Thematic analysis can be used within narrative research, and is initially concerned with identifying themes within the written and (if available) visual information (Riessman, 2002; Hunter, 2010). A ‘theme’ is a pattern in the data that, at a foundational level, describes and organises possible observations and can extend to interpreting aspects of a given phenomenon (Boyatzis, 1998). To analyse the data, I used the guidance of a number of narrative researchers: Minister (1991), Lieblich (1998), Riessman (2002, 2003), and Holloway and Jefferson (2000). I also utilised an adaptation of a six-stage thematic analysis process (Table 6 p.125), which helped to frame the evaluation of the data (Braun and Clarke, 2006).
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Table 6: Adaption of the six-stage process (Braun and Clarke, 2006)
Phase 1
Become familiar with the data
Transcription of the interviews (multiple readings)
Listening to the audio-tapes a number of times
Phase 2
Generate initial codes
Interesting patterns of the text are coded systematically across the data set
Phase 3
Search for themes
Codes are collated into potential themes, and all the data relevant to a theme are gathered
Phase 4
Reviewing the themes
Themes are checked in relation to the coded extracts and then across the entire data set
Phase 5
Define and Name the Theme
Analysis continues until the specifics of each theme are refined and story of analysis told
Phase 6
Produce the report
Analysis finalised
Although the framework (Table 6 p.125) was helpful in providing structure in the early stages of the analysis, I did not follow a clear linear pathway from Phase 1 to Phase 6. There were times during the analysis where I moved back and forth between the phases, to check that my interpretations were fair and accurate. In addition, I supplemented the analysis with guidance from the narrative literature, paying attention to both structure and performance (Riessman, 2002, 2003). Thematic analysis was also selected as it would facilitate the mapping of individual assets.
3.17 (i) Phase 1 Becoming familiar with the data
I listened to the tape recordings three or four times, taking note of the tone of voice during the interactions between myself and each of the participants; this stage of the process was carried out by hand. The tape recordings were transcribed verbatim using Microsoft Word, and these were read and re-read to identify initial thoughts and coding; at the same time notes within my reflective diary were reviewed. The photographs
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that the women had taken were also viewed in the context of the women’s narratives. During this stage, certain quotes were highlighted and some initial coding was carried out. This was completed by hand rather than by the use of a computer software programme.
The next step involved a decision to use a computer software package MAXqda10, allowing for the transcriptions, recordings and photographs to be stored in one place. MAXqda10 provided a system that made it relatively easy to navigate across the files. Computer software programmes have been increasingly used for the analysis of qualitative data and these can help to organise, develop linkage and theory (McLafferty and Farley, 2006). However, it has been argued that computer software programmes can create distance between the researchers and the data (Cresswell, 2007, p.165). Therefore, although a computer programme was used as the main tool to analyse the data, it was supplemented with handwritten charts, and notes that were used during each stage of the analysis. These handwritten records enabled me to remain close to the data, and to share early thoughts and emerging findings with the expert reference group.
3.17 (ii) Phase 2 Generation of initial codes
Once the data (transcriptions and photographs) were imported into the MAXqda10 programme, I was able to review the data and identify some initial codes. The codes were taken directly form the text data in a process that was inductive (Asieh and Shannon, 2006). At this stage of the data analysis, 74 codes were identified, and these were then reviewed and checked against the transcribed interviews and photographs to ensure nothing had been missed. The 74 initial codes were entered onto a set of
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hand-written charts and these facilitated discussions with the expert reference group, and later helped with discussions about linkage across the coding.
3.17 (iii) Phase 3 Searching and Reviewing Potential Themes
During this next phase of the analysis, linkage and connections were made across the 74 codes and these were refined by moving them into potential categories where there was some natural linkage. At this stage of the analysis there were 54 categories. Although the expert reference group had not been trained to undertake analysis of research data, they were involved in discussions about the early coding process and the potential themes. Their lived experiences as older women were valuable, and supported my analysis by ensuring that the emerging findings held some resonance for them. There were times when a longer debate took place with the expert reference group; an example of this was the potential theme of dolls and toys that had emerged from the early analysis of the data. One of the experts in the group did not think that dolls or toys were a good thing for older women to have, whereas the other three women disagreed. Having the photographs that the women had taken of the soft toys and dolls supported the dialogue, and it became clear that there were different views held by the women experts, and these had been influenced by their individual life journeys. However, the group decided that although they had different views, dolls were a feature of the narrative and visual data, and they were categorised as an emerging theme together with the other 53 categories.
3.17 (iv) Phase 4 Reviewing Themes and Categories
Having agreed the potential categories with the expert reference group, I then reviewed each of the coded sections of the data to ensure that these were aligned to each of the 53 categories. The next step involved reviewing the categories and
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identifying broader themes across the whole data set looking closely for linkage. During this stage of the analysis categories were integrated; for example, concepts of ‘equanimity’ and ‘resilience’ were combined under the theme of ‘equanimity’, as the narratives and photographs were more closely aligned to a balanced outlook on life.
3.17 (v) Phase 5 Defining and Naming the Theme
The next phase of the analysis involved analysing the data for common themes that told the story of their lived experiences. Nineteen themes had been initially defined, and the asset framework was then applied to map these themes to external or internal assets (Table 7 and 8, pp.130-131). During this process, special attention was also made to identify any deficits that emerged from the data.
Despite acknowledging that I would not be involving the expert reference group in the analysis, inclusivity in terms of sharing and consulting with the group could have been improved. The photographs that the women had taken supported the discussions within the expert group, and support their inclusion. Equanimity proved to be a complicated concept to explain, and Pat queried how good and bad times (equanimity) was an asset. Assets were explained as things that help us in our life, and that some things involve other people (external) and other things such as equanimity are within ourselves (internal). To support this, we talked about the things that we have that have helped us to pull through during difficult times e.g. strength, happiness, courage, love. The term equanimity was a very complex concept and was explained by using the words of two of the participants ‘Life is not a bowl of cherries but you just get on with it’ and by looking at some of the photographs the women had taken.
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This final stage of the process of data analysis resulted in identifying three internal assets and five external assets and one deficit, all of which represented the capacity and capabilities of the women’s narratives (Tables 7 and 8, pp. 130-131).
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Table 7 – Data Analysis Internal Assets
Potential Categories Themes Related Core Internal Asset Old age Young adult Childhood Health Wishes Hopes
Self-perception of old age
Future
‘I am not old yet’. This asset represented the women’s self-perception of old age.
‘My life and hopes for the future’ Value Independence Voluntary work Friends Staff Value Independence
‘Being Valued’. This reflected the women’s view of a place in the community and also a sense of accomplishment. Strength Determination Resourceful Balance Hope Perseverance Courage Independence Abuse
Equanimity ‘You just get on with it’. This asset reflects the women’s balanced approach to their life (equanimity). Fear Worry Sad Scared Lonely Abuse Womanhood and Vulnerability Abuse
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Table 8 Data Analysis - External Assets
Categories Themes Related Core External
Assets Shopping Eating out Walking Employment Voluntary employment College Clubs Church Community Gym Classes Cinema Holidays TV, Radio Hobbies Independence Choice Leisure and Recreation Employment Clubs and Connections Independence Hobbies
‘Getting out and meeting people’.
These reflected the importance of keeping busy, and seeing people.
‘Keeping Busy at home’.
Dolls Soft toys Babies Children Family Holidays Motherhood Family
‘My Babies’. This asset reflected the important role that dolls and soft toys, such as teddy bears, had in the women’s lives. Family Partners Best Friend Friends Staff Neighbours Choice Relationships - family Marriage Choice Friends Staff
‘My Family’. This represented the important role that family had for the women.
‘My Friends’. This asset represented the important role that friends had for the women; a further division between best and other friends was also made.
‘My support staff’. This represented the importance of good support with reference to facilitation of connections rather than direct care support.
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The data analysis revealed a shared story that the group named ‘having a good’ (Fig 1. p.68). This diagrammatic representation of the key findings, using the asset framework to underpin it, was developed with the expert reference. This diagrammatic model facilitated the group discussions and held resonance with the expert group, and it was hoped that this would support the dissemination of this research across the learning disability community.
During the data analysis stage of this research, I worked with the expert reference group. The health asset framework became instrumental in interrogating the data, and it was fundamental to understanding the lived experiences of the ten women participants in this study (pp.125 - 134). The health asset framework helped us discuss how the initial protective factors and strengths of the women participants had been identified during the data analysis. Working with the expert group, we discussed how critical these were for the individual. Morgan and Hernán (2013) argue that protective assets often lie within the social context of people’s lives, and identifying these is the first stage in the mapping of health assets. By mapping the internal and external assets with the expert reference group, we could see how these resources could be strengthened through family and friends. The health asset framework has an alliance with the QOL theory, whilst offering a distinctive position by prioritising the narratives of the participants in terms of the resources and support systems that were important to them in their everyday life as older women. For example, the prominence of relationships that were based within the learning disability community was an external support that the women trusted, and this could be mobilised.
Thematic analysis was applied to this study as it provides both an accessible method that could analyse experiences from an individual perspective (inductive), and is more
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accessible in terms of working with an expert group of women with learning disabilities. The flexibility of taking a thematic approach to the analysis enabled the analytical claims to be underpinned by the health asset framework, and to identify the internal and external resources of the women participants.
The health asset framework enabled the identification of capabilities, strengths and resources of the women participants in a concrete way, and provided a more tangible pathway into how these strengths and resources needed to be valued and organised. This was an important lens through which to view the data, as the internal resources of individuals have often been underestimated. Martensson et al. (2008), and Rotegard et al. (2012) demonstrate how you can maximise benefit by using personal strengths. Other studies have also indicated that health assets maybe essential for living a good life (Cochrane, 2006, Rotegard et al, 2012).