| name | thematic-analysis |
| description | Use when conducting Braun & Clarke's reflexive thematic analysis, identifying and analyzing patterns of meaning across qualitative data. |
Reflexive Thematic Analysis (Braun & Clarke)
This skill supports reflexive thematic analysis (TA) as articulated by Virginia Braun and Victoria Clarke—an approach for identifying, analyzing, and reporting patterns of meaning (themes) across a qualitative dataset. TA is widely used across disciplines and is methodologically flexible when executed with explicit reflexivity and transparent reporting.
Braun & Clarke’s six-phase process
Braun and Clarke describe TA as iterative rather than strictly linear. Phases overlap and researchers revisit earlier work as understanding deepens.
- Familiarization — Read and re-read the data (transcripts, field notes, documents). Note early impressions. Audio playback, margin notes, and preliminary memos support deep engagement.
- Generating initial codes — Produce descriptive labels that capture semantic and latent features of the data. Coding can be inductive (data-driven) or deductive (theory-informed), depending on the analytic stance.
- Searching for themes — Sort codes into candidate themes: broader patterns that unite multiple codes. Consider how themes relate and whether subthemes are needed.
- Reviewing themes — Collapse, split, or merge themes against the full dataset. Check internal homogeneity (coherence within a theme) and external heterogeneity (clear distinctions between themes). Revisit coded extracts.
- Defining and naming themes — Write analytic narratives for each theme: what it captures, what it excludes, how it fits the overall story. Names should be concise yet theoretically meaningful.
- Producing the report — Select vivid, representative extracts. Integrate analysis with research questions. Position the analysis theoretically and acknowledge researcher subjectivity.
Reflexive TA vs codebook TA vs coding reliability approaches
- Reflexive thematic analysis treats themes as interpretive stories the researcher actively constructs from data. It rejects the idea that themes “emerge” independently of the analyst. Reflexivity—ongoing reflection on how identity, assumptions, and context shape interpretation—is central.
- Codebook TA (sometimes associated with structured coding for team consistency) emphasizes predefined or iteratively refined code definitions and documented application rules. It can support collaboration but risks mechanistic application if reflexivity is weak.
- Coding reliability approaches (e.g., inter-coder agreement metrics) assume a stable “correct” coding of text. Braun & Clarke critique this as misaligned with interpretive TA, where multiple valid readings may exist. Use reliability metrics only when the epistemological stance explicitly warrants them.
Thematic analysis vs grounded theory
- Themes vs concepts — TA organizes data around themes (patterns of shared meaning). Grounded theory builds concepts and categories integrated into an explanatory substantive theory with relationships (e.g., core category, coding families).
- Theoretical ambition — Classic GT aims for theory that explains a process or behavior. TA often addresses research questions about patterns of meaning without necessarily producing formal theory.
- Literature — Reflexive TA does not prescribe Glaser’s “delay literature” rule; positioning relative to literature is explicit and reflexive.
- Sampling — GT uses theoretical sampling directed by emerging analysis. TA more often uses sampling aligned with the study design (e.g., purposive, convenience), though iterative designs exist.
When to use thematic analysis vs grounded theory
Choose TA when the goal is to synthesize patterns of meaning across cases, retain accessibility for multidisciplinary audiences, or align with a constructionist/essentialist question that does not require full GT theory-building.
Choose GT when the primary aim is to generate an explanatory model of a social process, with categories earned through constant comparison and integration (especially in Glaser’s classic tradition).
Coding practices that support reflexive TA
- Stay close to language first — Begin with participant vocabulary before abstracting; note when you import analytic language and why.
- Code for latent and semantic content — Semantic coding stays near explicit meaning; latent coding addresses underlying assumptions, ideologies, or social meanings. Reflexive TA welcomes both when justified.
- Maintain a decision log — Record why codes were merged, split, or abandoned. This log becomes the backbone of the audit trail.
- Use software deliberately — CAQDAS tools aid retrieval but can encourage premature quantification of code frequency. Treat counts as heuristic, not evidence of importance.
- Hold tension between themes — Contradictions and ambivalence are findings, not problems to erase. Reflexive TA can report patterned heterogeneity across and within cases.
Positionality and epistemological clarity
State whether the analysis leans essentialist (themes as context-independent patterns) or constructionist (themes as situated constructions). Braun & Clarke’s reflexive TA is often allied with constructionism but can be articulated differently if justified. Align ontology (what you believe about reality), epistemology (how you know), and method (what you do) in the methods section to forestall reviewer confusion.
Team-based reflexive TA
When multiple analysts code:
- Co-develop shared definitions while preserving space for interpretive difference.
- Use reflexive dialogue sessions rather than reliability as the primary quality metric.
- Assign a reflexivity lead to track how group dynamics and seniority may shape consensus.
Common pitfalls
- Theme sprawl — Too many thin themes; fix by merging, elevating to subthemes, or returning to phase 4.
- Paraphrase masquerading as analysis — Results sections that only restate quotes need analytic narrative explaining so what.
- Overclaiming frequency — “Most participants said X” requires systematic support, not impressionistic recall.
- Ignoring outliers — Negative cases and dissenting accounts strengthen reflexive TA when integrated theoretically.
Recommended output format
- Audit trail — Brief log of coding and theme revision decisions.
- Codebook or code list — Definitions and illustrative extracts (even in reflexive TA, transparency helps).
- Theme tables — Theme name, definition, boundaries, relation to research question, example quotes.
- Narrative results — Each theme as a subsection with argument, evidence, and reflexive commentary on interpretive choices.
- Method section — Epistemological stance (reflexive TA), phase description, and alignment with Braun & Clarke’s reporting checklist (see their published guidance for journal-specific expectations).
Key references
- Braun, V., & Clarke, V. (2006, 2019, 2021). Foundational and updated statements on TA; see also Thematic Analysis: A Practical Guide (2022).
- Braun, V., & Clarke, V. (2014). What can “thematic analysis” offer health and wellbeing researchers?
Use this skill whenever the user asks for Braun & Clarke–style analysis, theme development, or comparison between TA and grounded theory.
