How To Understand And Understand The Purpose Of Research

Transcription

1 MASTER OF BUSINESS ADMINISTRATION Research Methodology Contact details: Regenesys Business School Tel: +27 (11) Fax: +27 (11)

2 Version Control: 15_e_f Date of Publication: July 2014 Publisher: Regenesys Management Place of Publication: Sandton Document Change History Date Version Initials Description of Change 23 December DL New Masters Research Study Guide 29 December KG Reviewed 2 January PL Reviewed 27 January _f FVS Formatting 30 January _e CJ Proofreading 31 January _e_f DL Incorporating suggested editing changes and addressing editing queries 4 February _e_f FVS Final formatting 5 February KG Reviewed 5 February _e_f FVS Addressing comments from SME 5 February _e_ LS Edited and proofread 6 February _e_f FVS Formatting 17 June _e_f TS Updated definitions 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, 2014 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.

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4 CONTENTS 1 WELCOME TO REGENESYS INTRODUCTION TEACHING AND LEARNING METHODOLOGY ALIGNING ORGANISATIONAL, TEAM AND INDIVIDUAL OBJECTIVES ICONS USED IN THIS STUDY GUIDE STUDY MATERIAL FOR THE MODULE RECOMMENDED RESOURCES RECOMMENDED READING RECOMMENDED ARTICLES RECOMMENDED MULTIMEDIA ADDITIONAL SOURCES TO CONSULT LEARNING OUTCOMES CONTENT SCOPE AND LEARNING GUIDANCE INTRODUCTION TO RESEARCH METHODS AN OVERVIEW OF RESEARCH THE PURPOSE OF RESEARCH THE CHARACTERISTICS OF RESEARCH TYPES OF RESEARCH RESEARCH ETHICS INTRODUCTION ETHICAL CONSIDERATIONS FOR RESEARCHERS THE RESEARCH PROBLEM, OBJECTIVES AND RATIONALE INTRODUCTION THE RESEARCH PROBLEM IDENTIFYING A RESEARCH PROBLEM GENERATING RESEARCH IDEAS IDENTIFYING AN ORGANISATIONAL ISSUE REFINING A PROBLEM STATEMENT CHARACTERISTICS OF A RESEARCH PROBLEM RESEARCH OBJECTIVES THE RESEARCH QUESTIONS THE RATIONALE FOR THE RESEARCH FORMULATING AND CLARIFYING THE RESEARCH TOPIC FORMULATING AND CLARIFYING THE RESEARCH TITLE CONDUCTING A CRITICAL LITERATURE REVIEW INTRODUCTION WHAT IS A LITERATURE REVIEW? THE PURPOSE OF THE LITERATURE REVIEW CRITERIA FOR A LITERATURE REVIEW STEPS IN THE LITERATURE REVIEW WRITING THE LITERATURE REVIEW THEORETICAL FRAMEWORK THE RESEARCH PHILOSOPHY AND APPROACH THE RESEARCH PHILOSOPHY DEDUCTIVE VERSUS INDUCTIVE RESEARCH THE QUANTITATIVE VERSUS QUALITATIVE RESEARCH APPROACH FORMULATING THE RESEARCH DESIGN THE RESEARCH DESIGN INTRODUCTION TO RESEARCH DESIGNS THE TYPES OF RESEARCH STRATEGIES THE RESEARCH PROCESS SAMPLING DESIGN... 72

5 7.8.1 INTRODUCTION TO SAMPLING DESIGNS POPULATION VERSUS A SAMPLE SAMPLING CAUSES OF SAMPLING ERROR SAMPLING PROCEDURE TYPES OF NON-PROBABILITY SAMPLES TYPES OF PROBABILITY OR RANDOM SAMPLES COMBINATION OR MIXED PURPOSEFUL SAMPLING CONCLUSION PLANNING YOUR DATA COLLECTION DESIGN DATA COLLECTION METHODS VARIABLES IN THE RESEARCH PROBLEM QUESTIONNAIRES DATA COLLECTION TECHNIQUES CONSTRUCTING QUESTIONNAIRES SCALE DEVELOPMENT MEASUREMENT SCALES DATA ANALYSIS DESCRIPTIVE STATISTICAL ANALYSIS INFERENTIAL STATISTICAL ANALYSIS HYPOTHESIS TESTS HYPOTHESIS TEST USING THE ANOVA INFERENTIAL STATISTICAL ANALYSIS SIGNIFICANCE TESTS ANALYSING QUALITATIVE DATA REFERENCES APPENDIX 1: RESEARCH PROPOSAL AND MINI-DISSERTATION GUIDELINES GLOSSARY OF TERMS

6 List of Tables TABLE 1: PURPOSE OF RESEARCH TABLE 2: RESEARCH CONCEPTS AND TERMINOLOGY TABLE 3: REAL-WORLD PROBLEMS TABLE 4: POPULAR, TRADE AND SCHOLARLY PUBLICATIONS TABLE 5: DETERMINANT OF QUALITY OF INFORMATION SOURCES TABLE 6: THE DIFFERENCES BETWEEN THE QUALITATIVE AND QUANTITATIVE RESEARCH APPROACH TABLE 7: DIFFERENCES BETWEEN THE RESEARCH DESIGN AND THE RESEARCH METHODOLOGY TABLE 8: TERMS COMMONLY USED IN THE RESEARCH PROCESS TABLE 9: TERMINOLOGY TABLE 10: EXAMPLE OF NOMINAL SCALE TABLE 11: EXAMPLE OF NOMINAL SCALE TABLE 12: TYPES OF SCALES TABLE 13: T-TESTS TABLE 14: T-TEST TWO-SAMPLE ASSUMING EQUAL VARIANCE TABLE 15: SUMMARY OF STATISTICAL TESTS TO EXAMINE RELATIONSHIPS BETWEEN VARIABLES 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

7 1 WELCOME TO REGENESYS Have a vision. Think big. Dream, persevere and your vision will become a reality. Awaken your potential knowing that everything you need is within you. Dr. Marko Saravanja At Regenesys, we assist individuals and organisations to achieve their personal and organisational goals, by enhancing their management and leadership potential. We approach education and development holistically, considering every interaction not only from an intellectual perspective but also in terms of emotion and spirituality. Our learning programmes are designed to transform and inspire your mind, heart and soul, and thus allow you to develop the positive values, attitudes and behaviours, which are required for success. Having educated over students based in highly reputable local and international corporations across over 100 countries since Regenesys' inception in 1998, we are now one of the fastest-growing and leading institutions of management and leadership development in the world. Regenesys ISO 9001:2008 accreditation bears testimony to our quality management systems meeting international standards. Regenesys is accredited with the Council on Higher Education. Our work is rooted in the realities of a rapidly changing world and we provide our clients with the knowledge, skills and values required for success in the 21 st century. At Regenesys, you will be treated with respect, care and professionalism. You will be taught by business experts, entrepreneurs and academics who are inspired by their passion for human development. You will be at a place where business and government leaders meet, network, share their experiences and knowledge, learn from each other, and develop business relationships. You will have access to a campus, in the heart of Sandton, with the tranquillity of a Zen garden, gym and meditation room. We encourage you to embark on a journey of personal development with Regenesys. We will help you to awaken your potential and to realise that everything you need to succeed is within you. We will be with you every step of the way. We will work hard with you and, at the end celebrate your success with you. Areas of Expertise Regenesys Business School 1

8 2 INTRODUCTION Welcome to the module on Research Methodology. This module will guide and assist you in understanding research philosophies, research methodology, research design, and conducting and analysing data to produce meaningful information. This will assist you and provide you with a sound basis for research, which can be, used both in your personal and work life. There are references to video clips, which are recommended to complete the Mini-dissertation and will assist in gaining more insight into this subject. The cases and exercises will reinforce the theoretical concepts and it is highly recommended that you do there exercises as you work through this study guide.the study guide consists of the main body of knowledge and is supported by two annexures which are specifically designed to allow you to gain insight into the marking and structure of a dissertations. 2.1 TEACHING AND LEARNING METHODOLOGY Regenesys uses an interactive teaching and learning methodology that encourages self-reflection and promotes independent and critical thinking. Key to the approach utilised is an understanding of adult learning principles, which recognise the maturity and experience of participants, and the way that adult students need to learn. At the core of this is the integration of new knowledge and skills into existing knowledge structures, as well as the importance of seeing the relevance of all learning via immediate application in the workplace. Practical exercises are used to create a simulated management experience to ensure that the conceptual knowledge and practical skills acquired can be directly applied within the work environment of the participants. The activities may include scenarios, case studies, self-reflection, problem solving and planning tasks. Training manuals are developed to cover all essential aspects of the training comprehensively, in a user-friendly and interactive format. Our facilitators have extensive experience in management education, training and development. Please read through this Study Guide carefully, as it will influence your understanding of the subject matter and the successful planning and completion of your studies. Regenesys Business School 2

9 2.2 ALIGNING ORGANISATIONAL, TEAM AND INDIVIDUAL OBJECTIVES This course will draw on a model developed by Regenesys Management, which demonstrates how the external environment, the levels of an organisation, the team and the components of an individual are interrelated in a dynamic and systemic way. The success of an individual depends on his/ her self-awareness, knowledge and ability to manage successfully these interdependent forces, stakeholders and processes. The degree of synergy and alignment between the goals and objectives of the organisation, the team and the individual determines the success or failure of an organisation. It is, therefore, imperative that each organisation ensures that team and individual goals and objectives are aligned with the organisation s strategies (vision, mission, goals and objectives, etc.); structure (organogram, decision-making structure, etc.); systems (HR, finance, communication, administration, information, etc.); culture (values, level of openness, democracy, caring, etc.). Hence, an effective work environment should be characterised by the alignment of organisational systems, strategies, structures and culture, and by people who operate synergistically. Regenesys Integrated Management Model Regenesys Business School 3

10 3 ICONS USED IN THIS STUDY GUIDE Icons are included in the study guide to enhance its usability. Certain icons are used to indicate different important aspects in the study guide to help you to use it more effectively as a reference guide in future. The icons in this study guide should be interpreted as follows: Definition The definitions provide an academic perspective on given terminology. They are used to give students a frame of reference from which to define a term using their own words. Examples The example icon is used to indicate an extra/ additional text that illustrates the content under discussion. These include templates, simple calculation, problem solution, etc. Video clip or presentation This icon indicates a URL link to a video clip or presentation on the subject matter for discussion. It is recommended that students follow the link and listen/ read the required sources. Interesting source to consult The source icon is used to indicate text sources, from the Internet or resource centre, which add to the content of the topic being discussed In a nutshell This icon indicates a summary of the content of a section in the workbook and to emphasise an important issue. Calculations This icon indicates mathematical or linguistic formulae and calculations. Self-reflection Students complete the action of selfreflection in their own time. It requires students to think further about an issue raised in class or in the learning materials. In certain instances, students may be required to add their views to their assignments. Tasks The task icon indicates work activities that contact students must complete during class time. These tasks will be discussed in class and reflected upon by students and facilitators. E-learning students can use these tasks simply to reinforce their knowledge. Note This icon indicates important information of which to take note. Regenesys Business School 4

11 4 STUDY MATERIAL FOR THE MODULE You have received material that includes the following: Study Guide Recommended reading Assignment These resources provide you with a starting point from which to study the contents of this module. In addition to these, other resources to assist you in completing this module will be provided online via the link to this module. Guidance on how to access the material is provided in the Academic Handbook that you received when you registered for this qualification. 5 RECOMMENDED RESOURCES A number of recommended and recommended resources have been identified to assist you in successfully completing this module. 5.1 RECOMMENDED READING The following textbook is highly recommended and must be used to complete the module: Saunders, M., Lewis, P. and Thornhill, A. 2013, Research Methods for Business Students, 6 th ed., Cape Town: Pearson Education. Please ensure you order, or download your textbook, before you start with the module. 5.2 RECOMMENDED ARTICLES Collins, J. and Hussey, R. 2003, Business Research: A Practical Guide for Undergraduate and Postgraduate Students', 2 nd edition, Palgrave Macmillan, Humphrey, C. 2008, Auditing research: A review across the disciplinary divide, Accounting, Auditing and Accountability Journal, (21) 2, Hyde, K. F. 2000, Recognising deductive process in qualitative research, Qualitative Market Research, (3) 2, Regenesys Business School 5

12 Jack, E. P. and Raturi, A. S. 2006, Lessons learned from methodological triangulation in management research, Management Research News, (29) 6, McNamara, C. 2008, General guidelines for conducting research interviews, (accessed 7 January 2009). Woodside, A.G. and Wilson, E. J. 2003, Case study research methods for theory building, Journals of Business and Industrial Marketing, (18) 6/7, Additional articles that may prompt discussions and further assist you in completing this course will be saved on Regenesys Online under the relevant course. Please visit the site regularly to access these additional sources. 5.3 RECOMMENDED MULTIMEDIA Cranfield SoM. 2012, 'Management research: Delivering business results', [video clip], (accessed 16 January 2014). DrSamFiala. 2012, 'Research ethics, [video clip], (accessed 16 January 2014). Meeng Uofu. 2012, 'How to write a problem statement (review for ME1010), [video clip], (accessed 16 January 2014). Massey University. 2010, The literature review, [video clip], (accessed 16 January 2014). UELRDBS. 2013, Postgraduate research planning workshop Research process and philosophy, [video clip], (accessed 16 January 2014). Ignousohs. 2011, 'Sampling issues in research studies, [video clip], (accessed 16 January 2014). Regenesys Business School 6

13 5.4 ADDITIONAL SOURCES TO CONSULT You are responsible for sourcing additional information that will assist you in completing this module successfully. Below is a list of some sources that you may consult to obtain additional information on the topics to be discussed in this module: Emerald: NetMasters: MindTools: Brunel Open Learning Archive: ProvenModels: 12manage.com: Alliance Online: The Free Management Library: The Charity Village: This is an online database containing journal articles that are relevant to your modules. Please refer to the attached Emerald manual to assist you to download required articles. Information on how to access Emerald is provided to you in your Academic Handbook. You will receive access to the database once you register as a student. This is one of several web addresses that provide a selection of Masters constructs and discussion. It is one of the better of these addresses. MindTools.com is a very useful source of ideas, constructs, management models, etc. with even more useful commentary and description. A Brunel University support-site that provides an easily accessible library of ideas, concepts, constructs techniques, tools, models, etc. ProvenModels' Digital Model Book presents digitalised management models categorised in a clear, consistent and standardised information structure to improve the usability and reusability of management literature. Management models are important generalisations of business situations when applied in context and are powerful tools for solving business issues. This is a website on which one can access numerous models as well as global comments on the models and principles. This could also serve as a place where you could voice your ideas and get feedback from all over the world. The Alliance for Non-profit Management's general introduction to strategic planning is built around 15 questions that cover just about all aspects in brief. (Click on Strategic Planning ) The Free Management Library can be used to improve your organisation, and for your own personal, professional and organisational development. This is by far the most comprehensive overview of all aspects of strategic planning covering all stages of the process. A series of twelve very short articles, by Ron Robinson, an independent Canadian consultant, appeared on Charity Village between November 2001 and October These articles are refreshing in that they do not advocate a one best way for all types of non-profit organisations. They discuss various way of approaching the strategic planning process. There are many more sites and articles available that can help you to successfully complete this module. You are encouraged to post the website addresses or URLs of any additional interesting sites that you come across on the Regenesys Learning Platform. In this way, you can assist other students to access the same wonderful information that you have discovered. A word of caution not all information available on the Internet is necessarily of a high academic standard. It is, therefore, recommended that you always compare information that you obtain with that contained in accredited sources such as articles that were published in accredited journals. Regenesys Business School 7

14 6 LEARNING OUTCOMES Upon completing this course, students should be able to: Critically explain research terminology, concepts and principles Evaluate and compare the various types of research philosophies Apply and critique various research methods Comply with ethical issues in business research Understand the relationship between information resources and the knowledge management process of a specific organisation Collect and analyse research data and demonstrate its value in business decision-making; Apply the research process in resolving a business problem Demonstrate the ability to apply advanced statistical and other scientific data analysis techniques Collect data by using the appropriate research methods, and Collate and analyse data by performing the relevant descriptive and inferential statistical analysis using appropriate tools and techniques Develop and present a professional research proposal Regenesys Business School 8

15 7 CONTENT SCOPE AND LEARNING GUIDANCE A number of topics will be covered to assist you in successfully achieving the learning outcomes of this module. It is important to study each of these sections to ensure that you expand your knowledge on the subject and are able to complete the required assessments. The sections that will be dealt with include: Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7 Section 8 Section 9 Section 10 Introduction to Research Methods Research Ethics The Research Problem, Objectives and Rationale Formulating and Clarifying the Research Topic Conducting a Critical Literature Review The Research Philosophy and Approach Formulating the Research Design Sampling Design Planning your Data Collection Design Data Analysis A more detailed framework of what is required for each of these topics follows under each section heading. A number of questions to initiate discussion and guide you towards better comprehension and greater insight are also provided. The timetable under each section heading provides guidance on the time to be spent to study each section. It is recommended that you follow the given timetable to ensure that you spend the appropriate amount of time on each section. Following the timetable will ensure that you have covered the required sections relevant to each assignment and have appropriate time to prepare for the examination. Regenesys Business School 9

16 7.1 INTRODUCTION TO RESEARCH METHODS Timeframe: Learning outcomes: Recommended reading: Recommended multimedia: Minimum of 4 hours Critically explain research terminology, concepts and principles Saunders, M., Lewis, P. and Thornhill, A., 2013, Research Methods for Business Students, 6 th ed., Cape Town: Pearson Education. Collins, J. and Hussey, R. 2003, Business Research: A Practical Guide for Undergraduate and Postgraduate Students', 2 nd ed., Palgrave Macmillan, Cranfield SoM. 2012, 'Management research: Delivering business results', [video clip], (accessed 16 January 2014). This section introduces you to the research concept and the research need. The characteristics of research are discussed to allow contextualisation of the research. This will allow you to understand the relevant research concepts and their definitions. Section overview: This section will cover the following: The research concept Conducting research Understanding the need for research as a core business driver to remain competitive How things (study objects) are defined i.e. to define the nature of the study object(s) Explaining why things (study objects) are the way they are and to explain the relationship between things (study objects) How to predict phenomena, such as human behaviour in the workplace, with the aim of using this information in future (e.g. for screening job applicants) The characteristics of research, and The purpose of research so as to apply it in a pragmatic and systematic manner to solve an organisational problem An Overview of Research To a certain extent, most of us have been exposed to the research process and we have often been research subjects, without actually realising it. We are often approached to participate in surveys, for example on our preferences or experiences with regard to services, holiday experiences and household products, or on our preferences with regard to magazines, newspapers 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 specific 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. Regenesys Business School 10

17 In this Research Methodology module, you will come across a number of frequently used research concepts, with which you should become familiar. We will start by describing the research-related concepts so that you have a clear understanding of them. To increase your understanding on research. revisit the following descriptions from time to time. The term research is derived from the French word recherché, which means to travel through or to survey. Research is defined as follows: A systematic investigation to establish facts or collect information on a subject. (Collins English Dictionary, 2004:1379) 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 the above 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 specific 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. As a researcher you will want to conduct research into your area of interest and this suggests that research involves enquiry or examination to find things out on what you want to study in your mini-dissertation (Ghauri and Grønhaug, 2010). 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 specifically 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) Regenesys Business School 11

18 These definitions are essential as they establish a common understanding of what research means to you as the researcher. Research students should, therefore, follow a systematic process to investigate a management-related problem in order to compile their mini-dissertation. According to Saunders et al. (2013), there are three main factors which a researcher needs to take cognisance of when conducting research. These are as follows: Firstly, Saunders views the practice of management as being largely 'eclectic' as it is influenced by other disciplines and a variety of sources, such as physical sciences, pure sciences, social sciences, economics, statistics and maths. Students must be able to work across spiritual, emotional, technical, cultural and functional boundaries. They need to draw on knowledge from all modules, inter-alia, Leadership, Emotional and Spiritual Intelligence, Human Resources, Financial Management, Marketing Management, etc. The dilemma for any researcher, who attempts to compile a mini-dissertation, is whether to examine management from the perspective of one discipline, or whether to adopt an inter-disciplinary approach. Secondly, a researcher will most likely conduct research within organisations, either public or private. Note that, as a researcher, you may be constrained to access the organisation you want to research, unless such an organisation can see some intrinsic, commercial or personal advantage to be derived from the research study. Research can become challenging as it involves issues such as confidentiality, ethics, moral issues and consent from the organisation. Thirdly, as a researcher you will need to appreciate that research will require both critical analysis and theory application to resolve the research problem. The researcher must be able to critically compare the various theories and models in the context of the research objectives. The final research report should add value to organisations and society The Purpose of Research The purpose of our research is to conduct a research study in a pragmatic and systematic manner to solve an organisational problem. Welman and Kruger (1999:19) identify the purpose of research as follows: Table 1: Purpose of Research Describing Explaining Predicting To describe how things (study objects) are i.e. to define the nature of the study object(s) To explain why things (study objects) are the way they are and explain the relationship between things (study objects) To predict phenomena, such as human behaviour in the workplace, with the aim of using this information in future (e.g. for screening job applicants) Regenesys Business School 12

19 7.1.3 The Characteristics of Research Although research may vary in complexity and duration, Leedy and Ormond (2003:2 3) argue that research typically has the following eight distinct characteristics: 1. Research originates with a question or a problem. Research will usually begin with a problem statement, such as: Organisation X lost 10% per annum over the last three years in the area of technical skills. The researcher needs to ensure that the real problem (root cause) is identified and correctly defined, and not the symptoms of the problem, as this will lead to incorrect research with meaningless results. 2. The research goal requires a clear articulation because research is time consuming and usually costly to conduct. A research goal that is not clearly defined, may lead to research findings that differ from what is required by the researcher. This may nullify the research study conducted. 3. Research follows a specific format. Leedy (2013:75) views the basic format of the research process as having the following steps: Step 1: The researcher asks a question to which there is no (currently) known solution. Step 2: Convert the research question into 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 relevant literature and relevant secondary data, which already exists and is relevant to this problem. Conduct a critical analysis of 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. Collate the data and synthesised it into a logical structure to analyse through the appropriate data analysis tools and techniques, such as, hypothesis testing. Interpret the data and link it back to the previous steps to ensure a logical research flow and link back to the research objectives. Compare the data analysis and information produced from the data with the research problem statement and the extent to which the hypothesis tests, validates or solves the problem. 4. Research usually divides the principal problem into more manageable sub-problems. This allows the researcher to manage the research by focusing on more manageable areas to research. Regenesys Business School 13

20 5. Research is guided by the specific research problem, question or hypothesis. The researcher must ensure a clear link between the research goal, objectives, problem statement, research questions and the hypothesis. 6. Research accepts certain critical assumptions and limitations, as well as delimitations to ensure that the parameters of the research scope are 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 the researcher to understand that his/her research may be influenced by the market, organisation, product or other relevant cycles Types of Research Before we discuss the types of research you need to read through some useful definitions to assist you in this section of the Study Guide. You should familiarise yourself with these definitions, as they will be referred to throughout the research module. Most of the commonly-used terms are explained in Table 2 below. Table 2: Research Concepts and Terminology Applied research Assumption Basic research Bias Concept Construct Correlate Deduction Dependant variable Descriptive statistics Empirical Epistemology Ethnography External validity Hypothesis Research conducted to find solutions for specific problems in real situations. A basic premise that we believe is true. Pure, theoretical or scientific research, with the main purpose of creating new knowledge. Prejudice or distortion. An abstract idea representing a real phenomenon. To create or build (verb). An association between two or more variables determined statistically. Going from the general to the specific. A variable that is influenced or changed. Mathematical techniques used to see underlying patterns of data. Based on observation and experience. A branch of philosophy dealing with the nature of knowledge. Comprehensively describing situations. The extent to which results can be generalised to other populations. A tentative, testable statement about the relationship between two or more variables. Regenesys Business School 14

21 Independent variable Internal validity Interval Literature review Methodology Norms Ontology Population Prediction Qualitative research Quantitative research Random assignment Rank Reliability Research Research design Sample Sampling error Theory Theoretical framework Validity Variable A variable that changes or influences the independent variable. The extent to which the study confirms the existence of a cause-effect relationship. The difference between two points on a scale. An exhaustive review of a wide range of existing literature on the research topic. The rules and procedures of research work. Customary behaviour created by society and organisations which are standardised and usually followed by members of society and organisations A branch of philosophy dealing with the nature of reality. The entire group of persons or objects and events of interest to the researcher. Statement that tells us of a future outcome. 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. A highly structured research approach that involves the quantification of concepts, in order to do measurements and conduct evaluations. Every subject has an equal chance of being in a group. Arrange in hierarchy. This means that if an identical investigation was repeated, similar research results would be obtained. The systematic process of collecting and analysing information, in order to increase understanding of the research subject(s) or phenomenon involved. A plan or a set of guidelines and instructions that enable the researcher to determine the research methodology and to address the research problem. A subset of a research population. Differences between population parameters and sampling statistics. A framework of ideas that provides an explanation of something. A collection of interrelated concepts, similar to a theory but not necessarily well worked out in its initial stages. A methodological requirement for research methods. A property that changes empirically. (Saunders et al., 2013) Saunders et al. (2013) distinguishes 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 specific problems in real situations. In other words, one could say that applied research is used to investigate and find solutions for real-world problems. Regenesys Business School 15

22 Saunders et al. (2013:11) argues that much of the business and management research projects can be placed on a continuum with two opposing types of research. These are basic versus applied research. Basic research focuses on expanding the existing body of knowledge in the academic literature, where the knowledge of business paradigms and constructs are supported by grounded theory. The mini-dissertation will most likely be applied in nature. This implies that the researcher will focus on identifying an organisational challenge, typically a problem within the organisation, in which the researcher will apply the research process to solve an organisational problem. This is a more pragmatic and action-oriented approach and Saunders et al. (2013:12) liken this to consulting. As mentioned earlier, the research is focused on developing graduates who can add value to both society and organisations. From the above it is clear that the researcher needs to possess 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 the situation. Task Questions Read through the case study below, 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, 6 th ed., Cape Town: Pearson Education, Case 1 Reporting Evidence from Business and Management Research 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. Regenesys Business School 16

23 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!) 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) think 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? Regenesys Business School 17

24 Before deciding on the research you want to undertake, you need to ask the following questions? 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) Watch the following short video clip: Cranfield SoM. 2012, 'Management research: Delivering business results', [video clip], (accessed 16 January 2014). Task Questions 1. Select a possible research topic and then answer the questions listed above to determine whether the topic warrants research. You need to provide a detailed motivation of why you think this topic warrants research. Once the task above has been completed, finalise the recap question below to clarify your research topic. Regenesys Business School 18

25 Recap Questions 1. Answer the following questions to clarify your research topic with the resources that are available to you: 1.1. Is your topic clear and easy to understand? 1.2. Is your topic focused and realistically designed? 1.3. Is there relevant data and information (secondary information is data and information which exists already)? 1.4. Is there appropriate data and information? 1.5. Is there accurate and reliable data and information? 1.6. Is there relevant and reputable data and information? 1.7. Is there accessible data and information? 1.8. 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 19

26 7.2 RESEARCH ETHICS Timeframe: Learning outcomes: Recommended reading: Recommended multimedia: Minimum of 8 hours Comply with ethical issues in business research Saunders, M., Lewis, P. and Thornhill, A. 2013, Research Methods for Business Students, 6 th ed., Cape Town: Pearson Education. Chapter One. Chapter Six Collins, J. and Hussey, R. 2003, Business Research: A Practical Guide for Undergraduate and Postgraduate Students, 2 nd ed., Palgrave Macmillan, Bak, N. 2004, Completing your Thesis: A Practical Guide, Pretoria: Van Schaik. Dr Sam Fiala. 2012, 'Research ethics, [video clip], (accessed 16 January 2014). All research subjects have ethical rights. 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. It may happen that you elicit information that could potentially compromise either a person or an organisation, and this could result in misuse. The implication is that there should be mutual trust between the researcher and the participants. Section overview: This section will cover the following: 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 Introduction Ethics forms an integral part of any research. In this section we emphasise ethical consideration. We will consider a few ethical considerations It is essential that the researcher acts ethically responsible when dealing with both individuals (research subjects) and organisations that are involved in any research you undertake. According to Gorman and Clayton (2005:43), ethical considerations are essential, regardless of the research approach adopted. 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) reinforces the fact that all research subjects have ethical rights. These rights 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' and/or organisations' personal information. It may happen that you elicit information that could potentially compromise either a person or an organisation. The implication is that there should be mutual trust between the researcher and the participants. Regenesys Business School 20

27 7.2.2 Ethical Considerations for Researchers In every research endeavour, the researcher must take cognisance that the research process should abide by ethical principles. It is essential that as a researcher, you make yourself aware of and are sensitive to these issues and identify their impact on the nature and design of your research. You must be aware that the research philosophy you adopt in your dissertation will drive the research design you choose, be it principally quantitative or qualitative in nature. This choice will determine the appropriateness of your research process. This will need to be approved by the Regenesys Academic Department. Because the course focuses on people and their behaviour, ethical factors need to 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 mini-dissertation as you will be to produce an intellectually coherent and compelling one. This means attempting not only to carry out data generation and analysis in an ethical manner, but also to begin by framing research questions ethically. Saunders et al. (2013:52) discusses the need for an Ethics Committee and also suggests 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 dissertation, relying on your learning from your other modules, such as Spiritual and Emotional Intelligence. Dr Sam Fiala. 2012, 'Research ethics, [video clip], (accessed 16 January 2014). The diagram below illustrates the essential considerations in research ethics. Participants should be voluntarily and knowingly involved in the study. You have to make sure that participants do it 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 below. Regenesys Business School 21

28 Figure 1: Ethical Considerations in Research Informed consent Cultural sensitivity Deception Codes of ethics Ethical considerations in research Participant's right to privacy Confidentiality Disclosure of findings/ results (Smith, 2008) Go to the following site to expand your understanding on ethical issues in research. Smith, L. 2008, Ethical principles in practice, Kairaranga Special ed., NZ: Volume 9, (accessed 2 December 2013). Regenesys Business School 22

29 Task Questions Read through the case study below and summarise two key learning s. 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. This month, David Willetts, the UK universities minister, 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. British academics, he added, should concentrate more on teaching rather than publishing research in US journals. We have created a system in which research has much greater incentives and rewards than teaching, which I think is very bad for our universities. Though it is rare for a minister to question the role of business schools, the comments were familiar to deans and other academic staff. Dan LeClair, senior vice president at the Association to Advance Collegiate Schools of Business (AACSB), which accredits more than 500 institutions world-wide, says deans are under more pressure than ever to justify what they do. The deans have been telling us that major donors are asking tough questions like you have all these faculty members who you are very proud of, but can you tell me how this research has made a difference?, he says. It s also the alumni and even the provosts and presidents of the institutions. They are all asking schools to not only describe what they are trying to achieve, but also to demonstrate it. Business schools are frequently criticised for over-emphasising academic rigour over relevance to practice. And many believe the structures of the business school world feed the tendency: that promotion is based on articles few managers read; and that accreditation bodies and rankings providers count journal entries, and citations, to assess worthiness. Mr LeClair says the Florida-based AACSB has sometimes encouraged research that is narrow and theoretical and more mathematical because it is easier to quantify. By focusing on that, it takes some of the uncertainty away about whether a school is accreditable. It gives us something to count. Applied research is more difficult to measure. Following a 2008 report calling for schools to have greater contact with business, the AACSB has been studying how to measure the impact of faculty intellectual contributions on targeted audiences. Ten schools are taking part in a study where they self-assess their work against five criteria each taken from mission statements. Saint Joseph s University in Philadelphia, for example, is assessing whether it meets the needs of key industries and strategic niches, contributes to the practice of management and teaching and upholds its Jesuit values. Although the exercise is not finished, Mr LeClair said it has helped to develop measures for impact in areas such as executive education and the work of research centres. In future, it may be possible to assess how customised teaching programmes, for example, help companies reach their objectives. Other schools are framing similar exercises. The Erasmus Research Institute of Management (ERIM) in Rotterdam is introducing a dual impact system where it measures both academic influence (through journal articles and citations) and managerial relevance (consultancy requests and advisory board memberships). ERIM is also beginning to collect stakeholder data from government agencies and even the general public. Regenesys Business School 23

30 Scientific director Ale Smidts estimates that ERIM faculty are now appraised 80 per cent by standard academic criteria and 20 per cent by managerial relevance. He notes the influence of the national funding agency, the Netherlands Organisation of Scientific Research. It used to be that you only had to focus on originality and rigour, and if you had a relevant aspect it counted as a plus. Now it [relevance] is more of a necessity. If you can t show relevance, you get a negative on that aspect, he says. Robin Wensley, director of the UK s Advanced Institute of Management and professor of policy and marketing at Warwick Business School, says it is vital that academics become more engaged with business, seeing businesspeople as knowledgeable actors in situations, as much as thinking we have all the answers. He is also in favour of changing incentive structures to promote more relevant research. But he cautions against academics becoming the the same people as the subjects they are trying to analyse. Mr LeClair stresses that the AASCB s relevance initiative is designed for schools to meet their own criteria for relevance, rather than a general standard. And Esade dean Professor Alfons Sauquet argues that it is vital for schools to have a mixture of practice-focused and more theoretically minded staff. As deans we cannot fall too much into either camp. If we follow the business side position we would end up as consultants. If we followed just the academic research, we would be ivory tower people. I think we have to play both roles, and that s the tricky thing. (Adapted from Schiller, B. 2011, Academia strives for relevance, Financial Times, 25 April Copyright 2011 The Financial Times Ltd) Regenesys Business School 24

31 7.3 THE RESEARCH PROBLEM, OBJECTIVES AND RATIONALE Timeframe: Learning outcome: Chapter in textbook: Additional sources: 24 hours Critically explain research terminology, concepts and principles Identify and describe the research problem Evaluate the research problem Distinguish a research problem from a symptom Chapter 1: The research question Chapter 8: The quantitative research process Chapter 15: Foundations and approaches to mixed methods research Saunders, M., Lewis, P. and Thornhill, A. 2013, Research Methods for Business Students, 6 th ed., Cape Town: Pearson Education. 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, Hyde, K. F. 2000, Recognising deductive process in qualitative research, Qualitative Market Research, (3) 2, The concept and development of creating a research problem is vital. The very first question that faces any researcher is: What do I want to investigate (research)? The first, concrete step in the scientific research process, therefore, is to identify and formulate the specific problem that is to be investigated or examined. Section overview: This section will cover the following: 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 Creating a problem statement with supporting evidence Introduction The very first question that any researcher is faced with is: What should I investigate (research)? In other words, the researcher has 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 specific problem that is to be investigated or examined i.e. the researcher has to identify and formulate a research problem. The research problem can be defined as a difficulty that the researcher experiences in the context of a theoretical or a practical situation, for which a solution is required (Welman and Kruger, 1999:12). Regenesys Business School 25

32 Leedy (2013:27) views the research problem as the pivotal point around which the research problem exists. 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 the researcher to formulate a clearly defined problem statement, it is recommended that the following questions be answered. Task Questions Answer the self-assessments questions that follow: 1. Identify 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 (in the context of the group) 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 perfect research problem, or have definite research potential at this stage. 4. Write down any real-world problem that you can identify from any of your sources i.e. 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. Make sure that you 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 that you identified. This can be a page or two long. Please make sure that your rationale includes the context from which the research problem originated and the justification for the research 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 or study revolves. It is impossible for a researcher to commence with a research project if s/he does not pinpoint and clearly formulate a research problem. However, 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. 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 the researcher to understand this session, as it is the pivotal point of the research. Regenesys Business School 26

33 7.3.3 Identifying a Research Problem During the identification of the research problem the researcher needs to make certain assumptions. An assumption is a supposition, which is taken for granted in the research study, as it is not the main focus of the study but may impact the study. The intervening variable (the variable which 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. For example, if you are studying the performance output of employees in a production environment, 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 specific factory only. Delimitations are choices made by the researcher which describe the boundaries that have been set for this study, as such the assumption will be that the taxi violence will not occur in the vicinity of this factory for at least another year based on newspaper articles and union agreements. If there is indeed taxi violence and your assumption was incorrect, this will impact the research and outcome of the study. Limitations are, therefore, influences that you as a researcher cannot control and as such place restrictions on your research. Delimitations are specific choices made by the researcher to set boundaries for the research study (the scope of the study). As we indicated in the previous sub-section, the identification of a valid research problem is the first step in any research project. There are a number of individual tasks that the researcher can perform, in order to identify (develop) and formulate a problem that reflects meaningful research. It is essential in the mini-dissertation to provide supporting evidence to prove that you have identified and analysed a problem rather than a symptom. Developing an acceptable research problem involves the following tasks: 1. Generate research ideas by observing what is going on around you as a researcher. Identify questions that need to be answered in the organisation 2. Identify real-world problems in the organisation and from the vast amount of literature available in your environment 3. Gather and analyse relevant background research on the problem 4. Compile a motivation to support that there is a valid problem statement 5. Understand the relevant assumptions, limitations and delimitations of the research study 6. Refining real-world problems (Adapted from Leedy, 2013:29) Refer the case study abstract at the following link: Regenesys Business School 27

34 Case Study Answer the following questions based on the case study referred to above: 1. Define the problem statement you would use if you were to research the issues in this case study. 2. Critically discuss your supporting motivation for selecting your problem statement. Complete the activity below to develop real-world problems into research problems. Task Questions 1. Critically evaluate the following statements and decide if the researcher knows 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. Meeng Uofu. 2012, How to write a problem statement (review for ME1010), [video clip], (accessed 16 January 2014) Generating Research Ideas Research problems usually have their origin in research ideas. The first task in the minidissertation 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. Regenesys Business School 28

35 Research ideas usually originate from one (or more) of the following sources: 1. Previous research 2. Personal experience 3. Practical problem Task Questions 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 (in the context of the group) 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 will focus on the second task in identifying a research problem: Identifying real-world problems as research problems 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. As indicated in the previous section, 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 basically means that the researcher becomes aware of an existing problem in the organisation 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 research context will be seen as organisational issues. These issues often present themselves in the working environment in order to aid strategy formulation; decisionmaking or policy formulation. They are frequently aimed at improving organisational effectiveness and efficiency. The research that grows from these problems may, therefore, be aimed at improving the service objectives of institutions. Regenesys Business School 29

36 Nevertheless, real-world problems may also occur in a non-working environment, e.g. in a country, or in a particular community (society). In this case, the real-world problems usually relate to political, social or economic changes, challenges and problems. Please note that 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. For your research it is recommended that you narrow the scope so that you select a problem that is related to a management discipline. Task Questions 1. Write down any real-world problem that you can identify from any of your sources i.e. newspapers, TV, Internet, personal experience, work environment, etc. 2. Write a brief note on the real-world problem identified above 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 need to avoid many real-world problems, but try to focus only on one that you are most interested in studying. Steps for refining a real-world problem or an organisational issue For the purposes of this module, we recommend the following steps for refining a real-world problem: Step 1: Formulate the topic: o I am exploring/ examining/ developing/ studying/ investigating Step 2: Give a reason: o Because I want to find out what/ why/ who/ when/ whether Step 3: State the rationale or motivation for the project: o In order to understand how/ why/ whether Regenesys Business School 30

37 See the example below: The researcher is studying the decline in sales of fast foods in Sandton, (Gauteng) in 2013, 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 more practical problem. However, before the researcher can solve this real-world problem, he/she 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 the researcher to obtain more knowledge and a better understanding of matters related to the real-world problem; this enables him/ her 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 Research question Research problem In January 2012, there was an active drive by promoters of healthy foods in Sandton (Gauteng). Did the promotion of healthy foods impact on the fast food industry in Sandton (Gauteng)? The researcher has to determine if eating habits have changed in Sandton (Gauteng) and caused the decline in the sales of fast foods in any way. By following the process outlined here, the researcher creates a focused research problem and determines a scope within which the problem has to be solved. This becomes very important, especially when the researcher is conducting the literature review. If the researcher is not clear about the focus and scope of the research problem, it may happen that he/she reads too broadly and starts including information that is not relevant to the research problem. Regenesys Business School 31

38 Task Questions 1. 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/ investigating Step 2: Give a reason: Because I want to find out what/ why/ who/ when/ whether Step 3: State the rationale or motivation for the project: In order to understand how/ why/ whether Please take note that in the formulation of the research problem you have a verb, variables, justification of the study and the period you are researching 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 very 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 significantly assist the researcher in developing a true research problem. Activities such as studying a particular subject field and consulting earlier research findings are more likely to lead to a conceptually developed research problem. 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. Regenesys Business School 32

39 The research problem should represent a reasonably new area of research. Although it serves no purpose to repeat existing research results or findings, the research problem does not actually need 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 field. 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 field. The research problem should be researchable (manageable). This means that the researcher should consider practical aspects such as time, funding and the availability of resources make compromises, if necessary. Ethical issues should be considered when establishing the research problem. Here is a list of some of the ethical questions the researcher should consider: o What ethical consideration must be taken into account when conducting this research? o Who are the relevant stakeholders who have to approve this research study? o Is the researcher adequately qualified and skilled to conduct this research project? o 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 as to whether your research problem can actually be researched or warrants research. Once the research problem has been finalised, it is possible to determine sub-problems, also known as research objectives Research Objectives Research objectives or sub-problems will assist you as the researcher to decompose and break down the research problem into more manageable or researchable parts that can be investigated separately. Each research objective (sub-problem) 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 the researcher 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 the following 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 33

40 The following criteria should assist you in formulating your research objectives or subproblems: Each objective should have one focus. In other words, you should formulate the research objective in such a way as to ensure 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 asks: Do students at Regenesys use the portal to access the relevant study guide?, you will have to work analytically and do research to teach an acceptable answer. When the research objectives are combined, they should equal the whole of the research problem. The researcher should not omit any important aspect of the research problem. However, it is equally important that the researcher does 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 the researcher should revisit the research problem and adjust the formulation. The researcher should be careful not to include so-called pseudo-sub-problems as research objectives. Pseudo-sub-problems are not directly related to the research problem they have more to do with the research methodology. Examples of pseudo-subproblems include: 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 the researcher to combine the findings for the whole of the research problem. Once the research objectives or sub-problems have been spelt out, the researcher is in a position to clarify the scope and limitations of the study. This is important, because the researcher has to avoid investigating related issues that have no direct bearing on the research problem. The scope of the research indicates what the researcher includes in the study (investigation), while the limitations indicate what is not included in the research. Regenesys Business School 34

41 7.3.9 The Research Questions 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. The questions ensure the objectives of the study are researched correctly. Please be aware that the quality of the research questions will determine 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 need to be clear on what your study strives to answer. These are not like the interview questions or questions in a questionnaire, but are broader questions that should be answered by the entire study/ dissertation. A research question should be linked to the main objectives of the 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)?" The Rationale for the Research The researcher is expected to develop a rationale and background for his/ her research. In the rationale, the researcher justifies his/ her reasons for conducting research: He/she indicates why the study is worth undertaking and why he/she is interested in undertaking it. In other words, the function of the rationale is to indicate the general importance (significance) of the research (investigation). The rationale should include the following information (Leedy, 2013:31 31): The problem statement must be clearly stated and easily understood by anyone reading it. There should be supporting evidence for this problem statement. The context that gives rise to the research project should address the following questions: o What are the conditions, events, situations or processes that have led the researcher to the investigation or project? o Is the researcher of the opinion that the current knowledge of an issue is inadequate, or that certain issues have been poorly researched? o Does the researcher 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. Regenesys Business School 35

42 The justification for the research project should address the following questions: o What is the researcher s interest in the project? o What motivates the researcher to conduct the research? o Why is the project worthy of scientific investigation? o What does the researcher regard as the significance of the research? o In what way can the solution of the research problem be applied to the real-world problem? o What contribution should the research make in terms of current knowledge around the issue or problem that is 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, the researcher has to conduct a literature review and establish a theoretical framework to develop the rationale for the research problem. Task Questions 1. Identify research problems and develop a rationale for the research problem that you have identified. This can be a page or two long. Please make sure that your rationale includes the context from which the research problem originated and the justification for the research. Regenesys Business School 36

43 7.4 FORMULATING AND CLARIFYING THE RESEARCH TOPIC Timeframe: Learning outcomes: Recommended reading: Recommended multimedia: Section overview: Minimum of 5 hours Critically explain research terminology, concepts and principles Apply and critique various research methods Saunders, M., Lewis, P. and Thornhill, A. 2013, Research Methods for Business Students, 6 th ed., Cape Town: Pearson Education. Chapter One Chapter Two Collins, J. and Hussey, R. 2003, Business Research: A Practical Guide for Undergraduate and Postgraduate Students, 2 nd edition, Palgrave Macmillan, Humphrey, C. 2008, Auditing research: A review across the disciplinary divide, Accounting, Auditing and Accountability Journal, (21) 2, Meeng Uofu. 2012, How to write a problem statement (review for ME1010), [video clip], (accessed 16 January 2014). Having completed the previous task where you defined the problem, you now need to refine and finalise the research title. You probably get the idea that most people are exposed to the process of research. We are now going deep into the process of research. Before that, let us have a look at what characterises the research process. A research title is regarded as the essential link to the research design. It is therefore essential to get the heading correct and to ensure that the topic sets the scene for the research. The research title must align to organisational studies, as this is the focus of the research. This section will cover the following: Identifying a suitable research topic title Formulating a research topic Clarifying a research topic, and The relevance of the research topic Formulating and Clarifying the Research Title Having completed the previous task where you defined the problem, you now need to refine and finalise the research title. You probably get the idea that most people are exposed to the process of research. 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 research topic being studied aligns with the research title. The topic is contextualised and sets the scene for the research, whereas the title allows the reader to understand what the research topic covers. 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 37

44 In summary: Topic: Expression of broad research interest Topic: Associated with some academic discipline could be inter-multi-disciplinary Topic: Research question research objectives Title: Re/shaping of the broad topic/ research interest Title: 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. Do not use the words such as "a study of ", or similar, because all research is a study / investigation / examination of Before deciding on the research topic for your study, you need to ask the following questions: 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? Are there studies that can be linked to this title? Task Questions 1. Critically discuss how you would go about selecting a topic and relevant title for your research. Regenesys Business School 38

45 7.5 CONDUCTING A CRITICAL LITERATURE REVIEW Timeframe: Learning outcomes: Recommended reading: Recommended multimedia: 25 hours Critically explain research terminology, concepts and principles Evaluate and compare the various types of research philosophies Apply and critique various research methods Understand the relationship between information resources and the knowledge management process of a specific organisation Collect and analyse research data and demonstrate its value in business decision-making Saunders, M., Lewis, P. and Thornhill, A. 2013, Research Methods for Business Students, 6 th ed., Cape Town: Pearson Education. Chapter Three Collins, J. and Hussey, R. 2003, Business Research: A Practical Guide for Undergraduate and Postgraduate Students, 2 nd ed., Palgrave Macmillan, Mouton, J. 1996, Understanding Social Research, Pretoria: Van Schaik. Massey University. 2010, The literature review, [video clip] (accessed 16 January 2014) If you revisit the research problem that you identified earlier, you will be able to identify keywords in the research problem that can be used as search terms for an information search on the Internet. You can use relevant search terms and conduct a search on Google Advanced Search. You can also study the search results and print or record the results that you regard as most useful for your research. Section overview: This section will cover the following: The purpose of a literature review Identifying and using various literature sources Evaluating the context of the theory, assumptions and limitations of the literature Understanding how to review literature and compile a critical analysis of the literature 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 that need to be explored. Regenesys Business School 39

46 Whatever the research problem may be, the researcher has to conduct an investigation into the literature related to their research problem i.e. the researcher will need to find informative sources, determine their relevance, read them thoroughly and synthesise the information, make informed judgements and finally, report on the information provided in each source. In this section, we will provide you with 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 (e.g. 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. You would have already completed many assignments and exams before attempting this Research module. You would have noted already 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 and relevant 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 will apply in conducting the research for your dissertation. Ensure that you align the literature review to your dissertation/ research topic, research objectives, problem statement and research questions. Use research that matters and is aligned and relevant to your study. In order to address the mechanics of a literature review, the focus of this section is on conducting a literature review. 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 first draft of the review itself. The primary goal of the literature reviewer is to be comprehensive and up-to-date. The literature review plays an integral part of the mini-dissertation when designing the data collection method and the types of questions to ask. 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 paper, article, or thesis itself. Regenesys Business School 40

47 7.5.2 What is a Literature Review? Saunders (2013) points out that literature not only includes printed information and all the information sources. In actual fact, literature includes information in any format, be it in hard-copy print, audio, visual and electronic. It can then be argued that a literature review is a structured and systematic process in which the researcher finds 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 issue to remember when conducting the literature review is to understand the assumptions and limitations that the author made. In line with 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 create drawbacks within the assumptions and limitations made, as well as the models created by the author. This is essential when a critical analysis of the literature assumptions, limitations, delimitations, models and theories is conducted. Task Questions 1. Select at least three authors of relevant academic textbooks, and then critically review the assumptions and limitations made by these authors 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 will provide a sound academic basis for the dissertation/ research It strives 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 the researcher 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 the researcher in explaining the contribution of his/ 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 Regenesys Business School 41

48 It brings together all the work of experts on the research topic Criteria for a Literature Review In order for the literature review to meet its purpose, it should satisfy the following criteria: It should take into account all the assumptions made by the authors It should take into account all the limitations made by the authors It should take into account all the delimitations made by the authors It should take into account all the construct variables made by the authors It should take into account 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 be specific and address the research problem and objectives not other marginally related or interesting information that is unnecessary It should include all the authoritative authors or experts in the field It should not leave out important key words It should do justice to the authors and researchers arguments and reasoning before criticising them It should be critical and provide an analysis of the information not only a representation of it It should be logically structured It should be well organised (Leedy, 2013:66-67) Steps in the Literature Review The process involved in the literature review consists of the following steps: 1. Searching for information and information sources 2. Organising the information sources 3. Reading the information sources and determining their relevance and quality 4. Writing the literature review, and 5. Referencing 1. Searching for information and information sources The researcher will require both secondary research (data and information which already exists in the form of quantitative or qualitative data) and primary research (data and information which does not exist and will need to be collected for the specific research study). Information and information sources can come from virtually anywhere and can be either primary or secondary sources. Regenesys Business School 42

49 The researcher must base the literature on scholarly sources. Some examples of the 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. The table below highlights a sample of the combination of popular, trade and scholarly publications are the starting point. However, keep in mind the differences between these three categories of information as shown in the table below. Table 4: Popular, Trade and Scholarly Publications Criteria Popular Publications Trade Publications Scholarly Journals Nose week Engineering News Cross Cultural Management Financial Mail Water and Sanitation Africa International Journal of Managing The Economist Transport World Africa Projects in Business National Geographic Examples Time Magazine Popular Mechanics Forbes Africa Purpose Authors Language Article appearance References Accountability Inform the general public; may target a specific demographic Magazine employees, journalist or freelance writers Generally non-technical use of language; understandable to broad audiences; informal, very current, anecdotal, personal or entertaining Relatively brief articles accompanied by glossy/ colour graphics, photos and general advertisements Rarely include reference lists/ bibliographies Not evaluated by experts in the field but may attract comment; reviewed by the editor but not a panel of experts Provide news, trends, and practical information to professionals working in a particular industry (e.g. engineering) or profession (e.g. accountants) Trade journal employees, members of associations, entrepreneurs, leaders, and professionals in the field Specialised/ technical terminology (including jargon) used in the industry or profession; may have a public relations focus Brief to mid-length articles including photographs, charts, illustrations, and advertisements targeted at professionals in the field Occasionally include reference lists/ bibliographies Reviewed by the editor; may be evaluated by experts in the field but not peer-reviewed In depth analysis of topics; report research findings and promote further scholarly communication and research Scholars and researchers in the field; name and credentials are provided in the article including educational institution to which author is affiliated Specialised terminology; objective view 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 Always include extensive footnotes, reference lists or bibliographies Usually reviewed and critically evaluated by board of subject experts (peer-reviewed) (Saunders, et al., 2013) Regenesys Business School 43

50 Ideally, you must locate peer-reviewed research articles located 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 other government departments Librarians (who can assist in refining your literature search) Subject-matter experts Regenesys subscribes to Emerald (a global publisher with a significant number of journals). Refer to the Emerald Manual for instructions on how to use this facility. You must achieve knowledge literacy, in respect of which you are 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 and information service, such as a university library. If you are a member of an academic library, an information librarian (subject librarian) will assist you with the information searches for your research. The librarian will ensure that you consult all the relevant databases available. However, if you are not a registered post-graduate student at a tertiary institution, you should find alternative channels to access information. You may consider the following databases and resources for information searches: The Emerald database The World Wide Web (WWW) Academic research databases Task Questions 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

51 2. Organising information sources Once you have retrieved the information sources, the next question relates to 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 time period. You should create a file 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 sub-problems or research objectives, or if applicable, any other relevant topic title/ name. The idea is that you should be able to easily find a document when you need it. You should keep the documents until you have completed your research report in case it is necessary to refer to them again. Apart from organising the documents, it is also very 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 packages 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 do the compilation of 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 need to look at the assumptions and limitations, as well as the delimitations made by the authors. The economic and political environment also needs to be contextualised around the theories. 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. The reading of texts 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 referred to as retrospective reading. This method of reading helps you to establish right away what the latest state of the research is, and how developments to took place. Read the abstract (if available) of an article before you start with the article as such. In the case of a book, you should scan through the table of contents and read the preface and the 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

52 You should then read the introduction and concluding summary of the article. In the case of a book, it would be 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. Once you have established that the source is relevant to your study, you should proceed to reading it in-depth and systematically. Evaluating the quality of information As indicated earlier, it is essential for researchers to evaluate the content and quality of information and information sources before they actually use the content in their research. All information sources are not equally reliable; all authors are not equally competent and all journals and publishers are not equally respected. In other words, not every claim that appears in print or on the World Wide Web is true! Because researchers want their research to be based on the most accurate, authoritative and upto-date information, they have to exercise discretion in evaluating their information and information sources. Criteria to determine the quality of information sources The following criteria can be used to determine the quality of information sources: Table 5: Determinant of Quality of Information Sources First-hand presentation of information Evidence of research Up-to-date (recent) Relevance Agreement with other information sources Bibliographic references The researcher should be able to establish whether the author can confirm given facts and/ or statistics. If the researcher has no proof that the information is accurate and objective, s/he should obtain the original information source and use the information from that source in his/ her research. The information should include references and statements by reliable and accredited scientists in the field of study. 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. The relevance of information sources is not always that obvious: Titles, for example, may be misleading and vague. Therefore, the researcher has to evaluate the information in terms of relevance. If an information source correlates to other, reliable information sources on the subject field, it may be regarded as reliable. A scientific information source has to include bibliographic references and a bibliography at the end of the source. 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

53 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 into. This will assist you reconstruct what the author is trying to say. You should summarise each information 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 the authors writings and then you 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, you should group them together in a logical sequence. You should now identify the following: 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 becomes difficult to conduct these comparisons. When you compile the summaries, you should accurately reflect the authors opinions whether you feel that you agree with them or not Writing the Literature Review The actual writing of the literature review is problematic for many novice researchers and it confronts them with questions such as: 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? Do I discuss the findings from the literature in the literature review and repeat them when I relate my findings to the literature? The above 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 in which the literature review is represented depends on the type of research report you have to compile. 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. Regenesys Business School 47

54 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. Approaches to writing the literature review The literature review can be written in a number of ways, depending on how the researcher wants to approach the overall research report presentation. We will only briefly explain two of the more popular approaches: The literature review may be presented thematically i.e. according to specific themes around the research problem. These themes would be closely related to the specific research objectives. The researcher may also choose to follow a chronological approach in the literature review, in which case the earliest research on the topic is presented first in order to create the context for later research as well as the researcher s 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). The literature review is often a separate section in a research report/ mini-dissertation in which the researcher synthesises the literature about the research problem and critically analyses it. If the researcher decides on the thematic approach, a different section could cover each theme, with a title related to that theme. 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 and the research objectives could be used as the main headings; the following process can be applied: Write an introduction Define the concepts that the author(s) 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 Determine the literature on the cause and effect analysis At this point, the researcher is ready to start writing his/ her literature review. This will be the first draft of the literature review, which is never perfect, but it does help the researcher to put his/ her thoughts in writing and clarify his/ her thinking. The first draft will be unedited and re-edited at a later stage. Massey University. 2010, The literature review, [video clip], (accessed 16 January 2014). Regenesys Business School 48

55 Higson-Smith et al. (2000:30) suggest that answering the following questions helps the researcher to develop a literature review: Questions to ask and answer when conducting a literature review: Select the possible topic you wish to research and then conduct a literature research. Answer the questions below: Has the author provided an adequate background to the literature? Has the author stated limitations in the theory concepts, constructs and models? Has the author given relevant assumptions around the theory concepts, constructs and models? Has the author given any delimitations around the theory concepts, constructs and models? Has the author formulated a problem/ issue? Has the author given assumptions and limitations? What was the landscape (economical, political, etc.) like during the era when the text was published? What constructs are used and how are they created? What variables are used in creating the models/ constructs? Is the theory clearly defined? Is its significance (scope, severity, relevance) clearly established? Could the problem have been approached more effectively from another perspective? What is the author's research orientation (e.g., interpretive, critical science, combination)? What is the author's theoretical framework (e.g., psychological, developmental, feminist)? What is the relationship between the theoretical and research perspectives? Has the author evaluated the literature relevant to the problem/ issue? Does the author include literature taking positions she or he does not agree with? In a research study, how good are the basic components of the study design (e.g., population, intervention, and outcome)? How accurate and valid are the measurements? Is the analysis of the data accurate and relevant to the research question? Are the conclusions validly based upon the data and analysis? Is the material written for a popular readership, does the author use appeals to emotion, one-sided examples, or rhetorically-charged language and tone? Is there an objective basis to the reasoning, or is the author merely proving what he or she already believes? How does the author structure the argument? Can you deconstruct the flow of the argument to see whether or where it breaks down logically (e.g., in establishing cause-effect relationships)? In what ways does this book or article contribute to our understanding of the problem under study, and in what ways is it useful for practice? What are the strengths and limitations? How does this book or article relate to the specific thesis or question I am developing? What is the broad basis of national and international literature that has relevance for my research topic? What methods and results have previous researchers in my field produced? What is the history of my area of study? What theoretical model(s), relate to my research topic? What different methodologies have other researchers in my area used? Try to identify the key methodological issues that have to be addressed, since these will determine your own choice of methodology. What is the most recent research finding in my area of study? What gaps and contradictions exist among these findings and what new questions do these research findings suggest? What structure suits my literature review the best? What should I leave out? What quotations should I include? Regenesys Business School 49

56 7.5.7 Theoretical Framework A theoretical framework is derived from a collection of inter-related concepts. This framework is important in exploratory studies where the researcher may not know enough about the topic and is 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 mini-dissertation must demonstrate an in-depth 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, there a lot of the information may appear appropriate and the researcher may want to include everything into the research framework. This can become problematic and the solution to this is to have a focused research problem topic and clear research objectives (Saunders et al., 2013). To develop a clear theoretical framework, the researcher 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 which characterise the field of study Once the theoretical framework is done, you need to support your arguments with relevant and academically sound evidence, to assist in creating the relevant recommendations. Regenesys Business School 50

57 Task Questions Based on your own ideas for your possible dissertation, answer the questions below: 1. What is the organisational issue or challenge? 2. What is the problem? 3. Do you have a problem statement? 4. Do you have supporting data to show that this a real organisational challenge or problem? 5. Is there academic literature available on this topic? 6. Is the literature relevant and up-to-date? 7. Have you reviewed the assumptions, limitations and models used by the relevant authors? 8. Is there supporting data and statistical analysis in the literature to support this mini-dissertation or must more data be collected? Regenesys Business School 51

58 7.6 THE RESEARCH PHILOSOPHY AND APPROACH Timeframe: Learning outcomes: Recommended reading: Recommended multimedia: Section overview: Minimum of 30 hours Evaluate and compare the various types of research philosophies Saunders, M., Lewis, P. and Thornhill, A. 2013, Research Methods for Business Students, 6 th ed., Cape Town: Pearson Education. Chapter Four UELRDBS. 2013, Postgraduate research planning workshop Research process and philosophy, [video clip], (accessed 16 January 2014). Research philosophy is an over-arching term that relates to the development of knowledge, the nature of that knowledge, and the way in which the student will apply that knowledge in completing the mini-dissertation (Saunders et al., 2013). This section will cover the following topics: Different research philosophies Theory principles of the different research philosophies The Research Onion Different research assumptions, and applications thereof The Research Philosophy The research philosophy is seen an over-arching 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 the mini-dissertation (Saunders et al., 2013). Saunders et al. (2013) views a research paradigm as breaking down the complexity of the research and the manner in which social phenomenon from which particular understandings of these phenomena can be gained and explained. In essence, the research paradigm is the interpretative framework within which research is conducted. As the researcher you will be required to make assumptions, which are relevant for your dissertation. 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). Regenesys Business School 52

59 As a researcher, the specific research philosophy you decide to adopt will be seen as the assumptions used in your mini-dissertation when contextualising the manner in which you view 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:) both agree that, as researchers, we need to be aware of the philosophical commitments we make through our choice of research strategy since, this will have a significant impact not only on what we do but how we understand what it is that we are investigating. The philosophy you adopt will be influenced by the pragmatic considerations around the research conducted. This will impact on the research design and the study itself. The main influence is likely to be your particular view of what is acceptable in your mini-dissertation and will be aligned to the knowledge and the process by which this mini-dissertation is developed. Saunders et al. (2013) views the research philosophy as being researcher driven. This implies that the student conducting research and is concerned mostly with facts and figures, such as the detailed costing of material and labour required in a specific project, will tend to have a different stance on the way the research should be conducted compared to a researcher who is concerned with the emotions, perceptions, and attitude of the workers towards the construction manager on the project site. Not only will their strategies and methods probably 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) who argue that the important issue is not so much whether our research should be philosophically informed, but how well we are able to reflect upon 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. 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 achieving different outcomes form the research conducted. This depends on the research question(s) that the researcher wants to find answers to. UERLDBS, 2013, Research process and philosophy, [video clip] (accessed 16 January 2014). Regenesys Business School 53

60 Figure 2: The Research Onion (Saunders et al., 2013) Saunders (2013) explains that the way in which a researcher views the world will, together with his/her own disposition in life, influence the assumptions he/she makes with regards 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 will be 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 specific human resources required in a construction project. This may differ from a student who comes from a Psychology or Human Resources background, since the aforementioned students may tend to focus more the emotional intelligence factors, such as feelings and attitudes of the workers involved on the sale project. This will impact on their methodological choice and strategies for the research. Saunders et al. (2013) considers positivism as the viewpoint that research needs to be scientific, even if in a fairly crude sense. The reality researched is viewed as external and objective, and the methods used should be values free and, as far as possible, quantitative. 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

61 As a researcher who will be using the positivism philosophy, you will seek to observe and strive to predict outcomes. This can be compared to the way in which an inventor who is concerned with facts and rule-based generalisations such as cause and effect analysis; reflecting the philosophy of positivism. For example, we can see this in television series, or similar programmes such as Myth Busters who try to prove various myths against known sets of rules. This is based on well-known scientific facts such as Newton s Law of Gravity to propose and test theories with data, which is well defined and logically structured. This data is generally measurable and is not influenced by the researcher s values and perceptions. This will involve the collection of representative quantitative data to be used as a basis for conduction statistical hypothesis testing. When the known theory cannot be confirmed by the findings, there will be a need to revise the theory. Blackburn (2005) and Saunders (2013) argue that contemporary philosophical realism is regarded as the belief that a person s reality, or some aspect of it, is ontologically independent of that person s conceptual schemes, perceptions, linguistic practices, beliefs. Likewise, from a Regenesys perspective, the types of modules 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 mini-dissertation 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 as the foundation for the research. Saunders et al. (2013) proposes that realism exists independently of the mind and that what a researcher senses show them is the truth, although the researcher can be influenced by the variety of world views and the researchers own experiences and viewpoint of what reality is. You should understand that this research philosophy requires data to be collected by utilising an appropriate method. If you select the interpretive research philosophy you will be required to focus your minidissertation on your ability to analytically evaluate and apply the practices that are focused on 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) recommends that the researcher, who is more concerned with gathering rich insights into subjective meanings than striving to provide law-like generalisations, should be using the research philosophy of interpretivism. This would be relevant in dissertations where the researcher wishes to focus on people rather than objects. An example may be to study how organisational values impact on the 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 takes the view 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, and emphasises what is independent of or external to the mind. Regenesys Business School 55

62 Task Questions 1. Critically compare objectivism and constructivism and use your own example to support your theoretical discussion. Saunders et al. (2013) view subjectivism as social phenomena, 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 judgement and views these as being the only valid judgements. This approach by itself in not recommended for your dissertation, as this critical analysis required in the literature review depends of using multiple academic approaches and constructs to come up with new ideas. Task Questions 1. Critically discuss the benefit of having at least five years of managerial experience before attempting to do a dissertation. Task Questions 1. 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 argue that a Masters is based on pragmatism. This implies that your assignments and mini-dissertation must be focused on solving practical problems, typically back in your workplace and in the real world. This implies that your mini-dissertation should create value to the workplace. For students who adopt the philosophy of pragmatism, the importance of research will be the findings practical consequences. Saunders et al. (2013) proposes that no single viewpoint can ever give the entire picture and that there may be multiple realities. 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 does ensure credible, reliable and relevant data (secondary and primary) to be collected that support subsequent action. Regenesys Business School 56

63 Task Questions 1. Critically discuss two benefits and two challenges of having a pragmatic approach to the dissertation. The researcher s approach in applying functionalism in research is to apply the relevant social theory, which will see society as being 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. Task Questions 1. Discuss how a researcher can use functionalism. You can use your own example to support your discussion. Saunders et al. (2013) argues that interpretive research is best described as being qualitative which is not numerically based, to distinguish it from the quantitative research, which is numerically based. The interpretative method starts from the position, which assumes that your knowledge as a researcher is based on social construction, such language, consciousness and shared meanings where the research equally applies. If you use this approach, you need to take cognisance of the fact that interpretive research does not predefine any of the dependent and independent variables, instead the focus will be on understanding and researching the full complexity of human sensemaking as the situation emerges while conducting the research. Task Questions 1. Critically discuss how interpretive can be used by a researcher. 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 the researcher uses this approach, they will need to take a critical perspective on organisational life. This implies that you will need to 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. Regenesys Business School 57

64 Task Questions 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 need to adopt a research with a focus on view what is needed to achieving fundamental change based upon an analysis of such organisational phenomena as power relationships 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 within 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). Task Questions 1. Discuss how a researcher can use the radical structuralist approach. 2. You can use your own example to support your discussion Deductive versus Inductive Research Deductive reasoning: This follows a structured, rule based, top-down approach for reasoning, where you will begin by identifying the research topic to research, and then to narrow this topic down into specific hypothesis (or research questions) that you wish to test (or find answers to), and thereafter, further focus when you collect the data which is to be used to test the hypothesis (or answer the research questions). The hypothesis is then tested using quantitative data and this will allow you to either accept or reject the stated hypothesis. Inductive reasoning works in the opposite direction where you will move from the very specific observations to the more generalised theories. Saunders et al. (2013) views this as a bottom-up approach. If you use this method in your research, you will need to 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. Where applicable, the hypothesis may need to be refined to finally allow the researcher to end up with some general conclusions or relevant theories. Regenesys Business School 58

65 Task Questions 1. Give a practical example of the type of mini-dissertation in which the inductive and deductive approaches would be relevant, and discuss why you think so. Table 2: A Summary of the Various Research Assumptions Assumption Question Characteristics Ontological This will refer to the way in which you view the nature of reality or being Epistemological This will refer to the way in which view what constitutes acceptable knowledge Axiological This will refer to the way in which view the role of values in research What is the nature of reality? What is the relationship between the researcher and that being researched? What is the role of values? Reality is subjective and multiple, as seen by participants in the study. Researcher attempts to lessen distance between them self and that which is being researched. Researcher acknowledges that research is value laden and that biases are present. Implications for Practice (Examples) As a researcher, you must use quotes and themes in words of participants and provides evidence of different perspectives. As a researcher, you will need to collaborate, spends time in field with participants, and becomes an insider. As a researcher, you will need to openly discuss values that shape the narrative and includes own interpretation in conjunction with interpretations of participants. Rhetorical This will refer to the way in which you a question. A rhetorical question implies that you do not necessarily expect an answer, but you do want an occasion to talk about something. What is the language of research? Researcher writes in a literary, informal style using the personal voice and uses qualitative terms and limited definitions. As a researcher, you will need to use an engaging style of narrative, may use first-person pronoun, and employs the language of qualitative research. Methodological This will refer to the way in which you define the research process to be followed in the dissertation. What is the process of research? Researcher uses inductive logic, studies the topic within its context, and uses an emerging design. As a researcher, you will need to work with particulars (details) before generalisations, describe in detail the context of the study, and continually revises questions from experiences in the field. (Saunders et al., 2013). Regenesys Business School 59

66 7.6.3 The Quantitative Versus Qualitative Research Approach The research approach is important as this will determine the type of data to be collected and the level of statistical analysis, which 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. The quantitative research approach If you decide to adopt this approach, you will need to realise that this will be a logically and systematically structured process that will require you (the researcher) 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 utilised and to conduct evaluations. Leedy (2013:270) views this approach as a formalised approach with a carefully defined scope and set boundaries. This implies that that you will be required to select and apply an appropriate survey method. This will require of 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 non-probability methods). As a researcher you will need to ensure that you have correctly selected and applied the relevant data collection and data analysis methods. The qualitative research approach If you decide to use the qualitative approach then you will need to observing 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 will be placed 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 the researcher to interact with the individual participants or groups whose behaviour, experiences, attitudes, perceptions, etc., need to be 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 is limited to the specific research study. This implies that the results may not necessarily be inferred onto the population as a while and this becomes a limitation of your dissertation. Although the qualitative research methodology is not as rigid as the quantitative research methodology, it still requires the researcher to follow a set of well-defined data collection methods and structured research designs. The typical data-collecting instruments used in qualitative studies 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. Regenesys Business School 60

67 Recap Questions 1. Critically analyse the differences between the quantitative and qualitative research approaches. 2. Discuss how the research design, methodology and research methods are decided within the context of either the quantitative or qualitative approaches. The key differences between the qualitative and quantitative approaches are summarised in Table 6 below. Table 6: The Differences Between the Qualitative and Quantitative Research Approach Qualitative Research Scope can be less defined and processes are not so formalised Is generally associated with descriptive enquiry that draws on interviews, documents, narratives and observations (observable patterns) Studies social phenomena from the perspective of the participants Used to study social phenomena and the emphasis is on human beings as research subjects The design evolves during the study Generally generates new theory (inductive) Natural setting Uses small samples Uses face to face interaction and the researcher interacts with individual respondents Narrative description and the researcher s observations, which may be based on experience, knowledge or judgment Research phenomena are social in nature and cannot be quantified Uses open unstructured questionnaires and interviews Quantitative Research Formalised approach with carefully defined scope Is associated with statistical and experimental studies that rely on various numerical manipulations to research a problem Uses objective measurements and statistical measurements to explain a phenomena Is used in natural sciences and to study cause and effect relationships The design develops prior (before) the study Aims to test a theory (deductive) Laboratory setting Uses big samples of large populations in wide geographical areas Uses standardised instruments and the researcher seldom interacts with the respondents Statistical analysis of numeric data Accurate measurements Uses surveys and structured questionnaires (Adapted from Leedy, 2013: ) 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 specific 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. Regenesys Business School 61

68 7.7 FORMULATING THE RESEARCH DESIGN Timeframe: Learning outcomes: Recommended reading: Recommended multimedia: Minimum of 30 hours Apply and critique various research methods Saunders, M., Lewis, P. and Thornhill, A. 2013, Research Methods for Business Students, 6 th ed., Cape Town: Pearson Education. Chapter Five UELRDBS. 2013, Postgraduate research planning workshop Research process and philosophy, [video clip], (accessed 16 January 2014). Research design can be thought of as the structure of research it is the glue that holds all of 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 will cover the following: 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 relationship among the study s variables Describing the design outlines and procedures for research activities, and Applying the design outlines and procedures for research activities The Research Design Leedy (2013:74-77) views the research approach as being closely linked to both the research design and research methodology. Before we can discuss research methodology, we should explain the concept of research design. Mouton (1996:107) defines the term 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. In the research design (research plan), the researcher describes 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, then the research man not yield the desired research output. Regenesys Business School 62

69 Since the research design will involve the research subjects (participants or respondents), it is essential for the researcher to fully understand the concepts of population (i.e. the entire group of research subjects) and sampling. The differences between the research design and the research methodology are captured in Table 7 below. Table 7: Differences between the Research Design and the Research Methodology Research Design The focus is on the end product: What kind of study is being planned and what kind of result is aimed at? The point of departure is the research problem or question. The focus is on the research logic: What kind of evidence is required to address the research problem adequately? Research Methodology The focus is on the research process and on the tools and procedures to be used. The point of departure is the specific tasks (such as data collection and sampling) at hand. 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) Task Questions 1. Critically discuss the key differences between a research design and a research methodology. Use a practical example to illustrate your understanding of the key differences Introduction to Research Designs The research design is viewed as the key area, which defines and gives the much-needed framework to the dissertation. This can be likened to a Blue print in engineering design. The research design will give the researcher a well-thought-out and clearly defined structure for the research that needs to be conducted. This research design will show the researcher 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 that are common amongst all the definitions. The aforementioned 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. According to the aforementioned authors, 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) utilises the analogy on an onion with rings to describe and define the research process. Regenesys Business School 63

70 The Research Onion in Figure 2, provided earlier, will be used to refine the research process and discuss the research design to be applied in your dissertation The Types of Research Strategies A research strategy is critical as it is seen as the plan for the dissertation. This implies that it will specify methods and procedures for the collection, measurement, and analysis of data. According to Saunders et al. (2013), the main research strategies are experiment, survey, case study, action research, grounded theory, ethnography and archival research. You should not think of these as discrete entities because they may be used in combination in the same research project. As a researcher, you will most likely 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 in most other studies, exploration is the first stage of a project and. 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 based on their past experiences. 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 need to be done. This could also lead to new opportunities and problems to overcome, which could then become the organisational issue that your mini-dissertation will focus on. In Information Communication and Technology (ICT) this is referred to as Fuzzy Logic. The research becomes more defined as the 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 very specific hypothesis to be tested, or research questions to be answered. Descriptive studies are those that are used to describe phenomena associated with a subject population or to estimate proportions of the population that have certain characteristics (Ghauri and Grønhaug, 2010:56 58). Regenesys Business School 64

71 Observation versus interrogation/communication research If you decide to do an observation study, you will need to establish sound monitoring tools and techniques. These may be linked to technology and are considered to be a monitoring approach to collecting data where the researcher inspects the activities of the subject or the nature of some material without attempting to elicit responses from anyone. You need to take heed that the necessary ethical clearance is obtained and the method approved, as this is a sensitive type of research. If you choose to use an observation type of research design in your dissertation, you will need to decide if the observations are ethical and morally acceptable. There will need to obtain permission from the relevant stakeholders and respondents. The observations may be direct (where you will do the actual observations yourself of by a team of researchers) or indirect (cameras and other surveillance equipment may be used) and as a research student, you do not have to be at the physical location. During the observation, you will need to be very clear about what must be observed and how it will link back to the mini-dissertation 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 you may want to record, such as: spending patterns, patterns in social gatherings, etc. Observation requires a focused researcher with lots of attention to detail. This method is also used by Work Study Officers in organisations to conduct time and motion studies and, create effective and efficient work places through a detailed work and job analysis. As a researcher, if you decide to use the interrogation/ 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 mirrors for external observations and is equipped with recording and video equipment. The interrogators are expertly trained in interrogation methods and lie detection, as well as the ability to interpret body language. Task Questions 1. Give a practical example of the type of research where the two approaches above would be relevant, and discuss why you think so. Regenesys Business School 65

72 Experimental versus ex-post facto All the various types of experimental studies involve some sort of intervention (manipulation of one or more variables such as the taste, smell, etc.) by the researcher beyond that required for measurement to determine the effect of another variable. This would not be highly recommended as it is usually costly to implement and requires equipment to be applied under very specifically controlled environments, by specialists in that area. Saunders et al. (2013) support the notion that research experiments are derived from the natural sciences, although he argues that research experiments are also frequently used in the 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 one 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. In the mini-dissertation you may have more complex experiments, which 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 impact on the sales within the organisation being studied. Saunders et al. (2013) is of the opinion that experiments will tend to be used more in exploratory and explanatory research to answer how and why questions. That is, how do the advertising spending, competitor size and market size affect the sales? And why does it affect the sales? In your studies, most of the assignments and research are based on cases or past events. In simple terms, this is an ex post facto design, which you have used to complete the cases 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 which impacted, both negatively and positively on the project management process and make recommendations on how to improve the project success rate within the project management process. Task Question 1. Critically compare experimental versus ex-post facto approaches discussed above and discuss where each one would be relevant. Regenesys Business School 66

73 Descriptive versus causal Ghauri and Grønhaug (2010:56 58) view a descriptive study as a study that attempts 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 into a doctor's waiting room. This will require you to collect data on the times of arrival and analysis the data to be able to describe what is occurring, usually the analysis is conducted by applying descriptive statistical analysis. This will have to be carried out through the correct collection of data and the tabulation of the frequencies on the research variables (in this case, the times of patient arrivals into the doctors rooms). You will need to report on what the study reveals in terms of: 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 with regard to any causal relationships between the variables. This is now discussed below. 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 will, for example explore relationships between the times of patient arrival and the time of the day or the type of weather conditions to investigate possible relationships between these variables. This is done through the application of various statistical tests in an attempt to reveal the relationship between variables. The variables were defined and discussed in detail earlier in this study guide. This type of study is recommended where you want to prove relationships between variables in the research. Task Question 1. Give a practical example of the type of research where the two approaches above would be relevant, and discuss why you think so. Cross-sectional versus longitudinal A cross-sectional study is usually conducted once. It reveals a snapshot at one point in time within the study. This may be likened to analysis of the ratios used in Financial Management when analysing a Balance Sheet (Statement of Financial Position) as at a certain date, usually the financial year-end. In contrast to a cross-sectional study, a longitudinal study is one that is repeated over an extended period of time. This is used more in medical and psychological tracking studies, which 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 limited start and end date at a specific time period. Regenesys Business School 67

74 Task Question 1. Give a practical example of the type of research where the two approaches above would be relevant, and discuss why you think so. Case versus statistical In organisational learning, there is a strong emphasis on the use and application of case studies to emphasise the key theoretical principles and contextual analysis of the events. The researcher may wish to create a case study to be able to capture these principles and contextualise what is happening within the organisation. The case study approach is very time-consuming in terms of collection all the data and relevant data and information required to write-up the case study. There are often gaps in the information where relevant assumptions will need to be made. There may also be confidentiality clauses and ethical considerations that need to be referred to the relevant stakeholders to provide the necessary ethical clearances. This type of study is usually a qualitative study. In contrast to the case method, a statistical study attempts to capture the defined population s characteristics by making inferences from a probability sample being selected around the sample s characteristics. You must be aware that hypothesis testing needs to be conducted. This will require detailed statistical analysis when calculating and validating the results. This type of study is usually a quantitative study. (Saunders et al., 2013) Task Questions 1. Critically compare, by using a practical example, the two approaches above. Refer the article above obtainable at the following link: Perry, C. 1998, Processes of case study methodology for postgraduate research in marketing, European Journal of Marketing, University of Southern Queensland, Toowoomba, Australia, MCB University Press, (32) 9/10, , (accessed 27 January 2014). Case Study Answer the following question based on the article above: 1. Critically evaluate the processes of a case study methodology. Regenesys Business School 68

75 7.7.4 The Research Process For the purposes of this module we will follow the business research process shown in Figure 3, with the exception of Step 7, which is a requirement for the Regenesys' Master's degree only (the research proposal). The research process provided is aligned to a 'mini-dissertation', which is a requirement for this module. Figure 3: 10-Step Research Process 1. Choose a research topic 2. Develop a research ques5on and objec5ve 3. Compile a (cri5cal) 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 / Disserta5on (Saunders et al., 2013) Regenesys Business School 69

76 A 'Research Report' follows the formalised and very structured process. Figure 4: Research Process and the Research Report (Saunders et al., 2013) Regenesys Business School 70

77 The table below explains research related terminology. The research process provides the logical flow required for conducting research. Table 8: Terms Commonly Used in the Research Process Term Research topic (or problem area) Research question Hypothesis Variable Research objectives (Critical) Review Data Secondary data Primary data Literature 'Research onion' (metaphor research design) Validity Reliability Generalisability for Explanation The focus of the research the problem area is the axis around which the entire research process revolves. This may be one overall question or a number of key questions that the research process will address. These questions precede the research objectives. A testable proposition stating that there is a significant difference or relationship between two or more variables. Again, this is a precursor to the research objectives.. An individual element or attribute on which data is to be collected, e.g. when a research problem involves a possible cause-and-effect relationship the researcher will be looking at the extent to which one variable (the hypothesised cause) influences another variable (the hypothesised effect). We refer to variables throughout this study guide. Clear, specific statements that identify what the research process seeks to achieve as a result of doing the research. Refer to Section 8.2 A detailed overview of the significant literature available about the chosen topic providing discussion and critical evaluation. It reflects clear argument to contextualise and justify the research. Refer to Section 8.4. Facts, opinions, and statistics that have been collected and recorded for analysis. We refer to data throughout this study guide. Data used for a research project that were originally collected for some other purpose (e.g. census data, company financial reports, etc.). Refer to Section 8.4. Data collected specifically for the research project being undertaken. We consider this in detail in Sections 8.5 and 8.6. The route map to chart the researcher's way through choosing an appropriate research design. The outer layers of the onion contain thinking about research philosophies and approaches. The central layers reflect the need to consider research strategies and choices, while at the centre of the 'onion' data collection and analysis are the central concern. We consider this in detail in Section 8.5. The extent to which (1) the data collection method or methods accurately measure what they were intended to measure and (2) the research findings are really about what they profess to be about. Refer to Section 8.5. The extent to which the data collection methods and analysis procedures will produce consistent (repeatable) findings. Refer to Section 8.5. The extent to which the results of the research can be applied more generally (more widely) than the study itself or to a general population (may only be relevant to the specific context of the study). Refer to Section 8.5. (Saunders and Lewis, 2012; Leedy and Ormrod, 2013) Regenesys Business School 71

78 7.8 SAMPLING DESIGN Timeframe: Learning outcomes: Recommended reading: Recommended multimedia: Section overview: Minimum of 20 hours Understand the differences between a population and a sample Identify and select a census Identify the types of sampling methods Select an appropriate sample Identify and select a sampling frame Identify and describe sampling methods Identify and describe sampling bias and the impact of this on the research Saunders, M., Lewis, P. and Thornhill, A. 2013, Research Methods for Business Students, 6 th ed., Cape Town: Pearson Education. Chapter Seven Ignousohs. 2011, Sampling issues in research studies, [video clip], (accessed 16 January 2014). 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 Introduction to Sampling Designs A sample is a representative subset of the known and defined population, which is identified for the relevant research study. When we refer to 'population' (or sampling frame) this does not necessarily refer to people only it could, for example, refer to places or objects. Researchers will 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 Financial constraints In order to contextualise this section you need to understand the following terminology in Table 9: Regenesys Business School 72

79 Table 9: Terminology Population Census Sample Sampling frame The group of people, items or units under investigation. This is the universe of units from which the sample is to be selected. Obtained by collecting information about each member of a population Obtained by collecting information only about some members of a population The list of people from which the sample is taken. It should be comprehensive, complete and up-to-date. Examples of sampling frame: Electoral Register; Postal code Address File; telephone book (Leedy, 2013:215 ; Ghauri, 2010:140) Population versus a Sample A population can be defined as the entire group of persons or research subjects that the researcher wants to study, because it contains all the variables of interest to the researcher. We also refer to the population as the target population. Some examples of populations include: all first-year students at a particular university, all non-government organisations, all primary schools in a certain area, etc. We will now outline a number of basic concepts relating 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 geographical area. For example, consider the population of Regenesys Master s students in South Africa, a group numbering say students. This large population size of Master s students 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 from the Master s students. In organisations, which produce physical products, such as motor vehicles, cannot afford to crash, test every motor vehicle, as there would be no output. They use of specific sampling methods to ensure that the sample selected from the population of all motor vehicles produced in the factory within 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 the researcher identifies, defines and describes his/ her target population carefully in the research design and stipulates the criteria to be included in the population. For example: If a researcher wants to know how women feel about the glossy cover of a particular women s magazine (i.e. s/he wants to determine their response to the glossy cover), the selection criteria for the population will include women who are regular readers of the magazine. Regenesys Business School 73

80 The accessible population Researchers seldom have access to the entire population: The population that the researcher does have access to and actually does study usually differs from the entire population in one or more aspects. The population that the researcher can reach is known as the accessible population or study population. 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 (e.g. people in the same profession), or social groups, organisations, documents, or nations. The population for the Master s mini-dissertation 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 will 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 and inaccessible Sampling Leedy (2013:206) describes a sample is 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, the mini-dissertation could prove to 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?), time and cost of the study. Regenesys Business School 74

81 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. These are the key reasons why you will use a sample instead of doing a census. These are (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 lesser amount of data to deal with in the sample size. This reduces data collection and data interpretation time The large size of many populations may be too time consuming and not possible to reach. Inaccessibility of some of the population due to accessibility and security issues. Destructiveness of the observation. This is where you need to observe an event or an experiment and the event may not be able to be repeated again. Accuracy and reliability of the population data A sample may provide you with needed information more readily. This is especially true in emergency situations such as when there is an outbreak of a virus or a new disease. 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). 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. For example, if you want to study the level of employee satisfaction within an organisation, you may randomly choose all the disgruntled employees or all the satisfied employees by chance. 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). An interview bias is where the person collecting the data influences the respondents of the event. For example, if the researcher does not allow each interviewee to give his/ her opinion during a group interview, the researcher could be guilty of bias. When you conduct an interview, you have to be careful how you react to responses. If you overreact, it may result in you being seen as biased. The best approach would be to keep the interview as formal as possible. When you ask leading questions in a questionnaire or interview, the respondents may feel pressurised to respond positively instead of answering truthfully. This also results in biased responses. Try to avoid rhetorical questions and any double-barrelled question. Organisations operate in a global village with diverse cultures, customs, languages, etc., which needs to be taken into account to reduce interview bias. Regenesys Business School 75

82 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 the researcher is actually investigating (using a particular research instrument) what s/he says s/he is 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: ). 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). We will now briefly explain two types of validity that should be considered when designing and evaluating research: internal validity and external validity. Sample 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 utilised 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 will be 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 that was conducted on 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 Causes of Sampling Error There are two basic causes for 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 76

83 The second cause of sampling is sampling bias. Sampling bias occurs where there is a tendency to favour the choice selection of units from the population that are deemed to possess certain characteristics which, you as the researcher may be looking for, or are more readily available. Leedy (2013: ) argues that bias is usually the result of a poor sampling plan. The most notable is the bias of non-response when for some reason some units have no chance of appearing in the sample. For example, take a hypothetical case where the Revenue Services asking respondents to disclose their levels of honesty in their tax returns conducted a survey recently. If a mail questionnaire was sent to randomly selected tax payers, would the results be reliable and valid? You need to take cognisance of the fact that bias can be very costly as 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 prices in a residential suburb and you decide to use the telephone numbers from the telephone directory (sampling frame) to select a sample from the total population in a locality where only the expensive houses are, and those who 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. Ignousohs. 2011, Sampling issues in research studies, [video clip], (accessed 16 January 2014). Task Questions Read the example of sampling in South Africa below and discuss 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 would be able to locate all South African men with nursing qualifications, but it would be possible to locate all practising male nurses in South Africa, because all practising nurses have to 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/ her to come to a conclusion about the accessible population Non-sampling error (measurement error) The other main cause of unrepresentative samples is non-sampling error. This type of error can occur whether a census or a sample is being used. Like sampling error, non-sampling error may either be produced by the participants in the statistical study or be an innocent by-product of the sampling plans and procedures. Regenesys Business School 77

84 A non-sampling error is an error that results solely from the manner in which the observations are made. The simplest example of non-sampling error is inaccurate measurements due to malfunctioning instruments or poor procedures utilised in your research. For example, consider the observation of individual spending in a shopping mall. If persons 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 themselves on the different goods, and their responses may me incorrect. To reduce this error, you may have to physically check the goods purchased against the till slips. Biased observations This is caused when you as the researcher may try to see what is important to you. This may be caused by 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 without critically evaluating them. The interviewer s effect No two interviewers will be 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 just to lie to you 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 and a date of birth will definitely be more accurate. The respondent effect Respondents may also 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 samples ability to represent the population and allow you to make inferences in 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 for the same population. Regenesys Business School 78

85 Induced bias Induced sampling bias is caused when the personal prejudices of the researcher or the data collector may tend to induce bias. 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 it does not allow for personal interpretation Sampling Procedure Sampling procedures are divided into two main broad categories: non-probability and probability samples. For the mini-dissertation 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 in the sample. This allows you to make statistical inferences on the population. This simply means that the sample represents the population and can be statistically proven. In non-probability 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: ) Selecting the sample The preceding section has 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, to reduce error often requires an increased expenditure of resources. 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. As a researcher you need to understand that there will always be a chance error when dealing with sample data. There will always be the chance of drawing a good sample from a bad population or drawing 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 79

86 The two main methods used in survey research are probability sampling and non-probability sampling. The main and most important distinction between the two methods is that, in probability sampling all the people in the population will 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. These are usually the availability, cost, time, research objectives and reporting requirements Types of Non-Probability Samples There are three primary types of non-probability sampling methods: Convenience Judgement sample, and Random 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 the travel time and costs for the researcher. An example of this will 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 the Master s 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 judgement sample Researchers can use this type of method where they need to gain expert opinion from people who are seen to be experts within the industry. The assumption is that only expert s 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 judgements about the performers to determine the level of skills the performers have. A judgement sample is obtained in accordance with the discretion of someone who is familiar with the relevant characteristics of the population. Snowball sample This is a well-known technique 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, they may be able to refer you to another respondent and this eventually grow, hence the name, snowball sampling. Regenesys Business School 80

87 The random sample This may be the most important type of sample, since it allows for a known probability that each unit will be chosen. For this reason, it is sometimes referred to as a probability sample. This is the type of sampling that is used in lotteries and raffles. For example, if you want to select 10 players randomly from a population of 100, you can write their names, fold them up, mix them thoroughly then pick ten. In this case, every name had any equal chance of being picked. Random numbers, which are generated by a calculator, are numbers over which the researcher has no control, can also be used (Saunders et al., 2013) Types of Probability or Random Samples The probability or random sampling techniques are described below. A simple random sample A simple random sample is obtained by choosing elementary units in search 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 often very 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 10 th or 100 th unit after the first unit has been chosen randomly, as discussed below. Such a procedure is called systematic random sampling. A systematic random sample Selecting one unit on a random basis and choosing additional elementary units at evenly spaced intervals until the desired number of units is obtained obtains 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 may be in an 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 different groups may be based on some characteristic or variable such as levels of spending, income, education, etc. Regenesys Business School 81

88 The aforementioned groups are referred to as strata (layers or as marketers as 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, 5 from group B and 3 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. You want 10 town house 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 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 due to the fact that they interact with one another. They may even go to the same social gatherings or attend the same schools 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 being asked and the constraints being faced. This holds true for sampling strategy as well as sample size. Sample size Before deciding how large a sample should be, you have to define your study population. For example, all children below age three in New York. Then determine your sampling frame, which could be a list of all the children below three as recorded during 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 one 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). Regenesys Business School 82

89 In general, it can be seen that the sample size will depend 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 impact on the sample size. The required sample size is a function of the level of precision you will require as a researcher when submitting your research proposal.. The sample size n required estimating 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 R , the required sample size is 983:[(1.96)(1600/100)] squared. Deciding on a sample size for qualitative inquiry can be even more difficult than quantitative because there are no definite rules to be followed. The sample size may be based on data saturation. In essence, when the researcher finds that the respondents are providing the same or similar information. This shows that the researcher has 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 specific 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/ 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 on it. For example, if the population consists of all individuals living in a particular city and the researcher uses the phone directory as the sampling frame or list, they will ignore individuals with unlisted numbers or who cannot afford a phone. Regenesys Business School 83

90 Foreign elements constitute the second sampling problem. These are elements, which should not be included in the population, and sample appears on the sampling list. Thus, if the researcher were to use property records to create the list of individuals living within 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 the researcher is studying patient satisfaction within an emergency room at a hospital, they can possibly include the same patient more than once in the study. If the patients are completing a patient satisfaction questionnaire, the researcher will need to 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 represents a duplicate Conclusion In conclusion, it can be said that using a sample in research saves money and time. A valid research study that uses a qualitative approach will have a sample size that represents the population through a probability sampling method. If a suitable sampling strategy is used, the appropriate sample size would be selected and the necessary precautions would be taken to reduce sampling and measurement errors. In such a case, the sample should yield valid and reliable information. This implies that the statistical results obtained from the sample can be inferred onto the population. Regenesys Business School 84

91 7.9 PLANNING YOUR DATA COLLECTION DESIGN Timeframe: Learning outcomes: Recommended reading: Recommended multimedia: Minimum of 50 hours Apply the research process in resolving a business problem Demonstrate the ability to apply advanced statistical and other scientific data analysis techniques Collect data by using the appropriate research methods Collate and analyse the data by performing the relevant descriptive and inferential statistical analysis tools and techniques Collect and analyse research data and demonstrate its value in business decision-making Saunders, M., Lewis, P. and Thornhill, A. 2013, Research Methods for Business Students, 6 th ed., Cape Town: Pearson Education. Chapter Eight, Nine, tten Cranfield SoM. 2012, 'Management research: Delivering business results', [video clip], (accessed 16 January 2014). The basic idea behind the survey methodology is to collect and analyse the relevant data by asking your target population people questions and then to examine relationships among the variables. Your survey will attempt to record the relevant data around facts, perceptions and fractals (patterns) behaviour. Section overview: This section will cover the following topics: 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 Data Collection Methods The key data collection techniques are: Surveys Questionnaires Panel questionnaire designs Interviews Experimental treatment Regenesys Business School 85

92 Surveys Leedy (2013:189) views surveys as a study, which is designed to determine the incidence, frequency, as well as the distribution of certain characteristics in a population. Surveys are commonly used in business and government when research is conducted. The basic idea behind the Regenesys mini-dissertation 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 will attempt to record the relevant data around facts, perceptions, and fractals (patterns) behaviour. The most common mini-dissertation survey is a cross-sectional design, which asks respondents to answer the survey at a point in time. You need to be aware that this in limited as you may or may not be able to analyse the direction of causal relationships. This is critical if your mini-dissertation is based on systems thinking concepts (Leedy (2013: ). Ghauri (2010: ) views a survey as a method of data collection where the term survey refers to one, 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 out themselves. An interview typically occurs whenever a researcher and respondent are face-to-face or communicating via some technology like telephone or computer. The types of interviews typically used are: unstructured interviews, which will allow for instant and more sporadic, often more honest responses during the interview process; a structured interview is where the researcher will restrict the possible responses; and when conducting semi-structured interviews, the researcher will restrict certain kinds of responses allowed but will allow freedom on discussion of certain selected topics. Leedy (2013:189) argues that in adding retrospective (past behaviour) and prospective (future propensities) items to a cross-sectional survey may reduce this limitation, but generally it's more useful to have a longitudinal design, which asks the same questions at two or more points in time. The time constraint in completing the mini-dissertation will be a limiting factor to being able to do a longitudinal study. A trend study is basically 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 points in time. This can track changes in the industry over time; and supplemented by a panel study, which asks the same questions to the same people time after time will be most beneficial for you as a researcher to begin implementing within your organisation to ensure sustainability of the organisation. Although surveys can be a cost-effective type of research, survey research design suffers from inherent weaknesses. The greatest weakness as argued by Leedy (2013: ) is probably due to the fact that surveys are basically exploratory in nature. This implies that in your minidissertation you can make inferences, but will be limited in systems thinking when reviewing 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 high non-response rate and measurement errors may occur causing the results to be unreliable or invalid. Regenesys Business School 86

93 7.9.2 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, person-to-person, or even within one person over time. There are six common types of variables that you will be required to define in the research in terms of identifying and formulating the research problem: A research problem is always based on a specific 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; the researcher should always distinguish between the dependent and independent variables in his/ her 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 i.e. 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 together in a certain manner. If your passing the assignments depends on the quality of the course, then the academic support processes will be the intervening variable. The moderating variable influences the relationship between the independent and dependent variables by modifying the effect of the intervening variable(s) through possibly limiting the scope of the intervening variable. If your passing the assignments depends on the quality of the course, then the academic support processes will be the intervening variable and the moderating variable(s) may be gender, age, culture, or academic competency levels in the specific subjects. The control variable refers to the variables that are not measured in the research and must be held constant, neutralised/ balanced, or eliminated, so they will not have a biasing effect on the other variables. Variables controlled in this way are called 'control variables'. An example of this is the physical environment in which the research interview is conducted. The extraneous variables are those variables in the research environment which may have an impact on the dependent variable(s) but which is not controlled. Extraneous variables must be observed very closely and isolated before the study commences. The reason for this is that they may impact on the success of the research. They may adversely impact on the study's validity, and thereby make it impossible to know whether the effects were caused by the independent and moderator variables or some extraneous factor. If they cannot be controlled, extraneous variables must at least be taken into consideration when interpreting results. If your passing the assignments depends on the quality of the course, then the academic support processes will be the intervening variable and the moderating variable(s) may be gender, age, culture, or academic competency levels in the specific subjects. Extraneous variables may be the stress level or adverse conditions that may impact on the success of the student in their studies. (Adapted from Ghauri, 2010: and Leedy, 2013: ) Regenesys Business School 87

94 Task Questions 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 in May Write down your own research problem and identify the dependent and independent variables Questionnaires As a researcher, if you decide to use questionnaires, you will need to start by writing the questions themselves based on your research questions. After a rough draft is created, you have to ensure that these questions cover the scope of those research questions and the dissertations objectives. You must link these questions to the variables discussed earlier in this study guide. 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 ask for responses around only one research variable at a time. While designing the questionnaire, you need to take into account methods to increase the response rate. Some popular methods to improve the response rate are to increase response rate involve timing and remuneration. Timing is the name for a variety of techniques involving pre-survey phone calls or postcards telling respondents that a survey is coming their way soon. After the survey has been mailed or delivered, timing also involves a follow-up friendly reminder to complete the survey (Saunders et al., 2013). Sometimes, respondents will 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 tend to 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 money, attached to the questionnaire, so those respondents feel guilty about keeping the money and not answering the survey. Personalisation also increases response rate (Saunders et al., 2013). The order of questions is an important consideration in your Master's mini-dissertation 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 only 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 88

95 It is essential in research to ensure that you are targeting the correct sample frame members and you will need to 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 you a great deal of frustration and time in gathering data for your research. Interviews The general rule for interviewing is to record responses verbatim. This actually means you should use some type of voice/ 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 pre-coded response categories (SA, A, D, SD) which 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 like it is leading to interviewer bias. Interviews are wonderful opportunities to impress the importance of confidentiality on respondents. 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 criminal justice 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 webbased series of questions allowing for chat or bulletin board posting. Various software programs exist that 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. Regenesys Business School 89

96 7.9.4 Data Collection Techniques In line with Leedy (2013:279), we can point out that there are two main categories of data: Qualitative (not numerical data and quantitative data). 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 or numerical data). Quantitative data is a numerical measurement expressed not by means of a natural language description, but rather in terms of numbers. Quantitative data uses four levels of measurement: nominal, ordinal, interval, and ratio. These go from the lowest level to highest level of statistical analysis being able to conduct on the data. As a research student, you will need to be very clear about the levels of data as this will impact on the ability to test the hypothesis. As a general rule, try to always use interval and ratio data, preferably ratio data. Note that the data is classified according to the highest level, which it fits. Each additional level will add something onto the previous and lower level. This assumes that the lower level never has the ability to do that and limits the statistical analysis, which can be done. It is essential as a researcher that you are fully aware and understands 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 analyses tends to be less sensitive, but fewer statistical analysis methods can be applied. Nominal data is the lower level of measurement and only the mode, percentages and graphs can be applied. The response categories on the questionnaire are mutually exclusive, meaning that you have to choose one category over another. An example of this is typically the question: Are you male or female? Or What race are you? The order of the response categories is not important and may be interchanged without having any impact on the outcome. The second lowest level of data is ordinal data where there is a certain order and logic that needs to 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 (you have to choose one category over another). An example of this is typically 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 having an impact on the outcome. The third lowest 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 the motor vehicle. There are intervals of say 0 10 Km, KPH 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 (you have to 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 in. Regenesys Business School 90

97 The top level of the data and the level where all the descriptive statistical measures can be applied is the 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 the motor vehicle. There are intervals of say 0 10 Km, KPH and so on. With ratio data the speedometer needle will fall within a certain interval and be on an exact speed. Example: Within the KPH (interval data) with the speedometer needle may be on 55 KPH (ratio data) Constructing Questionnaires Nominal scales These are the simplest of measurement scales, which classifies 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 modules are you currently registered for? Table 10: Example of Nominal Scale 1. Finance 5. ICT 2. Economics 6. Knowledge Management The numbers have no arithmetic properties and act only as labels for Master's modules. The only statistical measure, which can be used, is the mode because this is simply a set of frequency counts. Ordinal scales These are also a simple type of measurement scale, which classifies individuals, companies, products, brands or other entities into categories where order is implied. Indeed 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, and if desired, numbers can be nominally assigned to label each category as in the example below: Regenesys Business School 91

98 What size shirt do you wear? Table 11: Example of Nominal 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 is same as distance from 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. As a researcher 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 below. 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: Knowledgeable Engaging Able to transfer concepts Professional Caring (a) Circle the appropriate score on each line Regenesys Business School 92

99 Please indicate your views on the Master's facilitator 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 that they might 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 module in a past semester. Why? Because you can have zero Master s students registered for an elective module in past semester and because it is meaningful to say that "...we had twice as many clients 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 out of all the other types of data and relevant scales. This has the properties of an interval scale together with a fixed origin or zero point. Examples of variables which are ratio scaled include assignment and exam marks, mass, lengths, speed, salaries, etc. Ratio scales will allow you to compare both differences in scores and the relative magnitude of scores. For instance, the difference between 50 and 60 percent is the same as that between 70 and 80 percent, and 60 percent is twice as large as 30 percent Scale Development In questionnaire design it is essential to make sure that the appropriate response is obtained. This is achieved through the use of appropriate scales. A scale typically uses 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 are to treat them as interval or higher. 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). Regenesys Business School 93

100 There are 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 part of their attitude 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 will overcome this limitation by obtaining a panel of judges and then asking them questions on why a Master s student will choose to study at Regenesys. By administering the questionnaire to the panel, you can then analyse inter-item agreement among the judges, and then eliminate what are called the non-homogenous items. Scaling seems to be driven by the need to be homogeneous in nature. In applying the Thurstone scaling, your objective is to favour the more useful respondents and look for higher-scoring items in high clusters of homogeneous responses to answer the question on 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 today. Think about the feedback cards received at most restaurants today. 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 mini-dissertation 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) Guttman scaling This is a technique whereby 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 introduced more of a randomness approach and takes away the predictability element, which 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: Regenesys Business School 94

101 For each of the following, indicate if you SA, A, 50/50, D, or SD: 1. Crime is out of control in South Africa. 2. Police should be given more powers. 3. More criminals should be given the death penalty. 4. South Africans ought to do something about drug exporting countries. 5. The military ought to be used to patrol our streets. 6. Inmates on death row ought to be executed quickly. 7. Most politicians are too soft on crime. 8. Lethal injection is too merciful for those who deserve it. 9. Crime is destroying the social fabric of our society. 10. 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 and harsher 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 all that well received by the scientific community. A variation is the Bogardus social distance scale, but it has properties of the semantic differential also 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, rightwrong, strong-weak, 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: 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 module? 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. Regenesys Business School 95

102 7.9.7 Measurement Scales There are various types of measurement scales recommended for the researcher to utilise in their research. These are based on the following two categories: comparative and non-comparative measurement scales. In comparative scaling, the respondent will be asked to compare one item with another. With non-comparative scaling the respondents will only be required to evaluate an item. The respondent s evaluation will be independent of the items, which you will be studying. Table 12: Types of Scales Types of Scales Comparative scales Non-comparative scale A line marking scale A semantic scale A Likert scale Description 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 ever put to the respondent at any one time. 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. Can be used to measure perceived similarity differences 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 items which best describes the item being studied. Will use words rather than numbers. This is used if you want to ask your respondents to describe their feelings about the item being studied. You will need to use antonyms at the end points of the scale; these are termed semantic differential scales. The semantic scale and the semantic differential scale are illustrated below: 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 will have 5 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: Strongly Strongly Agree Neither Disagree Agree Disagree The e-learning books add value to my Master's studies The student portal is simple to use The facilitators are keen to assist with student questions Regenesys Business School 96

103 7.10 DATA ANALYSIS Timeframe: Learning outcomes: Recommended reading: Recommended multimedia: Section overview: Minimum of 40 hours Understand the essential descriptive analysis tools and techniques Do basic descriptive analysis Calculate and describe measures of central tendency. Calculate and describe measures of dispersion Report on the relevant descriptive statistical measures Understand the basics of inferential statistical analysis Structure a hypothesis Use the popular inferential statistical measures to test the hypothesis Saunders et al., M., Lewis, P. and Thornhill, A., 2013, Research Methods for Business Students, 6 th ed., Cape Town: Pearson Education. Cranfield SoM. 2012, 'Management research: Delivering business results', [video clip], (accessed 16 January 2014). The more critical part of research is to be able to test the hypothesis and explore relationships within the data. This will then be linked back to the research questions and problem statement in the dissertation. Once the hypotheses testing are 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 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. 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 and correctly captured to be able to co crosstabulations Endure that the data is linked to the correct questions Ensure that the data is collected in the format of either: nominal, ordinal, interval or ratio data Validate the accuracy and reliability of the data captured compared to the questionnaires Regenesys Business School 97

104 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 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 needs to be done by using inferential statistical analysis. The more critical part of research is to be able to test the hypothesis and explore relationships among variables. This will then be linked back to the research questions and problem statement in the dissertation. Once the hypotheses testing are 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 being 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: Regenesys Business School 98

105 For example, take the following set of data: {1,2,3,4,5}. The mean of this data would be: Median This is the middle value in a set of data. This is also known as an array of data. The essential thing you need 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 the data in our second data set from above: {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. 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. ( ) / 2 = 82 / 2 = 41. 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. Regenesys Business School 99

106 Mode According to Leedy (2013), who considers mode as the single number or score which will occur the most times in the data range and 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 in the exam papers? 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, 9}. The mode for this data set is 4. An example of a bimodal data set is {1, 2, 2, 3, 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, i.e. 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. For example, in a sample with values , 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 a 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. Regenesys Business School 100

107 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: 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. 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 the n elements x1,...,xn, taken from the population. The (sample) mean is: 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: Regenesys Business School 101

108 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 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: and the population variance:, The population standard deviation is:. Notice how this standard deviation is somewhere in between the possible deviations. Regenesys Business School 102

109 Task Questions 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 will need to 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. Dialog boxes Choose Tools/ Data Analysis, and select the kind of analysis you want to do. The typical dialog box will have the following items: Input range Type the upper left and lower right corner cells, e.g. 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. Other items, depending on the analysis. Regenesys Business School 103

110 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 which will begin with the 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. 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 Master's Finance Exam Results Area 1 Master's Finance Exam Results Area Regenesys Business School 104

111 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 the 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 Regenesys Business School 105

112 The table below will appear in the new worksheet Area 1 Mean Range 86 Standard Error Minimum 11 Median 54 Maximum 97 Mode 50 Sum 2076 Standard Deviation Count 35 Sample Variance Largest (1) 97 Kurtosis Smallest (1) 11 Skewness Confidence Level (95.0%) For Area 2: 1. Go the 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 Area 2 Mean Range 87 Standard Error Minimum 11 Median 60 Maximum 98 Mode 60 Sum 2075 Standard Deviation Count 35 Sample Variance Largest (1) 98 Kurtosis Smallest (1) 11 Skewness Confidence Level (95.0%) Regenesys Business School 106

113 The two areas can now be combined into one table to be able to make comparisons between the two areas sampled. Descriptive Statistical Measures Area 1 Area 2 Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Largest (1) Smallest (1) Confidence Level (95.0%) Interpretation of the above 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 in 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 the study guide. 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. Regenesys Business School 107

114 Task Questions For Area 1: The average (i.e. mean) (59.31%) is above the median (54%) and also above the mode (50%). Critically evaluate the average (i.e. 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 above 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 will need to 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 below by using the data above to create a grouped frequency distribution, commonly known as a histogram. In Excel you need to 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 (classed) 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 108

115 Master's Finance Exam Results Number of Students Area 1 Area 2 Bin Range Regenesys Business School 109

116 In Excel you need to 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. Select the Data for Area 1 in the Input Range Select the Bin Range Select New Worksheet Ply Select Chart Output You will have the following histogram for Area 1 displayed on a new worksheet. Bin Frequency Figure 5: Histogram Area 1 Histogram Frequency Frequency More Bin Regenesys Business School 110

117 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 you need to create the Histogram In Excel, go to: Data, Data Analysis, and Histogram. Select the Data for Area 2 in the Input Range Select the Bin Range Select New Worksheet Ply Select Chart Output You will have the following histogram for Area 2 displayed on a new worksheet. Bin Frequency Figure 6: Histogram Area Histogram Frequency Frequency More Bin Regenesys Business School 111

118 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) as, well as, the confidence level. The larger these values are, the greater the width of the graph will become 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 minidissertation 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. Some examples of the more popular statistical hypothesis tests are demonstrated below. To illustrate this, we will use the same example as the above and 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. Regenesys Business School 112

119 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 Financial Exam Results. Ha (Alternate Hypothesis): There is a significant difference between Area 1 and Area 2 in terms of the Master s Financial 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 around 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 of 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<.05 (5%) the Ho will be rejected and the Ha accepted. Step Three: Analyse the data you have sampled Find the value of the test statistic (mean score, proportion and p-value.) described in the analysis plan. In Excel do the following: Go to Data, Data Analysis, Anova-Single Factor, select all the data under area 1 and area 2, Alpha must be 0.05 (default setting), Select New worksheet Ply. Regenesys Business School 113

120 The following table will be generated: Anova: Single Factor SUMMARY Groups Count Sum Average Variance Column 1 Area Column 2 Area ANOVA Source of Variation SS df MS F P-value F critical Between Groups Within Groups Total SS stands for Sum of Squares. It is the sum of the squares of the deviations from the means. When we introduced variance, we called that a variation. In other words, each number in the SS column is a variation. MS stands for Mean Square. It is a kind of average variation and is found by dividing the variation by the degrees of freedom. F stands for an F variable. F was the ratio of two independent chi-squared variables divided by their respective degrees of freedom. Step Four: Interpret your results Apply the decision rule described in the analysis plan. If the value of the test statistic is unlikely, based on the null hypothesis, reject the null hypothesis. Look at the P-value of This is not than the test value (critical value) of Decision errors: Two types of errors can result from a hypothesis test. Type I error A Type I error occurs when the researcher rejects a null hypothesis when it is true. The probability of committing a Type I error is called the significance level. This is the type of error allowed for. The level of significance was set at 5%. This is also named the alpha error, and is often denoted by. This is the default in testing a hypothesis. Type II error A Type II error occurs when the researcher fails to reject a null hypothesis that is false. The probability of committing a Type II error is called Beta, and is often denoted by. The probability of not committing a Type II error is called the Power of the test. Regenesys Business School 114

121 Decision rules The analysis plan includes decision rules for rejecting the null hypothesis. In practice, statisticians describe these decision rules in two ways with reference to a P-value or with reference to a region of acceptance. P-value. The strength of evidence in support of a null hypothesis is measured by the P-value. Suppose the test statistic is equal to S. The P-value is the probability of observing a test statistic as extreme as S, assuming the null hypothesis is true. If the P-value is less than the significance level, we reject the null hypothesis. Look at the P-value of This is not less than the test value (critical value) of 0.05 and so the Ho is accepted. Accept the Ho (Null Hypothesis): There is no significant difference between Area 1 and Area 2 in terms of the Master s Financial Exam Results. One-tailed and two-tailed tests A test of a statistical hypothesis, where the region of rejection is on only one side of the sampling distribution, is called a one-tailed test. A test of a statistical hypothesis, where the region of rejection is on both sides of the sampling distribution, is called a two-tailed test. (Adapted from Leedy, 2013: ) Inferential Statistical Analysis Significance Tests One of the most popular tests is the regression and correlation coefficients, which measures the strength of association between two variables. These two variables are on the X and Y-axis. The X-axis contains the independent variable and the Y-axis the dependent variable (the variable we want to predict). The correlation coefficient, called the Pearson product-moment correlation coefficient, will be used to measure the measures the strength of the linear association between the X and Y variables. There is a measure of linear correlation, which implies that the two variables have a linear relationship. The population parameter is denoted by the Greek letter rho and the sample statistic is denoted by the roman letter r. The value of a correlation coefficient ranges between -1 and 1. The greater the absolute value of a correlation coefficient, the stronger the linear relationship. A 1 will be a strong positive linear relationship and a -1 will be a strong inverse linear relationship. A correlation of 0 does not mean zero relationship between two variables; rather, it means zero linear relationship. Regenesys Business School 115

122 Calculation of correlation on MS Excel Do demonstrate statistical analysis using correlation coefficients, the previous example will be used. The data is shown below. Number of Students Randomly Sampled in Each Area Doing the Same Exam Master s Finance Exam Results Area 1 Master s Finance Exam Results Area Regenesys Business School 116

123 By using correlation analysis, we can determine if there is a linear correlation between the Master s Finance Exam Results for Area 1 and the Master s Finance Exam Results for Area 2. In this example we will assume that the students from Area 1 are contact class students and they are well known by the facilitators at Regenesys (the X axis and independent variable) while the students for Area 2 (the Y axis and dependent variable) are only distance education students. We want to determine if there is a linear relationship and a correlation between the Area 1 and Area 2 students who wrote the Master s Finance Exam. In Excel this can be done graphically as follows: Select the two Areas data (exclude the headings), Go to Insert Scatter Plot graph, Right click on any data point in the graph, Add a Linear trend line, Select the Display Equation on Chart and Display R-Squared value on Chart. The following graph will appear: Figure 7: Correlation Analysis Correla5on Analysis with Trend Line Equa5on y = 0,2078x + 46,962 R² = 0, There is a weak, positive correlation between Area 1(X axis) and Area 2 (Y Axis). This is seen by the wide scattering of the points around the straight line (y = x ). This straight-line equation can be used of make predictions of the X and Y values. Pearsons correlation co-efficient of R² = indicates a low correlation. The CORREL and PEARSON worksheet functions both calculate the correlation coefficient between two measurement variables when measurements on each variable are observed for each of N subjects. Any missing observation for any subject causes that subject to be ignored in the analysis. The Correlation analysis tool is particularly useful when there are more than two measurement variables for each of N subjects. It provides an output table, a correlation matrix, which shows the value of CORREL (or PEARSON) applied to each possible pair of measurement variables. Regenesys Business School 117

124 The correlation coefficient is a measure of the extent to which two measurement variables vary together. It is scaled so that its value is independent of the units in which the two measurement variables are expressed. For example, if the two measurement variables are weight and height, the value of the correlation coefficient is unchanged, if weight is converted from pounds to kilograms. The value of any correlation coefficient must be between -1 and +1 inclusive. You can use the correlation analysis tool to examine each pair of measurement variables to determine whether the two measurement variables tend to move together that is, whether large values of one variable tend to be associated with large values of the other (positive correlation), whether small values of one variable tend to be associated with large values of the other (negative correlation), or whether values of both variables tend to be unrelated (correlation near 0 (zero)). Correlation dialog box Input Range: Enter the cell reference for the range of data that you want to analyse; this will be the data for Area 1 and Area 2. The reference must consist of two or more adjacent ranges of data arranged in columns or rows. Grouped By To indicate whether the data in the input range is arranged in rows or in columns, click Rows or Columns. Labels in First Row/ Labels in First Column: If the first row of your input range contains labels, select the Labels in First Row check box. If the labels are in the first column of your input range, select the Labels in First Column check box. This check box is clear if your input range has no labels. Microsoft Office Excel generates the appropriate data labels for the output table. Output Range: Enter the reference for the upper-left cell of the output table. Excel populates only half of the table, because correlation between two ranges of data is independent of the order in which the ranges are processed. Cells in the output table with matching row and column coordinates contain the value 1, because each data set correlates exactly with itself. New Worksheet Ply: Click to insert a new worksheet in the current workbook and paste the results starting at cell A1 of the new worksheet. To name the new worksheet, type a name in the box. The following correlation table will be displayed: Area 1 1 Area 1 Area 2 Area This confirms the results discussed above that there is a very weak positive correlation between Area 1 and Area 2. Note that the Pearson s value shown above is R² = You need to look at the table value above of and Square this to get the value of Pearson s correlation coefficient of Regenesys Business School 118

125 Regression analysis hypothesis test using the Pearsons correlation coefficient The regression analysis tool performs linear regression analysis by using the least squares method to fit a line through a set of observations. You can analyse how a single dependent variable is affected by the values of one or more independent variables. 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 a linear correlation between Area 1 and Area 2 in terms of the Master s Financial Exam Results Ha (Alternate Hypothesis): There is no linear correlation between Area 1 and Area 2 in terms of the Master s Financial 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 around a single test statistic. This hypothesis will be tested by using the Anova the to test for linear Regression Step Three: Analyse the data you have sampled In Excel do the following: Go to Data, Data Analysis, Regression, select X range as area 1 and Y range as area 2, Select New worksheet Ply. Find the value of the test statistic, which is the p_value. The following table will be generated: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 35 ANOVA df SS MS F Significance F Regression Residual Total Regenesys Business School 119

126 Look at the P-value of This value is greater than the 0.05 critical p-value to test against. This is less than the test value (critical value) and so the Ho is accepted. There is a linear regression line between Area 1 and Area 2 in terms of the Master s Financial Exam Results Accept the Ho (Null Hypothesis): There is a linear correlation between Area 1 and Area 2 in terms of the Master s Financial Exam Results Hypothesis testing using the t-test To demonstrate how this can be applied on the dissertation, where applicable, the same data will be used as for the other statistical tests above. Refer back to the descriptive statistical analysis conducted above on this data. Master s Finance Exam Results Number of Students Area 1 Area Regenesys Business School 120

127 Table 13: t-tests Paired two sample for Means You can use a paired test when there is a natural pairing of observations in the samples, such as when a sample group is tested twice. This t-test form does not assume that the variances of both populations are equal. A pooled variance is calculated by Excel in this t-test and is an accumulated measure of the spread of data about the mean, which is derived from the following formula. Paired two sample for means dialog box Two-sample assuming equal variances Two-Sample assuming unequal variances This t-test assumes there is a shift in sample means. A value of 0 (zero) indicates that the sample means are hypothesised to be equal. This analysis tool performs a two-sample student's t-test. This t-test form assumes that the two data sets came from distributions with the same variances. In your dissertation, you can use this t-test to determine whether the two samples are likely to have come from distributions with equal population means. This test is used if the assumption is that the two data sets came from distributions with unequal variances. This is typically used on the Mini-dissertationwhen there are distinct subjects in the two samples. The following formula is used to determine the statistic value t. The following formula is used to calculate the degrees of freedom, df. Because the result of the calculation is usually not an integer, the value of df is rounded to the nearest integer to obtain a critical value from the t table. The Excel worksheet function TTEST uses the calculated df value without rounding, because it is possible to compute a value for TTEST with a non-integer df. Because of these different approaches to determining the degrees of freedom, the results of TTEST and this t-test tool will differ in the Unequal Variances case. Regenesys Business School 121

128 In the example used above the t-test: Two-Sample Assuming Equal Variances will be used as we assume there will be Equal Variances for the same exam between Area 1 and Area 2. 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 Area 1 and Area 2 in terms of the Master s Financial Exam Results. Ha (Alternate Hypothesis): There is a significant difference Area 1 and Area 2 in terms of the Master s Financial 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 around a single test statistic. This hypothesis will be tested by using the Two-Sample Assuming Equal Variances to test the equality of the two Area Means at one time by using variances. This will be a two-tailed test as the Ho is that Area 1 = Area 2. The test will be done at a 95% level of confidence and, therefore, a 5% level of significance (100% 95%). The decision rule is as follows: If the t-calculated value is less than the t-critical value the Ho will be accepted. These values will be calculated by Excel and shown in the table below. Step Three: Analyse the data you have sampled In Excel do the following: Go to Data, Data Analysis, t-test Two Sample Assuming Equal Variances, select the data under Area 1 as Variable 1 Range and Area 2 as Variable 2 Range, Alpha must be 0.05 (default setting), Select New worksheet Ply. Regenesys Business School 122

129 The following table will be generated: Table 14: t-test Two-sample assuming equal variance Area 1 Area 2 Variable 1 Variable 2 Mean Variance Observations Pooled variance Hypothesized mean difference 0 df 68 t Stat P(T<=t) one-tail t Critical one-tail P(T<=t) two-tail t Critical two-tail Step Four: Interpret your results Apply the decision rule described in the analysis plan. If the value of the test statistic is unlikely, based on the null hypothesis, reject the null hypothesis. The decision rule is as follows: If the t-calculated value is less than the t-critical value the Ho will be accepted. These values will be calculated by Excel and shown in the table below. In this case: P(T<=t) two-tail = and is less than the t Critical two-tail of The decision is to accept the Ho. Ho (Null Hypothesis): There is no significant difference Area 1 and Area 2 in terms of the Master's Financial Exam Results Decision errors: Two types of errors can result from a hypothesis test Type I error: A Type I error occurs when the researcher rejects a null hypothesis when it is true. The probability of committing a Type I error is called the significance level. This is the type of error allowed for. The level of significance was set at 5%. This is also named the alpha error, and is often denoted by. This is the default in testing a Hypothesis. Type II error: A Type II error occurs when the researcher fails to reject a null hypothesis that is false. The probability of committing a Type II error is called Beta, and is often denoted by. The probability of not committing a Type II error is called the Power of the test. Regenesys Business School 123

130 Decision rules The analysis plan includes decision rules for rejecting the null hypothesis. In practice, statisticians describe these decision rules in two ways with reference to a P-value or with reference to a region of acceptance. P The decision rule is as follows: If the t-calculated value is less than the t-critical value the Ho will be accepted. These values will be calculated by Excel and shown in the table below. In this case: P(T<=t) two-tail = and is less than the t Critical two-tail of The decision is to accept the Ho. Ho (Null Hypothesis): There is no significant difference Area 1 and Area 2 in terms of the Master s Financial Exam Results. One-tailed and two-tailed tests A test of a statistical hypothesis, where the region of rejection is on only one side of the sampling distribution, is called a one-tailed test. A test of a statistical hypothesis, where the region of rejection is on both sides of the sampling distribution, is called a two-tailed test. (Adapted from Leedy, 2013: ) Examining relationships using statistics According to Saunders and Lewis (2012:179), when you examine relationships between two or more variables you are looking at one of five things: 1. The association between two variables 2. The correlation between two variables 3. The difference between two or more variables 4. The explanation of one (dependent) variable by one or more other (independent) variables, and 5. The prediction of one (dependent) variable by one or more other (independent) variables Saunders and Lewis (2012:180) point out that, "when you do this you're also seeing how likely the relationship between your variables is to have occurred by chance" this is significance testing. Significance test: "A statistical test that challenges a hypothesis to determine whether the alternative hypothesis produces a pre-established significance level. The significance test attempts to disprove the concept of chance and reject a null hypothesis by adhering to observed patterns." (Business Dictionary, 2013) Depending on the data you are using and what you wish to examine, different tests are available. The tests are summarised in the table below. Regenesys Business School 124

131 Table 15: Summary of Statistical Tests to Examine Relationships Between Variables Type of Data Use: (Symbol in Brackets) To Examine: Which Represents the: Categorical data Chi-square test ( X 2 ) Association Probability of association between two variables occurring by chance Spearman's rank correlation coefficient ( ) Correlation Strength of the relationship between two variables and the probability of this occurring by chance Categorical ranked data Or Kendal's rank correlation coefficient Correlation (when data contains tied ranks) Strength of the relationship between two variables and the probability of this occurring by chance Numerical data split into two groups using a categorical variable Numerical split into threeplus groups using a categorical variable Pairs of numerical data for two variables measuring the same feature under different conditions Numerical data Independent groups t-test (t) Analysis of variance (ANOVA) (F) Difference Difference Probability of the differences between the values in the two groups occurring by chance Probability of the differences between the values in the groups occurring by chance Paired t-test (t) Difference Probability of the differences between each half of the pair (the two variables) occurring by chance Pearson's product moment correlation coefficient (r) Regression coefficient explanation (coefficient of determination) (r 2 ) Regression equation (y = a + b x) Correlation Explanation Prediction Strength of the relationship between the two variables and the probability of this occurring by chance Strength of a cause and effect of relationship between a dependent and one or more independent variables and the probability of this occurring by chance Formula to predict the values of a dependent variable, given the values of one or more independent variables (Saunders and Lewis, 2012: ) Significance testing means: "You have to work out both a statistic and the probability (likelihood) of this statistic occurring by chance. If this probability is small (usually 0.05 or lower), then you can say your relationship is statistically significant. Statisticians refer to this as 'rejecting the null hypothesis and accepting the hypothesis'." (Saunders and Lewis, 2012:180) Regenesys Business School 125

132 To get a sense of how the tests work, watch the following short video clip. Davis, J. 2010, 'Chi-squared test', [video clip], (accessed 8 January 2014). Now complete the task below. Watch the video clip on the 'Chi-Square Test' and answer the following questions. 1. What did you learn from the video clip 'Chi-Square Test'? 2. Why is it important to carry out such tests on quantitative data? 3. What did this example prove and as a consequence what should the researcher do? Analysing Qualitative Data The actual process of qualitative data analysis is less structured due to the nature of the data. (Please note: The popular computer package for analysing data is the Atlas and Nvivo package.) Saunders and Lewis (2012:264) recommend the following general analytical procedure: 1. Convert your rough field/ interview notes into a written record adding your own thoughts and reflections as soon as possible this will be the start of your tentative analysis. Use columns to distinguish between factual notes and your interpretations and speculations. 2. Make sure that data sets collected from interviews, observations, or original documents are properly referenced (e.g. who was involved, the date and time, the context, the circumstances leading to the data collection, and any implications in terms of the research which might include issues around validity and reliability). 3. Start coding the data as early as possible. This means allocating specific codes to each variable, concept, and/ or theme that you have identified. Consider allocating codes to words and phrases and explain the significance in terms of your research (refer to your research questions and objectives to maintain focus and the logical development of your analysis). Start with as many codes as you see fit you can always collapse them into groups at a later stage. 4. If you are not using a strong theoretical framework do not attempt to impose coded categories but allow them to emerge from the data. As you collect further data, compare this to your existing coded data (and groups) and modify the codes as required. Do not treat this as a mechanical task take time to think about the data that is emerging. 5. At regular intervals, write summaries of your findings. This will help you to synthesise your analysis and point to any deficiencies in the data collection (e.g. the data is or is not sufficient in terms of a particular research question). Regenesys Business School 126

133 6. Use your summaries to construct generalisations with which you can confront existing theories or use to construct a new theory. 7. Continue this process until you are satisfied that the generalisations arising from the data and your summaries are sufficiently robust to stand the analysis of existing theories or the construction of a new theory. During your journey through this Research Module, you were introduced to various research philosophies, all related to the development of knowledge. The focus was on the nature of that knowledge and the researcher's application of that knowledge in completing the mini-dissertation (Saunders et al., 2013). The research approaches helped you to critically evaluate concepts and reasoning and this ensured that you approached and presented your research arguments critically. Once you critically evaluated the reasoning, you explored some of the more popular research strategies. You then adopted specific choices in conducting research, and backed this up with appropriate data collection and analysis techniques to ultimately synthesize the final research report. Regenesys Business School 127

134 8 REFERENCES Asher, H. 1983, Causal Modeling, Beverly Hills, CA: Sage. Babbie, E. 1990, Survey Research Methods, Belmont, CA: Wadsworth. Bednar, A.K., Cunningham, D., Duffy, T.M. and Perry, J.D. 1991, 'Theory into practice: How do we link?', In G. Anglin (Ed.), Instructional Technology: Past, Present and Future. Englewood, CO: Libraries Unlimited, Inc. Bevir, M. and Kedar, A. 2008, 'Concept formation in political science: An anti-naturalist critique of qualitative methodology', Perspectives on Politics 6(3), Blackburn, S. 2005, Truth: A Guide. Oxford University Press, Inc. ISBN Bolman, L. G. and Deal, T. E. 1997, Reframing Organizations: Artistry, Choice and Leadership, 2 nd ed., San Francisco: Jossey-Bass. Campbell, D. and Stanley, J. 1963, Experimental and Quasi-Experimental Designs, Chicago: Rand McNally. Converse, J. and Presser, S. 1986, Survey Questions, Beverly Hills, CA: Sage. Cook, T. and Campbell, D. 1979, Quasi-Experimental Design, Chicago: Rand McNally. Cooper, D. and Schindler, P. 2001, Business Research Methods, 7 th ed., McGraw-Hill. Cresswell, J.W. 2003, Research Design, Qualitative, Quantitative, and Mixed Methods Approaches, 2 nd Ed, Thousand Oaks: Sage. Dillon, W. R., Madden, T. S and Firtle, N. H. 1994, Marketing Research in a Marketing Environment, 3 rd ed., Irwin, 298. Hagan, F. 2000, Research Methods in Criminal Justice and Criminology, Boston: Allyn and Bacon. Lapin, L. L. 1987, Statistics for Modern Business Decisions, Harcourt Brace Jovanovich, Inc. Lasley, J. 1999, Essentials of Criminal Justice and Criminological Research, NJ: Prentice Hall. Neuman, L. and Wiegand, B. 2000, Criminal Justice Research Methods, Boston: Allyn and Bacon. Patton, M.Q.1990, Qualitative Evaluation and Research Methods, London: SAGE Publications. Regenesys Business School 128

135 Pervez Ghauri, P and Kjell Gronhaug, K. 2010, Research Methods in Business, NJ: Prentice Hall: Rosenberg, M. 1968, The Logic of Survey Analysis, NY: Basic. Salant, P. and Dillman, D.A. 1994, How to Conduct Your Own Survey, John Wiley and Sons, Inc. Saunders, M., Lewis, P. and Thornhill, A. 2013, Research Methods for Business Students, 6 th ed., Cape Town: Pearson Education. Schiler, B. 2011, Academia strives for relevance, Financial Times, 25 April Senese, J. 1997, Applied Research Methods in Criminal Justice, Chicago: Nelson Hall. Smith, L. 2008, Ethical principles in practice, Kairaranga Special ed., NZ: Volume 9, (accessed 2 December 2013). Spector, P. 1981, Research Designs, Beverly Hills, CA: Sage. Webster, M. 1985, Webster s Ninth New Collegiate Dictionary, Merriam-Webster Inc. Regenesys Business School 129

136 9 APPENDIX 1: RESEARCH PROPOSAL AND MINI- DISSERTATION GUIDELINES Refer to the Mini-Mini-dissertationGuidelines manual that is given to you with your other study materials. 10 GLOSSARY OF TERMS Term Cluster sampling Continuous variables Convenience sampling Descriptive statistics Discrete variables Inferential statistics Interval level Nominal level Ordinal level Parameter Population Qualitative variables Quantitative variables Random sampling Random variable Ratio level Sample Explanation Sampling in which the population is divided into groups (usually geographically). Some of these groups are randomly selected, and then all of the elements in those groups are selected. Variables, which assume an infinite number of possible values. Usually obtained by measurement. Sampling in which data is which is readily available is used. Collection, organisation, summarisation, and presentation of data. Variables, which assume a finite or countable number of possible values. Usually obtained by counting. Generalising from samples to populations using probabilities. Performing hypothesis testing, determining relationships between variables, and making predictions. Levels of measurement, which classifies data that can be ranked, and differences are meaningful. However, there is no meaningful zero, so ratios are meaningless. Level of measurement, which classifies data into mutually exclusive, all-inclusive categories in which no order or ranking, can be imposed on the data. Level of measurement, which classifies data into categories that can be ranked. Differences between the ranks do not exist. Characteristic or measure obtained from a population. All subjects possessing a common characteristic that is being studied. Variables, which assume non-numerical values. Variables, which assume numerical values. Sampling in which the data is collected using chance methods or random numbers. A variable whose values are determined by chance. Level of measurement, which classifies data that can be ranked, differences are meaningful, and there is a true zero. True ratios exist between the different units of measure. A subgroup or subset of the population Regenesys Business School 130

137 Statistic (not to be confused with Statistics) Statistics Stratified sampling Systematic sampling Variable Characteristic or measure obtained from a sample. Collection of methods for planning experiments, obtaining data, and then organising, summarising, presenting, analysing, interpreting, and drawing conclusions. Sampling in which the population is divided into groups (called strata) according to some characteristic. Each of these strata is then sampled using one of the other sampling techniques. Sampling in which data is obtained by selecting every kth object. Characteristic or attribute that can assume different values Regenesys Business School 131