Data Analysis: Questionnaires and Interviews

The coding and analysis of the survey data was done using the Statistical Package for the Social Sciences (SPSS). Following inputting all 871 questionnaires, it was necessary to do some initial coding of the variables to convert them into a useable format. The main variable which required such work was ‘parental occupation’. This was an open question for pupils to write in their parent(s) jobs which needed to be coded into an occupational classification scheme. I decided to use both the Standard Occupational Classification 2010 (SOC2010) codes and the National Statistics Socio-Economic Classification (NS-SEC); SOC2010 enabled me to retain a level of detail around the specific occupational groups whilst NS-SEC would allow for a more broad coding in line with the dominant

classification framework. I created individual variables for each parent’s occupation38_{. This}

coding was not straightforward, at times there was a lack of detail given about a parent’s job making it impossible to ascertain the relevant code for the occupation listed. Examples of this include ‘manager’, ‘owner’ and ‘accountant’. I was often left to make decisions using additional information (such as parental educational level) to aid with the coding of

occupations.

Another difficulty I encountered through the coding process was that some pupils had ticked two boxes instead of one or had otherwise indicated being ‘in the middle’ of two responses. In these instances I used logical reasoning to make a decision about which box to assign them to. Through my coding I gained some insight into the problematic nature of rigid questionnaires with fixed option boxes which force people to select one predefined response. The young people in my research who ticked two boxes may be understood as vehemently opposing such rigidity and challenging my predefined categories39_.

In addition to the issues involved in coding the quantitative data it is important to point out that whilst it formed a central part of the original research design, the results of the survey have only limited analytical use in this thesis. This is because the initial survey was designed to address the original research questions and was thus not able to speak to all parts of the analysis following a change in focus. For example, there was no question on the survey about young people’s career aspirations, something which became important to this project

38 _{It is often the case that when classification systems are used the two parents in the household’s occupations}

become merged giving a household NS-SEC. I instead chose to keep both parents coded individually as this allows for a more nuanced analysis.

39 _{I do not wish to imply that this was necessarily undertaken in a conscious manner, rather their responses}

62

at a later stage. In this way it is notable that the survey is not drawn upon in relation to all aspects of the analysis, nevertheless parts of the survey remain important and have been useful in contextualising and supporting certain arguments in the thesis.

The method of qualitative data analysis undertaken in this thesis is not easily classifiable in line with the traditional sociological methods. I do not wish to force my data analysis process into a specific box but rather to be honest about how I came to the conclusions of this thesis and discuss how certain elements of my analysis may fit or differ from dominant approaches. Hammersley and Atkinson (1997) argue that the data analysis stage of research is not necessarily a distinct phase and highlight the ways it is prevalent throughout the course of research. They write: ‘Formally, it starts to take shape in analytic notes and memorabilia; informally, it is embodied in the ethnographer’s ideas and hunches’ (: 205). This was indeed my experience of the analysis process. Throughout my fieldwork I made notes on issues I felt were emerging strongly from my observations across the interviews. For me, analysis started during the data collection phase. At this point I was so close to the data, I had a real sense of what was going on.

I began the stage of formal data analysis once I had left the field and had a psychological break, as such I was deliberately one step removed from the data. This was an important process as gaining a distance from the field enabled me to relook at the data collected with greater objectivity40_{. Nevertheless the notes made in the field on ‘initial ideas and hunches’}

provided crucial insights to aid this process. My approach to analysis was thematic and I used NVivo to sort and manage the mass of data collected. I coded the transcripts into themes initially guided by the different sections of the interview and subsequently added codes to account for themes emerging directly from the data. Hollway and Jefferson (2013) warn against the problems of data fragmentation and assert the importance of ‘keeping the whole in mind’. When beginning analysis I was cautious about fragmenting and dissecting transcripts, feeling that each individual’s complete story was important. In order to avoid this I began to read through whole transcripts. This started to become difficult and I was faced with the question: how do I keep ‘the whole’ in mind when there are 60 transcripts? What I began to realise through analysis was that the context and bigger picture (or unit of analysis) for my specific research project was not each individual’s whole story; rather the ‘whole’ was the context of participants in relation to each other and the schools. This

40 _{What I mean by this is that my data analysis process was able to be less guided by my emotions of the field,}

63

understanding enabled me to cut up my data in multiple ways to explore the relations within it. For example, I constructed large mind maps of individual responses to specific questions asked about the vignettes in order to visually compare responses between each school and year group41_.

My approach to analysis was not wholly directed by my original research questions. Whilst they remained present in guiding the analysis, my approach also involved a close reading of the data to allow themes to emerge directly from it. Whilst this was daunting and

uncomfortable at first, guided by my supervisor who encouraged me to get ‘comfortable with the uncomfortableness’ of this phase, I found it to be a refreshing and liberating process as I was free to explore my data from multiple angles. Through this process I constructed a list of themes which I felt were of central importance. This was based not only on my interviews but also on what I had seen and experienced throughout the fieldwork. Of course this list was far too long for a thesis, as such I had to make some important decisions about the direction of the analysis. Following discussion with my supervisor, I re-evaluated my original research questions considering what was now apparently at the heart of the thesis. I then began to write starting with what I felt was of central importance to my participants, academia and wider society. I began by describing and unpacking various areas of the data and subsequently built up the analysis with theory. I utilised Bourdieu in this process as a tool to inspire and help me ‘think’ about my data and its implications. Bourdieu has been described as good to ‘think with’ by both his sympathisers (Grenfell and James, 1998) but also his by critics (Jenkins, 1992). Jenkins (1992) argues that this is because Bourdieu’s theorising is deeply rooted in empirical research and also because he provides rich and continuous reflections on the complexities of social life and the practice of research. In sum, the process of understanding, analysing and writing about my data involved a careful crafting. I centralised the participants’ narratives over any particular analytic framework and rather used theory to think through and understand their discussions in relation to broader structures of inequality.

It is important to note that this approach whilst seemingly inductive is notably different from the ‘grounded theory’ approach prescribed by Glaser and Strauss (1967) in that I am conscious that it is never entirely possible (or indeed desirable) to view the data free from any framework; my personal subjectivities and affinities to Bourdieusian scholarship clearly

64

influenced the process as is discussed below in section 3.9.3. Moreover, even when the research questions were put to one side, they had already influenced and framed the data through their original role in the design of the methodological tools. Data analysis is a messy and continual process. Whilst I have presented the data in this thesis as polished and conclusive, clearly there are alternative ways to understand it and indeed additional avenues which are yet to be explored.