come

1. Complete Phase 1: Data Familiarization of your interview data

Phase 1: Familiarising yourself with the dataset. Here, you become deeply and intimately familiar with the content of your dataset, through a process of immersion. Practically, this involves reading and re-reading your data (and, if working with transcripts of audio data, listening to the recordings at least once), and making (brief) notes about any analytic ideas or insights you may have, both related to each data item and the dataset as a whole.

2. Complete Phase 2: Coding of your interview data

Phase 2: Coding. Here, you work systematically through your dataset in a fine-grained way. You identify segments of data that appear potentially interesting, relevant or meaningful for your research question, and apply pithy, analytically-meaningful descriptions (code labels) to them. Your focus is specific, and detailed, with coding aimed at capturing single meanings or concepts. In reflexive TA, you can code at a range of levels – from the very explicit or surface meaning (we and many others term this semantic), through to the more conceptual or implicit meaning (we and others term this latent). Coding isn’t just about summarising and reducing content, it’s also about capturing your ‘analytic take’ on the data. You code the entire dataset, systematically and thoroughly. When done, you collate your code labels and then compile the relevant segments of data for each code.

3. BRING to THURSDAY’S CLASS

• Your data familiarization notes — these may be visual (see TA, p. 46 & 47, Figures 2.1 & 2.2 as examples) or they may be written. If written, follow the example in TA, p. 48, Box 2.4 “Ginny’s Overall Dataset familiarization Notes” 
• Your code labels with relevant segments of data compiled under each. For examples see: Table 3.3, p. 63 in TA