Thematic analysis
| Steps | Procedural comments |
|---|---|
| 1. Familiarizing yourself with your data: Transcribing data (if necessary), reading and re-reading the data, noting down initial ideas | As we went through the data, excerpts and related meta-data were entered into an Excel sheet as quotes and translated from Danish to English. The translation process served as a helpful way to become immersed and familiar with the collected data. Ideas and labels for coding were noted into the Excel sheet |
| 2. Generating initial codes: Coding interesting features of the data in a systematic fashion across the entire data set, collating data relevant to each code | Based on the organized data stored in our Excel sheet, we performed open coding by assigning meaningful codes to the translated quotes in the form of short sentences (e.g. “before, peers came with solutions instead of asking questions”). During this iterative process, we started to identify commonalities and patterns and align the codes' phrasing where it made sense, mainly in a theory-driven approach. We occasionally went back to our observational and interview notes to extract quotes that were initially discarded and added these to our analysis |
| 3. Searching for themes: Collating codes into potential themes, gathering all data relevant to each potential theme | Once the first round of first-order codes was done, we conducted an axial coding to group these into a higher level of category candidates, followed by the last selective coding of theme candidates. The second and third-order codes were labeled with short states (e.g. safe learning environment, problem-framing ability). Similar to the previous step, we occasionally went back to our observational and interview notes to extract quotes that were initially discarded and added these to our analysis. During this iterative process of aligning the categories, we used visual representations in PowerPoint to aid us in sorting the different codes into categories and themes |
| 4. Reviewing themes: Checking if the themes work in relation to the coded extracts (level 1) and the entire data set (level 2), generating a thematic “map” of the analysis | As we reviewed the themes and their underlying codes and excerpts, it became evident that some were not themes, some were divided into two, and some were collapsed with others. Based on this process, a thematic map was created and validated to represent our data set's meaning accurately |
| 5. Defining and naming themes: Ongoing analysis to refine the specifics of each theme and the overall story the analysis tells, generating clear definitions and names for each theme | We arrived at systems Alpha, Beta and Gamma through several iterations of defining and redefining the essence of the themes in relation to the underlying story we wanted to convey in this paper and our theoretical approach to the thematic analysis. Figure 2 (in Section 5) depicts the final analysis |
| 6. Producing the report: The final opportunity for analysis. Selection of vivid, compelling extract examples, final analysis of selected extracts, relating back of the analysis to the research question and literature, producing a scholarly report of the analysis | Following the AR tradition, the focus of the final steps was to tell the story of our data in a convincing way for the merit and validity of our analysis. For this, we engaged the participants/co-researchers from VELUX and academic colleagues to review the report and provide feedback |
| Steps | Procedural comments |
|---|---|
| As we went through the data, excerpts and related meta-data were entered into an Excel sheet as quotes and translated from Danish to English. The translation process served as a helpful way to become immersed and familiar with the collected data. Ideas and labels for coding were noted into the Excel sheet | |
| Based on the organized data stored in our Excel sheet, we performed open coding by assigning meaningful codes to the translated quotes in the form of short sentences (e.g. “before, peers came with solutions instead of asking questions”). During this iterative process, we started to identify commonalities and patterns and align the codes' phrasing where it made sense, mainly in a theory-driven approach. We occasionally went back to our observational and interview notes to extract quotes that were initially discarded and added these to our analysis | |
| Once the first round of first-order codes was done, we conducted an axial coding to group these into a higher level of category candidates, followed by the last selective coding of theme candidates. The second and third-order codes were labeled with short states (e.g. safe learning environment, problem-framing ability). Similar to the previous step, we occasionally went back to our observational and interview notes to extract quotes that were initially discarded and added these to our analysis. During this iterative process of aligning the categories, we used visual representations in PowerPoint to aid us in sorting the different codes into categories and themes | |
| As we reviewed the themes and their underlying codes and excerpts, it became evident that some were not themes, some were divided into two, and some were collapsed with others. Based on this process, a thematic map was created and validated to represent our data set's meaning accurately | |
| We arrived at systems Alpha, Beta and Gamma through several iterations of defining and redefining the essence of the themes in relation to the underlying story we wanted to convey in this paper and our theoretical approach to the thematic analysis. | |
| Following the AR tradition, the focus of the final steps was to tell the story of our data in a convincing way for the merit and validity of our analysis. For this, we engaged the participants/co-researchers from VELUX and academic colleagues to review the report and provide feedback |
Source(s): Braun and Clark (2006, p. 87)
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