Pinning Down What Interpretative Analysis Actually Entails

Pinning down what interpretative analysis actually entails

It is difficult to specify exactly what interpretative analysis actually entails, particularly as the specifics of it will vary from study to study. As a first step, we recommend looking at published examples of thematic analysis, particularly of the specific version you are planning to use (this is made somewhat more difficult in that thematic analysis is often not a named method, but you can find examples, e.g., Ellis & Kitzinger, 2002; Kitzinger & Willmott, 2002; Toerien & Wilkinson, 2004). In order to provide a sense of the sorts of questions you should be asking of your data, and the sorts of analytic claims you should be seeking to make, we will discuss a particularly good example of an inductive thematic analysis, which emphasises understanding men‟s experiences in relation to the broader social context (see Frith & Gleeson, 2004).

Frith and Gleeson (2004) aim to explore how men‟s feelings about their bodies influence their clothing practices, and they use data gathered in qualitative questionnaires from 75 men to answer this question. They report four themes: the practicality of clothing choices; lack of concern about appearance; use of clothing to conceal or reveal the body; use of clothing to fit cultural ideals. Each theme is clearly linked back to the overall research question, but each is distinct. They provide a clear sense of the scope and diversity of each theme, using a combination of analyst narrative and illustrative data extracts.

Where relevant, they broaden their analysis out, moving from a descriptive to an interpretative level (often relating their claims to existing literature). For example, in „men value practicality‟ they make sense of men‟s accounts in relation to gender norms and stereotypes, linking the accounts individual men provided to the expectations that men – as members of society – face. What they do, as analysts, is relate the patterns of meaning in men‟s responses to an academic analysis of how gender operates. In so doing, they demonstrate the dual position that analysts need to take: as both cultural members and cultural commentators. Their

„discussion‟ section makes broader analytic statements about the overall story that the themes tell us about men‟s relationship with clothing. This story reveals that men “deliberately and strategically use clothing to manipulate their appearance to meet cultural ideals of masculinity” (Frith & Gleeson, 2004: 45), in a way more traditionally more associated with women. This analysis makes an important contribution in that it challenges perceived wisdom about clothing/appearance and masculinity.

As this example demonstrates, your analytic claims need to be grounded in, but go beyond, the surface of the data, even for a „semantic‟ level analysis. The sort of questions you need to be asking, towards the end phases of your analysis, include: „what does this theme mean?

‟What are the assumptions underpinning it?‟ What are the implications of this theme?‟ „What conditions are likely to have given rise to it?‟ „Why do people talk about this thing in this particular way (as opposed to other ways)?‟ and „What is the overall story the different themes reveal about the topic?‟ These sorts of questions should guide the analysis once you have a clear sense of your thematic map.

Potential pitfalls to avoid when doing thematic analysis

Thematic analysis is a relatively straightforward form of qualitative analysis, which does not require the same detailed theoretical and technical knowledge that approaches like DA or CA do. It is relatively easy to conduct a good thematic analysis on qualitative data, even when you are still learning qualitative techniques. However, there are a number of things that can result in a poor analysis. In this section, we identify these potential pitfalls, in the hope that they can be avoided.

The first of these is a failure to actually analyse the data at all! Thematic analysis is not just a collection of extracts strung together with little or no analytic narrative. Nor is it a selection of extracts with the analytic comments that simply or primarily paraphrases their content. The extracts in the thematic analysis are illustrative of the analytic points the researcher makes about the data and should be used to illustrate/support an analysis that goes beyond their specific context, to make sense of the data, and tell the reader what it does or might mean – as discussed above.

A second, associated, pitfall is the use of the data collection questions (such as from an interview schedule) as the „themes‟ that are reported. In such a case, no analytic work has been done to identify themes across the entire data set or make sense of the patterning of responses.

The third is a weak or unconvincing analysis, where the themes do not appear to work, where there is too much overlap between themes, or where the themes are not internally coherent and consistent. All aspects of the theme should cohere around a central idea or concept. This pitfall has occurred if, depending on what the analysis is trying to do, it fails adequately to capture the majority of the data, or fails to provide a rich description/interpretation of one or more aspects of the data.

A weak or unconvincing analysis can also stem from a failure to provide adequate examples from the data – for example, only one or two extracts for a theme. This point is essentially about the rhetorics of presentation and the need for the analysis to be convincing to someone who has not read your entire data set:

“The analysis of the material … is a deliberate and self-consciously artful creation by the researcher, and must be constructed to persuade the reader of the plausibility of an argument” (Foster & Parker, 1995: 204). In so doing, you avoid (the appearance of) what Bryman (1988) has referred to as “anecdotalism” in qualitative research – where one or a few instances of a phenomenon are reified into a pattern or theme when it or they are actually idiosyncratic. This is not to say that a few instances cannot be of interest, or revealing, but that it is important not to misrepresent them as an overarching theme.

The fourth pitfall is a mismatch between the data and the analytic claims that are made about it. In such an (unfounded) analysis, the claims cannot be supported by the data, or, in the worst case, the data extracts presented suggest another analysis or even contradict the claims. The researcher needs to make sure that their interpretations and analytic points are consistent with the data extracts.

A weak analysis does not appear to consider other obvious alternative readings of the data or fails to consider variation (and even contradiction) in the account that is produced. A pattern in data is rarely, if ever, going to be 100% complete and non-contradicted, so an analysis that suggests that it is, without a thorough explanation, is open to suspicion. It is important to pick compelling examples to demonstrate the themes, so give this considerable thought.

The fifth involves a mismatch between theory and analytic claims, or between the research questions and the form of thematic analysis used. A good thematic analysis needs to make sure that the interpretations of the data are consistent with the theoretical framework. So, for instance, if you are working within an experiential framework, you would typically not make claims about the social construction of the research topic, and if you were doing constructionist thematic analysis you would not treat people‟s talk of experience as a transparent window on their world. Finally, even a good and interesting analysis that fails to spell out its theoretical assumptions, or clarify how it was undertaken, and for what purpose, is lacking crucial information (Holloway & Todres, 2003), and thus fails in one aspect.