Data analysis

The analysis of qualitative data is based on two important principles: transcribing the interviews and immersing oneself in the data in order to gain detailed insights into the phenomena explored (Smith & Frith, 2011). Once this has been achieved the task of analysing the data may commence. Smith and Frith (2011) indicates that there are predominantly three categories in which methods of analysing qualitative data may be classified:

1. Socio-linguistic methods that explore the use and meaning of language such as discourse and conversation analysis;

2. Methods that focus on developing theory, typified by grounded theory; and

3. Methods that describe and interpret participants’ views such as content and thematic analysis (p. 13).

For this study I have adopted the third approach listed above. A thematic approach requires the use of analytical categories to analyse qualitative data. These categories may be derived inductively, that is, obtained gradually from the data or used deductively, either at the beginning or part way through the analysis as a way of approaching the data. Deductive analysis is less common in qualitative research, but is increasingly being used, for example, in the framework approach (Pope, et al, 2000).

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The conceptual framework developed in Chapter 2, (see Appendix I) directed my data analysis towards the framework approach as described by Pope et al (2000). In the framework approach, the analytical categories are determined at the beginning of the data gathering process. These a priori categories in the case of my investigation relates the features of a PLC I explored, namely, Shared norms and values; Supportive and shared leadership; Reflective dialogue; Collaborative inquiry and Deprivitisation of practice.

The framework approach was developed in the 1980s by social policy researchers and is useful for the systematic analysis of qualitative data relating to the experiences of participants who share a common practice. See Table 18 below for an overview of all the processes involved. This feature of the framework approach ensures that it is analytically robust. The other distinctive aspect of the method of the framework approach is that although it uses a thematic analysis, it allows for flexibility between the themes since there may be links between these a priori categories or themes. Remaining true to the descriptions and the narratives of the interviewees is central to the framework approach (Smith & Frith, 2011, Ritchie & Lewis, 2003). Table 18 provides an overview of the framework approach

Table 18: Overview of the framework approach

Pr

oc

esses

Stages

Data management Descriptive accounts Explanatory accounts  Becoming familiar

with that data  Identifying initial

themes/categories  Developing a coding

system

 Assigning data to the themes and

categories in the coding index

 Summarising and synthesising the range and diversity of coded data by refining initial themes and categories  Identify associations

between the themes until the whole picture emerges

 Developing more abstract concepts

 Developing

associations/patterns within concepts and themes

 Reflecting back on the original data and

analytical stages in order to ensure participants accounts are accurately presented

 Interpreting and

explaining the concepts and themes

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 Seeking wider

application of concepts and themes

Continuum (Adapted from Ritchie and Lewis, 2003)

Data analysis does not take place in a linear form and one part of the process overlaps another. Nevertheless, I followed the guidelines provided by Ritchie and Spencer (1994). They posit that the process of analysis involves a number of distinct though interconnected stages. They outline six key stages in the process of analysing qualitative data: Familiarisation, Identifying of themes, Indexing, Charting, Mapping, and Interpretation

The process of data analysis begins during the data collection, by skilfully facilitating the discussion and generating rich data from the interviews. As Smith and Frith (2011) noted qualitative data is obtained mostly though participant interviews, this stage is not included in the categories identified by Ritchie and Spencer (1994) above.

The first stage of the data analysis process therefore involves the procedure of familiarising oneself with the data. This was achieved through listening to audio recordings, reading the transcripts in their entirety several times. The aim of this phase in the analysis process was to immerse oneself in the details of the interviewee narrative as a whole before breaking it into parts for further analysis. It is during this process that patterns begin to emerge and I noted these patterns as written comments made in the margin of the printed interview transcripts. This process then also involved stages 2 and 3 of the Ritchie and Spencer stages above, namely identifying common themes and indexing those themes.

I then organised interview data in a Table in such a manner that allows for the extraction of common elements or themes on the basis of the conceptual framework adopted for my study. This stage in the data reduction process also provided a mechanism to explore the links that

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may emerge between the categories or themes based on the data. In this phase of that data analysis I employed stages 3 and 4, namely, charting and mapping.

Central themes and connecting concepts between them were explored using the conceptual framework adopted to the point of theoretical saturation. Saturation means that no matter how much more data is collected there would be no more concepts or themes emerging. Although I followed the framework approach, I also drew on the principles of the constant comparative method of analysing the data (Glaser & Straus, 1965). The purpose of the constant comparative method of joint coding and analysis is to generate theory more systematically. Glaser and Strauss (1965) theorise that: “ …the basic, defining rule for the constant comparative method is that while coding an incident for a category the researcher may compare it with previous incidents in the data, coded in the same category” (p.439). This coincided with the final step in the process of data analysis according to the scheme by Ritchie, et al (1994)