Using Thematic Analysis in Research & Psychology

Using Thematic Analysis in Research & Psychology

Thematic analysis is a poorly demarcated and rarely-acknowledged, yet widely-used qualitative analytic method (see Boyatzis, 1998; Roulston, 2001) within and beyond psychology. In this paper, we aim to fill what we, as researchers and teachers in qualitative psychology, have experienced as a current gap: the absence of a paper which adequately outlines the theory, application, and evaluation of thematic analysis, and one which does so in a way accessible to students and those not particularly familiar with qualitative research. That is, we aim to write a paper which will be useful as both a teaching and research tool in qualitative psychology. Therefore, in this paper we discuss theory and method for thematic analysis, and clarify the similarities and differences between different approaches that share features in common with a thematic approach.

Qualitative approaches are incredibly diverse, complex and nuanced (Holloway & Todres, 2003), and thematic analysis should be seen as a foundational method for qualitative analysis. It is the first qualitative method of analysis that researchers should learn, as it provides core skills that will be useful for conducting many other forms of qualitative analysis. Indeed, Holloway and Todres (2003: 347) identify “thematizing meanings” as one of a few shared generic skills across qualitative analysis.2 For this reason, Boyatzis (1998) characterises it not as a specific method but as a tool to use across different methods. Similarly, Ryan and Bernard (2000) locate thematic coding as a process performed within „major‟ analytic traditions (such as grounded theory), rather than a specific approach in its own right. We argue thematic analysis should be considered a method in its own right.

One of the benefits of thematic analysis is its flexibility. Qualitative analytic methods can be roughly divided into two camps. Within the first, there are those tied to, or stemming from, a particular theoretical or epistemological position. For some of these – such as conversation analysis ([CA] e.g., Hutchby & Wooffitt, 1998) and interpretative phenomenological analysis ([IPA] e.g., Smith & Osborn, 2003) – there is (as yet) relatively limited variability in how the method is applied, within that framework. In essence, one recipe guides analysis. For others of these – such as grounded theory (e.g., Glaser, 1992; Strauss & Corbin, 1998), discourse analysis ([DA] e.g., Burman & Parker, 1993; Potter & Wetherell, 1987; Willig, 2003) or narrative analysis (e.g., Murray, 2003; Riessman, 1993) – there are different manifestations of the method, from within the broad theoretical framework. Second, there are methods that are essentially independent of theory and epistemology, and can be applied across a range of theoretical and epistemological approaches.

Although often (implicitly) framed as a realist/experiential method (e.g., Aronson, 1994; Roulston, 2001), thematic analysis is actually firmly in the second camp, and is compatible with both essentialist and constructionist paradigms within psychology (we discuss this later). Through its theoretical freedom, thematic analysis provides a flexible and useful research tool, which can potentially provide a rich and detailed, yet complex account of data.

Given the advantages of the flexibility of thematic analysis, it is important that we are clear that we are not trying to limit this flexibility. However, an absence of clear and concise guidelines around thematic analysis means that the „anything goes‟ critique of qualitative research (Antaki, Billig, Edwards, & Potter, 2002) may well apply in some instances. With this paper, we hope to strike a balance between demarcating thematic analysis clearly – i.e., explaining what it is, and how you do it – and ensuring flexibility in relation to how it is used, so that it does not become limited and constrained, and lose one of its key advantages. Indeed, a clear demarcation of this method will be useful to ensure that those who use thematic analysis can make active choices about the particular form of analysis they are engaged in. Therefore, this paper seeks to celebrate the flexibility of the method, and provide a vocabulary and „recipe‟ for people to start doing thematic analysis in a way that is theoretically and methodologically sound.3 As we will show, what is important is that as well as applying a method to data, researchers make their (epistemological and other) assumptions explicit (Holloway & Todres, 2003). Qualitative psychologists need to be clear about what they are doing and why, and include the often-omitted „how‟ they did their analysis in their reports (Attride-Stirling, 2001).

In this paper we outline: what thematic analysis is; a 6-phase guide to doing thematic analysis; potential pitfalls to avoid when doing thematic analysis; what makes good thematic analysis; and advantages and disadvantages of thematic analysis. Throughout, we provide examples from the research literature, and our own research. By providing examples we show the types of research questions and topics that thematic analysis can be used to study.

Before we begin, we need to define a few of the terms used throughout the paper. Data corpus refers to all data collected for a particular research project, while data set refers to all the data from the corpus that is being used for a particular analysis. There are two main ways of choosing your data set (which approach you take depends on whether you are coming to the data with a specific question or not – see „a number of decisions‟ below). First, your data set may consist of many or all individual data items within your data corpus. So, for example, in a project on female genital cosmetic surgery, Virginia‟s data corpus consists of interviews with surgeons, media items on the topic, and surgeon websites. For any particular analysis, her data set might just be the surgeon interviews, just the websites (Braun, 2005b), or it might combine surgeon data with some media data (e.g., Braun, 2005a). Second, your data set might be identified by a particular analytic interest in some topic in the data, and your data set then becomes all instances in the corpus where that topic is referred to. So in Virginia‟s example, if she was interested in how „sexual pleasure‟ was talked about, her data set would consist of all instances across the entire data corpus that had some relevance to sexual pleasure. These two approaches might sometimes be combined to produce the data set. Data item is used to refer to each individual piece of data collected, which together make up the data set or corpus. A data item in this instance would be an individual surgeon interview, a television documentary, or one particular website. Finally, data extract refers to an individual coded chunk of data, which has been identified within, and extracted from, a data item. There will be many of these, taken from throughout the entire data set, and only a selection of these extracts will feature in the final analysis.