What Involves in Thematic Analysis?

Thematic Analysis

The thematic analysis involves a number of choices that are often not made explicit (or are certainly typically not discussed in the method section of papers), but which need explicitly to be considered and discussed. In practice, these questions should be considered before analysis (and sometimes even collection) of the data begins, and there needs to be an ongoing reflexive dialogue on the part of the researcher or researchers with regards to these issues, throughout the analytic process. The method section of Taylor and Ussher‟s (2001) thematic discourse analysis of S&M provides a good example of research that presents this process explicitly; the method section of Braun & Wilkinson (2003) does not.

What counts as a theme?

A theme captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the data set. An important question to address in terms of coding is what counts as a pattern/theme, or what „size‟ does a theme need to be? This is a question of prevalence both in terms of space within each data item, and prevalence across the entire data set. Ideally, there will be a number of instances of the theme across the data set, but more instances do not necessarily mean the theme itself is more crucial.

As this is qualitative analysis, there is no hard-and-fast answer to the question of what proportion of your data set needs to display evidence of the theme for it to be considered a theme. It is not the case that if it was present in 50% of one‟s data items, it would be a theme, but if it was present only in 47%, then it would not be. Nor is it the case that a theme is only something that many data items give considerable attention to, rather than a sentence or two.

A theme might be given considerable space in some data items, and little or none in others, or it might appear in relatively little of the data set. So researcher judgement is necessary to determine what a theme is. Our initial guidance around this is that you need to retain some flexibility, and rigid rules really do not work. (The question of prevalence gets revisited in relation to themes and sub-themes, as the refinement of analysis [see later] will often result in overall themes, and sub-themes within those.)

Furthermore, the „keyness‟ of a theme is not necessarily dependent on quantifiable measures – but in terms of whether it captures something important in relation to the overall research question. For example, in Victoria‟s research on representations of lesbians and gay parents on 26 talk shows (Clarke & Kitzinger, 2004), she identified six „key‟ themes. These six themes were not necessarily the most prevalent themes across the data set – they appeared in between 2 and 22 of the 26 talk shows – but together they captured an important element of the way in which lesbians and gay men normalise” their families in talk show debates.

In this instance, her thematic analysis was driven by this particular analytic question. How she „measured‟ prevalence is relevant, as prevalence can be determined in a number of different ways. Prevalence was counted at the level of the data item (i.e., did a theme appear anywhere in each individual talk show). Alternatively, it could have been counted in terms of the number of different speakers who articulated the theme, across the entire data set, or each individual occurrence of the theme across the entire data set (which raises complex questions about where an ‘instance’ begins and ends within an extended sequence of talk, see Riessman, 1993).

Because prevalence was not crucial to the analysis presented, Victoria chose the most straightforward form, but it is important to note there is no right or wrong method for determining prevalence. Part of the flexibility of thematic analysis is that it allows you to determine themes (and prevalence) in a number of ways. What is important is that you are consistent in how you do this within any particular analysis.

There are various „conventions‟ for representing prevalence in thematic (and other qualitative) analysis that does not provide a quantified measure (unlike much content analysis, Wilkinson, 2000)  for instance: “the majority of participants” (Meehan et al., 2000: 372), “many participants” (Taylor & Ussher, 2001: 298), or “a number of participants” (Braun, Gavey, & McPhillips, 2003: 249).

Such descriptors work rhetorically to suggest a theme really existed in the data, and to convince us they are reporting truthfully about the data. But do they tell us much? This is perhaps one area where more debate needs to occur about how and why we might represent the prevalence of themes in the data, and, indeed, whether, if, and why prevalence is particularly important.

A rich description of the data set, or a detailed account of one particular aspect It is important to determine the type of analysis you want to do, and the claims you want to make, in relation to your data set. For instance, you might wish to provide a rich thematic description of your entire data set, so that the reader gets a sense of them predominant or important themes.

In this case, the themes you identify, code, and analyse would need to be an accurate reflection of the content of the entire data set. In such an analysis, some depth and complexity is necessarily lost (particularly if you are writing a short dissertation or article with strict word limits), but a rich overall description is maintained. This might be a particularly useful method when you are investigating an under-researched area, or with participants whose views on the topic are not known.

Alternative use of thematic analysis is to provide a more detailed and nuanced account of one particular theme, or group of themes, within the data. This might relate to a specific question or area of interest within the data (a semantic approach – see below), or to a particular „latent‟ theme (see below) across the whole or majority of the data set. An example of this would be Victoria‟s talk show paper, discussed previously (Clarke & Kitzinger, 2004), which examined normalisation in lesbians‟ and gay men‟s accounts of parenting.