Data Analysis

The tapes were transcribed verbatim, and thematic analysis was employed to analyse the data. Thematic analysis is a method for analysing and reporting patterns or themes within the data (Braun and Clarke 2006). According to Boyatzis (1998), a theme is a pattern found in the data which describes and organises potential observations or interprets aspects of the phenomenon in question. According to Elo and Kyngas (2008), thematic content analysis is commonly used in nursing studies, but there has been a limited publication of the actual analysis process and generally they only provide a brief description. However Braun and Clarke (2006) have provided comprehensive guidelines on the process of applying thematic analysis. These guidelines have been followed in the present study and are discussed below.

Ryan and Bernard (2000) contend that thematic analysis is a process performed within ‗major‘ analytic traditions, such as grounded theory, rather than a specific approach in

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its own right. However, Braun and Clarke (2006) contest this and argue that there are two types of qualitative analytic methods. Within the first, there are those stemming from a particular theoretical or epistemological position, such as Interpretative Phenomenological analysis; IPA. Second, there are methods that are essentially independent of theory and epistemology, and can be applied across a range of theoretical and epistemological approaches. Braun and Clarke (2006) argue that thematic analysis is positioned in the second perspective, and is compatible with both essentialist (universal truth, not dependent on context) and constructionist (where truth is constructed by our social worlds) paradigms.

As thematic analysis facilitates a flexible approach when analysing data, according to Braun and Clarke (2006) it is important to recognise that qualitative analysis guidelines should be applied in a flexible manner to suit the data and the research question. Boyatzis (1998) reports that thematic analysis is flexible, meaning that and what researchers do with the themes once they uncover them differ based on the intentions of the research and the process of analysis. Many researchers use thematic analysis as a way of getting close to their data and developing some deeper appreciation of the content. Therefore, thematic analysis was chosen as it offers an accessible and theoretically flexible approach, which has the potential to provide a rich and complex account of the data (Braun and Clarke 2006). In the process of thematic analysis there are two ways to identify themes or patterns. Firstly, an inductive approach means that the themes identified are strongly linked to the data. Secondly, a theoretical deductive approach is when the structure of the analysis is influenced by researcher‘s theoretical or analytic interest (Braun and Clarke 2006).

According to Braun and Clarke (2006) another important decision for the researcher is the level at which the themes are to be identified: at a semantic or explicit level, or at a

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latent or interpretative level (Boyatzis 1998). With a semantic level the themes are identified within the explicit or surface meanings of the data, which means that the researcher is not looking for anything beyond what a participant has said. In contrast, at a latent or interpretive level, the researcher begins to identify or examine the underlying ideas or assumptions, and concepts that are theorised as shaping or informing the semantic content of the data. The present study tends to analyse the data in an inductive interpretive level. The inductive approach, in this study, refers to an approach that primarily uses detailed reading of raw data to derive concepts, themes, and models, through the researcher‘s interpretations of the raw data (Strauss and Corbin 1998).

Braun and Clarke (2006) suggest six steps to follow when doing thematic analysis. The six steps suggested by Braun and Clarke (2006), are employed when doing thematic analysis regardless of a semantic or interpretative level of analysis being adopted. A visual diagram to illustrate the steps takes to analyse the data may be seen in Figure 6.

Step1: Familiarising self with data: I transcribed each of the interviews to ensure full immersion and connection with the data. I continued to immerse myself in the data, through repeated reading and listening to the transcripts in order to search for meaning and patterns. I also took copious notes of initial thoughts and ideas

Step 2: Generating initial codes: Following on from this initial stage, I generated an initial list of ideas about what was in the data. This phase involved the production of initial codes and building on the notes and ideas generated through transcription and data immersion. I began at the beginning at each transcript and systematically worked my way to the end. The data were manually coded by writing initial notes/words in the

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texts, and highlighting key words. This helped to indicate potential patterns. The codes identified features of the data that I felt were central to the research question.

Step 3: Searching for themes: This step involved sorting the codes into potential themes. By this stage I had a long list of codes. To help me with this step I manually cut out all of the codes and laid them out on the floor. All similar codes were sorted and grouped together in to separate piles to form initial themes. At this point any themes that did not have enough data to support them were discarded. At the end of this phase I had a collection of themes and subthemes.

Step 4: Reviewing themes: During step 4 I re-read all of the data extracts and checked the themes against the data extracts. This was to ensure that themes reflected the meaning of the data,

Steps 5 & 6: Defining and naming themes: In this phase I wanted to ensure that I had captured the essence of what each theme was about. After on-going analysis and continuous revisions of the themes, they were given final names. Suitable quotations from participants were identified to reflect the focus of the theme.