THEMATIC ANALYSIS

Before I started thematic data analysis, I repeatedly listened to the voice recorded data and began to identify specific ways in which the participants talk about, understand and think about an issue. The voice-recorded data were then transcribed into written text. The next phase was to read and re-read the data in order for me to get a better understanding of the participants’ experiences and perspectives about the topic being explored.

Thematic analysis can broadly be defined as a method for identifying, analysing and reporting themes within data. These themes are an invaluable research tool as they emphasised and described patterns which occurred frequently across the data set (Brandt, Dawes, Africa & Swartz, 2004). According to Saldana (2013), any researcher that wants to become proficient and effective at doing qualitative analysis must learn how to code well. He further noted that coding refers to the transitional process between data collection and data analysis and a code refers to a word or short phrase that symbolically assigns a summative, salient and essence- capturing, for a portion of data.

The data can consist of interview transcripts, participant observation, field notes, journals, documents, literature, etc. (Saldana, 2013). Charmaz (2001) describes coding as the “critical link’ between data collection and their explanation. Although there is no perfect or right way to do coding the act of coding requires that you wear

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your researchers’ analytic lens. In the same way, Sipe and Ghiso (2004 p.482) stated that “all coding is a judgment call since we bring our subjectivities, our personalities, our predispositions and our quirks” to the process.

Furthermore, through coding researchers can organize and group similarly coded data into categories or ‘families’ because they share some characteristics that are usually the beginning of a pattern. During this data analysis or coding process, researchers should use their classification reasoning as well as their intuitive senses to determine which data ‘look alike’ and ‘feel alike’ when grouping them together. Because qualitative analysis is about meaning making it is important to synthesize the collective not necessarily to obtain a reduced answer but to establish consolidated meaning. That meaning may take the symbolic form of a category, theme, and concept or furthermore categories to subcategories or themes to sub- themes. Categories are not cast in stone and can sometimes overlap whilst codes can be placed in more than one category (Saldana, 2016).

During the data analysis process of this research, I applied the analysis process as proposed by Saldana (2013). Data were therefore analysed by identifying codes followed by the search for possible themes in response to research questions.

Quality of data of the case study

The quality of the data collected, the analysis of the data and the interpretation thereof in a case study design are very important factors that need attention throughout the entire study (Patton, 2008; Yin, 2009). Henning (2004) refers to sound data and good analysis as ‘precision’ and she further notes that precision refers to how well the entire research process was managed and how the quality and trustworthiness of the research was assured.

Checking should be done throughout the process in order to identify issues such as biases, neglect and a lack of precision, and to rectify any of these if needed. All procedures and decisions should be questioned critically, and the researcher should be aware of his/her own biases through the entire research process (Denzin & Lincoln, 2000; Henning 2004).

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During the entire research process and especially during the data analysis phase I had to be consciously aware of the impact of my own potential biases and involvement in the process. I wanted the results of this study to be regarded as trustworthy and authentic. For this reason, I checked all my decisions, actions and interpretations as the process unfolded.

Therefore, during all the interview sessions I was mindful to regularly ‘mirror’ participants responses in order to check whether or not I ‘got it right' and understood correctly what they are trying to say or tell me. This process not only assist me to establish trustworthiness of the data but it also allowed me to gain greater insight into participants' perspectives and experience. It furthermore assisted me to ensure that the selected data and my analysis of the data remained as close as possible to reflecting the authentic voices of the participants. I also had to ensure that the findings would reflect a realistic and reasonable account of all the participants' narratives (Creswell, 2009).

After I had analysed the first set of data, I shared the findings with my supervisor, as well as a peer-reader. Their comments and feedback helped me to look anew at the analysis and the way in which I categorised data. In this regard Marshall and Rossman (2011) recommend on-going peer scrutiny of the research project as they are of the view that engaging with knowledgeable critical peer-readers brings a fresh perspective on our processes and actions, and that this may challenge our own conscious and unconscious assumptions.

Working through the categories and themes again allowed me to make adjustments and to pick up on issues that I did not previously observe.

In the final analysis, I presented the findings as authentically and holistically as possible in Chapters 6. In Chapter 7, I focused on the meanings and experiences of the participants. Throughout this final phase, I reflected critically on what I do, how I do it, and being acutely aware of my own positionings that might impact on how

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I interpret, discuss and understand the data (Creswell 2007; Denzin & Lincoln 2008). A copy of the preliminary data analysis process is included as Addendum 4.