A piece from my own thesis written in 2017:

To guide my analysis, the data for this study were used to identify themes appropriate to my overarching research aim. The six phases of thematic analysis, based on the work of Braun and Clarke (2006), served to guide my analysis. Firstly, this is because thematic analysis is relatively easy to learn, is flexible enough to be applied across a wide range of theoretical frameworks, and is beneficial as a rudimentary method for the following reasons: (1) it can be applied to a wide range of research questions; (2) it can be used to analyse diverse data types; (3) it can be applied to large or small data sets; and (4) it is able to produce either data or theory-driven analyses (Clarke & Braun, 2013). Secondly, according to Burnard and colleagues (2008), thematic analysis is considered the most used method of data analysis in qualitative research: this appealed to me as a novice researcher (Burnard, Gill, Stewart, Treasure & Chadwich, 2008). Finally, because of my desire for structure, I made this choice because thematic analysis presents a clear sequence of steps to guide the analysis process, which is not a linear, but a recursive process (Clarke & Braun, 2013).

Data were analysed in accordance with Braun and Clarke’s (2006) guidelines on thematic analysis.

The six phases of thematic analysis (Braun and Clarke, 2006: 87):

(1) Familiarisation with the data

To become immersed in the data, I transcribed the audio recordings verbatim and verified the content by listening to the recordings a second time. I read and re-read the transcripts, taking cognizance of initial potential codes.

(2) Coding

During this phase, I generated succinct labels (codes) for features of the data appropriate to my overarching research aim. According to Braun and Clarke (2006), this process must capture a semantic and conceptual reading of the data: it must not just simply involve reducing the data to a code. By the end of this phase, I had collated all of my codes into separate and meaningful categories. I managed the data by hand, assembling paper piles for each thematic category. These categories were given an identifying title and the relevant data extracts were re-produced (to keep the original data intact) and added to the appropriate pile, which were secured together using binder clips. I returned to this phase often during the analysis process as I continually developed and refined my coding.

(3) Searching for themes

Next, I organised the codes into relevant themes. I used a paper system to organise the themes into packs secured together using binder clips. I transferred the data to an electronic mind map for clarity and kept this maintained throughout the process. During this phase, I constantly referred to my research aim to ensure alignment with the codes and themes. By the end of this phase, I had assembled all the relevant coded data extracts into their respective themes and sub-themes under the appropriate title.

(4) Reviewing the themes

During this phase, I created mind-maps of the main themes and refined them by collapsing and splitting themes. This iterative process continued until I was satisfied that the themes created a story about the data.

(5) Defining and naming themes

Once I had constructed a representative thematic map of the data (Appendix B), I wrote a concise analysis of each theme by identifying the theme’s individual story. Thereafter, I determined how each of these stories blended into the larger story of the entire data set.

(6) Writing up

Phase six constitutes the narrative that binds the story together by merging the analysis and data. To persuade the reader of the validity of my account both in relation to the data and to the existing literature, I constructed a framework using the Nelson’s (1993) poem “Autobiography in five short chapters” (p. 2-3), with the identified themes. I told the stories that I believe needed to be told, whilst continually connecting to the literature through the theoretical lenses of the key constructs: readiness for change, the TTM, and autoethnography. As it was used during the coaching process, the poem provided a metaphor to help navigate the narrative of my analysis and findings and provided a key element to my interpretation of the data. Figure 4.1 depicts the Readiness for change framework.

To conclude the data analysis section, I wish to note the following:

·      Before embarking on the analysis process, I spent some time reflecting on my topic. The purpose of this exercise was to identify any assumptions I held regarding the topic and to consider the values and life experiences that might have shaped how I thought about and decoded my data. I have described these assumptions in point 3.5 on reflexivity.

·      My data analysis process was not a discrete and separate stage of my research: it was a continual process throughout and beyond the lifetime of this project (Mauthner & Doucet, 2003; Watt, 2007).

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