Thinking with the data

I begin with thinking with the data (instead of thinking with theory and thinking

reflexively) because I consider it the primary concern. While the influence of theory and

the interpreting researcher must be recognized, by my reasoning they must remain ancillary modes of knowledge production. By raising the status of theory, one risks the research becoming simply a canvas on which to paint pre-established theoretical concepts (Tomlie and Rouncefield, 2013). Similarly, focusing on reflexive interpretations might be criticized for being, merely idiosyncratic individual interpretations and, at the other end, self-indulgent autobiography. In recognition of the primary importance of data, I will explain how my interpretation ‘emerged’ from the body of data at hand. To do this I will outline how the data was organised, reduced and coded to themes.

The data set was stored and organised in the NVivo 9 software programme before systematic analysis began. Interviews were transcribed verbatim by myself which provided a preliminary opportunity to re-listen to the conversations after a period of time since conducting the interviews. Once all data was compiled and organised into categories describing its type and source (interview audio, participant photo, etc.) the technical process of coding and categorizing – for which there is a spectrum of approaches available – began. This technical process is most often performed as part of certain established data analysis procedures. Such procedures include thematic analysis (TA), interpretive phenomenological analysis (IPA), grounded theory (GT), Foucauldian discourse analysis (FDA), critical discourse analysis (CDA), conversation analysis (CA) and narrative analysis – all of which can be found in contemporary textbooks on qualitative research (Sparkes and Smith, 2013; Bryman and Burgess, 1999; Silverman, 2010). I made the decision not to follow any of these prescribed procedures. My decision was based on a concern for ‘off-the-shelf’ methodologies and their inherent alignment with particular theories. As was argued in my justification of data collection methods in the previous chapter, these procedures carry with them a set of assumptions which have enormous value in highlighting certain aspects of the

phenomena but simultaneously constrain what can be made visible. Remaining consistent with the bricolage approach taken in data production which allows for the opportunity to use whatever analytic ‘tools’ that are available and place the responsibility on the researcher, not the methodology textbook, I chose to allow a combination of ideas to influence the coding process. Coding, then, became a principle- driven task aiming to identify moments in the data that reveal something relevant for the aims of the research.

This initially was simply a process of data reduction. The first stage involved coding data at ‘nodes’ denoting what topic the date refers to. These nodes began being broad and general with the knowledge that they would be returned later for more detail interrogation. For example, the following interview quote: “I’d say I’m quite healthy. I like to do a couple of sit ups at night before I go to bed” (Richard, St Andrews High School) was coded simply with the node “health”. In this respect, as with coding commonly described in the methods literature, coding was ‘iterative’ and often ‘messy’ (Froggatt, 2001). At times it was unclear how a particular bit of data should be coded, or even if it should be included in the coding process at all. Indeed, the quote above could have coded as ‘exercise’ ‘motivation’ or in a number of other ways. It should also be said that this data reduction was carried out first, but also returned to on occasions when I spontaneously recalled particular moments of data that might be relevant, although were not originally coded. This first stage of coding was somewhat resonant of thematic analysis. Segments of transcript and visual data were coded by themes which were considered relevant. These themes were sometimes broad (such as PE,

health, leisure time) and sometimes more specific (such as competitiveness, boredom, the Olympics). To provide an example, Table 7 (p.69) shows how raw data was

abstracted into the higher order theme ‘health as fitness’. Savage (2000) notes that thematic analysis can be seen as data reduction if codes are kept at the general level, but also as data expansion through the inclusion of multiple interpretive codes. The end goal was to develop categories and concepts that would be helpful and meaningful in the explanation of the data (Coffey and Atkinson, 1996). It should also be said that this process could be described as ‘abductive’ and perhaps even ‘retroductive’. These terms acknowledge that both inductive and deductive thinking can be helpful (Coffey and Atkinson, 1996) and that there is a process of thinking ‘backwards’ to consider what

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Table 7. Showing the construction of the ‘health as fitness’ theme

Raw data Early theme Established theme

Richard: I’d say I’m quite healthy. I like to do a

couple of sit ups at night before I go to bed

Exercising perceived as healthy activity

Health as fitness

GW: how healthy would you guys say you are? Aaron: not very! I run up stairs and I get puffed

out – I have to go and lie down!

Being out of breathe perceived as sign of poor health

[image taken from student PE booklet]

Directly equating fitness with health

must have been true for the past event to have been possible (Easton, 2010). This on-going process resulted in the clustering of themes and the emergence of hierarchal categories.