Analyzing Qualitative Data

Graham R Gibbs

• Publisher: SAGE Publications Ltd
• Year: 2018
• ISBN:
  9781526426147
• Edition: 2
• Topic: Social Sciences
• Subtopic: Social Science Research & Methodology
• Index: Social Sciences

This book tackles the challenges of how to make sense of qualitative data. It offers students and researchers a hands-on guide to the practicalities of coding, comparing data, and using computer-assisted qualitative data analysis. Lastly, Gibbs shows you how to bring it all together, so you can see the steps of qualitative analysis, understand the central place of coding, ensure analytic quality and write effectively to present your results.

Chapter One The Nature of Qualitative Analysis

Contents

• Analysis2
• Qualitative data3
• Practicalities of qualitative analysis4
• Methodology5
• The aim of qualitative analysis10
• Ethics13

Objectives

After reading this chapter, you should:

• see that some features of qualitative analysis are distinctive, but at the same time they are features over which there is often disagreement between qualitative researchers;
• know about some of the different understandings of qualitative research; and
• understand that they bear upon analysis and map out the limits of the qualitative ‘territory’, and understand some of the distinctive styles and approaches qualitative analysts adopt.

Analysis

The idea of analysis implies some kind of transformation. You start with some (often voluminous) collection of qualitative data and then you process it, through analytic procedures, into a clear, understandable, insightful, trustworthy and even original analysis. There is disagreement even about this transformation. Some researchers focus on the ‘office’ processes involved; the sorting, retrieving, indexing and handling of qualitative data, usually with some discussion of how these processes can be used to generate analytic ideas (Maykut and Morehouse, 2001; Miles et al., 2013; Spencer et al., 2014). The processes are designed to deal with the sheer amount of data that is created in qualitative research, in interview transcripts (see Brinkmann and Kvale, 2018), field notes (see Coffey, 2018), collected documents, video and audio recordings (see Rapley, 2018), etc. Sorting and searching through all these data while at the same time creating a consistent and perceptive analysis that remains grounded in that data – that is, so the data provide good supporting evidence – is a major headache. It requires good organization and a structured approach to the data. This is one of the reasons why CAQDAS, computer-assisted qualitative data analysis, has become so popular. The software doesn’t do the thinking for you, but it helps enormously with these ‘office’ processes.

Other researchers emphasize the idea that analysis involves interpretation and retelling and that it is imaginative and speculative (Denzin, 1997; Giorgi and Giorgi, 2003; Mishler, 1986; Riessman, 1993). There are a range of approaches involved here including conversation and discourse analysis (see Rapley, 2018), some forms of phenomenology, biographical and narrative approaches as well as recent ethnographic methods (see Coffey, 2018). These approaches emphasize the idea that qualitative data are meaningful and need to be interpreted in analysis, not just to reveal the range of subject matter people are talking about but also to recognize and analyze the ways in which they frame and mould their communications and even the ways these communications frame and mould them and their actions.

Most writers about qualitative data analysis recognize that it involves both these aspects of analysis; data handling and interpretation (Coffey and Atkinson, 1996; Flick, 2014, 2018b; Mason, 2002; Bazeley, 2013). Sometimes they are used simultaneously, but often they are used in sequence starting with the use of the ‘office’ procedures then moving to the reduction of data into summaries or displays, before finishing with interpretative analysis and drawing conclusions.

Qualitative data

As I have suggested above, qualitative data are essentially meaningful, but aside from that they show a great diversity. They don’t include counts and measures, but they do include just about any form of human communication – written, audio or visual – behaviour, symbolism or cultural artefacts. This includes any of the following:

• individual and focus group interviews and their transcripts;
• ethnographic participant observation;
• email;
• web pages;
• advertisements – printed, film or TV;
• video recordings of TV broadcasts;
• video diaries;
• videos of interviews and focus groups;
• video recordings of laboratory sessions;
• records of meetings and other organizational documents;
• various documents such as books and magazines;
• diaries;
• online discussion group conversations;
• online social networking pages;
• online news libraries;
• still photos;
• film;
• home videos.

The most common form of qualitative data used in analysis is text; this can either be a transcription from interviews or field notes from ethnographic work or other kinds of documents. Most audio and video data are transformed into text to be analyzed. The reason for this is that text is an easy form of recording that can be dealt with using the ‘office’ techniques mentioned above. However, with the development of digital audio and video recordings and the availability of software to sort, index and retrieve them, the need and desire to transcribe might be reduced in the future. Moreover, using video data preserves some of the visual aspects of the data that are often lost when conversations are transcribed. Nevertheless, when it comes to the fluent, rapid and accurate examination of qualitative data, most of us still find it easiest when dealing with textual data.

Practicalities of qualitative analysis

Qualitative analysis involves two activities: first developing an awareness of the kinds of data that can be examined and how they can be described and explained, and, second, a number of practical activities that assist with the kinds of data and the large amounts of it that need to be examined. The latter are what I refer to as the practicalities of qualitative analysis. I will discuss these more in the rest of the book, but two of them distinguish qualitative analysis from other approaches

Merging collection and analysis

In some kinds of social research you are encouraged to collect all your data before you start any kind of analysis. Qualitative research is different from this because there is no separation of data collection and data analysis. Analysis can, and should start in the field. As you collect your data by interviewing, taking field notes, acquiring documents and so on, you can start your analysis. I examine these issues in more detail in Chapter 3, but things like keeping field notes and a research diary are both ways to collect data and ways to begin its analysis. You don’t even need to wait till your first interviews or field trips to start analysis. There is often plenty of data you can examine, in existing documents as well as in previous studies.

In fact, not only is concurrent analysis and data collection possible, but it can actually be good practice too. You should use the analysis of your early data as a way of raising new research issues and questions. To that extent qualitative research is flexible. Research questions can be decided late in the study; for instance, if the original questions make little sense in the light of the perspectives of those you have studied.

Expanding the volume of data not reducing it

A further key difference between the procedures of qualitative and quantitative analysis is that the former does not seek to reduce or condense the data; for example, to summaries or to statistics. Qualitative data analysis often involves dealing with large volumes of data (transcripts, recordings, notes, etc.). Most analysis simply adds to this volume even though, at the final stage of reporting about the research, the analyst may have to select summaries and examples from the data.

Thus qualitative analysis usually seeks to enhance the data, to increase its bulk, density and complexity. In particular, many of the analytic approaches involve creating more texts in the form of things like summaries, précis, memos, notes and drafts. Many of the techniques of qualitative analysis are concerned with ways to deal with this large volume of data. This is particularly the case with coding. Whereas coding in quantitative analysis is for the explicit purpose of reducing the data to a few ‘types’ in order that they can be counted, coding in qualitative analysis is a way of organizing or managing the data. All the original data are preserved. Codes (and their associated analytic documents) add interpretation and theory to the data. In fact, typically, text may be densely coded; not only will most text be assigned a code but much will have more than one code attached to it.

Methodology

The second activity that qualitative analysis involves is an awareness of the kinds of things that can be found in qualitative data and how they can be analyzed. There is a wide range of these ways of looking at the data, and qualitative analysts have adopted a variety of methodologically based analytic styles to do so. Consequently, there are still various contested views about methodology.

Rich description

A major concern of qualitative analysis is to describe what is happening, to answer the question ‘What is going on here?’. This is because very often what is described is novel or at least forgotten or ignored. The description is detailed and contributes to an understanding and eventual analysis of the setting studied. In particular, the focus is on giving a ‘thick’ description, a term popularized by Geertz (1975) (see also Mason, 2002). This is one that demonstrates the richness of what is happening and emphasizes the way that it involves peoples’ intentions and strategies. From such a ‘thick’ description it is possible to go one stage further and offer an explanation for what is happening.

Induction, deduction and abduction

One of the functions of qualitative analysis is to find patterns and produce explanations. There are two contrasting logics of explanation, induction and deduction, and qualitative research actually uses both.

• Induction is the generation and justification of a general explanation based on the accumulation of lots of particular, but similar circumstances. Thus repeated, particular observations that fans of football clubs that are doing well, or fans of those that are doing very badly, are more ardent supporters than those of clubs that languish in the middle of their league, sustain the general statement that the fervour of fans’ support is greatest when their clubs are at the extremes of success.
• Deductive explanation moves in the opposite direction, in that a particular situation is explained by deduction from a general statement about the circumstances. For example, we know that as people get older their reaction times slow down, so we could deduce that Jennifer’s reaction times are slow because she is over 80 years old. Much quantitative research is deductive in approach. A hypothesis is deduced from a general law and this is tested against reality by looking for circumstances that confirm or disconfirm it.

An important development of this deductive approach is the hypothetico-deductive model developed by the philosopher Karl Popper (1989). In this model, the scientist (or the social scientist) makes a bold conjecture or hypothesis deduced from what they believe is the correct theory. This is then tested by empirical examination. But it leaves it to the genius and imagination of the researcher to come up with the putative theory. Actually, as other philosophers have pointed out (Peirce, 1958), in ordinary life (and qualitative researchers do this too) we often come up with general theories to explain the phenomena we experience. What we do combines aspects of deduction and induction and is called abduction or retroduction.

• An abductive argument is one in which an explanation is proposed to account for an observed fact or group of facts. This is not deduction because we do not start with our general theory, but rather with the phenomena we experience, the facts. It is not induction as we do not generalize from a large number of similar observations. For example, we might notice in a group of young people, that those who come from low-income families have lower levels of educational achievement. We might then offer the explanation that low-income families are unable to afford all kinds of opportunities (formal and informal) that might improve their children’s education.

One problem with abduction is that there are typically several different explanations that might explain the phenomena we observe. For example, both the low income levels and the lack of educational achievement might be explained by inherited intelligence. One option is to pick the best explanation. However, there is debate as to what ‘the best’ means: it could be the most powerful explanation, or the most general, plausible or simple, or the one that coheres best with existing theories or with our own experience, or the one that is most parsimonious, or any combination of all these. In many cases the explanation we come up with is simply satisfactory or good enough.

A lot of qualitative research explicitly tries to generate new theory and new explanations. In that sense the underlying logic is inductive or abductive. Rather than starting with some theories and concepts that are to be tested or examined, such research favours an approach in which they are developed in tandem with data collection in order to produce and justify new generalizations and thus create new knowledge and understanding. Some writers reject the imposition of any a priori theoretical frameworks at the outset. However, it is very hard for analysts to eliminate completely all prior frameworks. As we have seen, previous experience and knowledge may affect the selection of an explanation in abductive reasoning. Inevitably, qualitative analysis is guided and framed by pre-existing ideas and concepts. Often what researchers are doing is checking hunches; that is they are deducing particular explanations from general theories they have established inductively or abductively, and seeing if the circumstances they observe actually correspond (Strübing, 2010).

Nomothetic and idiographic

Both inductive and deductive approaches are concerned with general statements but much qualitative research examines the particular, the distinctive or even unique.

• The nomothetic approach takes an interest in the general dimensions on which all individuals and situations vary. The approach assumes that the behaviour of a particular person is the outcome of laws that apply to all. To put it less formally, the approach tries to show what people, events and settings have in common and to explain them in terms of these common features. In qualitative research this is done by looking for variations and differences and trying to relate or even correlate them with other observed features like behaviours, actions and outcomes.
• The idiographic approach studies the individual (person, place, event, setting, etc.) as a unique case. The focus is on the interplay of factors that might be quite specific to the individual. Even though two individuals might share some aspects in common, these will inevitably be materially affected by other differences between them. Thus two heterosexual couples may have a lot in common: same ages, same culture, same number of children and similar houses in the same location. But there will be many differences too. They may have di...

Table of contents

• Cover
• Half Title
• Acknowledgements
• Title Page
• Copyright Page
• Contents
• Illustration List
• Editorial Introduction Uwe Flick
• About this book and its second edition
• Chapter One The Nature of Qualitative Analysis
• Chapter Two Data Preparation
• Chapter Three Writing
• Chapter Four Thematic Coding and Categorizing
• Chapter Five Analyzing Biographical, Narrative and Discursive Elements
• Chapter Six Comparative Analysis
• Chapter Seven Analytic Quality and Ethics
• Chapter Eight Getting Started with Computer-Assisted Qualitative Data Analysis
• Chapter Nine Searching and Other Analytic Activities Using Software
• Chapter Ten Putting it All Together
• Glossary
• References
• Index