APPENDICIES
4.5. Mixed Method Research Design
4.7.6. Data Analysis
Given that the interest of this study was in exploring and understanding how nurses experience and perceive Job Satisfaction, Job Strain, PsyCap, Burnout and Wellbeing within the context of the Re-engineered PHC system, IPA was deemed to be valuable in unearthing these subjective meanings. As stated by Dey (1993), qualitative data analysis deals with
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meaning and by using an IPA, it enabled the researcher to examine how nurses made sense of the changes within the health care system and how this influenced their perception of their Job Satisfaction and Wellbeing. Researchers using the IPA approach are interested in lived experiences that take on significance for the person concerned by unpacking their experience and the parts that make up this experience with emphasis on having the person reflect on the significance of the experience and engaging with these reflections (Smith et al. 2009). Nurses were asked questions that elicited this type of information from them regarding their
experience of the Re-Engineering PHC process and its effect on their professional and personal life.
In line with the IPA approach, thematic analysis was used as it offered an accessible and theoretically flexible approach to analysing qualitative data through the search for themes and patterns in content (Braun & Clarke, 2006). Unlike other word-based analyses such as key-in-word-context or semantic network analysis, thematic analysis requires the researcher to be more involved and requires a higher degree of interpretation on the part of the
researcher (Bernard & Ryan, 1998). Furthermore, thematic analysis goes beyond just counting words or phrases and focuses on identifying implicit and explicit ideas within the data and describing these ideas. These ideas are more commonly referred to as themes. A theme represents categories that were identified by the researcher in terms of how they relate to the research questions (Braun & Clarke, 2006). The argument has been made that from all the qualitative approaches to data analysis, thematic content analysis should be seen as the foundational method for qualitative analysis (Holloway & Todress, 2003). Hence, it is postulated by Braun and Clarke (2006) that it should be the first method of qualitative
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analysis that students and researchers should learn because it provides core skills that will be useful for future use in conducting qualitative analysis.
Thematic content analysis is flexible and this was one of the major benefits the researcher drew from when she used this approach to analyse the data. Thematic analysis is a method for identifying, analysing and reporting themes within data which are then marked as codes. According to Boyatzis (1998), it minimally organises and describes research data set in rich detail. Given this description, in the context of the present study, thematic analysis provided the researcher with the flexibility of interpreting the data in such a way that highlighted the aims and objectives of this study.
Within the IPA approach, the researcher engages in an interpretive relationship with the transcript so as to be able to understand the context and complexities of the data (Smith &
Osborn, 2007). The researcher creates names for the themes from the actual words of participants and groups them in such a manner that directly reflects the texts as a whole.
Interpretation on the part of the researcher is kept to a minimum and the feelings and thoughts of the researcher make little difference in thematic content analysis (Anderson, 2004).
According to Smith et al. (2009), the researcher need not follow a set of steps rigorously, but rather focus on following the guidelines of IPA. The steps of IPA and are outlined in Figure 4.8.
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Figure 4.9. Schematic Representation of Thematic Analysis. Adapted from Smith et al.
2009.
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In conjunction with the guidelines of the IPA, the researcher used the guidelines set out in Braun and Clarke (2006) for conducting the thematic analysis of the data. The first step in the data analysis process was the transcription of the interviews. Although the interviews were conducted in English, some of the participants unconsciously switched between their native tongue and English; consequently, independent parties were used to carry out the translation and transcription work. Through reading and re-reading the interview transcripts, the researcher became familiar with the data. Initial analyses of the interviews were done manually by reading and re-reading the transcribed interviews and highlighting key words and phrases. During this process, it was important to not impose the researcher’s own
interpretation on to the data. The initial coding of the first interview was guided by the broad areas that were discussed in the interview schedule. This initial coding was done by looking for patterns and potential themes. This process is supported by Braun and Clarke (2006) who advise that one “code for as many potential themes/patterns as possible” (p. 89). The
interviews were also given to a group of postgraduate students to analyse as a means of confirming the codes and subsequent themes the researcher had identified. This process of triangulation of the data is supported by Marlow and Boone (2011) and Royse (2011) who state that triangulation can be used to validate the data through the use of “different people to collect and analyse the data” or through the use of multiple sources of information or use of different methods.
The next step following the coding process, was the search for themes. Braun and Clarke (2006) describe this part of the data process as the phase where the analysis is re- focused at a broader level of themes. Here, the researcher considered which codes were
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related and could be grouped under one category. For example, the codes “helping people, improving lives, patient feedback” were all grouped into the sub-theme: Other People Matter.
Connections and relationships were found between some of the themes, and these were then all placed into one main theme.
During the next step, the themes were first reviewed, after which using Patton’s (1990) criteria for how themes should be broken down, the researcher proceeded to create main themes and sub-themes. During this process of reading all the extracts under each theme, it came to light that some of the themes did not form a coherent pattern and these themes were then reworked until the themes as adequately represented the coded data.
However, it is important to note that coding is an ongoing process and therefore the themes that the researcher began with initially changed and were reworked as re-reading of the data set occurred numerous times.
The last few stages of the analysis process involved “defining and naming” the themes. Braun and Clarke (2006) explains this as the stage whereby the researcher can
“identify the essence of what each theme is about” while remembering to keep it simple and not have complex themes that are too diverse (p. 92).