This section describes the analytical process undertaken for each phase of the research project. Interviews were digitally recorded, with participants’ permission. Immediately following each interview, the researcher made notes that reflected his initial impressions as well as possibilities for data codes (Burnard, 1991). The recordings enabled verbatim transcription, which was viewed as a precursor to thorough, robust analysis. The researcher chose to transcribe the interviews himself, despite the quantity of data and the potential for outsourcing, because this process encouraged deeper immersion in the data and therefore close reflection. The transcription process was supported by InqScribe; software that enabled the recordings to be played at slow speed, and paused and rewound. After each transcript was made, short summaries were written that reflected initial themes seen to emerge (Conger, 1998), for later reflection and as a sense check of the analysis. At this stage, the transcripts were imported into the qualitative analysis software, NVivo. The reasons for and benefits of this decision are explored in Section 3.5.1.
The subsequent content analysis was based on the approach of Cappellen and Janssens (2010). First, the narrative fragments - or sections of stories - within the transcripts
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were broken down into ‘thought units’ (Gioia & Sims Jr, 1986) reflecting distinct thoughts, which tend to correspond loosely to sentences. This was performed utilising the coding function within NVivo, in which the researcher highlights chunks of text before labelling them. In the second stage, termed the ‘categorising’ phase (Cappellen & Janssens, 2010), the thought units were read several times and then labelled with codes. The codes were compared with those created immediately after the interviews, as a form of verification. Codes predetermined by insights from the literature were not used because the since SIR is under-researched, doing so had the potential to suppress new theories or models, therefore predetermining outcomes.
The codes were then sorted into categories (n=47) and sub-categories (Conger, 1998) (n=144). Each category was assigned a label that reflects its shared message (Cappellen & Janssens, 2010). At this stage, the transcripts were re-read with the categories and labels to determine appropriateness (Burnard, 1991). In the third, ‘classifying phase’ (Cappellen & Janssens, 2010), the labels were grouped into themes (Graneheim & Lundman, 2004) to enable the development of new models and theories (Usinger & Smith, 2010). This step also enabled the creation of flowcharts of themes (e.g. Richardson, 2006), for example, in Figures 7.1 and 7.2., as well as comparison with prior research.
Crucially, this also enabled the comparison of key findings between the two phases of the study. The research questions had called for information on pre-repatriation expectations as well as post-repatriation experiences, and this necessitated separate, distinct analytical phases for the two corresponding sets of interview data. However, the research questions also called for a comparison of expectations with experiences,
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and this meant that the transcript of each participant’s experiences-phase interview had to be compared with that of their expectations-phase interview. At the theory building stage, common themes from this individual-level analysis were examined at phase level. Outputs of analyses from the phases appear in this thesis in Chapters Four (expectations) and Five (experiences), as well as Chapter Six (comparison between expectations and experiences).
In the experiences-phase interviews, participants tended to provide a self-assessment comparison of their experiences with their pre-move expectations. Clearly, they had to draw on their memories of their expectations to do this, and consequently their memories of their expectations did not always correspond with the content of their own first-phase interview data. Where discrepancies arose, priority was given to the phase one transcripts over the memories of expectations reflected in the phase two transcripts. This is an advantage of the two interview phase methodology as a more robust approach than the conventional singularly reflective design, which is more likely to accept at face value the participants’ own recollections of their pre-repatriation expectations from before their return move.
3.5.1 NVivo CAQDAS Software
As mentioned above, NVivo was used to support the data analysis. NVivo 10 is a computer aided qualitative data analysis software (CAQDAS) program. Although “computers cannot replace the contextual processes required of the researcher” (Fossey et al., 2002, p. 729), the use of software supports the adaptive process of developing codes (Hudson & Inkson, 2006) and facilitates the exploration of connections with existing theory (Richardson, 2006). It can also help determine the relative importance
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of themes through quantitative assessment of their frequency. The reason NVivo was selected for this project is that it offered the closest match between the requirements of the project and the capability of the software. These requirements included supporting the systematic categorising and classifying stages of analysis, quantifying the codes, and exporting demographic and coding data into Excel for further analysis. The researcher attended a two-day classroom style course run by the software’s manufacturer, QSR International, which included significant one-on-one time with an experienced NVivo tutor to discuss leveraging the features of the software in this study.
The manual and computer-aided approaches to coding and categorising of the data were complementary. The NVivo software proved useful in managing and analysing the interview transcripts, especially considering the volume of data; sixty interviews across two interview phases. Attempting to manage this data without the support of NVivo would likely have resulted in less methodical analysis. Moreover, NVivo enabled a more sophisticated interrogation of the data in relation to exploring connections between themes, than would have otherwise been possible. The queries functions within NVivo were particularly useful. For example, a ‘word frequency query’, used to find words or meanings participants are commonly discussing, helped reveal the ‘luck’ theme discussed in Section 6.4.1. In addition, other queries were used to explore whether demographic variables explained variance in participants’ views and experiences. Finally, the code-organising functionality within NVivo effectively supported the iterative process of adding, removing and merging codes (developing coding hierarchies), as well as exploring higher-level themes emerging from the codes.
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However, the researcher did not depend solely on NVivo to execute the analysis. As mentioned above, computer assisted analysis does not negate the need for a researcher’s own analytical ability and reflection. For this reason, the NVivo-supported analytical stages of this project took place in addition to the manual reflective and confirmatory processes described in Section 3.5. NVivo supported the analysis rather than driving it. Additionally, the researcher actively sought potentially conflicting data and explanations for emerging themes (Miles & Huberman, 1994), as part of these processes. This was executed primarily through running of queries within NVivo, and included, for example, searching for evidence of connections between categories and sub-categories which appeared unlikely, and counter to themes that were emerging.