thematic-analysis

To turn interview transcripts into candidate themes, this skill applies Braun and Clarke reflexive thematic analysis by performing initial coding, cross-interview pooling, and theme clustering to output structured candidate themes.

Clustering initial codes into themes for qualitative research requires defensible code-to-theme grouping across interviews, reviewing theme boundaries for coherence, and tracking ambiguous codes without forcing premature conclusions.

You can start thematic analysis directly from an existing coding pool, skipping raw transcript processing to immediately cross-interview pool codes and generate structured candidate themes tied to your research question.

Reflexive thematic analysis enforces researcher-first guardrails, meaning the AI provides theme naming suggestions and explicit handoffs while the researcher owns all final decisions regarding what matters thematically.

When clustering themes across multiple interviews, ambiguous codes are tracked and captured without forcing premature conclusions, allowing the researcher to review boundary-unclear coding and determine final theme inclusion.

Thematic analysis of interview text mandates saving intermediate outputs like initial coding pools and cross-interview code groupings, alongside final structured candidate theme tables and naming option suggestions.