Data Analysis

To a certain extent, a pocket of participation (Franks, 2011) can be seen in the data analysis of this project (see also Kramer et al., 2011; Lushy and Munro, 2014). To explain, data analysis was ongoing, not a one-off task at the end of the data collection. Participatory activities such as the co-production of the ‘Community to Me is…’ audio documentary, and focus group discussions, enabled me to obtain young people’s reflections on my findings at different stages of the project. I used these methods to illuminate the fit between the data I gathered and my interpretation of this data. The codes were largely generated/informed by the audio documentary and discussions I had with young people, which guided the analysis. Further to this, co-production of the three-part radio series ‘What we Found’ gave young people opportunities to clarify key findings, discuss the message, and to communicate this with the listening audience.

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I adopted the task of transcribing and coding as I considered this a time-intensive burden to young people (see also Byrne et al., 2009). Further, my research is funded (through an ESRC studentship); therefore, following Lushy and Munro (2014), I considered it an unethical demand to expect un-salaried young people to have equal involvement in the project. I transcribed, verbatim, qualitative data from interviews and focus groups with young people, interviews with stakeholders, listener diaries and follow-up interviews, and recordings from the audio artefacts. I reread the transcripts whilst listening back to recordings to guarantee accuracy in transcription. After this, I began the coding process. Certain authors (Weitzman, 2000) speak of the worth of using Computer Assisted Qualitative Data Analysis Software, such as NVivo, stating that it can help in theory-building. Though not disputing the benefits of such software, I am in agreement with Fossey et al. (2002) that it should not be used as a substitute for a researcher’s own careful analysis. Although I used a Word document to store my transcribed data, I analysed the data by hand, as I felt that this facilitated greater closeness. I considered this “human as analyst” (Robson, 2011:463) stance important due to the ethnographic nature of my study. In line with Mauthner and Doucet (2003), reflexivity is important at the interpretation stage of the research. For, as Taylor and Bogdan (1998) argue, researchers draw on their first- hand experience with the research setting and participants to make sense of their data. To borrow from Hatch (2002:148), I see data analysis as “a systematic search for meaning”. Following Ely et al. (1991), this search for meaning is influenced by the respective disciplines, mentors, and past readings of the researcher.

After reading through my data set multiple times, I separated the data into smaller, significant parts. Essentially, this required me to sort the data thematically. I labelled each of these smaller parts with a code. I then compared each new segment of data with the previous codes that had emerged. This ensured that similar data were labelled with the same code. I dismissed any preconceived data categories and loosened the initial focus of the study in an effort to “generate as many codes as possible” (Emerson et al., 1995:152). I wrote memos about parts of the text which intrigued me, or that I considered particularly important. I enjoyed the approach of coding by hand. MacLure (2008:174) likewise speaks of the pleasure derived from manual analysis, particularly “poring over the data, annotating, describing, linking, bringing theory to bear, recalling what others have written, and seeing things from

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different angles”. Writing memos has been long considered an essential phase in generating theory (see Glaser, 1978). Crucially, this enabled me to ask questions about what had emerged through the data. Resultantly, I changed and made linkages between some codes, dropped and added others. Following from this, I undertook a process of abstracting, whereby I condensed the codes into deeper conceptual constructs. I continued this until all coded sections were saturated. Category names ranged from technical terms from within the literature, for instance ‘social capital’ to, as Strauss and Corbin (1990) endorse, the spoken words of the research participants themselves, such as ‘radio voice’.

Believing that much of what is heard is lost in transcription, the verbatim words of young people have been included in this thesis to illustrate the varied and nuanced realities of youth voice. I have included any grammatical inaccuracies and I have tried, as best as possible, to capture the accents (predominantly Scouse15) of the respondents. I have presented these in a spelling form that gives the reader an idea of the pronunciation in order to retain closeness with the original spoken words. This adheres to what Blauner (1987:48) describes as the “preservationist” approach, whereby I am reproducing the sounds as they appear on the recording, thereby staying faithful to participants. Following Corden and Sainsbury (2005), this also aids the trustworthiness of results.

Concerning the survey data, I exported results into a Microsoft Excel spreadsheet with individual user responses on each row. Each respondent was given a unique identifier; this was numerical and ensured that respondents were non-identifiable within my dataset. I then cleaned the raw data, amending or removing data that were incorrect or duplicated. I assigned numbered codes (value labels) to variables, except for those already in quantitative format; for instance, the number of people in a household. Data from open-ended questions within the survey were recorded verbatim, instead of converting these to a number code (see Acton et al., 2009). I then imported the data into IBM SPSS Statistics 22 package to analyse. Karanja et al. (2013) call for researchers to be open about missing data and to discuss remedial techniques. Where respondents missed or elected not to answer certain questions, I

15 _{Although negative stereotypes have come to be associated with the terms ‘Scouse’ and ‘Scouser’,}

for instance criminality, guns, drugs, and the image of ‘scallies’ and ‘chavs’ (see Boland, 2008), I use these terms throughout this thesis in keeping with the young people’s self-identifications.

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used a missing values code, choosing a number to represent the missing data point. Before analysis, I conducted validation of the dataset using consistency checks, to check for invalid and inconsistent codes (see Acton et al., 2009). Deeming that all questions could be answered with summaries of the characteristics, I used Descriptives. Descriptive statistics usefully allow for understanding of the relationships between dependent and independent variables; they summarise the data using frequencies and percentages (Wetcher-Hendricks, 2011). I used Univariate analysis to summarise the characteristics of just one variable (Blaikie, 2003); for instance, how many people listen to KCC Live. When I required two variables to answer questions; for instance, what age groups listen to KCC Live, I used the cross- tab function in SPSS. This enabled me to look within the population who listen to KCC Live. This Bivariate analysis enabled me to explore the patterns or relationships between two variables. Within the relevant empirical chapter, I have used statistical representations of distributions in the form of tables and stacked bar graphs.