:

A clear statement of the research problem

Procedure and techniques proposed for gathering information

The population involved in the study

The methods to be used in processing and analyzing the data

1.10.1 Need for research design

The research design has to be prepared on account of the following reasons:

Research design is the blueprint of the proposed research to be conducted. Itenables to plan the various activities and provides an insight into the type ofdifficulties that may arise, so that the researcher may be prepared to tackle thesame.

Since the research design is the plan regarding the sampling procedure, datacollection method and various other activities to be performed in the proposedresearch , the same can be discussed with others and based on the criticalcomments, the flaws and inadequacies can be tackled leading to an effectiveresearch design.

It gives an idea regarding the type of resources required in terms of money,manpower, time and efforts

It enables the smooth and efficient conduct of various research operations

The research design affects the reliability of the research findings and as such itconstitutes the foundation of the entire research work

1.10.2 Classification of research designs

Every researcher is faced with the task of identifying a suitable research designto carry out the study. The following section explores the types of research design,based on select criteria:


31

NOTES


Criteria Types

Method of data collection MonitoringInterrogation/communication

Researcher’s control on variables ExperimentalEx post facto

Purpose of study DescriptiveCasual

Time dimension Cross-sectionalLongitudinal

Scope CaseStatistical study

Research environment Field settingLaboratory researchSimulation

Participants perception Actual routineModified routine

Types of investigation CasualCorrelational

Unit of analysis SingleDyadGroupOrganization/Nation

Extent of crystallization Formal studyExploratory study

1. Method of data collection

The research study may assume the characteristics of monitoring andinterrogation. Monitoring includes the studies where the researcher inspects the activitiesof a subject without attempting to elicit response from anyone. For e.g., observing thebehaviour of consumers in a departmental store.

In the interrogation/communication study the researcher questions the subjectsand collects their responses. The data can be collected through questionnaires, interviewsor experimental methods.


32

NOTES


2. Researcher’s control of variables

On the basis of researcher’s ability to control or manipulate the variables twotypes of research design could be arrived at viz., experimental and ex post facto designs.In an experimental design, the researcher attempts to control or manipulate the variablesin the study. The variables may be kept constant or may be changed to know theeffects. Experimental design is appropriate when one wishes to understand and explorethe effects of certain variables on the other.

In an ex post facto design investigators have no control over the variables. Thevariables cannot be manipulated. The researcher can only report what has happened orwhat is happening.

3. Purpose of the study

On the basis of the purpose of the study two categories of research design canbe arrived at viz., descriptive and casual studies.

Descriptive studies

The research concerned with finding out who, what, where, when or how muchis a descriptive study. The descriptive studies are more formalized and has a structurewith clearly stated hypotheses or investigative questions. They can serve a variety ofobjectives viz.,

Description of phenomena or characteristics

Estimates of the proportion of a population that have a certain set ofcharacteristics

Discovery of association among different variables. This is commonly called ascorrelational study

The Descriptive studies present data in a meaningful form and thus helps tounderstand the characteristics of a group in a given situation. It enables to thinksystematically about the aspects in a given situation. It offers ideas for further researchand help to make simple decisions. A descriptive study may be simple or complex andcan be done in many settings. A simplest descriptive research can study about the size,form, distribution etc.

Casual studies

If the researcher is concerned with analyzing how one variable produces changesin another, it is called a casual study. The casual study attempts to explain the relationshipamong variables. The concern in casual analysis is to analyse how one variable affects


33

NOTES


or ‘is responsible for’ changes in the other variables. There are three possibilities ofrelationship that can occur between variables:

1. Symmetrical

2. Reciprocal

3. Asymmetrical

In the case of Symmetrical relationship two variables fluctuate together but it isassumed that changes in neither variable are due to changes in the other. Symmetricalconditions are usually found when two variables are alternate indicators of anothercause or independent variable.

Reciprocal relationship exists in the case where two variables mutually influenceor reinforce each other. Asymmetrical relationship exists where the changes in onevariable viz., the independent variable is responsible for changes in another variableviz., the dependent variable. The dependent and independent variables are identifiedon the basis of :

The degree to which each variable may be altered. The variable which arerelatively unalterable is called independent variable

The time order between the variables. The independent variables precedes thedependent variable.

Four types of asymmetrical relationship can exists:

1. Stimulus – response; where an event or change results in a response from someobject. A stimulus is an even or force. A response is a decision or reaction. e.g.,decrease in price might result in increase in the number of units sold

2. Property – disposition; where an existing property causes a disposition. Aproperty is an enduring characteristic of a subject that does not depend oncircumstances for its activation. Disposition is a tendency to respond in a certainway under certain circumstances. eg. gender and attitude towards genocide

3. Disposition – behaviour; where a disposition causes a specific behaviour. Abehaviour is an action e.g., opinion about the stores image and purchase

4. Property- behaviour; where an existing property causes a specific behavioure.g., family size and purchase of car.

Testing casual hypotheses

To test casual hypotheses, three types of evidence can be opted:


34

NOTES


1. Covariation between the variables

2. Time order of events moving in the hypothesized direction

3. No other possible causes for change in the dependent variable.

Causation and experimental Design

In case of experimental design apart from the above three conditions two otherrequirements must be met:

All factors except the independent variable must be held constant and notconfounded with another variable that is not part of the study. This is called acontrol group.

Each person in the study must have an equal chance for exposure to each levelof the independent variable. This is called random assignment of subjects togroups

Causation and Ex Post Facto Design:

In case where research studies cannot be carried out experimentally bymanipulation of variable, the subjects which has been exposed to an experimental variableand those which are not exposed are studied

4. The time dimension

On the basis of time involved in conducting the study two classifications ispossible; Cross sectional and longitudinal study. The cross - sectional studies are carriedout once and represent a snapshot of the happening in a study at a point of time.Longitudinal studies are repeated over an extended period. In longitudinal studies thechanges over a period of time can be tracked.

In the case of longitudinal studies where a panel is used, the same members ofthe panel can be used for the entire period of study. The longitudinal studies involvemore time and budget. Hence, attempt can be made to use adroit questions involvingpast, present and future expectations in the cross sectional study itself. However, caremust be taken in interpreting the findings.

5. Scope

The topic of the research may involve a particular case study or it may be ageneral study. The general study attempts to capture a population’s characteristics basedon the inference drawn from sample characteristics. Hypotheses are formulated andtested based on quantitative data. Generalizations of the findings are made based onthe findings of the sample study. These studies have breadth rather than the depth.


35

NOTES


Case studies place more importance on a holistic analysis of a fewer events orconditions and their interrelations. The study relies to a greater extent on the qualitativedata. It provides more input and valuable insight to problem solving, evaluation andstrategy. The details regarding the problem in hand are collected from multiple sourcesof information. Generalizations cannot be made from the case study as the findings arespecific to the particular problem in hand. However, a single, well designed case studycan provide a major challenge to a theory and also provide the source for framing newhypotheses.

6. The research environment

Research may be conducted in actual, manipulated or simulated conditions.Research conducted in actual environmental conditions is called as field study. Thestudies conducted under staged or manipulated conditions are termed as laboratorystudies. Simulated studies involve replication of the essence of a system or process.Simulations are used to a wider extent in operations research where the majorcharacteristics of various conditions and relationships in actual situations are oftenrepresented in mathematical models. Simulations can also take the form of role playingand other behavioral activities.

7. Participants perceptions

The usefulness of a design may be reduced when people in a disguised studyperceive that the research is being conducted. Participants’ perceptions influence theoutcomes of the research. When participant perceive that something out of the ordinaryis happening, they may behave less naturally. In this context, three situations are likelyto arise:

Participants perceive no deviations from the routine.

Participants perceive deviations, but as unrelated to the researcher.

Participants perceive deviations as researcher-induced.

8. Type of investigation

Research study can take the form of casual or correlational investigation. Acasual study is conducted to establish a definitive cause and effect relationship. In thiscase the objective of the research is to delineate one or more factors that are causingthe problem. The intention of the researcher conducting a casual study is to be able tostate that variable X causes variable Y. Thus the study in which the researcher wants todelineate the cause of one or more problems is called a casual study.


36

NOTES


If the researcher wants to identify or delineate the important factors associatedwith the problem, then a correlational study is suitable. The type of questions asked andthe way in which problem is defined determines whether a study is casual or correlational.

9. Units of Analysis

The research question decides the unit of analysis. The research study may beinvolved in collecting information from the individual units involved in the study in whichcase it is an individual study. If the research involves studying the interaction betweentwo or more individuals then several two person groups also known as dyads willbecome the unit of analysis. The group study involves studying a group for e.g.,Comparison of the motivation level among the workers in the different departments is agroup study. Likewise the unit of study may involve the organization or nation.

10. Extent of crystallization of Research question

The research study can be classified as formal or exploratory. The classificationis made on the base of the degree of structure and the objective of the study.

(a)Formal study

Formal study begins where exploratory study ends. It begins with a hypothesisor research question and involves precise procedures and data source specification.The goal of formal research design is to test the hypothesis or answer the researchquestions.

(b)Exploratory study

Exploratory study has loose structures with the objective of discovering furtherresearch tasks. An exploratory study is undertaken when the existing knowledge baseon the problem selected is very limited or not available. In such cases preliminary researchwork needs to be done to gain familiarity with the situation. It is undertaken with theidea of comprehending the nature of the problem since very few studies have beenconducted in that area. The data are mostly collected through interviews or observation.When data reveal some pattern regarding the problem at hand, theories are developedand hypotheses are postulated for subsequent testing. The immediate purpose is todevelop hypotheses or questions for further research. Exploratory studies are alsoconduced when some facts are known and more information is needed for developinga viable theoretical framework.

The exploratory study is finished when the researcher has achieved the followingpurpose:


37

NOTES


Establish the major dimensions of the research task

Defined a set of subsidiary investigative questions that can be used as guidelineto a detailed research design

Developed hypotheses about possible causes of the problem

Learned the boundaries and scope of the proposed research study

Decided that additional efforts or further research is not feasible.

The objectives of exploration may be accomplished with different techniques.Exploratory study to a greater extent depends on the qualitative techniques. Howeverquantitative techniques may also be used. Several techniques are available for conductingexploratory investigations;

In-depth interviewing

Participant observation

Films, photographs and videotapes

Projective techniques and psychological testing

Case studies

Street ethnography

Document analysis

Proxemics and Kinesics

Combining the approaches listed above four techniques could be derived

Secondary data analysis

Experience survey

Focus groups

Two-stage design

i. Secondary data analysis

Data collected by others for the purpose of conducting their research is calledsecondary data. If the present study, the researcher is conducting, can use the samedata then, time and money can be saved by means of not conducting the study again.


38

NOTES


The researcher can explore the organization’s archives for the data. Report ofprior research studies would reveal the successful and unsuccessful methods adoptedin the previous research studies. Browsing through the earlier research studies will alsoreveal the less attempted problem areas which can be addressed in the present research.

The researcher can look into the published documents, in the form of books/journals by outside organizations. They can be a rich source of hypotheses. The e-sources and the library will provide the needed information.

The search of secondary sources will provide the background information aboutthe research to be conducted and also will also provide a fair idea about the areas to bepondered.

ii. Experience survey

Experience survey involves collecting information from the people experiencedor knowledgeable in the particular area of study. The data would be collected fromtheir memories and experiences .The ideas on important issues and the subject mattercan be explored. The investigative format is more flexible. The outcome of the interviewwould be a new hypothesis, discarding an old one or information, for doing the study ina better manner.

iii. Focus groups

A focus group is a panel of people who meet for about 90 minutes to 2 hoursand discuss about the subject matter led by a trained moderator. The facilitator usesgroup dynamics to focus or guide the group in the exchange of ideas, feelings andexperiences on a specific topic. The focus group is made up of 6 to 10 respondents.Too small or too large a group may not be effective in meeting the objective. Theoutcome of the focus group will be a list of ideas and behaviourial observations with theobservations and recommendations made by the moderator. The qualitative dataproduced from the focus group can be used for enriching the knowledge.

Depending on the topic, separate focus groups could be run for different subsetof the population. Homogeneity in the focus group will be more effective and producemaximum results. The focus groups can be conducted in a face to face manner, throughtelephones, internet (e-groups) and through video conferencing.

iv.Two stage design

In the exploratory stage, the researcher does not know much about the problemin hand but needs to know more before proceeding further in terms of time and resources.


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A two stage design would be useful in this situation. With this approach explorationbecomes a separate first stage with limited objectives: (1) clearly defining the researchquestion and (2) developing research design. A limited exploration at a lesser costcarries little risk for the researcher and enables to uncover information that reduces thetotal research cost.

SUMMARY:

This unit has examined some of the basic aspects of research. The importanceof knowledge of research in business setting was emphasized. The hallmarks of scientificresearch viz., purposiveness, rigor, testability, replicability, precision and confidence,objectivity, generalization and parsimony were described. The steps involved inhypothetico-deductive research were discussed. The various steps involved inundertaking the research were dealt in detail. The issues involved in development ofhypothesis and the parametric and non parametric tests were examined.

With the impetus to the background of research, the next unit deals with theissues concerning the research design and its types.

Have you understood?

What is research? Explain the need for the same

Discuss the hallmarks of scientific research.

Explain the process of deduction and induction with an example

Discuss the building blocks of scientific research.

What are the steps in Hypothetico-dedcutive research?. Explain them using anexample

Describe the research process in detail.

Discuss the steps involved in problem identification

Why should literature survey be conducted?

Discuss the need for theoretical framework and highlight the features of thesame

What is hypothesis? Discuss the types.


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Explain the steps involved in formulation and testing the hypothesis

Discuss the various methods of testing the hypothesis

Explain the meaning and significance of research design

What are the basic research design issues? Discuss them in detail.

Is single research design suitable for all research studies? If not why?

Discuss the exploratory research design in detail.

Discuss the different types of research design. Site a situation to which eachdesign is applicable to.


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Experimental DesignUnit structure:

2.1 Introduction2.2 Learning Objectives2.3 The benefits and drawbacks2.4 Activities involved in conducting an experiment2.5 Validity in Experimentation

2.5.1 Factors affecting internal validity2.5.2 Factors affecting external Validity

2.6 Experimental research designs2.6.1. Pre-experimental designs2.6.2 True experiments/ Lab experiments2.6.3. Field experiments: Quasi or semi - experiments

2.7 Measurement2.8 Measurement Scales

2.8.1 Selection of measurement scale2.8.2 Methods of scaling2.8.3 Construction of measurement scales

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2.1 INTRODUCTION

Experimental design enables a researcher to alter systematically the variablesinvolved in the study. The experimental design involves intervention by the researcher.The researcher intervenes by way of manipulating the variables in a setting and observesthe effect on the subjects studied. Under experimental design the independent variablesare manipulated and the effects of the same on the dependent variables are observed.

This units deals with the discussion on activities involved in conducting anexperiment, the factors affecting the validity in experimentation and the various types ofexperimental designs. Measurement of variables is necessary for testing the hypotheses.

Unit -2


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The nominal, ordinal, interval and ratio scales are dealt in detail. The process involvedin selection and construction of measurement scales are discussed in detail.


After reading this unit you should be able to:

Define experimental design and elucidate the benefits and drawbacks

Have insight on the internal and external validity of experimental designs

Distinguish the different types of experimental designs

Understand the concept of measurement, scales and the major sources ofmeasurement error

Understand the meaning of scaling and the six critical decisions involved inselecting an appropriate measurement scale

Know the different forms of rating and ranking scales

Be conversant with the five ways of constructing the measurement scales

2.3 THE BENEFITS AND DRAWBACKS

The benefits of an experimental design are listed below:

The researcher can manipulate the independent variable and thereby understandthe effect on the dependent variable. This will lead to understand the existenceand potency of the manipulation.

The effect of the extraneous variables can be eliminated by means of effectivecontrol which will lead to authentic findings.

The experimental design offers convenience and is economical compared toother methods as the scheduling of data collection and control of variables aredecided by the researcher himself.

The experiment can be replicated with different subject groups and conditionsand thereby enables to understand the effect of independent variables acrosspeople, situations, conditions and time.

The researcher in some situations can use field experiments to reduce thesubjects’ perception of the researcher as intervention or deviations in theireveryday lives.

The experimental design suffers from the following limitations:

The research is undertaken in artificial settings and hence the subjects may notbehave as they do under normal circumstances.


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Genaralization from non - probability sampling can lead to difficulty in extendingthe findings.

Many times, cost overruns arise in the experimental designs

Experimental studies are conducted to solve present or future problems.However, the past or occurred events cannot be dealt with, in the experimentaldesigns.

Management research is mostly concerned with people and hence manipulationand control of human elements are subject to ethical considerations

2.4 ACTIVITIES INVOLVED IN CONDUCTING AN EXPERIMENT

The following activities are involved in the experimental research:

2.4. 1. Selecting relevant variables

The researcher in the course of the conduct of the study develops hypothesesto meet the objectives of the research. The hypothesis describes the relationship betweentwo or more variables. The researcher should select the variables that best representthe concepts to be tested, determine the number of variables to be tested and select ordesign appropriate measures for them. The number of variables selected in a researchstudy is subject to the budget allocated, the time frame available, number of subjectsbeing tested and the like.

2.4. 2. Specifying the levels of treatment

The treatment levels of the independent variable are the distinctions, theresearcher makes between different aspects of the treatment conditions. For eg., ifattitude is hypothesized to have influence on the purchase behaviour, the attitude maybe grouped into three levels viz., positive, negative and neutral.

2.4. 3. Controlling the experimental environment

The control may be exercised on the experimenter/researcher, subject or theenvironment. The environmental control is concerned with holding the physicalenvironment in which the experiment is conducted as constant. The subjects may notknow that the experiment is being conducted. This situation is mentioned as blinding thesubject. When the experimenter/researcher is also unaware of the experiment, then it iscalled as double - blinded. The control refers to avoiding the effect of extraneous variableson the research study conducted.

2.4. 4. Choosing the experimental design

Choosing an apt experimental design improves the probability that the observedchange in the dependent variables are caused by the manipulation of the independentvariable only and not by another variable. It strengthens the generalization of the result.


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2.4.5. Selecting and assigning the subjects

The question of selecting and assigning the subjects do not arise in case wherethe entire population is considered for the study. However, mostly the researcher willdepend on sample to conduct the study. In order to validate and generalize the findingsof the research study, the samples selected should be representative of the population.The sample may be selected on the basis of random selection or systematic sampling.Random assignment of subjects to groups should be followed. When it is not possibleto randomly assign the groups then matching may be used. Matching is based on non-probability quota sampling approach. The object of matching is to ensure that eachexperimental and control subject matches on every characteristics used in research.

2.4.6. Pilot-testing, revising and testing

Pilot - testing reveals error in the design and improper control of extraneous orenvironmental conditions. Pretesting the instruments enables refinement of the samebefore the final test. It enables to revise scripts, take stock of control problems withlaboratory conditions and scan the environment for factors that might confound theresults.

2.4.7. Analyzing the Data

Pretesting and proper planning enable to have an order and structure in theexperimental data collected. The data are more conveniently arranged as a result of thelevels of treatment condition, pretest and post-test and the group structures. This enablesto apply statistical technique in a simplified manner.

2.5. VALIDITY IN EXPERIMENTATION

The findings of Experimental design are judged by measuring the internal andexternal validity. The Validity is the extent to which a measure accomplishes its claims.Two groups of validity exists; internal and external. Internal validity is concerned withidentifying whether the conclusions drawn from a demonstrated experimental relationshiptruly imply cause. External validity is concerned with the generalization of observedcasual relationship across persons, settings and times.

2.5.1 Factors affecting internal validity:

Internal validity refers to the confidence that one can place in the cause-effectrelationship. In other words, it addresses the question, “To what extent does the researchdesign permit us to say that the independent variable “A” causes the change in thedependent variable “B” ?”. Factors affecting internal validity causes confusion as to whetherthe observational differences are due to experimental treatment or extraneous factors.An experiment has high internal validity if the researcher has the confidence that the


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experimental treatment has been the source of change in the dependent variable. Thefactors listed below affects the internal validity:

History

Maturation

Testing

Instrumentation

Selection

Statistical regression

Experimental mortality

Diffusion or imitation of treatment

Compensatory equalization

Compensatory rivalry

Resentful demoralization of the disadvantaged

Local history

1. History

In the experimental designs a control measurement (O1) of dependent variableis taken before introducing the manipulation (X). After the manipulation an aftermeasurement (O2) of the dependent variable is taken. Then the difference between O1

and O2 is attributed to the manipulation. However, some events may occur during thecourse of the experimental study which will affect the relationship between the variablesunder the study.

2. Maturation

The subjects considered for experimentation might change with the passage oftime and may not be due to the occurrence of any specific event. This happens particularlywhen the study covers a long period of time.

3. Testing

The process of taking a test can affect the scores of further tests. The first testwould have created some awareness and learning experience which influences the resultsof the subsequent tests.


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4. Instrumentation

The threat to validity may arise due to the observer or the instrumentation.Using different observers or interviewers affects the validity of the study. If the sameobserver is used for a longer period of time, it may affect the validity due to observer’sexperience, boredom, fatigue and anticipation of results. Difference in the questions foreach measurement affects the validity

5. Selection

Differential selection of subjects for experimental and control groups affectsthe validity. Validity considerations require the groups to be equivalent in every aspect.The problem can be overcome by randomly assigning the subjects to experimental andcontrol groups. In addition, matching can be done. Matching is a control procedure toensure that experimental and control groups are equated on one or more variablesbefore the experiment. Matching the members of the groups on key factors also enhancesthe equivalence of the groups.

6. Statistical Regression

This factor operates especially when members chosen for the experimentalgroup have extreme scores on the dependent variable. For eg., If a manager wants totest if he can increase the salesmanship qualities of the sales personnel through trainingprogram, he should not choose those with extremely low or extremely high abilities forthe experiment. This is because, those with very low score i.e., those with low currentsale abilities have a greater probability of showing improvement and scoring closer tothe mean test after being exposed to the treatment. This phenomenon of low scorerstending to score closer to the mean is known as “regressing towards the mean”. Likewise,those with very high abilities would also have a greater tendency to regress towards themean – they will score lower on the post-test than on the pretest. Thus, those who areat either end of the continuum with respect to the variable would not “truly” reflect thecause-and-effect relationship. This phenomenon of statistical regression is a threat tointernal validity.

7. Experimental mortality

This factor arises due to the changes in the composition of study groups duringthe test. There may be drop outs in the study group leading to the changes in themembership of the group. This problem does not arise for the control group as they arenot affected by the testing situation and they are less likely to withdraw.

All the above threat factors can be controlled to a certain extent by randomassignment. However, the following factors affecting internal validity cannot be controlled


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by randomization. Both the control group and the experimental group are affected bythe first three factors.

8. Diffusion or imitation of treatment

The interaction between the experimental and the control group may lead thecontrol group to learn about the experiments eliminating the difference between thegroups.

9. Compensatory equalization

If the experimentation treatment leads to a desirable and beneficial outcome,then it may lead to an administrative reluctance to deprive the control members.Compensatory action directed at the control groups may confound the experiment.

10. Compensatory rivalry

Compensatory rivalry arises when the member of the control group know thatthey are in the control group. This will generate competitive pressures causing them totry harder which will affect their normal behaviour

11. Resentful demoralization of the disadvantaged

When the treatment is desirable and the experiment is obtrusive, control groupmembers may become resentful of their deprivation and lower their cooperation andoutput.

12. Local history

When all experimental persons are assigned to one group or session and allcontrol people to another, there is a chance for some idiosyncratic event to confoundresults. This problem can be handled by administering treatments to individuals or smallgroups that are randomly assigned to the experimental or control sessions.

2.5.2 Factors affecting External Validity

External validity is concerned with the interaction of the experimental treatmentwith other factors and the resulting impact on the ability to generalize the findings acrosstimes, settings or persons. External validity is high when the results of an experiment areapplicable to a larger population. The following are the threats to external validity:

1. The reactivity of testing on the experimental treatment

In the case of conducting pretest in an experimental design the subjects aresensitized and they react to the experimental stimulus in a different manner. The before-measurement effect can be particularly significant in case of experiments where theindependent variable involved in the study is concerned with the change in the attitude.


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2. Interaction of selection and the experimental treatment

This threat is concerned with the process by which the test subjects are selectedfor the experiment. The population from which the subjects are selected may not be thesame as the population to the result are extended to. It limits the generalizations of thefindings.

3. Other reactive factors

The experimental settings may have a biasing effect on the subject’s responseto the experimental treatment. An artificial setting will produce results that are notrepresentative of the larger population. If subjects know that they are participating in anexperiment, they may not behave in a normal way; this affects the validity of theexperimental treatment. In addition, yet another reactive effect is the possible interactionbetween X and the subject characteristics.

2.6 EXPERIMENTAL RESEARCH DESIGNS

Many experimental designs are available and they widely vary on the basis oftheir power to control contamination of the relationship between independent anddependent variables. On the basis of the characteristics of control the experimentaldesign can be grouped under the following three heads:

1. Pre-experiments

2. True experiments/Lab Experiment

3. Field experiments

A set of commonly used symbols are:

X = The exposure of an independent variable to a group of test subjects for whichthe effects are to be determined

O = The process of observation or measurement of the dependent variable (effectoutcome ) on the test subjects

R = The random assignment of test subjects to separate treatment groups

2.6.1. Pre-experimental designs

Pre- experimental design represents the crudest form of experimentation and isundertaken only when nothing stronger is possible. The designs are characterized bythe absence of randomization of test subjects. The pre-experimental designs are weakin their scientific measurement power because they fail to control adequately the variousthreats to the internal validity. As a result they fail to meet the internal validity criteria.Three pre-experimental designs are detailed below:


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1. One-Shot Case Study

2. One-Group Pretest-Post-Test Design

3. Static Group Comparison

1. One-Shot Case Study:

A single group of test subjects is exposed to the independent variable treatmentX, and then a single measurement on the dependent variable is taken (01).One-Shotcase study does not use pretest and control group. As a result this design is inadequatefor establishing causality. For e.g., a study on the employee education campaign aboutthe automation of the office activities without a prior measurement of employeeknowledge. Result would reveal only how much the employees know after the educationcampaign, but there is no way to judge the effectiveness of the campaign. This may berepresented as shown below:

X OTreatment or manipulation of Observation or measurement of independent variable dependent variable

2. One-Group Pre-Test – Post test design

First a pretreatment measure of the dependent variable is taken ( 01), then thetest subjects are exposed to the independent treatment X, and there after a post treatmentmeasure of the dependent variable is taken(( 02). This design meets the threats to internalvalidity better than the One-Shot Case study. Continuing the example given above itmeasures the awareness of the employees before the campaign and after the campaign.This can be represented in the following manner:

O X O

Pre- test Manipulation Post-test

However, other aspects of internal validity like the history, maturation, testingeffect etc are not taken into account. Hence, it is still a weak design only.

3. Static Group Comparison

This design uses two groups; one receives the experimental stimulus and theother serves as a control group and is not given the treatment. The dependent variableis measured in both groups after the treatment. For e.g., in a field setting an experimentis designed to study the effect of a natural disaster (experimental treatment) on thepsychological trauma ( measured outcome). A pretest before the natural disaster say


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tsunami is possible but not on a large scale. Moreover, the timing of the pretest wouldbe problematic. The control group, receiving the post-test would consist of subjectwhose property is safe. The effect can be represented in the following manner:

X = O1

—————-

O2

The addition of a comparison group increases the validity over the previous two designs.However, there is no way to be certain that the two groups are equivalent.

2.6.2 True experiments/ Lab experiments

The lab experiments are conducted in order to ascertain the cause and effectrelationship between independent and dependent variables. In order to understand thecause-effect relationship all other variables which might contaminate the relationshipshould be controlled so that the actual cause effects of the investigated independentvariable on the dependent variable can be determined. Thus, the experiments performedin an artificial or contrived environment is known as lab experiments. In the labexperiments the researcher has complete control over all aspects. The researcher has acontrol over the experiment, who, what, when, where and how. The researcher canassign subjects to conditions randomly. Random assignment is an unbiased assignmentprocess that gives each subject an equal and independent chance of being placed inevery condition. Random assignment is preferable because it allows one to concludethat any other variable could be confounded with the independent variable only bychance. Control over what, where, when and how of the experiment means that theexperimenter has complete control over the way the experiment is to be conducted.

Terms used:

The following are the meaning of the terms normally used in experimental design:

Factors: The independent variables of an experiment are often called the factorsof the experiment. Active factors are those the experimenter can manipulate bycausing a subject to receive one level or another. Blocking factor is one wherethe experimenter can only identify and classify the subject on an existing level.

Level: A level is a particular value of an independent variable.

Condition: The term condition is used to discuss the independent variables. Itrefers to a particular way in which subjects are treated.

Treatment: this is another word used for condition. It also refers to the statisticaltest of the effect of various conditions of the experiment.


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Test unit: the experimental subjects are referred as test unit. The test unit maybe people, organizations, machine type, materials and other entities.

The basic principles of experimental research design are:

1. The existence of a control group or a control condition and

2. The random allocation of the subjects to groups

The internal validity is higher in case of lab experiments. Internal validity as alreadyexplained increases the degree of confidence in the casual effects. In lab experimentsthe cause-and –effect relationship are substantiated and hence the internal validity ishigher. However the external validity i.e., the extent of generalizations of study is lesseras the research is executed in a contrived environment and the real world situation maynot be same.

Some of the true experimental designs are discussed below:

1. Pre-test and post-test Control Group Design

Test subjects are assigned to the groups by a random procedure ( R ) to eitherthe experimental or control group. Each group receives a pretreatment measure of thedependent variable. Then the independent treatment is exposed to the experimentalgroup, after which both groups receive a post treatment measure of the dependentvariable. The concept is represented in the following manner:

R 01 X 02

R 03 04

The effect can be measured by

E = (02 - 01 ) – (04 - 03 )

The internal validity problems discussed earlier are addressed to a greater extentin this design. Local history may occur in one group and not in the other. Maturation,testing and regression are handled well as the same would be equally felt in experimentaland control groups. Selection is equally dealt by random assignment. Mortality can bea problem if there are different drop out rates in the study groups. The external validityof the design is however questionable.

2. Post- Test Only Control Group Design

Test subjects are randomly assigned to either the experimental or control group.The experimental group is then exposed to the independent treatment, after which bothgroups receive a post treatment measure of the dependent variable. In this design, thepretest measurements are omitted. The design is

R X O1

R O2


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The experimental effect is measured by the difference between O1 and O2. Thedesign is more simple and attractive. Internal validity threats from the history, maturation,selection and statistical regression are adequately controlled by random assignment.Since the subjects are measured only once, the threats of testing and implementationare handled. The different mortality rates between experimental and control groupscontinue to be a problem. The design reduces the external validity problem of testinginteraction effect.

3. Extensions of True Experimental Design

The researcher normally uses an operational extension to the basic design.These extensions differ from classical design in terms of

The number of different experimental stimuli that are considered simultaneouslyby the experimenter and

The extent to which assignment procedures are used to increase precision.

a. Completely Randomized Design

It involves two principles viz., the principle of replication and the principle ofrandomization. The essential characteristic feature of the design is that the subjects arerandomly assigned to experimental treatments. This design is generally used to whenexperimental areas happen to be homogeneous. In case of this design all the variationsdue to uncontrolled extraneous factors are included under the heading of chance variation.Two forms of such design are:

1. Two-group simple randomized design: In this design first all the populationis defined and then from the population a sample is selected randomly. Theselected sample is then randomly assigned to experimental and control groups.Thus, the design yields two groups as representatives of the population viz theexperimental and control group. The two groups are given different treatmentsof the independent variable. This design of experiment is quite common in researchstudies concerning behavioural sciences.

2. Random replication design: The effect of the extraneous variables is nottaken into consideration in the case of two-group simple randomized design. Inthe case of random replication design, the effect of the extraneous variables isminimized by providing a number of repetitions for each treatment. The repetitionis called replication. Random replication design provides control for thedifferential effects of the extraneous independent variables and also it randomizesany individual differences among those conducting the treatment


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b. Randomized block design

In the randomized block design the subjects are first divided into groups knownas blocks such that within each group the subjects are relatively homogenous in respectto some selected variable. The number of subjects in a given block would be equal tothe number of treatments and one subject in each block would be randomly assigned toeach treatment. This design is used when there is a single major extraneous variable.Random assignment is the basic way to produce equivalence among treatment group.Blocking is done to learn whether treatments bring different results among various groupsof subjects. In this design two effects could be studied

1. The main effect is the average direct influence that a particular treatment hasindependent of other factors.

2. The interaction effect is the influence of one factor on the effect of another.

The precision of this experimental design depends on how successfully thedesign minimizes the variance within blocks and maximizes the variance between blocks.If the response patterns are about the same in each block, there is little value to themore complex design and blocking in case may be counter productive. The randomizedblock design is analysed by the two-way ANOVA technique.

c. Latin Square Design

The randomized block design is used to minimize the effects of one extraneousvariable whereas the Latin square design is used when two blocking factors are to becontrolled. Each treatment occur an equal number of times in any one ordinal positionin each row. Treatments are randomly assigned so that each treatment appears onlyonce for each factor. Due to this aspect, the Latin square should have the same numberof rows, columns and treatments. For example, in order to find the effect of offeringdiscount at 5%, 10%, 15%, two blocking factors can be identified viz., customer incomeand size of the store. Three levels can be identified on the basis of the blocking factorCustomer income viz., high, medium and low. On the basis of store size, three levelscan be identified viz., large, medium and small. On the basis of both the blocking factorscustomer income and size of the store, nine groups can be identified. For each of thegroup one treatment will be given and in the row or column it will not be repeated. Thisis illustrated below:

Store size

Customer IncomeHigh Medium Low

Large X3 X1 X2

Medium X2 X2 X1

Small X1 X2 X3


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Treatments are assigned based on random number tables. From the above, the effectsof price reduction can be ascertained. The major limitation of Latin square is that it isassumed that there are no interaction between treatments and the blocking factors.

d. Factorial design

In case of factorial design a researcher can deal with more than one factorsimultaneously. This design is especially important in several economic and socialphenomena where usually, large number of factors affect a particular problem. Factorialdesigns can be of two types:

(1) Simple factorial designs

(2) Complex factorial designs

(1). Simple Factorial designs: When the effect of varying two factors on the dependentvariables is dealt, the design is called simple factorial design. This design is also knownas two-factor-factorial design. A simple factorial design may be either a 2 X 2 , 3 X 4or 5 X 3 or the like type of design. A 2 X 2 simple factorial design can be depicted asbelow:

Experimental variable

Control variables

Treatment A Treatment B

Level I Cell 1 Cell 3

Level II Cell 2 Cell 4

In this design extraneous variable to be controlled by homogeneity is called asthe control variable and the independent variable which is manipulated is calledexperimental variable.

(2). Complex factorial design: A design which considers three or more independentvariables simultaneously is called a complex factorial design. This is also known asmulti-factor factorial design.

2.6.3. Field Experiments: Quasi or Semi Experiments

Field experiment is done in natural environment, but treatments are given toone or more groups. It is not possible to control all extraneous variables in field conditionsas the stimulus condition occurs in a natural environment. This situation warranties afield experiment. In the quasi experiment it is not possible to know when or to whom toexpose the experimental treatment, however, when and whom to measure can bedetermined. A quasi experiment is inferior to a true experimental design but is usuallysuperior to pre-experimental designs.


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The results are more generalizable and so the external validity is more in case offield experiments. However, the internal validity is lesser as the extent, to which variableX alone causes variable Y, cannot be ascertained

A few quasi experiments are listed below:

1. Nonequivalent control group design

This is a strong and widely used quasi experimental design. The test and controlgroups here are not assigned randomly. The design is diagrammed as below:

O1 O2

———— x ———O3 O4

Two designs are possible viz., intact equivalent design and the self-selectedexperimental group design

In intact equivalent design members of the experimental and control groups arenaturally assembled. This design is useful when any type of individual selection processwould be reactive.

In the self-selected experimental group design the volunteers are recruited toform the experimental group, while the non volunteer subjects are used for control

Comparison of the pretest result (O1 – O2) is one indicator of the degree ofequivalence between test and control groups. If the pretest results are significantlydifferent, there is a real question about comparability. On the other hand, if pretestobservations are similar between groups, there is more reason to believe internal validityof the experiment is good.

2. Sepearate Pretest and post - test design

This design is most applicable in the situations when the researcher cannotknow when and to whom to introduce the treatment but can decide when and whom tomeasure. The basic design is

R O1 ( X )

R X O2

The bracketed treatment ( X ) suggests that the researcher cannot control the treatment.This is not a strong design because of several threats to internal validity are not handledadequately. History can confound the results but it can be overcome by repeatedexperiments. This design is considered to be superior to true experiments in external


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validity. The strength is that samples are drawn from general population which extendsthe generalization of the study

3. Group Time Series design

This design introduces repeated observations before and after the treatmentand allows subjects to act on their own control. The single treatment group design hasbefore and after measurements as the only controls. There is also a multiple design withtwo or more comparison groups as well as the repeated measurements in each treatmentgroup. This format is useful where regularly kept records are a natural part of theenvironment and are unlikely to be reactive. It is also a good way to study unplannedevents in an ex post facto manner.

2.7 MEASUREMENT

In normal parlance, measurement refers to an attempt to fix quantitatively theform or other features of a physical object. In research, measurement refers to assigningnumbers to empirical events in compliance with a set of rules. This definition brings outthe three steps involved in the process of measurement:

1. Selecting the observable empirical events.

2. Developing a set of mapping rules i.e. a scheme for assigning numbers or symbolsto represent aspects of the event being measured.

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

The goal of measurement is to provide the highest quality, lowest error data forthe purpose of testing the hypotheses identified and other related analysis andinterpretations. Variables dealt in research studies can be classified as objects andproperties. Objects include the things of ordinary experience, such as the laptop, chairand car. It also includes things which are not concrete such as attitude, peer grouppressures, perception etc. Properties are the characteristics of the objects. It includeslevel of motivation, leadership skills etc. Strictly speaking researchers are not involvedin measuring objects or properties but rather they measure the indicants of the propertiesor indicants of the properties of the objects.

2.7.1 Mapping rules

Measurement involves developing mapping rules and applying the same torecord the happenings. The assumptions regarding the mapping rules also affects thechoice of data. The mapping rules have the following four characteristics:

1. Classification: numbers are used to group or sort responses. Order does notexist


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2. Order: Numbers are arranged in some order in such a way that one number isgreater than /smaller than / equal to another number.

3. Distance: differences between numbers are ordered. The difference betweenany pair of numbers is greater than, less than or equal to the difference betweenany other pair of numbers

4. Origin: the number series has an unique origin indicated by the number zero

Combination of these four characteristics gives raise to data types.

2.7.2 The scales

Based on the characteristics of the mapping rules i.e., classification, order,distance and origin, four classifications of measurement scales could be arrived at:nominal, ordinal, interval and ratio scale. A detailed discussion follows:

1. Nominal scale

A nominal scale allows the researcher to assign subjects to certain categoriesor groups. For e.g., the respondents can be grouped as male and female. The twogroups can be assigned numbers for the purpose of coding and further analysis as 1 and2. These numbers are simple and convenient labels and have no intrinsic values. It onlyassigns subjects into either of the two mutually exclusive categories. In other words,nominal scale allows the researcher to collect information on a variable that naturally orby design can be grouped into two or more categories that are mutually exclusive andare collectively exhaustive.

The nominal scale provides only the basic, categorical, gross information.Counting of members in each group and calculation of frequency or percentage ispossible when nominal scale is employed. The researcher is restricted to the use ofmode as the measure of central tendency. One can conclude which category has moremembers. Chi-square test can be used to measure the statistical significance and formeasures of association, phi, lambda or other measures may also be appropriate.

Nominal scales are weak but they are still useful to classify the data. It is valuablein exploratory work where the objective is to uncover relationships rather that to secureprecise measurements. Nominal data type is also widely used in survey and ex postfacto research when data is classified by major subgroups of the population.

2. Ordinal scale

Ordinal scale indicates the order. It includes the characteristics of nominal scalealso. Thus an ordinal scale not only categorizes the variables but also rank-orderscategories in some meaningful way. The use of ordinal scale implies a statement of


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‘greater than’ or ‘less than’ or ‘equal to‘ without stating how much greater or less.Other descriptors may also be used viz., ‘superior to’, ‘happier than’, ‘poorer than’,‘above’. It is also possible to rank more than one property at a time. For e.g., researchercan ask the respondent to rank various air lines on the basis of certain properties.

In ordinal scaling the differences in the ranking of objects, persons or eventsinvestigated are clearly known. However, the ordinal data does not give any indicationof the magnitude of the differences among the ranks

3. Interval scale

Interval data has the power of the nominal and ordinal data and in addition itincorporates the concept of equality of interval. The interval scale allows to measurethe distance between any two points on the scale. It not only enables to group theindividuals according to certain categories and taps the order of the groups; it alsomeasures the magnitude of differences in the preferences among the individuals. Theinterval scale is more powerful than the nominal and the ordinal scales. The measure ofcentral tendency the arithmetic mean, is applicable. Its measures of dispersion are therange, the standard deviation and the variance.

4. Ratio scale

Ratio data has the power of the nominal, ordinal and interval scale in addition italso has the provision for absolute zero or origin. It covers the disadvantage of thearbitrary origin point of the interval scale, i.e., it has an absoultue zero point. The ratioscale not only measures the magnitude of the differences between points on scale butalso the proportion in the differences. Multiplication or division would preserve theratios. It is the most powerful of the four scales because it has a unique zero origin andsubsumes all the properties of the other three scales.

The measure of central tendency of the ratio scale could be either the arithmeticor the geometric mean and the measure of dispersion could be either the standarddeviation or variance or the coefficient of variation. Some examples of ratio scales arethose pertaining to actual age, income and work experience in organizations.

2.7.3 Sources of measurement differences

Normally, any variation of scores among the respondents would reflect truedifferences in their opinions about the object/issue. However, four major sources maycontaminate the results: the respondent, the situation, the measurer and the data collectioninstrument


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.1. The Respondent

Opinion differences that affect measurement come from relatively stablecharacteristics of the respondent. The skilled researcher will anticipate many of thesedimensions, adjusting the design to eliminate, neutralize or otherwise deal with them.However, the respondents may still suffer from temporary factors like fatigue, boredom,anxiety or other distractions which may limit their ability to respond accurately and fully.Likewise variations in mood due to hunger, impatience etc may also have an impact.

2. The Situational factors

Situational factors include any condition that places a strain on the interview ormeasurement session which may have a serious effect on the interviewer-respondentrapport. If another person is present during the interview or if the respondents believethat anonymity is not ensured then they may be reluctant to express their true feelings.

3. The Measurer

The interviewer can distort responses by rewording, paraphrasing or reorderingquestions. The tone used, the body language, smiles, nods and so forth may encourageor discourage replies. Likewise in data analysis stage, incorrect coding, careless tabulationand faulty statistical calculation may introduce errors.

4. The Instrument

A defective instrument can cause distortion in two major ways. First, the use ofcomplex words and syntax beyond respondents’ comprehension may cause confusionand ambiguity. Leading questions, ambiguous meanings, mechanical defects and multiplequestions suggest the range of problems.

2.7.4 The characteristics of sound measurement

The instrument developed to measure the concept should be an accurateindicator of the aspects that are being measured. The scale developed cannot be imperfectand prone to errors. The use of better instruments will ensure accuracy in results andwill enhance the scientific quality of research. It should also be easy and efficient touse. The ‘goodness’ of the measures developed should be assessed on the basis ofthree major criteria viz., validity, reliability and practicality.

I. Validity

Validity refers to the extent to which a test measures what we actually wish tomeasure. It refers to the extent to which differences found with a measuring tool reflecttrue differences among respondents being tested. Validity can be classified into threemajor forms viz., content validity, criterion-related validity and construct validity.


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1.Content Validity

The content validity refers to the extent to which a measuring instrument providesadequate coverage of the investigative questions guiding the study. Content validity isgood if the instrument contains a representative sample of the universe of subject matterof interest. Determination of content validity is judgmental and can be approached inseveral ways. Generally the content validity is treated to be higher , if the scale itemsused represents to a greater extent the domain or universe of the concept being measured.The researcher may determine the content validity through a careful definition of thetopic of concern, the item to be scaled and the scales to be used. Another way is to usea panel of persons to judge whether the instrument meets the standards.

Face validity is considered as a basic and very minimum index of content validity.It indicates that on the face of it the, items look as if they measure the intended concept.

2.Criterion-Related Validity

Criterion related validity reflects the success of measures used for prediction orestimation. Predictive validity refers to the extent to which an outcome could be predictedand concurrent validity refers to the extent to which estimate of current behaviour orcondition could be made. The researcher must ensure that the validity criterion used isitself ‘valid’. This can be judged in terms of four qualities viz., relevance, freedom frombias, reliability and availability.

3.Construct validity

This is the most complex and abstract feature. Construct validity testifies thatthe results obtained from the use of measure fits the theories around which the test isdesigned. In other words a measure has construct validity to the degree that it conformsto predicted correlations of other theoretical propositions. The researcher may wish tomeasure or infer the presence of abstract characteristics for which no empirical validationseems possible. Attitude, aptitude and personality scales generally fall in this category.Although it is difficult, assurance is still needed that the measurement has an acceptabledegree of validity. This is assessed through convergent and discriminant validity.Convergent validity is established when the score obtained with two different instrumentsmeasuring the same concept are highly correlated. Discriminant validity is establishedwhen, based on theory two variables are predicted to be uncorrelated and it is alsoempirically proved. The validity can be proved through the use of correlational analysis,factor analysis etc.

II. Reliability

Reliability refers to consistency i.e. a measure is reliable to the degree that itsupplies consistent results. Reliability is concerned with estimates of the degree to which


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measurement is free from random or unstable error. Reliable instruments can be usedwith confidence that transient and situational factors are not interfering. Reliableinstruments are robust and they work well at different times under different conditions.The reliability of an instrument is measured on the basis of the stability, equivalence andinternal consistency.

1.Stability

Stability is securing consistent results with repeated measurements of the sameperson with the same instrument. An observation is said to be stable if it gives the samereading on a particular person when repeated one or more times. Stability measurementin survey situations is more difficult then in observational studies. Observation can bedone repeatedly but the resurvey can be conducted only once. Two tests of stability aretest-retest reliability and parallel-form reliability.

(a)Test-Retest Reliability

The conduct of resurvey is called test-retest arrangement which involves comparisonsbetween the two tests to learn about the reliability. The reliability coefficient obtainedwith a repetition of the same measure on a second occasion is called test-retest reliability.When a questionnaire containing some items that are supposed to measure a concept isadministered to a set of respondents and if the same questionnaire is administered aftersome time again, then the correlation between the scores obtained at two differenttimes from the same set of respondents is called test-retest co-efficient. Higher thecoefficient, better the reliability and stability.

The following difficulties can occur in test-retest methodology;

Time delay in measurement may give rise to changes in situational factors

Insufficient time between measurements will enable the respondent to rememberprevious answer and repeat them resulting in bias.

Respondents’ discernment of a disguised purpose may introduce bias if therespondents hold opinion related to the purpose but not assessed with currentmeasurement questions.

Topic sensitivity occurs when the respondents seek to learn more about thetopic or form new and different opinions before the retest.

Introduction of extraneous moderating variables between measurements mayresult in a change in the respondents’ opinion from factors unrelated to research

(b)Parallel-Form reliability

Parallel form reliability occurs when two comparable sets of measures tappingthe same construct are highly correlated. The forms have similar items and the same


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response format. The wording and the order or sequence of the question is only changed.It is done in order to establish the error variability arising due to change in the wordingsor ordering of questions. High correlation between two forms ensures that the measuresare reasonably reliable, with minimal error variance caused by wording, ordering orother factors.

2.Equivalence

Equivalence is concerned with how much error may be introduced by differentinvestigators or different sample of items being studied. Equivalence is concerned withvariations at one point of time among observers and samples of items. One way to testfor the equivalence of measurements by different observers is to compare their scoringof the same event. One test for item sample equivalence is by using alternative or parallelforms of the same test administered to the same person simultaneously. The results ofthe two tests are then correlated.

The major interest with equivalence is typically not how respondents differfrom item to item but how well a given set of items will categorize individuals. Theremay be differences in responses between two samples of items, but if a person isclassified the same way by each test, then the test has good equivalence.

3. Internal consistency

The internal consistency indicates the homogeneity of the items in the course ofmeasuring a construct. The items should hang together as a set and should also becapable of independently measuring the same concept so that the respondents attachthe same overall meaning to each of the items. This can be ensured by examining if theitems and the subsets of items in the measuring instrument are correlated highly. Theinternal consistency among the items can be measured by using the split-half reliabilityand the inter-item consistency reliability test.

(a)Split-Half Reliability

Split-half reliability reflects the correlations between two halves of an instrument.This technique can be used when the measuring tool has many similar questions orstatements. The instrument is administered and the results are separated by items intoeven and odd numbers or randomly selected halves. When correlation is performed, ifthe results of correlation are high the instrument is said to have high reliability as regards-the internal consistency.

(b)Inter-item consistency reliability

It is a test of the consistency of respondent’s answers to all the items in ameasure. If the items are independent measures of the same concept, they will becorrelated with one another. The most popular test of inter-item consistency reliability


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is the Cronbach’s coefficient alpha. This test is used for multipoint-scaled items. Fordichotomous items the Kuder-Richardson formula is are used. Higher coefficients willresult in a better measuring instrument.

III Practicality

The operational requirements of the project require it to be practical. Practicalityis defined as economy, convenience and interpretability. Economy is concerned withminimizing the cost concerned with conducting the research project. The method ofdata collection, length of the instrument etc will have an implication on the researchbudget. Convenience refers to ease in administering the questionnaire. This can beachieved by giving clear and complete instructions and by paying proper attention todesign and layout. The interpretability issue arises in case when the persons other thanthe test designers must interpret the results. To enable interpretation, the designer of thedata collection instrument should provide enough information regarding the scoring keys,norms, guidelines for test use etc.

2.8 MEASUREMENT SCALES

Scaling is a procedure for the assignment of numbers (or other symbols) to aproperty of objects in order to impart some of the characteristics of numbers to theproperties in question. The numbers are assigned to indicants of the properties of objects.In case of measuring the attitude of respondents towards a new product introduced inthe market numbers may be assigned. ‘1’ may be assigned to positive attitude, ‘2’ toneutral and ‘3’ to negative attitude.

Measurement can be performed using standardized scales or through customdesigned scales. Standardized scales may be opted in case of measuring concrete objects.Developing customized scale is needed in the case where a researcher wants to measuremore abstract and complex constructs like the customer attitudes towards a new productintroduced in the market. In this case standardized scales may not exist. This situationwarrants the development of customized scales.

2.8.1 Selection of measurement scale

Selection or construction of a measurement scale requires decision in the followingsix key areas:

1. Study objective: Researchers may have two general study objective viz., tomeasure the characteristics of the respondents and to use respondents as judgesof the objects or indicants presented to them.

2. Response form: Three types of measuring scales viz., rating, ranking andcategorization can be used. Rating scale is used when respondents score anobject or indicant without making a direct comparison with another object or


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attitude. Ranking scales enable to make comparison among two or more indicantsor objects. Categorization enables to put the subjects involved in groups orcategories

3. Degree of preference: Measurement scales may involve preference measurementor non preference evaluation. In case of preference measurement respondentsare asked to choose the object preferred. In case of non preference evaluationthe respondents are asked to make judgments without any personal preferencetowards objects or solutions.

4. Data properties: The data properties should also be viewed in case of decisionregarding measurement scales. The data can be classified as nominal, ordinal,interval and ratio. The statistical application depends on the assumptionsunderlying each data type.

5. Number of Dimensions: Measurement scales can be unidemensional ormultidimensional. In case of unidimensional scale only one attribute of therespondent is measured. Multidimensional scaling recognizes objects asconsisting of n dimensions.

6. Scale construction: Five construction approaches are available viz., arbitrary,consensus, item analysis, cumulative and factoring. The researcher should takeinto consideration of both the type of measurement and the scale’s constructionwhen selecting an appropriate scale.

2.8.2 Methods of scaling

The methods of assigning numbers or symbols to the attitudinal responses ofthe respondents towards objects, events or persons is an important aspect of the research.There are two main categories of attitudinal scales - the rating scales and the rankingscale. Rating scales have several response categories and are used to elicit responseswith regard to the object, event or person studies. Ranking scales are used to makecomparison between or among objects, events, persons and elicit the preferred choicesand ranking among them.

1. Rating scales

Rating scales are used to judge properties of objects without reference to othersimilar objects. In rating scales, an object is judged in absolute terms against certainspecified criteria. The scale can be used to elicit responses with regard to an object,event or person studied.

The number of scale points may range from three to five or ten. Researchersbelieve that more points on a rating scale provide an opportunity for an accuratemeasurement of variance.


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Some of the rating scales used often by researchers are explained below;

The dichotomous scale offers two mutually exclusive response choices. Itmay be used to elicit a “Yes” or “No” answer, “agree” and “disagree” etc., Thisis useful to elicit responses for demographic questions or where dichotomousresponse is adequate. e.g., Do you have a credit card? Yes No

The category scale uses multiple items to elicit a single response. The multiplechoice, single-response scale is appropriate when there are multiple optionsbut only one answer is sought.

o Eg., Age - less than 20 years

o - 21 to 40 years

o - 41 to 50 years

o - Above 50 years

The check list or a multiple choice, multiple- response scale allows therespondent to select one or several alternatives. E.g., in eliciting the responseregarding the source through which the information about a new product isobtained, a respondent may select all or more than one of the choices givenbelow:

Source of information - Advertisement

- Sales person

- Sales materials

- Showrooms

- Friends/ relatives/ Neighbours

- Other sources

The Likert scale is designed to examine how strongly the respondents agreeor disagree with statements relating to the attitude or object on a 5-point scale.The scores on the individual items are summed to produce a total score for therespondent and hence it is also called summated scales. A Likert scale usuallycontains two parts, the item part and the evaluative part. The item part usuallycontains statement about a product, event or attitude. The evaluative part is alist of response categories ranging from “ strongly agree” to “strongly disagree”.The item and evaluative part are shown below:


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Strongly StronglyDisagree

Disagree Neutral Agree Agree

1 2 3 4 5

I am satisfied withthe working environmentI am happy with thework assigned

The responses over a number of items or statements tapping a particular conceptor variable are summated for every respondent. It is assumed that all the statementsmeasure some aspect of a single common factor. This is an interval scale and thedifferences in the responses between any two points on the scale remain the same.

The Semantic Differential Scales are used widely to describe the set ofbeliefs a person holds. Several bipolar attributes are identified at the extremesof the scale and respondents are asked to indicate their attitudes on semanticspace toward a particular individual, object or event on each of the attributes.The semantic space may consist of five or seven-point rating scales bounded ateach end by polar adjectives or phrases. There may be as many as 15 to 25semantic differential scales for each attitude or object. The procedure is alsoinsightful for comparing the images of competing brands, stores or services.The semantic differential also may be analyzed as a summated rating scale.Each of the scale is assigned a value from -3 to 3 or 1 to 7 and the scoresacross all adjective pairs are summed for each respondent. Individuals can becompared on the basis of the total scores. An example of semantic differentialscale is given below;

Responsive _ _ _ _ _ Unresponsive

Beautiful _ _ _ _ _ Ugly

Courageous _ _ _ _ _ Timid

The semantic differential has several advantages. It produces intervaldata. It is an efficient and easy way to elicit responses from a large sample. Theattitudes can be measured both in terms of direction and intensity. The total setof responses provides a comprehensive picture of the meaning of an object. Itis a standardized technique which can be easily repeated and at the same timeescapes many problems of response distortion.

The Numerical scale is similar to the semantic differential scale with thedifference that numbers on a 5 point or 7 point scale are provided with bipolaradjectives at both ends. This is also an interval scale. The scale provides bothan absolute measure of importance and a relative measure of the various items


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rated. The scale’s linearity, simplicity and production of ordinal or interval datamakes it very popular. An example :

Extremely Pleased 7 6 5 4 3 2 1 Extremely displeased

The itemized rating scale is a 5 point or 7 point scale with anchors providedfor each item and the respondent states the appropriate number on the side ofeach item or circles the relevant number against each item. The responses tothe items are then summated. This uses an interval scale . Example is shownbelow;

Indicate your response number on the line for each item.

1 2 3 4 5

Very Unlikely Unlikely Neither Unlikely Likely Very Likely nor likely

1. I like to take more responsibility —

2. If additional responsibility is not provided I will be dissatisfied —

3. I am interested in a job which provides me more salary —

The itemized rating scale provides the flexibility to use as many points in the scale asconsidered necessary ranging from 4,5,7,9, etc., It is also possible to use differentanchors. When a neutral point is provided, it is a balanced rating scale. When a neutralpoint is missing it is an unbalanced rating scale. The itemized rating scale is frequentlyused in business research as it adapts to the number of points desired to be used , aswell as the nomenclature of the anchors can be accommodated to suit the needs of theresearcher.

In Fixed or constant sum scale the respondents are asked to distribute a givennumber of points across various items. It enables the researcher to discover theproportions and is more in the nature of ordinal scale. A minimum of two categoriesand a maximum of ten can be presented to the respondents. Presenting too manystimuli will be a hindrance to the precision and the patience of the respondents. Arespondent’s ability to add is also taxed if too much of stimulus is provided. Forexample in selecting a particular brand of computer a respondent may be asked torate the following aspects;


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Hardware configuration -----

Freebies given -----

Brand image ----- ------------

Total points 100 ------------

Staple Scales are simplified version of semantic differential scales. It is usedwhen it becomes difficult to find bipolar adjectives that match the investigativequestions. It uses only one pole rather than two. Respondents are asked to indicatethe object by selecting a numerical response category. The higher the positivescore the better the adjective describes the object. Similarly , the less accuratethe description, the larger the negative number chosen. Ratings may range from +3 to -3, or + 5 to -5 , very accurate to very inaccurate. It produces interval data.It is easy to administer and construct as there is no need to provide adjectives toassure bipolarity. For eg, the respondents may be asked to rate their job usingstaple scales as follows:

+3 +3 +3

+2 +2 +2

+1 +1 +1

Challenging suits my skill satisfactory

-3 -3 -3

-2 -2 -2

-1 -1 -1

The graphic rating scale is simple and commonly used in practice. In thisscale various points are marked along the line to form a continuum. The respondentindicates his rating by simply making a mark at the appropriate point on a line that runsfrom one extreme to the other. A brief description on the scale points are given to act asa guide in locating the rating. The faces scales depicting faces ranging from smiling tosad can be used on a rating scale to obtain responses regarding people’s feelings withrespect to some aspect. A major limitation of this scale is that the respondent may selectalmost any position on the line which will pose difficulty in analysis.

The Consensus scale – as the name suggests is developed by consensus by apanel of judges. The judges select certain items which enable to measure a concept.The items are selected based on pertinence or relevance to the concept. The items are


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also tested for validity and reliability. For eg. Thurstone Equal Appearing Interval Scaleis a consensus scale. A panel of judges selects the statements which describe the conceptunder study. The scale is developed based on the consensus. Developing this scaleinvolves time and as such is rarely used in the organizational concept.

Errors in Rating scales

The respondent’s rating should be evaluated taking into consideration the followingthree types of errors; leniency, central tendency and halo effect.

(1) The error of leniency occurs when the respondent is either a ‘easy rater’ or‘hard rater’. Respondents are inclined to give higher score to people they knowwell. The opposite is also possible where a lower score may be given.

(2) The central tendency refers to the respondents reluctance to give extremejudgments which will lead to the error of central tendency. This happens becausethe respondent may not know the object or property being rated.

(3) The halo effect happens because of carrying over a generalized impression ofthe subject from one rating to another. Halo is a pervasive error. It is difficult toavoid when the property being studied is not clearly defined, not easily observed,not frequently discussed, involves reactions with others or is a trait of highmoral importance.

2. Ranking scales

The ranking scales are used to tap the preferences of respondents among twoor more objects and make choices among them. The respondents are usually asked toselect the ‘best’ or ‘most preferred’. This approach is satisfactory when there are onlytwo choices involved. When more than two choices are present, it results in ties. Fore.g., in a response if 40 % choose product A , 35% choose Product B and 25%choose product C, then it cannot be concluded that ‘A’ is the most preferred productbecause 60% did not prefer ‘A’. Three techniques can be used to avoid this ambiguityviz., Paired Comparisons, forced choice and the comparative scale. A brief discussionfollows;

Paired comparison scale

The paired comparison scale is used when the respondents are expected toexpress attitudes or choice between two objects at a time. It helps to assess thepreferences. In the previous example, the preference for Product A over B and C willenable to make better decision. However, as the number of objects to be comparedincreases the number of paired comparisons also increases. The paired choices for nobjects will be [(n)(n-1)/2]. When three products are presented to the respondents the


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number of paired comparison would be three [(3)(3-1)/2]. If the number of products isfour then the number of paired comparisons would be six. More the number of objectsmore will be the number of paired comparisons presented to the respondents. Pairedcomparison is a good method, if the number of objects to be compared is small. If toomuch of comparisons are to be made then the respondents may become tired andprovide wrong answers or refuse to continue. It is suggested that 5 or 6 stimuli are notunreasonable if other questions are to be accompanied with the comparisons. If pairedcomparisons are only to be dealt with, then upto 10 stimuli can be accommodated.

Forced Ranking scale

Forced ranking scale is easier and faster compared to the paired comparisonmethod. It requires the respondents to rank a list of attributes. It enables respondentsto rank objects relative to one another among the alternatives provided. It is moresuitable in case where the number of alternatives to be ranked is limited in number.

For eg. rank the following newspapers in the order of preference

The Hindu -

Business Line -

Indian Express -

If the number of stimuli to be ranked is 5 or less, then it is comparatively aneasy task. The respondents may be normally careless if the items exceed 10.

Comparative scale

It involves a standard against which comparison is done. The comparison scaleprovides a point of reference against which the current object under study is compared.It enables benchmarking. However this can be used only when the respondents havethe knowledge regarding the standard against which comparison is made. The researcherscan treat the data produced by comparative scales as interval data since the scoresreveal the interval between the standard and the actual. It can also be treated as ordinaldata as the rank or position of the items are dealt with.

2.8.3 Construction of measurement scales

Five techniques are available to construct the measurement scales viz., Arbitraryapproach, Consensus Item analysis, Cumulative methods and Factoring. They areexplained below;

1. Arbitrary scaling

Arbitrary scales are developed on ad hoc basis. It is largely based on researcher’sown subjective selection of items. Several items which are appropriate and unambiguous


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to the theme of study may be selected. Each item is scored from 1 to 5 depending onthe responses obtained. The results are then totaled. Arbitrary scales are easy to develop,inexpensive and highly specific to the theme of the study. However the major limitationis that the design approach is subjective. There is no assurance other than researcher’sinsight that the items chosen are representatives of the universe of content.

2. Consensus Scaling

In consensus scale the items are selected by a panel of judges after evaluationon the basis of some criteria like - relevance to the topic area, the risk of ambiguity andthe level of attitude represented by the items. This approach is widely known asThurstone equal appearing Interval Scale. The procedure followed in construction ofthe scale is described below

(i) A large number of items/statements expressing different degrees offavourableness towards an object relating to the subject of the study, usuallymore than twenty are collected by the researcher.

(ii) A panel of judges evaluates the statements. The statements are written in thecard. One statement is written in each card. The judges sort each card into one ofthe 11 piles representing the degree of favourableness the statement expresses.

(iii) The sorting yields a composite position for each of the items. In case of disagreementbetween the judges, the item is discarded.

(iv) For the items that are retained, median scale value between one and eleven is assigned.

(v) A final selection of statements are made on the basis of the median score. Ofthe 11 piles 3 are identified by the judges as ‘favourable’ , ‘unfavourable’ and ‘neutral’.The eight intermediate piles are unlabelled.

The Thurstone method is widely used for developing differential scales to measureattitudes. The scale is more reliable for measuring a single attitude. This method ofconstruction involves cost, time and people and hence it is impractical. The values areassigned to the items by judges which is subjective.

3. Item Analysis scaling

In Item analysis scaling, an item is evaluated on the basis of how well itdiscriminates between those persons whose score is high and those whose total scoreis low. It involves calculating the mean score for each scale item among the low scorersand high scorers. The item means between the high-score group and the low-scoregroup are then tested for significance by calculating t values. Finally the items that havethe greatest t values are selected for inclusion in the final scales.


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Summated scales or Likert scales are developed by the item analysisapproach. Summated scales consist of a number of statements which express eitherfavourable or unfavourable attitude towards an object to which the respondents isrequired to react. The respondents indicate the agreement or disagreement with eachof the statement. Each response is given a numerical score and the total is obtained tomeasure the respondents’ attitude. The procedure for developing a Likert –type scaleis described below;

(i) A large number of statements relevant to the object being studied is collected.The statements express definite favourableness or unfavourableness towardsthe subject

(ii) A trial test can be conducted with a small group of respondents who formpart of the final study. The agreement or diasagreement towards eachstatement is obtained on a five point scale.

(iii) The response is scored in such a way that the response indicating the mostfavrourable attitude is given the highest score of 5 and the most unfavourableattitude is given the lowest score ‘1’.

(iv) The total score of each respondent is obtained by adding the score foreach individual statements

(v) The next step is to array the total scores and find out those statementswhich have a high discriminatory power. For this purpose the researchermay select some part of the highest and the lowest total scores, for eg, top25 percent and bottom 25 percent. These two extreme groups areinterpreted to represent the most favourable and the least favourable attitudesand are used as criterion groups by which to evaluate individual statements.Thus the statements which consistently correlate with low favourability andwith high favourability are identified.

(vi) The statements which correlate with the total test are retained in the finalinstrument and all others are discarded.

The advantages of Likert scale is that it is relatively easy to construct, consideredto be more reliable and less time consuming. One of the major limitations is thatthe scale simply examines whether respondents are more or less favourabletowards the subject under study, but it cannot reveal how much more or lessthey are. There is no basis for belief that the five positions indicated on the scaleare equally spaced.


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4. Cumulative scales

Cumulative scales consist of series of statements to which a respondent expresseshis agreement or disagreement. The special feature in this scale is that it forms a cumulativeseries. The statements are related to one another in such a way that an individual whoreplies favourably to Item No.3 also replies favourably to Item no. 2 and 1. An individualwhose attitude is at a certain point in a cumulative scale will answer favourably all theitems on one side of this point and answer unfavourably all the items on the other side ofthis point. The individual’s score is arrived at by counting the number of points concerningthe number of statements answered favourably. If the total score is known, it is easy toestimate the respondents’ answer to individual statements constituting the cumulativescales. A major scale of this type is the Guttman’s scalogram.

Scalogram analysis refers to the procedure for determining whether a set ofitems forms a unidimensional scale. A scale is unidimensional if the responses fall into apattern in which endorsement of the item reflecting the extreme position results also inendorsing all items which are less extreme. Under this technique, the respondents areasked to indicate in respect of each item whether they agree or disagree with it. If theitems form a unidimensional scale , the response patter will be in the following manner

Item Number Respondent score

3 2 1

X X X 3

_ X X 2

_ _ X 1

_ _ _ 0

A score of ‘3’ means that the respondent agrees with all the statements which ispositive or expresses favourable attitude. A score of ‘2’ reveal that the respondentdoes not agree with the third statements but agrees with all other statements. In thisway, the scores can be interpreted.

The procedure for developing a scalogram is described below;

(i) The issue or concept or subject under study must be clearly defined

(ii) A number of items relating to the subject under study is to be developed.Care should be devoted to eliminate the items that are ambiguous orirrelevant.

(iii) The next step is to pretest the items to determine the scalability. The pretestshould include a minimum of 12 items and should be administered on atleast


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20 respondents. In the pretest, the respondents’ opinions are obtained on5 point Likert scale. The most favourable response is scored as 5 and theleast favourable response is scored as 1. If there are 10 items then the totalscore can range between 5 and 50. For the purpose of analysis andevaluation, the respondents opinions are arrayed according to the totalscore. If the responses of an item form a cumulative scale, the responsecategory scores should decrease in an orderly fashion as indicated below.Failure to follow the decreasing pattern revels an overlapping and showsthat the item is not a good cumulative scale item.

Scale type Item Errors Number of9 6 2 5 7 Per case errors

4(perfect) X X X X X 0 0

3(perfect) - X X X X 0 0

nonscale - X - X X 1 1

nonscale - X X - X 1 2

3(perfect) - - X X X 0 0

2(perfect) - - - X X 0 0

1(perfect) - - - - X 0 0

nonscale - - X - - 2 2

0(perfect) - - - - - 0 0

(iv) The total scores of the various opinions are obtained. The order is thenshifted such that it results in a reduced number of items. The above exampleshows that five items ( 9,6,2,5 and 7) are selected for final scale. Perfectscales are those in which the respondents answers fit the pattern that wouldbe reproduced by using the person’s total score. Non-scale types are thosein which the category pattern differs from that expected from therespondent’s total score ie non scale items have deviations fromunidimensionality or errors. The selection of an item in the final unidimensionalscale is made on the basis of the coefficient of reproducibility. Guttman hasset 0.9 as the level of minimum reproducibility in order to select a scale.The following formula is used for measuring the reproducibility:


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Guttman’s Coefficient of Reproducibility = 1 – e/n(N)

e = number of errors

n = number of items

N = Number of cases

5. Factor scales

Factor scales include a variety of techniques that has been developed to addresstwo issues viz, the problem of dealing with the universe of content that is multi dimensionaland the problem of uncovering the underlying dimensions that has not been identified bythe exploratory research. Factor scales are developed through factor analysis or on thebasis of intercorrelations of items which indicate the common factor responsible for therelationships between items. The techniques are designed to intercorrelate items so thatthe degree of interdependence may be detected. An important factor scale based onfactor analysis is semantic differential scale and multi dimensional scales. They arediscussed below:

(a) Semantic Differential Scale

The semantic differential scale (S.D) was developed by Osgood and hisassociates to measure the psychological meaning of an object to an individual. Thescale is made on the presumption that the object under study can have different dimensionsof connotative meaning which can be located in multidimensional property space or inthe semantic space in the context of S.D scale. The scaling consists of a set of bipolarrating scales, usually of 7 points on the basis of which the respondents rate each concepton the scale item. An example of the scale being used by a panel of corporate leadersto rate the candidates for a leadership position are shown below. Three factors contributeviz., evaluation (E), potency (P) and activity (A) are considered.

(E) Sociable (7) ____ ____ ____ ____ ____ (1) Unsociable

(P) Strong (7) ____ ____ ____ ____ ____ (1) Weak

(A) Active (7) ____ ____ ____ ____ ____ (1) Passive

(E) Progressive (7) ____ ____ ____ ____ ____ (1) Regressive

(P) Tenacious (7) ____ ____ ____ ____ ____ (1) Yielding

(A) Fast (7) ____ ____ ____ ____ ____ (1) Slow

The nature of the problem determines the selection of dimensions and bipolarpairs. The SD scale is adapted to each research problem. The construction of SD scaleinvolves the following steps:


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(1) The concept to be studied, is selected based on personal judgment. It shouldreflect the nature of the problem.

(2) The next step is to select the scales. In selecting the scale, the scale’s relevanceto the concepts being judged and factor composition should be kept in mind.Atleast three bipolar pairs for each factor should be taken into consideration tomeasure the evaluation, potency and activity. The scale should also be stableacross subjects and concepts.

(3) A panel of judges are used to rate the various stimuli on the various selectedscales and the responses of all the judges are combined to determine thecomposite scaling.

The Semantic differential scale is efficient and easy way to secure attitude froma large sample. The scale measures both the direction and intensity. The testcan be easily repeated as it is a standardized technique.

(b) Multidimensional scaling(MDS)

Multidimensional scaling is relatively a more complicated scaling device whichcan be used to scale objects, individuals or both with a minimum of information. Itenables to provide visual impression of the relationship between variables. MDS isused when all the variables both metric and non metric are to be analyzed in a studysimultaneously and all such variables happen to be independent. The data handlingcharacteristic of MDS provides several options: ordinal input, fully metric and nonmetric modes. The various techniques use proximities as input data. Proximity is anindex of perceived similarity or dissimilarity between objects. The respondents areasked to judge in pairs the possible combinations regarding their similarity. Throughcomputer program, the ranked or rated relationship is represented as points on a mapin multi dimensional space. For example, if respondents are asked to identify similarproducts among a group of products and if product X and Y are similar, MDS techniquewill position X and Y in such a way that the distance between them in multidimensionalspace is shorter than that between any two other objects.

Two approaches viz., the metric approach and the non-metric approach areavailable in the context of MDS. The metric approach to MDS treats the input data asinterval scale data. The non-metric approach first gathers the non-metric similarities byasking respondents to rank order all possible pairs that can be obtained form a set ofobjects. Such non-metric data is then transformed into some arbitrary metric space andthen the solution is obtained by reducing the dimensionality.

The MDS enables the researcher to study the perceptual structure of a set ofstimuli and the cognitive process underlying the development of this structure. It enables


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perceptual mapping in a multidimensional space. However MDS is not widely usedbecause of the computational complications involved.

SUMMARY:

This unit examined the meaning and the various types of experimental design.The various activities involved in conducting the experiment were discussed. The variousfactors affecting the internal and the external validity were dealt. The pre-experiments,true/lab experiment and the field experiments were covered. The various rating andranking scales were discussed. Construction of arbitrary, cumulative, consensus, itemanalysis and factor scales were examined.

Equipped with the knowledge on the research design and measurement scales,the next unit presents the various data collection methods, sampling techniques and theparametric and non-parametric tests available to test the hypothesis.

Have you understood?What is experimental design? Elucidate the benefits and drawbacks

What essential characteristics distinguish a true experiment from other researchdesign?

Distinguish between the following:

o Internal validity and external validity

o Pre-experimental and quasi-experimental design

o History and maturation

o Random sampling, randomization and matching

o Active factors and blocking factors

What is internal validity and what factors affect the same?

What factors contribute to the external validity?

Discuss the different experimental designs. Illustrate the same.

A retail grocery chain wants to study the effects of the various levels of advertisingeffort and price reduction on the sale of specific branded grocery products.What type of experimental design would you recommend? Suggest in detail thedesign for the study.

What are the essential differences among nominal, ordinal, interval and ratioscales? How do these differences affect the application of statistical techniques?

What are the four sources of measurement error? Illustrate by example howeach of these might affect the measurement results in a face-to-face interview.


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How is the interval scale more sophisticated than the nominal and ordinal scales?

Why is ratio scale considered to be the most powerful of the four scales?

How do you ensure the soundness of a measure?

Describe the difference between the rating scales and ranking scales and indicatethe application areas where they can be used

Discuss the relative merits and problems with:

o Rating and ranking scales

o Likert and differential scales

o Unidimensional and multidimensional scales

Discuss the techniques of constructing the measurement scales with examples.


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Data collection MethodsUnit structure:

3.1. Introduction

3.2 Learning objectives

3.3. Sources of data

3.3.1 Primary data sources

3.3.2 Secondary sources

3.3.3 Tertiary sources

3.4. Methods of data collection

3.4.1 Interviews

3.4.2 Observation

3.4.3 Questionnaires/interview schedules

3.4.4 Other methods

3.5. The basics of sampling

3.5.1 Why sampling?

3.5.2 Steps in sampling

3.5.3 Characteristic features of a good sample

3.6 Types of sampling

3.6.1 Probability sampling

3.6.2 Non probability sampling

3.7 Determination of Appropriate Sampling Design

3.8 Sampling decisions: Some Issues

3.8.1 Precision and Confidence in sample size estimation

3.8.2 Sample data and hypothesis testing

3.8.3 Determining the sample size

Unit - 3


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3.1 INTRODUCTION

Once the problem is defined and research design is finalized, the various sourcesof data and the ways in which it can be collected for the purpose of analysis, testing ofhypotheses and answering research questions should be explored. Data collection couldbe made through the primary, secondary and tertiary sources which are dealt in detail.This unit also highlights the sources of data and the methods of collecting the same. Thedata cannot be collected always form the entire population due to various reasons likedifficulty in estimating the population, cost constraints, time etc., Sampling techniquehas to be adopted by a researcher for collection of data. This unit provides a detailedaccount of the probability and the non probability sampling techniques. The issuesregarding determination of sample sizes are also presented.


After completing this unit you will be able to:

• Know the difference between primary and secondary data and their sources

• Understand the various data collection methods and the advantages anddisadvantages of each method

• Know the criteria to be considered in making decisions regarding the methodof data collection

• Understand the need for sample

• Understand the different types of sampling designs

• Identify the appropriate sample design for different research purposes

• Describe the issues to be considered in determining the sample size

3.3 SOURCES OF DATA

Data sources can be broadly categorized into three types viz., primary,secondary and tertiary.

3.3.1 Primary data sources

Primary data refers to information gathered firsthand by the researcher for thespecific purpose of the study. It is raw data without interpretation and represents thepersonal or official opinion or position. Primary sources are most authoritative since theinformation is not filtered or tampered. Some examples of the sources of primary dataare individuals, focus groups, panel of respondents, internet etc. Data collection fromindividuals can be made through interviews, observation etc.


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Focus groups

Focus group involves a formalized process of bringing small group of peopletogether for an interactive and spontaneous discussion on any one particular topic.Focus group generally consists of 6 to 12 participants with a moderator leading theunstructured discussions which can last between 90 minutes to two hours in general. Byfacilitating the discussions the moderator elicits as many ideas, attitudes, feeling andexperiences as possible regarding the concerned issue. Participants are generally chosenon the basis of their expertise in the topic on which the information is sought.

The goal of conducting focus group is to give researchers the access to asmuch information as possible regarding the product, service concept or organization.The focus group does not restrict to only asking and answering questions. The successof focus group relies to a greater extent on the group dynamics, the willingness of theparticipants to engage in interactive dialogue and the ability of the moderator to keepthe discussion on the track. The fundamental idea behind the focus group is that oneparticipant’s remark or response may initiate comments and discussions from the otherparticipant thus generating spontaneous and free interplay among all the participants.Focus groups are relatively inexpensive and can provide access to dependable datawithin a short period of time.

Focus groups objectives

The objectives of forming focus groups are listed below:

The focus groups provide data for defining and refining the problems. In situationswhere it is difficult for the researcher to pinpoint the specific problems, thefocus group aids to differentiate between symptoms and root cause problems.

In certain situations, researchers may not be sure about the specific types ofdata or information that should be investigated. In these situations, focus groupsreveal unexpected components of the problem and thus can help researchersto determine the specific data that are to be collected.

There are situations when the quantitative research investigations leads to resultswhich are not understandable or explainable. In such situations the focus groupenables to provide data for a better understanding of results derived fromquantitative studies.

Focus group interviews provide researchers excellent opportunities to gaininsight into the respondents hidden needs, wants, attitudes, feelings andbehaviours. It opens the door to realism.


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The general interactions and discussions among the focus group membersenables to generate new ideas, products or services or innovative ways ofsolving problems which are unexplored hitherto.

The focus group plays a critical role in the process of developing new constructsand creating reliable and valid measurement scales. In the exploratory stage,the focus group reveals additional insights into the underlying dimensions thatmay or may not make up the construct. This insight can help researcher todevelop scales that can be later tested and refined through larger survey researchdesigns.

Conducting focus group interviews:

The process of conducting focus group interviews can be divided into threelogical phases: planning, conducting the discussion, analyzing and reporting the results.

I. Planning the Focus group study

Planning phase is critical for the conduct of a successful focus group interview.The researcher should have a clear idea of the purpose of the study, problem definition,and specific data requirement. In the planning phase the decision regarding the type ofparticipants in the focus group, the process of selecting and recruiting them, the size ofthe focus group and the location of the focus group session should be made. Theseaspects are discussed below:

Focus group participants

The decisions regarding the type of participants must be made in tune with thepurpose of the study. The focus group should be made as homogenous as possible, butit should also provide room for variations so as to encourage contrasting opinions.Important factors to be considered in the selection process are the potential groupdynamics and the willingness of members to engage in dialogue. The knowledge levelof the participants regarding the topic to be discussed should also be considered in theselection process.

Selection and recruitment of participants

To select the participants for a focus group, the researcher should develop ascreening form. The screening form should access the characteristics, the respondentmust possess in order to qualify as a prospective participant. Researcher should alsochoose a method of reaching the prospective participants. They can use the existingparticipants list, on-location interviews, snowball sampling, random telephone screening,placing advertisements in newspapers and on bulleting boards.


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The issue of sampling, needs to be addressed carefully while planning for thefocus group. Random sampling may eliminate bias and produce dependable conclusions .However, it may not be possible or necessary in certain situations. For example, inqualitative research, a more flexible research design can be followed.

Recruitment involves the process of securing willingness from the participantsto be a part of the focus group. It is not an easy task, however, professional recruiterswho are good in interpersonal communication skills and social skills can be identified toperform the task successfully. The recruiters should clearly communicate the importanceof the topic and specify the date, starting and ending time, location, incentives forparticipating and method of contacting the recruiter. In order to reinforce the participantscommitment to participate in the focus group, the researcher should send out a formalcommunication/invitation letter incorporating all the important information about themeeting. The recruiter should also remind the participants of the focus group meetingthe day before the scheduled meeting.

Size of the focus group

The optimal number of participants in the focus group interview may vary from6 to 12. In case of less number of participants, creating the right type of group dynamicsmay be difficult and a few people may dominate the others. Too many participants willalso limit each person’s opportunity to contribute to the discussions. However, theresearcher should provide room for the fact that some of the participants who haveagreed may not turn for the discussion. Incentives may be provided to motivateparticipants.

Focus group locations

The focus group sessions can last between 90 minutes to two hours. The locationassumes importance because of the length of the discussions. The location should becomfortable, spacious, uncrowded and conducive to elicit spontaneous, unrestricteddialogue among all the group members. Depending on the resource constraints thefocus group may be conducted in locations like conference rooms, office or hotel rooms.However, the ideal location would be specially designed rooms with facilities like largetable, comfortable chairs, relaxed atmosphere, build in audio equipment and a one-way mirror so that the researcher can view and hear the discussion without interferingor being seen. Video equipment should also be available to capture the nonverbalcommunications and behaviour during the course of discussion.

II. Conducting the focus group discussions

The focus group is conducted by a moderator. The moderator’s communication,interpersonal, probing, interactive and observation skills plays a major role in the


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successful conduct of the focus group discussions. The moderator should be able tostimulate and control the focus group discussions over the predetermined topics in askillful manner. He should be able to draw the best and most innovative ideas from theparticipants regarding the topic or problem under discussion. The moderator isresponsible for creating a positive group dynamics and a comfort zone between himselfand each group member as well as among the group members.

The moderator should have enough background knowledge regarding the topicof discussion. Apart from skill set discussed above, moderating the session requiresobjectivity, self-discipline, concentration and careful listening. The moderator shouldbe completely prepared with the questioning route yet should allow flexibility dependingon the situation.

The actual conduct of the focus group discussion can be arranged under threephases viz, opening the session, main session and closing the session. They are dealtbelow:

Opening the session

The moderator should warmly receive the participants and make them feelcomfortable. The participants should be instructed to write their names in the namecards. A few minutes should be allowed for socializing before seating the participantsso that a warm, friendly and congenial environment can be set. The socializing sessioncan be used by the moderator to observe the participants and place them in groups.The moderator should discuss the ground rules for the session; one person should onlytalk at a time, each one should be given a chance, brief about the purpose of the sessionand so on. The moderator can begin the discussion with an open question designed toengage all participants in the discussion. This breaks the ice and enables to build apositive group dynamics and comfort zone.

The main session

The topic area is introduced in the main session and as the discussion starts themoderator gears the direction using probing techniques to get as many details as possible.As there is no hard and fast rules regarding how long a discussion can be carried out themoderator should use his judgment in deciding when to close one topic and move to thenext. The critical question should be provided with more time so that the ideas, feelingand thoughts can be elicited to the maximum.

Closing the session

After covering all the topics for which the focus group is formed, the sessioncan be wound up. In this process the moderator can summarize the conclusion around


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at the discussion and also invite the closing comment from the participants regardingfurther contributions or disagreements over certain ideas. If nothing else arises, themoderator can close the discussion after thanking the participants and distributing thepromised incentives.

III. Analyzing and reporting the results

For the purpose of analyzing the happenings in the focus groups session twotechniques are available viz., debriefing analysis and content analysis. Debriefing analysisis an interactive procedure in which the researcher and moderator discuss the subjects’responses to the topics that outlined the focus group session. Insight and perceptionscan be expressed concerning the major ideas, suggestions, thoughts and feelings fromthe session. Ideas for improving the session can be uncovered and applied to furtherfocus group sessions.

Content analysis is a systematic procedure of collecting individual responsesand grouping them into larger theme categories or patterns. It is the most widely usedformalized procedure by the researchers in an effort to create data structures fromfocus group discussion. From the group discussion session responses are recordedand translated. The researcher reviews the raw responses and creates data structuresaccording to the common patterns. The process requires several analysis and interpretivefactors.

For creating the report, the researcher should first understand the audience, thepurpose of the report and the expected format. The report should be clear,understandable and should provide data to support findings. The researcher shouldinclude quotations, examples wherever needed. The report should be presented in alogical sequence. The fact that the report will be a historical record should also be givendue consideration.

Advantages and disadvantages of focus group interviews

The following are some of the advantages of conducting focus group interviews:

The spontaneous and unrestricted interaction among the participants give raiseto new ideas, thoughts and feelings which cannot be elicited in an one to oneinterview. The respondents will provide creative and honest opinion. Theconducive environment enhances creativity.

The underlying reason to the attitude, feeling, emotions, behaviour etc can bedealt as in case of the focus groups discussion.

The researcher will have a first hand information and the opportunity to beinvolved in the overall process right from starting the focus group till closing it.


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This gives an in depth insight into the various dimensions of the problem whichare hitherto unexplored.

Focus group interview can cover a number of topics. The discussion can bedirected at successfully over a number of issues.

It enables to bring together participants from diverge segments which willotherwise be difficult.

The disadvantages of the focus group interview are dealt below:

Identifying the participants and gathering them together in a location is difficult

The data structures developed from focus group interview cannot be as suchapplied to the target population. The generalization of the research findings arequestionable.

The researcher has only limited ways to substantiate the data reliability. Addedto this the data collected from the participants may not be structured andamenable to further statistical inferences.

The data collected from the focus group can be subjectively interpreted by theresearcher according to the researchers’ preconceived views. The bias willreduce the credibility and trustworthiness of the data and the information derived.

The cost per participant in terms of identifying, recruiting and compensating arerelatively quite high.

Online focus group

One of the major problems in collecting data through focus group interview isgathering the participants in a particular location. This issue can be solved by administeringonline focus group interviews. The developments in internet, telecommunication andcomputer technological advancements have led to the online focus group. In onlinefocus groups the researcher or moderator and the participants meet and conduct theinterview across the Internet on a real time basis. The participants need not be physicallygathered in a common location for conducting the session. E-mail, websites and Internetchat rooms facilitate online focus group discussions. However, the cost of collectingdata through online focus groups is high.

Panels

Panels refer to the sample of individuals, households or firms from whominformation may be collected in successive time periods. Like the focus group, panelsenable us to collect primary data. However, the focus groups meet for a one-timegroup session, panels meet more than once. Panel studies are useful to track the effectof changes over a period of time.


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The members of the panel are randomly chosen. They may be exposed to anadvertisement or their attitude towards a particular brand may be recorded. After a fewdays or month the panel may be exposed to a different set of advertisement or theirattitude may be measured again to identify the changes in the behaviourial pattern.Thus, the continuing set of members form the sample base or the platform for assessingthe effects of change. Such members are called panels and the research that uses themis called as panel study.

The panels can be static or dynamic. In case of a static panel the same membersform part of the panel over an extended period of time. In case of the dynamic panelmembers change from time to time as the study progresses to successive phases. Thestatic panel offers a good and sensitive measure of changes. However, due to continuousinterviews the panel members are over exposed to the issues on hand and they may notreflect the view of population. The members may also not continue to be a part of panelfor a longer period of time. There may be dropouts. The major drawback in dynamicpanel is that it deals with different people which may give raise to different opinions andthe changes cannot be tracked in a objective manner.

3.3.2 Secondary data sources

Secondary data refers to the information gathered from already existing sources.Secondary data may be either published or unpublished data. The published data areavailable in the following forms:

Publications of central, state and local governments

Publications of foreign governments, international bodies and their subsidiaryorganizations

Technical and trade journals

Books, magazines and newspapers

Reports and publications of various business and industrial associations, stockexchanges, banks and other financial institutions

Reports prepared by research scholars, universities, economists in differentfields

Public records and statistics, historical documents and other sources of publishedinformation.

Online and real time databases etc.,

The unpublished sources include the company records or archives, diaries,letters, biographies and autobiographies and other public/private organizations. Collection


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of secondary data involves less time and cost. However, a researcher should not solelydepend on the secondary data due to the following reasons; the data can becomeobsolete and may not provide current and updated information, data would have beencollected for some other purpose and hence it may not meet the specific requirementsof the researcher.

The researcher before using secondary data should ensure the following:

• The reliability of data should be ensured by way of finding out the type ofpeople involved in data collection, the sources from which the data is collected,the methods used to collect the data, the time of data collection and the level ofaccuracy associated.

• The secondary data used by the researcher would have been collected for adifferent problem other than what the researcher is presently attempting to solve.Hence the researcher should ensure that the data collected is suitable for thepurpose of the study for which it is attempted.

• The secondary data should be adequate for the conduct of the study. It shouldbe related to the area and should neither be narrower nor wider than the problemattempted by the researcher.

3.3.3 Tertiary sources

Tertiary sources are an interpretation of a secondary source. It is generallyrepresented by index, bibliographies, dictionaries, encyclopedias, handbooks, directoriesand other finding aids like the internet search engines.

3.4 METHODS OF DATA COLLECTION

Data collection method is an integral part of the research design. There arevarious methods of data collection, each method has its own advantages anddisadvantages. Selection of an appropriate method of data collection may enhance thevalue of research and at the same time wrong choice may lead to questionable researchfindings. Data collection methods include interviews, Self-administered questionnaires,observations and other methods. The choice of a method depends on the followingfactors:

• Nature , scope and objectives of the research

• Availability of resources

• Degree of accuracy required

• Expertise of the researcher

• Time span of the study

• Cost involved and the like


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The data collection methods are discussed below in detail:

3.4.1 Interviews

In this method the respondents are interviewed for the purpose of obtaininginformation on the issues pertaining to the reserch. The interview may be eitherunstructured or structured and it can be a personal interview or conducted throughtelephone, mail , internet or a combination of all these.

Unstructured interviews

In the unstructured interviews, the interviewer does not conduct the interviewwith a planned sequence of questions. The aim of this interview is to highlight thepreliminary issues so that the researcher can determine the variables which needs furtherin-depth investigation. The researcher resorts to the unstructured interviews when theproblem is not clearly formulated or when a clear understanding of the variables involvedis not present. The researcher in the attempt to obtain information may adopt differentstyles and sequencing of questions to various respondents. Some may provide informationwith open ended questions, whereas some may require more directions. Somerespondents may be more defensive and may not be willing to share information. Somemay even be reluctant to undergo the interview and may refuse to respond. Theresearchers have to employ various questioning techniques so as bring the respondent’sdefenses down and make them more amenable to reveal information. The researchershould also know when to retreat or terminate the interview when the respondentscannot be convinced to participate or impart the information.

The unstructured interview will direct the researcher to understand the variableswhich need greater focus based on which a structured interview can be planned.

Structured interviews

The structured interviews are conducted when the interviewer knows the typeof questions to be asked to the respondents or when the information needs are clearlyknown. The questions may focus on the issues that have been highlighted during theunstructured interviews and are considered relevant to the problem identified. Theinterview may be conducted by the researcher himself or by a team of interviewers.The researcher/ interviewer should be very clear about the purpose of each questionparticularly when a team of interviewers conduct the survey. The same questions areposed in the same sequence or manner to all the respondents and the responses arenoted down. Depending on the situations and the respondent’s willingness and knowledgethe researcher can also ask other relevant questions which may not be in the list so asto gain more insight into the identified problem. The researcher may also include visual


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aids, drawings , pictures and other materials in conducting the interviews. In situationswhere the ideas cannot be clearly articulated only with words visual aids are moreuseful.

1. Personal interviews

Personal interviews or face to face communication is a two-way conversationinitiated by the interviewer to obtain information from the participants. The interviewerand the participants may be strangers. The interviewer controls the topic and pattern ofdiscussion. The participant or the respondents may not gain anything out of theirparticipation in the interview.

The success of the personal interview lies among other things on the respondent’sability to provide the information needed and the ability to understand the importanceof information provided by him. The researcher should take necessary steps to motivatethe respondents to cooperate so as to ensure successful conduct of the interview.

Increasing participation

The researcher can enhance the respondent’s participation by way of explainingthe kind of answer sought, the terms that should be expressed, the depth and clarity ofinformation needed etc. Coaching can be provided to the participants but care shouldbe taken to avoid the biasing factor. The interviewer can make the session an interestingand enjoyable experience by means of administering adequate motivation techniques.Some of the techniques for successful interviewing of the participants are listed below:

The interviewer should introduce himself by name and the organizations to whichthey are affiliated to. The interviewer can identify himself with the introductoryletters or other information that confirms the legitimacy of the work. Enoughdetails regarding the work to be done should be given, wherever demandedmore information may be provided. The interviewer should be able to kindlethe interest of the respondent.

If the participant is busy, the interviewer should try to stimulate interest so as toarrange for an interview at another time.

The successful conduct of interview requires a good rapport and understandingbetween the interviewer and participant. The interviewer should earn theconfidence of the respondent so as to elicit response without censure, coercionor pressure.

In the process of gathering data the interviewer should ensure that the objectiveof each question is achieved and the needed response is obtained. Theinterviewer can resort to probing, but steps should be taken to avoid the bias.


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The interviewer should record the answers of the participant in an efficientmanner. The interview should record responses as they occur, recording theresponse later will lead to loss of information. Shorthand mechanism like recordingonly the keywords can be done in the case of time constraint.

Interviewers should have good communication skills, should be able to adaptto flexible schedules, be willing to work during intermittent work hours andshould be mobile. If the interview is conducted by the researcher himself, thereis no need for much training else proper training should be provided so that theinterviewer is able to understand the objective of the study, the purpose of eachquestion, the possible responses and an outline of the research work conducted,its importance etc. Written instructions can be provided wherever needed.

Questioning techniques should be followed by the interviewer. Funnelingapproach can be practiced i.e. in the beginning of the unstructured interviewopen-ended questions can be asked to get a broad idea and create an impressionabout the situation. Care should be taken to see that the questions are unbiased.The interviewer should restate or rephrase important information so as to ensurethat the issues are recorded as how the respondent intends to represent thesame. The researcher can also help the respondent to verbalize the perceptions.

Problems in conducting personal interview

The two problems in conducting personal interview are the increased cost andthe problem of biased results. Biased results arise out of three types of errors viz.,sampling, response and non response error

i.Sampling error

One of the major criteria of a good sample design is the precision of estimatemade with the samples. The sample respondents selected for conducting the interviewmay not fully represent the population in all aspects. The numerical descriptors thatdescribe the sample may differ from those that describe the population because of therandom fluctuations inherent in the sample process. This is called as sampling error. Thesampling error reflects the influence of chance in drawing the sample members. Thesampling error is that which is left after accounting for all known sources of systematicvariance.

ii.Non-response error

Non-response error occurs when the responses of participants differ in somesystematic way from the responses of non-participants. The error occurs due to theinability to locate and access the selected sample respondent or the selected samplerespondent may not be willing to participate in the interview. This problem specifically


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arises due to selection of samples through the probability sampling method. The problemcan be tackled by way of attempting to contact the respondent again. Another approachis to treat all the remaining non-participants as a new sub-population after a few callbacks.A random sample is drawn from the non participant group and attempt is made tocontact and complete this sample at hundred percent success rate. Finding from thisnon-participant sample can be then weighed into the total population estimate. Theresearcher can also try to substitute the missing participant but care should be taken tosee that the substitute participant possess the significant character of the replacedparticipant. For e.g., the respondent should belong to the same occupation, educationalstatus, income level etc.,

iii.Response error

Response error occurs when the data reported differ from the actual data. Theerror can be caused by the respondent or the interviewer or during the preparation ofdata for analysis. Participant initiated error occurs when the participant fails to answeraccurately either by choice or due to lack of knowledge. Interviewer error arises due tothe inability to conduct the interview in a controlled manner. This may take many formslike the failure to secure cooperation, lack of consistent interview procedures, inabilityto establish appropriate interview environment, bias due to physical presence, failure torecord answers correctly. These errors affect the quality of the data collected.

iv. Cost

To conduct the personal interview, the respondents should be met individually.They might be scattered geographically and the time and cost involved in administrativeand travel task is higher. Sometimes, the respondents may not be available and repeatedcontacts have to be made which adds to the cost. In addition to this the researcher mayemploy interviewers who have to be paid. To reduce the cost telephonic interviews andself-administrated surveys can be attempted.

Advantages and drawbacks

The major advantage of personal interviewing is the ability to secure in-depthinformation and detail. The ability to harness information is more in personal interviewingas compared to telephone, mail survey and through internet. The researcher can adoptthe questioning technique in tune with the respondent’s ability to understand. Furtherclarification can be immediately made by repeating or rephrasing the questions concerned.The researcher can also get information from the nonverbal clues exhibited through thebody language of the respondent.

However, the personal interviewing involves cost in terms of both money andtime. Costs may escalate in case, where the study covers a wide geographic area or


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has a large sample to be covered. The chance of the outcome being affected by theinterviewer’s bias is more in the case of personal interviews. The respondents may feeluneasy about the secrecy of their responses in case of the face to face interaction.

2 Telephonic interviews

Interviewing through telephones enables to gain the following advantages:

Conducting interview through telephone enables to reduce the cost. The costreduction arises due to reduction in traveling and administrative expenses involvedin training and supervision. It is enough to train less number of interviewerssince the interview is conducted through telephone. Coverage per person throughtelephone will be more than the face to face interviews.

Telephonic interview enables to screen and cover large population spread overa wide geographical location. It enables to have a much more representativesample.

Use of computer assisted telephonic interviewing enables to enter data collectedin interview directly in the computers by means of terminals or through voicedata entry. This helps in further cost and time reduction.

Computer administered telephone survey can also be conducted where thecomputer can replace the interviewer. A computer calls the phone number,conducts interview and place data into a file for later tabulation.

The interviewer’s bias caused by physical appearance, body language andactions are reduced by using telephones. The respondent may feel more relaxed,comfortable and unhesitant to reveal information as the face to face contact isnot present.

Unlike face to face interview where the respondent may avoid contact with theresearcher, the contact rate is higher in telephonic interviews as the respondenthas to pick up the ringing phone. However, the use of caller identification facilitymay reduce the contact rate.

The following drawbacks arise out of telephonic interviews:

Though the penetration rate of telephones is increasing in India, still there is avast population without the telephone facility. Also the number of users withonly cell phone connection is increasing. Their numbers are not listed and reachingthem would be difficult.

The random sample identified through telephone directories may be sometimesnot available in the number given or may be malfunctioning.


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The length or duration for which the telephonic interview can be conducted islimited. Ten minutes interview is considered ideal. However sometimes theinterview may extent to more than an hour also.

It is difficult or impossible to use maps, illustration, visual aids , measurementscale techniques in the telephonic interview. The researcher cannot dependmore on the visualization techniques.

The interview can be terminated by the respondent as easily as the contactcould be made. Also the level of interest and rapport in the telephonic interviewis much lesser when compared to the face to face interview.

The challenging and distracting physical environment either at home or officemay reflect on the quality of data collection and may also result in refusal toparticipate in the interviews.

3.4.2 Observation

Observation is most commonly used data collection method in many of thestudies relating to behavioral sciences. Observation enables to collect data withoutasking question from the respondents. The respondents can be observed in the naturalwork environment or in lab setting and their activities and behaviors of interest can berecorded.

In conducting research, casual examination without purpose cannot be calledas observation. Observation becomes a scientific tool for data collection, if it is conductedspecifically to answer a research question. It should be systematically planned andexecuted using proper controls and should provide a reliable and valid account of whathas happened.

Types of observation

Observation can be grouped under the following categories:

1. Type of activity under observation

Observation includes monitoring both behavioral and non-behavioral activitiesand conditions. Behavioral observation includes nonverbal analysis, linguistic analysis,extra linguistic analysis and spatial analysis.

Nonverbal analysis includes body movement, motor expression and exchangedglances. Body movement indicates interest, boredom, anger or pleasure. Motorexpression includes facial movements, blink of eye and exchanged glances.


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Linguistic behaviour includes the number of repeated words used by personsin a conversation. It also includes the type of interaction process that occurs,between two persons or in small groups.

There are four dimensions to extralinguistic behaviour viz., (i) vocal whichincludes pitch, loudness and timbre (ii) temporal which includes the rate ofspeaking, duration of utterance and rhythm (iii) interaction which includes thetendencies to interrupt, dominate or inhibit and (iv) verbal stylistic includingvocabulary and pronunciation, peculiarities, dialect and characteristicexpressions.

Spatial relationship refers to how a person relates physically to others. Foreg., proxemics is a study which relates to how people organize territory aboutthem and how they maintain discrete distances between themselves and others.

The non-behavioral analysis includes record analysis, physical conditionanalysis, and physical process analysis.

The records include the historical or current record and public or private records.It may be written or printed.

Physical condition analysis includes conducting store audits, studies relatingto plant safety compliance, analysis of financial statements etc.

Process refers to series of steps taken to complete an activity. It includes time/motion studies relating to the manufacturing process, analyzing the traffic flowin distribution system, financial flow in organization etc.,

2.Directness of the observation

Based on the directness of observation, it can be grouped as direct or indirect.Direct observation happens when the observer is physically present and monitors whilethe event is taking place. This is highly flexible as the observer can decide what toobserve, how much time to spend on observation of an aspect, when to shift focus etc.The observer may feel bored or frustrated by constantly being on the watch and maytend to loose focus. This might reduce the accuracy and completeness of the observation.Another weakness is that the observer may be overloaded when the events takes placequickly which cannot be kept track of or recorded.

Observation carried out using mechanical, photographic or electronic meansare grouped under indirect observation. For example the uses of video cameras,pupilometric devices etc to capture the behaviour of consumers are grouped underindirect observation. Indirect observation can be carried out in an unbiased manner.Further, loss of information due to boredom, fatigue, overloading etc is avoided. However,the indirect observation is less flexible as they may be programmed earlier.


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3. Concealment

This categorization is based on whether the participant is aware of the observer’spresence. The presence of observer may cause the participant to behave in a differentmanner which might arrest the very purpose of observation. If the activity in which theparticipants are involved is highly absorbing then there is a high chance that the participantmay remain unaffected by the presence of the observer. However, the potential biasdue to the presence of the observer cannot be totally ruled out.

In order to rule out the bias in behaviour the observers may conceal themselvesfrom the object being observed using some mechanical means. For e.g., one way mirror,camera, microphone etc. However, this has to be carefully evaluated on the basis ofethical grounds.

Partial concealment is where the presence of the observer is not concealed buthis objectives or interest is not revealed. In order to evaluate the performance of a salesperson, a sales manager may be present when the salesman is dealing with the customer.However the purpose of the sales manager’s presence may be concealed and he maypretend to be involved in some other task.

4.Participation

The presence of the observer and his involvement in the research setting iscalled participant observation. He plays the role of observer as well as the participant.The participants may or may not know about the same. The observer should be moreefficient as he has to play a dual role.

Non-participant observation occurs when the observer collects the data withoutbecoming an integral part of the research setting. The observer merely observes theactivities, records them and tabulates them in a systematic manner. This type ofobservation requires the observer to be physically present in the research setting for aextended period of time which makes it a time consuming task.

5. Definiteness of structure

The observation can be grouped as structured and unstructured observation.Clear definition of various aspects of observation viz., the units to be observed, methodof recording, extent of accuracy needed, conditions of observation and selection ofpertinent data of observation etc are the characteristic of structured observation.Structured observation is appropriate in case of descriptive studies.

If the observation is conducted without the above characteristics defined inadvance, it is termed as unstructured observation. This method of observation is usuallyfollowed in exploratory studies.


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6. Extent of control

The observation can be carried out in controlled or uncontrolled settings.Uncontrolled observation is carried out in a natural setting. No attempt is made to useprecision instruments. The main aim of using this method is to get a spontaneous pictureof reality. It provides naturalness and completeness to observation. However, it maylead to subjective interpretation and over confidence that the observer knows moreabout the observed phenomena than the actual. It is usually used in exploratory research.

Controlled observation takes place according to a definite predetermined plan.It involves experimental procedure and involves the use of precision instruments torecord the observation. The observation is usually carried out in a standardized andaccurate manner leading to certain assured degree of generalization. It is usually carriedout in the form of experiments in laboratory or under controlled conditions.

Decision involved in conducting the observational study

Observational studies involve the decision regarding the type of the study, contentto be observed, training requirement of the observer/researcher and the data collection.

1.Type of the study

Observation in various forms is practiced in different type of studies. Inexploratory studies data collection is done through simple observation which may notbe carried out in a structured manner. In case of studies other than the exploratorynature, systematic observation employing standardized scientific procedure will befollowed.

2.Content specification

In observational studies the variables to be observed and other variables thatmay affect them should be specified. From the specified variables, the variables that areto be observed should be selected. The variables should be operationally defined so asto avoid confusion in the minds of observers.

3. Training the observers

The validity and reliability of the findings from observation depends on theobserver. If the observer is not trained properly, the data collected may not lead tovalid results. The observer is prone to fatigue, halo effects and observers drift whichwill affect the dependability of the data collected. Hence, in selection of observerscertain guidelines should be followed. The observer should have the ability to functionamidst lot of distractions, remember details of the activity observed, blend with thesettings being observed and should have the ability to extract the most from theobservational study. The observer should be given clear instructions regarding the outcomesought and the precise content to be observed.


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4.Data collection

Data collection plans deals with answers to question like who, what, when,how and where. The qualification of a participant to be observed, the characteristics ofthe observation, the time of observation, the method of recording data by the observersand the place where the observation is to be conducted.

3.4.3. Questionnaires

Most of the research studies carried out for solving business problems requirethe researcher to depend on primary data. The researcher should collect data throughquestionnaires/ interview schedules and process the same so as to provide solution tothe identified problem. A questionnaire is a formalized framework consisting of a set ofquestions and scales designed to generate primary raw data. It is a preformulated writtenset of questions to which the respondents record their answers. The answers are mostlychosen by a respondent from within the closely defined alternatives. The questionnairescan be administered personally, mailed to the respondents or electronically distributed.

A.Personally administered questionnaire

If the study is confined to a local area, the questionnaires can be collected bypersonally administering the same. The main advantage is that the researcher can collectall the completed responses within a short period of time. The researcher has anopportunity to introduce the research topic and motivate the respondents to offer frankanswers. Any doubts that the respondents have on any questions is clarified on thespot.

Administering the questionnaire to a large number of respondents at a timewould save time and expenses and also ensure quick collection of data as againstpersonal interviewing. Hence, wherever possible group administration of questionnaireshould be opted for depending on the sample frame work. The major drawback willbe the reluctance of organizations to give time to conduct survey among group ofemployees.

B. Mail questionnaire

Where the respondents are scattered over a wide geographical area, theresearcher has to resort to mail questionnaires. The questionnaires are mailed to therespondents, who can complete them at their convenience, in their home at their ownpace. The main advantage is that the anonymity of respondents is maintained and thiswill lead to a free and frank disclosure of information. The respondents spread over awide , geographical area can be reached and the respondents can take more time attheir convenience and fill the questionnaire. It can also be administered electronically.


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However, the return rates of mail questionnaires are typically low. The doubtsin the questionnaire cannot be cleared as easily as in the case of personally administeredquestionnaire. The representativeness of the sample is questionable due to the lowreturn rates. The respondents can be motivated by sending follow-up letters, enclosingsmall monetary amounts as incentives, providing respondents with self-addressed,stamped return envelopes and keeping the questionnaire as brief as possible

Development of questionnaire requires both creativity and scientific approach.It involves creativity because the researcher should use creative words in communicatingto the respondents. Writing of questions alone does not make up a questionnaire. Itshould be scientific as it integrates the established rules of logic, objectivity, discriminatorypowers and systematic procedures.

Guidelines for questionnaire design

A good questionnaire accomplishes the research objectives. The logicalsequences of the steps involved in the development of a good questionnaire are discussedbelow:

I. Deciding the information to be collected

II. Formulate the questions needed to obtain the information

III. Decide on the wordings of the questions and layout of thequestionnaire

IV. Pretesting the questionnaire and correcting the problem

I. Deciding the information to be collected

The researcher should have a clear idea of exactly what information is to becollected from each respondent. Lack of clarity will lead to collection of irrelevant andincomplete information which does not contribute towards the research purpose. Thesituation will diminish the value of the study. Clarity can be facilitated by,

1. Clear research objectives that will provide an insight into the kind of informationneeded, the hypotheses and the scope of the research

2. Exploratory research will reveal the variables to be explored and will enable tounderstand the point of view of the respondents

3. Experience with similar studies

4. Pretesting the preliminary version of the questionnaire

In deciding the content of the questionnaire the following guiding factors shouldbe considered:


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• The question may be asked to get information regarding objective or subjectivevariables or both. In the case of objective variables like age, gender, incomeetc a single direct question can be asked. However, if the question is regardingsubjective variable for eg., regarding attitude, feeling, satisfaction etc then thequestions should tap the dimensions and elements of the concept concerned.

• The researcher should challenge each questions in terms of its contributiontowards providing an answer for the objectives. Questions which merelycontribute interesting information and not towards the fulfillment of the objectivesshould be avoided. The researcher should learn the art of getting moreinformation with fewer questions.

• The question should have a proper scope and should cover the issue. Thequestions asked should reveal all that is needed to know. Questions areconsidered to be ineffective if they do not provide the right information that isneeded.

• The question should ask precisely what is needed. For e.g., if the researcherneeds to know the ‘family income’ of the respondent but the question is askedregarding ‘income’ then it may mean to the respondent as the respondents’income and not family income. Unambiguous words can be used so that claritycan be ensured.

• The question asked by the researcher may be contributing towards the themeand may be precise but it may not be possible for the respondent to answer thesame adequately. The respondent may require time to think and answer certainquestions. Sometimes the respondent may not be able to give an accurateanswer due to his inability to recall things from memory.

II. Formulating the questions

Before formulating the questions a decision has to be made by the researcherregarding the degree of freedom to be given to the respondents in answering the questions.The various types of the question that can be included in a questionnaire are discussedbelow:

1. Open-ended versus closed questions:

Unstructured questions or open-ended questions allow respondents to reply tothe questions in own words. It enables the respondent to answer in any way he chooses.Predetermined responses are not given to aid the respondent. For example a questionasking the respondent to list five factors which made him to choose a particular investmentproposal. This type of question requires more thinking and effort on the part ofrespondents. In most cases an interviewer is required to prompt the response by asking


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probing questions. If correctly administered the open ended question can provide theresearcher with a rich array of information.

Structured or close-ended question in contrast provides a set of predeterminedresponses and the respondents is required to choose among the same. This questionreduces the amount of thinking and effort required by the respondent. Instead of askingthe respondent to list five factors, the questionnaire may provide a set of 10 to 15factors and ask the respondent to rank the first five among the list, in the order of theirpreference. All items in the questionnaire using nominal, ordinal or Likert or ratio scaleare considered closed. The close-ended questions enable the researcher to code theresponses easily for the purpose of carrying out subsequent analysis. Care should beexercised in making the alternatives provided as mutually exclusive and collectivelyexhaustive. Even a well – delineated category in closed question may make therespondent feel confined and he may be willing to provide additional comments. Theresearcher can tackle this issue by substantiating the close-ended questionnaire with afinal open ended question.

2. Dichotomous questions

Two alternatives are suggested in dichotomous questions. The choices presentedshould be mutually exclusive i.e. the respondent should choose either of the answeronly. At the same time the given choices should be collectively exhaustive.

3. Multiple choice questions

Multiple choices offer more than one alternative answer and from which therespondent to makes a single choice. The list of answers provided should be collectivelyexhaustive. The alternatives provided should represent different aspects of the sameconceptual dimension. The multiple choice question usually generates nominal data.When the choices are numbers, the response structure will produce at least interval andsometimes ratio data.

4. Checklist questions

Checklist questions are used when the researcher wants the respondent to givemultiple responses to a single question. For e.g., the factors leading to the choice of aparticular brand laptop. The same information can be obtained from the respondentusing a series of dichotomous selection questions, one for each factor. However, itwould be time and space consuming. Checklists are more efficient.

5. Ranking questions

Ranking question is used when the response regarding the relative order of thealternatives are important. For e.g., the check list question regarding the factors leading


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to the choice of laptop will only provide the factors considered but not the order ofimportance. The ranking question will lead the respondent to rank the most importantfactor as ‘1’ the next important as ‘2’ and so on.

6. Positively and negatively worded questions

The questionnaire should include both positively and negatively wordedquestions. If all the questions are positively worded then the respondent will tend tomechanically circle all the points toward one end of the scale. A respondent who isinterested in completing the questionnaire soon will tend to circle all the questions toone end. The researcher can keep a respondent more alert by including both positiveand negative worded questions. The use of double negatives and excessive use ofwords such as ‘not’ , ‘only’ etc., should be avoided in the negatively worded questionas they will tend to confuse the respondents.

7. Double-barreled questions

A question that leads to different possible responses to its sub-parts is called adouble-barreled question. Such questions should be avoided by way of breaking thequestions into two or more parts. For example the question – do you like the flavourand the taste of the soft drink?. The question may lead to ambiguous reply. It should bebroken into two questions addressing flavour and taste separately so as to obtainunambiguous response.

The type of question dealt below should be carefully avoided or used withcaution by the researcher.

8. Ambiguous question

The question may not be double-barreled but still it may lead to ambiguity. Fore.g., if the researcher involved in the study of the job satisfaction asks the respondentto rate the level of satisfaction, the respondent may be confused as to whether thequestion is addressing satisfaction related to work environment, salary, team spirit oroverall satisfaction. The question should not give raise to ambiguous response and bias.

9. Memory related questions

If the questions require respondents to recall experiences from a distance pastthat are very hazy in their memory, then the answers to such question might be biased.

10. Leading / Loaded questions

Questions should not be asked in such a way that the respondents are forcedor directed to respond in a manner that he would not have, under normal situationswhere all possible alternatives are given. Questions should not prompt the respondentsto answer in the way the researcher wants it answered. For example, “Don’t you think


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that salary is the main reason for software employees to quit the job”?. Questionswhich are emotionally charging the respondents are called as loaded questions. Suchquestions would lead to bias in response and should be avoided.

11. Bad questions

Any question that prevent or disturbs the fundamental communication betweenthe researcher and the respondent is considered to be a bad question. Some examplesof the bad questions are incomprehensible questions, unanswerable question, leadingor loaded questions, double-barreled question etc.

III.Decide on the wordings of the questions and layout of the questionnaire

The basic component of a questionnaire is the words. The researcher shouldbe careful in considering the words to be used in creating the questions and scales forcollecting raw data from respondents. The words used can influence respondents,reaction to the question. Even a small change in the words can affect the respondentanswers, but it is difficult to know in advance whether or not a change in wording willhave an effect.

The wording used in the questionnaire and the language used should beappropriate and understandable by the respondents. Certain guidelines in deciding thewordings of the questionnaire are given below:

• The vocabulary should be simple, direct and familiar to all respondents. If thewordings / jargons used or the language is not understood by the respondent,then it may lead to wrong or biased answers. The wording and language shouldbe selected keeping in mind the educational level of the respondents, the termsused in the culture and the frames of reference of the respondents.

• The words used should not give raise to ambiguity or vagueness. This problemarises because of not giving the respondent an adequate frame of reference , intime and space for interpreting the question. Words such as ‘often’, ‘usually’lack an appropriate time referent leading the respondents to choose their ownwhich will lead to answers not comparable. Similarly, appropriate space orlocation is not often specified. For eg.,the question “ Mention your place oforigin” Does it elicit response as the district, state or country?.

• Double-barreled question should be avoided. The respondent may agree withone part of the question but not the other. For e.g., Are you satisfied with thesalary and increments given? The question should be broken else it would leadto confusion and incorrect answers


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• The instructions provided to answer the question should not be confusing therespondent. The questions should be directed more towards measuring therespondent’s knowledge or interest in the subject

• The questions asked should be applicable to all the respondents. Otherwise itwill make a respondent to answer a question though they don’t quality to do soor may lack an opinion. For e.g., Which other airways have you traveled before?.This situation can be avoided by asking a qualifying or filter question and limitfurther questioning to those who qualify.

• Simple short questions should be asked instead of long ones. Researcher shouldsee that a question or a statement in the questionnaire should be worded asminimum as possible.

• Questions should not be asked in such a manner that it will elicit socially desirableresponse. For example, “ Do you think that physically challenged people shouldbe given more weightage in employment opportunities?”. Irrespective of thetrue feeling of the respondent a socially desirable answer would be provided.

Sequencing and layout decisions

The order in which the questions are to be presented can encourage ordiscourage the commitment and promote or hinder the development of researcher-respondent rapport. The sequence of questions asked in the questionnaire should leadthe respondents from questions of general nature to specific nature. It should start withrelatively easy questions which does not involve much thinking and should progress todifficult questions. This facilitates easy and smooth progress of the respondents throughthe various items in the questionnaire. Care should be taken to see that the positivelyand negatively worded questions addressing the same issue or concept are not placedcontiguously. For eg.,

I am satisfied with the working environment

I am not satisfied with the working environment

If the above questions appear in the same order it will appear meaningless tothe respondent. The two questionnaires should be placed in different places of thequestionnaire. The way in which questions are sequenced would introduce bias in theresponse which is frequently referred to as the ordering effects. Randomly placing thequestions in the questionnaire would reduce bias in the response, however, it is notattempted as it would lead to difficulty in categorizing, coding and analyzing the responses.

Layout of the questionnaire

The appearance of the questionnaire is as important as its content. A neat,properly aligned and attractive questionnaire with a good introduction, instructions and


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well sequenced questions and response alternatives will make things easier for therespondents to answer. These aspects are explained below:

• In the introduction section, the researcher can disclose his identity andcommunicate the purpose of the research. It is also used to motivate therespondents to answer the questions by conveying the importance of the researchwork and by specifying the importance of contribution from the respondent.The researcher should also ensure the confidentiality of the information provided.The introduction section should end with a courteous note, thanking therespondent for the time devoted to respond to the survey.

• The questions should be organized in a logical manner and numbered sequentiallyunder appropriate sections. Proper instructions should be provided to completethe questions in an unambiguous manner. The questions should be neatly assignedso as to enable the respondent to read and answer the same without difficulty.The questionnaire should be designed in such a way that the respondent spendsonly minimum time and effort in completing the same.

• Questions relating to the personal profile of the respondents viz., name, gender,age, education, income, marital status etc., can appear in the beginning or at theend of the questionnaire. The questions should provide a range of responseoptions rather than seeking an exact figure. The personal profile related questionsasked at the end may have a greater chance of response because the respondentwould have gone through other questions which would have convinced himabout the legitimacy and genuineness of the questions framed. This would makethem more amenable to reveal the personal information. Some researchers feelthat asking personal data in the beginning would enable the respondent topsychologically identify themselves with the questionnaire and enhance thecommitment to respond.

Avoiding the question relating to the name of the respondent would bebetter as it ensures anonymity and enhances the probability of response. Theidentification of the questionnaire with a particular respondent can be made byassigning number instead of asking name. A separate private document can bemaintained connecting the name and number given to identify the respondent.

• The open ended questions should be put at the end so the respondent may findit easy to comment on the various aspects.

• The questionnaire should end with an expression of sincere thanks to therespondent for spending their valuable time and effort. The researcher can alsoinclude a courteous note, reminding the respondents check that all the itemshave been completed properly.


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iv. Pretesting the questionnaire

The purpose of a pretest is to ensure that the questionnaire meets the researcher’sexpectations in terms of the information to be obtained. The objective of the pretest isto identify and correct the deficiencies in the questionnaire. It may lead to revisingquestions many times. It involves the use of a small number of respondents to test theappropriateness of the questions. 15 respondents are sufficient for a short andstraightforward questionnaire, whereas 25 may be needed in case of a long and complexquestionnaire with many branches and multiple options. Feedback is obtained from therespondents involved in the pretest on the general reaction to the questionnaire andregarding the effort involved in completing the questionnaire. Any difficulty or ambiguitycan be identified and rectified before administering the questionnaire to a large numberof respondents. This helps to rectify any mistakes in time and enables to reduce thebiases.

Various type of pretesting can be carried out ranging from informal reviews bycolleagues to creating conditions similar to the final study. Some types are discussedbelow:

• The researcher pretesting is conducted in the initial stages so as to buildmore structure in to the test. Fellow researchers can be involved. Manysuggestions and discussion may take place leading to a refined questionnaire

• Participant pretesting involves testing the questionnaire in the field by involvingthe participants or participant surrogates. Surrogates are those individuals withcharacteristics and backgrounds similar to the desired participants.

• Collaborative pretest can be conducted by the researcher where theresearcher informs or alerts the participants of their involvement in the preliminarytest of questionnaire. This makes the participants as the collaborators in theprocess of refinement of the questionnaire. A detailed probing of the parts ofthe question, including the words and phrases is carried out.

• Non-collaborative pretest is where the researcher does not inform theparticipant that the activity is a pretest. However, the probing of the questionnaireis done.

The pretest is conducted for the following reasons:

• The most important purpose for pretesting is to know whether the meaning ofthe questions is interrupted in the manner in which it is intended to. This problemmay arise because, the respondent may not be familiar with the word whichwill result in distortion of the meaning of the question. The respondent is likelyto modify a difficult question in a way that makes it easier for him to respond.


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• Flow of the questionnaire should be tested to know whether the transition fromone topic to another is natural, logical and ensures a coherent flow.

• Many questionnaires have instructions on what question to skip, depending onthe answer to a previous question. The skip pattern must be clearly laid out. Inthis context a questionnaire is like a road map with signs. Researchers whohave been involved with the questionnaire design may not spot any inconsistenciesor ambiguities as they are highly involved in the task. Pretesting will ensure thecorrect layout of the questionnaire

• The length of the questionnaire is pretested as a lengthy questionnaire will oftenlead to fatigue among the respondent, interview break-off and refusal if therespondents know in advance, the expected length.

• Task difficulty should also be identified through pretest. The respondent maybe confused if the question requires that a respondent make connections or puttogether information in an unfamiliar way. For e.g., questions related to annualincome. It involves calculation by the respondent. Instead the researcher canget monthly income and calculate the annual income on his own.

• Ability to capture and maintain the interest of the respondent throughout theentire questionnaire is a major challenge. The extent to which this is successfulshould be pretested

• Testing the items for an acceptable level of variation in the target population isone of the common goals of pretesting. The researcher should lookout foritems showing greater variability. Very skewed distributions from a pretest canserve as a warning signal that the question is not tapping the intended construct.

The flaws identified in the questionnaire should be corrected. Finally the pretestanalysis should return to the first step in the design process. Each question should bereviewed again and again regarding its contribution to objectives of the study, leading toother steps. The last step in the process may be another pretest, if major changes areneeded again.

Interview Schedules Vs Questionnaire

Use of interview schedules method to collect data is much the same likequestionnaire method except the fact that in the case of schedules, the same is filled upby researcher himself or by enumerators who are appointed for this purpose. Theschedule is a proforma containing a set of questions and the space to record the answerfor the same. The enumerators along with the interview schedules meet the respondents,put the questions to them from the proforma in the order the questions are listed andrecord the replies in the space meant for the same. In certain situations, schedules may


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be handed over to the respondents and researcher may help them in recording theiranswers to various questions in the schedules. The researcher can explain the aims andobjectives of the investigation and also can clear doubts and difficulties which therespondents feel in understanding the implications of a particular question.

The success of this method depends on the selection of enumerators for fillingup schedules or assisting respondents to fill up schedules. The enumerators should betrained to perform their job well and nature and scope of the investigation should beclearly explained to them. The purpose of each question and the type of responseexpected should be informed to them. Enumerators should posses patience to tacklethe respondents and should also be intelligent to cross examine and find the truth. Theyshould be sincere, hardworking and should have perseverance.

Collection of data using interview schedules and enumerators lead to fairlyreliable results and extensive inquiry. However it is expensive and takes time.

Difference between questionnaire and interview schedule

Questionnaire and interview schedule are both used for data collection andthey resemble each other. However, the important points of difference are highlightedbelow:

i. The questionnaire can be sent thought mail with covering letter and the samedoes not require further assistance. The schedule is filed out by the researcherwho interprets the question whenever needed.

ii. Collecting the questionnaire requires less expense as it is filled by the respondenthimself. In the case of schedules, enumerators should be appointed. This involvesadditional expenses in terms of payments made to them and training provided.

iii. The rate of non-response is usually higher in case of mailed questionnaire. Incase of schedules the non-response rate is lesser as the enumerator himself fillsthe schedules and is personally present. However, the danger of bias and cheatingprevails.

iv. The identity of the respondent is not clear in the case of the questionnaire, butin case of the schedules the identity is known.

v. The questionnaire method of data collection involves time as it requires severalreminders inspite of which it may not be returned. In case of schedules directpersonal contact is established and responses are elicited soon.

vi. Questionnaire method can be used only in case of educated or literaterespondents but the interview schedules can be administered even in case ofilliterate persons


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vii. Wider and more representative population is possible in the questionnaire methodof data collection, but it remains as a difficulty in case of schedules particularlywhen the respondents are distributed over a wide geographical area.

viii. Risk of collecting incomplete and wrong information is more in case ofquestionnaire method, but in case of schedules, the enumerators are present tosee that the questions are properly filled in. As a result the information collectedthrough the schedules are more accurate than those obtained through thequestionnaire.

ix. The success of the questionnaire method depends to a greater extent on thequality of the questionnaire, but in case of the interview schedules it depends onthe honesty, sincerity and perseverance of the enumerators.

x. The physical appearance of the questionnaire is very important to attract andretain the respondents attention, however, the level of importance is not thesame in case of the interview schedule.

xi. Additional data can be obtained by the enumerator apart from what is asked inthe schedules by personal observation. This is not possible in case of the mailedquestionnaire.

Electronic questionnaire design and surveys

Electronic questionnaires or online questionnaries combines questionnaire basedsurvey functionality with that of a webpage or website. It also includes mailing of datadisk to the respondents, who may use their own personal computers for responding tothe questions. However, web surveys are rapidly gaining popularity as they have majorspeed, cost, and flexibility advantages. Factors such as layout or organization, formatting,structure of the questions and technical requirements should be taken into considerationwhile designing and implementing an electronic questionnaire. Decision regarding theabove listed aspects should be made keeping in consideration, the profile of the targetaudience identified for the survey. The guidelines relating to the factors to be consideredare discussed below:

1. General organization

The overall structure and organization of electronic question can follow thepattern depicted in the figure below. A welcome page should be used to motivaterespondent to participate in the survey. Where the entry is restricted a login facility canbe provided to allow the user to enter the password. A short introductory page shouldprovide general information about the survey, including specific direction if any to fill thequestionnaire. If a screening test is required for the survey it is delivered beforeproceeding to the main section of the questionnaire. Every online questionnaire should


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conclude with the acknowledgement to the respondent for the time and effort spent incompleting the questionnaire. These aspects are detailed below:

i. Welcome:

The site or domain name that brings the respondents to the survey page shouldbe easy to remember and should reflect the purpose of the questionnaire. Several domainname could be used to attract the respondents. The welcome page should be designedin such a way that it is loaded quickly. The page should provide information regardingthe organization on whose behalf the questionnaire is administered. It should motivatethe respondent to take part in the survey and emphasize on the ease of responding. Theprocedure to start should also be made evident. For questionnaire with passwordrestriction, the fact should be mentioned clearly in the welcome screen so that therespondent does not waste time over the same. Too much of animations and gimmicksshould be avoided as it may take more time to download and may also distract therespondents attention.

ii. Registration/login

The registration or login screen is needed if the access to the questionnaire isrestricted to specific people. The passwords access should be provided to theappropriate respondents so as to enable them to participate in the survey. Whileprocessing the pin number and password it is better to accept dashes and hyphens aspart of string of numbers. In order to alleviate the respondents’ frustration, soon after

Welcome

Registration/ Login

Introduction

Additional information

Questionnaire questions

Screening test

Thank you


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the data is entered in the required fields, all correct data should be accepted and onlythe fields that have been erroneously omitted or completely incorrect should behighlighted for reentry with a proper explanation regarding the required data. Sufficienttime should be provided to read and complete the registration forms before automatictime out.

iii. Introduction

This section should provide a brief description of the survey, the purpose andthe importance of the response received. It should also outline all the security andprivacy practices associated with the survey so as to reassure the respondents.Alternatively, these informations can also be included in the registration/login page.

iv. Screening test

If the screening test is very simple it can be located within the introductionpage. If it is more extensive, it should be dealt in a separate page but should be linkedto the preceding and the succeeding pages. If a respondent fails a screening test, still thechance to participate in the survey should not be denied as it will offend the respondent.However the contribution can be discarded in the study.

v. Questions

The questions should follow all the basic guidelines of the offline questionnaire.In addition the following should also be considered in the designing of the electronicquestions:

1. The total number of questions should be as minimum as possible.

2. Initial questions should be routine, easy to answer questions so as to ease therespondents mindset. The first question should be engaging and should attractthe attention and interest of the respondent.

3. Difficult, sensitive and most important questions should appear after therespondent has completed atleast 1/3 of the questionnaire at a point when therespondent would have settled down.

4. In order to ensure consistency of responses, repeated questions which areworded in a different manner often forms part of the questionnaire. Suchquestions should be placed apart.

5. Open-ended questions should appear before close-ended on the same topicso as to prevent influencing respondents with the fixed option choices of theclose-ended questions.


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vi. Additional information/links:

In order to ensure that the main questionnaire is simple not cluttered withunnecessary information, additional information can be included in a separate pagewhich is linked to the main page. The respondent should be able to return to the mainquestionnaire easily at any point of time.

vii Note of gratitude:

The questionnaire should end with a note of gratitude to the respondent for thetime and effort spent by him. The same should be expressed in a friendly and gentletone. This page should also include the facility for respondents to e-mail feedback orcomments to the questionnaire administrators.

2. Layout

Online questionnaires should create a distincitive, positive visual impression soas to evoke a feeling of trust and assist the respondents in the process of completing thequestionnaire. Some guidelines are given below:

1. The question and answering process should be attractive. It should be presentedin an uncluttered manner evoking ease in completion. Too much informationshould not be squeezed in one page. Information should be aligned horizontallyand vertically to enable easy reading.

2. A question should never be separated from its response set. The question andall elements of response should appear in the same page/screen.

3. The questions related to a given topic should be presented together and clearlysectioned from questions related to other topics. Section heading andsubheadings should be used to clearly differentiate sections. Too many sectionsshould also be avoided as it will lead to confusion and lack of focus.

4. It should not be made compulsory for a respondent to answer a question, soas to move to the next question, except in case of screening question.Respondents should be provided the freedom of moving back and forth in theprocess of completing the questionnaire

5. A questionnaire can be designed within a single screen with scrolling page ordispersed across several, linked, non-scrolling pages. It should be noted thatlengthy questionnaire with a scroll may frustrate a respondent and give theimpression that a questionnaire will take a long time to complete. Only a fewquestions should appear in a page and clear and easy links to preceding andsucceeding pages should be provided.


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6. Frames can make pages difficult to read, print, increase load time and causeproblems. So the use of frame should be minimized or avoided.

7. Forms and fields are commonly used for data entry. The field labels should beplaced close to the associate fields. The ‘submit’ button should be locatedadjacent to the last field. The tab order for key navigation around the fields inquestionnaire should be logical and reflect the visual appearance as far aspossible. Fields should be stacked in vertical column and any instructionpertaining to a given field should appear before and not after the field.

3. Navigation

To enable easy navigation within the website presenting questionnaire, onlinequestionnaire usually includes buttons, links, site maps and scrolling. All mechanismsfor navigation should be clearly defined and should be placed in such a manner that itcan be easily identified and accessed by the respondents. The navigational aids aretypically located at the top right hand corner of web pages and it should appearconsistently in the same place on each page of a quesionnaire’s website. The guidelinesregarding the navigational elements are listed below:

1. Buttons enables a respondent to exit a questionnaire or return to the previous/next section of a questionnaire. This should be placed consistently in the sameplace in all the pages and should be designed in a easily identifiable manner.Graphical presentation can be used to name the button.

2. Links are commonly used in web pages. It should be designed in a simplemanner and used sparingly. It should be placed in a clearly identifiable manner.Bold, coloured, underlined text can be used. A link that has been visited by therespondent should be indicated by a change in the colour. Text based linkshould be used rather than image based links. Clear distinctions should bemade between links to locations within the same page and different page.

3. Site maps provide an overview of the entire webpage at a single glance. Theyhelp users navigate through website and enables saving time and frustration.The path way is usually in a linear manner and therefore the orientation shouldnot be overly complex. The site maps should be scaleable and should beconsistently placed. It should be downloadable in minimum time and used onlywhen there are more number of pages in a questionnaire.

4. Scroll bar should be avoided as some respondent find scrolling hard to useand it can also be overlooked by them. The welcome page should fit into asingle screen and not require scrolling. If scrolling cannot be avoided then therespondents should be informed of the need to scroll. Scrolling can be avoidedby using jump buttons which takes the respondents to the ‘next screen’ full ofinformation or questions.


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4. Formatting

Formatting of a questionnaire includes several aspects i.e. text, colour, graphics,flash, frames and tables, feedback and other miscellaneous factors. Guidelines pertainingto each of these aspects are discussed below:

A. Text

i. The font used should be readable and the text should be presentedin standard sentence format. Capital letters should be used only foremphasizing title, captions etc.

ii. Sentences should not have minimum words and should be presentedwith minimum characters per line. Paragraph should be of minimumsize.

iii. Technical instructions should be written in such a way that non-technical people can understand them.

iv. Questions should be easily distinguishable in terms of formattingfrom instructions and answers.

v. The relative position of questions and answers should be consistentthroughout the questionnaire. Where different types of questionsare to be included in the same questionnaire, each question typeshould have an unique visual appearance

vi. A minimum font size of 12 pt should be used. The font colour shouldcontrast significantly with the background colour.

vii. The text should be left justified and use of italics should be avoided.

B. Colour

Colour has a great impact on the respondents and their responses andso it is important to use colours in a wise manner. Consistent colour codingshould be used throughout the questionnaire to reinforce meaning or informationin an unambiguous fashion. Neutral background colour excluding patterns shouldbe used to make text easy to read. When using two colours, the colours of highcontrast can be used to ensure maximum discernability. The use of followingcombination of colours can be avoided since visual vibrations and after imagescan occur; red and green, yellow and blue, blue and red, blue and green. Whileusing colours the standard cultural colour association should be kept in mind


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C.Graphics

In order to minimize the download time, the graphics should be kept tominimum. Some guidelines to be followed are listed below:

i. Cluttering the questionnaires with graphs should be avoided as it repelsthe participants

ii. Small graphics that can be downloaded quickly should be used.Individual images should not exceed 5KB in size and a single webpage should not have graphics exceeding a total of 20KB size

iii. It is essential to provide text also along with the graphics. Progressiverendering method of allowing the text to download before the graphicsshould be used so as to reduce the access time.

iv. The number of colours used in the graph should be kept minimum andit should be ensured that the graphics do not resemble other items on atypical website.

v. Overlapping of menus, blurring of picture, graying out areas etc., shouldnot be done. Crisp and clear images should be used.

vi. Multimedia and audio clips should not be associated with the graphicsif plug - ins should be downloaded to play the same. The users shouldbe allowed the freedom of skipping the multimedia content.

D. Flash

Online questionnaire should use minimum of flash including blinkingtext since it requires certain browser versions or plug - ins. It makes it difficultfor users to download the animation or flash which takes much of the time toload. If it is a must to use flash then the site should provide the user an option ofusing a flash or non-flash format. It should provide for static navigation i.e. itshould not use disappear and reappear format and should always include away to navigate back to the location from which a user encountered a flash. A‘close window’ option should be provided in all windows that open.

E. Tables and Frames

Tables and frames are used in website design for alignment and otheraesthetic purposes. If tables are used to convey structured information, theyshould be kept short and simple. All information should be included withintables in straight text and should be in a standard format.


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F. Feedback

It is important to get feedback on the online questionnaire so as tounderstand whether a respondent will abandon completion or will perseverewith it. With each new section/page respondent should be given real timefeedbacks to their degree of process through questionnaire. This may take theform of ‘ 30 % completed ’ in a progressive bar. Respondent’s answers to thequestions should be made immediately visible to them in a clear and concisemanner to reinforce the effect of their action.

G. Miscellaneous

The following guidelines relating to formatting does not fall in any of thepreviously discussed categories. The total website content should remain below60 KB of text and graphics. A version of questionnaire as well as all referencedarticles or documentation should be provided in an alternative format that canbe printed fully. All introductory pages in the survey website should include adate-last-modified notification as well as a copyright notice if applicable.

5. Response Formats

Electronic equivalents to the various paper-based response styles have to beselected to best meet the needs of the questionnaire and target audience. Some guidelinesin this respect are discussed below;

A. Matrix questions:

If a question involves many response options, matrix formats can be used tocondense and simplify questions. it should be used sparingly as they require a lot ofwork to be done in a single screen. It is also hard to predict how such questions willappear on the respondent’s web browsers and size and format of such questionsdemands a significant amount of screen which cannot be guaranteed on smaller-scaletechnology.

B. Drop-down boxes:

A drop-down text box appears in one line text format. When collapsed itcontains a list or response options from which a respondent can select one or more.Drop-down boxes are fast to download and can be used when very long lists of responseoptions are required. It should be used sparingly as it requires very accurate mouseclick and should be avoided when it would be faster to simply type the response. It isimportant that the first option in the drop-down list box is not visible by default as it canlead respondents to select the same.


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C. Radio Buttons:

Radio buttons are small circles that are placed next to response options of aclose ended question. By default, only one radio button within any given group of radiobuttons can be selected at a time. It can be used in case of mutually exclusive options.They closely resemble the paper based questionnaire answer formats. It demands arelatively high degree of mouse precision and users with limited computer exposuremay find it frustrating to click the options or to change the options.

D.Check boxes

Check boxes are typically small squares that contain a tick mark when checkedand allow multiple options rather than exclusive options. They also require high degreeof mouse precision. The advantage of using check boxes and radio buttons within thesame questionnaire is that their appearance is visibly different and so, respondents aregiven visual clues as to how to answer any question using either of the two responseformats.

6. General technical guidelines

In addition to the above technicalities of a online-questionnaire design, thefollowing additional details should also be taken into consideration in the design of anonline questionnaire:

i. Privacy and Protection

It is important to ensure that the respondents’ privacy and perception of privacyare protected. The survey data should be encrypted and the anonymity of the respondentshould be assured.

ii. Computer literacy

The questionnaire should be designed keeping in mind the less knowledgeable,and low-end computer user. Specific instructions should be provided without offendingthe inexperienced respondents. Prior knowledge or preconceptions in terms oftechnological know-how should not be assumed. The need for double-click should beeliminated since it can be difficult for an inexperienced user.

iii. Automation

Many aspects of the online questionnaire can be automated unlike the paperbased questionnaire. For e.g., the skip questions. Skip questions are primarily used todetermine the basis of the individual respondent’s answer, which of the following questionsa respondent should jump(or ‘skip’) to when question path is response directed. Whenautomation is used it should be carefully designed in order to avoid disorientation orconfusion to the respondents.


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iv. Platforms & browsers

Before launching the questionnaire online it should be ensured that thequestionnaire operates effectively across all platforms and within all browsers. As ageneral rule, only a portion of the capacity of the most advanced browsers should beused in order to maximize the chance that all recipients of the questionnaire are havinga equal likelihood of responding to the same.

v. Devices

The technology is highly portable and information access is highly mobile. Henceit is important to design all elements of the online questionnaire in a scaleable form. It isunlikely that many respondents would choose to complete a lengthy questionnaire on ahandheld device, but this cannot be totally avoided and the range of the sizes to whicha respondent might resize their desktop browser can be anticipated. A well designedand tested questionnaire should take this aspect into consideration.

Advantages

• Web page surveys are extremely fast. A questionnaire posted on a popularWeb site can gather several thousand responses within a few hours. Manypeople who will respond to an email invitation on the first day, and most will doso within a few days.

• There is practically no cost involved once the set up has been completed. Large samples do not cost more than smaller ones

• The researcher can use audio visuals in collection of the data. Some Web surveysoftware can also show video and play sound.

• Web page questionnaires can use complex question skipping logic,randomizations and other features not possible with paper questionnaires ormost email surveys. These features can assure better data.

• Web page questionnaires can use colors, fonts and other formatting optionsnot possible in most email surveys.

• A significant number of people will give more honest answers to questionsabout sensitive topics, such as drug use when giving their answers to a computer,instead of to a person or on paper.

• On average, people give longer answers to open-ended questions on Webpage questionnaires than they do on other kinds of self-administered surveys.

• Some Web survey software can combine the survey answers with pre-existing information on the individuals taking a survey.


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• It is possible to link the online questionnaire to data base and as such theinformation received can be immediately updated without further need for manualdata entry as in the case of paper based questionnaire

Disadvantages

• The coverage error is prevalent in online survey i.e. all the members of thepopulation is not having an equal chance of being included in the survey. This isparticularly so in case of countries where the internet access is very low andcomputer illiteracy is higher.

• Non-responsive error is higher because respondent may not opt to fill the onlinequestionnaire. Also respondents can easily quit in the middle of a questionnaire.They are not as likely to complete a long questionnaire on the Web as theywould be if talking with a good interviewer.

• The survey on a web page cannot exercise a control over who replies - anyonefrom anywhere who is surfing may answer. We cannot restrict the demographicpattern of the respondent.

• There is often no control over people responding multiple times to bias theresults.

In the present context online surveys should be mainly used only when the targetpopulation consists entirely or almost entirely of Internet users. Business-to-businessresearch and employee attitude surveys can often meet this requirement. Surveys of thegeneral population usually will not. Web page surveys can be used when the researcheruses audio or video or both sound and graphics. A Web page survey may be the onlypractical way to have many people view and react to a video.

The researcher should make sure that the software used for conducting online surveysprevents people from completing more than one questionnaire. The access can also berestricted by requiring a password or by putting the survey on a page that can only beaccessed directly i.e., there are no links to it from other pages.

3.4.4 Other methods

In addition to the above discussed methods of data collection, the followingmethods can be used:

1. Warranty cards

These are post sized cards which are used by dealers of consumer durables tocollect information regarding the product. The information sought is printed in the formof questions on the ‘warranty cards’ which is placed inside the package along with the


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product with a request to the consumer to fill in the card and post the same to thedealer.

2. Store audits

Store audits are performed by distributors as well as manufacturers throughtheir salesmen at regular intervals. The information is used to estimate market size,market share, seasonal purchasing pattern etc. The data is obtained mostly byobservational method. Store audits are invariably panel operation, for the derivation ofsales estimates and compilation of sales trends are the base for the calculation. It providesan efficient way to evaluate the effect of various in-store promotions on the sales.

3. Pantry audits

This is used to estimate consumption of the basket of goods at the consumerlevel. The investigator collects an inventory of types, quantities and prices of thecommodities consumed. In pantry audits the data is recorded from the examination ofconsumer’s pantry. The objective in a pantry audit is to identify the type of consumerswho buy certain products and certain brands. The basic assumption is that the contentsof the pantry accurately portray consumer’s preference. Pantry audits are usuallysupplemented by direct questioning relating to reasons for preference of a product.

4. Consumer panels

The consumer panels consists a group of consumers who are interviewed on aregular basis over a period of time. The consumer panels may be transitory or a continuingpanel. A transitory panel is set up to measure the effect of a particular phenomenon.The panel is conducted on a before and after basis. Interview is conducted before thephenomenon takes place and another interview after the phenomenon has occurred soas to measure the changes in the attitude and behaviour of the consumers. A continuingconsumer panel is set up for an indefinite period with a view to collect data on a particularaspect of consumer behaviour over a time period.

5. Mechanical devices

Use of mechanical devices enables to record data accurately. Eye camera,Pupilometric camera, Psychogalvanometer, Motion picture camera and Audiometerare some of the devices used for data collection. Eye cameras are designed to recordthe focus of eyes of a respondent on a specific portion of a sketch or diagram or aproduct package etc. Pupilometric cameras record dilation of the pupil as a result ofvisual stimuli. The extent of dilation shows the degree of interest aroused by the stimuli.Pshchogalvanometer is used to measure the extent of body excitement as a result of thevisual stimulus. Motion pictures are used to record the movement of the buyer while


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deciding to buy a consumer good. Audiometers are used with television to find out thetype of programmes as well as channels preferred by viewers .A device is fitted in thetelevision itself to record the changes which can be used to ascertain the market share.

6. Projective techniques

Certain ideas and thoughts cannot be easily verbalized as it remains at theunconscious levels in the minds of respondents. This can be brought to the surface bytrained professionals who apply different probing techniques so as to bring to the surfacethe deep-rooted ideas and thoughts. Some techniques are explained below:

i. Word association test

The test is used to extract information regarding words which have maximumassociation. Respondents are asked to quickly associate a word say - happy with thefirst thing that comes to mind. This is often used to get true attitudes and feeling of therespondent. The same idea is used in marketing research to find out the quality that ismostly associated with a brand of product. This technique is quick and easy to use andyields reliable results, when applied to words that are widely known and possesessentially one meaning.

ii. Sentence completion tests

It is an extension of the word association tests. The respondent is providedwith a several half completed statement regarding a subject. Analysis of replies from therespondent reveals his attitude towards the subject. This technique not only permits thewords testing but ideas too. It is quick and easy to use, however, it leads to analyticalproblems as the responses are multidimensional.

iii. Story completion test

This test is a step further where the researcher may contrive stories instead ofsentences and the respondent to complete the same. The respondent is given just enoughof a story to focus attention on a given subject and is asked to provide a conclusion tothe story.

iv. Verbal projection tests

The respondent is asked to comment on or explain on what other people do.For example - Why people own a particular product? Answers may reveal therespondent’s own motivations.

v. Pictorial techniques

Several pictorial techniques are available. They are discussed below:


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a. Thematic appreciation test (T.A.T) requires respondent to weave a story arounda picture that is shown. Several need patterns and personality characteristicscould be traced through these tests.

b. Rosenweiz test uses a cartoon format where a series of cartoons with wordsinserted are given to the respondents. The respondents is asked to put his ownwords in the empty space provided for the purpose in the picture. From theresponse the attitude of the respondent can be inferred.

c. Rorschach test consists of ten cards having prints of ink-blots. The design happensto be symmetrical but meaningless. The respondents are asked to describetheir perception and the responses are interpreted on the basis of somepredetermined psychological framework.

d. Holtzman Inkblot Test(HIT) contains 45 inkblot cards which are based oncolour, movement, shading and other factors involved in inkblot perception.Only one response per card is obtained from the respondent and interrupted atthree levels i.e. accuracy(F) or inaccuracy (F-) of respondents percepts; shadingand color for ascertaining the affectional and emotional needs; and movementresponses for assessing the dynamic aspects of life.

vi. Play techniques

Under play techniques subjects are asked to act out a situation where variousroles are assigned to them. The researcher may observe such traits as hostility,dominance, sympathy, prejudice or absence of such traits.

vii. Quizzes, tests and examinations

This technique is used for extracting information regarding specific ability ofcandidates indirectly. The procedure uses both long and short questions to test thememorising and analytical skills of the respondents.

viii. Sociometry

It is a technique for describing the social relationship among individuals in agroup. It attempts to describe attractions or repulsions between individuals by askingthem to indicate whom they would choose or reject in various situations. It enables tostudy the underlying motives of the respondents.

Almost all the method of data collection discussed above have some biasassociated with them. Hence, collecting data through multi-methods and from multiplesources lends rigor to research. However, it would be costly and time consuming.


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3.5 THE BASICS OF SAMPLING

Sampling is an important concept which is practiced in every activity. Samplinginvolves selecting a relatively small number of elements from a large defined group ofelements and expecting that the information gathered from the small group will allowjudgments to be made about the large group. The basic idea of sampling is that byselecting some of the elements in a population, the conclusion about the entire populationis drawn. Sampling is used when conducting census is impossible or unreasonable. Ina census method a researcher collects primary data from every member of a definedtarget population. It is not always possible or necessary to collect data from every unitof the population. The researcher can resort to sample survey to find answers to theresearch questions. However, they can do more harm than good if the data is notcollected from the people, events or objects that can provide correct answers to theproblem. The process of selecting the right individuals, objects or events for the purposeof the study is known as sampling and the same is dealt in detail in this chapter.

The basic terminologies used in sampling are discussed below:

Population

Population is an identifiable total group or aggregation of elements that are ofinterest to the researcher and pertinent to the specified problem. In other words itrefers to the defined target population. A defined target population consists of thecomplete group of elements (people or objects) that are specifically identified forinvestigation according to the objectives of the research project. A precise definition ofthe target population is usually done in terms of elements, sampling units and time frames.

Element

An element is a single member of the population. It is a person or object fromwhich the data/information is sought. Elements must be unique, be countable and whenadded together make up the whole of the target population. If 250 workers in a concernhappen to the population of interest to the researcher, each worker therein is an element.

Population Frame

The population frame is listing of all elements in the population from which thesample is drawn. The nominal roll of class students could be the population frame forthe study of students in a class.

Sampling units

Sampling units are the target population elements available for selection duringthe sampling process. In a simple, single-stage sample, the sampling units and thepopulation elements may be the same.


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Sampling frame

After defining the target population, the researcher must assemble a list of alleligible sampling units, referred to as a sampling frame. Some common sources ofsampling frames for a study about the customers are the customer list from credit cardcompanies.

Sample

A sample is a subset or subgroup of the population. It comprises some membersselected from it. Only some and not all elements of the population would form thesample. If 200 members are drawn from a population of 500 workers, these 200members form the sample for the study. From the study of 200 members, the researcherwould draw conclusions about the entire population.

Subject

A subject is a single member of the sample, just as an element is a single memberof the population. If 200 members from the total population of 500 workers form thesample for the study, then each worker in the sample is a subject.

3.5.1 Why sampling?

There are several reasons for sampling. They are explained below:

• Lower cost: The cost of conducting a study based on sample is much lesserthan the cost of conducting the census study.

• Greater accuracy of results: It is generally argued that the quality of a study isoften better with sampling data than with a census. Research findings alsosubstantiate this opinion.

• Greater speed of data collection: Speed of execution of data collection is higherwith the sample. It also reduces the time between the recognition of a need forinformation and the availability of that information.

• Availability of population element: Some situations require sampling. When thebreaking strength of materials is to be tested, it has to be destroyed. A censusmethod cannot be resorted as it would mean complete destruction of all materials.Sampling is the only process possible if the population is infinite.

3.5.2 Steps in Developing a Sampling plan

A number of concepts, procedures and decisions must be considered by aresearcher in order to successfully gather raw data from a relatively small group ofpeople which in turn can be used to generalize or make predications about all theelements in a larger target population. The following are the logical steps involved in thesample execution.


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Define the target population

The first task of a researcher is to determine and identify the complete group ofpeople or objects that should be included in the study. With the statement of the problemand the objectives of the study acting as guideline the target population should be identifiedon the basis of descriptors that represent the characteristics features of element thatmake the target population’s frame. These elements become the prospective samplingunit from which a sample will be drawn. A clear understanding of the target populationwill enable the researcher to successfully draw a representative sample.

Select the data collection method

Based on the problem definition, the data requirements and the researchobjectives, the researcher should select a data collection method for collecting the

Define the target population

Execute the operational plan

Select the Data Collection Method

Identify the Sampling Frame Needed

Select the Appropriate Sampling Method

Determine necessary sample size and overall contact rates

Create an operating plan for selecting sampling units


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required data from the target population elements. The method of data collection guidesthe researcher in identifying and securing the necessary sampling frame for conductingthe research.

Identify the sampling frames needed

The researcher should identify and assemble a list of eligible sampling units.The list should contain enough information about each prospective sampling unit so asto enable the researcher to contact them. Drawing an incomplete frame decreases thelikelihood of drawing a representative sample.

Select the appropriate sampling method

The researcher can choose between probability and non-probability samplingmethods. Using a probability sampling method will always yield better and more accurateinformation about the target population’s parameters than the non-probability samplingmethods. Seven factors should be considered in deciding the appropriateness of thesampling method viz., research objectives, degree of desired accuracy, availability ofresources, time frame, advanced knowledge of the target population, scope of theresearch and perceived statistical analysis needs.

Determine necessary sample sizes and overall contact rates

The sample size is decided based on the precision required from the sampleestimates, time and money available to collect the required data. While determining thesample size due consideration should be given to the variability of the populationcharacteristic under investigation, the level of confidence desired in the estimates andthe degree of the precision desired in estimating the population characteristic. The numberof prospective units to be contacted to ensure that the estimated sample size is obtainedand the additional cost involved should be considered. The researcher should calculatethe reachable rates, overall incidence rate and expected completion rates associatedwith the sampling situation.

Creating an operating plan for selecting sampling units

The actual procedure to be used in contacting each of the prospectiverespondents selected to form the sample should be clearly laid out. The instructionshould be clearly written so that interviewers know what exactly should be done andthe procedure to be followed in case of problems encountered, in contacting theprospective respondents.

Executing the operational plan

The sample respondents are met and actual data collection activities are executedin this stage. Consistency and control should be maintained at this stage.


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3.5.3 Characteristic features of a good sample

The ultimate test of a good sample is based on how well it represents thecharacteristics of the population it represents. In terms of measurement the sampleshould be valid. Validity of the sample depends on two considerations viz., accuracyand precision.

Accuracy

The accuracy is determined by the extent to which bias is eliminated from thesample. When the sample elements are drawn properly, some sample elementsunderestimates the population values being studied and others overestimate them.Variations in these values offset each other. This counteraction results in sample valuethat is generally close to the population value. An accurate i.e., unbiased sample is onein which the underestimators and the overestimators are balance among the membersof the sample. There is no systematic variance with an accurate sample. Systematicvariance has been defined as “the variation in measures due to some unknown influencesthat ‘cause’ the scores to lean in one direction more than another”. Even a large size ofsamples cannot counteract systematic bias.

Precision

A second criterion of a good sample design is precision of estimate. No samplewill fully represent its population in all aspects. The numerical descriptors that describesamples may be expected to differ from those that describe population because ofrandom fluctuations inherent in the sampling process. This is called sampling error.Sampling error is what is left after all known sources of systematic variance have beenaccounted for. In theory, sampling error consists of random fluctuations only, althoughsome unknown systematic variance may be included when too many or too few sampleelements possess a particular characteristic. Precision is measured by standard error ofestimate, a type of standard deviation measurement; the smaller the standard error ofestimate, the higher is the precision of the sample. The ideal sample design produces asmall standard error of estimate.

3.6 TYPES OF SAMPLING DESIGN

The sampling design can be broadly grouped on two basis viz., representationand element selection. Representation refers to the selection of members on a probabilityor by other means. Element selection refers to the manner in which the elements areselected individually and directly from the population. If each element is drawn individuallyfrom the population at large, it is an unrestricted sample. Restricted sampling is whereadditional controls are imposed, in other words it covers all other forms of sampling.The classification of sampling design on the basis of representation and element selectionis shown below:


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Representation BasisElement Selection

Probability Nonprobability

Unrestricted Simple random ConvenienceComplex random Purposive

Restricted Systematic JudgementStratified QuotaCluster SnowballDouble

3.6.1 Probability Sampling

Probability sampling is where each sampling unit in the defined target populationhas a known non-zero probability of being selected in the sample. The actual probabilityof selection for each sampling unit may or may not be equal depending on the type ofprobability sampling design used. Specific rules for selecting members from theoperational population are made to ensure unbiased selection of the sampling units andproper sample representation of the defined target population. The results obtained byusing probability sampling designs can be generalized to the target population within aspecified margin of error. The different types of probability sampling designs are discussedbelow:

A. Unrestricted or Simple Random sampling

In the unrestricted probability sampling design every element in the populationhas a known, equal non-zero chance of being selected as a subject. For example, if 10employees (n = 10) are to be selected from 30 employees (N = 30), the researcher canwrite the name of each employee in a piece of paper and select them on a randombasis. Each employee will have an equal known probability of selection for a sample.The same is expressed in terms of the following formula:

Size of sampleProbability of selection = ———————

Size of population

Each employee would have a 10/30 or .333 chance of being randomly selectedin a drawn sample. When the defined target population consists of a larger number ofsampling units, a more sophisticated method can be used to randomly draw the necessarysample. A table of random numbers can be used for this purpose. The table of randomnumbers contains a list of randomly generated numbers. The numbers can be randomly


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generated through the computer programs also. Using the random numbers the samplecan be selected.

Advantages and disadvantages

The simple random sampling technique can be easily understood and the surveyresult can be generalized to the defined target population with a prespecified margin oferror. It also enables the researcher to gain unbiased estimates of the population’scharacteristics. The method guarantees that every sampling unit of the population has aknown and equal chance of being selected, irrespective of the actual size of the sampleresulting in a valid representation of the defined target population.

The major drawback of the simple random sampling is the difficulty of obtainingcomplete, current and accurate listing of the target population elements. Simple randomsampling process requires all sampling units to be identified which would be cumbersomeand expensive in case of a large population. Hence, this method is most suitable for asmall population.

B. Restricted or Complex Probability Sampling

As an alternative to the simple random sampling design, several complexprobability sampling design can be used which are more viable and effective. Efficiencyis improved because more information can be obtained for a given sample size usingsome of the complex probability sampling procedures than the simple random samplingdesign. The five most common complex probability sampling designs viz., systematicsampling, stratified random sampling, cluster sampling, area sampling and double samplingare discussed below:

i. Systematic random sampling

The systematic random sampling design is similar to simple random samplingbut requires that the defined target population should be ordered in some way. Itinvolves drawing every nth element in the population starting with a randomly chosenelement between 1 and n. In other words individual sampling units are selected accordingtheir position using a skip interval. The skip interval is determined by dividing the samplesize into population size. For e.g., if the researcher wants a sample of 100 to be drawnfrom a defined target population of 1000, the skip interval would be 10(1000/100).Once the skip interval is calculated, the researcher would randomly select a startingpoint and take every 10th until the entire target population is proceeded through. Thesteps to be followed in a systematic sampling method are enumerated below:

• Total number of elements in the population should be identified

• The sampling ratio is to be calculated ( n = total population size divided by sizeof the desired sample)


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• The random start should be identified

• A sample can be drawn by choosing every nth entry

Two important considerations in using the systematic random sampling are:

• It is important that the natural order of the defined target population list be

unrelated to the characteristic being studied.

• Skip interval should not correspond to the systematic change in the target

population.


The major advantage is its simplicity and flexibility. In case of systematic samplingthere is no need to number the entries in a large personnel file before drawing a sample.The availability of lists and shorter time required to draw a sample compared to randomsampling makes systematic sampling an attractive, economical method for researchers.The greatest weakness of systematic random sampling is the potential for the hiddenpatterns in the data that are not found by the researcher. This could result in a samplenot truly representative of the target population. Another difficulty is that the researchermust know exactly how many sampling units make up the defined target population. Insituations where the target population is extremely large or unknown, identifying thetrue number of units is difficult and the estimates may not be accurate.

ii. Stratified random sampling

Stratified random sampling requires the separation of defined target populationinto different groups called strata and the selection of sample from each stratum. Stratifiedrandom sampling is very useful when the divisions of target population are skewed orwhen extremes are present in the probability distribution of the target population elementsof interest. The goal in stratification is to minimize the variability within each stratum andmaximize the difference between strata. The ideal stratification would be based on theprimary variable under study. Researchers often have several important variables aboutwhich they want to draw conclusion. A reasonable approach is to identify some basisfor stratification that correlates well with other major variables. It might be a singlevariable like age, income etc or a compound variable like on the basis of income andgender.

Stratification leads to segmenting the population into smaller, more homogeneoussets of elements. In order to ensure that the sample maintains the required precision interms of representing the total population, representative samples must be drawn fromeach of the smaller population groups.


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There are three reasons as to why a researcher chooses a stratified randomsample;

• To increase the sample’s statistical efficiency

• To provide adequate data for analyzing various sub population

• To enable different research methods and procedures to be used in differentstrata.

Drawing a stratified random sampling involves the following steps:

1. Determine the variables to use for stratification

2. Select proportionate or disproportionate stratification

3. Divide the target population into homogeneous subgroups or strata

4. Select random samples from each stratum

5. Combine the samples from each stratum into a single sample of the targetpopulation.

There are two common methods for deriving samples from the strata viz.,proportionate and disproportionate. In proportionate stratified sampling, each stratumis properly represented so the sample drawn from it is proportionate to the stratum’sshare of the total population. The larger strata are sampled more because they make upa larger percentage of the target population. This approach is more popular than anyother stratified sampling procedures due to the following reasons:

• It has higher statistical efficiency than the simple random sample

• It is much easier to carry out than other stratifying methods

• It provides a self-weighing sample i.e., the population mean orproportion can be estimated simply by calculating the mean orproportion of all sample cases.

In disproportionate stratified sampling, the sample size selected from eachstratum is independent of that stratum’s proportion of the total defined target population.This approach is used when stratification of the target population produces samplesizes that contradict their relative importance to the study.

An alternative of disproportionate stratified method is optimal allocation. Inthis method, consideration is given to the relative size of the stratum as well as thevariability within the stratum to determine the necessary sample size of each stratum.The logic underlying the optimal allocation is that the greater the homogeneity of theprospective sampling units within a particular stratum, the fewer the units that would


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have to be selected to estimate the true population parameter accurately for thatsubgroup. This method is also opted for in situation where it is easier, simpler and lessexpensive to collect data from one or more strata than from others.


Stratified random sampling provides several advantages viz., the assurance ofrepresentativeness in the sample, the opportunity to study each stratum and make relativecomparisons between strata and the ability to make estimates for the target populationwith the expectation of greater precision or less error.

iii. Cluster sampling

Cluster sampling is a probability sampling method in which the sampling unitsare divided into mutually exclusive and collectively exhaustive subpopulation calledclusters. Each cluster is assumed to be the representative of the heterogeneity of thetarget population. Groups of elements that would have heterogeneity among the memberswithin each group are chosen for study in cluster sampling. Several groups with intragroupheterogeneity and intergroup homogeneity are found. A random sampling of the clustersor groups is done and information is gathered from each of the members in the randomlychosen clusters. Cluster sampling offers more of heterogeneity within groups and morehomogeneity among the groups.

Single stage and Multistage cluster sampling

In single stage cluster sampling, the population is divided into convenient clustersand required number of clusters are randomly chosen as sample subjects. Each elementin each of the randomly chosen cluster is investigated in the study. Cluster sampling canalso be done in several stages which is known as multistage cluster sampling. For exampleto study the banking behaviour of customers in a national survey , cluster sampling canbe used to select the urban, semi-urban and rural geographical locations of the study.At the next stage, particular areas in each of the location would be chosen. At the thirdstage, the banks within each area would be chosen. Thus multi-stage sampling involvesa probability sampling of the primary sampling units; from each of the primary units, aprobability sampling of the secondary sampling units is drawn; a third level of probabilitysampling is done from each of these secondary units, and so on until the final stage ofbreakdown for the sample units are arrived at, where every member of the unit will bea sample.

Area sampling

Area sampling is a form of cluster sampling in which the clusters are formed bygeographic designations. For example, state, district, city, town etc., Area sampling is aform of cluster sampling in which any geographic unit with identifiable boundaries can


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be used. Area sampling is less expensive than most other probability designs and is notdependent on population frame. A city map showing blocks of the city would be adequateinformation to allow a researcher to take a sample of the blocks and obtain data fromthe residents therein.

Advantages and disadvantages of cluster sampling

The cluster sampling method is widely used due to its overall cost-effectivenessand feasibility of implementation. In many situations the only reliable sampling unit frameavailable to researchers and representative of the defined target population, is one thatdescribes and lists clusters. The list of geographical regions, telephone exchanges, orblocks of residential dwelling can normally be easily compiled than the list of all theindividual sampling units making up the target population. Clustering method is a cost-efficient way of sampling and collecting raw data from a defined target population.

One major drawback of clustering method is the tendency of cluster to behomogeneous. The greater the homogeneity of the cluster, the less precise will be thesample estimate in representing the target population parameters. The conditions ofintracluster heterogeneity and intercluster homogeneity are often not met. For thesereasons this method is not practiced often.

Stratified random sampling Vs Cluster sampling

The cluster sampling differs from stratified sampling in the following manner:

• In stratified sampling the population is divided into a few subgroups, each withmany elements in it and the subgroups are selected according to some criterionthat is related to the variables under the study. In cluster sampling the populationis divided into many subgroups each with a few elements in it. The subgroupsare selected according to some criterion of ease or availability in data collection.

• Stratified sampling should secure homogeneity within the subgroups andheterogeneity between subgroups. Cluster sampling tries to secure heterogeneitywithin subgroups and homogeneity between subgroups.

• The elements are chosen randomly within each subgroup in stratified sampling.In cluster sampling the subgroups are randomly chosen and each and everyelement of the subgroup is studied indepth.

iv. Double sampling

This is also called sequential or multiphase sampling. Double sampling is optedwhen further information is needed from a subset of group from which some informationhas already been collected for the same study. It is called as double sampling becauseinitially a sample is used in the study to collect some preliminary information of interest


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and later a sub-sample of this primary sample is used to examine the matter in moredetail The process includes collecting data from a sample using a previously definedtechnique. Based on this information, a sub sample is selected for further study. It ismore convenient and economical to collect some information by sampling and then usethis information as the basis for selecting a sub sample for further study.

3.6.2 Non-probability Sampling

In nonprobability sampling method, the elements in the population do not haveany probabilities attached to being chosen as sample subjects. This means that thefindings of the study cannot be generalized to the population. However, at times theresearcher may be less concerned about generalizability and the purpose may be just toobtain some preliminary information in a quick and inexpensive way. Sometimes whenthe population size is unknown, then nonproability sampling would be the only way toobtain data. Some non-probability sampling technique may be more dependable thanothers and could often lead to important information with regard to the population. Thenon-probability sampling designs are discussed below;

A. Convenience sampling

Non-probability samples that are unrestricted are called convenient sampling.Convenience sampling refers to the collection of information from members of populationwho are conveniently available to provide it. Researchers or field workers have thefreedom to choose as samples whomever they find, thus it is named as convenience. Itis mostly used during the exploratory phase of a research project and it is the best wayof getting some basic information quickly and efficiently. The assumptions is that thetarget population is homogeneous and the individuals selected as samples are similar tothe overall defined target population with regard to the characteristics being studied.However, in reality there is no way to accurately assess the representativeness of thesample. Due to the self selection and voluntary nature of participation in data collectionprocess the researcher should give due consideration to the non-response error.


Convenient sampling allows a large number of respondents to be interviewedin a relatively short time. This is one of the main reasons for using convenient samplingin the early stages of research. However the major drawback is that the use ofconvenience samples in the development phases of constructs and scale measurementscan have a serious negative impact on the overall reliability and validity of those measuresand instruments used to collect raw data. Another major drawback is that the raw dataand results are not generalizable to the defined target population with any measure ofprecision. It is not possible to measure the representativeness of the sample, becausesampling error estimates cannot be accurately determined.


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B. Purposive sampling

A non-probability sample that conforms to certain criteria is called purposivesampling. There are two major types of purposive sampling viz., Judgment samplingand Quota sampling.

i. Judgment sampling

Judgment sampling is a non-probability sampling method in which participantsare selected according to an experienced individual’s belief that they will meet therequirements of the study. The researcher selects sample members who conform tosome criterion. It is appropriate in the early stages of an exploratory study and involvesthe choice of subjects who are most advantageously placed or in the best position toprovide the information required. This is used when a limited number or category ofpeople have the information that are being sought. The underlying assumption is thatthe researcher’s belief that the opinions of a group of perceived experts on the topic ofinterest are representative of the entire target population.


If the judgment of the researcher or expert is correct then the sample generatedfrom the judgment sampling will be much better than one generated by conveniencesampling. However, as in the case of all non-probability sampling methods, therepresentativeness of the sample cannot be measured. The raw data and informationcollected through judgment sampling provides only a preliminary insight.

ii. Quota sampling

The quota sampling method involves the selection of prospective participantsaccording to prespecified quotas regarding either the demographic characteristics(gender,age, education , income, occupation etc.,) specific attitudes (satisified, neutral,dissatisfied) or specific behaviours ( regular, occasional, rare user of product) .Thepurpose of quota sampling is to provide an assurance that prespecified subgroups ofthe defined target population are represented on pertinent sampling factors that aredetermined by the researcher. It ensures that certain groups are adequately representedin the study through the assignment of the quota.


The greatest advantage of quota sampling is that the sample generated containsspecific subgroups in the proportion desired by researchers. In those research projectsthat require interviews the use of quotas ensures that the appropriate subgroups areidentified and included in the survey. The quota sampling method may eliminate orreduce selection bias.


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An inherent limitation of quota sampling is that the success of the study will bedependent on subjective decisions made by the researchers. As a non-probability method,it is incapable of measuring true representativeness of the sample or accuracy of theestimate obtained. Therefore, attempts to generalize the data results beyond thoserespondents who were sampled and interviewed become very questionable and maymisrepresent the given target population.

iii. Snowball Sampling

Snowball sampling is a non-probability sampling method in which a set ofrespondents are chosen who help the researcher to identify additional respondents tobe included in the study. This method of sampling is also called as referral samplingbecause one respondent refers other potential respondents. Snowball sampling istypically used in research situations where the defined target population is very smalland unique and compiling a complete list of sampling units is a nearly impossible task.While the traditional probability and other non-probability sampling methods wouldnormally require an extreme search effort to qualify a sufficient number of prospectiverespondents, the snowball method would yield better result at a much lower cost. Theresearcher has to identify and interview one qualified respondent and then solicit hishelp to identify other respondents with similar characteristics.


Snowball sampling enables to identify and select prospective respondents whoare small, hard to reach and uniquely defined target population. It is most useful inqualitative research practices. Reduced sample size and costs are the primary advantageof this sampling method. The major drawback is that the chance of bias is higher. Ifthere is a significant difference between people who are identified through snowballsampling and others who are not then, it may give raise to problems. The results cannotbe generalized to members of larger defined target population.

3.7 DETERMINATION OF APPROPRIATE SAMPLING DESIGN

Determining an appropriate sampling design is a challenging issue and has greaterimplications on the application of the research findings. Apart from considering thetheoretical components, sampling issues, advantages and drawbacks of different samplingtechniques, the decision should take into consideration the following factors:

1. Research objectives

A clear understanding of the statement of the problem and the objectives willprovide the initial guidelines for determining the appropriate sampling design. If theresearch objectives include the need to generalize the findings of the research study,


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then a probability sampling method should be opted rather than a non-probabiolitysampling method. In addition the type of research viz., exploratory or descriptive willalso influence the type of the sampling design.

2. Scope of the research

The scope of the research project is local, regional, national or international hasan implication on the choice of the sampling method. The geographical proximity of thedefined target population elements will influence not only the researcher’s ability tocompile needed list of sampling units, but also the selection design. When the targetpopulation is equally distributed geographically a cluster sampling method may becomemore attractive than other available methods. If the geographical area to be covered ismore extensive then complex sampling method should be adopted to ensure properrepresentation of the target population.

3. Availability of resources

The researchers command over the financial and human resources should beconsidered in deciding the sampling method. If the financial and human resourceavailability are limited, some of the more time-consuming, complex probability samplingmethods cannot be selected for the study.

4. Time frame

The researcher who has to meet a short deadline will be more likely to select asimple, less time consuming sampling method rather than a more complex and accuratemethod.

5. Advanced knowledge of the target population

If the complete lists of the entire population elements are not available to theresearcher, the possibility of the probability sampling method is ruled out. It may dictatethat a preliminary study be conducted to generate information to build a sampling framefor the study. The researcher must gain a strong understanding of the key descriptorfactors that make up the true members of any target population.

6. Degree of accuracy

The degree of accuracy required or the level of tolerance for error may varyfrom one study to another. If the researcher wants to make predictions or inferencesabout the ‘true’ position of all members of the defined target population, then sometype of probability sampling method should be selected. If the researcher aims to solelyidentify and obtain preliminary insights into the defined target population, non-probabilitymethods might prove to be more appropriate.


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6. Perceived statistical analysis needs

The need for statistical projections or estimates based on the sample results isto be considered. Only probability sampling techniques allow the researcher toadequately use statistical analysis for estimates beyond the sample respondents. Thoughthe statistical method can be applied on the non-probability samples of people andobjects, the researcher’s ability to accurately generalize the results and findings to thelarger defined target population is technically inappropriate and questionable. Theresearcher should also decide on the appropriateness of sample size as it has a directimpact on the data quality, statistical precision and generalization of findings.

3.8 SAMPLING DECISIONS : SOME ISSUES

Sampling design and sample size are both important to establish therepresentativeness of the sample for generalizability. Even a large sample size cannotyield generalizable research findings if the appropriate sampling design is not used.Similarly unless the sample size is adequate and acceptable to ensure precision andconfidence, the sampling design however justifiable and sophisticated, may not be usefulto the researcher. Hence, a sampling design should give due consideration to bothsample size and design.

If the sample size is too large it would lead to Type II errors ie., the findings ofthe research would be accepted instead of rejection. Due to the large sample size, evenweak relationship might reach significance level and the researcher would be inclined tobelieve that these significant relationships found in the sample can be extended to thepopulation which may not be true. Likewise, if the sample size is too small, it may leadto generalization issues.

Even if the sample size is appropriate, whether the same is statistically significantand relevant, is to be considered. For example, there may be a statistically significantrelationship between two variables but if it explains only a very small percentage of thevariation then it may not have a practical utility.

The following rule- of - thumb proposed by Roscoe (1975) can be consideredin determining appropriate sample size.

1. Sample size larger than 30 and less than 500 are appropriate for most research.

2. If the samples are to be broken into sub samples and groups a minimum samplesize of 30 in each category should be fixed.

3. In multivariate research the sample size should be atleast ten times as large asthe number of variables in the study.

4. In case of simple experimental research a sample as small as 10 to 20 in sizewould yield good results.


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3.8.1 Precision and Confidence in sample size estimation

Since the sample data is used for drawing inference regarding the population,the inferences should be accurate to the extent possible and it should also be possibleto estimate the error. An interval estimation to ensure a relatively accurate estimation ofthe population parameter should be made. For this purpose, statistics that have thesame distribution as the sampling distribution of mean, usually a Z or t statistic is used.

For example the problem at hand is to estimate the mean value of purchasesmade by a customer from department stores. A sample of 64 customers are identifiedthrough systematic sampling method and it is found that the sample mean X = 105 andthe sample standard deviation S = 10. X, the sample mean is a point estimate of ì, thepopulation mean. A confidence interval could be constructed around X to estimate therange within which ì would fall. The standard error S X and the percentage or level ofconfidence required will determine the width of the interval which is determined by theformula.

XKSX ±=µ

nSS X =

25.164

10==XS

the cirtical value of tFor 90% confidence level the k value is 1.645For 95% confidence level the k value is 1.96For 99% confidence level the k value is 2.576If 90% confidence level is desired then

ì = 105 +- 1.645(1.25) ì would fall between 102.944 and 107.056.This indicates that using a sample size of 64, it can be stated with 90% confidence thatthe true population mean value of all customers would fall between Rs. 102.944 and107.056.

If it is required to increase the confidence level to 99% without increasing thesample size, then the precision has to be sacrificed, as could be seen from the followingcalculation:

ì = 105 + _ 2.576(1.25) ì would fall between 101.78 and 108.22


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The width of the interval has increased and as such the precision in the estimation iscomparatively less though the confidence level in the estimation has increased. A largersample size is required if the precision and confidence level has to be increased. Thesample size , n is a function of

• The variability in the population• Precision or accuracy needed• Confidence level desired• Type of sampling plan used.If the sample size cannot be increased, the only way to maintain same level of

precision would be by discarding the confidence level in the estimation. The confidencelevel or certainty of the estimate will be reduced. It is a must for researchers to considerfour aspects while making decisions regarding the sample size.

• The precision level needed in estimating the population characteristics ie theallowable margin of error.

• The level of confidence required ie., the percentage chance the researcher iswilling to take in committing error in the estimation of population parameters.

• The extent of variability in the population on the characteristics investigated• The cost - benefit analysis of increasing the sample size.

3.8.2 Sample data and hypothesis testing

In addition to estimating the population parameters, the sample data can alsobe used to test hypotheses about population values. For example, if we want to deter-mine whether a customer spent the same average amount in purchases at DepartmentA as in Department B a null hypothesis can be formed. Null hypothesis proposes thatthere is no significant differences in the amount spent by customers at the two differentstores. This would be expressed as:

H0 : ìA- ìB = 0 The alternate hypothesis can be states as follows:

H0 : ìA- ìB ? 0If a sample of 20 customers from each of the two stores and find that the mean

value of purchases of customers in Store A is 105 with a standard deviation of 10, andthe corresponding figures for store B are 100 and 15, respectively , it can be seen that

XA – X B = 105-100 = 5The null hypothesis states that there is no significant difference. The probability

of the two group means having a difference of 5 in the context of null hypothesis shouldbe determined. This can be done by converting the difference in the sample means to a


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t statistic and identify the probability of finding a t of that value. The t distribution hasknown probabilities attached to it. The critical values in t distribution for two samples of20 each with 38 as degrees of freedom (n1+n2)-2 = 38) is 2.021. A two tailed test isused to know whether the difference between Store A and Store B is positive or negative.The t statistics can be calculated for testing the hypothesis as follows:

( ) ( )2

2121

1 XX SSxxt

−−−−

=µµ

( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛+

−++

=−2121

222

211

2111

2 nnnnSnSnxSxS

( ) ( )( ) ⎟

⎠⎞

⎜⎝⎛ +

−+×+×

=201

201

2202015201020 22

( ) ( )136.4

BABA xxt µµ −−−=

It is known that 5=− BA xx (The difference in the mean of two stores)

0=− BA µµ (null hypothesis)

209.1136.4

05=

−=t

The t value of 1.209 is much below the value of 2.201at 95% significance level. Evenfor 90% probability requires a value of 1.684. Thus the difference of 5 found betweenthe two stores is not significant. The conclusion is that there is no significant differencebetween the spending pattern of the customers in Store A and in Store B. Thus the nullhypothesis is accepted and the alternate hypothesis is rejected.

3.8.3 Determining the Sample size

Sampling is done to reduce the cost of data collection and for the purpose ofconvenience. However, there is a likelihood of missing some useful information aboutthe population if the sample is inadequate. While deciding the sample size, care should


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be taken to ensure that neither a small sample is selected so as to enhance the risk ofsampling error nor too many units are selected to increase the cost of study. It is neces-sary to make a trade-off between (i) increasing sample size which would reduce thesampling error but increase the cost and (ii) decreasing the sample size which mightincrease the sampling error while decreasing the cost.

Several factors should be considered before deciding the sample size. The firstand the foremost is the size of the error that would be tolerable for the purpose of thedecision making. The second is the degree of confidence with the results of the study. If100 percent confidence of result is needed the entire population must be studied. How-ever it is impractical and costly. Normally, confidence limit is accepted at 99%, 95%and 90%. The confidence and precision aspects are discussed in detail under the head-ing “precision and confidence in sample size estimation” dealt earlier.

For determining the sample size the following relationship is used.

xσ = standard error of the estimate = nσ

can be calculated if we know the upper and lower confidence limits. If these limitsare assumed to be Y, then

Z xσ = Y

where Z is the value of the normal variate for a given confidence level.

The procedure for determining sample size can be illustrated through an example.

A management consultant concern is performing a survey to determine the annual sal-ary of managers numbering 3000 in the textile concern within a district. The sample sizeit should take for the purpose of the study should be ascertained in order to estimate themean annual earnings within plus and minus 1000 at 95 percent confidence level. Thestandard deviation of annual earning of the entire population is known to be Rs.3000.

The desired upper and lower limit is Rs.1000 ie., the estimate of annual earn-ings within plus and minus Rs.1000 should be ascertained.

Z = 1000

The level of confidence is 95 %, the Z value is 1.96.

100096.1 =xσ


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20.51096.1

1000==xσ

The standard error xσ is given by nσ

where the population standard deviation

20.510=nσ

i.e.,

20.5103000=

n

i.e., 88.520.510

3000==n

n = 34.57

Therefore the desired sample size is approximately 35.

SUMMARY

This chapter dealt in detail the various sources of data and the data collectionmethods. The primary data sources viz., the focus group and panels were discussed indetail. The data collection methods viz., the interview, questionnaire, observation andother methods were examined. Sampling design is an important element of research asit decides the validity and the reliability of the research findings. The various probabilityand non-probability techniques were discussed in detail. The method of determiningthe sample size, the precision and confidence desired in estimating the population sizewere explained.

With this background, the next unit provides a detailed discussion on the variousmultivariate techniques used to analyze the data collected.


• Discuss the different data sources, explaining their usefulness and disadvan-tages?

• Discuss the types of error and the steps to avoid the same.

• Discuss the important issues to be considered in designing a questionnaire

• What are projective techniques? Where can it be used?


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• How has the advancement in technology helped data gathering?

• Elucidate the differences between questionnaire and an interview schedule?

• What is an electronic survey? Discuss the issues to be considered in designingand electronic questionnaire

• When should a researcher opt for sampling and why?

• Discuss the steps involved in sampling plan.

• Discuss the various probability and non-probability sampling techniques.

• Non-probability sampling design ought to be preferred to probability samplingdesigns in some cases. Explain with example.

• Discuss the issues concerned with precision and confidence in sampling design.


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A Refresher on Some MultivariateStatistical techniques

Unit structure:

4.1 Introduction4.2 Learning objectives4.3 Multivariate techniques

4.4 Factor analysis4.4.1 Statistics and terms used4.4.2 Steps in conducting factor analysis4.4.3 Uses and limitations of factor analysis4.4.4 Application of statistical package for factor analysis

4.5 Cluster analysis4.5.1 Statistics and terms used4.5.2 Steps in conducting cluster analysis4.5.3 Uses of cluster analysis4.5.4 Application of for cluster analysis

4.6 Discriminant analysis4.6.1 Statistics and terms used4.6.2 Steps in conducting discriminant analysis4.6.3 Uses of discriminant analysis4.6.4 Application of statistical package for discriminant analysis

4.7 Multiple regression & correlation4.7.1 Statistics and terms used4.7.2 Steps in conducting multiple regression& correlation analysis4.7.3 Application of Statistical package for multiple regression & correlation analysis

Unit-4


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4.8 Canonical correlation4.8.1 Statistics and terms used4.8.2 Steps in conducting canonical correlation4.8.3 Application of Statistical packages for canonical correlation analysis

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4.1 INTRODUCTION

Business problems today are more complex. The various functional areas ofmanagement are confronted by multiple independent and/or dependent variables. Thisrequires the application of multivariate techniques to gain an insight into the problems orto make decisions regarding the choices involved. The availability of computers withfast processing speed and versatile software has enhanced the application of thesetechniques which involves complex mathematical calculations. Multivariate analysis canbe defined as “those statistical techniques which focus upon, and bring out in boldrelief, the structure of simultaneous relationships among three or more phenomena”.Thus multivariate analysis refers to a group of statistical techniques used when there aretwo or more measurements on each element and the variables are analyzedsimultaneously. It is concerned with the simultaneous relationship among two or morephenomena. Multivariate techniques are largely empirical and deal with the reality. Thebasic objective underlying the use of multivariate techniques is to represent a collectionof massive data in a simplified manner. In other words, multivariate techniques transforma mass of observations into smaller number of composite scores in such a way that theyreflect as much information as possible contained in the raw data obtained in a researchstudy.

This unit explains some of the multivariate techniques and the application ofstatistical package to solve the same.


After reading this unit, you will be able to:Classify and select appropriate multivariate techniques

Understand where ,why and how to use factor analysis

Know the use of cluster analysis techniques for grouping similar objects orpeople

Classify people or objects into groups using discriminant analysis

Predict a metric dependent variable from a set of metric independent variable


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using multiple regression & correlation

Need and application of canonical correlation

Use the statistical package to apply multivariate techniques

4.3 Multivariate Techniques

Selecting an appropriate technique requires an understanding of the distinctionbetween dependency and interdependency techniques. In dependence method,multivariate techniques is used to explain or predict the dependent variable on the basisof two or more independent variables. A dependence method can be defined as one inwhich a variable is identified as the dependent variable to be predicted or explained byother independent variables. Dependence techniques include multiple regression analysis,discriminant analysis, MANOVA and conjoint analysis.

In interdependence method, no single variable or group of variables is definedas being independent or dependent. The multivariate procedure here involves the analysisof all the variables in the data set simultaneously. The goal of interdependence methodis to group respondents or objects together. In this case no single variable is explainedor predicted by others. Cluster analysis, factor analysis and multidimensional scalingsare the most frequently used interdependence techniques.

In selecting a multivariate technique, two aspects should be considered viz.,

i. Whether the variables can be grouped as dependent and independent orwhether it is based on the interdependency or dependency based technique.

ii. Whether the data is metric or non metric.

The nature of the measurement scales will enable to determine the appropriate multivariatetechnique to be selected to analyze the data. The selection of technique requiresconsideration of the types of measures used for both dependent and independent set ofvariables. If the dependent variable is measured nonmetrically, the appropriate methodsare discriminant and conjoint analysis. If the dependent variable is measured metrically,the techniques like multiple regression, ANOVA, MANOVA and conjoint can be used.Multiple regression and discriminant analysis require metric independents, but they canuse nonmetric dummy variables. ANOVA, MANOVA and conjoint analysis areappropriate with nonmetric independent variables. The interdependence techniques offactor analysis and cluster analysis are most frequently used with metrically measuredvariables, but nonmetric adaptations are possible. The multivariate procedures areexplained in the following figure:


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(Metric) Dependent variable

Level of measurement

Ordinal

• Spearman’s Rank Correlation

• Discriminant analysis

• Conjoint

Interval or Ratio

• Multiple regression

• ANOVA • MANOVA • Conjoint

• Factor • Cluster • Perceptual

Mapping

(Nonmetric)

Nominal

Dependence Methods

Number of Dependent variables

Interdependence methods

Variables in multivariate analysis

Several variables are used in the context of multivariate analysis. They are classifiedinto different categories and explained below:

1. Explanatory variable and criterion variable

If X is considered to be the cause of Y, then X is described as explanatoryvariable. It is also called as causal or independent variable. Y is described as criterionvariable and it is also called as resultant or dependent variable. In some situationsboth explanatory variable and criterion variable may consist of a set of many variablesin which case set X1, X2,X3……Xn is called as set of explanatory variables and theset Y1, Y2,Y3……Yn is called as a set of criterion variables , if the variation in theformer is supposed to cause the variation in the latter as a whole. In economics theexplanatory variables are called as external or exogeneous variables and the criterionvariables are called endogeneious variables. The term external criterion forexplanatory variable and the term internal criterion for criterion variable are alsoused.

2. Observable variables and latent variables

If the explanatory variables described above can be observed directly,it is termed as observable variables. However, there are some unobservablevariables which may have an influence on the criterion variables. They are termedas unobservable or latent variables.

One None


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3. Discrete variables and continuous variables

Discrete variables are those variables which can be measured in termof the integer value only. Continuous variables can assume real value ie thedecimal points.

4. Dummy variable.

This is also called as Pseudo variable. The term is used in technicalsense and is useful in algebraic manipulations in the context of multivariateanalysis. Xi ( i = 1, …., m) is called as dummy variable if only one of Xi is 1 andthe others are all zero.

4.4 FACTOR ANALYSIS

Factor analysis is the most often used multivariate technique in research studiesespecially in studies pertaining to social and behaviourial sciences. It is a class ofprocedures primarily used for data reduction and summarization. Researchers can usefactor analysis for two primary functions in data analysis i.e., to identify the underlyingconstructs in data and to reduce the number of variables to a more manageable set. Infactor analysis there is no distinction between dependent and independent variables, allvariables under investigation are analyzed together to identify underlying factors. It isused to summarize the information contained in a large number of variables into a smallernumber of subsets or factors. The purpose of factor analysis is to simplify the data. Inreducing the number of variables, factor analysis procedures attempt to retain as muchof the information as possible and make the remaining variables meaningful and easy towork with.

Factor analysis resolves a large set of measured variables into relatively fewfactors and the factors so derived are treated as new variables. The value of the newvariables are derived by summing the original values which have been grouped into thefactors. The meaning and name of the new variable is subjectively determined by theresearcher. Factor is a linear combination of data and hence the coordinates of eachobservation or variable is measured to obtain the factor loadings. The factor loadingsrepresent the correlation between the particular variable and the factor and are usuallyplaced in a matrix of correlations between the variable and the factors.

The mathematical basis of factor analysis concerns a data matrix also termedas score matrix symbolized as S. The matrix contains the scores of N persons of Kmeasures.


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a b c k

1 a1 b1 c1 k1

2 a2 b2 c2 k2

3 a3 b3 c3 k3

Persons(objects)

N aN bN cN kN

a1 is the Score of person 1 on measure a , a2 is the score of person 2 on measurea and kN is the measure of person N on measure k. It is assumed that scores on each

measure are standardized The sum of scores in any column of the matrix

S is zero and the variance of scores in any column is 1. A factor is any linear combinationof the variable in a data matrix and can be stated in general manner :

A = Wa a + Wbb + …. Wkk . The values are obtained and factor loading iethe factor variable correlations is are calculated. Then communality symbolized as h2,the eigen value and the total sum of squares are obtained and the results are interpreted.The technique of rotation is done in order to obtain realistic results. The rotation revealsdifferent structures in the data. Finally the factor scores are obtained which enables toexplain the factors. After obtaining factor scores, several other multivariate analysis likecluster, multiple regression, discriminant analysis etc can be performed.

4.4.1 Statistics and terms associated with factor analysis

The statistics and some of the basic terms used in factor analysis are explainedbelow:

i. Factor : A factor is an underlying dimension that account for severalobserved variables. There can be more than one factor depending uponthe nature of the study and the number of variable involved in it.

ii. Factor – loadings: Factor-loadings are simple correlation between thevariables and the factors. It explains how closely the variables are relatedto each one of the factor discovered. This is also known as factor-variablecorrelations and acts as the key to understand the meaning of factor. Theabsolute size rather than plus or minus signs of the loading is important inthe interpretation of a factor.

iii. Communality (h2): communality sympolised by h2 shows how much ofeach variable is accounted for by the underlying factor taken together. A


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high value of communality means that not much of the variable is left overafter whatever the factor represent is taken into consideration. It is workedout for each variable as under:

h2 of ith variable = ( ith factor loading of factor A )2 + ( ith factor loading of factor B)2 + ….

iv. Eigen value (latent root): It represents the total variance explained byeach factor. Eigen value is the sum of squared values of factor loadingsrelating to a factor. It indicates the relative importance of each factor inaccounting for the particular set of variables analysed.

v. Total sum of squares: When the eigen values of all factors are totaled, theresulting value is termed as the total sum of squares. This value, when dividedby the number of variables involved in the study results in an index thatshows how the particular solution accounts for what, all the variables takentogether represent.

vi. Rotation: Rotation reveals different structures in data. Different rotationsgive results that appear to be entirely different, but from the statistical pointof view, all results are taken as equal. However, right rotation should beselected to make sense out of the results. If the factors are independentorthogonal rotation is done and if the factors are correlated, an obliquerotation is made. Communality for each variable will remain undisturbedregardless of rotation but the eigen values will change as a result of rotation.

vii. Factor scores: Factor scores are composite scores estimated for eachrespondent on the derived factors. With the factor scores several othermultivariate analyses can be performed.

viii. Bartlett’s test of sphericity : It is a test statistic used to examine thehypothesis that the variables are uncorrelated in the population.

ix. Correlation matrix: It shows the simple correalation r, between all possiblepairs of variables included in the analysis. The diagonal elements which areall 1, are usually omitted.

x. Kaiser-Meyer-Olkin(KMO) measure of sampling adequacy: The KMOmeasure of sampling adequacy is an index used to examine theappropriateness of factor analysis. High values between .05 and 1.0 indicatefactor analysis is appropriate. Values below .5 imply that factor analysismay not be appropriate.


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xi. Scree plot: Scree plot is a plot of the eigen values against the number offactors in order of extraction.

4.4.2 Steps in conducting factor analysis

The first step involved in conducting factor analysis is to define the problem andidentify the variables involved. A correlation matrix is to be constructed and a methodof factor analysis to be performed is to be selected. Decision regarding the number offactors to be extracted and the method of factor analysis is made. The rotated factorsare interpreted. Depending upon the objective the factor scores are calculated orsurrogate variables selected so as to represent the factors in subsequent multivariateanalysis. Finally, the fit of the factor analysis model is determined. The steps are illustratedbelow :

Formulate the problem

Construct the correlation matrix

Identify the method of factor analysis

Determine the number of factors

Rotate the factors

Select the surrogate variables Calculate the factor scores

Determine the model fit

Interpret the factors


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1. Formulate the problem

Problem formulation includes several tasks. The objectives of factor analysisshould be identified and the variables to be included in the factor analysis should bespecified based on the past research, theory and judgement of the researcher. Thevariables should be appropriately measured in an interval or ratio scale. An appropriatesample size should be identified. The sample size should be atleast four or five timesmore than the variables identified for the study. For e.g. , if the study includes 20 variables,then the sample size should be a minimum of 80 or 40. If the sample size is small andthe ratio is not maintained, the results should be interpreted cautiously.

2. Construct the correlation matrix

Correlation matrix provides valuable insight and is the basis for further analyticalprocess. The variables identified for the study should be correlated in order to conductthe factor analysis. If the correlation between the variables is small, factor analysis maynot be appropriate. It can also be expected that the variables that are highly correlatedwith each other would also highly correlate with the same factor or factors. Formalstatistics are available for testing the appropriateness of the factor model. Bartlett’s testof sphericity can be used to test the null hypothesis that the variables are uncorrelated inthe population. A large value of test statistics will favour the rejection of the null hypothesisand if the null hypothesis cannot be rejected, then the appropriateness of factor analysisshould be questioned.

Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy can also be used.The index compares the magnitudes of the observed correlation coefficients to themagnitude of the partial correlation coefficients. Small values of the KMO statisticsindicate that the correlation between pairs of variables cannot be explained by othervariables and that factor analysis may not be appropriate. Generally a value greaterthan 0.5 is desirable.

3. Identify the method of factor analysis

After determining the appropriateness of factor analysis for analyzing the data,a suitable method should be selected. The approach used to derive the weights orfactor scores coefficients differentiates the various method of factor analysis. The twomost commonly employed factor analytic procedures are principal component andcommon factor analysis. Based on the researcher’s objective the procedure to be usedis chosen. Principal component analysis is used when the objective is to summarizeinformation in a larger set of variables into a fewer factors. It is recommended if the


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primary concern is to determine the minimum number of factors that will account formaximum variance in the data for use in subsequent multivariate analysis. The factorsare called principal components. If the researcher is attempting to uncover underlyingdimensions surrounding the original variables, common factor analysis is used. Principalcomponent analysis is based on the total information in each variable, whereas commonfactor analysis is concerned only with the variance shared among all the variables.

Principal component analysis

Factor analysis begins with the construction of a new set of variables based onthe relationships in the correlation matrix. Principal component analysis method transformsa set of variables into a new set of composite variables or principal components that arenot correlated with each other. The linear combination of factors accounts for the variancein the data as a whole. The best combination makes up the first principal componentand is the first factor. The second principal component is defined as the best linearcombination of variables for explaining the variance not accounted for by the first factor.Likewise, there may be a third, fourth and kth component, each being the best linearcombination of variables not accounted for by the previous factors.

This process continues till all the variance is accounted. However, usually it isstopped after a small number of factors has been extracted. The output of the principalcomponent analysis might look like the data given below:

Extracted % of variance Cumulativecomponents accounted for variance

Component no. 1 74% 74%

Component no. 2 15% 89%

Component no. 3 11% 40 %

Numerical results from a factor analysis will be presented like the followingtable. The values in the table are correlation coefficients between the factor and thevariables.


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A B

Variable Unrotated factors Rotated factors

I II h2 I II

A .70 -.40 .65 .15

B .60 -.50 .61 .03

C .60 -.35 .48 . .4

D .50 .50 .50 .06 .

E .60 .50 .61 .13

F .60 .60 .72 .07

Eigenvalue 2.18 1.39

% of variance 36.30 23.20

Cumulative % 36.30 59.50

In the above table .70 is the correlation coefficient between variable A andfactor I. The correlation coefficients are called as loadings. Eigen values are the sum ofthe variances of the factor values. For factor I the eigenvalue is sum of .702

+602+602+502+602+602 which is 2.18. The eigen value 2.18 divided by the number ofvariables i.e., 6 yields an estimate of the amount of total variance explained by thefactor. In the example given above Factor I accounts for 36% of the total variance.The column heading h2 gives the communalities i.e., the estimates of the variance in eachvariable that is explained by the two factors. From the above table it can be seen thatin the case of Variable A the communality is .702 + (-402) = .65 which indicates that 65percent of the variance in variable A is statistically explained in terms of factors I and II.

In the unrotated factor, a loading does not provides much information. It is notpossible to find the variables with high loading in factor I and factor II. Rotation enablesto identify the variables associated with the factors.

4. Determine the number of factors

It is possible to compute as many principal components as there are variables,but it does not serve the purpose of conducting a factor analysis. In order to summarize

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.70

.77

.85


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the information contained in the original variables, smaller number of factors should beextracted. The question of how many factors are to be extracted arise. Several proce-dures are discussed below for determining the number of factors.

A Priori determination:

Due to prior knowledge the researcher knows how many factors to extractand thus can specify the number of factors to be extracted beforehand. The extractionof factors is completed as soon as the desired number of factors is extracted.

Determination based on Eigenvalues:

In this approach only factors with eigen values greater than 1.0 are retained,the other factors are not included in the model. An eigen value represents the amount ofvariance associated with the factor. Hence, factors with variance greater than 1.0 areincluded. If the number of variables is less than 20, this approach will result in conser-vative number of factors.

Determination based on Scree plot:

A scree plot is a plot of the eigen values against the number of factors in orderof extraction. The shape of plot is used to determine the number of factors. The plottypically has a distinct break between the steep slope of factors with large eigenvaluesand a gradual trailing off associated with the rest of the factors. The gradual trailing offis referred as scree. The point at which the scree begins denotes the true number offactors.


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Determination based on the percentage of variance:

In this approach the number of factors to be extracted is determined in such away that the cumulative percentage of variance extracted by the factors reaches asatisfactory level. Satisfactory level depends upon the problem at hand. However, it isrecommended that the factors extracted should account for at least 60 percent of thevariance.

Determination based on Split-Half reliability:

The sample is split in half and factor analysis is performed on each half. Onlyfactors with high correspondence of factor loadings across the two sub-samples areretained.

Determination based on significance tests:

It is possible to determine the statistical significance of the separate eigen val-ues and retain only those factors that are statistically significant. A drawback is that withlarge samples sizes greater than 200, many factors are likely to be statistically signifi-cant, although many of these may account for only a small proportion of the total vari-ance.

5. Rotate the factors

The initial or unrotated factor matrix indicates the relationship between the fac-tors and the individual variables. However, it is difficult to identify the variables with afactor or interpreting the factor is difficult from the unrotated matrix. Through rotationthe factor matrix is transformed into a simpler one that is easier to interpret. Throughrotation it is possible to see that each factor has a non-zero or significant loadings orcoefficients for only some of the variables. Likewise, it can also be ensured that eachvariable has a non-zero or significant loading with only a few factors, or with only onefactor. If several factors have high loadings with the same variable, it is difficult tointerpret them. Rotation does not affect the communalities and the percentage of totalvariance explained. However, the percentage of variance accounted for by each of thefactor changes. It can be seen from the table shown in previous pages that the varianceexplained by the individual factors is redistributed by rotation. Hence, different meth-ods of rotation results in identification of different factors.

Two basic types of rotation available are the orthogonal rotation and obliquerotation. If the axes are maintained at right angles then the rotation is called orthogonalrotation.. VARIMAX rotation. is the most commonly used rotation. Its goal is tominimize the complexity of the components by making the large loadings larger and thesmall loadings smaller within each component. There are other rotational methods.QUARTIMAX rotation makes large loadings larger and small loadings smaller within


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each variable. EQUAMAX rotation is a compromise that attempts to simplify bothcomponents and variables. These are all orthogonal rotations, that is, the axes remainperpendicular, so the components are not correlated with one another.

When the axes are not maintained at right angles and the factors are correlatedthen it is called oblique rotation. Oblique rotation should be used when factors in thepopulation are likely to be strongly correlated.

In the table given above, it can be seen that the factor interpretability is more incase of the rotated matrix than the unrotated matrix. Rotated factor matrix forms thebasis for interpretation of the factors.

6. Interpret factors

Interpretation is facilitated by identifying the variables that have large loadingon the same factor. The factor can be interpreted in terms of the variables that load highon it. In the above table it can be seen that variables A,B,C load high on Factor I andhence factor I is interpreted in terms of variables A,B and C. Likewise, the variablesD,E,F are interpreted in terms of Factor II.

Another useful method in interpretation of factors is to plot the variables usingthe factor loadings as coordinates. Variables at the end of the axis are those that havehigh loadings on only that factor and hence describe the factor.

7. Calculate factor scores

If the goal of factor analysis is to reduce the original set of variables to a smallerset of composite variables i.e., factors for application in subsequent multivariate analy-sis it is useful to compute factor scores for each respondent. A factor is simply a linearcombination of the orginal variable. the factor scores for the ith factor may be estimatedas follows:

Fi = Wi1X1 + Wi2X2+ Wi3X3+ ….+ WikXk

The weights or factor score coefficients are obtained from the factor scorecoefficient matrix. Only in principal component analysis it is possible to compute exactfactor scores and these scores are uncorrelated. In common factor analysis, estimatesof these scores are obtained, and there is no guarantee that the factors will be uncorrelatedwith each other. The factor scores can be used instead of the original variables in thesubsequent multivariate analysis.

8. Select the surrogate variables

Instead of computing factor scores, the researcher may select the surrogate orsubstitute variables. Selection of surrogate variables involves identifying some of theoriginal variables for use in the subsequent analysis. This allows the researcher to con-duct further analysis and interpret the results in terms of original variables rather than the


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factor scores. The variables can be selected by examining the factor matrix and select-ing for each factor the variable with the highest loading on that factor. The variablecould be as surrogate variables for the associated factor. This process will work well ifone factor loading for a variable is clearly higher than all other factor loadings. Howeverif two or more variables have similar loadings the choice will be difficult. In such casesthe choice of variables should be made on the basis of theoretical and measurementconsideration. For example, the theory may suggest that a variable with a slightly lowerloading is more important than one with slightly higher loading. Likewise, if variablehaving a slightly lower loading has been measured more precisely, it should be selectedas the surrogate variable.

9. Determine the Model fit

Determining the fitness of the model is the final step in factor analysis. A basicassumption underlying factor analysis is that the observed correlation between vari-ables can be attributed to common factors. The correlation between variables can bereproduced from the estimated correlations between the variables and the factors. Thedifferences between the observed correlations as given in the input crorrelation matrixand the reproduced correlations as estimated from the factor matrix can be examinedto determine the fitness of the model. The differences are called residuals. If there aremany large residuals, the factor model does not provide a good fit to the data and themodel should be reconsidered.

R-type and Q-type factor analysis

Factor analysis may be R-type factor analysis or Q-type. In R-type factoranalysis high correlations occur when respondents who score high on variable 1 alsoscore high on variable 2 and respondents who score low on variable 1 also score lowon variable 2. Factors emerge when there are high correlations within group of variables

In Q-type factor analysis, the correlations are computed between pairs ofrespondents instead of pairs of variables. High correlations occur when respondents Apattern of responses on all the variables is much like respondents B pattern of responses.Factors emerge when there are high correlations within a group of people. Q-typeanalysis is useful when the object is to sort out people into groups based on theirsimultaneous responses to all the variables.

4.4.3 Uses and limitations of factor analysis:

The benefits of using factor analysis are dealt below:

i. Interdependency and pattern delineation. If a researcher has a table of dataconcerning attitude, lifestyle, personality characteristics, or answers to a


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questionnaire—and if he suspects that these data are interrelated in a complexfashion, then factor analysis may be used to untangle the linear relationshipsinto their separate patterns. Each pattern will appear as a factor delineating adistinct cluster of interrelated data.

ii. Parsimony or data reduction. Factor analysis can be useful for reducing amass of information to an economical description. For example, data on fiftycharacteristics for 500 respondents are unwieldy to handle, descriptively oranalytically. Management of data analysis, and clear understanding of such dataare facilitated by reducing them to their common factor patterns. These factorsconcentrate and index the dispersed information in the original data and cantherefore replace the fifty characteristics without much loss of information.

iii. Structure. Factor analysis may be employed to discover the basic structure ofa domain. Data collected on a large sample of groups and factor analyzed canhelp disclose this structure.

iv. Classification or description. Factor analysis is a tool for developing anempirical typology. It can be used to classify respondent’s profiles into typeswith similar characteristics or behavior.

v. Scaling. A researcher often wishes to develop a scale on which individuals,groups, or nations can be rated and compared. A problem in developing ascale is to weight the characteristics being combined. Factor analysis offers asolution by dividing the characteristics into independent sources of variation(factors). Each factor then represents a scale based on the empirical relationshipsamong the characteristics. As additional findings, the factor analysis will givethe weights to employ for each characteristic when combining them into thescales. The factor score results are actually such scales.

vi. Hypothesis testing. Hypotheses are generally framed regarding dimensions ofattitude, personality, group, social behavior, and conflict. Factor analysis maybe used to test for their empirical existence. Which characteristics or behaviorshould, by theory, be related to which dimensions can be postulated in advanceand statistical tests of significance can be applied to the factor analysis results.

vii. Data transformation. Factor analysis can be used to transform data to meetthe assumptions of other techniques. For example, application of the multipleregression technique assumes independent variables that are statisticallyunrelated . If the predictor / independent variables are correlated in violation ofthe assumption, factor analysis can be employed to reduce them to a smallerset of uncorrelated factor scores. The scores may be used in the regression


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analysis in place of the original variables, with the knowledge that the meaningfulvariation in the original data has not been lost. Likewise, a large number ofdependent variables also can be reduced through factor analysis.

viii.Exploration The unknown domain may be explored through factor analysis. Itcan reduce complex inter-relationships to a relatively simple linear expressionand it can uncover unsuspected, startling, relationships. Unlike pure scienceresearchers, usually the social science researcher is unable to manipulate variablesin a laboratory but they deal with the manifold complexity of behaviors in theirsocial setting. Factor analysis thus fulfills some functions of the laboratory andenables the researcher to untangle interrelationships, to separate different sourcesof variation, and to partial out or control for undesirable influences on thevariables of concern.

ix. Mapping. Besides facilitating exploration, factor analysis also enables aresearcher to map the social terrain by the systematic attempt to chart majorempirical concepts and sources of variation. These concepts may then be usedto describe a domain or to serve as inputs for further research

x. Theory: The analytic framework of social theories or models can be built fromthe geometric or algebraic structure of factor analysis.

Application of Factor analysis:

Some areas in which factor analysis can be used are:

i. It can be used in market segmentation for identifying the underlying variableson which to group the customers.

ii. Factor analysis can be used in product research to determine the brand attributesthat influence the consumers’ choice.

iii. In advertising studies, factor analysis can be used to understand the mediaconsumption habits of the consumers.

iv. In pricing studies, it can be used to identify the characteristics of price-sensitiveconsumers.

Limitations:

i. Factor analysis involves laborious computations at a heavy cost burden. Withthe computer facility and statistical packages the factor analysis has becomerelatively faster and easier, however large factor analysis are still bound to bequite expensive.


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ii. The results of single factor analysis are considered generally less reliable anddependable as the factor analysis mostly starts with a set of imperfect data.The factor analysis should be done atleast twice. If similar results are obtainedthe confidence regarding the results will increase

iii. Factor analysis is a complicated decision tool that can be used only when onehas thorough knowledge and enough experience of handling this tool.

4.4.4 Application of Statistical package: Factor analysis

A marketing concern would like to predict the sales of the cars from a set ofvariables. However, many of the variables are correlated and this might adversely resultin a wrong prediction. The variables are vehicle type, price, engine size, fuel capacity,fuel efficiency, wheel base, horsepower, width, length. Factor analysis with principalcomponent extraction can be used to identify a manageable subset of predictors.

The steps to be followed in performing factor analysis and interpretation ofthe same output is discussed below:

From the Data Editor Window

Click on “Analyze”Click on “Data Reduction”Click on “Factor...”

The following Factor Analysis dialog box will appear.


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Select the variables you want to enter into the factor analysis by double clicking onthem, or use the shift or control keys to select them and click the right arrow key tomove the selected variables to the “Variables” list on the right. Click Extraction

Extracting factors and factor rotation:

There is no hard and fast rule to determine the number of factors. A commonlyused convention is to use the number of factors with eigen values greater than 1. Thestatistical package will select this number by default. The scree plot may also be usedto determine the number of factors

Click on “Rotation”. The Factor Analysis: Rotation dialog box appears.


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1 Under method, select “Varimax” by clicking on it2 Under Display, select “Rotated solution” by clicking on it3 Click on “Continue” which brings you back to the Factor Analysis dialog

box.4 Click on “OK” to run the analysis.5 Click “Continue”.6 Click “OK” in the Factor Analysis dialog box.

Interpretation of the output

The output obtained through performing the steps discussed is detailed in thefollowing pages. Communalities

Communalities indicate the amount of variance in each variable that is accountedfor. Initial communalities are estimates of the variance in each variable accounted for byall components or factors. For principal components extraction, this is always equal to1.0 for correlation analysis. The communalities in this table are all high, which indicatesthat the extracted components represent the variables well.


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Total variance explained

The variance explained by the initial solution, extracted components, and rotatedcomponents is displayed. This first section of the table shows the Initial Eigenvalues.The Total column gives the eigenvalue, or amount of variance in the original variablesaccounted for by each component. The % of Variance column gives the ratio of thevariance accounted for by each component to the total variance in all of the variables.The Cumulative % column gives the percentage of variance accounted for by the first ncomponents. For example, the cumulative percentage for the second component is thesum of the percentage of variance for the first and second components. For the initialsolution, there are as many components as variables.

The second section of the table shows the extracted components. They explainnearly 88% of the variability in the original ten variables, so the complexity of the dataset can be considerably reduced by using these components, with only a 12% loss ofinformation.

The rotation maintains the cumulative percentage of variation explained by theextracted components, but that variation is now spread more evenly over the


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components. The large changes in the individual total suggest that the rotated componentmatrix will be easier to interpret than the unrotated matrix.

The scree plot enables to determine the optimal number of components. Theeigen value of each component in the initial solution is plotted. Generally, the compo-nents on the steep slope are extracted. The components on the shallow slope contrib-ute little to the solution. The last big drop occurs between the third and fourth compo-nents, so the first three components are selected.

Rotated Component Matrixa

.935 -3.45E-03 4.136E-02

.933 .242 5.565E-02

.753 .436 .292

.155 .943 6.862E-023.616E-02 .884 .314

.384 .759 .231-.101 9.478E-02 .954-.543 -.318 -.681.398 .495 .676.519 .533 .581

Price in thousandsHorsepowerEngine sizeLengthWheelbaseWidthVehicle typeFuel efficiencyFuel capacityCurb weight

1 2 3Component

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

Rotation converged in 4 iterations.a.


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The rotated component matrix helps to determine what each componentsrepresent. The first component is most highly correlated with Price in thousands andHorsepower. Price in thousands is a better representative, however, because it is lesscorrelated with the other two components. The second component is most highlycorrelated with Length. The third component is most highly correlated with Vehicletype. This suggests that Price in thousands, Length, and Vehicle type can be focusedfor in further analysis.

4.5. CLUSTER ANALYSIS

Like factor analysis cluster analysis examines an entire set of interdependentrelationships. Cluster analysis, also called as classification analysis or numerical taxonomyis a class of techniques used to classify objects or cases into relatively homogeneousgroups called clusters. Objects in each cluster tend to be similar to each other anddissimilar to objects in other clusters. The primary objective of cluster analysis is toclassify objects into relatively homogeneous groups based on the set of variablesconsidered. Objects in a group are relatively similar in terms of these variables anddifferent from objects in other groups.

Cluster analysis does not distinguish between dependent and independentvariables. The independent relationship between the whole set of variables are examined.The following illustration shows an ideal clustering situation in which clusters are distinctlyseparated on two variables. Each object is assigned to only one cluster and there is nooverlapping areas.

Variable 2

Var

iabl

e 1


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The following illustration provides a clustering situation that is practicallyencountered. The boundaries for the clusters are not clear cut and the classification ofconsumers is not obvious, as many of them could be grouped into one cluster or another.

Cluster and discriminant analysis are both concerned with classification.However discriminant analysis requires prior knowledge of the cluster or groupmembership for each object included to develop the classification rule. In cluster analysisthere is no a priori information about the group or cluster membership for any of theobjects. Groups are suggested by data not defined a priori.

4.5.1 Statistics and terms associated with cluster analysis

Clustering methods are based on relatively simple procedures that are notsupported by an extensive body of statistical reasoning. The methods are based onalgorithms and hence differ from factor, discriminant, regression, ANOVA which arebased on extensive statistical reasoning. The terms associated are discussed below:

i. Agglomeration schedule: The schedule gives information on the objects orcases being combined at each stage of a hierarchical clustering process.

ii. Cluster centroid: The cluster centroid is the mean values of the variables forall the objects in a particular cluster.

iii. Cluster centers: The cluster centers are the initial starting points in non-hierarchical clustering. Clustering are built around these centers or seeds.

iv. Cluster membership: Cluster membership indicates the cluster to which eachobject or case belongs.

v. Dendrogram: Dendrogram or tree graph, is a graphical device for displayingclustering results. Vertical lines represent clusters that are joined together. Theposition of the line on the scale indicates the distance at which clusters werejoined. The dendrogram is read from left to right.


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vi. Distance between cluster centers: The distance indicates how separated theindividual pairs of clusters are. Clusters that are widely separated are distinctand desirable.

vii. Icicle diagram: An icicle diagram is a graphical display of clustering results. Itresembles a row of icicles hanging from the roof of a house. The columnscorrespond to the objects being clustered and the rows correspond to thenumber of clusters.

viii.Similarity/distance coefficient matrix: It is a matrix containing pair wisedistances between objects or cases.

4.5.2 Steps in conducting cluster analysis

The first step in cluster analysis is to formulate the clustering problem by definingthe variables on which the clustering will be based. Then an appropriate distance measuremust be selected. The distance measure determines how similar or dissimilar the objectsbeing clustered are. Several clustering procedure are available from which the researchershould select the appropriate one suitable to the problem. The researcher should decidethe number of clusters and the derived cluster should be interpreted in terms of thevariables used to cluster them. Finally the validity of the clustering process should beassessed.

The steps are explained in the clustering procedure are listed below: Formulate the problem

Select a distance measure

Select a clustering procedure

Decide on the number of clusters

Interpret and profile clusters

Assess the validity of clustering


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1. Formulate the problem

The most important aspect in formulating a problem is selecting the variable onthe basis of which clusters are to formed. Including irrelevant variables will affect theclustering solution. The variables selected should describe the similarity between objectsin terms of the problem selected. The variables should be selected based on past research,theory or in consideration of the hypothesis being tested. In exploratory research, theresearcher should act based on judgment and intuition.

2. Select a distance measure

The objective of clustering is to group similar objects together. For this purposesome measures should be adopted to assess how similar or different the objects are.The most common approach is to measure similarity in terms of distance between pairsof objects. Objects with smaller distances between them are most similar to each otherthan those at larger distances. The following are some of the methods available tomeasure the distance between objects:

i. The Euclidean distance is the most commonly used measure. It is the squareroot of the sum of the squared differences in values for each variable.

ii. The city-block or Manhattan distance measure the distance between twoobjects in terms of the sum of the absolute differences in values for each variable.

iii. The Chebychev distance between two objects is the maximum absolutedifference in values for any variable.

The variables involved in the study may be measured in terms of different units forexample in terms of Likert scale, frequency, percentages etc. in such cases beforeclustering the respondents , the data must be standardized by rescaling each variableto have a mean of zero and a standard deviation of unity. The outliners or caseswith non-conforming values should also be eliminated.

3. Select a Clustering Procedure

Clustering procedures may be broadly categorized as hierarchical or non-hierarchical. Hierarchical clustering is characterized by development of hierarchy ortree-like structure. Hierarchical methods can be of two types viz., divisive oragglomerative. Divisive clustering starts with all the objects grouped in a single cluster.Clusters are divided until each object is in a separate cluster. Agglomerative clusteringstarts with each object in a separate cluster. Clusters are formed by grouping objectsinto bigger and bigger clusters. This process is continued until all objects are formedinto a single cluster. Agglomerative consists of (i) Linkage methods, (ii) Variance methodsand (iii) Centroid methods.


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(i). Linkage methods

Linkage methods include single linkage, complete linkage and average linkage.The single linkage method is based on minimum distance or the nearest neighbour rule.The first two objects clustered are those that have the smallest distance between them.The next shortest distance is identified, and either the third object is clustered with thefirst two, or a new two-object cluster is formed. At every stage the distance betweentwo clusters is the distance between their two closest points as illustrated below:

Single linkage method does not work well when the clusters are poorly defined.The complete linkage method is similar to single linkage except that it is based on themaximum distance or the furthest neighbour approach. The distance between twoclusters is calculated as the distance between their two furthest points.

In the average linkage method the distance between two clusters is defined asthe average of the distances between all pairs of objects, where one member of the pairis from each of the clusters. This method uses information on all pairs of distances, notmerely the minimum or maximum distances. Hence it is preferable to single and completelinkage method.

Cluster 1 Cluster 2

Minimum distance

Single Linkage

Cluster 1 Cluster 2

Maximum distance

Complete Linkage

Cluster 1 Cluster 2

Average distance

Average Linkage


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(ii). Variance methods

The variance method attempts to minimize within cluster variance. Wardsprocedure is a commonly used variance method. For each cluster, the means of all thevariables are computed. Subsequently for each object the squared Euclidean distanceto the cluster means is calculated. The distances are summed for all the objects. At eachstage, the two clusters with the smallest increase in the overall sum of squares within thecluster distances are combined. This is illustrated below:

(iii). Centroid methods

In the Centroid methods, the distance between two clusters is the distancebetween their centroids i.e., means of all the variables. Every time objects are grouped,new centroid is computed.

The average linkage method and ward’s method perform better than otherprocedures.

Non-hierarchical clustering

The non-hierarchical clustering method is also known as k-means clustering.This method includes sequential threshold, parallel threshold and optimizing partitioning.

i. In sequential threshold method, a cluster center is selected and all objectswithin a prespecified threshold value from the center are grouped together.Next a new cluster or seed is selected and the process is repeated for the

Ward’s Method

Centroid Method


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unclustered points. Once an object is clustered with a seed, it is no longerconsidered for clustering with subsequent seeds.

ii. The parallel threshold method operates similarly, however several clustersare selected simultaneously and objects within the threshold are groupedwith the nearest center.

iii. The optimizing partitioning method differs from the other threshold methodi.e., the objects can later be reassigned to clusters to optimize an overallcriterion, such as average within-cluster distance for a given number ofclusters.

Non-hierarchical clustering is faster than the hierarchical methods and ispreferable when the number of objects or observation is large. The major drawback ofnon-hierarchical procedure is that the number of clusters must be prespecified and theselection of cluster centers is arbitrary. The clustering results depend on how the centersare selected.

The hierarchical and non-hierarchical methods can be used together. An initialclustering solution can be obtained using a hierarchical procedure and the number ofcluster and cluster centroids so obtained are used as inputs to the optimizing portioningmethod.

4. Decide on the number of clusters

Some guidelines to make decision regarding the number of clusters are:

Theoretical, conceptual or practical considerations may suggest the number of clusters.

In hierarchical clustering , the distance at which clusters are combined can beused as criteria. This information can be obtained form the agglomerationschedule or from the dendrogram.

In non-hierarchical clustering, the ratio of total within-group variance to betweengroup variance can be plotted against the number of clusters. The point atwhich a sharp bend occurs indicates an appropriate number of clusters.Increasing the number of clusters beyond this point will not be useful

The relative sizes of the cluster should be meaningful with each cluster having moreelements. It is not useful to have only one element in a cluster.

5. Interpret and profile the clusters

Interpreting and profiling clusters involves examining the cluster centroids. Thecentroids represent the mean values of the objects contained in the cluster on each of


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the variables. The centroids enable us to describe each cluster by assigning it a name orlabel. It will be more helpful to profile the clusters in terms of variables that are not usedfor clustering. The demographic, psychographic, product usage, media usage or othervariables can be used for profiling. The variables that significantly differentiate betweenclusters can be identified via discriminant analysis and one-way analysis of variance.

6. Assess Reliability and Validity

Several decisions are made on the basis of cluster analysis, hence clusteringsolutions should not be accepted without assessing the reliability and validity. Thefollowing procedure can be followed to provide adequate checks on the quality ofclustering results.

Perform cluster analysis on the same data using different distance measure.Compare the results across measures to determine the stability of the solutions.

Use different methods of clustering and compare the results.

Split the data randomly into halves, perform clustering separately on each halfand compare the cluster centroids across the two sub-samples.

Delete variables randomly. Perform clustering based on the reduced set ofvariables. Compare the results with those obtained by clustering based on theentire set of variables. In non-hierarchical clustering, the solution may dependon the order of cases in the data set. Multiple runs using different order of casescan be performed until solutions are stabilized.

4.5.3 Uses of Cluster analysis:

Some practical area where cluster analysis can be used is explained below;

i. Segmenting the market: The consumers may be clustered on the basis of thebenefits sought from the purchase of a product. Each cluster would consist ofconsumers who are relatively homogeneous in term of the benefit they seek.This is called benefit segmentation.

ii. Understanding buyer behaviour: Cluster analysis can be used to identifyhomogeneous groups of buyers. The buying behaviour of each group may beexamined separately.

iii. Identifying new product opportunities: clustering brands and products enablesto identify the competitive sets within the market. Brands within the same clustercompete more fiercely with each other than with brands in other clusters. A


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firm can examine its current offerings compared to those of the competitors toidentify potential new product opportunities.

iv. Selecting test markets: Clustering geographical areas enables to selectcomparable cities to test the various marketing strategies.

v. Reducing data: Clusters analysis can be used as data reduction tool to developclusters or subgroups of data that are more manageable than individualobservations.

4.5.4 Application of Statistical package : Cluster analysis

A car manufacturing concern would like to ascertain the current market for itsvehicles. For this it needs to group cars based on the information available regardingvarious models of vehicles. The information regarding the vehicle type, price, enginesize, fuel capacity, fuel efficiency, wheel base, horsepower, width, length are available.The segmentation could be performed using the Hierarchical Cluster Analysis procedure.The steps are discussed below:

To perform cluster analysis from the menu choose:

Analyze Classify Hierarchical Cluster...


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Select the variables on basis of which clusters are to be formed. Also select thecase labeling variable

Click Plots.

Select Dendrogram.

Select None in the Icicle group.

Click Continue.

Click Method in the Hierarchical Cluster Analysis dialog box


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Select Nearest neighbor as the cluster method.Select Z scores as the standardization in the Transform Values group.Click Continue.Click OK in the Hierarchical Cluster Analysis group.

Interpretation of the output

The output of cluster analysis is discussed below: The dendrogram is a graphicalsummary of the cluster solution.


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Cases are listed along the left vertical axis. The horizontal axis shows thedistance between clusters when they are joined. Parsing the classification tree to deter-mine the number of clusters is a subjective process. Generally, the “gaps” betweenjoinings along the horizontal axis is looked for . Starting from the right, there is a gapbetween 20 and 25, which splits the automobiles into two clusters. There is anothergap from approximately 4 to 15, which suggests 6 clusters

The agglomeration Schedule

The agglomeration schedule is a numerical summary of the cluster solution

At the first stage, cases 8 and 11 are combined because they have the smallestdistance. The cluster created by their joining next appears in stage 7. In stage 7, theclusters created in stages 1 and 3 are joined. The resulting cluster next appears in stage8. When there are many cases, the table becomes rather long, but it may be easier toscan the coefficients column for large gaps rather than scan the dendrogram. A goodcluster solution sees a sudden jump (gap) in the distance coefficient. The solution be-fore the gap indicates the good solution. The largest gaps in the coefficients columnoccur between stages 5 and 6, indicating a 6-cluster solution, and stages 9 and 4,indicating a 2-cluster solution. These are the same as the findings from the dendrogram.

4.6. DISCRIMINANT ANALYSIS

Discriminant analsyis is a dependence multivariate technique. The purpose ofdependence technique is to predict a variable form a set of independent variables. It isalso used for predicting group membership on the basis of two or more independentvariables. Discriminant analysis is a technique for analyzing data when the criterion ordependent variable is categorical and the predicator or independent variables are intervalin nature. For eg., The dependent variable may be the choice of a brand and theindependent variable may be the ratings of attributes of soft drinks on 5 point Likertscale.


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The objectives of discriminant analysis are as follows:

1. Development of discriminant fuctions which will best discriminate betweenthe categories of the dependent variable. Discriminant function is the linearcombination of the predictor or independent variables that will best discriminatebetween the categories of dependent variable.

2. To examine whether significant differences exist among the groups in terms ofthe predictor variables.

3. Determination of the predictor variables which contributes to most of theintergroup differences.

4. Classification of cases to one of the groups based on the values of the predictorvariables

5. Evaluation of the accuracy of classification.

The discriminant analysis techniques are described by the number of categoriespossessed by the dependent variable/ criterion variable. When the ciriterion variablehas two categories, the technique is known as two-group discriminant analysis. Whenthree or more categories are involved the technique is referred to as multiple discriminantanalysis. In two group discriminant analysis it is possible to derive only one discriminantfunction. In multiple discriminant analysis, more than one function may be computed.

The discriminant analysis model involves the linear combination of the followingform:

D = b0 + b1X1 + b2X2+ b3X3 + ……..+ bkXk

D = Discriminant scorebn = Discriminant coefficients or weightsXn = Predictors or independent variable

The coefficients or weights(b) are estimates so that the groups differ as muchas possible on the values of the discriminant function. This will happen when the ratio ofbetween group sum of squares to within-group sum of squares for the discriminantscores is at the maximum. Any other linear combination will result in a smaller ratio.Several statistics are associated with discriminant analysis which are dealt below:

4.6.1 Statistics associated with discriminant analysis

The important statistics associated with discriminant analysis are:

Centroid :

The centroid is the mean value for the discriminant socres for a particular group.The means for a group on all functions are the group centroids.


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Classification matrix:

This is also called as confusion matrix or prediction matrix. It contains the numberof correctly and misclassified cases. The correctly classified cases appear on the diagonalbecause the predicted and actual groups are the same. The off diagonal elementsrepresent cases that have been incorrectly classified. The sum of the diagonal elementsdivided by the total number of cases represent the hit ratio.

Discriminant function coefficients:

The unstandardised discriminant function coefficients are the multipliers ofvariables, when the variables are in the original units of measurement.

Discriminant scores:

The unstandardized coefficients are multiplied by the values of the variables.These products are summed and added to the constant term to obtain the discriminantscores.

Eigenvalue.

For each discriminant function, the eigen value is the ratio of between group towithin group sums of squares. Large eigenvalues imply superior functions.

F values and their significance:

These are calculated from a one-way ANOVA, with the grouping variableserving as the categorical independent variable. Each predictor serves as metric dependentvariable in the ANOVA.

Group means and group standard deviation:

These are computed for each predictor for each group.

Pooled within- group correlation matrix:

The pooled within group correlation matrix is computed by averaging theseparate covariance matrices for all the groups.

Structure correlations:

This is also referred to as discriminant loadings , the structure correlationsrepresent the simple correlations between the predictors and the discriminant function.

Total correlation matrix:

If the cases are treated as if they were from a single sample and the correlationscomputed, a total correlation matrix is obtained.


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Wilks’ ë :

Sometimes also called the U statistic, Wilks’ ë for each predictor is the ratio ofthe within group sum of squares to the total sum of squares. The values range between0 and 1. Large values of ë nearing 1 indicates that the group means are not different.Small values of ë nearing 0 indicate that the group means are different.

4.6.2 Steps in conducting two group discriminant analysis

The steps in conducting two group discriminant analysis are discussed below:

1. Formulate the problem:

The first step in discriminant analysis is to formulate the problem by identifyingthe objectives, the criterion variable and the dependent variables. The criterion variablesmust consist of two or more mutually exclusive and collectively exhaustive categories.

Formulate the problem

Research design issues

Assumptions

Estimating the discriminant functions

Assess the validity of discriminant analysis

Interpretation of discriminant functions

Validation of discriminant results


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When the dependent variable is interval or ratio scaled, it must first be converted intocategories. The predictor variable should be selected based on a theoretical model orprevious research or in the case of exploratory research, the experience of the researchershould guide the selection.

2. Research design issues

Research design for discriminant anlysis requires consideration of the followingissues (1) selection of both dependent and independent variables, (2)deciding the samplesize needed for estimation of discriminant function and (3) division of sample for validationpurpose.

(i) Selection of dependent and independent variable

To apply discriminant analysis the researcher should specify the dependent andthe independent variables. Dependent variable should be categorical and the independentvariables are metric. The number of dependent variables categories can be two ormore, but these groups must be mutually exclusive and exhaustive. Each observationshould be such that it can be placed into only one group. The dependent variable insome cases may involve two groups eg., purchasers and non purchasers. In somecases it may also involve several groups such as heavy users, medium users, light usersand non-users of a product.

After the decision regarding the dependent variables, the researcher must decide aboutthe independent variables to be included in the analysis. Independent variables can beselected in the following two ways.

• Identifying the variables from the previous research or from the theoretical modelthat is underlying the basis of research question.

• The second approach is intuition i.e., utilizing the researchers’ knowledge andintuitively selecting variables for which previous research is not available.

(ii) Sample size

The ratio of sample size to the number of predictor variables should beconsidered in discriminant analysis. Many studies suggest a ratio of 20 observations foreach predictor variable. If adequate sample is not maintained the results become unstable.The minimum size recommended is five observations per independent variable. Theratio applies to all variables considered in the analysis, even if all of the variables consideredare not entered into the discriminant function. In addition to the overall sample size, theresearcher must also consider sample size of each group. The smallest group size mustexceed the number of independent variables. The practical guideline is that each groupshould have atleast 20 observations.


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(iii) Division of sample

The sample should be divided into two groups called as estimation or analysissample and the holdout or validation sample. The analysis sample is used for estimationof the discriminant function. The hold out or validation sample is reserved for validatingthe discriminant function. It is essential that each subsample should be of adequate sizeto support conclusions from the results.

If the sample is large enough, it can be split in half. One half serves as theanalysis sample and the other is used for validation. The analysis sample is used todevelop the discriminant function and the validation sample is used to test the discriminantfunction. This method of validating the sample is referred to as the split-sample orcross-validation approach. The role of the halves is then interchanged and the analysisis repeated. This is called double cross- validation. The distributions of the number ofcases in the analysis and validation samples follow the distribution in the total sample.For example, if the total sample contains 60 percent users and 40 percent non-usersof the product, then the analysis and validation sample would each contain 60 percentusers and 40 percent non-users.

3. Assumptions

The key assumptions in deriving the discriminant function are multivariatenormality of the independent variables and the unknown dispersion and covariancestructures for the groups as defined by the dependent variable. As in the case of allmultivariate techniques, the implicit assumptions that all relationship are linear applies todiscriminant analysis also. The researcher should examine the data and if assumptionsare violated, the researcher should identify the alternative methods available and theimpacts on the results that can be expected. Data not meeting the multivariate normalityassumption can cause problems in the estimation of the discriminant function.

4. Estimating the discriminant function

To derive at the discriminant functions, the researcher must decide on the methodof estimation and then determine the number of functions to be retained. After theestimation of the function the overall model fit can be assessed in several ways.

Methods to derive discriminant function:

Two computational methods are used to derive the discriminant function viz.,simultaneous/direct method and the step-wise method. The direct method involvesestimating the discriminant function so that all the predictors are included simultaneously.In this case each independent variable is included regardless of its discriminating power.This method is appropriate when the researcher wants to include all the independent


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variables for theoretical reasons and is not interested in viewing the intermediate resultsbased only on the most discriminating variables. In step-wise discriminant analysis theindependent variables are entered one at a time, based on their ability to discriminateamong groups. The stepwise method is useful when the researcher wants to consider arelatively large number of independent variables for inclusion in the function.

Statistical significance

The researcher must assess the level of significance of the discriminant functioncomputed. It would not be meaningful to interpret the analysis if the discriminant functionsestimated were not statistically significant. Significance test can be done on the basis ofa number of statistical criteria viz., wilk’s lambda, Hotelling’s trace and Pillai criterion.The significant criterion of .05 or beyond is often used. If the higher levels of risk forincluding non-significant results are acceptable, significance level at .2 or .3 may befixed.

If the number of groups is three or more, the researcher must decide not only ifthe discrimination between groups is significant but also if each of the estimateddiscriminant function is statistically significant.

Assessing Overall Fit

Assessing overall fit of the selected discriminant function involves three tasks:calculating discriminant Z scores for each observation, evaluating group differences onthe discriminant Z scores and assessing group membership predication accuracy.

5. Interpretation of discriminant functions

Interpretation involves examining the discriminant functions to determine therelative importance of each independent variable in discriminating between the groups.Three methods are available to assess the importance of the discriminating function.

i. The sign and magnitude of the standardized discriminant weights ordiscriminant coefficient assigned to each variable is taken into consideration.A small weight may indicate that the corresponding variable is irrelevant indetermining the relationship

ii. Discriminant loadings also referred as structure correlations, measure thesimple linear correlations between each independent variable and thediscriminant functions. Variables are associated with the functions in whichit has a higher loading

iii. If step-wise method is selected in deriving discriminant functions, anadditional means of interpreting the relative discriminating power of theindependent variable is available through partial F values. The absolute


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sizes of the significant F values are examined and ranked. Large F valuesindicate greater discriminatory power.

6. Validation of the discrimination results

The final stage in discriminant analysis involves validating the discriminant resultsto provide assurance that the results have external as well as internal validity. The mostfrequently used procedure to validate the discriminant function is to divide the groupsrandomly into analysis and holdout sample. This involves developing a discriminantfunction with the analysis sample and applying the same to the holdout sample.

Instead of randomly dividing the total sample into analysis and holdout samplesonce, the total sample can be divided randomly divided into analysis and holdout samplesseveral times, each time testing the validity of the function through the development of aclassification matrix and a hit ratio.

Either one of these two approaches can be used only when the smallest groupsize is atleast three times the number of predictor variables.

4.6.3 Uses of Discriminant analysis:

• A medical researcher may record different variables relating to patients’backgrounds in order to learn which variables best predict whether a patient islikely to recover completely (group 1), partially (group 2), or not at all (group 3).

• A biologist could record different characteristics of similar types (groups) offlowers, and then perform a discriminant function analysis to determine the setof characteristics that allows for the best discrimination between the types.

• Discrimininant analysis can help to distinguish between heavy, medium and lightusers of a product in terms of consumption habits and lifestyles

• It enables to carry out image research i.e., it enables to distinguish betweencustomers who exhibit favorable perceptions of a store and those who do not

• It assists in distinguishing how market segments differ in media consumptionhabits

1.6.4 Application of Statistical package : Discriminant analysis

Using cluster analysis a telephone company has categorized the customers intofour groups viz., Basic service, e- service, plus service and total service. The concernwants to predict group membership so as to customize offers for individual prospectivecustomers. The predication should be based on the demographic data viz., gender,age, marital status, income, education, number of years in current address, years withcurrent employer, retired and number of people in family. The Discriminant Analysisprocedure can be used to classify customers.


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The steps are discussed below:

To run the discriminant analysis, from the menus choose:

AnalyzeClassifyDiscriminant...

Select the grouping variable.

Click Define Range.

Enter the Minimum

Enter the Maximum


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Click Continue

Click Classify in the Discriminant Analysis dialog box.

Select Summary table and Territorial map.Click Continue.Click OK in the Discriminant Analysis dialog box.

These selections produce a discriminant model using the step-wise method ofvariable selection.Interpretation of the Output

The discriminant model produced using the step-wise method of variableselection is discussed below:

Variables Not in Analysis


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When there are lots of predictors, the stepwise method can be useful by auto-matically selecting the “best” variables to use in the model. The stepwise method startswith a model that doesn’t include any of the predictors. At each step, the predictor withthe largest F to Enter value that exceeds the entry criteria (by default, 3.84) is addedto the model. The variables left out of the analysis have F to Enter values smallerthan 3.84, so not added.

The following table displays statistics for the variables that are in the analysis at eachstep.

Variables in Analysis

Tolerance is the proportion of a variable’s variance not accounted for by otherindependent variables in the equation. A variable with very low tolerance contributeslittle information to a model and can cause computational problems.

F to Remove values are useful for describing what happens if a variable isremoved from the current model (given that the other variables remain). F to Removefor the entering variable is the same as F to Enter at the previous step (shown in theVariables Not in the Analysis table

From the Summary of the Canonical functions – eigen values table it can beseen that nearly all of the variance explained by the model is due to the first two dis-criminant functions


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Eigen Values

Three functions are fit automatically, but due to its minuscule eigenvalue, thethird function can be ignored.

Wilks’ lambda shows that only the first two functions are usefulWilks’ Lamda

Structure Matrix

The structure matrix enables to identify the significant variables within eachfunction.

When there is more than one discriminant function, an asterisk(*) marks eachvariable’s largest absolute correlation with one of the canonical functions. Within each


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function, these marked variables are then ordered by the size of the correlation. Levelof education is most strongly correlated with the first function, and it is the only variablemost strongly correlated with this function. Years with current employer, Age in years,Household income in thousands, Years at current address, Retired, and Genderare most strongly correlated with the second function, although Gender and Retiredare more weakly correlated than the others. The other variables mark this function as a“stability” function. Number of people in household and Marital status are moststrongly correlated with the third discriminant function, but since this is a useless function,predictors are also useless.

The territorial map

The territorial map helps to study the relationships between the groups and thediscriminant functions. Combined with the structure matrix results, it gives a graphicalinterpretation of the relationship between predictors and groups.

The territorial map offers a comprehensive view of the discriminant model.Thefirst function, shown on the horizontal axis, separates group 4 from the others. SinceLevel of education is strongly positively correlated with the first function, this suggests


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that group 4 customers are, in general, the most highly educated. The second functionseparates groups 1 and 3. Since, the third function was found to be rather insignificant,only the first two discriminant functions are plotted.

From Wilks’ lambda, it can be understood that the model is doing better thanguessing, but the classification results should be considered to determine how much

better the model is.

Given the observed data given in the above table it can be seen that, the “null”model (that is, one without predictors) would classify maximum number of customers


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into the model group, Plus service. Thus, the null model would be correct 281/400(281 customers out of 400 customers ) = 28.1% of the time.

The discriminant model gets 11.4% more or 39.5% of the customers. Inparticular, the model excels at identifying Total service customers. However, it does anexceptionally poor job of classifying E-service customers.

4.7 MULTIPLE REGRESSION & CORRELATION

Multiple regression is a multivariate statistical technique used to examine therelationship between a single dependent variable and a set of independent variables.The objective of multiple regression analysis is to use the independent variables whosevalues are known to predict the single dependent variable. Each independent variableis weighted by the regression analysis procedure to estimate the maximal predictionfrom the set of independent variables. The weights denote the relative contribution ofthe independent variables to the overall prediction and facilitate interpretation as to theinfluence of each variable in making the prediction.

Regression analysis is the most widely used technique for business decision-making. It is the foundation for building business forecasting models. It can also be usedto study the factors influencing consumer decisions. It enables to evaluate the expectedreturn from a stock option etc.,

4.7.1 Statistics associated with multiple regression and correlation analysis

The statistics and some of the basic terms used in multiple correlation andregression analysis are explained below:

Beta coefficient : It is a standardized regression coefficient on the basis of which thedirect comparison between coefficients regarding their relative explanatory power ofdependent variable can be made.

Correlation coefficient( r): It indicates the strength of the association between anytwo metric variables. The sign (+ or - ) indicates the direction of the relationship. Thecorrelation value can range from -1 to + 1 with +1 indicating a perfect positiverelationship, 0 indicating no relationship and – 1 indicating a perfect negative or reverserelationship

Coefficient of determination(R2): It is the measure of proportion of the variance ofthe dependent variable about its mean that is explained by the independent variables.The coefficient vary between 0 and 1 . Higher value of R2 greater the explanatorypower of the regression equation and therefore better prediction of dependent variableis possible.


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Collinearity : It is an expression of relationship between two(collinearity) or moreindependent variables( multicollenearity). Two independent variables are said to exhibitcomplete collinearity if their correlation coefficient is 1, and complete lack of collinearityif their correltion coefficient is 0. Multicollinearity occurs when any single independentvariable is highly correlated with a set of other independent variables.

Regression coefficient (bn): Numerical value of the parameter estimate directlyassociated with an independent variable. In the model Y = b0+ b1x1, the value b1 is theregression coefficient for the variable X1 .

Residual (e or E): Error in predicting the sample data. It is an estimate of the truerandom error in the population and not just the error in the prediction of the sample.

4.7.2 Steps in conducting multiple regression analysis

The steps in conducting multiple regression analysis are discussed below:

1. Formulating the research problem

The starting point in multiple regression is identification of the research problem.In selecting suitable application for multiple regression , three issues are to be consideredviz., the appropriateness of the research problem, specification of a statistical relationshipand selection of the dependent and independent variables.

(i)Appropriateness of research problem

Multiple regression is an appropriate tool for research problems concernedwith prediction and explanation. These problems are not mutually exclusive, an applicationof multiple regression analysis can address either or both types of research problem.The fundamental purpose of multiple regression is to predict the dependent variablewith a set of independent variables. In predicting the dependent variables, two moreobjectives are fulfilled viz., it provides an objective means of assessing the predictivepower of a set of independent variables and also enables comparing two or more set ofindependent variables to ascertain the predictive power of each variate.

It also provides a means of objectively assessing the degree and character ofthe relationship between dependent and independent variables. The independent variablesin addition to their collective prediction of the dependent variable, may be consideredfor their individual contribution to the variate and its predictions. The variate may beinterpreted on any of the three perspectives: the importance of independent variables,the type of relationships found, or the interrelationships among the independent variables.


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(ii)Specifying a statistical relationship

Multiple regression is appropriate when the researcher is interested in a statisticalnot functional relationship. In case of specifying the functional relationship there will beno error in prediction. In specifying a statistical relationship there will always be somerandom component to the relationship being examined. In statistical relationship morethan one value of the dependent value will be usually be observed for any value of anindependent variable.

(iii)Selection of dependent and independent variables

The success of multiple regression techniques depends on the selection of thevariables that are to be used in the analysis. The selection of dependent and independentvariable should be based on conceptual or theoretical grounds. In selecting the variablesthe measurement error and specification error should be taken into consideration.Measurement error refers to the degree that the variable is an accurate and consistentmeasure of the concepts that are being studied. Measurement error may happen more,in case of selection of the dependent variable. The most problematic issue in independentvariable selection is specification error which is concerned with the inclusion of irrelevantvariables or the omission of relevant variables from a set of independent variables.

2. Research design issues

In the design of a multiple regression analysis the researcher must consider theissues regarding the sample size, the nature of the independent variables and the possiblecreation of new variables to represent special relationship between the dependent andthe independent variables. The sample size used in multiple regression is most importantas the effect of sample size is most directly felt in the statistical power of the significancetesting and the generalization of the result. Power in multiple regression refers to theprobability of detecting as statistically significant a specific level of R2 or a regressioncoefficient at a specified significance level for a specific sample size. Sample size has adirect and sizable impact on power. Sample size also affects the generalization of theresults by the ratio of observations to independent variables. There should be atleastfive observations for each independent variable in the variate. If the ratio falls below thisstipulation, the risk of ‘over fitting’ the variate to the sample i.e., making the result toospecific to the sample result which leads to lack of generalization.

Multiple regression deals with the linear association between metric dependentand independent variables. If non-metric data needs to be included or to represent anyeffects other than non-metric variable, new variables must be created by transformations.


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The transformations can be performed by using simple commands in various statisticalpackages.

3. Assumptions

In carrying out multiple regression analysis several assumptions about thedependent and independent variables and about the relationships as a whole are made.once the variate has been derived through multiple regression, it acts collectively inpredicting the dependent variable. The assumption is made not only for the individualvariables but also for the variate itself. The variate and its relationship with the dependentvariable should also meet the assumptions of multiple regression. The assumptions are;

(i) The linearity of the relationship between dependent and independentvariables is assumed. This represents the degree to which the changein the dependent variable is associated with the independent variable.Partial regression slots can be used to show the relationship betweena single independent variable and dependent variable. A curvilinearpattern of residuals indicate a non-linear relationship between aspecific independent variable and the dependent variable.

(ii) The presence of unequal variances i.e., heteroscedasticity is one ofthe most common assumption violations. Plotting the residuals againstthe predicted dependent values and comparing them to the nullplot shows a consistent pattern , if the variance is not constant.

(iii) Independence of the error terms is assumed in regression. Eachpredicted value is assumed to be independent, it is not related toany other prediction i.e., it is not sequenced by any other variable.

(iv) Normality of the dependent and independent variables or both isassumed. However, this is the most frequently encounteredassumption violation.

4. Estimating the regression model

In order to estimate the regression model and to assess the overall predictiveaccuracy of the independent variables three tasks must be performed viz.,(i) selectinga method for estimating the regression model,(ii) assessing the statistical significance ofthe overall model in predicting the dependent variable and (iii)determining whether anyobservation exert undue influence on the results.


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(i) Method for estimating the regression model

The regression model can be estimated using confirmatory approach , sequentialsearch methods and combinational approach.

(a) The confirmatory approach is used when the set of independent variables iscompletely specified. The researcher has total control over the variable selection.

(b) In sequential search method the regression equation is estimated using a set ofvariables and then selectively adding or deleting variables until some overall crieteria isachieved. This approach provides an objective method for selecting variables thatmaximizes the prediction with the smallest number of variables employed. There aretwo types of sequential approach viz., step-wise estimation and forward addition andbackward elimination.

(i). Step-wise estimation allows the researcher to examine the contribution ofeach independent variable to the regression model. Each variable is considered forinclusion prior to developing the equation. The independent variable with the greatestcontribution is added first, then based on incremental approach further variables areselected.

(ii). Forward addition and backward elimination procedures are based on trialand error approach. The forward addition model is similar to the step-wise procedurementioned above. The backward elimination procedure computes a regression equationwith all the independent variables and then deletes independent variables that do notcontribute significantly.

(c) The combinational approach is where all possible combinations of the independentvariables are used using a procedure called all-possible-subsets regression. All possiblecombinations of the independent variables are examined as the best fitting set of variablesis identified.

(ii) Assessing the statistical significance of the overall model in predicting thedependent variable

Testing for statistical significance is needed when the analysis is based on samplerather than census. Significance testing of regression coefficient provides a statisticallybased probability estimate of whether the estimated coefficients across a large numberof samples of a certain size will be different than zero. The test is done to determinewhether the impacts represented by the coefficients are generalizable to other samplesfrom the population.


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(iii). Identifying the influential observations

Individual observation should be focused so as to identify the observation thatlie outside the general patterns of the data set or that strongly influence the regressionresults. Influential observations are of three basic types: outliers, leverage points andinfluentials. Outliers are observations that have large residual values and can be identifiedonly with respect to a specific regression model. Leverage points are observations thatare distinct form the remaining observations based on their independent variable values.Influential observation includes all observations that have a disproportionate effect onthe regression results. It includes outliers and leverage points and may include otherobservations as well.

5. Interpreting the regression variate

Each of the independent variables should be standardized before the regressionequation is estimated. The coefficients resulting from standardized data are called betacoefficients. The advantage is that they eliminate the problem of dealing with differentunits of measurement thus reflecting the relative impact on the dependent variable of achange in one standard deviation in either variable. Since there is a common unit ofmeasurement, it enables to identify the variable which is having the highest impact.

6. Validation of the results

After identifying the regression model, the next step is to ensure that it representsthe general population and is appropriate for the situation in which it will be used. Themost appropriate empirical validation approach is to test the regression model on anew sample drawn form the general population. The ability to collect new data is limiteddue to cost, time pressures or availability of respondents. In this case, the split samplescan be used i.e., the sample can be divided into two parts viz., an estimation model forthe purpose of creating the regression model and the holdout or validation sub-sampleto ‘test’ the equation.

4.7.3 Application of Statistical package : Multiple Regression and Correlation

An automobile concern wants to identify the sales for a variety of personalmotor vehicles so as to identify over and under performing models. This necessitatesestablishing a relationship between vehicle sales and vehicle characteristics. Informationconcerning different makes and models of cars like the vehicle type, price, engine size,fuel capacity, fuel efficiency, wheel base, horsepower, width, length are available.

Linear regression can be performed in STATISTICAL PACKAGEto identifymodels that are not selling well. Steps are discussed below:


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To run a linear regression analysis, from the menu choose:

Analyze Regression Linear

Select the dependent variable


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Select the Independent variables.

Select Stepwise as the entry method.

Select the case labeling variable.

Click Statistics

Select Casewise diagnostics and type 2 in the text box.

Click Continue.

Click Plots in the Linear Regression dialog box.

Select the y variable and the x variable.

Select Histogram.

Click Continue.

Click Save in the Linear Regression dialog box.


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Select Standardized in the Predicted Values group.

Select Cook’s and Leverage values in the Distances group.

Click Continue.

Click OK in the Linear Regression dialog box

Interpretation of output

The collinearity among the variables needs to be verified from the outputcollinearity diagnostics. If the eigenvalues are close to 0, it means that the predictorsare highly inter-correlated and that small changes in the data values may lead to largechanges in the estimates of the coefficients. Condition index values greater than 15indicates a possible problem with collinearity; greater than 30, a serious problem.

The following collinearity table show that there are no eigenvalues close to 0,and all of the condition indexes are much less than 15. The model built using step-wisemethods does not have problems with collinearity.


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Collinerity diagnostics

Checking the model fit

The ability of the model to predict the dependent variable can be checkedthrough the model fit summary

Model Summary

The adjuster R square value predicts the fitness of the model. Higher value ispreferable.

Stepwise Co-efficients

The step-wise algorithm chooses price and size of the vehicle wheelbase aspredictors. Sales are negatively affected by price and positively affected by size. Hencethe conclusion is that cheaper, bigger cars sell well.


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4.8 CANONICAL CORRELATION

Canonical correlation analysis is a multivariate statistical model that facilitatesthe study of interrelationship among set of multiple dependent variables and multipleindependent variables. Multiple Regression predicts a single dependent variable from aset of multiple independent variables, canonical correlation simultaneously predictsmultiple dependent variables from multiple independent variables.

Canonical correlation has fewest restrictions on the types of data on which itoperates. Other techniques impose high level of restriction and hence the informationobtained from them is of higher quality. However in case of situations with multipledependent and independent variables, canonical correlation is the most appropriateand powerful multivariate technique. Canonical correlation’s goal is to quantify the strengthof the relationship between two sets of dependent and independent variables.

Canonical correlation deals with the association between composite of sets ofmultiple dependent and independent variables. During the process it develops a numberof independent canonical functions that maximize the correlation between the linearcomposites, also known as canonical variates, which are sets of dependent andindependent variables. Each canonical function is actually based on the correlationbetween two canonical variates, one variate for the dependent variables and one forthe independent variables. The variates are derived to maximize their correlation.

4.8.1 Statistics and Key terms associated with Canonical correlation

The statistics and some of the basic terms used in multiple correlation andregression analysis are explained below:

Canonical variable or variate: A canonical variable, also called a variate, is a linearcombination of a set of original variables in which the within-set correlation has beencontrolled. That is, the variance of each variable accounted for by other variables in theset has been removed. It is a form of latent variable. There are two canonical variablesper canonical correlation (function). One is the dependent canonical variable, theindependent variable are called the covariate canonical variable.

Canonical correlation, also called a characteristic root, is a form of correlationrelating two sets of variables. There may be more than one canonical correlation, eachrepresenting an orthogonally separate pattern of relationships between the two latentvariables. The maximum number of canonical correlations between two sets of variablesis the number of variables in the smaller set.


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Pooled Rc2 (pooled canonical correlation) is the sum of the squares of all the canonical

correlation coefficients, representing all the orthogonal dimensions in the solution bywhich the two sets of variables are related. Pooled Rc

2 is used to assess the extent towhich one set of variables can be predicted or explained by the other set.

Eigenvalues They reflect the proportion of variance in the canonical variate explainedby the canonical correlation relating two sets of variables.

Canonical weight. This is also called as the canonical function coefficient or thecanonical coefficient: The standardized canonical weights are used to assess the relativeimportance of individual variables’ contributions to a given canonical correlation.

Structure correlation coefficient is also called as canonical factor loadings Astructure correlation is the correlation of a canonical variable with an original variable inits set. Structure correlations are used for the following purposes.

Interpreting the Canonical Variables: The magnitudes of the structurecorrelations help in interpreting the meaning of the canonical variables with which theyare associated. Larger canonical factor loadings should be weighted more when assigningan interpretive label to the given canonical correlation. A rule of thumb is for variableswith correlations of 0.3 or above to be interpreted as being part of the canonical variable,and those below not to be considered part of the canonical variable.

Calculating Variance Explained in a Given Original Variable: The squareof the structure correlation is the percent of the variance in a given original variableaccounted for by a given canonical variable on a given canonical correlation.

Canonical communality coefficient is the sum of the squared structure coefficientsfor a given variable. The canonical communality coefficient measures how much of agiven original variable’s variance is reproducible from the canonical variables.

Redundancy coefficient, d, also called Rd, measures the percent of the variance ofthe original variables of one set may be predicted from a (usually the first) canonicalvariable from the other set. High redundancy means high ability to predict.

4.8.2 Steps in conducting Canonical correlation

The steps involved in building canonical correlation are discussed below:

1. Formulating the objectives

The canonical correlations analysis is highly flexible in terms of both the numberand types of variables handled and hence more complex problems could be addressed.


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Two sets of variables - dependent and independent are identified in the canonicalcorrelation. Once the variables are identified, the canonical correlation can be performedfor the following purposes;

(i) Determining the magnitude of relationship between two sets of variables

(ii) Deriving a set of weights for each set of dependent and independent variablesso that the linear combinations of each set are maximally correlated.

(iii) Explaining the relationship between the sets of dependent and independentvariables by measuring the relative contribution of each variable to theextracted canonical functions.

2. Designing a Canonical Correlation Analysis

The researcher in case of canonical analysis may add more number of dependentand independent variables without understanding the implications on the sample size.The issues on the sample size and the necessity for a sufficient number of observationsper variable are frequently encountered. Small samples will not represent the correlationswell and very large samples will indicate statistical significance in all instances, evenwhen practical significance is not indicated. The sample should constitute of at least 4observations for one variable to avoid overfitting the data.

The classification of data as dependent and independent variables does notassume much significance for statistical estimation, as canonical correlations weightsboth variates to maximize the correlation and it does not place any particular emphasizeon either variate. However a researcher must have conceptually linked set of the variablesbefore applying canonical correlation analysis. This makes the specification of dependentand independent variables essential so as to establish a strong conceptual foundationfor the variables.

3. Assumptions

The following assumptions are made;

Multivariate normality is required for significance testing in canonical correlation.This assumption is violated when dichotomous, dummy, and other discrete variablesare used.

Low multicollinearity: To the extent that the variables within the independent sets ofvariables are highly intercorrelated, the canonical coefficients will be unstable. Thecoefficients for some variables may be misleadingly low or even negative becausevariance has already been explained by other variables.


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Homoscedasticity and other assumptions of correlation are assumed.

Minimal measurement error is assumed since low reliability affects the correlationcoefficient. Canonical correlation also can be quite sensitive to missing data.

Adequate sample size must exist to reduce the chances of Type II error (thinkingyou don’t have something when you do). Stevens (1986) recommends at least 20times as many cases as variables in the analysis in order to interpret the first canonicalcorrelation only. For two canonical correlations, Barcikowski and Stevens (1975)recommend 40 to 60 times as many cases as variables.

No or few outliers. Outliers can substantially affect canonical correlation coefficients,particularly if sample size is not very large.

5. Deriving the Canonical Functions and Assessing Overall Fit

The first step on canonical correlation analysis is to derive one or more canonicalfunctions. The canonical correlation analysis focuses on accounting for maximum amountof relationship between the two sets of variables. The first pair of canonical variate isderived so as to have the highest intercorrelation between two sets of variables. Thesecond pair of canonical variates is then derived so that it exhibits the maximumrelationship between the two sets of variables not accounted for by the first pair ofvariates.

Three criteria can be used in conjunction with one another to decide whichcanonical function should be interpreted. They are the:

(1) Level of statistical significance of the function. The level of significance generallyconsidered to be minimum acceptable is the .05 level

(2) Magnitude of the canonical correlation represented by the size of canonicalcorrelations should be considered when deciding which functions to interpret and

(3) Redundancy measure for the percentage of variance accounted.

5. Interpreting the Canonical Variate

Interpretation involves examining the canonical functions to determine the relativeimportance of each of the original variables in the canonical relationships. Thefollowing three methods can be used to interpret the variate:


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(a) Canonical weights can be used to interpret the canonical functions. This involvesexamining the sign and magnitude of the canonical weight assigned to eachvariable in its canonical variate. Variables with relatively larger weights contributemore to the variates and vice versa.

(b) Canonical loadings can be used to interpret the functions. It measures the simplelinear correlations between an original observed variable in the dependent orindependent set and the set’s canonical variate. The larger the coefficient, themore important it is in deriving the canonical variate.

(c) Canonical cross loading can be used as an alternative to canonical loadings.This involves correlating each of the orginal observed dependent variables directlywith the independent canonical variate and vice versa.

6. Validation and Diagnosis

Canonical correlation analysis should be subjected to validation methods toensure that the results are not specific only to the sample data and can be generalizedto the population. For the purpose of validation two sub samples can be createdand analyses can be performed on each sub sample separately. Then the results arecompared for similarity of canonical functions, variate loadings etc., If markeddifferences are found additional investigation should be performed.

Another approach is to assess the sensitivity of the results to the removal ofdependent or independent variable. To ensure the stability of the canonical weightsand loadings, multiple canonical correlations can be performed each time byremoving a different independent and dependent variable.

4.4.3 Application of Statistical Package : Canonical correlation

Canonical correlation can be carried out in SPSS using syntax. There are twoways to perform the same. One is to use the Canonical correlation.sps macro. Theother way is to use MANOVA with DISCRIM subcommand.

(1) Canonical correlation.sps macro

The macro is a part of the SPSS package and can be found in a subdirectorywhere SPSS is installed. To use the canonical correlation macro, locate the file Canonicalcorrelation.sps on the computer. Suppose that it is in c:\Program Files\spss. In thesyntax window, type

include file ‘c:\Program files\spss\canonical correlation.sps’.


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cancorr set1=var1 var2 var3

/set2=var4 var5 var6.

In the above syntax replace var1-var6 with variable names to be used in thecanonical correlation analysis.

(2) MANOVA

To use MANOVA the following syntax should be typed in the window:

MANOVA set1 WITH set2

/DISCRIM ALL ALPHA(1)

/PRINT SIG(EIG DIM).

Replace set 1 and set2 with the variable lists. Then run the program by selectingRun from the menu. The data set is to be kept open in the data window while runningthe program. The MANOVA output contains also multivariate regression results inaddition to canonical correlation analysis. The canonical correlation coefficients in themacro output have the same values, but opposite signs to the ones in the MANOVAoutput. The table names are also different, for example, the correlations between thevariables under analysis and canonical variables are called loadings in the macro output.

SUMMARY

Selection of multivariate techniques to analyze the data is based on two criteria:dependent or independent variables and the type of data ie metric or non metric. Thevarious multivariate techniques like factor, cluster, multiple regression and correlationdiscriminant analysis and canonical correlation were presented. The criteria for applyingthe statistical tests and the steps involved in conducting the same is explained in detail.Applications of these statistical tests using the software package were also discussed.Once the data analysis is done, the report has to be prepared to communicate theresults to all concerned. The next unit on report writing deals with the same.


Explain the significance of multivariate techniques in the context of researchstudies.

Identify a situation where factor analysis can be used. Discuss the steps involvedin performing the same.


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Explain the application of cluster analysis with example. Elucidate the processof performing the same in SPSS. How will you interpret the results?

What are the uses of discriminant analysis? Explain the process of building adiscriminant model.

What is multiple regression? Explain the steps involved in the application of thesame .

When can you apply canonical correlation? Explain the steps involved in buildingthe model.


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The Research ReportUnit structure:

5.1 Introduction5.2 Learning objectives5.3 Purpose of business reports5.4 Types of reports5.5 The concept of audience5.6 Basics of written reports

5.6.1 Stages in writing reports5.6.2 Characteristics of a good report

5.7 Integral parts of report5.8 Research proposal5.9 Visual aids in reports

5.9.1 Steps in creating visual aids5.9.2 Guidelines5.9.3 Types of visual aids

-------------------------------------------------------------------------------------

5.1 INTRODUCTION

Report writing is an integral part of a research process. Research reports arewritten to communicate to the world at large the results of the research, field work, andother activities. Research report is a concrete outcome of the research work undertaken.The quality of the research is judged by the quality of the writing and how well theimportance of the findings is conveyed. A research carried out very scientifically revealingfindings of great importance may not be of value if the same is not communicatedeffectively. In the context of business, the report assumes importance as it is through thereports the management gets information regarding the activities performed at variouslevels of the organization. The management takes decisions and controls various activitiesof the business on the basis of information provided through the business reports.According to Louis L.N. “Business report is an unbiased and arranged presentation offacts by one or more than one persons for a definite and specified important business

Unit 5


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purpose”. Koontz and O’Donnell define report as, “a documentation in which by thepurpose of providing information a specified problem is researched and analyzed andconclusions, thoughts and sometimes references are presented”. In a nut shell, a businessreport is any factual, objective document that serves a business purpose. This chapterprovides an insight into the basics of writing research reports in addition to the contentsand characteristic features of a good report. The contents of a research proposal andthe use of visual aids in preparing reports are dealt in detail.


After reading this unit you will be able to understand;

The basics of research report

The importance and types of report

The characteristics of a good report

The need for audience analysis

The contents of a report

The steps in generating a report

The contents of a research proposal

Use of visual aids in report

5.3 PURPOSE OF BUSINESS REPORTS:

The business reports are prepared for the following purposes:

Report enables the management to monitor the operations undertaken at variouslevels and control the same

The written report acts as a guideline for future course of action. It enables toplan and organize things in an effective manner.

The feed back regarding the various aspects, controls and processesimplemented in the organization can be obtained through the reports.

The information regarding specific problems or issues can be obtained by wayof report. This report may be narrowly focused and provide the desiredinformation to the management in a brief format.

Information provided in the reports enables decision making.

6 Report may also be prepared to convince the reader or to sell an idea. Thereport in this case would be more detailed and convincing as to how the proposedidea could add to the organizations value or the justification as to why it shouldbe adopted.


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Report may also be prepared to provide several alternative solutions orrecommendations so as to compare the pros and cons and select a best courseof action. A detailed discussion of methodology, criteria for comparison, dataanalysis etc should be provided

Reports may be prepared to provide an insight into the problem and may alsoprovide a final solution to the same.

5.4 TYPES OF REPORTS:

Reports can be classified on the basis of purpose, source, frequency, targetaudience, length, subject dealt, function performed and intention.

1. Source

Source refers to the person/persons who initiated the report. Voluntary reportsare prepared on own initiative and they require more detail. The background of thesubject should be more carefully planned. The authorized reports are those which areprepared as a response to a request made.

2. Frequency

Routine or periodic reports are submitted on a recurring basis which may beweekly, monthly, daily etc. Some routine reports may be prepared in preprintedcomputerized form. Due to the routine nature of report, it requires only less introductionthen the special reports. Special reports are nonrecurring in nature and they present theresults of specific, one time studies or investigations.

3. Length

A short report differs from a long report in scope, research and duration. Along report examines the problem in detail and requires more extensive time and effortin preparation. On the contrary a short report may discuss only a module of a problem.A summary is a short report which gives a concise overview of a situation. It highlightsthe important details but does not include background material, examples or specificdetails. A short report is suitable when the problem is well defined, is of limited scopeand has a simple methodology. It normally runs to five pages.

4. Intent

Informal reports focuses on the facts and explains or educates the readers.Analytical report is designed to solve a problem by convincing readers that theconclusions and recommendations reached are justified based on the data collected,analysis and interpretation. Information provided plays a supporting role in convincingthe reader.


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5. Function

The reports may be classified as informative and interpretative on the basis offunction performed. Informative reports present facts pertinent to the issue or situation.Common types of informational reports include those for monitoring and controllingoperations, statements of policies and procedures, compliance reports and progressreports. It may take the form of an operating or a periodic report. Operating reportsprovide managers with detailed information regarding all activities like sales, inventory,costs etc., Periodic reports which describe the activities in a department during a particularperiod.

Interpretative also knows as analytical or investigative report analyses the factsand presents recommendations and conclusions. The report presents facts and persuadesthe reader to accept a stated decision, action or the recommendations detailed throughoutthe report. It may take the form of problem solving report providing the backgroundinformation and analysis about the various options. Trouble shooting reports is a formof problem solving report which discusses the source of the problem, extent of damagedone and solutions possible. A feasibility report is a problem solving report that studiesproposed options to assess whether all or any one of them is sound.

6. Subject dealt

The reports may be categorized as problem determining, fact finding, performancereport, technical report etc. The problem determining report focuses on underlying aproblem or to ascertain whether a problem actually exists. Technical reports are concernedwith presenting data on a specialized subject with or without comments.

7. Legal reports

Reports may be prepared to meet the government regulations. For eg., Acompliance report explains what a company is doing to conform to the governmentregulations. It may be prepared on annual basis like the income tax returns, annualshare holders report etc. Interim compliance reports can also be prepared to monitorand control the licenses granted by the government.

5.5 The Concept of audience

Reports are written for the sake of audience i.e., the readers of the reports.The goal of report writer is to enable the audience to act and hence the audience shouldbe taken into consideration, right from word choice, planning, organizing, deciding aboutthe visual aids, sentence structure etc., A good report requires to tune up the variousaspects of the audience viz., their knowledge level, their role in the given situation, theirplace in the organization and their attitude.


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(1)Knowledge level of the audience

Knowledge level refers to the extent to which the audience are aware about thesubject matter discussed in the report. This level ranges from expert to non-expert. Anexpert audience understands the basic terminology, facts, concepts and implicationsassociated with the topic. Information about the extent of audience knowledge enablesto choose the information to be presented and the depth of explanation needed. Inascertaining the audience knowledge level the following aspects should be considered.

Understanding the knowledge level of the audience

The researcher should ascertain what the prospective reader of the report knowsabout the topic. The knowledge level can be ascertained by directly discussing with theaudience .The duties and responsibilities occupied by the audience will also provide akey to understand their familiarity with the concept.

Adapting to the knowledge level of audience

The report should be adapted to the knowledge level of the audience by buildingon their schemata i.e., based on the concepts they have formed from their priorexperiences. The basic principle here is that the reports add to the knowledge level ofthe audience and not to waste the time by concentrating on what the audience alreadyknows.

(2)Audience Role in the situation

Audience take decision and plan the further course of action based on thereport presented to them. A good report should be adapted to accommodate differentaudience roles. The topics and subtopics may be similar, but the report should be quitedifferent because of the different roles of the intended audiences. In order to determinethe audience roles the following aspects should be considered.

i. Type of audience

The audience for whom the report is drafted should be taken into account. Theaudience could be a single person or members of a committee or a large group.Sometimes there may be both primary audience as well as secondary audience. Primaryaudiences are those at whom the document is addressed. Secondary audiences arepeople who could read the report for information but not for immediate action.

ii. Audience need

Information in reports could be presented in different format in different way.The audience need should be considered in deciding the format of report, its content,the details needed, the level of precision required and the time period within which thereport is to be submitted.


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iii. Writer’s goal and audience’s need

The basic goal of a researcher/ the person who prepares the report is to enablethe audience to perform the act. This has to be fulfilled by delivering the basic messagethat has a specific purpose. Basic message consists of the basic facts that need to bepresented to filling the purpose which may be to inform, instruct or persuade the readers.If the goal is to persuade the reader to act in a certain manner because of the information,then the report should clearly point out the significance of the data and the action theysupport.

iv. Audience’s task

Audience’s task refers to the type of activity which the audience will indulge inafter reading the report. The audiences task may be different and hence the reportdirected at each one of audience should also be different. For eg., For a manager thereport should contain explanatory paragraphs rather than the numbered how-to-do-itsteps which will be apt for a operator.

v. Number of audience

In case of the presence of both the primary and the secondary audience, thereport writer must decide whether the concentration should be on the primary orsecondary audience. If a brief note is only needed than a lengthy format, the sameshould be preferred. If the same report is to be circulated among various members thenbrief informal report is inappropriate.

(3) Audience’s attitude

Audience attitude refers to the expectations of a reader while reading the report.The expectations arise due to the reader’s role played in the organization, the socialsituation ,the feelings about the message provided and the sender. These attitudespowerfully affect the way the readers read the message in the report. The followingfactors help to determine the attitudes of the individuals

The consequences which arise out of the information given in the report shouldbe considered. A positive message may be welcomed in a optimistic perspectiveby the readers.

Understanding the history may provide an insight into the attitude of the reader.History is the situation prior to the report writing. The report writers need toshow that the situation prior to writing report is understood. Otherwise thereaders may dismiss the same on the ground that the writer does not understandthe implications of what is being reported.

The reader’s power affects his attitude towards the information provided in thereport. Power is the supervisory relationship of the author and the reader. The


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more powerful the reader, the less likely the report will give orders and themore likely it is to make suggestions.

Formality is the degree of impersonality in the document. Written reportcommunicated to the reader is mostly presented in an official tone. The extentof formality depicted in the report will affect the perception of the readerregarding the message conveyed through the report

Reader’s feeling regarding the subject dealt may be positively inclined, neutralor negatively inclined towards the topic. If there is a positive inclination towardsthe subject then the reader may be more receptive towards the messageconveyed.

The writer should establish a relationship with the reader. The relationship isaffected by the writer’s credibility and authority. If the readers believe that thewriter has followed a clear and scientific method of investigating the topic, apositive image will be created.

The audience expect messages in a certain form. To stimulate a positive attitude,the document should be presented in the form expected.

5.6 Basics of written reports

This section deals with an overview of steps involved in writing reports andalso highlights the characteristics of a good report.

5.6.1 Stages in report writing

Report writing is a process which should be carried out at various stages. Thegoal of the writing process is to generate clear, effective document so as to enable theaudience to act. The writing process is performed in the following three stages viz., pre-writing, writing and post-writing stage. The stages are discussed below:

I. Pre-writing stage

Pre-writing stage involves planning the task for writing the reports. It includescollection of all the relevant information and deciding the steps to be followed. It involvesthree tasks viz., analyzing the situation, investigation and adaptation.

1. Analyzing the situation

A thorough analysis of the situation should be made to decide whether thesituation merits writing report. Sometimes, it may be enough to make a phone call oremail or conduct a meeting. If situation warranties writing reports, then the next step isto decide the type of report needed. It may be informational or an analytical report. Incase of informational report, the specific purpose of the report should be defined and


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report type that is appropriate should be selected. For analytical reports, the problemshould be defined before stating the purpose of the report.

Problem definition

The problem addressed by a report may be defined by the person who authorizesthe report or by the researcher himself. The readers of the report should be convincedabout the existence of the problem. This requires persuasive writing method. The problemdefinition can be made by answering the following issues:

What needs to be ascertained?

When did the problem start?

What is the importance of the issue?

Who are involved in the situation?

Where is the trouble located?

Problem factoring can also be done which involves breaking down the perceivedproblem into a series of logical, connected questions that try to identify the cause andeffect. Speculating the cause for a problem leads to forming a hypothesis. A hypothesisis a potential explanation that needs to be tested. Dividing the problem and framing thehypothesis based on the available evidence enables to tackle even the most complexsituation.

Developing the statement of purpose

The problem statement enables to define what is going to be investigated whereasthe statement of purpose defines, why the report is prepared. The purpose statementcan be started with an infinite phrase. For eg., “ To analyse the reasons for fall in theshare price”. Using an infinite phrase ( to plus a verb) encourages to take control anddecide where the starting should be made. The purpose statement should be highlyspecific and the same should be checked with the person who has authorized the report.The confirmed statement can be used as the basis for developing the preliminary outlineof the report.

Developing a preliminary outline

Preliminary outline establishes the framework for the report preparation. Itprovides a visual diagram of the report to be prepared, in important points, the order inwhich the discussion will take place and the details to be included. The preliminaryoutline might look different from the final outline of the report, however, the outlineguides the research effort and acts as a foundation for organizing and composing thereport. Since, outline is only a working draft it will be revised and modified in the further


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steps. The two common outline formats used to guide the writing efforts are; alphanumericand decimal. The grammatical parallelism should be ensured among the various itemspresented at the same level. Parallelism ensures generality by showing that the ideas arerelated and they are of similar importance.

Preparing the work plan

Most of the reports have a firm deadline to be met. A carefully prepared workplan ensures that the quality reports are produced on the schedule. If the work plan isprepared for the researcher himself, it can be prepared in an informal manner. Howeverin case of proposal, a detailed work plan should be prepared which becomes the basisfor the contract if the proposal is accepted. A formal work plan might include the followingelements:

Statement of the problem which enables to stay focused on the core problem

The purpose statement which describes the plan to be accomplished with thereport and the boundaries of the work.

A description of the product that will arise out of the investigation. Many timesthe report may be the only outcome.

A review of the project assignments, schedules and resource requirementsindicating who will be responsible for what, when the task will be completedand how much will be the investigation cost.

Plan for following up after delivering the report should be explained.

2. Investigating information

Information should be gathered for writing reports on various perspectivessuch as the specific company information, trends, issues, product, events, relatedliterature, micro and macro economic perspectives of the problem taken for the studyetc. The following tasks should be completed in investigating the information:

Identify the right questions

Find and access primary and secondary sources of information

Evaluate and finalize the resources

Process the information

Analyze the data

Interpret the finding

The tasks mentioned need not be performed in a specific order.


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3. Adapting the report

A good relationship with the audience should be maintained in order to ensurethat the report is audience centered. A report will be successful, only if it focuses on theaudience. The focus on the audience can be maintained by following the criteria givenbelow:

The “you” attitude should be followed and the report should answer the audiencequestions and solve their problems.

Emphasize should be given to the positive aspects. If the report recommends anegative action, the facts should be stated and the recommendation should bemade positively.

Credibility should be established by building audience trust. The trust can begained by researching the topic from all sides and documenting the findingswith credible sources.

The report should address the audience in a polite manner. The audience’srespect should be earned by being courteous, kind and tactful.

Bias- free language should be used. Unethical and embarrassing blunders inlanguage related to gender, race, age and disability should be avoided.

The style and language of the report should reflect and adapt to the image ofthe organization.

Selecting the appropriate channel and medium

A right medium should be selected for conveying the report. It may be in theform of oral presentation, e-format, email, letter or a formal written report. Writtenreports are opted to convey complex lengthy information which needs to be presentedin a structural format and is need for further reference. If immediate feedback is needed,oral reports are appropriate. Electronic reports are stored in electronic media and maybe distributed on disk, attached to an email or posted in the website. When comparedto paper based reports, electronic reports enable to save cost and space. It also enablesfaster distribution as well as include multimedia features. The appropriate channel shouldbe chosen based on the requirement of the audience and the researcher.

II. Writing stage

Actual composing of report should be preceded by organizing the materialcollected and arranging the same in a logical order that will meet the audience needs.The format, length, order and structure of the report should be decided before draftingthe report.


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Deciding the format and length

Four options are available to format the report viz.,

Pre-printed form: The pre-printed form is a fill-in-the-blank type report. These reportsare relatively short and deals with only routine information.

Memo: It is a short informal report distributed within the organization. It hasheadings and visual aids and if the length exceeds ten pages it is called as memoreport.

Letter: It includes the normal parts of a letter and in addition may have headings,footnotes, tables and illustrations. It is commonly used for reports of five orfewer pages that are directed to outsiders.

Manuscript: It is commonly used for reports that require a formal approachand may range from a few pages to several hundred pages. The prefatory partsand supplementary parts will have more number of pages as the length of thereport increases.

If the report is more of routine nature, the flexibility in deciding the format andlength is much lesser. The length of the report is often decided by the subject matter andthe type of relationship with the audience.

Choosing the approach

The researcher may choose a direct or indirect approach in writing the report.A direct report starts with the main idea first and thereby saves time and enables easierunderstanding of the report. The direct approach is used when the audience is morereceptive or open-minded. The report starts with the findings, conclusions andrecommendations. This method is mostly followed in the business reports.

The indirect approach withholds the main idea until the latter part of the report.If the audience is skeptical or hostile then the complete findings and all supportingdetails should be presented before presenting the findings and conclusions.

Structuring the reports

Structure of the report deals with the way in which the ideas will be subdividedand developed. The structure of the report depends on its type viz., informational,analytical, investigative etc. The reports may follow topical organization i.e., arrangingmaterials according to one of the following topics:

Materials may be organized on the basis of the importance of the subject matter.The most important topic may be presented first and least important at the endof the report


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If the report is presented on the process, then it should be arranged in sequentialorder of the process.

If events are reported in the study then the same should be reported in thechronological order.

If a physical object is discussed in a report ,then the same should be discussedfrom left to right, top to bottom, outside to inside.

If the report is organized on the basis of geographical area occupied it has to beorganized on the basis of the regions under study viz., city, district, state orcountry.

Composing Reports

Once the decision regarding the length, approach and structure is made,composing of first draft can begin. The writing task should start with preparation of afinal outline. This would act as a guide to the writing process and will also enable tocritically evaluate the selection and order of information to be presented in the report.The outline preparation may lead to rephrasing the points and tone of the report. Whilecomposing the reports, the researcher should only concentrate on drafting the messageand not editing or polishing the same which is done at a later stage. While composingthe reports the following points should be kept in mind:

Formal language should be used in writing reports. Obsolete and pompouslanguage should be avoided. Similarly using big words, trite expressions andoverly complicated sentences to impress others should not be attempted.

Correct words should be used in report. The words selected should conveythe meaning clearly, specifically and dynamically. The words that are familiar tothe audience should be chosen. Clinches and jargons can be used only when itis understood by the audiences for whom the report is directed to.

Due attention should be paid to the grammatical accuracy of the content deliveredas it affects the image of the researcher.

The report should concentrate on presenting the facts

The arguments for or against any aspect should be constructed in a rationalmanner

Active or passive voice should be used appropriately in composing the reports.Active voice can be used to emphasize the subject and to produce shortersentences. Passive voice is mostly used in research reports as it is prepared ina formal situation.


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To ensure readability, the report should be broken up into paragraphs withsuitable headings.

Consistent time perspective should be ensured in the report i.e., the reportshould be in past or present tense. The chronological sequence should also beadapted in presenting the events.

The reader’s perspective of the report might be different from the researcher’sperspective. Hence a preview or road map of the report structure should beincluded. This will clarify the reader regarding the overall organization and flowof report.

III. Post-writing stage

A research report will undergo many drafts before finalization. The report isrevised many times to ensure the content, organization, style and tone, readability, clarityand conciseness. Post-writing stage involves revision of the report, production andproofreading the same.

(1)Revision

Revision takes place during and after preparation of the first draft. It is anongoing process that occurs throughout the writing process. Revision involves searchfor best way of saying something, probing for right words, rephrasing sentences,reshaping, juggling elements etc. Revision is a never ending process, however, everyresearch report has a deadline and hence schedules should be drawn and met. Revisionconsists of three main activities viz,(i) evaluating content, organization ,style and tone(ii) reviewing for readability and scannability and (iii) editing for clarity and conciseness

(i) Evaluating content, organization, style and tone

During the process of evaluating the content the following aspects should begiven due attention:

Accuracy of the information presented

Relevance of the facts presented to the concerned audience

Completeness of information provided to suite the audience needs

Balance between specific and general information

While reviewing the organization the following aspects should be considered:

Logical order in presentation and coverage of all main points to be ensured

Assuring that the main theme is given more space and prominence


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Correctness in the sequence of presentation

Grouping of scattered details in an appropriate manner

More attention should be given to the introduction and conclusion of the reportas it has major impact on the audience. The words used should be of right style andtone. The opening statements should be relevant, interesting and enticing the reader toread further. It should establish the subject, purpose and organization of the informationin the report. The conclusion should be reviewed to ensure that it summarizes the mainidea and leaves the reader with a positive impression.

(ii) Reviewing for readability and scannability

Readability depends on choice of words, sentence length, sentence structure,organization and the physical appearance of the message. The following techniques canbe used to ensure readability:

Variety in sentence structure makes the information presented more appealingto the reader. While long sentences should be avoided, use of too many shortsentences should not be attempted. Average sentence length should consist of20 words or fewer.

Important ideas can be presented in the forms of list. Lists are effective toolsfor highlighting and simplifying the information presented. It provides the readerwith clues, simplifies the complex subjects, highlights the main point, breaks upthe pages visually and ease the skimming process for busy readers

Heading is a brief title that provides clues to the reader about the content of thesection that follows. Heading should be properly used to attract the readers’attention and to divide the material into shorter sections.

(iii) Editing for clarity and conciseness

Clarity in information presented should be ensured. Clarity prevents confusion.If the information is presented in a cluttered manner it can be interpreted by the readerin several ways which is not intended by the researcher. The following aspects shouldbe considered to ensure clarity:

Long sentences should be broken up. Connecting too many clauses with andshould be avoided.

Too many hedging statements should be avoided.

Parallelism should be ensured among related ideas. It can be achieved byrepeating the pattern in words, phrases, clause or entire sentences.

Long noun sequences should be avoided


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Words ending with -ion,-tion, -ing, -ment, -ant, -ent, -ance, and -ency shouldbe used with care as they change verbs into nouns and adjectives. Verbs shouldbe used instead of noun phrases

Reports use expressions like ‘ above-mentioned, as mentioned above, theformer, the latter, respectively ‘. These words cause reader to jump from pointto point and hence these awkward references should be kept to the minimum.

To ensure conciseness, every word in the report should be carefully scrutinized.Words which do not serve any function should be eliminated. Every long word shouldbe replaced with a short word. Conciseness should be ensured by way of deletingunnecessary words and phrases, shortening words and phrases and by eliminatingredundancies.

Use of computer enables to revise the report in a much faster and efficientmanner. Word processor helps to add, delete and move text with functions like cut andpaste, search and replace, replace all options etc. Autocorrect feature enables to storewords commonly misspelled or mistyped along with correct spelling. History of revisionsmade can also be fetched by enabling the software options. Three advanced softwarefunctions viz., spell checker, thesaurus and grammar checker enables to create an effectivereport.

(2) Production the report

Producing the report involves adding elements such as graphics and designingthe page layout to give the report attractive and contemporary appearance. Addinggraphics is dealt in detail in the latter part of this unit. The appearance of the reportmeets the eyes of the reader first and plays an important role in creating impression.Effective design should have the following elements:

Consistency should be ensured throughout the report in terms of the margins,typeface, type size, spacing, paragraph indent, borders, columns etc.

Proper balance should be maintained between the text, graphs and white space.

Too much of highlighting, decorative touches and design element should beavoided. Simplicity in design should be aimed at.

Attention should be paid to details like heading should not be separated fromthe information, avoiding narrow columns and the like.

Variety of design elements such as line justification, typefaces, styles etc can beused to create a professional and interesting report , but it should be kept inmind that too many design elements might confuse the reader.


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(3) Proof reading

While proofreading attention should be devoted to spelling, punctuations andtypographical errors. Credibility of the researcher is affected by the attention paidto details, mechanics and form. Researcher should carefully check the grammarusage, language errors, missing material, design errors and typographical errors.Design errors include elements like wrong typeface, wrong type style, misalignmentetc. typographical errors include uneven spacing between lines and words, headingat the bottom of a page, incorrect hyphenation, non confirmation with the guidelinesprovided etc. Attention should also be paid to overall format. Routine documentswill have only fewer elements to check. Longer more complex documents havemany components that need checking and more time should be devoted for thesame.

The three stages in report writing is summarized in the following pictorialrepresentation.

Stages in report writing

5.6.2 Characteristics of a good report

The desirable features of a good report is dealt under various sections of reportwriting discussed in previous pages. A summary of the salient features are listed below;

A good research report should focus on the purpose of the study and the typeof the audience

It should also have clarity, conciseness and coherence

Right emphasis should be placed on the important aspects of the problemidentified meaningful organization of paragraphs, sentences and smooth transitionfrom one topic to next should be achieved by ensuring , parallelism, specificityetc.

Prewriting Writing Post writing

Analyzing Investigating Adaptation

• Format &length

• Structure • Order • Composing

• Revision • Production • Proof reading


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The report should be free of technical or statistical jargon if the same is addressedto audiences who may not understand.

Care should be taken to avoid grammatical, spelling and typographical errors.Assumptions made by the researcher should be clearly spelled out.

Operational definitions of words used with specific meaning should be given inthe beginning of the report.

The report should be organized in a meaningful manner so as to enable smoothflow of information.

Side headings should be properly used to ensure smooth and logical flow ofmeaning Appearance should be given due emphasize so that a professionalimage can be created.

Ambiguity, multiple meanings and allusions should be avoided by choosing theright words and sentences.

The report should adhere to the guidelines specified regarding the format etcand should be prepared within the schedule provided.

5.7 INTEGRAL PARTS OF A REPORT

Research report has a set of identifiable components. The components of reportshould be decided keeping in mind the needs of the audience. The headings and sideheadings should also focus on the requirements of the audience and the problem identifiedfor the study. Generally, a research report consists of three parts; the preliminaries, thetext and the reference materials. Each of the main parts may consist of several subsectionsas shown below:

A. The preliminaries

The preliminaries do not make a direct contribution to the identified researchproblem. However, it assists the reader in using the research report. The subsections inpreliminaries are discussed below:

Letter of transmittal

A letter of transmittal is required in case of formal relationship between theresearcher and audience at whom the report is directed at. It is mostly used in case ofcarrying out research work for a specific client or for an outside organization. The lettershould highlight the authorization for conducting the project and the specific instructionsprovided to complete the study. It should also state the purpose and scope of the study.The letter of transmittal is not necessary if the report is aimed at authorities within theorganization.


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Title page

Most of the organization have their own form of title page for the researchreport and the same should be complied with. The title page generally has thefollowing information:

Title of the report.

The month and year of submission.

For whom and by whom the report is submitted.

If project report is submitted for award of degree, the degree for which thedissertation is submitted for should be listed.

The best practice is to centre the title of the report on the page in upper caseletters. If the title is too long to be centered on one line, an inverted pyramid principleshould be followed without splitting word or phrases.

Preface

The preface may include the writer’s purpose in conducting the study, a briefresume of the background, scope, purpose, general nature of the research for whichthe report is prepared and the acknowledgments. A preface can be prepared only afterthe final form of the report is ready. In the case of dissertation submitted for award ofdegree the preface is omitted and instead an acknowledgment is added.

Acknowledgment recognizes the persons to whom the writer is indebted forguidance and assistance during the study. It also credits the institution for providingfunds to conduct the study and for granting permission to use the facilities. The researchershould acknowledge the assistance provided by all concerned honestly in a simple andtactful manner.

Executive summary

An executive summary is a brief account of the research study. It is a report inminiature covering all aspects in the body of the report but in a brief manner. It providesan overview of the research problem identified and highlights the important informationsuch as the sampling design, data collection method used, results of data analysis, findingsand recommendation. The length of the executive summary will normally be two tothree pages. The executive summary is usually written after the completion of the report.

Sometimes a synopsis or an abstract may be included instead of the executivesummary, however, they are not one and the same. Executive summaries are morecomprehensive than a synopsis. It includes heading, visual aids and enough informationto help busy people to make quick decision. Although executive summaries are not


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designed to replace the report, in some cases it may be the only thing that may be readby the audience. By contrast, a synopsis is only a brief overview of the entire report andmay either highlight the main points as they appear in the report or simply inform thereader as to the content of the report. The purpose of synopsis is to entice the audienceto read the report.

Table of contents

The table of contents includes the major divisions of the report. It indicates inoutline form the topics included in the report. The purpose of a table of contents is toprovide an analytical overview of the topics included in the report together with thesequence of presentation. Depending on the length and complexity of the report, thecontent page may show only the top two or three levels of headings or only the first-level headings. Care should be exercised to see that the titles of chapters and captionsof subdivisions within chapters correspond exactly with those included in the body ofthe report. Page numbers for each of the divisions are given. The relationship betweenmajor divisions and minor subdivisions should be shown by using capital letters andindentation or by using numeric system.

The table of contents is prepared after the other parts of report have beentyped, so that the page numbers can be given. If they are fewer than four visual aids, thesame may be listed in the table of contents, but if there are more than four visual aids, aseparate list of illustration should be prepared. Some guidelines for writing table ofcontents are given below:

The page is titled as Table of Contents or Contents.

The name of each section should be worded and formatted as it appears in thetext.

The table of contents should not be underlined as they may overwhelm thewords.

Use only the page number on which the section starts.

The margins should be set such that the page numbers align on the right.

Not more than three levels of headings should be given.

The leaders, a series of dots can be used to connect the words to page numbers

List of Tables

The researcher should prepare a list of tables compiled under the heading LISTOF TABLES. It should be centered on a separate page by itself. Two spaces belowthe headings ‘Table number’, ‘Title’, and ‘Page number’ should be given. Table number


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should be aligned to the left, page number should be aligned at the right and the titleshould be centered.

List of Illustrations

The list of figures should be prepared in the same form as the list of tables. Thepage is headed as LIST OF FIGURES. The list includes the Figure number, title of thefigure and page number. Normally arabic numerals are used for numbering.

B. The Text

The text is the most important part of a report as it is in this section that thewriter presents the facts. The researcher should devote the greater part of attention tothe careful organization and presentation of his findings or arguments. The text may beorganized as introduction, methodology and as many chapters as required for presentingthe report.

Introduction

The introduction prepares the reader for the report by describing the variousparts; background, problem statement and research objectives.

Background

The background information provides a prelude to the reader of the researchreport. It may be the preliminary results of exploration the survey or any other source.The secondary data from the literature review could also be highlighted. Previous research,theory or situations that led to the research issue can be discussed. The literature shouldbe organized, integrated and presented in a logical manner. The background includesdefinitions, assumptions etc. It provides the needed information to understand theremainder of the research report. It contains information pertinent to the managementproblem or the situation that led to the study. It may be placed before the problemstatement.

Problem statement

The problem statement contains the need for the research project. The problemis usually represented by a management question. It is followed by a more detailed setof objectives. The guidelines are given below:

It gives basic facts about the problem.

It specify the causes or origin of the problem.

It explains the significance of the problem.

Research objectives

The research objectives provide the purpose of the research. The objectivesmay be research questions and associated investigative questions. In correlational study,


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the hypothesis statements are included. Hypothesis are declarative statements describingthe relationship between two or more variables. They state clearly the variables ofconcern, the relationships among them, and the target group being studied. Operationaldefinitions of variables should be included.

Methodology

The methodology contains the following sections:

The type of the study viz., descriptive, exploratory should be mentioned in themethodology.

The sampling design explains the sample method and sample size.

The data collection method is described in the report.

The tools used for analysis of data should be explained.

Findings and Conclusions

The findings section is generally the longest section of the report. The objectiveis to explain the data. Wherever needed the data should be supplemented with charts,and graphs. The conclusion serves the important function of tying together the wholethesis or assignment. The recommendations of the study are also presented in this section.It provides idea about the corrective actions. In academic research, the suggestionsbroaden the understanding of the subject area. In applied research, the recommendationincludes the guidelines for further managerial actions. Several alternatives may be providedwith further justifications. The conclusion should leave the reader with the impressionof completeness and of positive gain.

C. Reference material

The reference material includes, bibliography, appendix and index.

Bibliography

The bibliography follows the main body of the text and is a separate but integralpart of a thesis, preceded by a division sheet or introduced by a centered capitalizedheading BIBLIOGRAPHY. A bibliography is a list of secondary sources consultedwhile preparing the report. In a proper sense bibliography differs from the referencelist. A bibliography is the listing of the work that is relevant to the main topic of researchinterest arranged in the alphabetical order of the last names of the authors. A referencelist is a subset of bibliography. It includes details of all the citations used in the literaturesurvey and elsewhere in the research report, arranged in the alphabetical order of thelast names of the author. These citations are provided for the purpose of crediting theauthor and enabling the reader to find the works cited

Proper citation, style and formats should be followed in providing reference.Various methods of referencing are available viz., Publication manual of the American


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Psychological Association (APA), The Chicago Manual of Style, The Modern LanguageAssociation (MLA) System, American Chemical Society (ACS) system. Each of themanuals specifies with examples, how the books, journals, newspaper articles,dissertations and so on should be referenced.

For books the order may be as under:

1. Name of the author, last name first

2. Title of the book in italics

3. Place of publication and the publisher

4. Year of publication

Example:

Peeru Mohamed et.al, Customer Relationship Management, Delhi, Vikas publishinghouse, 2002.

References for articles in journals could be cited as under:

1. Name of the author, last name first

2. title of article in quotation marks

3. Name of periodical, in italics

4. The volume or volume and number.

5. The date of the issue

6. The pagination

Example

Chitra.K, “ In search of Green Consumer: A Perceptual Study”, Journal ofServices Research, Volume 7, No.1, April-September, 2007, pp.173-191.

The above examples are just samples for bibliography entries. There are manyother acceptable forms which can be used. However, a researcher should follow aconsistent style of reference throughout the report.

Appendix

The appendix contains information of a subordinate, supplementary or highlytechnical nature that the researcher does not want to place in the body of the report.Each appendix should be clearly separated from the other and should be listed in thetable of contents. The guidelines for preparing appendix are:

Each appendix item should be referred in the appropriate place in the body ofthe report.


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In short reports, the page number numbers may be continued in sequence fromthe last page of the body.

In long reports, a separate pagination system can be followed as the appendixesare often identified as Appendix A, Appendix B, and so on. The page numberscan be given along with the appropriate letter: A-1, A-2, B-1, B-2.

The illustrations in the appendix may continue with the sequence started in thebody of the report.

Index

The index should be included after bibliography and the appendix. It acts as agood guide to the reader. Index may be prepared both as subject index and authorindex. The subject index gives the names of the subject-topics or concepts along withthe number of pages on which they have appeared or discussed in the report. Theauthor index gives similar information regarding the names of the authors. The indexshould always be arranged alphabetically. An index is not required for an unpublishedthesis or a report. If the finding in the report is subsequently published as a book,monograph or bulletin, an index is necessary.

5.8 RESEARCH PROPOSAL

A research proposal is also a type of research report prepared for getting thepermission to proceed with the research work. It is a work plan, outline, statement orintent or draft plan of the proposed research work. It gives an insight into what, why,how, where and for whom the research is done for. It is a road map showing all elementsof the research process and resources required at every step right from the beginning tothe end.

The preparation of research proposal benefits both the researcher and theresearch sponsor. The research proposal enables the sponsor to assess the researchdesign and the validity of the same. The sincerity of the researcher can be evaluated bycomparing the completed work with the proposal. It serves as the basic for additionaldiscussion on the problem identified. The proposal benefits the researcher more thanthat of the sponsor as it necessitates the researcher to plan and review the logical stepsinvolved in the research process. This enables the researcher to revise the researchprocess where needed. It acts as an outline for preparing the final project report.

The research proposal can be prepared for the internal or external audiences.An internal proposal is prepared by the research department or staff within the firm.External proposal is sanctioned by outsiders like the government agencies or UniversityGrants Commission and the like sponsors.


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5.8.1 Structure of the research proposal

The research proposal may include the following modules. The modules areflexible. The contents and length can be altered to suit the requirement of the researcherand the sponsoring agents. A brief overview of the contents is dealt below:

Executive summary

Executive summary enables the sponsors to understand the core of the researchproposal within a short time. The goal of the summary is to secure a positive evaluationby the sponsors who will authorize the research work. It should include a brief backgroundof the research work proposed, its importance, the objectives, the proposed researchdesign, the deliverables and the implication of conducting the research work. It shouldhighlight the benefits of conducting the proposed research.

Problem statement

This section should provide the background of the problem, consequences andthe implication of the same to the management or the sponsor. The importance of thefinding answer to the research question should be asserted. It should also specify theboundary line of the problem and the issues which may not be addressed. The problemstatement should be clear to the management to make the decision regarding itssignificance and the future action required to solve the same.

Research objectives

This section highlights the purpose of conducting the research. It should givespecific, concrete and achievable goals. The objectives should be listed in the order ofimportance or it can be specified in a general term. Later on specific objectives couldbe highlighted. It is the core of the proposed research work and also for the finalresearch report.

Review of literature

This module examines and presents the recent research studies, industry reportsetc that supports the proposed study. Unnecessary information should be avoided. Abrief review of the information of interest should be highlighted. The objectives,methodology, results and conclusions of the similar studies should be presented. Theresearcher should discuss how the literature applies to the proposed study and the gapwhich will be addressed by conducting the study.

Benefits of the study

The explicit benefits that can be gained by conducting the study should behighlighted. The importance of doing the study is emphasized. This section gains moreimportance if the proposal is submitted to an external body, particularly if it is an


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unsolicited proposal. This section should be geared to convince the sponsor that theirneeds will be met by the conduct of the study.

Research design

The design module describes the technical issues involved in conducting thestudy. What is going to be done is described in technical terms. It can be divided intomany subsection viz., type of study, sampling design, data collection method tools foranalysis , scope of the study and limitations. The justification as to why the particularmethod of sampling or data collection is opted should also be discussed.

Qualifications of the researcher

This section should provide the names of the principal investigator and co-investigators, individuals involved in the project. The professional research competenceand experience of the researchers should be highlighted to assure the sponsor. Theacademic experience, research experience and similar projects conducted for internaland external agencies should be listed. The membership of the researcher in variousassociations and other relevant accomplishment can be mentioned. A profile of theresearcher can be enclosed in the appendix of the report.

Budget

Budget should be prepared in the format required by the sponsoring agents.The details to be presented in the budget varies depending on the sponsors’ requirements.It should not be more than one or two pages. All the expenses should be presented witha proper breakup.

Schedule

The schedule should indicate the major phases of the project, the time requiredat each phase and the milestones that determines the completion of the project. Forexample the major phases may be refining the problem based on interaction withmanagement, tuning up the objectives, designing the questionnaire, conducting pilotstudy, data collection, analysis and interpretation and report writing. Each of the phasesshould be presented along with the time schedule and the resources including the peopleassigned to complete the work.

Facilities and special resources

The special facilities or resources needed to complete the project should bedescribed in detail along with the justification for the same. The proposal should carefullylist the relevant facilities and the resources that will be used. The costs for such facilityshould also be detailed in the budjet.

Apart from the above, the bibliography listing the books, journals, websitesreferred should be mentioned in the alphabetical order. The appendixes including the


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glossary of terms, questionnaire, profile of the investigator etc should be prepared. Fora detailed discussion of the sections refer the integral parts of research report.

5.9 Visual aids in reports

Visual aids are an essential part of report. Carefully presented visual aids canmake the report more interesting and understandable. Visual aids have the simple purposeof “revealing the data”. It enables to understand complex data and the interrelationshipamong data in a easier manner. It also enables to view data from different perspectives.The purposes of using visuals are:

To summarize data and present information in concise form.

To provide an opportunity to the reader to explore data on their own. Thereader can focus on any aspects that are relevant to their needs.

To orient the readers to the topic even before the text is presented.

To communicate effectively with diverse audience.

To attract and hold readers attention.

To make the reader understand the text description and quantitative informationin a better manner.

To simplify information by breaking complicated description into componentsthat can be depicted with conceptual models, flowcharts, diagrams etc.,

To summarize major points in a narrative form with the help of charts that sumup data

To enhance the retention of important message in readers mind by presentingthe same in visual form.

5.9.1 Steps in creating visual aids

The steps involved in creating visual aids viz., Planning, drafting, finishing anddiscussing are presented:

Planning the visuals

The visual aid is an opportunity to present data and to engage the reader. Theoverall goal of presenting visuals is to help the reader to find the needed information.While planning the visuals the following aspects should be considered:

The level of knowledge of the reader.

The need for the information.

The researcher’s goal in presenting the visuals.


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The researcher should consider the following aspects:

Each visual aid has a one main point to communicate.

The time needed for presenting a clear visual.

The layout of the visuals.

Drafting the visuals

Drafting involves producing the visuals, revising the same until it produces thedata in the most effective manner. It involves selecting the type of visuals, selecting thewordings, tick marks, data line characteristics, type of legends, colours etc. the variouselements are to be selected and tried until the best visual is produced to present theconcept in a clear manner.

Finishing the visuals

Care should be exercised to finish all the visuals in a consistent manner. Thevisuals should create a pleasant view. Cluttering of pages with unnecessary visuals orpresenting simple facts that could be easily understood by text should be avoided.

Discussing the Visual aids

The readers’ attention should be carefully guided to the visual aids to bediscussed. The description of the visual aids could be done at three levels viz., elementary,intermediate or overall information. In addition, the background which necessitates thevisuals, the methodology viz the aspects used to represent the various components inthe visual and the overall significance of information derived out of visuals should beexplained. The visual can be referred by number. If it is presented after or beforeseveral pages, the page number should also be mentioned apart from the visual number.Either textual or parenthetical method can be followed in referring the visuals.

The textual reference is a statement in the text that calls attention to the visualaid. For example, Table 1 shows that the price of the product is declining gradually. Inparenthetical reference the visual aid is referred in parentheses in sentence. For example,the sales show an increasing trend (see figure 1).

5.9.2 Guidelines for creating effective visual aids

The following guidelines can lead to creation of an effective visual aid.

The researcher should plan for the visual aids and develop the same as early asplanning the first draft of report.

Each visual aid should be prepared in such a manner that it concentrates onconveying one point only. If too much of data is included, the reader may notbe able to grasp the meaning clearly.


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The visual aids should be positioned in the report at logical and convenientplaces.

The visuals should be revised to eliminate clutter in terms of unnecessary words,lines , three dimensions etc.

High quality visuals should be created in terms of clarity in lines, words, numbersand organizations as it is an important aspect which determines the effectivenessof a report.

Various types of visuals are available to present the data. Some types of visualsdepict certain kinds of data better than others:

Tables can be opted to present detailed, exact values.

Frequencies and pie chart can be represented better with pie chart,segmented bar chart or area chart.

Line chart or bar chart can be used to illustrate trend over a time period.

Bar chart is used to compare one item with another.

Pie chart is used to compare on part with the whole.

Line chart, bar chart or a scatter chart can be used to depict correlations.

Map is used to show the geographical relationship.

Flowchart or diagram is used to illustrate a process or a procedure.

5.9.3 Types of visuals

The various types of visuals are discussed in detail.

1. Tables

A table is a collection of information presented in columns and rows. Tablesshould contain enough information to enable the readers to understand its contents. Itshould have a caption that contains the table number and title, rules, column heads, dataand notes. The title should explain the subject of the table, details regarding the dataclassification and the time period or other related matters. A subtitle is sometimes includedunder the title to explain some aspects of a table like the statement explaining themeasurement units in which the data is expressed. The contents of the columns areexplained by the column heads and the row contents are explained by the stub. Thebody of the table contains the data and the footnote contains the needed explanation.Footnotes should be identified by letters or symbols such as asterisks. The source noteshould also be presented. Tables should be accompanied by text to direct the readers’attention to the important figures. The guidelines relating to creation of table are givenbelow:


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The tables should be numbered consecutively throughout the report. The numberand the title are given above the table.

Table title should be informative and identify the main points of the table.

Horizontal rules are used to separate the parts of the table. The rules are placedabove and below the column heads and below the last row of the table. Verticallines can also be used to separate the column.

Spanner head should be used to characterize the column headings. The spannerseliminate repetition in column headings.

Common understandable units should be used. All items in a column should beexpressed in the same unit and rounded off for simplicity.

Column or row total should be provided wherever needed.

Explanatory comments should be placed below the table with the word ‘Note’.

Source of the data given in the table should be mentioned.

2. Line graphs

Line graph depicts trends or relationship. It shows the relationship betweentwo variables by a line connecting points in X axis and Y axis. The line graphs usuallyshow trends over time. The line connects the points and its ups and downs illustrate thechanges. Line graphs have conventional parts; a caption that contains number and title,axis rules, axis labels and a legend. Some guidelines to create line graphs are givenbelow;

The figures should be numbered consecutively using Arabic numerals.

Brief clear title should be used to specify the content of the graph

The caption can be given either above or below the figure, but a consistentpattern should be followed throughout the report

The independent variables are recorded on the X axis and the dependentvariables on the Y axis

Clear axis labels should be provided

If the graphs have more than one line, visual distinct should be made betweenthe same. The lines should also be identified with labels or in a legend


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Example:

A surface chart, also called an area chart, is a form of line chart with acumulative effect; all the lines add up to the top line, which represents the total. Thisform of chart helps to illustrate the changes in the composition of something over time.In preparing the surface chart, the most important segment should be put in the baselineand the number of strata should be restricted to four or five.

3. Bar graphs

A bar chart depicts numbers by height or length of its rectangular bars. It makesnumbers easy to read and understand. Bar charts are very much useful to

Compare the size of several items at one time.


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To track changes over time.

To indicate the composition of several items over time.

To show the relative size of components of a whole.

The guidelines to prepare bar charts are:

Proper title that informs about the content should be given and the graphs shouldbe numbered consecutively in Arabic numerals.

The independent variables to be placed on horizontal axis and the dependentvariable on the vertical axis.

Proper axis labels and description of legends should be adhered to in a consistentmanner.

Elaborate cross-hatching and striping should be avoided.

Subdivision of bars can be made to show additional comparison.

A bar chart can be created in many ways depending on the need and creativityof the researcher. However care should be exercised to see that the width of all barsare be uniform and are placed evenly in logical order.

4. Pie charts

A pie chart is used to show the relative sizes of parts of a whole. It uses segmentsof a circle to indicate percentages of a total. The whole circle represents 100 percent,the segments of circle represents each item’s percentage of the total. Pie charts areeffective ways to show percentages or to compare one segment with another. Generalguidelines are:

Figure 3. Product wise sales

0%

20%

40%

60%

80%

100%

1 2 3 4 5

Year

Sal

es in

per

cent

age

A B C


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A pie chart should not be divided into more that five segments as the readermay have difficulty in differentiating the sizes of small segments.

Segments should be identified with legends or call outs.

The segments should be arranged in sequence clockwise form largest to smallest.

Different color s or patterns can be used to distinguish the various pieces.

All the segments put together should add up to 100 percent, if percentages areused. Percentages can be placed inside the segments.

The segment which needs greater attention can be exploded i.e pulled out fromthe rest of the segment.

Example:

5. Pictograms

A chart that uses symbols instead of words or numbers to portray data is knownas pictogram. It is very novel way of presentation and it conveys more literal visualmessages. Pictograms enhance reports value.

6. Flow charts

Flow charts are used to show a time sequence, decision sequence or conceptualrelationships. The flowcharts are indispensable when illustrating processes, proceduresand sequential relationships. Arrows indicate the direction of the action, and symbolsrepresent steps or particular points in the action. In case of computer programming thesymbols have special shapes for certain activities.


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7. Organization charts

The organization chart illustrates the positions, units or functions of an organizationand the way they interrelate. Organization charts are used to depict the interrelationshipsamong the parts of an organization. An organizations normal communication channelscan be explained in detail with the help of organization charts.

8. Decision charts

A decision chart or decision tree is a flow chart that uses graphs to explainwhether or not to perform a certain action in a certain situation. At each point, thereader must decide yes or no and then follow the appropriate path until the final goal isreached.


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9. Gantt Charts

A Gantt chart represents the schedule of a project. Unit of time is representedalong the horizontal axis and sub processes are explained on the vertical axis. The linesindicate the starting and ending point of each sub-process.


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10. Maps

Maps are used to represent statistics by geographical area. It is also used toshow location relationships. Maps can be used to show regional differences in sales ofthe company. The maps can be illustrated to suit the needs. It can use dots, shading,colour lines, labels, numbers and symbols. The computer softwares like Excel andCoral draw has templates which makes the production of maps easier.

11. Photographs

Photographs enable to capture the exact appearance of an object and usesvisual appeals to capture the readers’ attention. The advent in technology like the digitalcameras has reduced the cost of including photographs drastically. Further modificationof photos to the requirement can be made with the help of software. It duplicates theitems to be discussed and also shows the relationships among various parts. Photographscan be used to provide general introduction to orient the readers towards the object.

12. Drawings and diagrams

Drawings and diagrams are often used to show how something looks or operates.Diagrams can be much clearer than words in explaining the readers the process or theuses of an object. A variety of software programs can be used to add decorative touchto the report. The drawings/diagrams enables to eliminate unnecessary details so thatthe readers can focus on important aspect. Two commonly used drawings are theexploded view and the detailed drawing. An exploded view shows the parts disconnectedbut arranged in the order in which they fit together. They are used to show the internalparts of a small and intricate object or to explain how the objects are assembled.Manuals often use exploded drawings with named or numbered parts. Detailed drawingsare renditions of particular parts or assemblies.

SUMMARY

The research report is prepared to communicate the research findings. Thisunit covered the different types of reports. The importance of audience analysis wasexplained. The steps involved in the preparation of report and the integral parts of thereport were discussed. The contents of research proposal were highlighted. In addition,the basic guidelines to use the visual aids and the various types of visual aids weredealt.


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1 Discuss the need for understanding the audience while preparing the researchreport?

2 What are the steps involved in writing a research report?

3 Discuss the contents of a report?

4 Prepare a research proposal for identifying the market potential of a new productlaunched by your concern.

5 What type of visual aids can be used for the presenting a report on the customersatisfaction of a new brand of laptop introduced by your concern in the market.

Business Research Methodology

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