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distorted in the translation process. To mitigate this possibility, I listened to the original audio recording several times and re-read the transcripts to be sure I had captured the participants’
narratives as accurately as possible. Moreover, I am a first language isiZulu speaker, and the analysis of transcripts relied heavily on my translation. At the end of each session (photovoice, interviews, and FGD), I thanked the participants for their willingness to participate and engage in the research.
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(Nowell et al., 2017; Nusbaum et al., 2017). In essence, I created a table for the photovoice visual data, where I organised all the pictures and the participants' descriptions. I split the table into three columns: the first column was for the photographs; the second column was for captions (with the heading: Why Did You Take This Picture?); and the third column with the heading: Why Do You Feel It Was an Important Picture? I included this data alongside the translated transcripts and used all the data for the thematic analysis process.
4.8.1 Thematic Analysis
Thematic analysis focuses on identifiable themes or patterns in the data (Castleberry & Nolen, 2018; Nowell, Norris, White, et al., 2017; Vaismoradi et al., 2013). Essentially, the method is used for identifying, analysing, and reporting patterns or themes within the data (Braun &
Clarke, 2006). The benefit of thematic analysis is in its flexibility. Its variability in how the framework can be used makes it compatible with different theories such as grounded theory and constructivist theory. Therefore, thematic analysis provides a flexible and valuable research tool, which can potentially provide a rich and detailed, yet complex, account of data (Braun & Clarke, 2006). When conducting thematic analysis, the researcher becomes the instrument for analysis, making judgments about coding, theming, decontextualising, and recontextualising the data (Starks & Trinidad, 2007).Qualitative researchers can demonstrate how data analysis has been conducted by recording, systematising, and disclosing the analysis methods with enough detail to enable the reader to determine whether the process is credible (Hennink et al., 2020; Nowell et al., 2017).
Researchers who are relatively unfamiliar with qualitative methods may find that thematic analysis is easier to grasp and relatively quick to learn, as there are few prescriptions and procedures. Thematic analysis is a valuable method for examining different research participants’ perspectives, highlighting similarities and differences, and generating unanticipated insights (Braun & Clarke, 2006, Hansen, 2020; King, 2004).It is also helpful in summarising the key features of large data sets.
In this study, I used thematic analysis to analyse both the visual and textual data described above. Importantly, I understood the usefulness of each theme was not dependent on quantifiable measures but rather on whether or not the theme captured had an essential aspect of the participants’ experiences. Since this qualitative study sampled a small group of adults
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from one rural community, I was not interested in quantifiable data, but what was important were emerging themes that the data produced. Using thematic analysis in this study, I was guided by Braun and Clarke’s (2006) six phases of analysis. Using these phases, I took stock of these authors’ suggestion that analysis is not a linear process. Rather, in my analysis, I moved back and forth throughout all six phases and as needed.
To understand both the participants and their experiences, I had to fully immerse myself in the data and make sure I was familiar with it. I did this in various steps. As the primary researcher in this study, I was present at all the data generating engagements with the participants, so I watched the data develop first hand. I organised all the visual data and transcribed and translated the audio recordings and the textual data to engage with it. Finally, I reviewed the data after every data generation activity, which helped me to further familiarise myself with it.
This process helped me to understand and become acquainted with not just the data but also some emerging patterns from the data.
I reviewed all the transcripts to search for emerging themes in the data. To organise my data effectively, I had a worksheet with a table that had three columns. The first column was for the rough phrases I extracted from the transcripts, the second column was for the key phrases and words I identified a significant for analysis, and the last column was for the main themes that emerged from the data. As part of the data, I included my field notes. First, I identified and highlighted all the phrases and extracts that related to my research questions. For example, I extracted these phrases from the transcripts:
• That’s just the way it is. We grew up being taught that men can’t be hovering around in the kitchen. That’s the women’s role.
• So, it’s not because we’re unfair on women and so on. It is just how things are, how we were brought up and no one can blame us for that
By doing a closer reading of the data, the phrases above, for example, became relevant for the analysis of the data, which addresses the second research question: Do men and women in this rural community report different experiences and views about having limited access to safely managed water? Therefore, I followed this analysis procedure using the research questions to guide my analysis.
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In the next step, I identified and highlighted all the words and phrases that kept emerging from the first column. For example, if the word “conflict” or the phrase “we are neglected” appeared multiple times, I placed them in the second column. This step was to narrow down the ideas that were emerging from the data. For the third step, I identified key themes that were most prominent in the data taken from the second column phrases. This process helped me to identify the emerging themes from the data. This process entailed identifying and naming themes which enabled me to create an insightful and in-depth narrative of the participants’ experiences from the data generated. I ensured that each emerging theme corresponded accurately with the participants’ descriptions of their experiences by frequently reverting to the confirmation and clarification of the data (Clarke & Braun, 2013).
Finally, I used existing literature, the conceptual framework, and the theoretical framework to guide analysis in this study. I discuss the study findings in the following two chapters (Chapter Five and Six). This process provided a linkage between the analytic narrative and themes extracted from the data. It also provided a comprehensive and trustworthy way of presenting the data by creating a comparative narrative and analysis of the data (Clarke & Braun, 2013).