This study adopted the thematic analysis framework as described by Braun and Clarke (2006). They define thematic analysis as a method for identifying, analysing, and reporting patterns (themes) within data. They advised that it often goes further than this, rather it interprets various aspects of the research topic (Boyatzis, 1998 cited in Braun and Clarke, 2006). They provide a 6-phase process - the six phases of analysis, to be applied when analysing the data. They highlight that analysis is not a linear process, rather a process where one moves back and forth as required, during the data analysis process (Braun and Clarke, 2006). This method was selected because it is best suited to exploring the meaning and significance of experiences of participants to gain insight into the lived experience of dementia. Therefore, an inductive rather than deductive approach was applied, as there were no specified hypotheses to test and I wished to build a knowledge base up from the interview data, as is common practice in qualitative research (Seale et al. 2014). Thematic analysis was also seen as a means of analysing lived experience descriptions. Themes enable the researcher to capture ‘the phenomenon one tries to understand’ (Van Manen, 1990) by allowing the researcher to simplify and focus on description.
3.8.1 The experience of data analysis
In completing data analysis, I followed the Phases of Thematic Analysis as outlined by Braun and Clarke (2006 p.34) in table 3.2. The process that I followed in reference to the phases of thematic analysis by Braun and Clarke 2006) is further detailed below.
Table 3.2: Phases of Thematic Analysis
Phase Description of the process
Familiarising yourself with your data: Transcribing data (if necessary), reading and rereading the data, noting down initial ideas. Generating initial codes: Coding interesting features of the data in a
systematic fashion across the entire data set, collating data relevant to each code
Searching for themes: Collating codes into potential themes, gathering all data relevant to each potential theme.
Reviewing themes: Checking in the themes work in relation to the coded extracts (Level 1) and the entire data set (Level 2), generating a thematic map of the analysis.
Defining and naming themes: Ongoing analysis to refine the specifics of each theme, and the overall story the analysis tells; generating clear definitions and names for each theme.
Producing the report: The final opportunity for analysis. Selection of vivid, compelling extract examples, final analysis of selected extracts, relating back of the analysis to the research question and literature, producing a scholarly report of the analysis
Familiarising yourself with your data: In becoming familiar with the data, I listened to audio recordings of interviews within 24 hours to review each interview, with note writing to capture any additional or general observations, impressions or ideas. As the data collection process included a series of three interviews, when I reviewed the first and second audio recordings, when I identified areas that were not fully explored, these notes were used to help develop the interview guide for the third interview with the participant. Each interview was then transcribed verbatim by the transcribing service. On receipt of the transcript, I reviewed it again, to ensure that audio recording and transcript were accurate. This was a necessary step, as on one transcript, the transcriber noted: ‘Transcriber’s note: Strong accents. Mic poorly positioned’. I was able to use the Annotations function on N*Vivo to make further notes as I reviewed the received transcripts against the audio recording (see Appendix 23).
The object of this study was to interpret the experience of the participants living with dementia of Black ethnicity in the UK, and not just the story itself (Bernard and Ryan, 2010). The interviews gave the participants the opportunity to tell their stories which became the data for the research. For the data analysis to be effective within a phenomenological study the data needs to retell the story in such a way that it is understandable to the reader. Cohen et al.
(2000) write that analysis begins during the interviews, as the researcher actively listens to the participant and consciously thinks about the meaning of what is being said. As I was the interviewer I would agree with this – it was difficult not to get excited when I listened to some of the narrations. I recall that I would call my University supervisor to explain the preliminary observations. The more I read and re-read the data, the process of allocating then codes commenced.
Generating initial codes: The data were explored using coding techniques to establish common themes and any deviant themes that emerge. Once the transcripts were received, the transcripts were uploaded onto NVivo.
I used NVIVO software (QSR International Pty Ltd, V.10, 2012) for initial analysis. Care in coding the data had to be taken to ensure the excerpts documented were in context with the theme it had been placed in. For this study, a line by line scrutiny was undertaken of the 16 transcripts (made up of 5 x 3 interviews and 1 transcript from the participant who did not complete the study) from the semi-structured interviews.
Using N*Vivo, across the whole body of the transcripts, every identifiable significant statement or comment was assigned a code. For example, any content/statements related to participant agency employment -were coded initially under the code ‘agency’-see figure 3.0 below. The codes were generated as I was examining the data. I had no predefined codes. I developed a list of over 150 codes.
Searching for themes: I then reviewed the codes list and recognised that certain similar codes occurred more than once and there were related codes with logical connections. Therefore, I grouped these using parent codes to assist in commencing development of the identification of themes. For example, in figure 3.0 all codes relating to employment, types of employment, views about getting work, work patterns were grouped under Employment. Similarly, with regards to the participants views about how they viewed dementia as an illness, what they perceived caused dementia or was related to dementia as an illness was grouped under Dementia as an illness. If a statement fitted into more than one category they were placed in the one that fitted most in the context of the study. Once the codes were grouped under parent codes, I had a list of 51 parent codes. (see Appendix 24).
Figure 3.0: Example of N*Vivo coding
N*Vivo was quite helpful with this initial coding process with this process and I could use other features such as Word frequency queries, and this was presented visually as a word cloud (see Appendix 25). This N*Vivo function enabled me to see word frequency and word trees’ and also gave me direction for further analysis in the initial stages. Silverman (1993) thought simple counting of themes was of value, as the researcher found in the initial stages of analysis. But for a phenomenological approach, this was not enough, and more in-depth exploration of the data was carried out as described in the next section.
Searching for themes: In order to fully understand the phenomenon that was emerging from the data, I recognised that that functionality of N*Vivo was limited and that I required a deeper immersion. Therefore, I also reviewed the transcripts manually data and coded by the researcher to enable the feeling of the rich data to be experienced first-hand. This was done by a line by line study of each part of the data text and writing emerging themes. I was able to cross-reference to the work already completed on N*Vivo. A practical example of this is seen in the transcript image in Figure 3.1.
Figure 3.1: Example of data coding
Reviewing themes: The participants’ stories were told by the researcher through quotes from the transcripts under the headings of emerging themes. Selecting quotes that make it clear how a person really experiences something is challenging to the researcher, requiring an empathic understanding of the phenomenon being studied (Bernard and Ryan, 2010). The participants in the interviews gave a story through conversation with the researcher about their experiences. The process of reading and re-reading is sometimes known as “immersing oneself in the data” (Cohen et al. 2000) and is what, in this study, helped to develop an interpretation which later informed the theme development.
Through the repeated reading of the transcripts to allow me to become more familiar with the data and a process of reflective thinking, a gradual awakening of the hidden meanings of the narratives began to emerge. Streubert and Carpenter (2011) term this “interpretive reading”. I reviewed the codes and the parent codes and started developing a thematic map of the analysis with the initial development of the overarching themes (see Appendix 26). The data were grouped and re-grouped under the overarching themes to enable the essence of living with dementia as an individual of Black ethnicity to emerge
Defining and naming themes: Once the initial key themes and overall understanding had been developed, ongoing analysis allowed for the specifics of each theme to develop; I then
developed a diagram which helped me to see the whole picture: Life before dementia; Journey to diagnosis and then of course Living with dementia as demonstrated in Appendix 27
All themes were presented as relating to the entire sample. The overarching themes developed and sub-themes, which helped to capture the essential meanings of the overarching themes, were also noted. One interlining theme was also identified. It was a process of writing and rewriting reflexively and it was through this continuous process of re- reading and re-writing that the emerging themes were developed.
Producing the report: Producing the report: The findings of this process are presented fully in Chapter four with a summary of findings presented in section 3.11.