Data Analysis

Case study researchers, according to Johnson and Christensen (2008), tend to be pragmatic and they recommend that a researcher take an eclectic approach and use any data that will help them understand a case and answer the research question. So my approach involved examining and reporting on each of the participants in the focus groups and searching through the survey responses for data to support the findings from the interviews and focus group discussion. My goal was then to complete a simplified version of a cross-case analysis using thematic analysis to look for any similarities or patterns or conversely differences present in the data and report this in the context of themes. These themes were then be related back to the findings from the research literature where appropriate. The survey was also analysed to look for similarities and differences similarly to the case study. I had thought that with a potential survey response pool in excess of 1000 that a verbal coding tool like NVIVO would be needed to help with identification of themes. However, it transpired that only 73 surveys were returned making the use of NVIVO redundant as the Qualtrix tool enabled me to group data. I was able to use the coding process developed for

analysing the qualitative data from the case study and apply it to the qualitative responses in the survey. This will be discussed in the next section related to thematic analysis.

3.7.1 Thematic analysis

According to Braun and Clarke (2006) thematic analysis is “a method for identifying, analysing and reporting patterns (themes) within data.” (p. 79). This makes it particularly well suited to case study research given the amount of rich data that accrue from focus groups and interviews.

The qualitative data was gathered from the following sources; • The recorded responses from the two focus groups • The recorded responses from the three interviews

• The responses elicited from the open questions in the survey.

A benefit of thematic analysis according to Braun and Clarke (2006) is its flexibility along with ease of application across a range of theoretical and epistemological approaches. Braun and Clarke (2006) go on to say that “through its theoretical freedom thematic analysis provides a flexible and useful research tool, which can potentially provide a rich and detailed, yet complex account of data.” (p. 78). This is useful when analysing data gathered from a case study as the data set is a rich collection of transcribed and recorded information. To analyse text using a thematic approach, Mutch (2005) suggests that one needs to have an open mind and allow the text to reveal its contents asking what are the key messages, how are words used and what are the themes that emerge? Although describing themes as ‘emerging’ does not account for the active role a researcher plays in the identification of patterns/themes, as they carefully think about the data and create links to what they understand (Braun & Clarke, 2006). These authors further comment that thematic analysis can work to both reflect reality and to unravel the surface of reality making it very compatible with a critical theory approach. To elaborate on the nature of a theme Braun and Clarke (2006) attribute it as “capturing something important about the data in relation to the research question, and represents some level of patterned response or meaning within

the data set”(p. 82). This patterned response or meaning within the data set requires the use of coding which clusters data and allows a researcher to identify a theme. What is meant by coding and what is meant by a ‘theme’ along with what determines its importance and size, is discussed next.

Prevalence in and across data sets does not necessarily determine the importance of a theme; researcher judgement is necessary and this should link back to the overall research question. As the data was read and re-read specific features of the data became heightened to the researcher and they formed the initial set of codes against which the data was continuously assessed. In the phases of thematic analysis described by Braun and Clarke (2006, p. 87) this covered phases one and two.

1. Familiarising yourself with your data: I had transcripts of all the interviews and focus groups along with the qualitative comments from the survey.

2. Generating initial codes: as I coded manually, I wrote notes on the texts and cut and pasted excerpts according to the codes I developed.

3. Searching for themes: here the analysis was refocused at the broader level of themes where the coded data was collated into four themes.

4. Reviewing themes: the themes themselves were reviewed in relation to the coded extracts and to the whole data set using a latent thematic analysis which seeks to identify particular forms and features.

5. Defining and naming themes: clarifying the names of the themes so they told a coherent ‘story’ through the analysis.

6. Producing the report: this culminates in the chapters on findings and on emerging themes.

Braun and Clarke (2006) suggest that there are two primary ways by which themes within data can be identified; inductive or theoretical. The approach used in this study is an inductive one as the themes identified are data driven in that they are strongly linked to the data and not to the researcher’s theoretical interest in the area nor are they being fitted into a pre-existing coding frame. However as various researchers assert, researchers cannot completely free themselves of their theoretical and

epistemological viewpoints (Braun & Clarke, 2006; Cohen, et al., 2007; Johnson & Christensen, 2008; Mutch, 2005). Most researchers have already applied some degree of thematic analysis at a deeper level to examine the covert ideas and assumptions that shape the semantic content of the data and then from there choose codes to facilitate identifying themes “Thus for latent thematic analysis, the development of the themes themselves involves interpretive work and the analysis that is produced is not just description, but is already theorised.” (Braun & Clarke, 2006, p. 84).