MASTER OF BUSINESS ADMINISTRATION Business Research
Contact details: Regenesys Business School Tel: +27 (11) 669-5000 Fax: +27 (11) 669-5001 E-mail:
[email protected] www.regenesys.co.za
This study guide highlights key focus areas for you as a student. Because the field of study in question is so vast, it is critical that you consult additional literature.
Copyright © Regenesys, 2023 All rights reserved. No part of this publication may be reproduced, stored in or introduced into a retrieval system, or transmitted, in any form, or by any means (electronic, mechanical, photocopying, recording or otherwise) without written permission of the publisher. Any person who does any unauthorised act in relation to this publication may be liable for criminal prosecution and civil claims for damages.
CONTENTS 1 2
STUDY MATERIAL ................................................................................................................................... 1 PRESCRIBED RESOURCES ................................................................................................................... 1 2.1 BOOKS .............................................................................................................................................. 1 2.2 ARTICLES ......................................................................................................................................... 2 2.3 MULTIMEDIA .................................................................................................................................... 4 3 INTRODUCTION ....................................................................................................................................... 6 3.1 LEARNING OUTCOMES .................................................................................................................. 6 3.2 INTRODUCTION TO RESEARCH .................................................................................................... 7 3.2.1 AN OVERVIEW OF RESEARCH .............................................................................................. 8 3.2.2 THE PURPOSE OF RESEARCH ............................................................................................ 10 3.2.3 THE CHARACTERISTICS OF RESEARCH ........................................................................... 10 3.2.4 TYPES OF RESEARCH .......................................................................................................... 12 3.2.5 KEY POINTS ........................................................................................................................... 17 3.3 RESEARCH ETHICS ...................................................................................................................... 18 3.3.1 INTRODUCTION ..................................................................................................................... 19 3.3.2 ETHICAL CONSIDERATIONS FOR RESEARCHERS ........................................................... 20 3.3.3 KEY POINTS ........................................................................................................................... 22 3.4 THE RESEARCH PROBLEM, OBJECTIVES AND RATIONALE ................................................... 23 3.4.1 INTRODUCTION ..................................................................................................................... 24 3.4.2 THE RESEARCH PROBLEM .................................................................................................. 24 3.4.3 IDENTIFYING A RESEARCH PROBLEM ............................................................................... 25 3.4.4 GENERATING RESEARCH IDEAS ........................................................................................ 27 3.4.5 IDENTIFYING AN ORGANISATIONAL ISSUE ....................................................................... 27 3.4.6 REFINING A PROBLEM STATEMENT ................................................................................... 28 3.4.7 CHARACTERISTICS OF A RESEARCH PROBLEM .............................................................. 30 3.4.8 RESEARCH OBJECTIVES ..................................................................................................... 31 3.4.9 THE RESEARCH QUESTIONS .............................................................................................. 32 3.4.10 THE RATIONALE FOR THE RESEARCH .............................................................................. 33 3.4.11 KEY POINTS ........................................................................................................................... 34 3.5 FORMULATING AND CLARIFYING THE RESEARCH TOPIC ...................................................... 36 3.5.1 FORMULATING AND CLARIFYING THE RESEARCH TITLE ............................................... 36 3.5.2 KEY POINTS ........................................................................................................................... 38 3.6 CONDUCTING A CRITICAL LITERATURE REVIEW ..................................................................... 39 3.6.1 INTRODUCTION ..................................................................................................................... 40 3.6.2 WHAT IS A LITERATURE REVIEW? ...................................................................................... 41 3.6.3 THE PURPOSE OF THE LITERATURE REVIEW .................................................................. 41 3.6.4 CRITERIA FOR A LITERATURE REVIEW ............................................................................. 42 3.6.5 STEPS IN THE LITERATURE REVIEW ................................................................................. 42 3.6.6 WRITING THE LITERATURE REVIEW .................................................................................. 47 3.6.7 THEORETICAL FRAMEWORK ............................................................................................... 50 3.6.8 KEY POINTS ........................................................................................................................... 51 3.7 THE RESEARCH PHILOSOPHY AND APPROACH ...................................................................... 52 3.7.1 THE RESEARCH PHILOSOPHY ............................................................................................ 53 3.7.2 DEDUCTIVE VERSUS INDUCTIVE RESEARCH ................................................................... 59 3.7.3 THE QUANTITATIVE VERSUS QUALITATIVE RESEARCH APPROACH ............................ 61 3.7.4 KEY POINTS ........................................................................................................................... 64 3.8 FORMULATING THE RESEARCH DESIGN .................................................................................. 65 3.8.1 THE RESEARCH DESIGN ...................................................................................................... 65 3.8.2 THE TYPES OF RESEARCH STRATEGIES .......................................................................... 67 3.8.3 THE RESEARCH PROCESS .................................................................................................. 72 3.8.4 KEY POINTS ........................................................................................................................... 75 3.9 SAMPLING DESIGN ....................................................................................................................... 76 3.9.1 INTRODUCTION TO SAMPLING DESIGNS .......................................................................... 76 3.9.2 POPULATION VERSUS A SAMPLE ....................................................................................... 77 3.9.3 SAMPLING .............................................................................................................................. 78 3.9.4 CAUSES OF SAMPLING ERROR .......................................................................................... 80 3.9.5 SAMPLING PROCEDURE ...................................................................................................... 83 3.9.6 TYPES OF NONPROBABILITY SAMPLES ............................................................................ 84 3.9.7 TYPES OF PROBABILITY OR RANDOM SAMPLES ............................................................. 85 3.9.8 COMBINATION OR MIXED PURPOSEFUL SAMPLING ....................................................... 86
4 5 6 7
3.9.9 KEY POINTS ........................................................................................................................... 88 3.10 PLANNING YOUR DATA COLLECTION DESIGN ......................................................................... 89 3.10.1 DATA COLLECTION METHODS ............................................................................................ 90 3.10.2 VARIABLES IN THE RESEARCH PROBLEM ........................................................................ 91 3.10.3 RESEARCH INSTRUMENTS .................................................................................................. 92 3.10.4 DATA TYPES .......................................................................................................................... 94 3.10.5 CONSTRUCTING QUESTIONNAIRES .................................................................................. 95 3.10.6 SCALE DEVELOPMENT ......................................................................................................... 98 3.10.7 MEASUREMENT SCALES ................................................................................................... 100 3.10.8 KEY POINTS ......................................................................................................................... 101 3.11 DATA ANALYSIS .......................................................................................................................... 102 3.11.1 INTRODUCTION ................................................................................................................... 103 3.11.2 DESCRIPTIVE STATISTICAL ANALYSIS ............................................................................ 103 3.11.3 INFERENTIAL STATISTICAL ANALYSIS – HYPOTHESIS TESTS ..................................... 118 3.11.4 HYPOTHESIS TEST USING THE ANOVA ........................................................................... 118 3.11.5 INFERENTIAL STATISTICAL ANALYSIS – SIGNIFICANCE TESTS ................................... 121 3.11.6 ANALYSING QUALITATIVE DATA ....................................................................................... 133 3.11.7 KEY POINTS ......................................................................................................................... 136 REFERENCES ...................................................................................................................................... 137 APPENDIX 1: RESEARCH PROPOSAL AND MINI-DISSERTATION GUIDELINES ........................... 143 GLOSSARY OF TERMS ....................................................................................................................... 143 VERSION CONTROL ............................................................................................................................ 146
List of Tables TABLE 1: PURPOSE OF RESEARCH 10 TABLE 2: RESEARCH CONCEPTS AND TERMINOLOGY 12 TABLE 3: REAL-WORLD PROBLEMS 29 TABLE 4: POPULAR, TRADE AND SCHOLARLY PUBLICATIONS 43 TABLE 5: DETERMINING QUALITY OF INFORMATION SOURCES 46 TABLE 6: A SUMMARY OF THE VARIOUS RESEARCH ASSUMPTIONS 60 TABLE 7: THE DIFFERENCES BETWEEN THE QUALITATIVE AND QUANTITATIVE APPROACHES 63 TABLE 8: DIFFERENCES BETWEEN RESEARCH DESIGN AND RESEARCH METHODOLOGY 66 TABLE 9: DIFFERENCES BETWEEN INTENSIVE AND EXTENSIVE RESEARCH 70 TABLE 10: TERMINOLOGY 77 TABLE 11: EXAMPLE OF NOMINAL SCALE 95 TABLE 12: EXAMPLE OF ORDINAL SCALE 96 TABLE 13: TYPES OF SCALES 100 TABLE 14: T-TESTS 127 TABLE 15: T-TEST TWO-SAMPLE ASSUMING EQUAL VARIANCE 129 TABLE 16: SUMMARY OF STATISTICAL TESTS TO EXAMINE RELATIONSHIPS BETWEEN VARIABLES 131
List of Figures FIGURE 1: ETHICAL CONSIDERATIONS IN RESEARCH FIGURE 2: THE RESEARCH ONION FIGURE 3: 10-STEP RESEARCH PROCESS FIGURE 4: RESEARCH PROCESS AND THE RESEARCH REPORT FIGURE 5: HISTOGRAM – AREA 1 FIGURE 6: HISTOGRAM – AREA 2 FIGURE 7: CORRELATION ANALYSIS
21 54 73 74 116 117 123
1 STUDY MATERIAL Your material includes: • • • •
This study guide Prescribed reading and viewing Digital assessments at the end of each section of your course Individual assignment These resources provide a starting point for your studies. You are expected to make good use of your textbooks, the additional resources provided via online links, and wider reading that you, as a higher education student, will source yourself.
2 PRESCRIBED RESOURCES A number of resources are recommended to help you complete this course.
2.1 BOOKS The following textbooks are prescribed and should be used to complete the course. You may use either, or both. •
Saunders, M., Lewis, P. and Thornhill, A. 2019, Research Methods for Business Students, 8th ed., Cape Town: Pearson Education.
You can order a hard or soft copy of this book through the library, or through reputable academic bookstore.
OR
•
Adams, J., Raeside, R. and Khan, H.T.A. 2014, Research Methods for Business and Social Science Students, 2nd ed., New Delhi: Sage Publications.
You must have Ebscohost open through the Regenesys portal to access this link.
Please ensure you order or download your textbooks before you start the course.
© Regenesys Business School
1
You may also find these books useful: •
Bak, N. 2004, Completing Your Thesis: A Practical Guide, Pretoria: Van Schaik.
•
Collins, J. and Hussey, R. 2003, Business Research: A Practical Guide for Undergraduate and Postgraduate Students, 2nd ed., Palgrave Macmillan.
•
Edwards, R. and Holland, J. 2013, What Is Qualitative Interviewing? New York: Bloomsbury Academic.
•
Mouton, J. 1996, Understanding Social Research, Pretoria: Van Schaik.
•
Rubin, H.J. and Rubin, I.S. 2004, Qualitative interviewing: The Art of Hearing Data, 2nd ed., Thousand Oaks, CA: Sage Publications.
•
Welman, J.C. and Kruger, S.J. 1999, Research Methodology for the Business and Administrative Sciences, Johannesburg: International Thomson Publishing.
2.2 ARTICLES •
Borgatti, S. 2017, ‘Axial coding’, http://www.analytictech.com/mb870/introtogt.htm (accessed 26 March 2023).
•
City of Philadelphia, 2016, ‘A brief history of research ethics’, https://www.phila.gov/departments/department-ofpublic-health/ (accessed 26 March 2023).
•
Gläser, J. and Laudel, G. 2013, ‘Life with and without coding: Two methods for early-stage data analysis in qualitative research aiming at causal explanations’, https://www.qualitativeresearch.net/index.php/fqs/article/view/1886/3528 (accessed 26 March 2023).
•
Gosling, M. 2017, ‘San Council launches code of ethics for researchers’, News24, https://mg.co.za/article/2017-03-06-san-council-launches-code-of-ethics-for-researchers/ (accessed 26 March 2023).
•
Grounded theory, 2017, http://www.qualres.org/HomeGrou-3589.html (accessed 26 March 2023).
•
Humphrey, C. 2008, ‘Auditing research: A review across the disciplinary divide’, Accounting, Auditing and Accountability Journal, 21(2), 170-203, http://www.emeraldinsight.com/doi/full/10.1108/09513570810854392 (accessed 26 March 2023).
•
Hyde, K. F. 2000, ‘Recognising deductive processes in qualitative research’, Qualitative Market Research, (3) 2, 82-89, http://www.emeraldinsight.com/doi/full/10.1108/13522750010322089 (accesses 26 March 2023).
•
Jack, E.P. and Raturi, A.S. 2006, ‘Lessons learned from methodological triangulation in management research’, Management Research News, (29) 6, 345–357. http://www.emeraldinsight.com/doi/full/10.1108/01409170610683833 (accessed 26 March 2023). © Regenesys Business School
2
•
MCB UP, 2003, ‘Learning from the mistakes of Enron: the issue, in a word’, Strategic Direction, 19 (3), 23-25, https://www.emerald.com/insight/content/doi/10.1108/02580540310794417/full/html (accessed 26 March 2023).
•
McNamara, C. 2008, ‘General guidelines for conducting research interviews’, http://managementhelp.org/businessresearch/interviews.htm (accessed 26 March 2023).
•
Schiller, B. 2011, ‘Academia strives for relevance’, Financial Times, April 25 2011, http://www.ft.com/intl/cms/s/2/4eeab7d4-6c37-11e0-a049-00144feab49a.html#axzz3jLxUIXes (accessed 26 March 2023). Summarise two key learnings.
•
Simon, S. 2011, ‘Choosing your dissertation title’, http://dissertationrecipes.com/wpcontent/uploads/2011/04/Dissertation-TitleXY.pdf (accessed 26 March 2023).
•
Smith, J. and Noble, H. 2014, ‘Bias in research’, Evidence-based Nursing, Oct 17 (4), 100-101, http://ebn.bmj.com/content/17/4/100 (accessed 26 March 2023).
•
Smith, L. 2008, ‘Ethical principles in practice’, Kairaranga, 9, pp16-21, http://files.eric.ed.gov/fulltext/EJ908179.pdf (accessed 26 March 2023).
•
Stemler, S. 2001, ‘An overview of content analysis’, https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1100&context=pare (accessed 26 March 2023).
•
The European Code of Conduct for Research Integrity, 2011 https://www.allea.org/wpcontent/uploads/2017/03/ALLEA-European-Code-of-Conduct-for-Research-Integrity-2017-1.pdf (accessed 26 March 2023).
•
Wallace, J.S. 2010, ‘Family-owned businesses: determinants of business success and profitability’, http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1590&context=etd (accessed 26 March 2023).
•
Woodside, A.G. and Wilson, E. J. 2003, ‘Case study research methods for theory building’, Journals of Business and Industrial Marketing, (18) 6/7, 493-508, http://www.emeraldinsight.com/doi/full/10.1108/08858620310492374 (accessed 26 March 2023).
© Regenesys Business School
3
2.3 MULTIMEDIA •
Cranfield School of Management, 2012, ‘Management research: delivering business results’, [video clip], http://www.youtube.com/watch?v=R7XuQxukmb0 (accessed 26 March 2023).
•
Davis, J. 2010, 'Chi-squared test', [video clip], http://www.youtube.com/watch?v=UPawNLQOv-8 (accessed 26 March 2023).
•
Dr Sam Fiala, 2012, ‘Research ethics’, [video clip], http://www.youtube.com/watch?v=Ir3VvYNzHeM (accessed 26 March 2023).
•
Gibbs, G.R. 2010, ‘Approaches to open coding’, https://www.youtube.com/watch?v=Dfd_U-24egg ( 26 March 2023).
•
Gibbs, G.R. 2010, ‘Coding part 5: The code list or code hierarchy’, https://www.youtube.com/watch?v=DVpkuTdkZvA (accessed 26 March 2023).
•
Gibbs, G.R. 2010, ‘Grounded theory – axial coding’, https://www.youtube.com/watch?v=s65aH6So_zY (accessed 26 March 2023).
•
Gibbs, G.R. 2010, ‘Grounded theory – open coding Part 1’, https://www.youtube.com/watch?v=gn7Pr8M_Gu8 (accessed 26 March 2023).
•
Gibbs, G.R. 2010, ‘Grounded theory – open coding Part 2’, https://www.youtube.com/watch?v=vi5B7Zo0_OE (accessed 26 March 2023).
•
Gibbs, G.R. 2010, ‘Grounded theory – open coding Part 3’, https://www.youtube.com/watch?v=n-EomYWkxcA (accessed 26 March 2023).
•
Gibbs, G.R. 2010, ‘Grounded theory – open coding Part 4’, https://www.youtube.com/watch?v=AwmDRh5l7ZE (accessed 26 March 2023).
•
Ignousohs, 2011, ‘Sampling issues in research studies, [video clip], http://www.youtube.com/watch?v=jgLALMJ62-U (accessed 26 March 2023).
•
Massey University, 2010, ‘The literature review’, [video clip], http://www.youtube.com/watch?v=jKL2pdRmwc4 (accessed 2023).
•
Meeng Uofu, 2012, ‘How to write a problem statement (review for ME1010)’, [video clip] http://www.youtube.com/watch?v=JwdHL3U0eoc (accessed 26 March 2023).
© Regenesys Business School
4
•
Ruff, C. 2014, ‘Writing your thesis video 4 – Zotero is a hero’, [video clip], https://www.youtube.com/watch?v=5MjZ2urtk70 (accessed b 26 March 2023).
•
UELRDBS, 2013, ‘Postgraduate research planning workshop – research process and philosophy’, [video clip], http://www.youtube.com/watch?v=zjhrfqZTUD8 (accessed 26 March 2023).
•
Wilson, D. 2014, ‘Introduction to reference management software’, [video clip], https://www.youtube.com/watch?v=1YzkEf1aLsM (accessed 26 March 2023).
© Regenesys Business School
5
3 INTRODUCTION This course will guide you to understand research philosophies, research methodology, research design, and conducting and analysing data to produce meaningful information. This course provides a sound basis for research that can be used both in your personal and work life. Read the cases and do the exercises provided as you work through the course, as they will reinforce the theoretical concepts. Pay attention to the structures set out in this course, as they are designed to give you insight into the marking and structure of a dissertation. Please note that dissertation supervisors may require you to make changes to the research proposal you develop as the assignment for this course, even if your research is approved, as dissertations require long, deep and rigorous research. You might also be asked to return to earlier chapters to make changes, particularly if you transfer between supervisors. Regenesys accepts the use of any referencing style guide (eg APA, Harvard, etc) as long as a single style is chosen and employed consistently throughout. The APA (American Psychological Association) referencing style is recommended as it is easily integrated into Word documents.
3.1 LEARNING OUTCOMES On completing this course, you should be able to: • • • • •
Demonstrate an understanding of the research process and its application to resolve business problems; Review, apply, and critique various business research methods; Understand the need to, and the process of complying with, ethical issues in business research; Develop and present a professional research proposal for either a technical project or a single research or mini-dissertation; and Demonstrate the ability to apply statistical and other data analysis techniques to interpret research findings and solve business problems.
The number of notional learning hours set out in the table under each section heading provides guidance on how long to spend studying each section of this course. Set yourself a schedule to ensure that you spend a suitable period of time on each section, complete the assignment, and give yourself enough time to prepare for the examination.
© Regenesys Business School
6
3.2 INTRODUCTION TO RESEARCH Timeframe
Learning outcomes
Prescribed textbooks
Prescribed multimedia
Minimum of 4 hours •
Demonstrate an understanding of the research process and its application to resolve business problems;
•
Develop and present a professional research proposal for either a technical project or a single research- or mini-dissertation.
•
Saunders, M., Lewis, P. and Thornhill, A., 2019, Research Methods for Business Students, 8th ed., Cape Town: Pearson Education.
•
Adams, J., Raeside, R. and Khan, H.T.A. 2014, Research Methods for Business and Social Science Students, 2nd ed., New Delhi: Sage Publications.
•
Collins, J. and Hussey, R. 2003, Business Research: A Practical Guide for Undergraduate and Postgraduate Students, 2nd ed., Palgrave Macmillan.
•
Cranfield School of Management, 2012, ‘Management research: delivering business results’, [video clip], http://www.youtube.com/watch?v=R7XuQxukmb0 (accessed 26 March 2023).
This section introduces you to the concept of research and the need for research. The characteristics of research are discussed, and then we will tackle:
Section overview
•
The research concept;
•
Conducting research;
•
Understanding the need for research as part of remaining competitive;
•
How study objects are defined, ie their nature;
•
Why study objects are the way they are and the relationship between study objects;
•
How to predict phenomena, such as human behaviour in the workplace, with the aim of using this information in future (eg for screening job applicants);
•
The characteristics of research; and
•
The purpose of research (applying it in a pragmatic and systematic manner to solve an organisational problem).
© Regenesys Business School
7
3.2.1 An Overview of Research Most of us have been exposed to the research process in some way. We are often approached to participate in surveys, for example on our preferences or experiences with regard to services or household products, or on our preferences in magazines, newspapers, websites and radio stations. These surveys are typical examples of market research in which the service provider aims to determine customer needs, and or potential customers. A particular research process is followed and an appropriate research method (such as a survey) is employed to collect and analyse data in order to achieve the aim of the study. In this course you will come across a number of frequently used research concepts with which you should become familiar. We start by describing the research-related concepts so that you have a clear understanding of them. The term research is derived from the French word recherché, which means to travel through or to survey. Research is defined as:
“A systematic investigation to establish facts or collect information on a subject.” (Collins English Dictionary, 2004)
“The process of thoroughly studying and analysing the situational factors surrounding a problem in order to seek out solutions to it.” (Cavana, Delahaye and Sekaran, 2001:4)
“ … a systematic, careful inquiry or examination to discover new information or relationships and to expand/verify existing knowledge for some specified purpose”. (Bennett, 1991:68)
“A process that people undertake in order to find out things in a systematic way, thereby increasing their knowledge.” (Saunders et al, 2013)
From these definitions, it is evident that research involves systematic investigation (Ghauri and Grønhaug, 2010). The term “systematic” suggests that research is based on logical relationships and not just beliefs (Saunders et al, 2013). Research is not conducted haphazardly, but it is a systematic process with a particular purpose in mind. In other words, we can regard research as the systematic process of collecting and analysing information (data) to increase our understanding of the subject or phenomenon involved.
© Regenesys Business School
8
In essence, research is a process that is followed in order to find answers or to come up with findings regarding a certain topic. In other words, research is a process of investigation: It examines a particular subject from a variety of different points of view considering a variety of assumptions, limitations and models proposed by various authors. The following authors’ definitions focus on research in a business context:
“Undertaking systematic research to find out things about business and management.” (Saunders, Lewis and Thornhill, 2003:3) “An organised, systematic, data-based, critical, objective, scientific inquiry or investigation into a specific problem or issue with the purpose of finding solutions to it or clarifying it.” (Cavana et al, 2001:5)
These definitions establish a common understanding of what research means to the researcher. As a research student you should, therefore, follow a systematic process to investigate – for example – a management-related problem in conducting your research study. There are three main factors that a researcher should take cognisance of (Saunders et al, 2013). Firstly, Saunders views the practice of management as eclectic. It is influenced by other disciplines, such as physical sciences, social sciences, economics, statistics and mathematics. You must be able to work across spiritual, emotional, technical, cultural and functional boundaries. You will need to draw on knowledge from all your courses at Regenesys. The dilemma for any researcher is whether to examine management or any other business problem from the perspective of one discipline, or whether to adopt an interdisciplinary approach. Secondly, a researcher will most likely conduct research within organisations, either public or private. Note that your access the organisation you want to research may be constrained, unless such an organisation can see some intrinsic, commercial or personal advantage to be derived from the study. Research can be challenging as it involves issues such as confidentiality, ethics, moral issues and consent from the organisation. Thirdly, you should appreciate both critical analysis and theory application will be required to resolve the research problem. You must be able to critically compare various theories and models in the context of the research objectives. Your final research report should add to the body of knowledge for the benefit of society.
© Regenesys Business School
9
3.2.2 The Purpose of Research Welman and Kruger (1999:19) identify the purpose of research as shown in Table 1. TABLE 1: PURPOSE OF RESEARCH
Describing
To describe how things (study objects) are – ie to define the nature of the study object(s)
Explaining
To explain why things (study objects) are the way they are and explain the relationship between them
Predicting
To predict phenomena, such as human behaviour in the workplace, with the aim of using this information in future (eg for screening job applicants)
3.2.3 The Characteristics of Research Although research may vary in complexity and duration, Leedy and Ormond (2003:2-3) say that research usually has eight distinct characteristics: 1. Research originates with a question or a problem Research usually begins with a problem statement, such as: §
Organisation X lost 10% of its technical skills per annum over three years.
As a researcher, you must ensure that the real problem (root cause) is identified and correctly defined, and not the symptoms of the problem, or you run the risk of setting up a study that produces meaningless results. 2. The research goal must be clearly articulated A research goal that is not clearly defined may lead to findings that differ from what is required. Take, for example, a dress designer who wants to start a boutique selling exclusive matric dance dresses. Her research goal might be to know what her target market is prepared to pay. So while girls in matric might be her target market, because schoolgirls in her area rarely have part-time jobs, they are unlikely to fund their ensembles themselves. How accurate a price range would she be likely to define if she asked them what they would pay for an exclusive dress? So how would she best articulate this research goal in order to obtain usable results?
© Regenesys Business School
10
3. Research follows a specific format Leedy (2013:75) views the basic format of the research process as having these steps: o o o o o
o o o
Step 1: The researcher asks a question to which there is no (currently) known solution. Step 2: Convert the research question into a clearly stated research problem that is researchable. Step 3: Based on the problem statement, state the research questions and hypothesis. The hypothesis is what the researcher believes may be causing the problem. Step 4: Select literature and secondary data that already exist and are relevant to this problem. Critically analyse the literature. Step 5: Once the literature review is completed and the secondary data analysis has been exhausted, collect primary data, specifically for where there are gaps in the secondary data. Step 6: Collate the data and synthesise it into a logical structure to analyse through the appropriate data analysis techniques, such as hypothesis testing. Step 7: Interpret the data and link it back to the previous steps to ensure a logical research flow and link back to the research objectives. Step 8: Compare the data analysis and information produced from the data with the research problem statement. To what extent does the hypothesis test, validate or solve the problem?
4. Research usually divides the principal problem into more manageable subproblems This allows you to manage the research by focusing on more manageable areas to research. 5. Research is guided by the specific research problem, question or hypothesis You must ensure a clear link between the research goal, the research objectives, the problem statement, the research questions and the hypothesis. 6. Research accepts certain critical assumptions and limitations, and delimitations, to ensure that the scope of the research is clearly defined before any research is undertaken. 7. Research requires the collection and interpretation of data in an attempt to resolve the problem that initiated the research. 8. Research is, by its nature, cyclical It is critical for you to understand that the market, organisation, product or other relevant cycles, may influence your research.
© Regenesys Business School
11
3.2.4 Types of Research Before we discuss the types of research, familiarise yourself with the commonly used researchrelated definitions in Table 2. TABLE 2: RESEARCH CONCEPTS AND TERMINOLOGY
Applied research
Research conducted to find solutions for particular problems in real situations.
Assumption
A basic premise that we believe is true.
Basic research
Pure, theoretical or scientific research, with the main purpose of creating new knowledge.
Bias
Prejudice or distortion.
Concept
An abstract idea representing a real phenomenon.
Construct
To create or build (verb).
Correlate
An association between two or more variables determined statistically.
Deduction
Going from the general to the particular.
Dependant variable
A variable that is influenced or changed.
Descriptive statistics
Mathematical techniques used to see underlying patterns of data.
Empirical
Based on observation and experience.
Epistemology
A branch of philosophy dealing with the nature of knowledge.
External validity
The extent to which results can be generalised to other populations.
Hypothesis
A tentative, testable statement about the relationship between two or more variables.
Independent variable
A variable that changes or influences the independent variable.
Internal validity
The extent to which the study confirms the existence of a cause-effect relationship.
Interval
The difference between two points on a scale.
Literature review
An exhaustive review of a wide range of existing literature on the research topic.
Methodology
The rules and procedures of research work.
Norms
Customary behaviour created by society and organisations that are standardised and usually followed by members of society and organisations.
Ontology
A branch of philosophy dealing with the nature of reality.
Population
The entire group of persons or objects and events of interest to the researcher.
Prediction
Statement that tells us of a future outcome.
Qualitative research
A research approach that focuses on human beings as the research subjects and on the observation of events from the perspective of those involved in an attempt to understand the behaviour of individuals.
© Regenesys Business School
12
Quantitative research
A highly structured research approach that involves the quantification of concepts, in order to do measurements and conduct evaluations.
Random assignment
Every subject has an equal chance of being in a group.
Rank
Arrange in hierarchy (verb).
Reliability
This means that if an identical investigation were repeated, similar results would be obtained.
Research
The systematic process of collecting and analysing information, in order to increase understanding of the research subject(s) or phenomenon involved.
Research design
A plan or a set of guidelines and instructions that enable the researcher to determine the research methodology and to address the research problem.
Sample
A subset of a research population.
Sampling error
Differences between population parameters and sampling statistics.
Theory
A framework of ideas that provides an explanation of something.
Theoretical framework
A collection of interrelated concepts, similar to a theory but not necessarily well worked out in its initial stages.
Validity
A methodological requirement for research methods.
Variable
A property that changes empirically. (Saunders et al, 2013)
Saunders et al (2013) distinguish between two major types of research, namely: basic research and applied research: 1. Basic research is often referred to as pure, theoretical or scientific research and its purpose is mainly to create new knowledge. 2. Applied research is used to solve particular problems in real situations. One could say that applied research is used to investigate and find solutions for real-world problems. Saunders et al (2013:11) argue that many business and management research projects can be placed on a continuum between basic and applied research. Basic research focuses on expanding the existing body of knowledge in the academic literature, where the knowledge of business paradigms and constructs may be supported by grounded theory. Your research will most likely be applied in nature. This implies that you will focus on identifying an organisational problem, typically within the organisation, to which you will apply the research process to solve the problem. This is a more pragmatic and action-oriented approach. Saunders et al (2013:12) liken this to consulting. Researchers need in-depth knowledge of people, systems and processes. The research process requires the collection of (new) primary data, based on previously collected and analysed (secondary) data, in order to gain a revised view of a situation.
© Regenesys Business School
13
Reporting Evidence from Business and Management Research Read the case study and answer the questions that follow. •
Case 1, ‘Reporting evidence from business and management research’, extracted from Saunders, M., Lewis, P. and Thornhill, A. 2013, Research Methods for Business Students, 6th ed., Cape Town: Pearson Education, 22–24.
Katie is working in her local National Health System (NHS) hospital on a six-month internship. During her time there, the hospital plans to introduce what they call a ‘Leadership at all Levels’ programme. All staff members are encouraged to act as leaders, and Katie is asked to write a report for her manager setting out the best way to ensure that the aims of the programme actually happen. Her manager makes a special point of telling Katie that the hospital wants to make its introduction ‘evidence-based’. This means, he explains, that he would like her report to set out the scientific evidence about what works in these kinds of initiatives. Katie agrees to do the report, and she thinks it may also be suitable as the research project for her degree. ‘Where do you start with a project like this?’ Katie wonders. ‘Well’, she thinks, ‘I may as well type, “leadership at all levels” into Google!’ On the day she does this, the entry at the very top of the list takes her straight to ‘Leadership at all levels: Leading public sector organisations in an age of austerity ’. The title page says it is a ‘research paper’ and it is published by the prestigious firm of management consultants, Deloitte (Deloitte 2010). She reads it all carefully. While the report is very enthusiastic about leadership as a general idea for improving public services, she is surprised to see that it contains very few concrete details. Although it is 16 pages long, there is nothing specific about what leadership is, nothing about how ‘leadership at all levels’ is actually going to happen; no academic research at all, as far as she can see. In fact, the more she thinks about it, the more she feels its recommendations are vague with little justification. For instance, among a list of bullet points on page 12, it recommends that top public sector leaders ask themselves questions like: • •
Do you have a senior team that is ready for change and is working collectively to enable it? Can you articulate a brief, compelling message of change, framed appropriately to connect with your staff?
‘But how could chief executives really know whether their answers to such questions were correct?’ Katie ponders. She feels chief executives are likely to have a vested interest in making their answers fit with what they already believe to be the case. Even if they can put their managerial interests aside, she thinks that the questions arising from the bullet point list such as ‘how “ready for change” is my team?’ or ‘how “compelling [a] message” might I be delivering to staff?’ are never going to be things that can be measured with any degree of objectivity. They are quite different from the kind of medical questions a hospital generally deals with, such as: ‘What is this patient’s body-mass index and blood pressure?’ ‘So’, Katie thinks, ‘Deloitte’s is probably not the kind of scientific evidence my manager had in mind when he asked me for an evidence-based report!’ She decides to look instead at academic journals, thinking that they might be a better place to look for scientific evidence than the World Wide Web. But she soon finds it a rather daunting task. Not only are there an almost overwhelming number of potentially relevant research papers, when she starts reading them she gets very confused. Not primarily because she does not understand them (though because of the language that can sometimes be a problem!) © Regenesys Business School
14
Still, her confusion is more down to the fact that many of the articles apparently contradict one another – even within the same journal. What is worse, their disagreements are often over fundamentals, rather than over details. For example, in the journal Human Relations, Schippers et al (2008:1593) write that transformational leadership is key to the ‘adoption of a shared vision by the team’. However, Harding et al (2011:1) claim ‘that leaders evoke a homoerotic desire in followers such that followers are seduced into achieving organisational goals’. After a few weeks of reading this evidence, Katie starts to think that she has been asked to do something that misunderstands the nature of scientific evidence – at least that of business and management studies. Her manager appears to have assumed that ‘the evidence’ will all point in the same direction. But Katie has discovered that in the case of leadership, ‘the evidence’ cannot even agree what leadership is, or whether it is a good or a bad thing for managers to adopt – never mind the best way to get all staff to become leaders. Authors disagree so much – and so fundamentally – that she finds it impossible to extract ‘best practice’. Unfortunately, Katie did say she would write the report. It occurs to her that she could just mention those articles that imply leadership is a good thing, and that detail ways of involving staff in it. She thinks that is really what her manager would like. After all, it’s already been announced across the hospital that a Leadership at all Levels programme is going to happen, and her report would still enable him to tell people that what he was doing was ‘evidence-based’. After some soul-searching, Katie decides to write a partial and somewhat misleading report (recognising she will need a good reference from him if she wants to get a job). Nevertheless, she knows that all her other readings will not go to waste – at least she can include these in her research project for university! Questions 1. Consider that Katie is correct, and that evidence does not necessarily tell managers the best way to take action. Do we still need evidence? 2. Can Katie’s decision to submit a report she thinks is misleading be justified on ethical grounds? 3. In what ways are the kinds of research projects that most managers want to read likely to be different from the kinds of research projects that get high marks at university?
Before deciding on the research you want to undertake, ask yourself: • • • • • • • • • • • • • •
Am I interested in this study? Who will be interested in this topic? What is the significance of the topic? Why is there a need for this particular topic to be researched? Who are the stakeholders involved in this study? Do the stakeholders have any vested interest in this study? What are the main concepts? What are the main ideas and theories? What are key terms, phrases or vocabulary used? What are the issues to consider in this study? Use the following queries to clarify the topic: Who? What? Why? Where? When? How? Can this study be done? Are there any ramifications if these study findings are published? Are there other studies that can be linked to this study? (Adapted from Saunders et al, 2013) © Regenesys Business School
15
Learn more about management research: •
Cranfield School of Management, 2012, ‘Management research: delivering business results’, [video clip], http://www.youtube.com/watch?v=R7XuQxukmb0 (accessed 26 March 2023).
Research Topic Select a possible research topic and then answer the questions listed above to determine whether the topic warrants research. You should provide a detailed motivation of why you think this topic warrants research.
Once the task above has been completed, finalise your response to the recap questions that follow to clarify your research topic.
Recap Your Knowledge 1. Answer the following questions to clarify your research topic with the resources that are available to you: 1.1. 1.2. 1.3. 1.4. 1.5. 1.6. 1.7. 1.8.
Is your topic clear and easy to understand? Is your topic focused and realistically designed? Is there relevant secondary data and information (data and information that exists already)? Is there appropriate data and information? Is there accurate and reliable data and information? Is there relevant and reputable data and information? Is there accessible data and information? What is your view of the available data and information and is it available and readily accessible?
2. Based on the above questions, discuss the areas that require attention.
© Regenesys Business School
16
3.2.5 Key Points Key points covered in this section include: • •
•
Research can be defined as a process that people undertake in order to find out things in a systematic way, thereby increasing their knowledge In essence, research is a process that is followed in order to find answers or to come up with findings regarding a certain topic. In other words, research is a process of investigation: It examines a particular subject from a variety of different points of view considering a variety of assumptions, limitations and models proposed by various authors The purpose of our research (Welman and Kruger, 1999) is to: o o o
•
Although research may vary in complexity and duration, Leedy and Ormond (2003:2-3) say that research usually has the following eight distinct characteristics: o o o o o o o o
•
Describe; Explain; and Predict.
Research originates with a question or a problem; The research goal requires a clear articulation because research is time consuming and usually costly to conduct; Research follows a specific format; Research usually divides the principal problem into more manageable subproblems; Research is guided by the particular research problem, question or hypothesis; Research accepts certain critical assumptions and limitations; Research requires the collection and interpretation of data; and Research is, by its nature, cyclical
We can distinguish between two major types of research, namely: o o
Basic research; and Applied research.
© Regenesys Business School
17
3.3 RESEARCH ETHICS Timeframe
Learning outcomes
Prescribed books
Recommended books
Minimum of 8 hours •
Demonstrate an understanding of the research process and its application to resolve business problems;
•
Understand the need to, and the process of complying with ethical issues in business research;
•
Develop and present a professional research proposal for either a technical project or a single research- or mini-dissertation.
•
Saunders, M., Lewis, P. and Thornhill, A. 2019, Research Methods for Business Students, 8th ed., Cape Town: Pearson Education. Chapters 1 and 6.
•
Adams, J., Raeside, R. and Khan, H.T.A. 2014, Research Methods for Business and Social Science Students, 2nd ed., New Delhi: Sage Publications.
•
Bak, N. 2004, Completing Your Thesis: A Practical Guide, Pretoria: Van Schaik.
•
Collins, J. and Hussey, R. 2003, ‘Business Research: A Practical Guide for Undergraduate and Postgraduate Students, 2nd ed., Palgrave Macmillan.
•
City of Philadelphia, 2016, ‘A brief history of research ethics’, https://www.phila.gov/departments/department-of-public-health/ (accessed 26 March 2023).
•
Gosling, M. 2017, ‘San Council launches code of ethics for researchers’, News24, http://www.news24.com/SouthAfrica/News/san-council-launches-code-of-ethics-forresearchers-20170304 (accessed 26 March 2023).
•
Schiller, B. 2011, ‘Academia strives for relevance’, Financial Times, April 25 2011, http://www.ft.com/intl/cms/s/2/4eeab7d4-6c37-11e0-a04900144feab49a.html#axzz3jLxUIXes (accessed 26 March 2023). Summarise two key learnings.
•
Smith, L. 2008, ‘Ethical principles in practice’, Kairaranga, 9, pp16-21, http://files.eric.ed.gov/fulltext/EJ908179.pdf (accessed 26 March 2023).
•
The European Code of Conduct for Research Integrity, 2011, https://ec.europa.eu/info/funding-tenders/opportunities/docs/20212027/horizon/guidance/european-code-of-conduct-for-research-integrity_horizon_en.pdf (accessed 26 March 2023).
•
Dr Sam Fiala, 2012, ‘Research ethics’, [video clip], http://www.youtube.com/watch?v=Ir3VvYNzHeM (accessed 26 March 2023).
Prescribed reading
Prescribed multimedia
© Regenesys Business School
18
Section overview
All research subjects have rights that must be respected. These rights include the right to be consulted, to give or withhold consent, and the right to confidentiality. As a researcher, you investigate subjects in some depth and often have access to personal information. You may elicit information that could compromise a person or organisation, and this information could be misused. The implication is that there should be mutual trust between the researcher and the participants. This section covers: • • • •
Ethical issues in organisational research; The factors involved in the ethics for research; Ethical issues involved in doing research; and Drafting a code of ethics for the research problem you identified earlier.
3.3.1 Introduction Ethics forms an integral part of any research. In this section we emphasise ethical considerations.
Read more about the history of research ethics: •
City of Philadelphia, 2016, ‘A brief history of research ethics’, https://www.phila.gov/departments/department-of-public-health/ (accessed 26 March 2023).
It is essential that you act in an ethically responsible manner when dealing with individuals (research subjects) or organisations that are involved in any research you undertake. Ethical considerations are essential, regardless of the research approach adopted (Gorman and Clayton, 2004:43). Even so, the qualitative approach (as opposed to the quantitative approach) tends to result more in situations where ethics may become an issue (for example, where the researcher works in close collaboration with the participants as opposed to simply handing out questionnaires with minimal contact, if any with the respondent). Saunders et al (2013:43) emphasise that all research subjects have ethical rights. These include the right to be consulted, to give or withhold consent, and the right to confidentiality. As a researcher, you may investigate subjects in some depth and often access individuals’ or organisations’ private information. You may elicit information that could potentially compromise a person or an organisation. The implication is that there should be mutual trust between you and the participants.
© Regenesys Business School
19
3.3.2 Ethical Considerations for Researchers In every research endeavour, you must take care that the research process should abide by ethical principles. It is essential that as a researcher, you make yourself aware of these issues and identify their impact on the nature and design of your research. Researchers must be independent, impartial, open and honest. Moreover, all researchers have a responsibility of care towards the environment, animals or humans that they study. Download the European Code of Conduct for Research Integrity: •
The European Code of Conduct for Research Integrity, 2011, https://ec.europa.eu/info/funding-tenders/opportunities/docs/20212027/horizon/guidance/european-code-of-conduct-for-research-integrity_horizon_en.pdf (accessed 26 March 2023).
The research philosophy you adopt in your dissertation will drive the research design you choose, whether quantitative or qualitative. This choice will determine the appropriateness of your research process. This must be approved by the Regenesys Academic Department. Because the course focuses on people and their behaviour, ethical factors must be considered. The student, organisations and the Regenesys Academic Department should comply with ethical issues by completing ethical clearance documents. This is a prerequisite to conduct your research. You should be as concerned with producing an ethical research study, as you would be with producing an intellectually coherent and compelling one. This means not only carrying out data generation and analysis in an ethical manner, but also to begin by framing research questions ethically. Saunders et al (2013:52) discuss the need for an ethics committee and also suggest that, because of the complexities of research ethics, and because there is unlikely ever to be one clear ethical solution, that a practical approach to ethics is particularly appropriate. Such an approach may involve asking yourself to review the ethical and moral issues around your research project.
Learn more about research ethics: •
Dr Sam Fiala, 2012, ‘Research ethics’, [video clip], http://www.youtube.com/watch?v=Ir3VvYNzHeM (accessed 26 March 2023).
Participants should be voluntarily and knowingly involved in the study. You should make sure that participants take part voluntarily and have not perhaps been instructed by a superior to participate. The most important aspects related to ethics in research are indicated in Figure 1. The Regenesys Higher Degrees and Research Committee reviews and approves all research involving human subjects, thus acting as an institutional review board, which is in line with the Belmont Declaration.
© Regenesys Business School
20
FIGURE 1: ETHICAL CONSIDERATIONS IN RESEARCH
Informed consent
Cultural sensitivity
Deception
Ethical considerations in research
Participant's right to privacy
Codes of ethics
Confidentiality
Disclosure of findings/ results
(Smith, 2008)
Learn more about ethical issues in research: •
•
Gosling, M. 2017, ‘San Council launches code of ethics for researchers’, News24, https://mg.co.za/article/2017-03-06-san-council-launches-code-of-ethicsfor-researchers/ (accessed 14 December 2021). Smith, L. 2008, ‘Ethical principles in practice’, Kairaranga, 9, pp16-21, http://files.eric.ed.gov/fulltext/EJ908179.pdf (accessed 26 March 2023).
© Regenesys Business School
21
‘Academia Strives for Relevance’ Are business schools relevant? Given the expansion of management education in recent years, the question may seem moot. But with critics continuing to query the real-world value of research and teaching, relevance has remained an issue for school administrators. UK universities minister David Willetts criticised business schools for focusing on peer-reviewed research at the expense of applied studies. "I am very aware we have inherited a structure of rewarding research excellence in particular that can have a very damaging practical effect on the work of a business school,” he said. Read the rest of this article here: •
Schiller, B. 2011, ‘Academia strives for relevance’, Financial Times, April 25 2011, http://www.ft.com/intl/cms/s/2/4eeab7d4-6c37-11e0-a049-00144feab49a.html#axzz3jLxUIXes (accessed 26 March 2023). Summarise two key learnings.
3.3.3 Key Points This section introduced you to research ethics. Note that: • •
It is essential that you act in an ethical manner when dealing with both individuals (research subjects) and organisations that are involved in any research you undertake; All research subjects have ethical rights that include: o o o
• •
The right to be consulted; The right to give or withhold consent; and The right to confidentiality.
There should be mutual trust between you and the participants; and It is essential that as a researcher, you make yourself aware of these issues and identify their impact on the nature and design of your research.
© Regenesys Business School
22
3.4 THE RESEARCH PROBLEM, OBJECTIVES AND RATIONALE Timeframe
Learning outcomes
Prescribed books
Recommended reading
Multimedia
•
Minimum of 24 hours
• Demonstrate an understanding of the research process and its application to resolve business problems; and • Develop and present a professional research proposal for either a technical project or single research or mini-dissertation. •
Saunders, M., Lewis, P. and Thornhill, A. 2019, Research Methods for Business Students, 8th ed., Cape Town: Pearson Education.
•
Chapter 1: The research question
•
Chapter 8: The quantitative research process
•
Chapter 15: Foundations and approaches to mixed methods research
•
Adams, J., Raeside, R. and Khan, H.T.A. 2014, Research Methods for Business and Social Science Students, 2nd ed., New Delhi: Sage Publications.
•
Welman, J.C. and Kruger, S.J. 1999, Research Methodology for the Business and Administrative Sciences, Johannesburg: International Thomson Publishing.
•
Humphrey, C. 2008, ‘Auditing research: A review across the disciplinary divide’, Accounting, Auditing and Accountability Journal, 21(2), 170-203, http://www.emeraldinsight.com/doi/full/10.1108/09513570810854392 (accessed 27 March 2023).
•
Hyde, K. F. 2000, ‘Recognising deductive processes in qualitative research’, Qualitative Market Research, (3) 2, 82-89, http://www.emeraldinsight.com/doi/full/10.1108/13522750010322089 (accesses 27 March 2023).
•
MCB UP, 2003, ‘Learning from the mistakes of Enron: the issue, in a word’, Strategic Direction, 19 (3), 23-25, https://www.emerald.com/insight/content/doi/10.1108/02580540310794417/full/html (accessed 27 March 2023).
•
Meeng Uofu, 2012, ‘How to write a problem statement (review for ME1010)’, [video clip] http://www.youtube.com/watch?v=JwdHL3U0eoc (accessed 27 March 2023).
The concept and development of a research problem is vital. The first question any researcher faces is: What do I want to investigate (research)? In this section we examine: Section overview
• • • • •
The difference between an organisational issue (problem) and a research question; The rationale for selecting the problem; The difference between a problem and a symptom; The process to define and compile a problem statement; and Creating a problem statement with supporting evidence.
© Regenesys Business School
23
3.4.1 Introduction The very first question that any researcher faces is: “What should I investigate (research)?” In other words, you have to identify an area of interest, a research area, or a general research topic. The first, concrete step in the scientific research process, therefore, is to identify and formulate the particular problem to be investigated – ie you have to identify and formulate a research problem. The research problem can be defined as a difficulty that you experience in the context of a theoretical or a practical situation, for which a solution is required (Welman and Kruger, 1999:12). Leedy (2013:27) views the research problem as the pivotal point around which the research study revolves. The level of clarity in defining the problem cannot be stressed enough to ensure that there is a clear defined focus for the researcher. In order for you to formulate a clearly defined problem statement, it is recommended that the following questions be answered.
Research Rationale
1. Identify sources from which research ideas may originate, and give examples. 2. Use personal experience and observation, newspaper coverage of current events. and networking for the identification of your research ideas. 3. Write down your research ideas, with an indication of how each originated. Remember, we are dealing with the initial task in identifying the research problem and your research ideas do not need to be perfect research problems, or have definite research potential at this stage. 4. Write down any real-world problem that you can identify from any of your sources, ie newspapers, TV, internet, personal experience, work environment, etc. 5. Write brief notes on the real-world problem identified above. 6. Take the real-world problem that you identified in the previous task and develop it into a research problem. 7. Follow the exact steps for refining a real-world problem before you develop the actual research problem. 8. Write down your own research problem and identify the dependent and independent variables. 9. Develop a rationale for the research problem identified. This can be a page or two long. Make sure that your rationale includes the context from which the research problem originated and the justification for the research.
3.4.2 The Research Problem The research problem is not only the first step in the research process, it is also considered to be the axle around which a research project revolves. It is impossible for a researcher to start a research project if he or she does not pinpoint and clearly formulate a research problem. Identifying and formulating an appropriate and interesting research problem may be one of the most demanding tasks in the research process. Writing about a phenomenon or an issue that is straightforward and unproblematic does not warrant an investigation and a mere description cannot be regarded as research. © Regenesys Business School
24
In other words, not all problems are viable for scientific research. In the next section, we will outline the tasks (steps) involved in identifying and formulating a meaningful research problem. It is essential for you to understand this session, as it is the pivotal point of the research.
3.4.3 Identifying a Research Problem During the identification of the research problem, you need to make certain assumptions. An assumption is a supposition that is taken for granted in the research study. It is not the main focus of the study but may affect the study. The intervening variable (the variable that can influence the research and is generally external to the study and has an influence on the study) will typically be the variable around which the assumption is made. This is explained in the example below.
Setting boundaries If, for example, you are studying the performance output of employees in a factory, then an intervening variable may be taxi violence in the area. The taxi violence is not part of the study, as the study is limited to the manufacturing sector and delimited to this particular factory. Delimitations are choices made by the researcher describing the boundaries set for this study, such as the assumption, based on newspaper articles and union agreements, that the taxi violence will not occur in the vicinity of this factory for at least another year. If there is indeed taxi violence and your assumption was incorrect, this will affect the research and outcome of the study. Limitations are, therefore, influences that you as a researcher cannot control and which place restrictions on your research. Delimitations are particular choices made by the researcher to set boundaries for the research study (the scope of the study).
As indicated in the previous subsection, the identification of a valid research problem is the first step in any research project. There are a number of individual tasks that you can perform to identify and formulate a problem for meaningful research. It is essential to provide supporting evidence that you have identified and analysed a problem rather than a symptom. Developing an acceptable research problem requires you to: 1. 2. 3. 4. 5. 6.
Generate research ideas by observing what is going on around you; Identify real-world problems in the organisation and from the vast literature available; Gather and analyse relevant background research on the problem; Compile a motivation to show that there is a valid problem statement; Understand the relevant assumptions, limitations and delimitations of the research study; and Refine real-world problems. (Adapted from Leedy, 2013:29)
© Regenesys Business School
25
Enronitis
The consequences of the bankruptcy of the energy conglomerate Enron were a fall in the stock market and calls for broad reform in business practice. “Enronitis” became “a term associated with many of the problems coming to light in blue-chip companies”. Read the case study here, and then answer the questions that follow: •
1. 2.
MCB UP, 2003, ‘Learning from the mistakes of Enron: the issue, in a word’, Strategic Direction, 19 (3), 2325, https://www.emerald.com/insight/content/doi/10.1108/02580540310794417/full/html (accessed 27 March 2023).
Define the problem statement you would use if you were to research the issues in this case study. Critically discuss your motivation for selecting your problem statement.
Problem Statement
Now let’s try our hand at developing real-world problems into research problems: 1. Critically evaluate the following statements and decide whether, as a researcher, you know exactly what to do: 1.1. Satisfaction levels within the service industry; and 1.2. Savings levels of adults. 2. Now give an example of a problem statement and find evidence to support that this is problem.
Learn how to write a problem statement: •
Meeng Uofu, 2012, ‘How to write a problem statement (review for ME1010)’, [video clip] http://www.youtube.com/watch?v=JwdHL3U0eoc (accessed 27 March 2023).
© Regenesys Business School
26
3.4.4 Generating Research Ideas Research problems usually have their origin in research ideas. The first task in the research process is to identify a suitable research topic, including a clearly defined research problem. Or, if no problem exists, provide a suitable research question. The research problem is, therefore, to generate a research idea. As a researcher conducting the study, you are supposed to generate an interesting idea that may lead to research. Research ideas are formulated as questions. If you are not an experienced researcher yet, you may wonder how to generate ideas suitable for research and where research ideas come from. This research must also be of value to the organisation.
Research ideas usually originate from one (or more) of these three sources: 1. Previous research; 2. Personal experience; and 3. Practical problem.
Research Ideas 1. Identify more sources from which research ideas may originate and give examples. 2. Use the sources of personal experience and observation(s), newspaper coverage of current events and networking for the identification of your research ideas. 3. Write down your research ideas, with an indication of how each of them has originated. Remember, we are dealing with the initial task in identifying the research problem and your research ideas do not need to be a perfect research problem, or have definite research potential at this stage.
Now that you know how research ideas are generated, we focus on the second task in identifying a research problem: Identifying real-world problems as research problems.
3.4.5 Identifying an Organisational Issue You may have heard scientists, researchers and other professionals refer to their research topics. Essentially, research topics are the real-world problems that researchers have identified as research problems. For the purpose of this course, we will use the term organisational issue (research problem). However, keep in mind that it includes theoretical as well as practical potential problems for research and that we also use it to refer to the term research topic.
© Regenesys Business School
27
As we said, research areas and or research ideas often present themselves in a working environment (in a particular organisation), or in a social environment (in a particular community). This environment is what we refer to as the world. This means that you become aware of a problem that warrants scientific investigation (research). In this context, the world is the workplace or the community or any other environment in which a problem may occur. A research problem that originates in the real world does not necessarily have to be a practical problem; it could also represent itself as the identification of a gap in the body of knowledge in a particular discipline, or as a model that needs further exploration to refine it. Real-world problems in the context of this course are organisational issues. These issues often present themselves in the working environment and may have to do with strategy formulation, decision-making or policy formulation. The research that grows from these problems may, therefore, be aimed at improving the service objectives of institutions. Real-world problems may also occur in a nonworking environment, eg in a country, or in a particular community (society). In this case, the real-world problems usually relate to political, social or economic changes and problems.
It is not necessary to formulate a research problem at this stage: you simply have to identify the real-world problem that may lead to a research project. It is recommended that you narrow the scope of your research so that you select a problem that is related to a management discipline.
Your Real-World Problem
1. Write down any real-world problem that you can identify from any of your sources ie newspapers, TV, internet, personal experience, work environment, etc. 2. Write a brief note on the real-world problem identified above.
3.4.6 Refining a Problem Statement At this stage, the real-world problem is probably formulated in broad terms. For example, the realworld problems may read: “A lack of corporate governance on major stock exchanges is causing more corruption each year.” As you can see, these real-world problems are rather broad and they should be narrowed down (refined). One way of refining a real-world problem is to ask questions regarding the problem.
At this stage a word of caution is that you should avoid many real-world problems, but try to focus only on one that you are most interested in studying.
© Regenesys Business School
28
Steps for refining a real-world problem or an organisational issue For the purposes of this course, we recommend the following steps for refining a real-world problem: •
Step 1: Formulate the topic: o I am exploring, examining, developing, studying or investigating
•
Step 2: Give a reason: o Because I want to find out what, why, who, when or whether
•
Step 3: State the rationale or motivation for the project: o In order to understand how, why or whether
See the example below.
The researcher is studying the decline in sales of fast foods in Sandton, (Gauteng) in 2018, because the researcher wants to determine whether changing to healthier eating habits has resulted in the decline of sales of fast food over the past two years.
We have explained what a real-world problem is. But how does a real-world problem relate to a research problem? Doing something about it in the world or environment in which it exists can solve a real-world problem, which is often a practical problem. However, before you can solve this realworld problem, you may have to formulate and solve a research problem related to the organisation. The solution of the research problem for the organisation must then be applied to the real-world problem. The research problem helps you to obtain more knowledge and a better understanding of matters related to the real-world problem; this enables you to solve the real-world problem. The gaining of knowledge and understanding is achieved by means of the collection, analysis and interpretation of information. Let us return to our example in order to explain how that particular real-world problem can be developed into a research problem. TABLE 3: REAL-WORLD PROBLEMS
Real-world problem
South Africa has an obesity problem. In January 2016, there was an active drive by promoters of healthy foods in Sandton (Gauteng).
Research question
Did the promotion of healthy foods affect the fast-food industry in Sandton (Gauteng)?
Research problem
The researcher has to determine whether eating habits have changed in Sandton (Gauteng) and caused the decline in the sales of fast foods in any way.
© Regenesys Business School
29
By following the process outlined here, you create a focused research problem and determine a scope within which the problem has to be solved. This becomes important, especially when you are conducting the literature review. If you are not clear about the focus and scope of the research problem, it may happen that it reads too broadly and includes information that is not relevant to the research problem.
Research Problem Take the real-world problem that you identified in the previous task and answer the questions below to develop it into a research problem. Step 1: Formulate the topic: I am exploring, examining, developing, studying or investigating Step 2: Give a reason: Because I want to find out what, why, who, when or whether Step 3: State the rationale or motivation for the project: In order to understand how, why, whether…
Make sure that in the formulation of the research problem you have a verb, variables, justification of the study and the period you are researching.
3.4.7 Characteristics of a Research Problem At this stage, you should be familiar with the concept of the research problem and understand the critical importance of a focused and well-formulated research problem as part of the research process. This brings us to one last issue regarding the research problem: it is important that the stated problem can actually be researched. Powell and Connaway (2010:29) outline the following characteristics that a problem should exhibit in order to be suitable for research: •
The research problem should represent conceptual thinking, inquiry and insight – not merely activity. Simply collecting data and making comparisons is unlikely to assist you much in developing a true research problem. Activities such as studying a particular field and consulting earlier research findings are more likely to lead to a conceptually developed research problem;
© Regenesys Business School
30
•
•
•
•
•
There should be a meaningful relationship between the variables related to the problem. In other words, the study of unrelated facts cannot be regarded as true research. If a research problem represents some kind of meaningful relationship between variables, it must also reflect the cause of this relationship. In other words, it must show why this relationship exists between the variables. In essence, a conceptually developed research problem should reflect some interpretation of the nature and the cause of the relationship between variables; The research problem should represent a reasonably new area of research. While it serves no purpose to repeat existing research findings, the research problem does not have to be entirely new, original or unique in order to be worthy of scientific investigation. Research can also build on, expand, refine or improve previous research; The problem should represent research that will contribute to, and impact on the knowledge of the subject. If the research problem represents a trivial question of no real importance, it cannot result in meaningful research that can make a significant contribution to the subject; The research problem should be researchable (manageable). This means that you should consider practical matters (such as time, funding and the availability of resources), and make compromises if necessary. Ethical issues should be considered when establishing the research problem. Here are some of the ethical questions you should consider: o o o o
•
•
What ethical consideration must be taken into account when conducting this research? Who are the relevant stakeholders who have to approve this research study? Are you adequately qualified and skilled to conduct this research project? Is the research confidential and has the necessary approval been obtained to conduct this research?
Evaluate a research problem from your working environment against the above criteria (characteristics). This should give you an idea whether your research problem can actually be researched or warrants research; and Once the research problem has been finalised, it is possible to determine subproblems, also known as research objectives.
3.4.8 Research Objectives Research objectives or subproblems will assist you to break down the research problem into more manageable or researchable parts that can be investigated separately. Each research objective (subproblem) addresses one aspect of the research problem, which ensures that each aspect of the research problem is actually investigated. Clearly defined research objectives also enable you to identify the most appropriate research method(s) for investigating the objectives. According to Powell and Connaway (2010:29), the identification of research objectives involves these two basic steps: 1. Breaking the research problem down into its separate components. 2. Identifying the words that indicate a need for the collection and interpretation of data and information.
© Regenesys Business School
31
The following criteria should assist you in formulating research objectives or subproblems: • •
•
•
Each objective should have one focus. In other words, you should formulate the research objective so that you will not be investigating any more than one aspect; A simple yes or no cannot answer your research objective. For example, if your research question is: “Do students at Regenesys use the portal to access the relevant study guide?” you will have to work analytically and do research to reach an acceptable answer; When the research objectives are combined, they should equal the whole of the research problem. You should not omit any important aspect of the research problem. But it is equally important that you do not add research objectives that are not covered by the research problem. This could mean that the research problem actually involves more than one problem and that you should revisit the research problem and adjust the formulation; and You should be careful not to include pseudo-subproblems as research objectives. Pseudosubproblems are not directly related to the research problem. They have more to do with the research methodology. Examples of pseudo subproblems are: setting objectives on how to observe participants, or how to select a sample, or how to measure customer satisfaction.
It should be possible to identify applicable research methods for each research objective. The collected data or information should be interpreted within each objective. When the research report is written, it should be possible to report on the findings and interpretation as they relate to each objective. This will enable you to combine the findings for the whole of the research problem. Once the research objectives or subproblems have been spelt out, you can clarify the scope and limitations of the study. This is important, because you must avoid investigating related issues with no direct bearing on the research problem. The scope of the research indicates what you include in the study, while the limitations indicate what is not included in the research.
3.4.9 The Research Questions Any research has to answer some questions. To some extent, these questions are the reason for conducting the research. There must be a conceptual link between research objectives and questions. The questions ensure the objectives of the study are researched correctly. Be aware that the quality of the research questions determines the success of the research. These are not the questions you ask your research participants, but are strategic questions that the study has to provide answers for. As a researcher you must be clear on what your study is intended to answer. These questions are not like the interview questions or questions in a questionnaire, but are broader questions that should be answered by the entire study or dissertation. A research question should be linked to the main objectives of the research report or dissertation. A research question must also be clearly formulated, unambiguous and researchable. An example of a research question could be: "What are the main factors causing the decline of sales in fast food outlets at Sandton City (Gauteng)?"
© Regenesys Business School
32
3.4.10
The Rationale for the Research
You are expected to develop a rationale and background for your research. In the rationale, you justify conducting your research: you indicate why the study is worth undertaking and why you are interested in undertaking it. In other words, the rationale indicates the general importance (significance) of the research (investigation). The rationale should include the following information (Leedy, 2013:30-31): • •
The problem statement must be clearly stated and easily understood by anyone reading it. There should be supporting evidence for this problem statement. With regard to the context that gives rise to the research project, you should address the following questions: o o o
• •
What are the conditions, events, situations or processes that have led you to the investigation or project? Are you of the opinion that the current knowledge of an issue is inadequate, or that certain issues have been poorly researched? Do you disagree with the interpretation, results or methodology of previous research and or researchers?
Ensure that you do not have a symptom, but the real problem. The justification for the research project should address the following questions: o o o o o o
What is your interest in the project? What motivates you to conduct the research? Why is the project worthy of scientific investigation? What do you regard as the significance of the research? How can the solution of the research problem be applied to the real-world problem? What contribution should the research make to current knowledge of the issue or problem being researched?
As you can see, the rationale (background) to the research problem contains information that directly relates to the current knowledge, theories, research methodology and research results (findings) in the subject field in which the research is being conducted. This requires consulting relevant information sources or subject literature. In other words, you must conduct a literature review and establish a theoretical framework to develop the rationale for the research problem.
Rationale for the Research Problem
Identify two or three research problems and develop a rationale for each. This can be a page or two long. Make sure that your rationale includes the context from which the research problem originated, and the justification for the research.
© Regenesys Business School
33
3.4.11
Key Points
The following key points should be noted in this section: •
•
• • • • • •
The very first question that any researcher faces is: “What should I investigate (research)?” In other words, you have to identify an area of interest, a research area, or a general research topic; The first, concrete step in the scientific research process, is to identify and formulate the particular problem that is to be investigated or examined, ie you have to identify and formulate a research problem; The research problem is not only the first step in the research process, it is also considered to be the axle around which a research project or study revolves; It is impossible for a researcher to commence with a research project if he or she does not pinpoint and clearly formulate a research problem; During the identification of the research problem, you must make certain assumptions; An assumption is a supposition that is taken for granted in the research study. It is not the main focus of the study but may affect it; Research problems usually have their origin in research ideas; Research ideas usually originate from one (or more) of the following sources: o o o
•
Previous research; Personal experience; and A practical problem.
The following steps can be used to refine a real-world problem: o
Step 1: Formulate the topic: §
o
Step 2: Give a reason: §
o
Because I want to find out what, why, who, when or whether
Step 3: State the rationale or motivation for the project: §
•
I am exploring, examining, developing, studying or investigating
In order to understand how, why or whether
A research problem should exhibit the following characteristics in order to be suitable for research: o o o o o o
The research problem should represent conceptual thinking, inquiry and insight; There should be a meaningful relationship between the variables related to the problem; The research problem should present a reasonably new area of research; The problem should present research that will contribute to, and affect, knowledge of the subject field; The research problem should be researchable (manageable); and Ethical issues should be considered when establishing the research problem.
© Regenesys Business School
34
• •
•
Research objectives or subproblems will assist you to break down the research problem into more manageable or researchable parts that can be investigated separately; Any research has to answer some questions. To some extent, these questions are the reason for conducting the research. The research must ensure a conceptual link between research objectives and questions; and You are expected to develop a rationale and background for the research.
© Regenesys Business School
35
3.5 FORMULATING AND CLARIFYING THE RESEARCH TOPIC Timeframe
Learning outcomes
Prescribed books
Recommended book
Minimum of 5 hours •
Demonstrate an understanding of the research process and its application to resolve business problems; and
•
Develop and present a professional research proposal for either a technical project or a single research or mini-dissertation.
•
Saunders, M., Lewis, P. and Thornhill, A. 2019, Research Methods for Business Students, 8th ed., Cape Town: Pearson Education. Chapter One and Two
•
Adams, J., Raeside, R. and Khan, H.T.A. 2014, Research Methods for Business and Social Science Students, 2nd ed., New Delhi: Sage Publications.
•
Collins, J. and Hussey, R. 2003, ‘Business Research: A Practical Guide for Undergraduate and Postgraduate Students, 2nd ed., Palgrave Macmillan, 32-37.
•
Humphrey, C. 2008, ‘Auditing research: A review across the disciplinary divide’, Accounting, Auditing and Accountability Journal, 21(2), 170-203, http://www.emeraldinsight.com/doi/full/10.1108/09513570810854392 (accessed 27 March 2023).
•
Simon, S. 2011, ‘Choosing your dissertation title’, http://dissertationrecipes.com/wpcontent/uploads/2011/04/Dissertation-TitleXY.pdf (accessed 27 March 2023).
•
Meeng Uofu, 2012, ‘How to write a problem statement (review for ME1010)’, [video clip] http://www.youtube.com/watch?v=JwdHL3U0eoc (accessed 27 March 2023).
Prescribed reading
Prescribed multimedia Section overview
This section covers identifying a suitable research topic title, formulating a research topic, clarifying a research topic and the relevance of the research topic.
3.5.1 Formulating and Clarifying the Research Title Having defined the problem, you must now refine the research title. A research title is seen as the cornerstone of the research process. It is, therefore, essential to get the title correct and to ensure that the topic being studied aligns with the research title. The topic sets the scene for the research, whereas the title allows the reader to understand what the research topic covers. Your research title will be changed and refined several times as your work progress, and the final version will probably be produced shortly before submission of your final report. The title must be relevant and linked to the research problem statement. The research title must align to organisational studies, as this is the focus of the dissertation.
© Regenesys Business School
36
In summary: • • • •
Topic: expression of broad research interest; Topic: associated with some academic discipline; could be interdisciplinary or multidisciplinary; Topic: research question research objectives; and Title: shaping or reshaping of the broad topic or research interest.
The title should accurately reflect the content and scope of what you propose to study. It should be crisp and to the point. The title must not be a paragraph.
Learn more about choosing your dissertation title: •
Simon, S. 2011, ‘Choosing your dissertation title’ (https://paperzz.com/doc/9043908/choosing-your-dissertation-titleaccessed 27 March 2023).
Before deciding on the title of your study, ask: • • • • • • • • • • • • • •
Who will be interested in this topic? What will be the title you will develop from the topic? Is there a clear link between the topic and the title? What is the significance of the title? Is this title linked to this particular topic being researched? Who are the stakeholders involved in this deciding on the title? Do the stakeholders have any vested interest in this research title? Is the title linked to the main ideas, concepts and theories? Is the title linked to the key terms, phrases or vocabulary used? What are the issues to consider in this title? Use the following queries to clarify the title: Who? What? Why? Where? When? How? Can this title be used? Are there any ramifications if this title is published? and Are there studies that can be linked to this title?
Your research title, together with the research objectives and questions, might provide the keywords that will, in many instances, form the thematic macrostructure of your literature review.
Selecting a Research Topic
Critically discuss how you would go about selecting a topic and a relevant title for your research.
© Regenesys Business School
37
3.5.2 Key Points In this section we noted that: • • •
A research title is seen as the cornerstone of the research process; It is essential to get the title correct and to ensure that the topic being studied aligns with the research title; and The topic sets the scene for the research, whereas the title allows the reader to understand what the research topic covers.
© Regenesys Business School
38
3.6 CONDUCTING A CRITICAL LITERATURE REVIEW Timeframe
Learning outcomes
Prescribed books
Recommended books
Minimum of 25 hours •
Demonstrate an understanding of the research process and its application to resolve business problems; and
•
Develop and present a professional research proposal for either a technical project or a single research or mini-dissertation.
•
Saunders, M., Lewis, P. and Thornhill, A. 2019, Research Methods for Business Students, 8th ed., Cape Town: Pearson Education. Chapter Three.
•
Adams, J., Raeside, R. and Khan, H.T.A. 2014, Research Methods for Business and Social Science Students, 2nd ed., New Delhi: Sage Publications.
•
Collins, J. and Hussey, R. 2003, ‘Business Research: A Practical Guide for Undergraduate and Postgraduate Students, 2nd ed., Palgrave Macmillan.
•
Mouton, J. 1996, Understanding Social Research, Pretoria: Van Schaik.
•
Wallace, J.S. 2010, ‘Family-owned businesses: determinants of business success and profitability’, http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1590&context=etd (accessed 27 March 2023).
•
Massey University, 2010, ‘The literature review’, [video clip], http://www.youtube.com/watch?v=jKL2pdRmwc4 (accessed 27 March 2023).
•
Ruff, C. 2014, ‘Writing your thesis video 4 – Zotero is a hero’, [video clip], https://www.youtube.com/watch?v=5MjZ2urtk70 (accessed 27 March 2023).
•
Wilson, D. 2014, ‘Introduction to reference management software’, [video clip], https://www.youtube.com/watch?v=1YzkEf1aLsM (accessed 27 March 2023).
Reading
Prescribed multimedia
Section overview
In this section we’ll cover the purpose of a literature review; identifying and using various literature sources; evaluating the context of the theory, assumptions and limitations of the literature and understanding how to review literature and compile a critical analysis of the literature.
© Regenesys Business School
39
3.6.1 Introduction The literature review is an integral part of the research process. It forms part of the determination of the real-world problem, the development of the research problem and the development of the research rationale. As a researcher, you need to consult the relevant literature to understand the academic debates and arguments surrounding the topic. This will enable you to gain a deeper insight into the topic and to identify the key issues to be explored. Whatever the research problem may be, you have to conduct an investigation into the literature related to their research problem, ie you need to find informative sources, determine their relevance, read them thoroughly and synthesise the information, make informed judgments and finally, report on the information provided in each source. In this section, we provide practical guidelines on how to conduct a literature review in order to find information about the research problem and the rationale for the research. A literature review is an account of what has been published on a topic by accredited scholars and researchers. Occasionally, you will be asked to write one as a separate assignment, sometimes in the form of an annotated bibliography, but more often, it is part of the introduction to an essay, research report, or thesis. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (eg your research objective, the problem or issue you are discussing or your argumentative thesis). It is neither a descriptive list of the material available, nor is it a set of summaries. If you have completed many assignments and exams before attempting this course, you will have noted that in the process of seeking information you relied on your ability to scan the literature efficiently and effectively by using your study material, journals, etc to identify a set of useful articles and books. Once this was completed you conducted a critical appraisal (applied principles of analysis to identify unbiased and valid studies) of the literature, before writing up your assignment. The same principles apply in conducting the research for your business or your dissertation. Ensure that you align the literature review to your dissertation or research topic, research objectives, problem statement and research questions. Use research that matters and is aligned to your study. The process begins with finding literature related to the topic. The process continues with selecting appropriate literature from the search, reading the material and making notes for later reference. Themes will emerge from analysing the literature. These themes are the foundation of the outline and subsequently make out the first draft of the review itself. The primary goal of the literature reviewer is to be comprehensive and up-to-date. This provides the necessary grounded theory, which is defined as the seamless craft of a well-executed grounded theory study. However, this is the product of considerable experience, hard work, creativity and, occasionally, a healthy dose of good luck (Suddaby, 2006:640). From the above we can see that the literature review establishes an essential background to the thesis.
© Regenesys Business School
40
3.6.2 What is a Literature Review? Saunders (2013) points out that literature is broader than just printed information. Literature includes information in any format, hard-copy print, audio, visual or electronic. It can be argued that a literature review is a structured and systematic process in which you find all the relevant sources, which are then further critically evaluated against the research topic. The result of the literature review is a synthesis of the work of other authors, experts and researchers in the field. A key thing to remember when conducting the literature review is to understand the assumptions and limitations that the author made. Leedy (2013:5) argues that these assumptions and limitations should be linked to the environment in which the literature was developed (stable, turbulent, level of technology, etc). Such factors are important to consider as they could cause drawbacks within the assumptions and limitations made, and the models created by the author. This is essential when a critical analysis of the literature assumptions, limitations, delimitations, models and theories is conducted.
Assumptions and Limitations Select at least three authors of relevant academic textbooks, and then critically review the assumptions and limitations made by these authors.
3.6.3 The Purpose of the Literature Review We can summarise the purpose of a literature review as follows: • • • • • • • • • • • • •
It is an integral part of the research process as it provides a sound academic basis for the dissertation or research; It is meant to enhance the creative thinking ability of the students and thereby create new ideas and theoretical constructs; It aids in confirming that an appropriate research topic has been chosen; It aids in ensuring that no duplication of previous research takes place; It aids in identifying research ideas, and to refine the research problem; It ascertains what the most widely-accepted definitions of key concepts in the field are; It provides a critical analysis of the relevant literature and identifies the gaps in the research and knowledge in the subject field; It assists you in developing a rationale (background) and justification for the research; It helps to establish a theoretical framework to be used in the research; It assists you in explaining the contribution of his or her research to the study field and to the research in general; It helps to identify an appropriate research design, method and available instrumentation; It determines what the most widely accepted empirical findings in the field of study are; and It brings together all the work of experts on the research topic. © Regenesys Business School
41
3.6.4 Criteria for a Literature Review In order for the literature review to meet its purpose, it should take into account: • • • • • •
All the assumptions made by the authors; All the limitations made by the authors; All the delimitations made by the authors; All the construct variables made by the authors; The environment (stable, turbulent, etc) when the author wrote the book or article; It should be comprehensive and include all past and current information (the most recent thinking and writing) about the research problem;
It should also: • • • • • • •
Be specific and address the research problem and objectives – not other marginally related or interesting information that is unnecessary; Include all the authoritative authors or experts in the field; Not leave out important key words; Do justice to the authors’ and researchers’ arguments and reasoning before criticising them; Be critical and provide an analysis of the information – not only a representation of it; Be logically structured; and Be well organised. (Leedy, 2013:66-67)
3.6.5 Steps in the Literature Review Literature review consists of the following steps: 1. 2. 3. 4. 5.
Searching for information and information sources; Organising the information sources; Reading the information sources and determining their relevance and quality; Writing the literature review; and Referencing.
1. Searching for information and information sources You will require both secondary research (data and information that already exists, whether quantitative or qualitative) and primary research (data and information that does not exist and must be collected for your study). Information and information sources can come from virtually anywhere and can be either primary or secondary sources.
© Regenesys Business School
42
Base the literature on scholarly sources. Examples of sources of information are: media, blogs, personal experiences, books, academic journals and magazine articles, expert opinions, encyclopaedias, and web pages. The type of information you need require will depend on the question(s) you are trying to answer. Table 4 shows a sample of the combination of popular, trade and scholarly publications that can be a starting point. However, keep in mind the differences between these three categories of information as shown in the table. TABLE 4: POPULAR, TRADE AND SCHOLARLY PUBLICATIONS
Criteria
Popular Publications
Trade Publications
Scholarly Journals
Examples
Noseweek
Engineering News
Cross-Cultural Management
Financial Mail
Water and Sanitation Africa
The Economist
Transport World Africa
International Journal of Managing Projects in Business
National Geographic Time Magazine Popular Mechanics Forbes Africa Purpose
Inform the general public; may target a specific demographic
Provide news, trends, and practical information to professionals working in a particular industry (eg engineering) or profession (eg accountants)
In-depth analysis of topics; report research findings and promote further scholarly communication and research
Authors
Magazine employees, journalist or freelance writers
Trade journal employees, members of associations, entrepreneurs, leaders, and professionals in the field
Scholars and researchers in the field; name and credentials are provided in the article including educational institution to which author is affiliated
Language
Generally nontechnical use of language; understandable to broad audiences; informal, very current, anecdotal, personal or entertaining
Specialised or technical terminology (including jargon) used in the industry or profession; may have a public relations focus
Specialised terminology; objective view
Article appearance
Relatively brief articles accompanied by glossy or colour graphics, photos and general advertisements
Brief to mid-length articles including photographs, charts, illustrations, and advertisements targeted at professionals in the field
Lengthy articles using a formal research structure that includes abstracts, literature reviews, methodologies, results, conclusions and references; charts, maps, tables, photos support the text; little or no advertising
© Regenesys Business School
43
References
Rarely include reference lists or bibliographies
Occasionally include reference lists/bibliographies
Always include extensive footnotes, reference lists or bibliographies
Accountability
Not evaluated by experts in the field but may attract comment; reviewed by the editor but not a panel of experts
Reviewed by the editor; may be evaluated by experts in the field but not peer-reviewed
Usually reviewed and critically evaluated by board of subject experts (peer-reviewed)
(Saunders et al, 2013)
Ideally, you must locate peer-reviewed research articles in scholarly journals. However, your literature review may also include trade publications. Other sources of information include: • • • • •
Books specific to the subject matter by leading authors (including textbooks); Government publications including legislation; Managers in government departments; Librarians (who can assist in refining your literature search); and Subject-matter experts.
Regenesys subscribes to the Ebsco e-book, company profile and journal database, and the Emerald business journal database, which you can access through the Regenesys e-Library on the student portal. To pass this course you must be able to demonstrate the ability to interrogate multiples sources of knowledge in an area of specialisation and evaluate knowledge and processes of knowledge production (SAQA, 2013).
A researcher usually accesses a variety of appropriate information sources via an academic library, such as a university library.
Keywords Revisit the research problem that you identified earlier. 1. Identify keywords in the research problem that can be used as search terms for an information search on the world wide web. 2. Use the relevant search terms and conduct a search on relevant academic sources. 3. Study the search results and print or record the results that you regard as most useful for your research.
© Regenesys Business School
44
2. Organising information sources Once you have retrieved the information sources, the next question is what to do with or how to organise them. Electronic files from databases and websites can be saved on a disk on your computer or you can print them. You may have to make photocopies of printed documents such as chapters from books or articles if you are not allowed to keep the original documents for an extended period. You should have a system for filing all the copies of information sources. For electronic formats you should create folders in which to download the documents. These folders and files should be named according to the subproblems or research objectives, or if applicable, any other relevant topic title or name. You should be able to find a document easily when you need it. Keep the documents until you have completed your research report in case you need to refer to them again. Apart from organising the documents, it is also important to keep a record of the bibliographic details. At the end of your research report you have to compile a list of references or bibliography. It is easier to start with this process right from the beginning. You can do this in different ways. You could use index cards and record each document’s details on a card and file. Nowadays computers make it easy. Software such as Research Toolbox and Endnotes provide facilities with which to organise references and information sources. Alternatively, you can use your word processing programme and compile the bibliography as you progress with the literature review.
3. Reading information sources and determining their quality Once you have found all the relevant information sources, you have to read them and conduct a critical analysis of the literature. This implies that you look at the assumptions, limitations and the delimitations made by the authors. The economic and political context must also be considered. It is essential that you understand the main Regenesys model and are, therefore, able to link and compare to the authors’ models. These models will often create the basis of the research focus area. Reading for research is far more intensive than reading for leisure. You will probably have to read each text several times, analysing the text far more intensively. Reading for research Mouton (2001:90) provides the following tips on reading for research: •
Start with the most recent information sources and work your way backward to older sources. This is known as retrospective reading. This method of reading helps you to establish right away what the latest state of the research is, and how developments took place;
•
Read the abstract (if available) of an article before you start with the article itself. In the case of a book, scan the table of contents and read the preface and introduction. You will then have an idea what the article or book is about as well as its usefulness for your literature review;
© Regenesys Business School
45
•
Then read the introduction and concluding summary of the article. In the case of a book, read the introductory chapter and concluding chapter. After having read these parts, you should have a good indication of whether the source is really relevant to your study and worth further reading; and
•
Once you have established that the source is relevant to your study, read it in depth and systematically.
Evaluating the quality of information As indicated, it is essential for researchers to evaluate the content and quality of information and information sources before they use the content. Not all information sources are equally reliable; not all authors are equally competent; and not all journals and publishers are equally respected. In other words, not every claim that appears in print or on the world wide web is true! Because researchers want their work to be based on the most accurate, authoritative and up-to-date information, they must exercise discretion in evaluating their information and information sources. Criteria to determine the quality of information sources The criteria shown in Table 5 can be used to determine the quality of information sources: TABLE 5: DETERMINING QUALITY OF INFORMATION SOURCES
First-hand presentation of information
You should be able to establish whether the author can confirm given facts and or statistics. If you have no proof that the information is accurate and objective, look for the original information source and use the information from that source in your research.
Evidence of research
The information should include references and statements by reliable and accredited scientists in the field of study.
Up-to-date (recent)
The information should be based on the latest statistics and research findings (results), in order to ensure that it reflects the current state of affairs.
Relevance
The relevance of information sources is not always obvious: titles, for example, may be misleading and vague. Therefore, you must evaluate the information for relevance.
Agreement with other information sources
If an information source correlates to other, reliable information sources on the subject, it may be regarded as reliable.
Bibliographic references
A scientific information source has to include bibliographic references and a bibliography. These bibliographic references may help the researcher to determine the relevance of the source in hand, and to trace other relevant sources. (Saunders et al, 2013)
© Regenesys Business School
46
Synthesising the information During the in-depth reading phase, you have to study the text closely for the main claims, arguments, findings and interpretations. You can make notes or even draw mind map diagrams and affinity charts to group similar themes. This will help you reconstruct what the author is trying to say. You should summarise each source and pay particular attention to the most important conclusions and implications. If you find it difficult to paraphrase the source in your own words, it means that you have not fully understood it and should read it again or give it to a fellow researcher to read and then discuss it with them. Try to divide the literature into the assumptions, limitations, models, theory and pragmatic applications. This decomposition of the theory may make the analysis and paraphrasing simpler to synthesise and compile. The summary of a document is also referred to as a précis. Once you have compiled a précis for each document, group them together in a logical sequence. Now identify which texts cover the same issues; which authors have responded to others; what are the points of similarities and differences; what are the main contentious issues; what claims are made; and what gaps or shortcomings are identified (Saunders et al, 2013). Without summaries, it is difficult to conduct these comparisons. When you compile the summaries, reflect the authors’ opinions accurately, whether you agree with them or not.
3.6.6 Writing the Literature Review The actual writing of the literature review is difficult for many novice researchers. You might use: • • •
Do I first provide summaries of all the findings from the literature and then follow it with my own discussion, or do I critically discuss the text as I progress? Where do I report information on the research methodology and motivate my choice of research methods? and Do I discuss the findings from the literature in the literature review and repeat them when I relate my findings to the literature?
These questions can be difficult to answer, because there is no one set of rules or a recipe according to which the literature review has to be addressed (Bak, 2004:54). The presentation of the literature review also depends on the planning and structuring of the research report as a whole. The method and comprehensiveness with which the literature review is represented depends on the type of research report you are writing. For example, dissertations and theses require a more extensive review than a journal article or report to your organisation. If your research report is in the form of an article, conference paper or a report to your organisation, you will probably not make use of chapters, but divide the report into numbered sections of which at least one section will cover the literature review. The literature review is not only written as part of your final research report, but a shortened version is already compiled to form part of your research proposal as part of the background to your study. In your proposal (assignment) the emphasis is mainly on the findings of the preliminary literature review as pointed out earlier.
© Regenesys Business School
47
Approaches to writing the literature review The literature review can be written in a number of ways, depending on how you want to approach the overall research report presentation. We briefly explain two of the more popular approaches: •
The literature review may be presented thematically, ie according to particular themes around the research problem. These themes would be closely related to the specific research objectives; or
•
You may choose to follow a chronological approach, in which case the earliest research on the topic is presented first in order to create the context for later research as well as your own research. With this approach, you also point out the most important advances in the research about your topic that has already been made (Henning, Van Rensburg and Smit, 2004:28).
Note the following example of a study that investigated factors associated with family-owned businesses that lead to business success and profitability.
Read: •
Wallace, J.S. 2010, ‘Family-owned businesses: determinants of business success and profitability’, http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1590&context=etd (accessed 27 March 2023).
Note how the literature review is presented in line with the research problem and objectives, and findings of previous studies are also presented. Remember that the literature review can be written in a number of ways. In some cases, you may even have more than one literature chapter. For example, you may have one literature chapter focusing on specific themes around your dependent variable, and another chapter focusing on the independent variables of your study. Work closely with your research supervisor, who will guide you through the process of writing your literature review.
The literature review is often a separate section in a research report or mini-dissertation in which you synthesise the literature about the research problem and critically analyse it. If you decide on the thematic approach, a different section could cover each theme, with a title related to that theme.
© Regenesys Business School
48
The writing process It is always a good idea to compile a framework or an outline for the literature review before actually writing the review. The framework should include main headings and subheadings. The research objectives could be used as the main headings; the following process can be applied: • • • • • •
Write an introduction; Define the concepts that the authors introduce; Link these causes to the variable being studied; Look for similar or opposing, local and international viewpoints on what you are studying; Contextualise the viewpoints to the delimitations of the dissertation; and Determine the literature on the cause and effect analysis.
At this point, you are ready to start writing your literature review. This will be the first draft of the literature review, which is never perfect, but it helps you to put your thoughts in writing and clarify your thinking. The first draft will be re-edited at a later stage.
Learn more about the literature review and reference management software: • • •
Massey University, 2010, ‘The literature review’, [video clip], http://www.youtube.com/watch?v=jKL2pdRmwc4 (accessed 27 March 2023). Ruff, C. 2014, ‘Writing your thesis video 4 – Zotero is a hero’, [video clip], https://www.youtube.com/watch?v=5MjZ2urtk70 (accessed 27 March 2023). Wilson, D. 2014, ‘Introduction to reference management software’, [video clip], https://www.youtube.com/watch?v=1YzkEf1aLsM (accessed 27 March 2023).
Arshed and Danson (2015) suggest that answering the following questions can help the researcher to develop a literature review: • • • • • • • •
What do you know about the research area? What are the relationships between key ideas, dynamics and variables? What are the current theories, trends and themes? What are the inconsistencies, implications and shortcomings of previous studies? What needs further investigation because evidence is lacking, inconclusive, contradictory and or limited? What methodological approaches have been taken and why? Are the methodological approaches justified? Why does this area of research need to be studied further? and What contribution will your work make to the current debate?
© Regenesys Business School
49
3.6.7 Theoretical Framework A theoretical framework is derived from a collection of interrelated concepts. This framework is important in exploratory studies where you may not know enough about the topic and are trying to learn more. The theory of the subject field (discipline) involved should explanations for the questions relating to why and how we make sense of information. If you base your research on academically sound secondary theories, the various theories will be contextualised and allow you to create a theoretical framework. A theoretical framework is important, because it provides a context and basis for the research. It critically explains the structure and theoretical models you have decided on in the dissertation. Your research must demonstrate an indepth and critical understanding of the main theories, debates and constructs from the literature. This will form a basis for developing your own insights and theories. At the point of the literature review, much of the information may appear appropriate and you may want to include everything into the research framework. This can be a problem. The solution is to have a focused research problem topic and clear research objectives (Saunders et al, 2013).
To develop a clear theoretical framework, you should: • • •
Be clear on the research problem (topic) and the research questions, in order to direct research reading; Identify central texts and try to capture broad trends and debates in the main arguments, discussions and findings of other research texts; and Discover the main research approaches that characterise the field of study.
Once the theoretical framework is done, you should support your arguments with relevant and academically sound evidence, to assist in creating the relevant recommendations.
Dissertation Ideas Based on your own ideas for your dissertation, answer the questions below: 1. 2. 3. 4. 5. 6. 7. 8.
What is the organisational issue or challenge? What is the problem? Do you have a problem statement? Do you have supporting data to show that this a real organisational challenge or problem? Is there academic literature available on this topic? Is the literature relevant and up to date? Have you reviewed the assumptions, limitations and models used by the relevant authors? Is there supporting data and statistical analysis in the literature to support this mini-dissertation or must more data be collected?
© Regenesys Business School
50
3.6.8 Key Points In this section we discussed how to conduct a critical literature review. Note that: • • • • • •
The literature review is an integral part of the research process; It forms part of the determination of the real-world problem, the development of the research problem and the development of the research rationale; As a researcher, you need to consult the relevant literature to understand the academic debates and arguments surrounding the topic; Whatever the research problem may be, you have to conduct an investigation into the literature related to their research problem; A literature review is an account of what has been published on a topic by accredited scholars and researchers; The literature review consists of the following steps: o o o o o
Searching for information and information sources; Organising the information sources; Reading the information sources and determining their relevance and quality; Writing the literature review; and Referencing.
© Regenesys Business School
51
3.7 THE RESEARCH PHILOSOPHY AND APPROACH Timeframe
Learning outcomes
Prescribed books
Prescribed reading
Prescribed multimedia
Section overview
Minimum of 30 hours •
Demonstrate an understanding of the research process and its application to resolve business problems;
•
Review, apply, and critique various business research methods; and
•
Develop and present a professional research proposal for either a technical project or a single research or mini-dissertation.
•
Saunders, M., Lewis, P. and Thornhill, A. 2019, Research Methods for Business Students, 8th ed., Cape Town: Pearson Education. Chapter Four.
•
Adams, J., Raeside, R. and Khan, H.T.A. 2014, Research Methods for Business and Social Science Students, 2nd ed., New Delhi: Sage Publications.
•
Jack, E.P. and Raturi, A.S. 2006, ‘Lessons learned from methodological triangulation in management research’, Management Research News, (29) 6, 345–357. http://www.emeraldinsight.com/doi/full/10.1108/01409170610683833 (accessed 27 March 2023).
•
Woodside, A.G. and Wilson, E. J. 2003, ‘Case study research methods for theory building’, Journals of Business and Industrial Marketing, (18) 6/7, 493-508, http://www.emeraldinsight.com/doi/full/10.1108/08858620310492374 (accessed 27 March 2023).
•
UELRDBS, 2013, ‘Postgraduate research planning workshop – research process and philosophy’, [video clip], http://www.youtube.com/watch?v=zjhrfqZTUD8 (accessed 27 March 2023).
Here we’ll examine various research philosophies and the theories that underpin them, look at the “research onion” and explore various research assumptions and their applications.
© Regenesys Business School
52
3.7.1 The Research Philosophy “Research philosophy” is an overarching term that is related to the development of knowledge and the nature of that knowledge, and the way in which you, as a researcher, apply that knowledge (Saunders et al, 2013) in completing your research report Saunders et al (2013) view a research paradigm as breaking down the complexity of research and the manner in which social phenomena can be understood and explained. In essence, the research paradigm is the interpretative framework within which research is conducted. As a researcher you will be required to make assumptions that are relevant for your dissertation or other research. Your assumptions about human knowledge and about the nature of the realities you encounter in your research inevitably shape how you understand your research questions, the methods you use and how you interpret your findings (Saunders et al, 2013). The research philosophy you adopt will be seen as the assumptions used when viewing the environment around you. These assumptions will underpin your research strategy and the methods you choose as part of that research strategy. Saunders et al (2013) and Leedy (2013) agree that we should be aware of the philosophical commitments we make through our choice of research strategy, since this has a significant impact not only on what we do but how we understand what we are investigating. The philosophy you adopt will be influenced by pragmatic considerations around the research conducted. This will affect the research design and the study itself. The main influence is likely to be your particular view of what is acceptable in your research report and will be aligned to the knowledge and the process by which this research is developed. Saunders et al (2013) view the research philosophy as researcher-driven. This implies that the researcher concerned mostly with facts and figures, such as the detailed costing of material and labour required in a particular project, will tend to have a different stance on the way the research should be conducted from a researcher concerned with the emotions, impressions and attitude of the workers towards the construction manager on the project site. Not only will their strategies and methods differ considerably, but also so will their views on what is important and, perhaps more significantly, what is useful. In summary, we agree with Johnson and Clark (2006), Saunders et al (2013) and Leedy (2013:100) that the important issue is not so much whether our research should be philosophically informed, but how well we reflect on our philosophical choices and defend them in relation to the alternatives we could have adopted. This is critical when defending the research you have selected, especially in the proposal approval phase. It is easy to fall into the trap of thinking that one research philosophy is better than another. This is not true, as each is designed to achieve a different outcome, and these outcomes depend on the question(s) that you want to find answers to.
© Regenesys Business School
53
Learn more about the research process: •
UELRDBS, 2013, ‘Postgraduate research planning workshop – research process and philosophy’, [video clip], http://www.youtube.com/watch?v=zjhrfqZTUD8 (accessed 27 March 2023).
FIGURE 2: THE RESEARCH ONION
(Saunders et al, 2013) Saunders (2013) explains that the way a researcher views the world will, together with his or her own disposition in life, influence the assumptions he or she makes with regard to human knowledge. The same applies to the nature surrounding the realities encountered. Saunders et al (2013) further argues that this will guide the manner in which the research question is understood and the associated research design that will be followed. As a researcher, you will have gained practical work experience and this may influence your research philosophy. If you are more technically inclined, you may be interested in observable phenomena, such as the number of human resources required in a construction project. This may differ from a student who comes from a psychology or human resources background, who may focus more on the feelings and attitudes of the workers involved on the same project. This will affect their methodological choice and strategies for the research. Saunders et al (2013) consider positivism as the viewpoint that research must be scientific. There are many different versions of positivism. The confusion is intensified by the fact that much of modern physics is far closer to phenomenology than positivism as it is usually understood, and some branches of management science have a lot to say about values. © Regenesys Business School
54
As a researcher using the positivist philosophy, you will seek to observe and strive to predict outcomes. This can be compared to the way an inventor is concerned with facts and rule-based generalisations such as cause-and-effect analysis (reflecting the philosophy of positivism). For example, we see this in the television series Myth Busters. The myth busters try to prove or disprove various myths with data that is well defined and logically structured. This data is generally measurable and is not influenced by the researcher’s values and assumptions. Representative quantitative data is collected to be used for conducting statistical hypothesis testing. When the known theory cannot be confirmed by the findings, the theory must be revised. Blackburn (2005) and Saunders (2013) argue that contemporary philosophical realism is the belief that a person’s reality, or some aspect of it, is ontologically independent of that person’s conceptual schemes, perceptions, linguistic practices, and beliefs. Likewise, from a Regenesys perspective, the types of courses you have completed and those you have enjoyed may be more closely aligned to your realism of the qualification. If you come from a strong financial background, your reality may be financially based, and your research may not be “complete” unless it has a “sound” financial basis. You should be aware that like positivism, realism is also a philosophical position associated with a sound scientific basis for research. Saunders et al (2013) propose that the basis of realism is that what our senses indicate as reality is the truth. Therefore, realism maintains that reality is independent of the mind. It should however be noted that researchers can be influenced by the variety of worldviews and the researcher’s own experiences and view of what reality is. If you select the interpretive research philosophy you will focus your research on your ability to analytically evaluate and apply the practices that are investigated, making sense and creating meaning of the practices while showing how those practices configure to generate observable outcomes of your study. Saunders et al (2013) recommend that the researcher who is more concerned with gathering rich insights into subjective meanings than striving to provide law-like generalisations should use the research philosophy of interpretivism. This is relevant in research where the researcher wishes to focus on people rather than objects. An example may be to study how organisational values affect loyalty towards the organisation. In this case you may research the impact of emotional and spiritual intelligence in an organisation, say on organisational performance. You must be aware that your data collection and analysis will involve qualitative data from in-depth investigations with small, well-defined and carefully selected samples. The constructivist stance is that there is no single reality; instead, an understanding of reality is socially constructed, building on what was historically known and taking into account the views of multiple people (Cresswell, 2003:6). This view is appropriate where a researcher seeks a complexity of views and can address the process of interaction between people. Objectivism, on the other hand, is the philosophy that things exist independently of human perception. Objectivism emphasizes what is independent of or external to the mind.
© Regenesys Business School
55
Objectivism and Constructivism
Critically compare objectivism and constructivism. Use your own example to support your theoretical discussion.
Saunders et al (2013) say that subjectivism views reality as a social phenomenon, created from the perceptions and consequent actions of social actors. This is a continual process: through the process of social interaction, these social phenomena are in a constant state of revision. The students’ own organisational and personal experiences to create the basis for factual knowledge from which deductions can be made in the dissertation. Subjectivism relies on the student’s judgment and views these as being the only valid judgments. This approach by itself in not recommended for your dissertation, as the critical analysis required in the literature review depends on using multiple academic approaches and constructs to come up with new ideas.
Managerial Experience
Critically discuss the benefit of having at least five years of managerial experience before attempting to do a dissertation.
Subjectivists Critically discuss the following paragraph. You may use your own example in the discussion. “So what is truth? Is truth something that is sitting out there waiting for us to discover it? Are there objective certainties that sit outside personal experience? Or are we part of the truth and is the truth a part of us? Well, subjectivists would say that the only truth is that of the individual – his or her perspective or point of view.”
During your studies, you would have heard facilitators say that a master’s degree is based on pragmatism. This implies that your assignments and research study must be focused on solving practical problems, typically in your workplace and in the real world. This implies that your research should create value to the workplace. For students who adopt the philosophy of pragmatism, the importance of research will be the findings’ practical consequences.
© Regenesys Business School
56
“
Pragmatism research philosophy accepts concepts to be relevant only if they support action. Pragmatics recognise that there are many different ways of interpreting the world and undertaking research, that no single point of view can ever give the entire picture and that there may be multiple realities.” (Saunders et al, 2013) Pragmatism is a deconstructive paradigm that advocates the use of mixed methods in research, sidesteps the contentious issues of truth and reality (Feilzer, 2010), and focuses instead on what works as the truth regarding the research questions under investigation.” (Tashakkori & Teddlie, 2003)
This does not mean that a researcher who uses this research philosophy would always use a variety of data collection techniques and analysis procedures; rather check that the research design ensures credible, reliable and relevant data (secondary and primary) to be collected that support subsequent action.
Pragmatic Approach
Critically discuss two benefits and two disadvantages of having a pragmatic approach to a dissertation.
A functionalist sees society as a complex system whose components and subcomponents interact and work together to achieve synergy, solidarity and stability within the integrated system. This will be a useful approach if your research is focused on systems thinking or if you are using an integrated approach by incorporating learning from various theories across various disciplines. An example of this may be the impact of emotional intelligence on the strategic planning process.
Functionalism
Discuss how a researcher can use functionalism. You can use your own example to support your discussion.
© Regenesys Business School
57
Saunders et al (2013) argue that interpretive research is best described as being qualitative (not numerically based), to distinguish it from quantitative research, which is numerically based. The interpretative method assumes that your knowledge as a researcher is based on social construction, such as language, consciousness and shared meanings. If you use this approach, you should be aware that interpretive research does not predefine any of the dependent and independent variables. Instead, the focus is on understanding and researching the full complexity of human sense-making as the situation emerges while conducting the research.
Interpretive Research Critically discuss how a researcher can use interpretive research. You can use your own example to support your discussion.
The radical humanist paradigm is located within the subjectivist and radical change dimensions (Saunders et al, 2103). If you use this approach, you will need to take a critical perspective on organisational life. This implies that you will research and integrate both the political nature and the consequences that one’s words and deeds have upon others (Kelemen and Rumens, 2008). This would be more relevant if you are doing an integrated study across disciplines.
Radical Humanist Approach 1. Discuss how the radical humanist approach can be used in a dissertation. 2. You can use your own example to support your discussion.
Saunders et al (2013) are of the opinion that the researcher who is concerned with the radical structuralist paradigm will focus on what is needed to achieve fundamental change based on an analysis of such organisational phenomena as power relations and patterns of conflict. The radical structuralist paradigm focuses on understanding structural patterns, such as organisational structures, team structures, etc within work organisations such as hierarchies and reporting relationships, and the extent to which these may produce conflict and problems in the organisation. It adopts an objectivist perspective because it is concerned with objective entities, unlike the radical humanist ontology, which attempts to understand the meanings of social phenomena from the subjective perspective of participating social actors (Saunders et al, 2013).
© Regenesys Business School
58
Radical Structuralist Approach
1. Discuss how a researcher can use the radical structuralist approach. 2. You can use your own example to support your discussion.
3.7.2 Deductive versus Inductive Research Deductive research: This follows a structured, rule based, top-down approach, where you begin by identifying the topic to research, and then narrow this topic down into specific hypotheses (or research questions) that you wish to test (or find answers to), and thereafter, further focus when you collect the data that is to be used to test the hypotheses (or answer the research questions). The hypotheses are then tested using quantitative data and this will allow you either to accept or reject the stated hypotheses. Inductive research works in the opposite direction. You will move from very specific observations to more generalised theories. Saunders et al (2013) view this as a bottom-up approach. If you use this method in your research, you will begin by detecting patterns of fractals (patterns that repeat infinitely), and any irregularities you may come across in the research. Once this is complete the hypothesis can be formulated, but will only be a temporary hypothesis, as you will need to do more research and explore the hypothesis. The hypothesis may have to be refined to allow you to end up with some general conclusions or relevant theories. Consider the following example. Differences between inductive and deductive research: Concepts associated with quantitative methods Type of reasoning
Type of question Type of analysis
Concepts associated with qualitative methods
Deduction
Induction
Objectivity
Subjectivity
Causation
Meaning
Pre-specified
Open-ended
Outcome-oriented
Process-oriented
Numerical estimation
Narrative description
Statistical inference
Constant comparison (Dudovskiy, 2016)
Note that the statements above are not unconditional. In some instances, an inductive approach can be followed in quantitative research.
© Regenesys Business School
59
Inductive Versus Deductive Approaches
Give a practical example of the type of dissertation in which the inductive and deductive approaches would be relevant, and discuss why you think so.
TABLE 6: A SUMMARY OF THE VARIOUS RESEARCH ASSUMPTIONS
Assumption Ontological Refers to the way in which you view the nature of reality or being. Epistemological Refers to the way in which you view what constitutes acceptable knowledge. Axiological Refers to the way in which you view the role of values in research.
Rhetorical Refers to the way in which you question. A rhetorical question implies that you do not necessarily expect an answer, but you do want an occasion to talk about something. Methodological Refers to the way in which you define the research process to be followed in the dissertation.
Question
Characteristics
Implications for Practice (Examples)
What is the nature of reality?
Reality is subjective and multiple, as seen by participants in the study.
Researcher must use quotes and themes in words of participants and provide evidence of different perspectives.
What is the relationship between the researcher and what is being researched?
Researcher attempts to lessen distance between him- or herself and what is being researched.
Researcher must collaborate, spends time in field with participants, and become an “insider”.
What is the role of values?
Researcher acknowledges that research is value laden and that biases are present.
Researcher must openly discuss values that shape the narrative and include own interpretation in conjunction with interpretations of participants.
What is the language of research?
Researcher writes in a literary, informal style using the personal voice and uses qualitative terms and limited definitions.
Researcher must use an engaging style of narrative, may use first-person pronoun, and employ the language of qualitative research.
What is the process of research?
Researcher uses inductive logic, studies the topic within its context, and uses an emerging design.
Researcher must work with particulars (details) before generalisations, describe in detail the context of the study, and continually revise questions from experiences in the field. (Saunders et al, 2013)
© Regenesys Business School
60
3.7.3 The Quantitative Versus Qualitative Research Approach The research approach is important, as this determines the type of data collected and the level of statistical analysis that can be conducted with the data. The key differentiator between quantitative and qualitative is the data collected. Quantitative approaches require the respondent to answer questions by providing a numerical response, whereas qualitative data is not numerically based.
“Qualitative research methods are interpretative and aim to provide a depth of understanding. Qualitative methods are based on words, perceptions, feelings etc. rather than numbers and they include experiments, interviews, focus groups, and questionnaires with open-ended questions. Qualitative data collection methods emerged after it has become known that traditional quantitative data collection methods were unable to express human feelings, perceptions, and motivations.” (William, 2005) “Qualitative methods are often regarded as providing rich data about real life people and situations and being more able to make sense of behaviour and to understand behaviour within its wider context. However, qualitative research is often criticised for lacking generalisability, being too reliant on the subjective interpretations of researchers and being incapable of replication by subsequent researchers.” (De Vaus, 2002)
The quantitative research approach If you decide to adopt this approach, you must realise that this will be a logically and systematically structured process that will require you to fully understand and quantify the concepts researched. This must be critically applied in the dissertation. The concepts must be quantified for measurements to be used, and to conduct evaluations. Leedy (2013:270) views this approach as a formalised approach with a carefully defined scope and set boundaries. You will be required to select and apply an appropriate survey method. This will require you to use data collection instruments to collect the data in the correct format (nominal, ordinal, interval or ratio data) for the specified sample group using the appropriate sampling method (probability or nonprobability methods). As a researcher you must ensure that you have correctly selected and applied the relevant data collection and data analysis methods.
© Regenesys Business School
61
The qualitative research approach If you decide to use the qualitative approach then you will observe events from the perspective of those involved and attempt to understand behaviour and perceptions of individuals in an organisation. You need to understand that the main emphasis is on human behaviour, perceptions, experiences, etc. This research approach may be focused on the individual or the organisation. You could for example focus on the behaviour and their experience of situations and events, which have occurred on an individual or organisational level. This type of research is far more time consuming as it requires you to interact with the individual participants or groups whose behaviour, experiences, attitudes, perceptions, etc are researched, collected, collated and analysed to be reported on. Although the scope of qualitative research can be less defined and processes are not very formalised, the interpretation and reporting of the research findings can be time consuming and limited to the particular study. The results may not necessarily be true of the population as a while, and this is a limitation of your dissertation. Although the qualitative methodology is not as rigid as the quantitative methodology, it still requires you to follow a set of well-defined data collection methods and structured research designs. The typical data-collecting instruments used include observation, open, unstructured questionnaires and interviews. These are often difficult to analyse, as you need to find patterns (referred to as “fractals”), common statements, etc.
Recap Questions 1. Critically analyse the differences between the quantitative and qualitative research approaches. 2. Discuss how the research design, methodology and r methods are decided in the context of either the quantitative or qualitative approaches.
Key differences between the qualitative and quantitative approaches are summarised in Table 7.
© Regenesys Business School
62
TABLE 7: THE DIFFERENCES BETWEEN THE QUALITATIVE AND QUANTITATIVE APPROACHES
Qualitative Research
Quantitative Research
Scope can be less defined and processes are not so formalised
Formalised approach with carefully defined scope
Generally associated with descriptive enquiry that draws on interviews, documents, narratives and observations (observable patterns)
Associated with statistical and experimental studies that rely on various numerical manipulations to research a problem
Studies social phenomena from the perspective of the participants
Uses objective measurements and statistical measurements to explain a phenomenon
Used to study social phenomena and the emphasis is on human beings as research subjects
Used in natural sciences and to study cause and effect relationships
The design evolves during the study
The design develops before the study
Usually generates new theory (inductive)
Aims to test a theory (deductive)
Natural setting
Laboratory setting
Uses small samples
Uses big samples of large populations in wide geographical areas
Uses face-to-face interaction, and the researcher interacts with individual respondents
Uses standardised instruments. Researcher seldom interacts with respondents
Narrative description and researcher’s observations, which may be based on experience, knowledge or judgment
Statistical analysis of numeric data
Research phenomena are social in nature and cannot be quantified
Accurate measurements
Uses open, unstructured questionnaires and interviews
Uses surveys and structured questionnaires (Adapted from Leedy, 2013:240-245)
Now that you understand the basic research approaches and the influence of the research approach on the research methodology, we should proceed to the collection of data according to a particular research methodology and according to recognised research techniques (methods). However, before we discuss the concept research methodology, we should distinguish between the research design and the research methodology. We’ll tackle that in the next section of this course.
© Regenesys Business School
63
3.7.4 Key Points In this section the following key points should be noted: •
• •
• •
•
• •
The research philosophy is seen an overarching term that is related to the development of knowledge and the nature of that knowledge and the way in which the student will apply that knowledge in completing his or her research; In essence, the research paradigm is the interpretative framework within which research is conducted; As a researcher, the particular research philosophy you decide to adopt will be seen as the assumptions used in your research when contextualising the manner in which you view the environment around you; The philosophy you adopt will be influenced by the pragmatic considerations around the research conducted; When thinking about philosophies it would be easy to fall into the trap of thinking that one research philosophy is “better” than another. This is not true as they are designed to achieve different outcomes; Deductive reasoning follows a structured, rule based, top-down approach for reasoning, where you will begin by identifying the research topic to research, and then narrow this topic down into specific hypotheses (or research questions) that you wish to test (or find answers to), and thereafter, further focus when you collect the data to be used to test the hypotheses (or answer the research questions); Inductive reasoning works in the opposite direction, where you will move from the very specific observations to the more generalised theories; Quantitative approaches require the respondent to answer questions by providing a numerical response, whereas qualitative data is not numerically based.
© Regenesys Business School
64
3.8 FORMULATING THE RESEARCH DESIGN Timeframe
Learning outcomes
Recommended reading Prescribed multimedia
Minimum of 30 hours •
Demonstrate an understanding of the research process and its application to resolve business problems;
•
Review, apply, and critique various business research methods; and
•
Develop and present a professional research proposal for either a technical project or a single research or mini-dissertation.
•
Saunders, M., Lewis, P. and Thornhill, A. 2019, Research Methods for Business Students, 8th ed., Cape Town: Pearson Education. Chapter Five.
•
UELRDBS, 2013, ‘Postgraduate research planning workshop – research process and philosophy’, [video clip], http://www.youtube.com/watch?v=zjhrfqZTUD8 (accessed 27 March 2023).
Research design can be thought of as the structure of research, or the “glue” that holds all the elements in a research project together. We often describe a design using a concise notation that enables us to summarise a complex design structure efficiently. This section covers: Section overview
• • • • • • •
Explaining research design is an activity – a time-based plan; Understanding that design is always based on the research question; Identifying and describe design guides; Evaluating the selection of sources and types of information; Understanding that design is a framework for specifying the relationships among the study’s variables; Describing the design outlines and procedures for research activities; and Applying the design outlines and procedures for research activities.
3.8.1 The Research Design Leedy (2013:74-77) views the research approach as closely linked to both the research design and research methodology. Before we discuss research methodology, we should explain the concept of research design. Mouton (1996:107) defines research design as “a set of guidelines and instructions that [are] followed in addressing the research problem … to enable the researcher to anticipate what the appropriate research should be as to maximise the volatility of the eventual results”. In other words, a research design is the plan according to which the researcher obtains research subjects (participants) and collects information from them.
© Regenesys Business School
65
In the research design (research plan), you describe what will be done with the research subjects, in order to reach conclusions about the well-defined research problem. It is critical to understand that if the research design is incorrect, the research may not yield the desired research output. Since research deals with research subjects (participants or respondents), it is essential to understand fully the concepts of population (ie the entire group of research subjects) and sampling. The differences between research design and research methodology are captured in Table 8. TABLE 8: DIFFERENCES BETWEEN RESEARCH DESIGN AND RESEARCH METHODOLOGY
Research Design
Research Methodology
The focus is on the end product: what kind of study is being planned and what kind of result is aimed at?
The focus is on the research process and on the tools and procedures to be used.
The point of departure is the research problem or question.
The point of departure is the particular tasks (such as data collection and sampling) at hand.
The focus is on the research logic: what kind of evidence is required to address the research problem adequately?
The focus is on the individual steps in the research process and the most objective (unbiased) procedures to be employed. (Adapted from Leedy, 2013:74-77)
Research Design and Research Methodology
Critically discuss the key differences between a research design and a research methodology. Use a practical example to illustrate your understanding of the differences.
The research design is viewed as the key area that defines and gives the much-needed framework to the dissertation. This can be likened to a blueprint in engineering design. The research design will give you a well-thought-out and clearly defined structure for the research that to be conducted. This research design will show you the logical flow and integration of all of the major components of the research required for the dissertation. Cooper and Schindler (2010:134) argue that although there are differing views on what a research design is, there are certain essentials common to all the definitions. The authors argue that the design is seen as an activity and follows the project management scheduling technique. Research should always be based on a research question. Cooper and Schindler say the design as a framework specifies the various types of relationships between the variables being studied. In essence, the design is considered the procedure for the research. Saunders et al (2013) use the analogy on an onion with rings to describe and define the research process. The research onion in Figure 2 will be used to refine the research process and discuss the research design to be applied in your dissertation. © Regenesys Business School
66
3.8.2 The Types of Research Strategies A research strategy is critical as it is the plan for the dissertation. It should specify methods and procedures for the collection, measurement, and analysis of data. The main research strategies are experiment, survey, case study, action research, grounded theory, ethnography and archival research (Saunders et al, 2013). You should not think of these as discrete entities because they may be used in combination in the same research project. You will probably use more than one type of research strategy to complete the research required.
Exploratory versus formalised research Exploratory research is appropriate if you decide that the research problem is not fully understood and there is limited data available. Ghauri and Grønhaug (2010:56) argue that the objective of exploration is to develop a hypothesis, and not necessarily the testing of it. This may identify a problem and narrow down the research. If you decide to use an exploratory research design, you will have a brief idea of what the organisational issue (problem statement) is. This lack of a clearly identified issue will require you to explore many options, and may be done through trial and error. An analogy can be seen in geology where a geologist may understand the physical environment (as you may understand the macro and market environments in which the organisation operates) and then begin to drill pilot holes into the earth in light of past experience. Many decisions in organisations are also based on past experience and intuition. Organisations also create test environments to assess concept ideas and products. The pilot result may yield positive or negative findings, and more tests may be needed. This could lead to new opportunities and problems to overcome, which could become the organisational issue that your research focuses on. In information and communication technology this is referred to as “fuzzy logic”. The research becomes more defined as exploration yields more clarity on the findings. In your dissertation, a formalised study, including descriptive and causal studies, will require a rigid and well-defined structure. This will contain a defined and known organisational issue (problem statement) with specific hypothesis to be tested, or research questions to be answered. Descriptive studies describe phenomena associated with a subject population or estimate proportions of the population having certain characteristics (Ghauri and Grønhaug, 2010:56-58).
Observation versus interrogation or communication research If you decide to do an observation study, you should establish sound monitoring tools and techniques. These may be linked to technology and are considered to be a monitoring approach to collecting data, where you inspect the activities of the subject or the nature of some material without attempting to elicit responses from anyone. Take heed that the necessary ethical clearance is obtained and the method approved, as this is a sensitive type of research.
© Regenesys Business School
67
If you choose an observation type of research design in your dissertation, you must decide whether the observations are ethically acceptable. You will need permission from the relevant stakeholders and respondents. The observations may be direct (where you do the actual observations yourself or through a team of researchers) or indirect (cameras and other surveillance equipment may be used) and you do not have to be present. During the observation, you should be very clear about what must be observed and how it links back to your research goals and objectives. You can look for fractals (patterns that repeat themselves), as this will allow you to identify and record behaviours and other variables such as: spending patterns, patterns in social gatherings, etc. Observation requires a focused researcher with attention to detail. This method is also used by work-study officers in organisations to conduct time and motion studies for making more effective and efficient workplaces. If you decide to use the interrogation or communication approach you will be required to question the sample members and collect their responses by personal or impersonal means (Ghauri and Grønhaug, 2010:56-58). This type of method is seen in the police force, where suspects are interrogated in a special interrogation room. The room has one-way glass for external observations and is equipped with recording and video equipment. The interrogators are trained in interrogation methods and lie detection, and in the interpreting of body language.
Research Types
Give a practical example of the type of research where the two approaches above would be relevant, and discuss why you think so.
Experimental versus ex-post facto All types of experimental studies involve some sort of intervention (manipulation of one or more variables such as taste, smell, etc) by the researcher beyond simple measurement to determine the effect of another variable. This is not highly recommended as it is usually costly to implement and requires equipment to be used in controlled environments by specialists. Saunders et al (2013) agree that research experiments are derived from the natural sciences, although they say that research experiments are also often used in social science research, particularly psychology. As a researcher using experiments you will want to study causal links between well-defined variables. For example, does the change in an independent variable (such as increased spending on advertising) cause a change in another, dependent variable (such as sales). The authors note that the simplest experiments are concerned with whether there is a link between two variables.
© Regenesys Business School
68
In your research you may have more complex experiments that consider the size of the change and the relative importance of two or more independent variables, such as the size of the market and the number of competitors and how they affect the sales of the organisation studied. Saunders et al (2013) note that experiments are used more in exploratory and explanatory research to answer “how” and “why” questions. That is, how do advertising spending, competitor size and market size affect sales? And why do they affect sales? In your studies, most assignments are based on cases or past events. This is an ex-post facto design, which you have used to complete the case study analysis. This study is useful where you want to compare completed projects, some successful and others not, to a formal project management process. With this study you can isolate the variables that impacted, both negatively and positively, on the project management process and make recommendations on how to improve the success rate in project management.
Experimental Versus Ex-Post Facto Approaches
Critically compare experimental versus ex-post facto approaches, and discuss where each one would be relevant.
Descriptive versus causal Ghauri and Grønhaug (2010:56-58) view a descriptive study as one intended to describe or define a topic, subject, or construct. The descriptive study, as the name implies, attempts to describe what is being studied. An example of this would be to describe the rate of arrival times at a doctor's waiting room. This will require you to collect data on the times of arrival and analyse the data to be able to describe what is occurring. The analysis is conducted by applying descriptive statistical analysis. This will have to be carried out through the collection of precise data and the tabulation of the frequencies of the research variables (in this case, the times of patient arrivals at the doctor’s rooms). You will report on what the study reveals with regard to who, what, where, when, or how much. The major limitation of this method is that it can only describe the variables of interest, but no inferences can be made about any causal relationships between the variables. In contrast to this, a causal study requires more detailed statistical analysis, such as inferential statistical analysis, where the relationship between the variables is studied. In the example above, inferential statistical analysis could explore relationships between the times of patient arrival and time of day or type of weather to show possible relationships between these variables. Statistical tests are applied in an attempt to reveal the relationship between variables. This type of study is recommended where you want to prove relationships between variables in the research.
© Regenesys Business School
69
Descriptive versus Causal Research Give a practical example of the type of research where the two approaches above would be relevant, and discuss why you think so.
Extensive versus intensive research Extensive methods have traditionally been regarded as the norm for social research. These methods search for consistencies, believing that large numbers of recurring observations will point to relationships that are significant. According to Sayer (2000), this involves finding a population, describing groups taxonomically based on their shared characteristics (eg white women over 60; houses worth less than $50 000), and seeking quantitative relationships among the variables. Extensive methods ignore or do not directly concentrate on the causal groups in which certain individuals (persons, institutions etc) are truly involved. This includes the groups or networks of certain people, institutions, discourses and things with which they interact (Sayer, 2000). An intensive approach starts with individuals and portrays the main causal relationships into which they enter. It is not always possible to describe these causal groups at the beginning of the research; however, a key objective of the research may be to discover and study how they operate (Sayer, 2000). Against this background, we can say that extensive research illustrates how extensive certain phenomena and patterns are in a population, while intensive research focuses on what makes things happen in certain instances. The following example highlights the main differences between intensive and extensive research. TABLE 9: DIFFERENCES BETWEEN INTENSIVE AND EXTENSIVE RESEARCH
Research questions
Intensive
Extensive
How does a process work in a particular case or small number of cases?
What are the regularities, common patterns, distinguishing features of a population?
What produces a certain change? What did the agents actually do?
How widely are certain characteristics or processes distributed or represented?
Relations
Substantial relations of connection.
Formal relations of similarity.
Type of groups studied
Causal groups.
Taxonomic groups.
Type of account produced
Causal explanation of the production of certain objects or events, though not necessarily representative ones.
Descriptive representative generalisations, lacking in explanatory penetration.
Typical methods
Study of individual agents in their causal contexts, interactive interviews, ethnography, qualitative analysis.
Large-scale survey of population or representative sample, formal questionnaire, standardised interviews. Statistical analysis.
© Regenesys Business School
70
Limitations
Actual concrete patterns and contingent relations are unlikely to be representative, average or generalisable. Necessary relations discovered will exist wherever their relata are present, for example, causal powers of objects are generalisable to other contexts as they are necessary features of these objects.
Although representative of a whole population, they are unlikely to be generalisable to other populations at different times and places. Problem of ecological fallacy in making inferences about individuals. Limited explanatory power.
(Adapted from Sayer, 1992)
Cross-sectional versus longitudinal A cross-sectional study is conducted once. It reveals a snapshot at one point in time. This may be likened to analysing a balance sheet (statement of financial position) at a certain date, usually the financial year-end. In contrast to a cross-sectional study, a longitudinal study is repeated over an extended period. This is used more in medical and psychological tracking studies, with changes in variables over time, and which includes panels or cohort groups. This would not be possible in many research studies since there is a defined start and end date.
Cross-Sectional versus Longitudinal Research
Give a practical example of the type of research where these two approaches would be relevant, and explain why you think so.
Case versus statistical In organisational learning, there is a strong emphasis on the use of case studies to explain the key theoretical principles and contextual analysis of events. You may wish to create a case study to be able to capture these principles and contextualise what is happening in the organisation. The case study approach is very time-consuming in collection of data required to write up the case study. There are often gaps in the information where relevant assumptions will be needed. There may also be confidentiality clauses and ethical considerations that must be referred to the relevant stakeholders for ethical clearances. This type of study is usually qualitative. In contrast to the case method, a statistical study attempts to capture the defined population’s characteristics by making inferences from a probability sample selected around the sample’s characteristics. You must be aware that hypothesis testing must be conducted. This will require detailed statistical analysis when calculating and validating the results. This type of study is usually quantitative. (Saunders et al, 2013) © Regenesys Business School
71
Case versus Statistical Research
Critically compare, by using a practical example, the two approaches above.
Case Study Methodology
Critically evaluate the processes of a case study methodology.
3.8.3 The Research Process For the purposes of this course, we will follow the business research process shown in Figure 3. The research process provided is aligned to a mini-dissertation, which is a requirement for this course.
© Regenesys Business School
72
FIGURE 3: 10-STEP RESEARCH PROCESS
1. Choose a research topic
2. Develop a research question and objective
3. Compile a (critical) literature review
4. Locate secondary data
5. Formulate the research design (using the research onion)
6. Make sure the research will be believable (valid, reliable and generalisable)
7. Complete the research proposal
8. Collect data
9. Record and analyse the data
10. Complete and submit a research report or dissertation
(Saunders et al, 2013) A research report follows a formalised and structured process.
© Regenesys Business School
73
FIGURE 4: RESEARCH PROCESS AND THE RESEARCH REPORT
RESEARCH PROCESS
RESEARCH REPORT TITLE PAGE
(1) DEVELOP A RESEARCH T OPIC
Approval by research panel
(2) DEVELOP RESEARCH QUEST IONS AND OBJECT IVES
(3) COMPILE A (CRIT ICAL) LIT ERAT URE REVIEW
ABSTRACT (max 500 words) DECLARATION OF ORIGINAL WORK ACKNOWLEDGEMENTS CONTENTS PAGE LISTS OF TABLES, FIGURES AND APPENDICES CHAPTER 1: INTRODUCTION 1.1 1.2 1.3 1.4 1.5
Background of the Study Research Problem Importance of the Research Research Questions and Objectives Delineation of the Report
% of report
10%
CHAPTER 2: LITERATURE REVIEW
(4) LOCAT E SECONDARY DAT A
2.1 Theoretical/conceptual Framework 2.2 Theme/category 1/link to research question 2.3 Theme/category 2/link to research question (Includes all secondary data)
20%
CHAPTER 3: RESEARCH DESIGN (5) FORMULAT E THE RESEARCH DESIGN (research 'onion')
(6) ENSURE RESEARCH WILL BE BELIEVABLE (valid, reliable, generalisable)
3.1 Research Philosophy 3.2 Research Approach 3.3 Research Strategy 3.4 Research Choices 3.5 Time Horizon 3.6 Techniques and Procedures 3.7 Validity, Reliability, and Generalisability 3.8 Data Collection 3.9 Ethical Issues 3.10 Limitations and Delimitations
20%
CHAPTER 4: RESEARCH FINDINGS
(7) SUBMIT RESEARCH PROPOSAL (ONLY for Masters' students)
4.1 Research Question 1 4.2 Research Question 2 4.3 Etc.
(8) COLLECT DAT A (using samples and tools)
15%
CHAPTER 5: ANALYSIS AND INTERPRETATION Triangulation of the literature reviewed, results and current reality (primary and secondary data)
20% CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS (9) CAPT URE, ANALYSE AND INT ERPRET T HE DAT A
6.1 Conclusions 6.2 Recommendations
REFERENCES (APA or Harvard) (10) COMPLETE AND SUBMIT RESEARCH REPORT (PDPM) (Refer to Regenesys mini-dissertation guidelines / Masters only)
APPENDICES
5% 10%
5%
(Adapted from Saunders et al, 2013)
© Regenesys Business School
74
3.8.4 Key Points In this section we focused on the research design. Note that: • • •
The research design is a set of guidelines and instructions that are followed in addressing the research problem; In the research design (research plan), you describe what will be done with the research subjects in order to reach conclusions about the well-defined research problem; Research strategies include: o o o o o o o
•
Exploratory versus formalised; Observation versus interrogation; Experimental versus ex-post facto; Descriptive versus causal; Cross sectional versus longitudinal; Case versus statistical; and Intrusive versus extensive.
For the purpose of this course, the following research process should be followed: o o o o o o o o o o
Choose a research topic; Develop a research question and objective; Compile a literature review; Locate secondary data; Formulate the research design; Make sure the research will be believable (valid, reliable etc); Complete the research proposal; Collect data; Record and analyse the data; and Complete and submit a research report.
© Regenesys Business School
75
3.9 SAMPLING DESIGN Timeframe
Learning outcomes
Recommended reading Prescribed reading Prescribed multimedia
Section overview
Minimum of 20 hours •
Demonstrate an understanding of the research process and its application to resolve business problems;
•
Review, apply, and critique various business research methods; and
•
Develop and present a professional research proposal for either a technical project or a single research or mini-dissertation.
•
Saunders, M., Lewis, P. and Thornhill, A. 2019, Research Methods for Business Students, 8th ed., Cape Town: Pearson Education. Chapter Seven.
•
Smith, J. and Noble, H. 2014, ‘Bias in research’, Evidence-based Nursing, Oct 17 (4), 100101, http://ebn.bmj.com/content/17/4/100 (accessed 27 March 2023).
•
Ignousohs, 2011, ‘Sampling issues in research studies, [video clip], http://www.youtube.com/watch?v=jgLALMJ62-U (accessed 27 March 2023).
Using a sample in research saves money and time. By using a suitable sampling strategy and appropriate selection of the sample size, and by considering the necessary precautions to reduce sampling and measurement errors, a sample will yield valid and reliable data. Details on sampling can be obtained from the references included below and from other sources on statistics or qualitative research.
3.9.1 Introduction to Sampling Designs A sample is a representative subset of the known and defined population, which is identified for the research study. When we refer to “population” (or sampling frame) this does not necessarily mean people only – it could, for example, refer to places or objects. Researchers use a sample of the population for practical reasons, including: • • •
The full extent of the population may be unknown or the entire population could be inaccessible; Time constraints; and Financial constraints.
In order to contextualise this section, you need to understand the terms in Table 10.
© Regenesys Business School
76
TABLE 10: TERMINOLOGY
Population
The group of people, items or units under investigation. This is the universe of units from which the sample is to be selected.
Census
Obtained by collecting information about each member of a population.
Sample
Obtained by collecting information only about some members of a population.
Sampling frame
The list of people from which the sample is taken. It should be comprehensive, complete and up-todate. Examples of sampling frame: electoral register; postal code address file; telephone directory. (Leedy, 2013:215; Ghauri, 2010:140)
3.9.2 Population versus a Sample A population can be defined as the entire group of persons or research subjects that you want to study, because it contains all the variables of interest to you. We also refer to the population as the target population. Some examples of populations: all first-year students at a particular university, all nongovernment organisations, all primary schools in a certain area, etc. We now outline a number of basic concepts relating to the research population. Many populations about which inferences must be made are large, and often the group of people, items or units under investigation are not in the same place. For example, consider the population of Regenesys master’s students in South Africa, a group numbering say 10 000 students. This large population makes it difficult to conduct a census. In such a case, selecting a representative sample may be the only way to get the information required. Organisations that produce physical products, such as motor vehicles, cannot afford to crash test every motor vehicle, as there would be no output. They use sampling methods to ensure that the sample selected from the population of all motor vehicles produced in the factory in the production period under review is statistically represented and the results from the sample can be inferred about the population as a whole. There are also some populations that are so difficult to get access to that only a sample can be used.
The target population It is essential that you identify, define and describe your target population carefully – in the research design – and stipulate the criteria to be included in the population. For example: you want to know how women feel about the glossy cover of a particular women’s magazine (ie you want to determine their response to the cover), the selection criteria for the population might include only women who are regular readers of the magazine.
The accessible population Researchers seldom have access to the entire population: The population that you do have access to and actually study usually differs from the entire population in one or more aspects. The population that you can reach is known as the accessible population or study population. © Regenesys Business School
77
The elements of the population (unit of analysis) An element can be defined as a unit in a defined target population about which the researcher is obtaining information. Elements may be people who share certain characteristics (eg people in the same profession), or social groups, organisations, documents, or nations. The population for the master’s research study should include all objects that are of interest to you as the master’s student conducting this research. The sample is a representative proportion of the population. The quantitative approach defines statistical parameters, which are associated with populations and statistics with samples. Population parameters are usually denoted by using Greek letters (mu, sigma). On the other hand, the sample statistics are usually denoted using Roman letters (x, s). There are several reasons why researchers do not work with populations. They are usually large, and it is often impossible to get data for every object you are studying. Sampling does not usually occur without cost, and the more items surveyed, the larger the cost. They are also sometimes incomplete, unknown or inaccessible.
3.9.3 Sampling Leedy (2013:206) describes a sample as a finite part of a statistical population whose properties are studied to gain information about the whole. When dealing with people, it can be defined as a set of respondents (people) selected from a larger population for the purpose of a survey. A population is a group of individual persons, objects, or items from which samples are taken for measurement – for example a population of presidents or professors, books or students (Leedy, 2013:206). Leedy (2013:206) views sampling as being the act, process, or technique of selecting a suitable sample, or a representative part of a population for the purpose of determining parameters or characteristics of the whole population. This is a critical component in the research process and if not well designed and correctly structured, your research could be unreliable and invalid. The main purpose of sampling is to be able to draw conclusions about populations from samples. It is advisable to use inferential statistics, which will enable you to describe and determine the relevant population’s characteristics by directly observing only a portion (or sample) of the population. There are limitations when using a sample as opposed to using the entire population, such as: accessibility to the population frame (is it well defined and accessible?), size of the population (is it known and defined, and is it accurate?), and the time and cost of the study. There would be no need for statistical theory if a census rather than a sample was always used to obtain information about populations. But a census may not be practical, and is almost never economical.
© Regenesys Business School
78
These are the key reasons to use a sample instead of doing a census (Leedy, 2013:206): • • • • • •
Economy of costs due to section of a sample, which represents the population size; Timeliness of the data and information as there is less data to deal with in the sample size. This reduces data collection and data interpretation time; The large size of many populations makes them too time consuming and not possible to reach; Inaccessibility of some of the population, eg due to security issues; Destructiveness of the observation. This is where you need to observe an event or an experiment, and the event may be repeatable; and Accuracy and reliability of the population data.
A sample may provide you with needed information more readily. This is especially true in emergencies, such an outbreak of a virus. Due to time constrains, a sample can give faster feedback to medical researchers to begin immediate treatment. In such a case just a few of those already infected could be used to provide the required information.
Sample and interview bias Bias is an unknown or unacknowledged error created during the design, measurement, sampling or another procedure of the research process. It can be any influence or condition or set of conditions that distort data or information (Powell and Connaway, 2004:88). Sample bias may occur when the sample is drawn from a population and there is no equal and fair representation of all the elements within the randomly drawn sample. For example, if you want to study the level of employee satisfaction within an organisation, you may, by chance, choose all the disgruntled employees or all the satisfied employees. This will distort the results or the findings. A method to overcome this is to take a second sample (called double acceptance sampling) and compare this result with the results of the first sample. If there is a difference, or to increase the reliability of the results, multiple samples can be drawn from the population and the results compared (multiple acceptance sampling). Interview bias occurs when the person collecting the data influences the respondents. For example, if you do not allow each interviewee to give his or her opinion during a group interview, you could be guilty of bias. When you conduct an interview, you have to be careful how you react to responses. If you overreact, you may be seen as biased. The best approach is to keep the interview as formal as possible. When you ask leading questions in a questionnaire or interview, the respondents may feel pressed to respond positively instead of answering truthfully. This also results in biased responses. Try to avoid rhetorical questions and any double-barrelled questions. Organisations operate in a global village with diverse cultures, customs, languages, etc, which should be taken into account to reduce interview bias.
© Regenesys Business School
79
Sample validity and instrument validity A sample is valid if it represents the population. Instrument validity refers to the question of whether the researcher measures what they are supposed to measure with the selected instruments. One way of determining validity is to ask whether you are actually investigating (using a particular research instrument) what you say you are investigating. Research is considered to be valid when the conclusions are true. Sample validity is where the sample represents the population, and each element of the population had a fair and equal chance of being chosen to be included in the sample (Leedy, 2013:216-219). In order to ensure validity, you have to control those factors that could interfere with what you want to investigate or with the cause-effect relationships (Saunders et al, 2013).
Research reliability Research is considered reliable when the findings are repeatable (Powell and Connaway, 2004:43). In other words, the repeated application of the same research sample size selected with the same method will yield the same results if the same instrument is used every time. In quantitative studies, a mathematical instrument could be used, which should give the same result when used repeatedly. In qualitative research the research instruments could be a questionnaire or an interview guide. There are different ways in which to ensure reliability. One way is to keep notes consistently during the application of the research method for example, during observation or while conducting the interview with your selected sample element. An alternative method is to conduct the research, especially observation, over an appropriate time span or at different times of the day as this may reduce the possibility of reducing the sample selection error discussed above. As a researcher you need to observe behavioural patterns and differences at different times. Reliability can also be achieved when you compare your research to other research conducted for similar dissertations. or generic research that has been conducted. This may be from previous projects or earlier aspects of the current research, or complementary work you or others have carried out.
3.9.4 Causes of Sampling Error There are two basic causes of sampling error: One is chance. That is the error that occurs just because of natural or chance causes. This may result in untypical choices. Unusual units in a population do exist and there is always a possibility that an abnormally large number of them will be chosen. For example, in sampling the numerical ability of master’s students at Regenesys, a sample of all the mathematically strong students is chosen, but the majority of students are actually not mathematically inclined. This will definitely skew the results and may result in inappropriate decisions being made.
© Regenesys Business School
80
The second cause of sampling error is sampling bias. Sampling bias occurs where there is a tendency to favour the selection of units from the population that are deemed to possess certain characteristics that the researcher is looking for, or that are more readily available. Leedy (2013:216219) says bias is usually the result of a poor sampling plan. The most notable is the bias of nonresponse when for some reason some units have no chance of appearing in the sample. For example, the revenue service is asking respondents to disclose their levels of honesty in their tax returns. If a mail questionnaire is sent to 5 000 randomly selected taxpayers, will the results be reliable and valid? You should be aware that bias can be very costly. It may nullify the research results and cause you to redo the study. Another example would be where you would like to know the average house price in a town and you use numbers from the telephone directory (sampling frame) to select a sample from the total population. But only better-off people have telephones. You may well end up with high average house prices, which will lead to the incorrect decisions being made based on the findings. This could result in losses in income and reputation.
Learn more about sampling problems: •
Ignousohs, 2011, ‘Sampling issues in research studies, [video clip], http://www.youtube.com/watch?v=jgLALMJ62-U (accessed 27 March 2023).
The challenges around sampling: A researcher intends to study the personality types of all South African men with nursing qualifications: The target population is qualified South African male nurses. It is highly improbable that the researcher could locate all South African men with nursing qualifications, but it would be possible to locate all practising male nurses, because all practicing nurses must be registered with the South African Nursing Council. Therefore, the accessible population may then be defined as all qualified, practising South African male nurses. It may, however, still be impossible to obtain information from the accessible population (all practising male nurses), in which case the researcher draws a sample from the accessible population. The researcher studies the personality types of the sample and this enable him or her to come to a conclusion about the accessible population.
© Regenesys Business School
81
Nonsampling error (measurement error) The other main cause of unrepresentative samples is nonsampling error. This type of error can occur whether a census or a sample is being used. Like sampling error, nonsampling error may either be produced by the participants in the statistical study or be an innocent by-product of the sampling plans and procedures. A nonsampling error results solely from the manner in which the observations are made. The simplest example of nonsampling error is inaccurate measurements due to malfunctioning instruments or poor procedures. For example, consider the observation of individual spending in a shopping mall. If people are asked to state their spending themselves, no two answers will be of equal reliability. The people will have spent money but may not have kept an accurate record of what they have spent, and their responses may be incorrect. To reduce this error, you may have to physically check the goods purchased against the till slips.
Biased observations These may occur when you try to see what is important to you. This may be attributable to a previous disposition or reluctance to have an open mind when conducting research. This may also be caused when certain factors are simply ignored in the research, as you may think they are not relevant.
The interviewer’s effect No two interviewers are alike and the same person may provide different answers to different interviewers. The manner in which a question is formulated can also result in inaccurate responses. Individuals tend to provide false answers to particular questions. For example, some people want to feel younger or older for some reason known to them. If you ask such a person their age in years, it is easier for the individual to lie by overstating their age by one or more years, than it is if you asked which year they were born since it will require a bit of quick arithmetic to give a false date.
The respondent effect Respondents may give incorrect answers to impress the interviewer. This type of error is the most difficult to prevent because it results from outright deceit on the part of the responder. An example of this is apparent in the following: Knowing the study’s purpose to assist in the sampling method Knowing why a study is being conducted may create incorrect responses, as you may simply want to complete the required study and thus select any type or size of sample, without thinking about the sample’s ability to represent the population and allow you to make inferences about the population. One way to guard against such a sampling method and size bias is to statistically calculate the sample size and use more than one sample from the same population.
© Regenesys Business School
82
Induced bias Induced sampling bias occurs when the personal prejudices of the researcher or the data collector are not suppressed. Asking neutral questions can reduce this bias. For example, a researcher may ask the respondents leading questions in a sample where the results can be predicted with more certainty. A remedy for this bias is to make all the questions very specific. This will remove any bias as personal interpretation is excluded. Bias can occur in both quantitative and qualitative research. Read the following article, which provides more insight into types of bias across research designs, and strategies to minimise bias. Note how bias can occur at any stage of a study (design, selection, data collection and analysis).
Learn more about bias in research: •
Smith, J. and Noble, H. 2014, ‘Bias in research’, Evidence-based Nursing, Oct 17 (4), 100-101, http://ebn.bmj.com/content/17/4/100 (accessed 27 March 2023).
3.9.5 Sampling Procedure Sampling procedures are divided into two main categories: nonprobability and probability samples. For your research you need to be very clear when this selection is made, as an incorrect method will have serious consequences on the inferences based on the sampled results. In probability sampling each unit in the population has a fair (known) and equal (non-zero) chance of being selected for the sample. This allows you to make statistical inferences about the population. This simply means that the sample represents the population and can be statistically proven. In nonprobability sampling methods, the sample is not representative of the population and it is, therefore, not possible to make valid inferences about the population, based on sample results. (Leedy, 2013:219 and Ghauri, 2010:139-140)
Selecting the sample The preceding section covered the most common problems associated with statistical studies. The desirability of a sampling procedure depends on both its vulnerability to error and its cost. However, economy and reliability are competing ends, because reducing error often requires increased expenditure. Of the two types of statistical errors, exercising care in determining the method for choosing the sample can control only sampling error. The previous section has shown that sampling error may be due to either bias or chance. There will always be a chance of error when dealing with sample data. You can always draw a “good sample from a bad population” or draw a “bad sample from a good population”. By knowing some basic information about survey sampling designs and how they differ, you can understand the advantages and disadvantages of various approaches.
© Regenesys Business School
83
The two main methods used in survey research are probability sampling and nonprobability sampling. The most important distinction between the two methods is that, in probability sampling, all the people in the population have an equal and fair chance of being included and selected, and the results are more likely to accurately reflect the entire population. While it would always be statistically correct and more reliable to have a probability-based sample it is not always possible, as there are usually a host of factors to be considered such as availability, cost, time, research objectives and reporting requirements.
3.9.6 Types of Nonprobability Samples There are three primary types of nonprobability sampling methods:
• • •
Convenience sampling; Judgment sampling; and Snowball sampling.
They differ in the manner in which the elementary units are chosen.
The convenience sample This method is used in research where it is convenient and easy to collect the data. This reduces travel time and costs for the researcher. An example of this would be to go to a business school and interview master’s students, if your research objective is to study the perceptions of master’s students about their course. A convenience sample results when the more convenient elementary units (master’s students) are chosen from a population (all master’s students) for observation.
The judgment sample Researchers can use this method where they need to gain expert opinion from people who are seen to be experts in the industry. The assumption is that only experts’ opinions will be valid due to their level of peer review and recognised expertise. An example of this is seen in the Idols television show, where experts are used to make judgments about the performers to determine the level of skills the performers have. A judgment sample is obtained in accordance with the discretion of someone who is familiar with the relevant characteristics of the population.
Snowball sample This technique is used where the population elements and, therefore, sample elements, are not well known. An example is where you may want to interview users of steroids in sports. Once you can find one respondent, he or she may be able to refer you to another respondent, and those respondents to others, and so on – hence the name “snowball” sampling.
© Regenesys Business School
84
3.9.7 Types of Probability or Random Samples The probability or random sampling techniques are described in this section.
A simple random sample A simple random sample is obtained by choosing elementary units in such a way that each unit in the population has an equal chance of being selected. A simple random sample is free from sampling bias. However, using a random number table or calculator with a random number generation button to choose the units can be time consuming and cumbersome. If the sample is to be collected by a person who is not well versed in statistics, then instructions may be misunderstood and selections may be made improperly. Instead of using a list of random numbers, data collection can be simplified by selecting say every 10th or 100th unit after the first unit has been chosen randomly. Such a procedure is called systematic random sampling.
A systematic random sample Selecting one unit randomly and choosing additional elementary units at evenly spaced intervals until the desired number of units is obtained gives a systematic random sample. For example, there are 100 students in your class, and you want a sample of 20. You also have the students’ names listed on a piece of paper, maybe in alphabetical order. If you choose to use systematic random sampling, divide 100 by 20, you will get 5. Randomly select any number between 1 and 5. Suppose the number you have picked is 4, that will be your starting number. So student number 4 has been selected. From here onwards, you will select every 5th name until you reach the sample size of 20.
A stratified sample A stratified sample is obtained by independently selecting a separate simple random sample from each population stratum. A population can be divided into groups according to some characteristic or variable such as levels of spending, income, education, etc. The aforementioned groups are referred to as strata (layers or, as marketers refer to them, segments). You can then randomly select from each stratum a given number of units, which may be based on a proportion. For example, if group A has 100 persons while group B has 50, and C has 30, you may decide that you will take 10% of each group. If you do so, you will end up with 10 from group A, five from group B and three from group C.
A cluster sample A cluster sample is obtained by selecting clusters from the population on the basis of simple random sampling. The sample comprises a census of each random cluster selected. For example, a cluster may be a townhouse complex. So you decide on all the townhouse complexes in a particular area, for example.
© Regenesys Business School
85
You want 10 townhouse clusters in a particular area to be selected. You can use a simple random or systematic random sampling method to select the 10 town house clusters in that particular area, and then every town house selected will become a cluster. If your interest is to interview teenagers on their opinion of some new cellular telephone, then all the teenagers in a cluster must be interviewed. This is a cost-effective sampling method, but it is very susceptible to sampling bias. In the above case, you are likely to get similar responses from teenagers living in one complex because they interact with one another. They may go to the same social gatherings or attend the same school.
3.9.8 Combination or Mixed Purposeful Sampling This combines various sampling strategies to achieve the desired sample. This helps in triangulation, allows for flexibility, and meets multiple interests and needs. When selecting a sampling strategy, it is necessary that it fits the purpose of the study, the resources available, the question asked and the constraints faced. This holds true for sampling strategy and sample size.
Sample size Before deciding how large a sample should be, you have to define your study population. For example, all children below the age of three in New York. Then determine your sampling frame, which could be a list of all the children below three as recorded in the New York census. You can then struggle with the sample size. The question now arises as to the size of the sample. Leedy (2013:298) is of the opinion that the sample size can be determined by understanding the various constraints. For example, the allocated funding may limit the sample size used. When research costs are fixed, a useful rule of thumb is to spend about half of the total amount for data collection and the other half for data analysis. This constraint influences the sample size as well as sample design and data collection procedures (Saunders et al, 2013). In general, it can be seen that the sample size depends on the type of research to be undertaken; the type of research philosophy used; the time frame and cost constraints; the type of data analysis to be done; and how homogeneous the population and sample are. These are the most common factors that will affect the sample size. The required sample size is a function of the level of precision you require when submitting your research proposal. The sample size ‘n’ required to estimate a population mean (average) with a given level of precision is:
The most common method to calculate the sample size is to use the level of confidence formula. This is the square root of N= (1.96)*(S)/precision, where S is the population standard deviation for the variable whose mean one is interested in estimating. Precision refers to width of the interval one is willing to tolerate and 1.96 reflects the confidence level. For example, to estimate the mean spending in a population with an accuracy of R100 per year, using a 95% confidence interval and assuming that the standard deviation of earnings in the population is R1600, the required sample size is 983:[(1.96)(1600/100)] squared.
© Regenesys Business School
86
Deciding on a sample size for qualitative inquiry can be even more difficult than quantitative because there are no definite rules to follow. The sample size may be based on data saturation. In essence, when you find that the respondents are providing the same or similar information. This shows that you have sampled a sufficient number of respondents as no new information is provided. In many cases it will depend on what you want to know, the purpose of the inquiry, what is at stake, what will be useful, what will have credibility and what can be done with available time and resources. With fixed resources, which are always the case, you can choose to study one particular phenomenon in depth with a smaller sample size or a bigger sample size when seeking breadth. In purposeful sampling, the sample should be judged on the basis of the purpose and rationale for each study and the sampling strategy used to achieve the study’s purpose. The validity, meaningfulness, and insights generated from qualitative inquiry have more to do with the information richness of the cases selected and the observational or analytical capabilities of the researcher than with sample size.
Sampling problems There are several potential sampling problems. When designing a study, a sampling procedure is also developed including the potential sampling frame. Several problems may exist within the sampling frame. First, there may be missing elements – individuals who should be on their list but for some reason are not. For example, if the population consists of all individuals living in a particular city and you use the phone directory as the sampling frame or list, you will ignore individuals with unlisted numbers or who cannot afford a phone. Foreign elements constitute the second sampling problem. These are elements that should not be included in the population, but which appear on the sampling list. Thus, if you were to use property records to make a list of individuals living in a particular city, landlords who live elsewhere would be foreign elements. In this case, tenants would be missing elements. Duplicates represent the third sampling problem. These are elements that appear more than once on the sampling frame. For example, if you are studying patient satisfaction with an emergency room at a hospital, you may include the same patient more than once in the study. If the patients are completing a patient satisfaction questionnaire, you must ensure that patients are made aware of this study to avoid them completing the form again. If they complete it more than once, their second set of data is a duplicate.
© Regenesys Business School
87
3.9.9 Key Points In this section the following key points should be noted: • •
A sample is a representative subset of the known and defined population, which is identified for the relevant research study; Researchers will use a sample of the population for practical reasons, including: o o o
• • • • • • • •
An element can be defined as a unit in a defined target population about which you are obtaining information; Bias is an unknown or unacknowledged error created during the design, measurement, sampling or another procedure of the research process; A sample bias may occur when the sample is drawn from a population and there is no equal and fair representation of all the elements within the randomly drawn sample; An interview bias is where the person collecting the data influences the respondents of the event; A sample is valid if it represents the population; Instrument validity refers to the question of whether the researcher measures what they are supposed to measure with the selected instruments; Research is considered reliable when the findings are repeatable; There are two basic causes of sampling error: o o
•
•
By chance; and Sampling bias.
A nonsampling error is an error that results solely from the manner in which the observations are made. The simplest example of nonsampling error is inaccurate measurements due to malfunctioning instruments or poor procedures utilised in your research; There are three primary types of nonprobability sampling methods, namely: o o o
•
The full extent of the population may be unknown or the entire population could be inaccessible; Time constraints; and Financial constraints.
Convenience sampling; Judgment sampling; and Snowball sampling.
Probability or random sampling techniques include: o o o o
Simple random sampling; Systematic random sampling; Stratified sampling; and Cluster sampling.
© Regenesys Business School
88
3.10
PLANNING YOUR DATA COLLECTION DESIGN
Timeframe
Learning outcomes
Prescribed textbook
Recommended books
Prescribed reading
Prescribed multimedia
Minimum of 50 hours •
Demonstrate an understanding of the research process and its application to resolve business problems;
•
Review, apply, and critique various business research methods; and
•
Develop and present a professional research proposal for either a technical project or a single research or research.
•
Saunders, M., Lewis, P. and Thornhill, A. 2019, Research Methods for Business Students, 8th ed., Cape Town: Pearson Education. Chapters Eight, Nine and Ten.
•
Rubin, H.J. and Rubin, I.S. 2004, Qualitative interviewing: The Art of Hearing Data, 2nd ed., Thousand Oaks, CA: Sage Publications.
•
Edwards, R. and Holland, J. 2013, What Is Qualitative Interviewing? New York: Bloomsbury Academic.
•
McNamara, C. 2008, ‘General guidelines for conducting research interviews’, http://managementhelp.org/businessresearch/interviews.htm (accessed 27 March 2023).
•
Cranfield School of Management, 2012, ‘Management research: delivering business results’, [video clip], http://www.youtube.com/watch?v=R7XuQxukmb0 (accessed 27 March 2023).
The basic idea behind the survey methodology is to collect and analyse the relevant data by asking your target population questions and then to examine relationships among the variables. Your survey is intended to record the relevant data around facts, perceptions and patterns of behaviour. This section covers: Section overview • • • • • •
The essential elements of a survey; The various types of survey methods; The various types of variables used in research; The types of scales used in questionnaire design; Elements of questionnaire design; and Designing and developing questionnaires.
© Regenesys Business School
89
3.10.1
Data Collection Methods
In this section we briefly discuss surveys and interviews as data collection methods. Leedy (2013:189) views a survey as a study that is designed to determine the incidence, frequency and distribution of certain characteristics in a population. Surveys are commonly used in business and government. The basic idea of your research survey methodology is to measure variables by asking your target population people questions, and then to examine relationships among the variables. For your dissertation, your survey should record the relevant data around facts, perceptions, and fractals (patterns) behaviour. The most common research survey is a cross-sectional design, which asks respondents to answer the survey at a point in time. Be aware that this has limitations. You may or may not be able to analyse the direction of causal relationships. This is critical if your research is based on systems thinking concepts (Leedy, 2013:212-213). Ghauri (2010:118-119) views a survey as a method of data collection where the term “survey” refers to one of two, or some combination of two, procedure(s): questionnaires and interviews. During the research you will notice that many researchers use questionnaire, which are mostly selfadministered, allowing respondents to fill them in themselves. An interview typically occurs whenever a researcher and respondent are face-to-face or communicating via telephone or computer. The types of interviews typically used are: unstructured interviews, which allow for instant and more sporadic, often more honest responses; structured interviews, where you restrict the possible responses; and semi-structured interviews, in which you restrict certain kinds of responses allowed but allow freedom on discussion of certain selected topics. Leedy (2013:189) argues that adding retrospective (past behaviour) and prospective (future propensities) items to a cross-sectional survey may reduce this limitation, but generally it is more useful to have a longitudinal design, which asks the same questions at two or more points in time. The time constraint in completing your research will be a limiting factor for a longitudinal study. A trend study is a repeated cross-sectional design, asking the same questions to different samples of the target population at different points in time. A trend study is commonly used in conducting strategic analysis by asking the same industry analysis questions to different samples of the target population at different times. This can track changes in the industry over time. Supplemented by a panel study, which asks the same questions to the same people time after time, a trend study will be most beneficial for you to begin implementing within your organisation to ensure its sustainability. Although surveys can be a cost-effective type of research, survey research design suffers from inherent weaknesses. The greatest weakness (Leedy, 2013:189-190) is probably because surveys are basically exploratory in nature. This implies that in your research you can make inferences, but will be limited in systems thinking when conducting cause-and-effect analysis. Other survey weaknesses may be: a lack of respondents’ willingness or ability to react; the sampling frame may be easy to generate, but difficult to access; there may be a low response rate; and measurement errors may occur, causing the results to be unreliable or invalid.
© Regenesys Business School
90
3.10.2
Variables in the Research Problem
As a researcher you need to understand that a variable is simply a measurable characteristic that varies. It may change from group to group, from person to person, or even within one person over time. It is important to recognise the different types of variables in your study so that you plan your data collection appropriately. There are six common types of variables that you will be required to define when identifying and formulating the research problem: a research problem is always based on a particular relationship between two variables, and this relationship implies cause and effect: Variable X is the cause of Variable Y, and Variable Y is the effect of Variable X. In other words, Variable X is dependent on Variable Y. Therefore, you should always distinguish between the dependent and independent variables in your research problem. The independent variable is the variable that determines, produces, influences or changes the dependent variable. Therefore, the independent variable (Variable X) is the cause. The dependent variable, by contrast, is the variable that is influenced or changed – ie the variable that is the effect (Variable Y). The concept of variables may become clearer if we study a number of examples. The intervening variable refers to abstract processes that are not directly observable. Nevertheless, they link the independent and dependent variables in a certain manner. If your passing assignments depends on the quality of the course, then academic support processes are the intervening variable. The moderating variable influences the relationship between the independent and dependent variables by modifying the effect of the intervening variable(s), possibly through limiting the scope of the intervening variable. If your passing the assignments depends on the quality of the course, and the academic support processes are the intervening variable, the moderating variable(s) may be gender, age, culture, or academic competency in certain subjects. The control variable is not measured in the research and must be held constant, neutralised, balanced, or eliminated, so it will not have a biasing effect on the other variables. An example of a control variable is the physical environment in which the research interview is conducted. The extraneous variables are those variables in the research environment that may affect the dependent variable(s), but which are not controlled. Extraneous variables must be observed closely and isolated before the study commences. The reason is that they may affect the success of the research. They may adversely affect the study's validity, and thereby make it impossible to know whether the effects were caused by the independent and moderating variables or some extraneous factor. If they cannot be controlled, extraneous variables must at least be taken into consideration when interpreting results. Extraneous variables may be the stress level or adverse conditions that may affect the success of the student in their studies. (Adapted from Ghauri, 2010:118-119 and Leedy, 2013:180-195)
© Regenesys Business School
91
Variables 1. Read the research problem below and identify the variables discussed above. “An investigation into unemployment and poverty as contributing factors to the xenophobic violence within area X in country A during May 2019.” 2. Write down your own research problem and identify the dependent and independent variables.
3.10.3
Research Instruments
Survey questionnaire If you decide to use questionnaires, you should start by writing the survey questions based on your research questions. After writing a rough draft, you have to ensure that these questions cover the scope of the research questions and the dissertation’s objectives. You must link these questions to the variables discussed earlier. The variables list will ensure that the key concepts or theoretical constructs are aligned to the relevant research questions and hypotheses where applicable. Ensure that questions are not ambiguous, and that they ask for responses regarding only one research variable at a time. While designing the questionnaire, consider methods to increase the response rate. Some popular methods to improve the response rate involve timing and remuneration. Timing is the name for a variety of techniques involving presurvey phone calls or postcards telling respondents that a survey is coming to them soon. After the survey has been mailed or delivered, timing also involves a followup friendly reminder to complete the survey (Saunders et al, 2013). Sometimes, respondents admit to things in completing the survey just to make the reminders stop. Remuneration takes many forms. “In the name of science” and “help me out with my class research project while in college” appeals do not usually increase response rates. Some respondents also take you up on any offer to receive a copy of your finished research report, when done. The best incentive is cash, attached to the questionnaire, so recipients feel guilty about keeping the money and not answering the survey. Personalisation also increases response rates (Saunders et al, 2013). The order of questions is an important consideration in your research as the respondents are more likely to answer the easy questions first. These are demographic information, such as age, gender, race, etc. You should begin with a filter question and a few questions to capture the respondents’ attention. You may increase the response rate by including reversal questions, which ask for the same information, but in reverse. For example, “Do you feel the employment practice is fair?”; and later in your questionnaire, you ask, “Do you feel the employment practice is unfair?” The responses should be roughly equivalent to both questions, although one should be “strongly disagree” while the other should be “strongly agree”. Reversal questions serve as a check on lying and complacency. © Regenesys Business School
92
It is essential in research to ensure that you are targeting the correct sample frame members. You do this by using filter questions. For example, if you are looking to research master’s students, start with a filter question such as: “Are you currently a master’s student?” This will save a great deal of frustration and time in gathering data for your research. Common qualitative techniques: structured, unstructured and semi structured interviews (interview schedule) Apart from a survey questionnaire, an interview schedule can be used as a research instrument to collect data (qualitative). The general rule for interviewing is to record responses verbatim. This means, you should use some type of voice or video recording device or write down the interview response verbatim. If you want to collect sensitive information you can stop recording the interview and then when you are not in the interview with the respondent try to write down what they said later. Structured interviews, of course, use precoded response categories (SA, A, D, SD for “strongly agree”, “agree”, “disagree”, “strongly disagree”) that you can tailor to more sophisticated responses (“a lot”, “a little”, “hardly any”, “none at all”). This requires you to be familiar with the terminology and jargon used in the population. An unstructured or semi structured interview permits you to explore various issues in depth with respondents. If you start getting into life history, you are probably doing depth interviewing, which is something completely different. It is all right, however, for you, the interviewer to talk about how you would answer a question, as long as this is to clarify the purpose of the question or set up an instructional pattern. Self-disclosure should be avoided if it seems it is leading to interviewer bias. Interviews are wonderful opportunities to impress the importance of confidentiality on respondents.
According to Edwards and Holland (2013), “a semi structured interview is a method of research used most often in the social sciences. While a structured interview has a rigorous set of questions which does not allow one to divert, a semi structured interview is open, allowing new ideas to be brought up during the interview as a result of what the interviewee says.”
An interesting and somewhat important issue with interviewing is the time of day. Some people are diurnal and others are nocturnal, which means they talk more during the day or at night. Many prison populations are nocturnal, so you get the best information at night. However, safety issues must be kept in mind. Interviewers should not be overdressed nor underdressed. Some time should be spent at the beginning to build up a rapport with the respondent. Be prepared to use probes. Probes, or probing questions, are whatever is necessary when you get responses like “hmm” or “I guess so”, and your probe should be “what did you mean by that?” Do not be satisfied with monosyllabic answers. Simple yes or no answers usually call for probing, unless the protocol suggests otherwise. Always exit the interview diplomatically. That way, you have not ruined it for others who might follow you. Telephone interviews are generally better than computer interviews, although neither substitutes for the good observational skills of face-to-face interviewing. The most common sampling procedure with telephones is random digit dialling. The most common computer method is a web-based series of questions allowing for chat or bulletin board posting. © Regenesys Business School
93
Various software programs can be loaded onto laptops and used to guide face-to-face interviews. Other technology exists to content analyse keywords captured by recording or computer devices.
Learn more about qualitative interviewing: • •
3.10.4
Rubin, H.J. and Rubin, I.S. 2004, Qualitative interviewing: The Art of Hearing Data, 2nd ed., Thousand Oaks, CA: Sage Publications. Edwards, R. and Holland, J. 2013, What Is Qualitative Interviewing? New York: Bloomsbury Academic.
Data Types
As noted previously, there are two main categories of data, qualitative and quantitative. Qualitative data is a categorical measurement expressed not in terms of numbers, but rather by means of a natural language description. In statistics, qualitative data is often used interchangeably with “categorical data”. Quantitative data is a numerical measurement expressed not by means of a natural language description, but in terms of numbers. Quantitative data uses four levels of measurement, namely nominal, ordinal, interval and ratio. These go from the lowest level to highest level of statistical analysis. As a research student, you should be very clear about the levels of data as this will affect your ability to test your hypothesis. As a general rule, try always to use interval and ratio data, preferably ratio data. Note that the data is classified according to the highest level that it fits. Each additional level will add something to the previous and lower level. This assumes that the lower level never has the ability to do that and limits the statistical analysis that can be done. It is essential as a researcher that you are fully aware that there is a formal and statistical hierarchy implied in the level of measurement idea. At lower levels of measurement, assumptions tend to be less restrictive and data analysis tends to be less sensitive, but fewer statistical analysis methods can be applied. Nominal data is the lowest level of measurement and only the mode, percentages and graphs can be applied. The response categories on the questionnaire are mutually exclusive, meaning that respondents must choose one category over another. “Are you male or female?” Or “What race are you?” are typical questions. The order of the response categories is not important and may be interchanged without any impact on the outcome. The second level of data is ordinal data, where there is a certain order and logic that must appear in the response categories when the question is asked. As with nominal data, the ordinal data response categories on the questionnaire are also mutually exclusive (respondents must choose one category over another). An example of this is the question: “What size of shirt will best fit you: small, medium or large?” As you can see, the order of the response categories is important, as there is certain logic to this and may not be interchanged without affecting the outcome.
© Regenesys Business School
94
The third level of data is interval data, which allows for more detailed descriptive analysis to be conducted on the data (the average, mode, standard deviation, range). The best way to describe this is by looking at the speedometer in a motor vehicle. There are intervals of say 0–10km/h, 1120km/h, and so on. Note the gap between the intervals so that the respondents are clear which interval to check. The response categories on the questionnaire are mutually exclusive (respondents must choose one category over another). The data will fall within an interval and can be grouped in meaningful and similar intervals. The main problem with interval data is that it does not tell the driver the exact speed, only the interval where the speedometer needle is. The top level of data and the level where all the descriptive statistical measures can be applied is ratio data. Note that the interval adds meaningful differences to the data. Ratio data adds a zero so that ratios are meaningful. The best way to describe this is by looking at the speedometer in a motor vehicle again. There are intervals of say 0-10km/h, 11-20km/h, and so on. With ratio data the speedometer needle falls within a certain interval and on an exact speed. Example: within the 5160km/h (interval data) with the speedometer needle may be on 55km/h (ratio data).
3.10.5
Constructing Questionnaires
Nominal scales These are the simplest measurement scales, classifying individuals, companies, products, brands or other entities into categories where no order is implied. This type of scale is sometimes referred to as a categorical scale. It is a system of classification and does not place the entity along a continuum. It involves a simple count of the frequency of the cases assigned to the various categories, and if desired numbers can be nominally assigned to label each category as in the example below:
Which master's courses are you currently registered for? TABLE 11: EXAMPLE OF NOMINAL SCALE
1. Finance
3. ICT
2. Economics
4. Knowledge Management
The numbers have no arithmetic properties and act only as labels for courses. The only statistical measure that can be used is the mode (value that appears most often in a set data) because this is simply a set of frequency counts.
© Regenesys Business School
95
Ordinal scales This is also a simple type of measurement scale that classifies individuals, companies, products, brands or other entities into categories where order is implied. It is often referred to as a categorical scale. It is a system of classification, and does place the entity along a continuum. It involves a simple count of the frequency of the cases assigned to the various categories. If desired, numbers can be nominally assigned to label each category as in the example following:
What size shirt do you wear? TABLE 12: EXAMPLE OF ORDINAL SCALE
1. Small
2. Medium
3. Large
4. Extra large
Interval scales In interval measurement the distance between attributes does indeed have meaning. For example, when we measure temperature (in Celsius), calibrated and equal intervals are the same. From 1020 is same as the distance from 40-50. The interval between values is interpretable. Because of this, it makes sense to compute an average of an interval variable, where it does not make sense to do so for ordinal scales. But note that in interval measurement ratios do not make any sense – 100 degrees is not twice as hot as 50 degrees (although the attribute value is twice as large). It is only with interval-scaled data that researchers can justify the use of the arithmetic mean as the measure of average. The interval or cardinal scale has equal units of measurement, thus making it possible to interpret not only the order of the scale scores but also the distance between them. However, it must be recognised that the zero point on an interval scale is arbitrary and is not a true zero. This has implications for the type of data manipulation and analysis you will be able to conduct on data collected in this format. It is possible to add or subtract a constant to all of the scale values without affecting the form of the scale, but one cannot multiply or divide the values. Interval scales may be either numeric or semantic. This is demonstrated next.
© Regenesys Business School
96
Examples of Interval Scales in Numeric (a) and Semantic Formats (b) Please indicate your views on the master's facilitators by scoring them on a scale of 1– 5, where, 5 = excellent and 1 = poor, on each of the criteria listed: Facilitators are: Circle the appropriate score Knowledgeable 5 4 3 2 1 on each line Engaging 5 4 3 2 1 Able to transfer 5 4 3 2 1 concepts Professional 5 4 3 2 1 Caring 5 4 3 2 1 (a)
Please indicate your views on the master’s facilitators by ticking the appropriate responses below: Excellent Very Good Good Fair Poor Facilitators are: Knowledgeable Engaging Able to transfer concepts Professional (b)
Most of the common statistical methods of analysis require only interval scales in order to be used.
Ratio scales It is important to understand that in ratio measurement there is always an absolute zero that is meaningful, as compared to interval scales, which have no absolute zero. This means that you can construct a meaningful fraction (or ratio) with a ratio variable. Weight is a ratio variable. In applied social science research most “count” variables are ratio: for example, the number of student registrations on a master’s elective course in a past semester. Why? Because you can have zero students registered for an elective course in a past semester and because it is meaningful to say that: “We had twice as many students in the past semester as we did in the previous year’s semester.” In your dissertation, try to use this ratio scale where you can, as this provides the highest level of measurement of all types of data and scales. This has the properties of an interval scale together with a fixed origin or zero point. Examples of variables that are ratio-scaled include assignment and exam marks, mass, lengths, speed, salaries, etc. Ratio scales allow you to compare both differences in scores and the relative magnitude of scores. For instance, the difference between 50% and 60% is the same as that between 70% and 80%, while 60% is twice as large as 30%.
© Regenesys Business School
97
3.10.6
Scale Development
In questionnaire design it is essential that the appropriate response is obtained. This is achieved through the use of appropriate scales. A scale has a range of guided responses. Rating questions have been combined to measure a wide variety of concepts such as customer loyalty, service quality and job satisfaction. A scale is always at the ordinal or interval level, but it is conventional for researchers to treat them as interval. Scales are seen to be predictors of question outcomes (like perceptions, behaviour, attitudes or feelings) because they measure the fundamental underlying traits (like introversion, patience or verbal ability) (Saunders et al, 2013).
Four ways to construct scales Thurstone scales This tool can be used for measuring core attitude when you have multiple dimensions or concerns around that attitude. Take the Regenesys master’s degree, for instance. A person might have one part of their attitude relating to employability; another part of their attitude relating to networking; and still another relating to entrepreneurship. How do you determine which part of the attitude goes to the core of the selection of the Regenesys master’s degree? In Thurstone scaling, you overcome this limitation by obtaining a panel of judges and then asking them questions about why a master’s student will choose to study at Regenesys. By administering the questionnaire to the panel, you can analyse inter-item agreement among the judges, and eliminate what are called the nonhomogeneous items. Scaling seems to be driven by the need to be homogeneous in nature. In applying Thurstone scaling, your objective is to favour the more useful respondents and look for higher-scoring items in high clusters of homogeneous responses to the question why master’s students choose to study at Regenesys (adapted from Saunders et al, 2013). Likert scales This tool is usually based on the five-point bipolar response format most people are familiar with. Think about the feedback cards received at many restaurants. These scales always ask people to indicate how much they agree or disagree, approve or disapprove, believe to be true or false. With a five-point scale the centre point is always neutral, to accommodate respondents with neutral feeling. This is essential to use in your dissertation, as you do not want to force a response and introduce a bias into the study. Below are some of the typical response categories you can use in your research questionnaire.
Never; Seldom; Sometimes; Often; Always, Strongly Agree; Agree; About 50/50; Disagree; Strongly Disagree; Don't Know, Strongly Approve; Approve; Need more information; Disapprove; Strongly Disapprove, Strongly Opposed; Definitely Opposed; A bit of both; Definitely Unopposed; Strongly Unopposed (Saunders et al 2013) © Regenesys Business School
98
Guttman scaling This is a technique where you can mix questions up in the sequence they are asked so that respondents do not see that several questions are related. This is good in the sense that it introduces randomness and takes away the predictability element that respondents may have when completing a questionnaire. The scoring system is based on how closely they follow a pattern of ever-increasing hardened attitude toward some topic in the important questions. Let us take the example of attitude toward capital punishment in South Africa:
For each of the following, indicate if you SA, A, 50/50, D, or SD: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Crime is out of control in South Africa. Police should be given more powers. More criminals should be given the death penalty. South Africans ought to do something about drug-exporting countries. The military ought to be used to patrol our streets. Inmates on death row ought to be executed quickly. Most politicians are too soft on crime. Lethal injection is too merciful for those who deserve it. Crime is destroying the social fabric of our society. They ought to jack up the voltage when they electrocute criminals.
In the above example, items #3, 6, 8, and 10 make up the scale for the attitude toward capital punishment. Everything else is irrelevant. You should see how the relevant items lead progressively to a harder attitude. If most of the respondents you study, or the top 27% of them, hold fast to this hierarchical pattern, you have captured a very one-dimensional aspect of your construct. In addition, you can calculate something called the coefficient of reproducibility, which is simply 1 minus the number of breaks with the hardened response pattern divided by the total number of responses. Guttman scaling is very appealing, but it is not very well received by the scientific community. A variation is the Bogardus social distance scale, which also has properties of the semantic differential (Leedy, 2013). The semantic differential A technique developed in the 1950s to deal with emotions and feelings. It is based on the idea that people think dichotomously or in terms of polar opposites such as good-bad, right-wrong, strongweak, etc. There are many varieties of the technique, the most popular one asking respondents to place their own slash mark along a line between adjectives. Let us take the example of a scale intending to measure feelings toward rap music as a cause of crime:
© Regenesys Business School
99
On each line below and between each extreme, place a cross closest to your first impression: How do you feel about the master’s Economics course? Too difficult ----------------------------------------------------------------------------Too easy Relevant content----------------------------------------------------------------------Irrelevant content Focused---------------------------------------------------------------------------------No focus Value for money-----------------------------------------------------------------------No value for money Modern ----------------------------------------------------------------------------------Traditional
You can use the semantic differential scale with any adjectives you choose to use in the dissertation. Your objective is to collect response patterns that you can analyse for scaling purposes. To quantify a semantic differential, all you do is overlay a Likert-type scale on top of it, and assume the endpoints are extremes such as “very bad” or “very good”. You can also use a ruler or a graph paper to obtain a precise numerical measurement.
3.10.7
Measurement Scales
There are various types of measurement scales to use in your research. These are based on the following two categories: comparative and noncomparative measurement scales. In comparative scaling, the respondent is asked to compare one item with another. With noncomparative scaling the respondent is required only to evaluate an item. The respondent’s evaluation is independent of the items which you will be studying. See Table 13. TABLE 13: TYPES OF SCALES
Types of Scales
Description
Comparative scales
You can use a paired comparison scale to determine which two items are preferred as a pair. In paired comparisons every factor has to be paired with every other factor in turn. However, only one pair is put to the respondent at any one time.
Noncomparative scales
You can use continuous rating scales where you will ask the respondents to give a rating by placing a mark at the appropriate position on a continuous line. If you are using interviews, it is recommended that you can draw the scale on card and show to the respondent during the interview. The respondent's score is determined either by dividing the line into as many categories as desired and assigning the respondent a score based on the category into which his/her mark falls, or by measuring the distance, in millimetres, from either end of the scale.
A line-marking scale
Can be used to measure perceived similarity or difference between items, whereas with itemised rating scales respondents are provided with a scale having numbers or descriptions associated with each item being studied and are asked to select one of the limited number of terms that best describes the item.
© Regenesys Business School
100
Types of Scales
Description
A semantic scale
Uses words rather than numbers. This is used if you want to ask your respondents to describe their feelings about the item being studied. You must use antonyms at the end points of the scale; these are termed semantic differential scales.
A Likert scale
Is known as a summated instrument scale. This implies that the items making up a Likert scale will be added up in total and used to create a total score. The Likert scale is a composite of itemised scales. Typically, each scale item has five categories; you may also use a 7-point scale, with scale values ranging from -2 to +2 with 0 as neutral response. This explanation may be clearer from the example below:
The e-learning books add value to my MBA studies The student portal is simple to use The facilitators are keen to assist with student questions
3.10.8
Strongly Agree 1
Agree
Neither
Disagree
2
3
4
Strongly Disagree 5
1
2
3
4
5
1
2
3
4
5
Key Points
In this section we discussed data collection. Note that: • • •
Surveys are commonly used in business and government when research is conducted; An interview typically occurs whenever a researcher and respondent are face-to-face or communicating via some technology like telephone or computer; There are various types of research variables, including: o o o o o o
• •
Independent variables; Dependent variables; Intervening variables; Moderating variables; Control variables; and Extraneous variables.
There are two main categories of data, namely qualitative and quantitative data; There are different types of scales that can be used in research, including: o o o o
Nominal scales; Ordinal scales; Interval scales; and Ratio scales.
© Regenesys Business School
101
3.11
DATA ANALYSIS
Timeframe
Learning outcomes
Prescribed textbook
Prescribed reading
Prescribed multimedia
Minimum of 40 hours •
Demonstrate an understanding of the research process and its application to resolve business problems;
•
Review, apply, and critique various business research methods;
•
Demonstrate the ability to apply statistical and other data analysis techniques to interpret research findings and solve business problems; and
•
Develop and present a professional research proposal for either a technical project or a single research or mini-dissertation.
•
Saunders et al, M., Lewis, P. and Thornhill, A., 2019, Research Methods for Business Students, 8th ed., Cape Town: Pearson Education.
•
Borgatti, S. 2017, ‘Axial coding’, http://www.analytictech.com/mb870/introtogt.htm (accessed 27 March 2023).
•
Gläser, J. and Laudel, G. 2013, ‘Life with and without coding: Two methods for early-stage data analysis in qualitative research aiming at causal explanations’, http://www.qualitativeresearch.net/index.php/fqs/article/view/1886/3528 (accessed 27 March 2023).
•
Grounded theory, 2017, http://www.qualres.org/HomeGrou-3589.html (accessed 27 March 2023).
•
Stemler, S. 2001, ‘An overview of content analysis’, https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1100&context=pare (accessed 27 March 2023).
•
Cranfield School of Management, 2012, ‘Management research: delivering business results’, [video clip], http://www.youtube.com/watch?v=R7XuQxukmb0 (accessed 27 March 2023).
•
Davis, J. 2010, 'Chi-squared test', [video clip], http://www.youtube.com/watch?v=UPawNLQOv-8 (accessed 27 March 2023).
•
Gibbs, G.R. 2010, ‘Approaches to open coding’, https://www.youtube.com/watch?v=Dfd_U-24egg (accessed 27 Msrch 2023).
•
Gibbs, G.R. 2010, ‘Grounded theory – open coding Part 1’, https://www.youtube.com/watch?v=gn7Pr8M_Gu8 (accessed 27 March 2023).
•
Gibbs, G.R. 2010, ‘Grounded theory – open coding Part 2’, https://www.youtube.com/watch?v=vi5B7Zo0_OE (accessed 27 March 2023).
•
Gibbs, G.R. 2010, ‘Grounded theory – open coding Part 3’, https://www.youtube.com/watch?v=n-EomYWkxcA (accessed 27 March 2023).
•
Gibbs, G.R. 2010, ‘Grounded theory – open coding Part 4’, https://www.youtube.com/watch?v=AwmDRh5l7ZE (accessed 27 March 2023).
© Regenesys Business School
102
Section overview
•
Gibbs, G.R. 2010, ‘Coding part 5: The code list or code hierarchy’, https://www.youtube.com/watch?v=DVpkuTdkZvA (accessed 27 March 2023).
•
Gibbs, G.R. 2010, ‘Grounded theory – axial coding’, https://www.youtube.com/watch?v=s65aH6So_zY (accessed 27 March 2023).
The most critical part of research is to be able to test the hypothesis and explore relationships within the data. This is then linked back to the research questions and problem statement in the dissertation. Once the testing of hypotheses is complete, you use inferential statistics to infer from the sample data what the population response would be. In essence, the sample represents the population. You can also apply inferential statistics when making probability judgments. You will mostly use inferential statistics to make inferences from your dissertation’s data to more general conditions; whereas you will use descriptive statistics simply to describe what is going on in your data. It is essential to fully understand these key differences.
3.11.1
Introduction
Before beginning to analyse any data set, the following should be done: • • • • •
Ensure that there is no missing or incorrect data; Organise the data to ensure that it is logical, correctly captured and cross-tabulation is possible; Endure that the data is linked to the correct questions; Ensure that the data is collected as nominal, ordinal, interval or ratio data; and Validate the accuracy and reliability of the data captured compared to the questionnaires.
3.11.2
Descriptive Statistical Analysis
For data analysis, you will begin with descriptive statistics. As the name implies, you will describe what you have found from the data. This will be in the form of graphs and descriptive statistical analysis techniques such as, the mean (average), mode (most common) and the median (middle of the data). These are all used to describe the basic measures of central tendency. The other measures used in descriptive statistical analysis are measures of dispersion. These measures show you the distance of the data from the mean (average). The above tell us about data variation. Here we typically calculate the range (highest to lowest values), the standard deviation from the average outwards on either side. These measures of central tendency and dispersion provide simple, yet powerful summaries about the sample and the measures. Together with simple graphic analysis, they form the basis of virtually every quantitative analysis of data. With descriptive statistics you are simply describing what is, what the data shows. This cannot be used to make inferences on the population or relationships between various variables, as there has been no hypothesis testing done. This must be done by using inferential statistical analysis.
© Regenesys Business School
103
The most critical part of research is to be able to test the hypothesis and explore relationships among variables. This is then be linked back to the research questions and problem statement in the dissertation. Once the testing of hypotheses is completed you will use inferential statistics to try to infer from the sample data what the population response would be. In essence, the sample represents the population. You can also apply inferential statistics when making probability judgments. You will mostly use inferential statistics to make inferences from your research data to more general conditions; whereas you will use descriptive statistics simply to describe what the data reveals.
Mean The mean, or more precisely the arithmetic mean, is calculated as the arithmetic average of a group of numbers (or data set) and is shown using bar symbol. So the mean of the variable is , pronounced “x-bar”. It is calculated by adding up all of the values in a data set and dividing by the number of values in that data set:
𝑥=
Σ𝑥 𝑛
For example, take the following set of data: {1,2,3,4,5}. The mean of this data would be:
𝑥=
Σ𝑥 1 + 2 + 3 + 4 + 5 15 = = =3 𝑛 5 5
Median This is the middle value in a set (or array) of data. The essential thing to remember when calculating this is that you need to sort the data from the lowest to highest value. This implies that the median is the number in the centre of a data set that has been ordered sequentially.
For example, let's look at this data set: {10, 14, 86, 2, 68, 99, 1}. What is its median? • • •
First, you will need to sort the data set sequentially: {1, 2, 10, 14, 68, 85, 99} Note that this is an odd number. Next, you need to determine the total number of points in the data set (in this case, 7.) Finally, you will need to determine the central position of or data set (in this case, the 4th position), and the number in the central position is our median – {1, 2, 10, 14, 68, 85, 99}, making 14 our median.
© Regenesys Business School
104
Another simple way to determine the central position or positions for any ordered set is to take the total number of points, add 1, and then divide by 2. If the number you get is a whole number, then that is the central position. If the number you get is a fraction, take the two whole numbers on either side. Because our data set had an odd number of points, determining the central position was easy – it will have the same number of points before it as after it. But what if our data set has an even number of points? Let us take the same data set, but add a new number to it: {1,2,10,14,68,85,99,100} What is the median of this set? When you have an even number of points, you must determine the two central positions of the data set. (See side box for instructions.) So for a set of 8 numbers, we get (8 + 1) /2 = 9 /2 = 4 1/2, which has 4 and 5 on either side. Looking at our data set, we see that the 4th and 5th numbers are 14 and 68. From there, we return to our trusty friend the mean to determine the median. (14 + 68) /2 = 82 /2 = 41. To find the median of 2, 4, 6, 8 => firstly we must count the numbers to determine its odd or even as we see it is even so we can write: M=4+6/2=10/2=5 5 is the median of above sequential numbers.
Mode The mode is the single number or score that will occur the most times in the data range. Leedy (2013) recommends researchers take cognisance of this when identifying trends in research data. An example of this would be seen in past exams that you have completed. How many times have you looked for the questions in the past exam papers, which keep coming up? You are in fact looking for the mode. The mode is the most common or most frequent value in a data set. Example: The mode of the following data set (1, 2, 5, 5, 6, 3) is 5 since it appears twice. This is the most common value of the data set. Data sets having one mode are said to be unimodal, with two are said to be bimodal and with more than two are said to be multimodal. An example of a unimodal dataset is {1, 2, 3, 4, 4, 4, 5, 6, 7, 8, 8, and 9}. The mode for this data set is 4. An example of a bimodal data set is {1, 2, 2, 3, and 3}. This is because both 2 and 3 are modes. Please note: If all points in a data set occur with equal frequency, it is equally accurate to describe the data set as having many modes or no mode.
Range The range of a sample (set of data) is simply the maximum possible difference in the data, ie the difference between the maximum and the minimum values. A more exact term for it is “range width” and is usually denoted by the letter R or W. The two individual values (the max. and min.) are called the “range limits”. Often these terms are confused and students should be careful to use the correct terminology.
© Regenesys Business School
105
For example, in a sample with values 2 3 5 7 8 11 12, the range is 10 and the range limits are 2 and 12.
The range is the simplest and most easily understood measure of the dispersion (spread) of a set of data, and though it is very widely used in everyday life, it is too rough for serious statistical work. It is not a robust measure, because clearly the chance of finding the maximum and minimum values in a population depends greatly on the size of the sample we choose to take from it and so its value is likely to vary widely from one sample to another. Furthermore, it is not a satisfactory descriptor of the data because it depends on only two items in the sample and overlooks all the rest.
Variance and standard deviation When describing data it is helpful (and in some cases necessary) to determine the spread of the data around the mean. One way of measuring this spread is by calculating the variance or the standard deviation of the data. In describing a complete population, the data represents all the elements of the population. As a measure of the spread in the population one wants to know a measure of the possible distances between the data and the population mean. There are several options to do so. One is to measure the average absolute value of the deviations. Another, called the variance, measures the average square of these deviations. A clear distinction should be made between dealing with the population or with a sample. When dealing with the complete population the (population) variance is a constant, a parameter that helps to describe the population. When dealing with a sample from the population the (sample) variance is actually a random variable, whose value differs from sample to sample. Its value is only of interest as an estimate for the population variance.
Population variance and standard deviation Let the population consist of the N elements x1,...,xN. The (population) mean is:
"
1 𝜇 = ( 𝑥! 𝑁 !#$
The (population) variance σ2 is the average of the squared deviations from the mean or (xi - μ)2 — the square of the value's distance from the distribution’s mean.
"
1 𝜎 % = ((𝑥! − 𝜇)% 𝑁 !#$
© Regenesys Business School
106
Because of the squaring the variance is not directly comparable with the mean and the data themselves. The square root of the variance is called the standard deviation σ. Note that σ is the root mean squared of differences between the data points and the average.
Sample variance and standard deviation Let the sample consist of n elements x1,..., xn, taken from the population. The (sample) mean is: "
1 𝑥̅ = & 𝑥! 𝑛 !#$
The sample mean serves as an estimate for the population mean μ. The (sample) variance s2 is a kind of average of the squared deviations from the (sample) mean: "
1 𝑠% = &(𝑥! − 𝑥̅ )% 𝑛−1 !#$
For the sample we also take the square root to obtain the (sample) standard deviations. A common question at this point is: “Why do we square the numerator?” One answer is: to get rid of the negative signs. Numbers are going to fall above and below the mean and, since the variance is looking for distance, it would be counterproductive if those distances factored each other out. Example When rolling a fair dice, the population consists of the 6 possible outcomes 1 to 6. In this example a sample of 1 000 was used for the purposes of illustrating the calculations. Saunders et al (2013) recommends a sample of at least 30 should be used by the researcher to gain some form of representation of the sample to the population. The population mean is: 1 𝜇 = (1 + 2 + 3 + 4 + 5 + 6) = 3.5 6 and the population variance: "
1 1 35 𝜎 = .(𝑖 − 3.5)! = (6.25 + 2.25 + 0.25 + 0.25 + 2.25 + 6.25) = ≈2 6 6 12 !
#
%$The population standard deviation is: 35 𝜎 = ( ≈ 1.708 12
Notice how this standard deviation is somewhere in between the possible deviations. © Regenesys Business School
107
Descriptive Statistical Analysis Type in some data in Microsoft Excel then follow these instructions to do descriptive statistical analysis on the data. Enable the Analysis ToolPak The Data Analysis ToolPak is not installed with the standard Excel setup. Look in the Tools menu. If you do not have a Data Analysis item, you will need to install the Data Analysis Tools. Search Help for “Data Analysis Tools” for instructions. Missing values A blank cell is the only way for Excel to deal with missing data. If you have any other missing value codes, you must change them to blanks. Data arrangement Different analyses require the data to be arranged in various ways. If you plan on a variety of different tests, there may not be a single arrangement that will work. You will probably need to rearrange the data several ways to get everything you need. Dialogue boxes Choose Tools/Data Analysis, and select the kind of analysis you want to do. The typical dialogue box will have the following items: Input range Type the upper left and lower right corner cells, eg A1:B100. You can only choose adjacent rows and columns. Unless there is a checkbox for grouping data by rows or columns (and there usually is not), all the data is considered as one glop. Labels There is sometimes a box you can check off to indicate that the first row of your sheet contains labels. If you have labels in the first row, check this box, and your output MAY be labelled with your label. Then again, it may not. Output location New Sheet is the default. Or, type in the cell address of the upper left corner of where you want to place the output in the current sheet. New Worksheet is another option, which I have not tried. Ramifications of this choice are discussed below.
© Regenesys Business School
108
Other items, depending on the analysis. Output location The output from each analysis can go to a new sheet within your current Excel file (this is the default), or you can place it within the current sheet by specifying the upper left corner cell where you want it placed. Either way is a bit of a nuisance. If each output is in a new sheet, you end up with lots of sheets, each with a small bit of output. If you place them in the current sheet, you need to place them appropriately; leave room for adding comments and labels; changes you need to make to format one output properly may affect another output adversely. Example Output from Descriptive statistics has a column of labels such as standard deviation, Standard Error, etc. You will want to make this column wide in order to be able to read the labels. But if a simple Frequency output is right underneath, then the column displaying the values being counted, which may just contain small integers, will also be wide.
Application exercise using Microsoft Excel The following exercise will guide you through a practical example that will begin with data capturing and then take you through the measures of central tendency (mean, median and mode), which will tell you how close the values are to the centre of the data. The ideal situation is to have the mean, median and mode to be the same values. Once this has been completed the measures of dispersion (range, variance and standard deviation). These measures of dispersion measure the spread of the data around the mean. The greater the dispersion, the more variation there will be around the average (also referred to as the mean). The ideal situation would be where there in on dispersion around the average.
© Regenesys Business School
109
The calculations will be supplemented with the appropriate graphs to create a more visual perspective on the data.
Application example: Number of Students Randomly Sampled in Each Area Doing the Same Exam 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Master's Finance Exam Results – Area 1
Master's Finance Exam Results – Area 2
57 50 36 47 80 81 72 35 41 81 24 37 91 70 52 46 93 50 66 71 85 95 50 82 12 97
38 34 69 83 66 88 90 46 52 70 90 18 88 44 11 20 63 83 46 50 60 91 56 57 95 55
© Regenesys Business School
110
To do the descriptive statistical analysis on the master's finance exam results for Area 1, do the following in Excel: For Area 1: 1. Go to “Excel Options”, click on: “Add-Ins”, “Analysis Tool pack” to add in the Excel Data Analysis Tool pack. 2. Click on: “Data” in the main menu, “Data Analysis”, “Descriptive Statistics”, “Input Range”, Select all the data in the “Master's Finance Exam Results – Area 1” column, Select “Output Options”, “Grouped by Column” and “New Worksheet Ply”. Select the four check boxes below “Summary Statistics”, “Confidence interval for mean”, “Kth Largest” and “Kth Smallest”. Click “OK”. The table below will appear in the new worksheet Area 1 Mean
59.31428571
Range
86
Standard Error
4.252882646
Minimum
11
Median
54
Maximum
97
Mode
50
Sum
2076
Standard Deviation
25.16039304
Count
35
Sample Variance
633.0453782
Largest (1)
97
Kurtosis
-1.08234994
Smallest (1)
11
Skewness
-0.131371503
Confidence Level (95.0%)
8.642897358
For Area 2: 1. Go to “Excel Options”, click on: “Add-Ins”, “Analysis Tool pack” to add in the Excel Data Analysis Tool pack. 2. Click on: “Data” in the main menu, “Data Analysis”, “Descriptive Statistics”, “Input Range”, Select all the data in the “Master's Finance Exam Results – Area 2” column, Select “Output Options”, “Grouped by Column” and “New Worksheet Ply”. Select the four check boxes below “Summary Statistics”, “Confidence interval for mean”, “Kth Largest” and “Kth Smallest”. Click “OK”.
© Regenesys Business School
111
Area 2 Mean
59.28571429
Range
87
Standard Error
4.08328658
Minimum
11
Median
60
Maximum
98
Mode
60
Sum
2075
Standard Deviation
24.15704918
Count
35
Sample Variance
583.5630252
Largest (1)
98
Kurtosis
-0.781417493
Smallest (1)
11
Skewness
-0.158179212
Confidence Level (95.0%)
8.298236685
The two areas can now be combined into one table to be able to make comparisons between the two areas sampled.
Descriptive Statistical Measures Mean
Area 1
Area 2 59.31
59.29
4.25
4.08
Median
54.00
60.00
Mode
50.00
60.00
Standard Deviation
25.16
24.16
633.05
583.56
Kurtosis
-1.08
-0.78
Skewness
-0.13
-0.16
Range
86.00
87.00
Minimum
11.00
11.00
Maximum
97.00
98.00
2076.00
2075.00
Count
35.00
35.00
Largest (1)
97.00
98.00
Smallest (1)
11.00
11.00
8.64
8.30
Standard Error
Sample Variance
Sum
Confidence Level (95.0%)
© Regenesys Business School
112
Interpretation of descriptive statistical calculations To begin discussion of the above results, it is advisable that you begin with the measures of central tendency (indicated in the above table as bold and blue) as this will indicate the tendency of the data to come towards the centre. The mean, median and mode are now compared. In analysing Area 1 it is evident that the mean, median and mode are not the same values. This implies that there is a level of skewness within the data. According to Leedy (2013), skewness refers to the situation in which the average, the mode and median are not all the same, and this moves the data towards the mode and results in skewness of the data. In the example shown above, you would have noticed the following: the mean (59.31%) is above the median (54%) and also above the mode (50%). The median is a good point to compare the mean and mode to, as it is the middle of the data. The kurtosis is simply the height of the distribution and will vary according to the sample size taken. A negative value indicates a flatter distribution of the data. This will be demonstrated and explained in more detail later on in this course. The mode indicates the most common exam result was 50%, which is just a pass mark. The mean was 59.31%, which is 9.93% greater that the mode. This indicates that most of the people just passed, but the mean was higher.
Evaluate the Average For Area 1: The average (ie mean) (59.31%) is above the median (54%) and also above the mode (50%). Critically evaluate the average (ie mean) as a reliable measure to use in research. What could cause the differences between the mean, median and the mode? And For Area 2: The mean (59.29%) is less than the median (60%) and also above the mode (60%). Why do you think the mean is less than the median and the mode?
In order to answer the above questions, you should look at the measures of dispersion (indicated in the above table in italics and yellow). The range indicated the difference between the highest and lowest exam marks. The standard deviation shows the distance from either side of the mean. This is a more acceptable method to use than the range. The confidence interval is usually set at 95%. This will show you the value on to add-on, and subtract from either side of the mean, which will accommodate 95% of all the data values. To assist in the interpretation of descriptive statistical analysis you can apply graphical analysis. This will be demonstrated next by using the data above to create a grouped frequency distribution, commonly known as a histogram.
© Regenesys Business School
113
In Excel, create a Bin Range to group the data. The following guidelines can be followed to set the size of the Bin Range, also called classes, can be followed: There should be between 5 and 20 classes, and the class width should be an odd number. This will guarantee that the class midpoints are integers instead of decimals. The classes must be mutually exclusive; l inclusive or exhaustive; continuous and must have no gaps in a frequency distribution. In data below, we will use a Bin Range of 10%. The marks will be grouped into Bins (classes) of: • • • • • • • • •
0% to 10% 11% to 20% 21% to 30% 41% to 50% 51% to 60% 61% to 70% 71% to 80% 81 to 90% 91 to 100%
© Regenesys Business School
114
Number of Students 1
Master's Finance Exam Results Area 1 57
Area 2 38
Bin Range 0
2
50
34
10
3
36
69
20
4
47
83
30
5 6
80 81
66 88
40 50
7
72
90
60
8
35
46
70
9 10
41 81
52 70
80 90
11
24
90
100
12
37
18
13
91
88
14 15
70 52
44 11
16
46
20
17
93
63
18
50
83
19 20
66 94
46 60
21
71
50
22
85
60
23
95
91
24 25
50 82
56 57
26
12
95
27
97
55
28
11
60
29 30
82 29
53 19
31
50
98
32
91
90
33
34
60
34
30
38
35
54
34
© Regenesys Business School
115
In Excel, create the histogram: The histogram analysis tool calculates individual and cumulative frequencies for a cell range of data and data bins. This tool generates data for the number of occurrences of a value in a data set. •
In Excel, go to: Data, Data Analysis, and Histogram. o Select the Data for “Area 1” in the Input Range; o Select the “Bin Range”; o Select “New Worksheet Ply”; and o Select “Chart Output”.
You will have the following histogram for Area 1 displayed on a new worksheet. Bin 0 10 20 30 40 50 60 70 80 90 100
Frequency 0 0 2 3 4 7 3 2 3 5 6
FIGURE 5: HISTOGRAM – AREA 1
8 7 6 5 4 3 2 1 0 More
100
90
80
70
60
50
40
30
20
10
Frequency
0
Frequency
Histogram
Bin
© Regenesys Business School
116
The graph should have a bell shape curve to be considered as a normal distribution curve. This is not the case in the above graph and can be seen in the differences between the mean, median and mode. The spread of the graph is shown by the range, standard deviation, variation (which is the standard deviation squared) as, well as, the confidence level. The larger these values are the more the width of the graph will become. In Excel, create the histogram: •
In Excel, go to: Data, Data Analysis, and Histogram. o Select the Data for “Area 2” in the Input Range; o Select the “Bin Range”; o Select “New Worksheet Ply”; and o Select “Chart Output”.
You will have the following histogram for Area 2 displayed on a new worksheet. Bin 0 10 20 30 40 50 60 70 80 90 100
Frequency 0 0 4 0 2 4 11 6 1 5 2 FIGURE 6: HISTOGRAM – AREA 2
Histogram 12
8 6 4
Frequency
2 More
100
90
80
70
60
50
40
30
20
10
0 0
Frequency
10
Bin
© Regenesys Business School
117
The graph should have a bell-shape curve to be considered a normal distribution curve. This is not the case on the above graph and can be seen in the differences between the mean, median and mode. The spread of the graph is shown by the range, standard deviation, variation (which is the standard deviation squared), and confidence level. The larger these values are, the greater the width of the graph will become.
3.11.3
Inferential Statistical Analysis – Hypothesis Tests
This section will focus on some of the more widely used inferential statistical methods, especially for testing hypotheses. For your research you may need to be able to conduct statistical tests to determine whether the hypothesis is to be accepted or rejected based on the sample of the data selected. If you chose to do quantitative research, you will need to apply the two common approaches to inferential statistical analysis, namely, significance testing and hypothesis testing. Hypothesis testing will require you to research and analyse the relevant evidence for a particular hypothesis to determine if it is accepted or rejected. From your research data you will most likely want to know something about the average (or mean), or about the variability (as measured by variance or standard deviation). This would have been calculated and described when doing the descriptive statistical analysis. In order to do the statistical tests, you need to begin by making certain assumptions, which is called the null hypothesis, and thereafter determine whether the data you have collected, described and observed is likely or unlikely to occur, given that assumption. To illustrate this concept your research hypothesis may be “determine if there is any difference between the Regenesys master's students writing the same finance exam, at the same exam times, in Area 1 and Area 2”. To be able to approximate a normal distribution with the intention to infer the results on the population (all Regenesys master’s finance students for that semester), you select and measure 30 women and 30 men. We assume the null hypothesis: there is no difference between the average marks of men compared to that of women. We can then test this hypothesis using the relevant statistical test. To illustrate this, we will employ the Anova technique, which is used to test a hypothesis concerning the differences between the means of the two or more groups. In the example the sample was randomly selected and represents the population.
3.11.4
Hypothesis Test Using the Anova
Step one: State the null and alternate hypothesis The hypotheses are stated in such a way that they are mutually exclusive. That is, if one is true, the other must be false. Ho (null hypothesis): There is no significant difference between Area 1 and Area 2 in terms of the master’s finance exam results.
© Regenesys Business School
118
Ha (alternate hypothesis): There is a significant difference between Area 1 and Area 2 in terms of the master’s finance exam results.
Step two: Formulate an analysis plan The analysis plan describes how to use sample data to evaluate the null hypothesis. The evaluation often focuses on a single test statistic. This hypothesis will be tested by using the anova (analysis of variances), which is a one-way analysis of variance, to test the difference between the two groups’ means at one time by using the variances to test the differences. The test will be done at a 95% level of confidence and, therefore, a 5% level of significance (100%– 95%). The decision rule will be as follows: If p