The analysis of the results was conducted using a Thematic Analysis (TA) framework. Section 5.3.1 provides an overview of TA and a detailed explanation of how TA was used in this study. In Sections 5.3.2 to 5.3.4, the identified themes are used to discuss the results, based on the research questions posed in Chapter One (see Section 1.4).
5.3.1. Thematic Analysis
Thematic analysis (TA) is a method used to identify, analyse, and report patterns or themes within data (Braun & Clarke, 2020). Themes are identified by searching across a data set to find repeated patterns of meaning. These themes allow the researcher to address the research by organising and describing the data set in rich detail (Nowell et al., 2017). Braun and Clarke (2006) and Maguire and Delahunt (2017, p. 3353) concur that a good thematic analysis does not merely summarize the
137
data, but “interprets and makes sense of it”. Additionally, Braun and Clarke (2006) assert that TA allows the researcher to interpret aspects of the research topic that stretch beyond the semantic content of the data.
TA is not tied to any specific epistemological or theoretical perspective (Clarke & Braun, 2013;
Maguire & Delahunt, 2017). Due to its flexibility, TA has been used in diverse fields ranging from psychology, to health services, tourism, and education (Maguire & Delahunt, 2017; Lester et al., 2020). Given the diversity of work in learning and teaching, this flexibility is significantly advantageous in the learning and teaching environment. Braun and Clarke (2006) and Nowell et al. (2017) list TA’s facility to summarize the essential features of a large data set, its flexibility, the capability of generating unanticipated insights and the usefulness of working within a participatory research methodology amongst the many advantages of TA. TA was chosen in this study due to its usefulness in summarizing key features and generating insights, and for its usefulness when participants are collaborators in a participatory design methodology. TA is particularly pertinent to this study, as open-ended responses from teachers and moderators can explore the moderation context at a deeper level, which, according to Castleberry and Nolen (2018) quantitative analysis lacks.
Braun and Clarke's (2006) framework is “arguably the most influential approach”, because it offers a clear, usable framework for doing thematic analysis (Maguire and Delahunt, 2017, p. 3353).
Accordingly, the semantic content of the data was investigated using the six phases of TA as postulated by Braun and Clarke (2006), namely,
1. Familiarizing yourself with your data;
2. Generating initial codes;
3. Searching for themes;
4. Reviewing themes;
5. Defining and naming themes; and 6. Producing the report.
138
Structuring qualitative data analysis into phases creates a systematic process for the researcher to conduct the analysis in a transparent way (Lester et al., 2020). Additionally, an inductive approach (see Figure 5-5) of using the actual data in developing the structure of the analysis (Burnard et al., 2008) was used to code the data in this study. Medelyan (2021) cautions that, when using pre- existing coding frames, bias is introduced, and the researcher may miss naturally emerging themes from participant responses. Accordingly, an open coding approach, as recommended by Maguire and Delahunt (2017), was used to iteratively develop and modify the codes during the coding process. The detailed activities that were followed in each phase are elaborated on in the following sections.
Phase One: Familiarising yourself with your data
Braun and Clarke (2006, p. 16) maintain that it is vital to become familiar with the “depth and breadth of the content”. Becoming truly immersed involves actively reading and re-reading the data to search for meanings and patterns (Belotto, 2018; Castleberry & Nolen, 2018; Nowell et al., 2017).
Compiling data into a useable form is the first step in finding meaningful answers to the research questions (Castleberry & Nolen, 2018). During phase one, the information provided by participants was transcribed into a spreadsheet consisting of four separate sheets based on the activities that were followed during the PD workshops.
Sheet one captured individual responses to the user feedback table (see Figure 5-1, responses from two groups are shown). This information was categorized into the groups that participants worked in and structured into separate columns for each participant of each group.
In this way, information was laid side by side so that it was easier to view all identified stakeholder needs and determine the commonalities within and amongst each group.
139
Figure 5-1: Data obtained from user interview forms
Sheet two included information that was grouped together from individual participant responses to the user feedback table (see Figure 5-2). Each group ranked the recurrent themes within their group. This data was captured in the exact format that participants used.
Figure 5-2: Segment of data illustrating participant identified themes
Sheet three of the spreadsheet (see Figure 5-3) contained a transcript of the data from each group’s idea webs. The data was captured using the concepts provided to participants, that is, stakeholders, requirements, constraint/challenges, important features, and questions we have.
140
Figure 5-3: Data captured in Idea Webs
Sheet four of the spreadsheet captured comments made by other teams on the initial screen designs (see Figure 5-4). The comments were categorized as “Ideas I Like” (pink sticky notes), “Questions I have” (green sticky notes), and “Suggestions for Improvement” (blue sticky notes).
The data was read several times during the process of transcribing, thus ensuring a greater understanding of the data collected. A note of aspects relevant to each research question (see Figure 5-1) was made to provide a “context to create categories of codes” (Belotto, 2018, p. 2625) related to the research questions, minimize the number of codes, and gain an overall sense of the data captured, thus allowing for the formation of ideas and the identification of patterns in the data, as recommended by Braun and Clarke (2006).
141
Figure 5-4: Design Comments
Phase one is acknowledged as an interpretative act that provides the foundation for the rest of the analysis (Braun & Clarke, 2006). The captured data was verified against the original information to ensure that the transcript contained accurate information, so that meaningful knowledge could be obtained.