Complete Phase 2: Coding Of Your Interview Data

Complete Phase 2: Coding of your interview data

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.

Bring to Thursday's class your code labels with relevant segments of data compiled under each.

Paper For Above instruction

The process of Phase 2 coding in qualitative research is a crucial step in analyzing interview data, as it allows researchers to systematically engage with their dataset to identify meaningful themes and concepts relevant to their research questions. This phase emphasizes a detailed, fine-grained approach to coding, ensuring that each segment of data is carefully examined and labeled with relevant codes that capture both surface (semantic) and deeper (latent) meanings.

Systematic coding begins with thorough familiarization with the dataset, often involving multiple readings of interview transcripts. The researcher then segments the data into manageable units—such as sentences, phrases, or even single words—that appear potentially interesting or relevant. Each of these segments is assigned a code label that is concise yet meaningful, reflecting the specific content or conceptual idea it represents. This process is recursive, meaning that codes can evolve as new insights are gained or as the researcher refines their understanding of the data.

Reflexive thematic analysis advocates for a flexible approach to coding, where levels of interpretation vary from explicit, surface-level meaning—descriptive, often directly linked to the interview content—to more implicit, underlying meanings that require a more interpretative stance. For example, at the semantic level, a coder might label a statement as “lack of confidence,” whereas at the latent level, they might interpret that statement as part of a broader theme of “professional identity struggles.” This nuanced approach enables a richer, more layered analysis, capturing the complexity of human experiences expressed in interview data.

Once coding is completed, the researcher organizes codes into categories or themes, collating all data segments associated with each code. This step facilitates pattern recognition and thematic development, which serve as the foundation for subsequent analysis and interpretation. The iterative nature of coding means that codes and categories may be refined multiple times, and integration with existing theoretical frameworks is encouraged to deepen understanding.

For students preparing for class, it is essential to bring their coded data—comprising code labels alongside the relevant data segments—organized in a way that clearly demonstrates the relationship between codes and data. This might include a coded data table or matrix, highlighting how each segment was labeled and how codes relate to the overall research focus. Such preparation supports rich discussion and critical reflection during class.

References

• Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.
• Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5(1), 80-92.
• Saldaña, J. (2015). The coding manual for qualitative researchers. Sage Publications.
• Charmaz, K. (2006). Constructing grounded theory. Sage Publications.
• Holton, J. A. (2007). The coding process and its challenges. ThequalitativeReport, 12(1), 8-15.
• Mason, J. (2002). Qualitative researching. Sage.
• Lewis, P. (2009). Reflexive thematic analysis. The Qualitative Report, 14(2), 472-483.
• King, N. (2004). Using interviews in qualitative research. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 11-22). Sage.
• Ritchie, J., & Lewis, J. (2003). Qualitative data analysis. In J. Ritchie & J. Lewis (Eds.), Qualitative research practice: A guide for social science students and researchers (pp. 219-262). Sage.
• Guest, G., MacQueen, K. M., & Namey, E. E. (2012). Applied thematic analysis. Sage Publications.