Steps used in analysing data

In this study, I used some of the stages prescribed by Braun and Clarke (2006). Before analysing the data, I transcribed all the interviews verbatim and typed them out. The scripts were labelled with the pseudonym of each participant and filed (see appendix B). Transcribing the interviews was very helpful because it enabled me to read over each script several times, in order to identify the main ideas or concepts in each response. Since some of the responses were rather long, I decided to break the responses into smaller units i.e. sentence by sentence, which became my unit of analysis. I wrote down the initial ideas from each sentence and prescribed a code to it. Table 3.5 below gives details of the steps I used in the analysis process.

Table 3.5

Thematic analysis – integrating Manual and NVivo procedures

Step 1 Transcribing of interviews

Verbatim transcription of the semi-structured and unstructured interviews.

Step 2 Selection of codes

I preselected codes to use for the coding of the interview script and I used line by line coding.

Step 3 Counting frequencies

First I coded the interviews manually. After that, I coded the new script using NVivo. I did a word similarity and word frequency also using NVivo to determine the ideas and themes within the interview script.

89 Step 4 Establishing

relationships between codes or themes

I produce a data cloud and carried out a cluster analysis of themes and printed it out to note the patterns and themes that were emerging. The next stage of analysis was done manually by cutting out the new themes and sub-themes to establish the relationship between them.

Step 5 Building themes and sub-themes

The themes and sub-themes were reduced to a smaller number for interpretation and discussing of the results.

Step 6 Narrative analysis

The themes and sub-themes were used as a narrative in the study

I began the next process of analysis by using my pre-selected codes of PLH for participants’ life history, LLC for language and literacy policy changes and SCC for socio-cultural changes, to code the interview script. These codes are described by Miles and Huberman (1994, p.56) as ‘tags or labels for assigning units of meaning to descriptive or inferential information compiled during a study’, which are attached to phrases or sentences. Rubin and Rubin, (1995, p.238) also describe coding as ‘the process of grouping interviewees’ responses into categories that bring together the similar ideas, concepts or themes …’ Furthermore, Clarke and Braun (2016, p.1) interpret codes as ‘the smallest units of analysis that capture interesting features of the data (potentially) relevant to the research question’. Although PLH, LLC and SCC were the main themes, I came up with small units of new codes to express these ideas and concepts in a simple way. For example, I had codes like participant’s name (PN), place of birth (POL) etc. After coding the first interview transcripts with these initial codes, I realised I had many themes, so I decided to group the codes expressing similar ideas together under new codes. For example, instead of having PN, POL, etc., I decided on using POP (profile of participant), to which I coded everything participants said about where they were born, the towns and villages where they attended school and anything to do with their upbringing. I did similar things for the other two interview scripts for individual participants. My main objective here was to be flexible on what I coded and why I chose a certain code.

I continued with different codes for all three interviews for each person, until I had coded all the interview scripts. To help speed up the process and to identify the relationships between the various themes emanating from participants’ life histories, I uploaded each interview transcript into NVivo Version 10 for Windows using the updated codes for a third coding. Although using NVivo was helpful, the process was very long as I had to do the coding myself. For me, the use of NVivo was to help narrow down the number of codes and to help generate themes that were emerging from the data (Veal, 2011).

90 To help gain more insight into the themes raised in participant interviews, I created a ‘data cloud’ diagram based on the selected codes (see Appendix C). I was able to picture the emerging themes, as the data cloud produced a number of ideas based on the number of times participant’s used certain words. I then carried out a cluster analysis based on code similarity and word similarity in NVivo to help me analyse the data (Crowley, Harre and Tagg, 2002). Since the cluster analysis had grouped all similar themes together for each participant (Appendix D), I spent time reading, identifying and selecting the themes that had emerged according to their importance within the structure. I printed out the cluster analysis diagrams for each participant and analysed them manually. I started by writing down all the themes and sub-themes for each interview and came up with a diagram (Appendix E). I cut out each theme and put them all in an envelope. I then put all the similar themes into one envelope and those themes which could not be grouped together under one theme into another envelope.

The themes in the two envelopes were further renamed and reframed because of the number of themes that had been generated. This was done to get the best out of them and to come up with a smaller and more manageable number to use in line with my research questions and objectives (Attride-Stirling, 2001 and Bryman, 2008). This procedure was also applied to my three expert interviews. As expected, this exercise was very laborious because I had twenty four (24) interview transcripts in total, on a wide range of issues. The nature of the association between themes provided the structure for the participant narrative, to make the life history more readable and coherent without compromising the voice of participants. At the end of the process, I came up with three main themes; namely, professional life history of participants, the different language and literacy policy changes, the impact of these policy changes on teachers’ practices and participants’ views on policy formulation and monitoring. I also had a number of sub-themes in relation to the above themes, which have all been discussed in the findings chapter with the help of relevant literature.