Data analysis adopted for this study was an iterative process in which I repeatedly went forward and backward in searching, coding, categorising, comparing and contrasting of the themes. The general principles of analysis were based on Charmaz’s (2006) grounded approach to data analysis. As in any qualitative research study, the data analysis in this study started with running through the data again and again to get a general sense of the whole data. After some key points had been noted several steps were carried out. These will be described in detail in the following sections.
4.4.8.1 Analysing individual cases
Identifying each teacher participant as a ‘sub-case’, I started to analyse the data inductively from individual teachers. Analysis of these data followed Charmaz’s (2006) practical steps. It began with the process of initial coding, which resulted in a list of open codes (or nodes). This coding process involved identifying meaningful segments (Tesch, 1990) that were found relevant to describe teachers’ beliefs and practices. Particular attention was paid to statements and classroom incidents related to principles of TBLT and characteristics of tasks. Each of these segments was coded using an appropriate ‘node’ labelled by myself. The first teacher’s data that I analysed resulted in a tremendous number of open nodes. However, as this process went on, the number of open nodes in the subsequent teachers’ data tended to decrease, as the themes and categories had emerged. Below is an example of the data segment coding:
130 Table 4.4: The initial coding process
Extracts Source Codes
So we […] replace the ‘discussion’ task by a gap-filling one. [Replace] this one, this later task [discussion], because our students will find it difficult. They can’t discuss, I believe.
Lesson planning; Teacher 7 Replacing activities concerning students’ language proficiency T: Task 2. Dialogue [writes on board, reading
along] A: What-kind-of-film-do-you-like/-want- to-see? B: I-like-love-story-film Observation; Teacher 3 Presenting language structures
For example, in Task 2, they had to use ‘may’. This was kind of basic requirement, which asked them to use this to agree or to disagree. Just kind of giving opinions SR; Teacher 2 Role of language features in production
Sometimes I feel that teaching using the new textbooks is somewhat non-sense. I mean, what are teaching and learning all for? While we spend all these three years teaching and learning communicatively, at the end point students do not seem to gain anything because the exams test different things.
Focus group; Teacher 1 Constraint between textbooks and exams
An example of what open nodes looked like in Nvivo in the initial state of data analysis is provided in Appendix F.
When open nodes had been established, the next step was to run through the nodes again and again so as to put them together, rename them, and organise them into categories. The categories were then re-organised to generate broader themes to form tree nodes. Figure 4.1 shows the initial outline of the tree nodes of the first teacher.
131 Figure 4.1: Initial tree nodes
This process was repeated for the data from all eleven teachers.
4.4.8.2 Analysing cross-case data
Once the data for each teacher were initially analysed and I had gained an overview of their beliefs and practices, I began to compare and contrast the themes, categories, and nodes across the teachers. I realised that the teachers in this study shared so many beliefs and practice patterns that it was possible to build a cross-case tree of nodes resulting from most commonly found themes, categories, and nodes in all the eleven teachers’ individual tree nodes.
Although the cross-case tree of nodes might have provided sufficient themes that described an understanding of the teachers’ beliefs and practices, I decided to take another step of cross-case analysis by independently analysing individual sources of the data collected. This process was less tedious than the earlier ones, given that now I had been informed by the themes and categories derived earlier. However, I was willing to add any new themes that emerged during this step (see Appendix G, for a snapshot of interactive data in Nvivo). In this process, I also looked for the opposites or contradictions of what had been found, as a procedure of data validation. In doing so, I was aware of the possibility warned in the
Beliefs
About language teaching About grammar About TBLT Etc. Practices Teaching vocabulary Teaching grammar Corrective feedback
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literature that research data are often used to support particular points or arguments, where data presented may miss ‘irrelevant’ or ‘inconvenient’ data (Leung, Harris, & Rampton, 2004). Therefore, whenever a seemingly contradictory piece of data was found, it was coded in the corresponding category with a subtraction mark (-), to make it available in the subsequent processes of review and re-categorisation. During this process, I also started to incorporate the principles of TBLT and task characteristics outlined in Table 3.2 to understand the relevance of what the teachers believed about language teaching and their practices with reference to TBLT. I realised that doing it this way gave me more insights into teachers’ beliefs and practices because I could view teachers’ meaning in context, i.e., within their discussions in which references to the textbooks were made. This process allowed me to generate a new cross-case tree node, consisting of themes and categories from all sources of data based on the initial nodes generated from individual teachers.
The list of themes, categories and nodes generated were used to compare and contrast against TBLT principles and characteristics I reviewed earlier. At this stage, following the ‘thick’ description of the teachers’ beliefs and practices, I started to establish a ‘rich’ interpretation of the data regarding my research questions. In presenting the themes and categories in my findings chapter, I decided to track the data down again in order to provide quantitative results of the trends happening in the data. For example, given my observation that the lesson planning data indicate some frequency in retention of textbook activities, I tracked this down to find out which types of activities (and how many) the teachers preferred to retain. This tracking process was facilitated by Nvivo since the programme allows users to view the number of references for a particular node. As a result, tables of these trends were presented in the finding sections involving lesson planning and observation data.