For the purpose of data analysis, a thematic analysis model was utilised. Thematic analysis is simply the process of identifying important and significant patterns in a pattern that is thematically relational and significant to the research questions of a given study (Braun &
Clarke, 2006; Clarke & Braun, 2013). The goal is not just to arrange the data in themes and patterns, but rather to use them to address the identified research problems. Such analysis brings out the meaning of the data collected mostly from the participants’ views, in a highly inductive way that does not allow the researchers themselves, to impose their own individual meanings on the data (De Vos et al., 2011).
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Thus, in accordance with Braun and Clarke (2006), the study has adopted a six-step approach, namely:
1. Familiarisation with the data 2. Generation of initial codes 3. Searching for themes 4. Reviewing themes
5. defining and naming themes 6. Producing the report
3.9.1 Step 1: Familiarising yourself with your data
After transcribing the data, the researcher had first immersed herself in the data to the extent that she was familiar with the depth and breadth of the content. The researcher repeatedly read the data in an active way by means of searching for meanings, patterns and so on. It was ideal to read through the entire data set at least three times before commencing the coding process.
This ensured the researcher that the data was making sense in terms of understanding the connection between the teachers’ mental health and their performance.
3.9.2 Step 2: Generating initial codes
The familiarisation stage was followed by the coding process. After the researcher had familiarised herself with the data, she generated the initial list of ideas about how the data speaks to the research questions. For instance, what part of the data answered specific research questions? How do I categorise that part? Reflecting on the data like this made it possible for the production of initial codes from the data. Codes identify a feature of the data (semantic content or latent) that appears interesting to the analyst, and refer to “the most basic segment, or element, of the raw data or information that can be assessed in a meaningful way regarding the phenomenon” (Boyatzis, 1998, p. 63).
The researcher coded her data by writing notes on the texts she was analysing, by using highlighters or coloured pens to indicate potential patterns. The researcher identified the codes, and then matched them up with data extracts that demonstrated that code, but it was important in this phase to ensure that all actual data extracts were coded, and then collated together within each code. This involved copying extracts of data from individual transcripts or photocopying
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extracts of printed data and collating each code together in separate computer files or using file cards. The researcher coded for as many potential themes/ patterns as possible.
3.9.3 Step 3: Searching for themes
This step began when all data have been initially coded and collated. Now with an initial list of different codes as a data set, the researcher was able to re-focus on identifying themes that speaks to the research questions on the broader level. This was arranged as patterns. In practical terms, this involved sorting the different codes into potential patterns and collating all of them in a meaningful organised way. This is then later combined to form overarching themes, in relation to the research questions.
3.9.4 Step 4: Reviewing themes
Having assembled some meaningful themes, the researcher began the fourth stage. The stage involves two levels: reviewing and refining of themes. While in the first level, the researcher reviewed the themes from the coded data extracts, in the second level she had read again all the collated extracts for each theme and considered whether they appear to form a coherent pattern. The researcher had considered other elements such as themes being coherent, whether or not they are meaningfully coherent, and whether they are strictly addressing the research questions and objectives. At the end of this stage, the researcher was confident that clear and refined themes had emerged from the data.
3.9.5 Step 5: Defining and naming themes
After having derived a satisfactory thematic map of the data, the researcher began defining and naming the themes. Here, the essences of the themes were critically identified and determined.
Measures were taken to make sure that each theme had an aspect of the research questions it captured. The researcher tried not to get a theme to do too much, or to be too diverse and complex. Above all, the researcher ensured that the themes carried a coherent and internal consistent narrative that represented the participants’ views.
3.9.6 Step 6: Producing the report
This was the final step. It involved a write up of the data. This stage meant that at the end of the analysis, a report is produced, in a way that makes sense for the study. Extracts from an
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analysis journal kept by the researcher throughout the data analysis process, were embedded within an analytic narrative that illustrated the story about the data.