Thematic analysis of the interview data

My research aimed to explore the patterns of the perceived impact of PISA in a specific educational context, namely, Fangshan District of Beijing, and describe and interpret the patterns within this context. Therefore, thematic analysis which identifies, describes and interprets patterns (themes) across data (Braun and Clarke, 2006; Braun et al., 2016) was employed in

analysing the interview data. Compared with other methods such as narrative analysis, thematic analysis is flexible because it is not tied to a particular theoretical framework or research paradigm (Braun and Clarke, 2006; Robson, 2011; Braun et al., 2016), and can be used to answer a wide range of types of research questions (Braun et al., 2016). For example, it can be employed to understand participants’ actions and practices, and their views and perspectives (Clarke and Braun, 2017). Its flexibility allows me to seek answers to RQ 1 and RQ 2 with interview data, and to synthesise the results from the qualitative strand and the quantitative strand, and interpret the results in detail.

In terms of the process of applying thematic analysis, Braun and Clarke (2006) propose a step-by-step guide which includes six phases: familiarising with data, generating initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the report. They note that the process of thematic analysis is recursive rather than linear (Braun and

Clarke, 2006). Perhaps because of this characteristic of the analysis process, later, Braun, et al. (2016) further developed the classification of the six

phases, proposing phases 1-2 “Familiarization and coding”, phases 3-5 “Theme development, refinement and naming”, and phase 6 “Writing up”. They additionally suggest that, at the phase of familiarization, one reads the data analytically and identifies ideas that are helpful for addressing research questions. It is also admitted that writing is along with the analytic process and is an integral part of it (Braun and Clarke, 2006; Braun et al., 2016). Hence, phase 6 involves “compiling, developing, and editing existing analytic writing, and situating it into an overall report” (Braun et al., 2016, pp.200- 201). In my research, I basically followed these phases for analysing interview data.

Firstly, I familiarised myself with the data. I transcribed the audio recordings of all 16 interviews manually by myself, and also captured some non-

language behaviours such as laughs and pauses, which I considered

meaningful, in transcripts. To annotate the details in the speech, I created a transcription notation system (see Appendix A.10). For example, “{ }” is used to indicate speakers’ nonverbal expression and behaviour, and bold

font indicates emphasised words. During transcription, I made comments

and marks on transcripts when I noticed information of potential importance for addressing my research questions. After I completed transcription, I checked all the transcripts against the audio recordings and then

anonymised transcripts. Through transcription I have become familiar with the data to some extent. However, that process focused more on the language of segments in speech, rather than the content of whole data. Therefore, I read and reread each transcript to further familiarise myself with the data, with attention on the meanings and patterns, and the potential meanings of non-language behaviours like laughs and pauses in the data. From this step, I started to write memos (see Appendix A.11) for recording the development of my thoughts (e.g. initial ideas about the patterns in the data) during interview data analysis.

Secondly, I moved on to generate codes. A code is a label that identifies an interesting aspect in the data, which is potentially relevant to the research questions (Braun et al., 2016). At the second step, I systematically identified and labelled (so-called “code”) important data extracts which would be potentially useful for answering my research questions. To assure the coherence of coding, as suggested by Braun et al. (2016), after the initial codes were generated, I read through the whole data for the second time to review all the codes and made them coherent. Since my research employed mixed methods design that the qualitative data analysis results were to be synthesised with quantitative results, after I got the quantitative results, I read the interview data and reviewed the codes again to identify whether I omitted any codes which are corresponding with some variables in

quantitative analyses.

Following coding, the third step was to group codes, develop and refine themes. At this step, first, I looked at the relationships amongst codes and their relevance to my research questions. Then I clustered the codes which share same broader patterned meaning or concept in relation to the use of PISA, perceived impact on the process of teaching and learning, perceived

impact on learning outcomes (non-cognitive and cognitive). After I had got candidate themes, I read all the transcripts again and moved on to review these themes to check: (1) each of the themes captures the ideas in the data well; (2) they are distinct with each other; and (3) they are tied to my

research focus, coherently telling a story about the data. The themes, each of which has a distinctive focus and scope were finally identified, when they did not need great changes for addressing the research questions. At this step, I also gave each theme an informative and concise name.

During thematic analysis, I also analysed whether there are links between the themes and whether the themes vary in the data of different groups (e.g. educational policymakers versus practitioners) of interviewees and in the data of different interviewees in the same group (e.g. school leaders; mathematics teachers), as suggested by Bryman (2016).

Coding and developing themes was conducted in QSR International's NVivo 11, a qualitative data analysis software (see the screenshot of categorising codes in Appendix A.12). Memos were written in NVivo 11 as well.

Compared with manual analysis on paper, employing this software in data analysis is more efficient to manage data extracts and visualise data results. The data extract used for inter-reliability check and the quotes employed in presenting and discussing findings in the thesis were all translated from Mandarin to English.