Methods of data analysis

According to Finlay (2014), when carrying out data analysis, we must engage a phenomenological attitude which embraces four aspects to this process: seeing afresh, dwelling, explicating, and the transformative power of writing. This requires immersion in the data after bracketing out personal

assumptions and opinion to understand what is being ‘said’ by the

participants. It also requires further analysis to look for meaning and ways to weave meaning together into a rich description to describe the whole. In considering how to get from data to conclusions, I have referred to Punch (2009) where he suggests a four-staged effective way to proceed, this is a set of questions and advice for helping to decide on a framework for data analysis. In assessing how my research questions have been framed and developed, I selected an approach consistent with my interpretivist

methodology and phenomenological approach. I used the Miles and Huberman approach, cited in Punch (2009, p174), which is labelled as ‘transcendental realism’ and has three main components:

 Data reduction

 Data display

In order to outline how I approached each of these components, I wish to explain my processes under each heading.

4.6.1 Data reduction

This proved to be a ‘messy’ stage and required constant ‘editing, segmenting and summarising of the data’ (Punch, 2009, p178). I appreciated that coding has a central role in qualitative analysis and to proceed it was crucial for me to really understand that role and its purpose in driving forward the overall project. I was helped by the various texts on qualitative analysis and their definitions and suggested processes. As outlined by Punch (2009, p178), ‘coding involves the process of putting labels on chunks of data which attach meaning to that data, this will index the data for storage and enable further analysis by pulling together themes’. Memoing is the second basic operation and happens alongside the coding. It allows ideas that occur to be recorded. More specifically, in carrying out this stage, I followed the suggested process by Gibbs (2007) for thematic coding and categorizing. He defines coding as defining what the data are about and suggests that ‘it involves identifying and recording one or more passages of text or other data items that …, in some sense, exemplify the same theoretical or descriptive idea’ (p38). I found that this method of coding worked for me as I was able to do two forms of

analysis. Firstly, I retrieved all the codes with the same label that were examples of the same phenomenon. Secondly, I used the list of codes, such as relationships between the codes and case-by-case comparisons (Gibbs, 2007, p39). I then developed these codes into a hierarchy in a codebook.

I started coding using the typed transcripts. At this stage, I made some notes about each code as I used it: these were memos and attempted to explain the nature of the code and the thinking behind it. These were kept separate from the transcript files. This helped with enabling me to apply the code in a consistent way. As suggested by Gibbs (2007, p41), this process required me to undertake ‘intensive reading’ of the transcripts and use basic questions such as: what is going on? What are people doing? What is the person

I realised that some of the codes used were simply descriptive and I needed to ‘move away from descriptions, especially using respondents’ terms, to a more categorical, analytic and theoretical level of coding’ (Gibbs, 2007, p42). At this stage, I wanted to construct the codes in the codebook using a data- driven coding approach or open coding approach and I did this with an open mind and without preconception – in effect bracketing out any preconceptions or own opinions in order to the true to the data. I made a hierarchy of the codes (see Appendix 5 and 6). Around the same time as I was carrying out this task, new literature on SE and students as partners was emerging and there was some backwards and forwards between this approach and a concept-driven coding approach. As Gibbs (2007, p46) postulates ‘a complete tabula rasa approach is unrealistic…the point is that, as far as possible, one should try and pull out from the data what is happening and not impose an interpretation based on pre-existing theory’.

4.6.2 Data display

In terms of data display, I found the use of tables to be very helpful in

enabling me to understand in a structured way what the data was telling me and as Miles and Huberman repeatedly utter ‘you know what you display’ (1994, p11). Displays have been helpful at all stages and required constant review and enhancement. I wished to move the analysis forward by choosing a display method that organises, compresses and assembles information and as Miles and Huberman (1994) state, “they have no doubt that better displays are a major avenue to valid qualitative analysis”. Repeated and iterative displays were used until conclusions could be reached. One of my next tasks was to start comparisons and as Gibbs (2007, p78) states ‘…coding hierarchy is just the starting point’. I did this by using tables to carry out cross comparisons across different subgroups of the dataset and between staff and students. I looked for patterns in the data. As Gibbs (2007, p86) states ‘the use of tables… suggests that any models produced will have arisen out of a close reading of the data and thus will be closely supported by the data (see Appendix 7). They are, in that sense, data-driven’.

4.6.3 Drawing and verifying conclusions

The three components of data analysis discussed in this section more or less happened concurrently. I found Miles and Huberman’s (1994) 13-point tactics for generating meaning and 13-point tactics for testing or confirming findings very useful at this stage. The tactics for generating meaning such as: noting patterns, clustering, making contrasts and comparisons have been discussed above and the tactics for testing or confirming findings such as: triangulating, checking meaning of outliers, following up surprises etc. were also used. Their final point of getting feedback from informants was carried out through the focus groups with student and staff groups (see above). The aim at this stage was to integrate what had been done into a meaningful and coherent picture of the data to provide answers to the research objectives of the overall project. In order to check the veracity of my emerging themes and mindful that there is no one correct way to do phenomenology (Wertz, 2011), I also used Braun and Clarke’s (2006) six-stage approach to data analysis in order to check that I had carried out the data analysis robustly and that I had captured something meaningful that expressed the lived experiences. This double-checking was also prompted by my engagement in reflexivity; this ‘second engagement’ (Fischer, 2009, p3) meant that I could bracket my earlier understanding of the data and re-examine it against emerging insights. In essence, I wanted to go back to the data and check I hadn’t missed anything or misinterpreted it. Braun and Clarke’s six stages involve: familiarising yourself with the data, generating initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the report. Carrying out the initial data analysis and then going back to the data again using the six-stage approach above gave me confidence and allowed me to proceed to the development of the initial staff and student Guide for partnerships (see Appendix 8, 9 and 10). At all times, I subscribed to the phenomenological approach to data analysis which was to push beyond what I already knew from experience or knowledge and to break away from my own ‘natural attitude’ to find a way to remain open to new understandings (Finlay, 2014; Merleau-Ponty, 1945).