Data analysis and coding

‘You should get this right. It will be exciting to see what you come up with’ (AC) According to Marshall and Rossman (2006), data analysis is focused on techniques and processes that enable the scientist to organise, structure and interpret data that have been gathered. The data analysis in my work was an iterative process, starting from the time of data collection and lasting throughout the research. This aligns with Miles and Huberman (1994), who believe that robust data analysis is best undertaken from the early stages of data

collection and throughout the research project. The data analysis was an ongoing iterative process that became noticeably more structured and organised through the employment of both the organisational software tool NVivo and the manual technique I employed myself. My analysis involved transcribing the interviews and analysing the 45 transcripts, in addition to cross-referencing the data with further sources. Examination of the fieldwork data exposed diverse observations on the same event, and substantiation with secondary sources therefore helped to support and contextualise their standpoints (Lilleker, 2003). In addition, this represented proof that there is no single reality. Coding is one of the main steps taken during data collection and analysis to organise and make sense of textual data. Codes serve to label, compile and organise data (Miles and Huberman, 1994). In other words, codes are tags for allocating units of meaning to the descriptive or inferential information compiled during a study. The core notion of coding is that the texts containing the raw data are indexed. Codes – keywords, phrases or mnemonics – that indicate the occurrence of specific information are assigned to segments of the transcript. Miles and Huberman (1994, p. 56) define codes as:

Tags or labels for assigning units of meaning to the descriptive or inferential information compiled during a study. Codes usually are attached to ‘chunks’ of varying size – words, phrases, sentences, or whole paragraphs, connected or unconnected to a specific setting.

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I decided to start with the coding method preferred by Miles and Huberman (1994), which creates a provisional start list of codes prior to the fieldwork. My main influences have been Miles and Huberman (1994), Miles et al. (2014) and Silverman (2000; 2016). Really what I was interested in was systematic classification and categorisations of themes and I have tried to be systematic to derive categories and constructs from both the literature and where relevant from my own research. I have also tried to be systematic with my definitions, with my classifications and with the categories I have used and I have tried to be as exacting as possible. I have also aimed throughout my thesis to map the data systematically to these constructs, to get the relevant headings, quotes and coding list. This initial list derived from my interview subject areas, problem areas and key variables. During the early stages of analysis, it became apparent that considering my findings in three broad categories would be useful and make the management of my data more practicable. This involved data relating to three strategies: the mobility patterns and strategies of the interview participants; the

predominant motives across the occupational fields and why the professional elite members are reliant on their local German-Turkish communities; and, finally, the motives of

networking with like-minded individuals from similar backgrounds. Afterwards, I went through all my textual data (interview transcripts, direct notes, field observations, etc.) in a systematic way. The ideas, concepts and themes were subsequently coded to fit the categories. In accordance with Seidel and Kelle (1995), I view the role of coding in my fieldwork as noticing relevant phenomena, collecting examples of these occurrences, and analysing these to find commonalities, differences, patterns and structures. This allowed me to compare categories across the data, to change or drop categories and to make a hierarchical order of codes.

Although I gained familiarity in using the Computer Assisted Qualitative Data Analysis Software (CAQDAS) package NVivo, I deliberately chose to use both NVivo and the manual data analysis method using a simple Microsoft Word processing package. This was mainly because of my own individual preference for working with verses that are on a page in front of me. I could straightforwardly make handwritten notes owing to the relatively small scope of my interview sample, which evidently was vital to make this style of analysis feasible and possible. Additionally, I used the NVivo qualitative software package to help manage the large volume of data. Undoubtedly, using NVivo had its benefits, as I employed it as a data analysis support to provide an accurate frequency of the mention of words and the density of coverage of particular themes, which I took notice of while doing my manual analysis. I believe that pre-use of NVivo gave me a comprehensive understanding of the significance of

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emergent themes relative to the entire data corpus and, hence, made the coding and segmenting more robust.

It is a fallacy to claim that the NVivo tool actually does any analysis, as every researcher must create categories, undertake segmenting and coding and decide what to retrieve and collect. For this reason, I decided to print out every single interview transcript, with three piles for each interviewee group, the business leaders, medical doctors and lawyers, and subsequently highlighted passages using different coloured pens. In this manner I used thematic analysis, where I gave labels to the files. According to Braun and Clarke (2006), thematic analysis, through its theoretical freedom, provides a highly flexible approach that can be adjusted for the essentials of many studies, providing a rich and detailed account of data. Braun and Clarke (2006) and King (2004) also argue that thematic analysis is a beneficial method for

researching and analysing the viewpoints of different research participants, emphasising parallels and differences. Passages were therefore frequently highlighted in more than one colour, as elite participants were able to express more than one idea in a sentence.

Consequently, I switched each time to the laptop and copied each colour into its own manuscript, providing each colour with a title based on its theme, and, lastly, printed these documents all over again. Likewise, I ensured that I always had the original transcripts readily to hand to make sure that I kept track of the tone and overall impact of the interview.

Accordingly, in the course of the analysis, I completed various evaluations of each text and continuously worked through the transcript over and over again, observing further themes, overlaps and also disagreement, not only between interviews but also between the different elite groups.

Coding allowed me to summarise and synthesise what had materialised in my fieldwork. In linking data assemblage and interpreting the data, coding became the basis for developing my analysis chapters. Nonetheless, it is vital to understand that coding and analysis are not

synonymous – instead, coding is a vital aspect of analysis. Moreover, qualitative data analysis was not a distinct route taken at the final stages of my research, but rather an all-

encompassing activity that endures throughout the thesis (Basit, 2003).