Applied Business Research

Module 1      Introduction to Business

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Module 2    Scientific Investigation and the Research Process

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Module 3    Preliminary Information Gathering and Problem Definition

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Module 4    Framework Development and Research Objectives

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Module 5    Research Design and Planning 

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Module 6    Qualitative Data Gathering


Qualitative research is open to criticism for being subjective and biased. Its advantage, however, is the ability to amass rich and highly useful data. Qualitative researchers, therefore, respond to the demands of accuracy and replicability in a number of ways.




To a qualitative researcher integrity is everything. A qualitative researcher always endeavour's to observe, report and interpret the complex field experience as accurately and faithfully as possible.




A basic tenet of qualitative research is not to accept anything at face value. Qualitative researchers try to ensure that their research accurately reflects the evidence, and they have checks on their evidence and interpretations.


As an aid to verification, triangulation is a common theme in qualitative research. Cohen and Manion (1989) suggest three types. Researcher-subject corroboration involves cross-checking the meaning of data between the researcher and the respondents. This cross-checking may occur during data gathering or after interpretations of the raw data have been made, for confirmation of accurate reporting. Second, confirmation from other sources about specific issues or events identified is always paramount. Third, two or more methods of data collection should be used and the resultant interpretations should be compared. A fourth triangulation option is called researcher convergence (Huberman & Miles1998) - using another researcher analyse the same raw data and then comparing the two analyses.


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Module 7   Qualitative Data Analysis and Interpretation

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Module 8    Measurement of Variables

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Module 9    Scaling, Reliability and Validity

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Module 10    Questionnaire Design

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Module 11    Sample Design

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Module 12   Experimental Designs

Consider the following two scenarios.


For some time now there has been the feelings that individual companies and the economy would be better served if executive compensation contracts were entered into that made the CEOs accountable for performance. Currently, the top executives are compensated irrespective of their performance, making them permanent corporate fixtures.


A change to the new mode is likely to annoy the CEOs, but it is definitely worth a try. How can we be sure that it would work?


Scenario B


A study of absenteeism and the steps taken to curb it indicates that companies use the following incentives to reduce it.


  • 14 per cent give bonus days.
  • 39 per cent offer cash.
  • 39 per cent give out recognition awards.
  • 4 per cent give prizes.
  • 4 per cent pursue other strategies.


Asked about the effectiveness of offering incentives for reducing absenteeism, 22 percent of the companies said they were very effective; 66 per cent said they were somewhat effective; and 12 per cent said they were not at all effective.


What does the preceding information tell us? How do we know what kinds of incentives cause people not to absent themselves? What particular incentive(s) were used by the 22 per cent of companies that found their strategies to be 'very effective'? Is there a direct causal connection between one or two specific incentives and absenteeism?


The answers to the questions raised in scenarios A and B might be found by using experimental designs in researching the issues. We will discuss both lab experiments and field experiments. We will also briefly discuss simulation experiments. Experimental designs, as we know are set up to examine possible cause and effect relationships among variables, in contrast to correlational studies, which examine the relationships among variables without necessarily trying to establish if one variable causes another.


To establish that a change in variable X causes a change in variable Y, all three of the following conditions should be met.



  1. Both X and Y should co-vary - that is, when one goes up, the other should also simultaneously go up (or down).

  2. The independent variable X (the presumed causal factor) should precede the dependent variable Y. In other words, there must be a time sequence in which the two occur.

  3. No other factor should possibly cause the change in the dependent variable Y.

It may therefore be seen that to establish causal relationships between two variables in a business setting, several variables that might co-vary with the dependent variable have to be controlled. This would then allow us to say that changes in the independent variable X and variable X alone cause the changes in dependent variable Y. Useful as it is to know the cause and effect relationships, establishing is not easy, because several other variables that co-vary with the dependent variable have to be controlled. It is not always possible to control all the co-variates while manipulating the causal factor (the independent variable that is causing the dependent variable) in business settings, where events flow or occur naturally and normally. It is however, possible first to isolate the effects of a variable in a tightly controlled official setting (the lab setting), and after testing and establishing the cause and effect relationship under these tightly controlled conditions, see how generalizable such relationships are to the field setting.


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Module 13    Quantitative Data Analysis and Interpretation

After data have been obtained through questionnaires, interviews, observation or secondary sources, they need to be edited. The blank responses, if any, have to be handled in some way; the data then have to be coded, and a categorisation scheme has to be set up. The data will then have to be keyed in, and a software program used to analyse the data.


Data have to be edited, especially when they relate to responses to open-ended questions of interviews and questionnaires, or unstructured observations. In other words, information that may have been written down in a hurry by the interviewer, observer or respondent must be clearly deciphered so that it may be coded systematically in its entirety. Lack of clarity at this stage will result in later confusion.

Therefore it is recommended that such editing should be done on the very same day that the data are collected so that the respondents (if not anonymous) may be contacted for any further information or clarification, as needed. The edited data should be identifiable through the use of a different colour pencil or ink so that the original information is still available in case of later doubts.


Incoming mailed questionnaire data have to be checked for incompleteness and inconsistencies by designated members of the research staff. Inconsistencies that can be logically corrected should be rectified and edited at this stage. For instance, the respondent might have inadvertently neglected to answer the item on a questionnaire about her marital status. Against the column asking for the number of years married, she might have responded 12 years; in the number of children column, she might have marked 2, and for ages of children, she might have answered 8 and 4. The latter three responses would indicate that the respondent is in all probability married. The unanswered response to the marital status question could then be edited by the researcher to read 'yes'. It is possible, however, that the respondent deliberately omitted responding to the item because she is either a recent widow or has just been separated, or for some other reason. If such is the case, we would be introducing a bias in the data by editing the data to read 'yes'. Hence, whenever possible it is better to follow up with the respondent and get the correct data while editing.


Once all the Data is entered, the next step is to code the responses. Using scanner sheets for collecting questionnaire data; such sheets facilitate entry of the responses directly into the computer without manually keying data. However, if for some reason this cannot be done, then it is perhaps better a coding sheet first to transcribe the data from the questionnaire and then key data.




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Module 14    Research Reporting

Once the data analyses are completed and conclusions drawn from the findings, the researcher is ready to present the results of the research study and make suitable recommendations. This is usually done in the form of a written report, frequently followed up by an oral presentation.


It is important that the results of the study and the recommended solutions to the problem are effectively communicated to the sponsors or clients, so that the suggestions made are accepted and implemented. Otherwise, all the effort expended on the investigation would be in vain. Writing the report concisely, convincingly and with clarity is perhaps even more important than

Conducting a perfect research study. Hence, a well-thought-out written report and oral presentation are critical.


The contents and organisation of both modes of communication – the written report and the oral presentation - depend on the purpose of the research study and the audience at which it is targeted.


The written report enables the manager to weigh the facts and arguments presented therein and implement the acceptable recommendations, with a view to closing the gap between the existing state of affairs and the desired state. To achieve its goals, the written report has to focus on the following issues.




Reports can be written for different purposes, so the form of the written report will vary according to the situation. It is important to identify the purpose of the report so that it can be tailored accordingly. If the purpose is simply to offer details on some specific factors requested by a manager, the report can be very narrowly focused and provide the desired information to the manager in a brief format.


If, on the other hand, the report is intended to 'sell an idea' to management, then it has to be more detailed and convincing as to why the proposed idea is an improvement and should be adopted. Here the emphasis should be on presenting all the relevant information, backed by the necessary data, to persuade the reader to 'buy into the idea'. A variation will be provided in cases where a manager asks for several alternative solutions or recommendations to rectify a problem in a given situation.


The researcher provides the requested information and the manager chooses from among the alternatives and makes the final decision. In this case, a more detailed report surveying past studies, the methodology used for the present study, different perspectives generated from interviews and current data analysis, and alternative solutions based on the conclusions drawn from the results of data analyses, will have to be provided. How each alternative will help to improve the problem situation must also be discussed. The advantages and disadvantages of each of the proposed solutions, together with a cost-benefit analysis in terms of dollars and/or other
resources, will also have to be presented to help the manager make the decision.



Still another type of report might require the researcher to identify the problem and provide the final solution as well. That is, the researcher might be called in to study a situation, determine the nature of the problem, and offer a report of the findings and recommendations. Such a report has to be very detailed, following the format of a full-fledged study.



A fifth kind of research report is the more scholarly publication presenting the findings of a basic or applied study that one usually finds published in academic journals.


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Module 15 Managerial Decision Making and Evaluating Research

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Applied Business Research

Qualitative and Quantitative Methods

Module 1

Introduction to business:

  • research

  • What is research?

  • Why managers should know about research

  • Business research

  • Definition of research

  • Research and the manager

  • The context of business research

  • Philosophical bases of business research

  • Positivist research

  • Interpretivist research

  • Critical research

  • An approach for business

  • Business research methods: Quantitative and qualitative

  • Types of business research: Applied and basic

  • Applied research

  • Basic or fundamental research

  • Managers and research The manager and the consultant or researcher

  • How to locate and select a researcher

  • The manager-researcher relationship

  • Values

  • Internal versus external consultants or researchers

  • Advantages of internal consultants or researchers

  • Disadvantages of internal researchers

  • External consultants or researchers

  • Advantages of external consultants

  • Disadvantages of external consultants

  • Knowledge about research and managerial effectiveness

  • Ethics and business research

  • Societal accountability

  • The sponsor or client of the research

  • The research subjects

  • Conflict among the accountabilities

Module 2

Scientific investigation and the research process:

  • The hallmarks of scientific research

  • Purposiveness

  • Rigour

  • Testability

  • Replicability

  • Accuracy

  • Objectivity

  • Generalisability

  • Parsimony

  • Limitations to scientific research in management

  • Some basic definitions

  • Observations

  • Concepts

  • Constructs

  • Approaches to research

  • Quantitative research

  • Qualitative research

  • Deduction

  • Induction

  • The research process

  • Catalyst for research

  • Preliminary information gathering and literature survey

  • Problem definition

  • Framework development

  • Research objectives

  • Research design

  • Data collection

  • Data analysis

  • Interpretation of findings

  • Report preparation and presentation

  • Management action

  • Review of the research process

Module 3

Preliminary information gathering and problem definition:

  • The practical application of the research process

  • Catalyst for business research

  • Preliminary information gathering

  • Nature of the information to be collected

  • Sources of information

  • The literature survey

  • Reasons for the literature survey

  • Conducting the literature survey

  • Writing up the literature survey

  • Examples of two literature surveys

  • Problem definition

  • Examples of well-defined problems

  • Managerial implications

  • Ethical issues

Module 4

Framework development and research objectives:

  • The need for a framework

  • Concepts and variables

  • Concepts and the conceptual framework

  • The components of the conceptual framework

  • Variables and the theoretical framework

  • Types of variables

  • Developing the theoretical framework

  • The components of the theoretical framework

  • Research objectives

  • Research questions

  • Hypothesis development

Module 5

Research design and planning:

  • The research design

  • The purpose of the study

  • Exploratory study

  • Descriptive study

  • Hypothesis testing

  • Case studies

  • Review of the purpose of the study

  • Type of investigation

  • Purpose of the study and the research method

  • Researcher interference

  • Study setting: contrived and non-contrived

  • Units of analysis: Individuals, dyads, groups, organisations or cultures

  • Time horizon: Cross-sectional versus longitudinal studies

  • Cross-sectional studies

  • Longitudinal studies

  • Review of elements of research design

  • The research proposal

  • Managerial implications

Module 6

Qualitative data gathering:

  • The assumptions of qualitative research

  • Accuracy and replicability

  • Sampling

  • Qualitative research methods

  • Interviewing

  • The pattern of an interview

  • Listening

  • Questioning

  • Paraphrasing

  • Probing

  • Summarising

  • N on-verbal behaviour

  • Structured and unstructured interviews

  • The three levels of interviewing

  • Types of interviews

  • The focus group

  • Structured and unstructured focus groups

  • Logistics

  • Group composition

  • Conducting the focus group

  • Summary of focus groups

  • Observational studies

  • Types of observer roles

  • Structured versus unstructured observational studies

  • Advantages and disadvantages of observational studies

  • Bias in observational studies

  • Summary of observational studies

  • Other special data sources

  • Projective methods Secondary data

  • Panels

  • Ethics in data collection

  • Ethics and the researcher

  • Ethical behaviours of respondents

Module 7

Qualitative data analysis and interpretation:

  • The overlap of data gathering and analysis

  • The purpose of qualitative analysis

  • Structure and unconstructed methods

  • Content analysis

  • Conducting content analysis

  • A rich, messy and complex process

  • Using computer package

  • The NVivo program

  • The NVivo program and content Analysis

Module 8

Measurement of variables:

  • How variables are measured

  • Operational definition: Dimensions and elements

  • What an operational definition is not

  • A measure of student learning

  • Review of operational definition

  • Measurement scales

  • Nominal scale

  • Ordinal scale

  • Interval scale

  • Ratio scale

  • Review of scales

Module 9

Scaling, reliability and validity:

  • Rating scales

  • Dichotomous scale

  • Category scale

  • Likert scale

  • Sematic differential scale

  • Numerical scale

  • Itemised rating scale

  • Fixed or constant sum rating scale

  • Stapel scale

  • Graphic rating scale

  • Consensus scale

  • Other scales

  • Ranking scales

  • Paired comparison

  • Forced choice

  • Comparative scale

  • Goodness of measures

  • Item analysis

  • Reliability

  • Stability of measures

  • Internal consistency of measures

  • Validity

  • Face validity

  • Content validity

  • Criterion-related validity

  • Construct validity

Module 10

Questionnaire design:

  • Why are questionnaires important?

  • Guidelines for questionnaire design

  • Principles of wording

  • Content and purpose of the question

  • Language and wording of the questionnaire

  • Type and form of questions

  • Biases in questions

  • Sequencing of questions

  • Classification data or personal information

  • Principles of measurement

  • General appearance of the questionnaire

  • Introduction to respondents

  • Instructions and organising questions

  • Demographic data

  • Sensitive personal data

  • Open-ended question at end

  • Concluding the questionnaire

  • Pre-testing questionnaires

  • Face validity

  • Content validity

  • Pilot study

  • Gathering the data

  • Personally administered questionnaires

  • Mail questionnaires

  • Electronic questionnaires

  • Cross-cultural research

  • Special issues in instrumentation

  • Issues in data collection

  • Multimethods of data collection

  • Comparison of data collection methods

  • Managerial perspective


Module 11

Sampling design:

  • Population, element, sampling frame,

  • sample and subject

  • Sampling

  • Reasons for sampling

  • Representativeness of samples

  • Probability and non-probability sampling

  • Probability sampling

  • Simple random sampling

  • Complex probability sampling

  • Review of probability sampling designs

  • Non-probability sampling

  • Convenience sampling

  • Purposive sampling

  • Review of non-probability sampling designs

  • Examples of sampling designs

  • Review of sampling plan decisions

  • Sampling in cross-cultural research

  • Issues of precision and confidence in

  • determining sample size

  • Precision

  • Confidence

  • Sample data, precision and confidence in estimation

  • Trade-off between precision and confidence

  • Determining the sample size

  • Importance of sampling design and sample size

  • Efficiency in sampling

  • Review of sample size decisions

Module 12

Experimental designs:

  • Introduction to experimental designs

  • Laboratory experiments

  • Controlling the extraneous variables

  • Manipulating the independent variable

  • Selecting and assigning subjects

  • Internal validity

  • External reliability

  • Field experiments

  • Validation issues

  • Validation comparison: Lab and field experiments

  • Trade-off between internal and external validity

  • Factors affecting internal validity

  • Scenario case: Identifying threats to internal validity

  • Internal validity in case studies

  • Factors affecting external validity

  • Review of factors affecting internal and external validity

  • Types of experimental designs

  • Quasi-experimental designs

  • True experimental designs

  • Solomon four-group design

  • Double-blind studies

  • Ex post facto designs

  • Simulation

  • Ethical issues in experimental research

  • Managerial implications

Module 13

Quantitative data analysis and interpretation:

  • Getting data ready for analysis

  • Editing data

  • Handling blank responses

  • Coding

  • Categorising

  • Entering data

  • Data analysis

  • Getting a feel for the data

  • Testing goodness of data

  • Reliability

  • Validity

  • Hypothesis testing

  • Some preliminary steps

  • Checking the reliability of measures: Cronbachs alpha

  • Obtaining descriptive statistics

  • Inferential statistics: Pearson correlation

  • Hypothesis testing

  • Overall interpretation and recommendations

  • Use of expert systems

Module 14

Research Reporting:

  • The written report 351

  • The written report and its

  • purpose 351

  • The written report and its

  • audience 353

  • Characteristics of a well-written

  • report 354

  • Contents of the research report


  • Integral parts of the report 356

  • The title page of the research

  • report 356

  • Table of contents 356

  • Authorisation letter 356

  • The executive summary or

  • synopsis 357

  • The introductory section 358

  • The body of the report 358

  • The final part of the report 358

  • Acknowledgements 358

  • References 358

  • Appendices 360

  • Oral presentation 360

  • Deciding on the content 360

  • Visual aids 361

  • The presenter 361

  • The presentation 361

  • Handling questions

Module 15

Managerial decision making and evaluating research:

  • Scientific research and managerial

  • decision making

  • Purposive research

  • Decision-making processes and different types of research

  • Evaluating business research

  • Conceptual/overview questions

  • Framework questions

  • Method questions

  • Results or conclusions questions

  • Reader's abstract

  • A final comment

  • Discussion points

  • References

  • Glossary of statistical symbols

  • Appendix I: A refresher on some

  • statistical terms and tests

  • Introduction

  • Descriptive statistics

  • Frequencies

  • Measurement of central tendencies

  • Measures of dispersion

  • Inferential statistics

  • Statistical hypothesis testing

  • Correlations

  • Relationship between two nominal variables: chi-square test

  • Significant mean differences between two groups: the t-test

  • Significant mean differences among multiple groups: ANOVA

  • Regression analysis

  • Factor analysis

  • Other tests and analyses

  • Managerial relevance