Chapter 4 Data analysis
4.1 Research question 1: What are educators’ perceptions of cultural wellbeing?
of cultural wellbeing to both help identify these influences and to assist in analysing their effect on classroom communities.
Table 4.1 Forms of analysis for each research question
Research Question Methodology Data Analysis 1. What are educators’
perceptions of cultural wellbeing?
Constructivist grounded theory (Charmaz, 2014)
Grounded coding using CGT; Sorting and grouping of codes into categories. Constant Comparative Analysis. 2. How do educators support cultural wellbeing in classroom communities?? Situational analysis methods (Clarke, 2005)
Messy situational map; ordered situational map; Social worlds analysis map.
Discourse Analysis. 3. What might a typology
of cultural wellbeing look like?
(This research question emerged during the study).
Constructivist grounded theory (Charmaz, 2014)
Constant Comparative Analysis
4.1 Research question 1: What are educators’
perceptions of cultural wellbeing?
4.1.1 Constructivist grounded theory analysis
I selected constructivist grounded theory as a methodological approach for generating data for this study and drew upon the analytical procedures described by Charmaz (2014). The analysis of research question 1 was the initial process of analysis for the study and the coding undertaken for this question informed the
analysis of research questions 2 and 3. The workflow for the analysis of this first research question is represented in
Figure 4.1. This workflow represents my analysis of participant interview data.
Figure 4.1 - Research question 1 data analysis workflow
The goal of the analysis of research question 1 was to theorise a schedule of categories representing the significant themes and relationships present in the
interview data. I began this by creating a set of labels, or “codes”, that were attached to appropriate passages in each transcript. This is described in detail below.
4.1.2 Coding the data
In constructivist grounded theory, the analysis commences from the very first interview, and emerging concepts and ideas are then explored in subsequent
interviews. In this way, the theorising becomes a process guided by both literature and data, and which responds to emergences as they surface in the interviews and in the analysis that follows. Identifying and capturing those emergences is fundamental to the data analysis process and this commenced with thoroughly familiarising myself with each interview transcript and identifying incidents of significance.
The next phase involved creating codes and assigning them to snippets of interview data. This was facilitated by the use of NVivoTM qualitative data
management software. The individual codes that I created can be represented by two types:
• researcher-applied codes were codes I created and titled to reflect the characteristics represented by the code. For example, the code
“Outdoor education” was applied to snippets of transcript that referred to educators engaging students in learning outside the classroom and in nature.
• in-vivo codes are the same as researcher applied codes, except a direct quote from the transcript is used for the code. For example, one educator described their experience of trying to manage their teaching workload as “running on the rev limiter” and I used that expression as a code to be applied to similar passages from educators about
struggling under heavy workloads or other demanding situations. Charmaz (2014) advocates employing in-vivo codes because of the
value they bring to analysis by giving representation in the data to the participant’s voice, effectively grounding the analysis in the
participants’ experiences.
As I added and coded more transcripts in NVivo, I continually refined these codes by adding new codes for new emergences from the data, and by culling or merging any codes that proved to be of insufficient value. Where appropriate, “parent codes” were created to aggregate collections of codes with similar characteristics.
My analysis of the initial interviews revealed there was considerable
uncertainty about the concept of cultural wellbeing amongst educators, and it became apparent that opportunities to explore pre-existing perceptions of cultural wellbeing would be limited. I coded transcript incidents such as “I haven’t heard that term before” and “I haven’t encountered this idea of cultural wellbeing really”, which indicated cultural wellbeing was a nascent concept to educators. This was an early finding of the research and it meant that to explore educator’s perceptions of cultural wellbeing I would have to co-construct meanings within the interview event. Despite there being initial uncertainty about the concept of cultural wellbeing for some participants, all educators interviewed were able to share their interpretations of the concept.
4.1.3 Constant Comparative Analysis
One of the values of incorporating constant comparative analysis is its capacity to identify common elements across multiple interview transcripts. Continually comparing newly acquired data with existing data surfaced significant details common to multiple interviews. For example, one participant mentioned that
an interesting data point. Its significance was more fully realised when a subsequent participant told me she had students in her class from “homes where there’s domestic violence”. Finding two references to unsafe home environments during interviews about cultural wellbeing is important to the analysis, as it helped to explain educators’ emphasis on students feeling safe and comfortable in the classroom community. The practice of comparing data against data ensured the combined importance of these isolated comments did not go unrecognised.
4.1.4 Creating categories in the data
After 15 interviews had been transcribed and coded, following the procedures described above, I considered “theoretical sufficiency” had been reached once the rate of new code creation had fallen to the point that no major new insights were being generated from subsequent interviews. This signalled the next step in shifting from the empirical to the theoretical phase of data analysis, which was to identify links among the schedule of open codes and to abstract a collection of categories based on the relationships and themes I had found. Appendix E presents a sample section of constructivist grounded theory coding, to show how open, axial and selective coding (Charmaz, 2014) was undertaken with the data. In the process of axial coding, I took the substantive codes developed during the transcript analysis and conceptualised relationships between those codes. I used these relationships to generate theoretical categories for use in the presentation of findings that follows in Chapter 5.