Qualitative Data Analysis Approaches

Whether researchers are using interviews, focus groups, or textual analysis, large amounts of data are produced. It can be daunting to manage and analyse the many thousands of words produced. The purpose of this chapter is to provide suggestions on how to move beyond describing what participants have said, to analysing the data. In this chapter researchers will learn more about the most common approaches to analysing qualitative data, namely, Grounded Theory, thematic and template analysis, discourse analysis and hermeneutics. Situations where each approach may be more suitable are suggested. By the end of the chapter readers should be able to identify which approach is appropriate to their data set. In addition, readers will be able to undertake robust analysis of their qualitative data.

The Literature Review

It is important to demonstrate and critically analyse your understanding of the literature within your chosen field of study or research. The chapter highlights the different types of literature reviews: the traditional literature review versus the systematic literature review. The literature review is often essential in establishing what is known about a subject area, and by association what is not yet known, allowing you to understand and explore gaps in knowledge by appraising, encapsulating, comparing, contrasting, and correlating scholarly books, research articles, reports and various other sources of information. This chapter provides guidance on undertaking a literature review by highlighting the key stages of the process: selecting a topic, searching the literature, reading and reviewing the literature, critiquing the literature, evaluating the literature, and finally writing the literature review. The aim of this chapter is to offer a clear, concise and structured way of undertaking a literature review to establish your theoretical framework and methodological focus.

Quantitative Data Analysis Approaches

In order to understand data and present findings in an accurate way, researchers and managers need to develop an awareness of statistical analysis techniques. This chapter focuses on two sets of the most widely used statistical tools – exploring relationships and comparing groups. It also briefly explains the nature of Big Data. This chapter explores the data preparation approach, how to conduct preliminary (descriptive) analysis as well as introducing some of the main statistical techniques used in quantitative research in business management. The key issues that should be related back to your study question are a) how to prepare your data; b) how to conduct preliminary data analysis; c) what analyses are appropriate for comparing groups; d) what techniques are most appropriate for exploring relationships.

Managing Ethics in Research Projects

The ethical dimension research is the one of the major topics that should be addressed before beginning any empirical work. In practice, what this means is that any research project, which is likely to involve distributing questionnaires, conducting interviews or conducting focus groups, brings with it various dilemmas in terms of moral conduct towards the people you are expecting to fill in questionnaires, be interviewed or take part in focus groups. Such dilemmas will be dependent on the design of your particular study. However, typical ethical dilemmas in relation to business management research include: the manner in which you intend to recruit people to your study, the extent to you will inform participants of what you research is about, dealing with research participants who may be concerned about how they are portrayed in your final dissertation. There are some simple steps which can help ensure that your research embodies the kinds of ethical principles that many institutions require, and we will use a range of real life ethical dilemmas to help you as you formulate your own research. The chapter concludes with checklist questions to consider.

Quantitative Data Gathering Techniques

The role of managers and researchers is concerned with analysing and solving problems. These problems come in many forms with common features and often numerical information can be useful for analysis. In order to perform quantitative analyses, we need data. This chapter illustrates how to collect quantitative data: sampling and measurement issues, surveys and experimental research. In this chapter the tools that are needed for collecting quantitative data are introduced. In particular, the key issues that should be related to your research question are a) defining what are your dependent and independent variables b) how to sample your participants and c) how to design appropriate questions to elicit the data you need to answer your research question.

Case Studies and Data

The backbone of every research project is the collection of data that a researcher has identified as worthy of analysis. Interviews, survey questionnaires, publically available information and audio-visual material are all potential sources of data to a researcher. However, collecting data can be a daunting experience, either because you have too little data or because what looked like a rich dataset has turned into a nightmarish sense of data-overload. It is therefore helpful to spend time thinking about where you will look for data. Evaluating data sources will save valuable time and resources but also tends to lead to better analysis and more robust results. What constitutes good practice when collecting data is very much dependent on the research tradition within which your project is placed. How do different research traditions define and use data? For those scholars whose work you are engaging with closely, where did they source data? The chapter will help you evaluate data quality, consider ways in which different sources of data be combined and will provide practical advice on data collection. Additionally, a great part of this chapter is dedicated to case studies as an example of a methodology that can be based on different types of data. By following the advice offered here, you will be able to collect data that are relevant to your research methodology and build high quality insights.

Qualitative Data Gathering Techniques

As a primary methodology in the Methods Map, qualitative research techniques promote the collection of rich and revelatory data. This chapter presents several qualitative data collection techniques appropriate to business management research, such as interviews, focus groups, ethnographic approaches, observations, and the use of diaries. To the inexperienced researcher, these approaches may initially appear a less challenging methodological option to pursue, however, this chapter serves to highlight the inherent complexities associated with qualitative techniques and offers accessible advice and guidance for researchers new to qualitative research, or those looking to formalise their current understanding.

Mapping Research Methods

In most peer-reviewed settings a claim to contribution is central to the evaluation of your research. It is therefore good practice to outline the philosophical orientation of your research and this requires that you address the particular relationship between ontology, epistemology, methodology, data gathering techniques and data analysis approaches. This chapter sets out definitions of these five key concepts, shows how they inter-relate and reviews common configurations in management and organizational research. Further, the chapter introduces the Method Map as a means of presenting a succinct, yet comprehensive account of the paradigm within which your research is conducted. In so doing, the chapter offers a clear, structured and explicit means of dealing with the philosophy of knowledge.

The Key is in the Reading Finding a Project

This chapter steers towards understanding ideas in the context of academic research so that, even if you thought your idea was a ‘eureka!’ style moment, you will also be able to give it roots in extant and firmly-founded scholarship. In Management studies, ideas are valued for their ‘innovation’, their relevance, and their introduction of the new. First the chapter discusses where ideas come from and how to make ideas ‘interesting’. Then it deconstructs Maclean, Harvey and Chia’s (2012) paper to provide an example of idea generation and justification in practice. It then moves on to explaining how to make sure ideas for projects are modest, delimited and realistic. The chapter concludes by working through how to construct and write a research proposal.

Writing Up Your Research Project

The purpose of this chapter is to highlight a few key points about the process of writing up a dissertation or other research project, as distinct from the process of doing the research itself, and offer some advice on writing effectively. Though obviously interrelated, it is worth teasing these two tasks apart since it can make the whole process more productive. The chapter begins with a look at mapping out your writing, before offering suggestions on how to find your focus and maintain it. The chapter then looks at overcoming writer’s block, rewriting and editing, and the use of technology. This is followed by a series of writing tips, before the chapter concludes with some practical advice on the relationship between you and your supervisor.