Thematic analysis does involve a number of choices that need to be made explicit during the analysis process. This includes identifying what counts as a theme and what size does a theme need to be. Braun and Clarke (2006) state that a theme should capture something important about the data in relation to the research question, and represent some level of patterned response or meaning within the data set. There are no hard and fast answers to the question of what proportion of the data set needs to display evidence of the theme for it to be considered a theme. Braun and Clarke (2006) do state that the ‘keyness’ of a theme is not necessarily dependent on quantifiable measures but in terms of whether it captures something important in relation to the overall research question. When identifying themes thematic analysis allows you to determine themes (and prevalence) in a number of ways. A ‘theoretical’ thematic analysis would tend to be driven by the researcher’s theoretical or analytic interest in the area. This form of analysis tends to provide a less rich description of the data overall, and a more detailed analysis of some aspect of the data. A semantic approach is different in that the themes are identified within the explicit or surface
meanings of the data and the analyst is not looking for anything beyond what a participant has said (Braun and Clarke 2006). There are six steps to
thematic analysis that closely follow the steps as already described by Creswell and Clark (2007). The next section details the steps to data analysis, using thematic analysis in relation to the current study.
138 4.10.4 Steps in Thematic Analysis
a) Familiarising yourself with your data
For this study the interviews were downloaded into a secure hospital file that only the research team could access to ensure confidentiality and security.
All interviews were transcribed verbatim. The researcher transcribed eight of the ten interviews, and had checked the remaining two interviews against the recordings for accuracy so was very familiar with the data from the beginning.
Any identifiable information, such as names, were removed to ensure the anonymity of the interviewee and all “erms,” “ahhs” and “mmms” were removed from the transcripts so the dialogues were less broken and more fluid when read. Within the interview transcripts, commas indicated a short pause by the speaker; full stops indicated a longer pause by the speaker;
ellipsis (…) represented the omission of one or more words; square brackets ([]) indicated an insertion by the researcher to indicate a change, such as the deletion of a name, or additional information relevant to the conversation;
and speech marks (“”) to indicate when the speaker was recounting a conversation. The transcriptions formed the formal text that was used for analysis. All transcripts were re-read through prior to coding and at this point the researcher had already identified codes and potential themes from the text. Any notes from the researcher were added at this stage of any general thoughts about the data.
b) Generating initial codes
In this study the researcher decided to code by hand as they had no previous experience of analysing qualitative data and wanted to remain as close to the data as possible. The amount of time to code the ten interviews was taken into consideration and adequate time was given to this process as it can be very time consuming. To assist in the credibility of this step a second
researcher, experienced in qualitative research, coded the interviews separately. Once the researchers had coded all the interviews separately they met to discuss and compare the codes identified. The second
researcher, working as part of the PD Care Home study team, has a
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background in health psychology so provides support to individuals to develop coping strategies and assists individuals to deal with stress. Having their background and insights into these issues was particularly helpful to ensure the researcher did not miss any important or interesting codes. The researchers developed the codes on the basis of the emerging information collected from participants, and so these were data-driven, and not by using pre-determined codes (taking the terms from the Goldsworthy and Knowles model for example) as the researchers were not trying to test a distinct theory. When the researchers met to discuss the codes, even though the researchers had different backgrounds, both identified the same codes and there was little in way of variation between codes. Only slightly different terms or words were used between the researchers and clarity of the final codes was achieved during our first meeting.
c) Searching for themes
Within this study similar codes were grouped into categories and placed together in theme piles. To assist in this process the researcher used a visual representation, via a mind-map, to sort the long list of different codes identified into initial categories. These initial categories were discussed with the second researcher and agreed to ensure they included all the original codes. The researcher then placed categories into different organisations to see if categories could be ‘clustered’ together to make overarching themes.
After discussion with the second researcher thirteen candidate themes (Appendix 7) were identified at this stage and named to encompass the meaning of the category.
d) Reviewing themes
This phase involved the researcher writing a synopsis for each of the
candidate themes that had been identified. During this phase it became clear that some of the candidate themes were interlinked, where two apparently separate candidate themes merged to form one theme, or there was not enough data or the data were too diverse and so some candidate themes collapsed into each other. Again these changes were discussed with the
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second researcher and following this process the number of themes was reduced to eight (Appendix 8). At this point the researcher reviewed the coded data extracts. All the extracts of data from each theme were collated to see if they appeared to form a coherent pattern. Again once the data extracts were reviewed there were further overlaps and inter-links and the candidate themes were refined.
e) Defining and naming themes
Braun and Clarke (2006) describe how it is important not to get the theme to do too much, be too diverse and complex but that it is necessary to identify what is interesting about them and why. Braun and Clarke (2006) also state that each theme should have detailed analysis and as well as identifying the
‘story’ that each theme tells. It was important to consider how it fits into the broader overall story that the researcher is trying to tell about the data. As part of this process the researcher should identify whether or not a theme contains any sub-themes. Sub-themes are essentially themes within a theme and can be useful for giving structure to a particularly large and complex theme (Braun and Clarke 2006). Having reviewed and discussed the candidate themes with the second researcher on multiple occasions, and after revisiting the transcripts and examining the data set, themes were given names that would give the reader a sense of what the theme was about.
f) Producing the report
As part of the data integration within mixed methods each theme was mapped across to the study objectives and combined with the quantitative data in either a side-by-side analysis or joint data display to bring the
different types of data together visually to draw out new insights (Guetterman et al., 2015) as already described in Chapter 3. All carer interview quotes used are followed (in brackets) by the interview participant number, line(s) number(s) to identify source of quote and theme number from which it was derived (e.g. T1).
141 4.11 Additional Qualitative Data
The primary source of qualitative data for this study are the carer interviews but the researcher also had access to other qualitative data, such as entries into medical notes, as part of the larger PD Care Homes study, that may also be relevant to this project. Additional qualitative data would also be included in the results section if it was felt to be pertinent to an issue or theme already identified from the quantitative or qualitative data of this study.
4.12 Chapter Summary
In this chapter the researcher has described in detail the quantitative and qualitative methods of data collection and analysis that have been employed in this study. Details of the questionnaire used have been described in terms of the adapted stress-appraisal model used and the approaches to
qualitative research have been described and the use of thematic analysis has been justified. Data integration has also been explained as a key component to mixed methods and how this will be achieved in the following results chapter.
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