Reflexive thematic analysis: what changed with Braun and Clarke's updated approach

Reflexive thematic analysis is Braun and Clarke's updated approach to thematic analysis, formalised in their 2019 paper and 2021 book. The core shift from their 2006 framework is philosophical: coding is no longer treated as systematic extraction of pre-existing meanings, but as an interpretive act shaped by the researcher's position, assumptions, and theoretical commitments. Reflexivity (actively documenting how your perspective shapes the analysis) is no longer optional... it's the core of the method.

Braun and Clarke's 2006 paper "Using Thematic Analysis in Psychology" is one of the most cited papers in all of psychology, with over 100,000 citations on Google Scholar. Ironically, this widespread adoption became part of the problem. The paper was read selectively, stripped of its epistemological context, and applied as a generic coding procedure across research traditions that are fundamentally incompatible with its assumptions. Their 2019 paper "Reflecting on Reflexive Thematic Analysis" and their 2021 SAGE book are, in part, a course correction.

This guide explains what changed, what reflexivity means in practice, and how to apply the updated framework in ways that will satisfy PhD examiners and peer reviewers.

What is the difference between the 2006 and the updated reflexive approach?

The 2006 paper presented thematic analysis as a flexible method that could be used across different epistemological positions — from realist to constructionist. It described a 6-phase process and set a relatively accessible bar for entry. That accessibility was both its strength and its weakness.

By the 2019 paper, Braun and Clarke had observed a recurring pattern: researchers were applying thematic analysis as though it were a mechanical coding procedure, trying to achieve inter-rater reliability, treating saturation as a sample size criterion, and presenting themes as though they were discovered rather than constructed. These practices, they argued, reflect a fundamentally different paradigm (broadly, post-positivist coding reliability TA) rather than the interpretivist approach they had always intended.

Their updated framework makes four major clarifications:

1. Reflexive TA is theoretically positioned, not generic. It sits within a broadly constructionist or critical realist epistemological framework. Researchers who want a more positivist, reliability-focused approach should use codebook TA or coding reliability TA — different approaches that Braun and Clarke now distinguish explicitly.

2. Themes are constructed, not found. A theme is not a pattern that exists in the data waiting to be discovered. It is a pattern of meaning that the researcher constructs through their engagement with the data. This is not weakness — it is the point.

3. Researcher subjectivity is a resource, not a bias. The researcher's knowledge, experience, and theoretical commitments actively shape the analysis. This is acknowledged and used, not bracketed or controlled for.

4. Inter-rater reliability is not appropriate and should not be used. Because coding is interpretive and subjective, having two people independently code the same material and then measuring agreement treats coding as though there is a correct answer to be checked. There is not.

What are the 6 phases of reflexive thematic analysis?

The 6-phase structure survives from 2006, but the spirit of each phase is now sharper. The phases are iterative, not linear — you will move between them repeatedly rather than completing each one before starting the next.

Phase 1: Familiarisation with the data. Read all your data multiple times. Write initial ideas. This is active engagement, not passive reading — you are already interpreting. Take notes on what strikes you.

Phase 2: Coding. Generate codes that capture something interesting about the data in relation to your research question. Codes are not labels; they are the researcher's interpretive observations. The same data item can carry multiple codes. Keep an analytic memo explaining your coding rationale.

Phase 3: Generating initial themes. Group codes into potential themes. At this stage, themes are provisional — working ideas about patterns of shared meaning, not finished products. Create a thematic map.

Phase 4: Reviewing and developing themes. Test your themes against the data. Does each theme have enough data to sustain it? Is the data within a theme coherent? Does it tell a clear story? Merge, split, discard, and rename.

Phase 5: Refining, defining and naming themes. Write a detailed analysis of each theme. What is the essence of this theme? How does it connect to your research question? Name each theme to capture its analytical content, not just its topic.

Phase 6: Writing up. The write-up is part of the analysis, not just a report of it. Weave together data extracts and your interpretation — themes are not illustrated by quotes, they are argued through them.

What does reflexivity actually mean in practice?

Reflexivity is one of those concepts that gets nodded at in methods sections and then disappears. In reflexive TA, it needs to be operational.

Keep an analytic journal throughout. Write regular entries documenting your analytical decisions: why you coded something one way rather than another, what surprised you, where you felt uncertain, how your thinking evolved. This journal serves two purposes — it develops your thinking and it provides evidence of your process.

Articulate your positionality before you start coding. Who are you in relation to this topic? What assumptions do you bring from your professional background, lived experience, or theoretical training? Write this down in your methods section. It does not need to be confessional — it needs to be relevant.

Document your theoretical framework explicitly. Reflexive TA is epistemologically flexible, but not free-floating. If you are working from a feminist perspective, or a critical realist one, or a social constructionist one, say so. The framework shapes what you see in the data.

Revisit your reflexive account as analysis proceeds. Your initial positionality statement may look different after 50 hours of coding. Note the evolution.

What are 5 common mistakes researchers make when claiming to use reflexive TA?

Braun and Clarke have written extensively about misapplication. These five errors appear frequently in published work:

1. Calculating inter-rater reliability. As noted above, this is incompatible with reflexive TA. If you see an IRR calculation in a paper claiming reflexive TA, the authors have misunderstood the approach (or applied a different one without naming it). Peer reviewers are increasingly catching this.

2. Stopping data collection when themes "saturate". Saturation is a concept from grounded theory's logic of theoretical sampling. Reflexive TA does not use saturation as a stopping rule. Braun and Clarke are explicit about this — sample size decisions should be made in advance based on the research question and practical context.

3. Treating codes as the data rather than interpretations of it. Mechanical coding (applying the same label every time a concept appears) produces a content count, not thematic analysis. Coding should be analytical — asking what this excerpt says about the research question, not just what topic it addresses.

4. Naming themes as topics rather than analytical claims. "Work-life balance" is a topic. "The impossibility of balance in a culture that rewards overcommitment" is a theme. The theme label should convey what you found, not just what you looked at.

5. Treating the 6 phases as a checklist. Phases are a heuristic for a deeply iterative process. Reporting that you "completed phases 1 through 6" suggests a linear march through a procedure — the opposite of how the method works.

How do you write up reflexive TA for journal submission?

This is where many researchers struggle. The write-up must convincingly show two things: the analytical rigour of your process and the interpretive depth of your themes.

Methods section. Explicitly name the approach as reflexive thematic analysis and cite Braun and Clarke (2019) or the 2021 book alongside the 2006 paper. Describe your positionality. Explain how coding proceeded (iteratively, with memoing). Justify your sample size in terms of depth rather than saturation. Do not claim inter-rater reliability.

Results section. Each theme gets a subsection with an analytical label, a few lines explaining the theme's meaning, and 2-4 verbatim quotes presented with contextual attribution. The quotes illustrate the theme — they do not constitute it. Your interpretive commentary must do work beyond paraphrase. See our guide on how to write a thematic analysis results section for the structural detail.

Quality criteria. Reflexive TA does not use traditional validity and reliability criteria. Instead, reviewers look for: coherence between epistemological position and method, richness and depth of analysis (not just the number of themes), transparency of the analytical process, and authentic reflexive engagement. Lincoln and Guba's trustworthiness framework (credibility, transferability, dependability, confirmability) is a recognised alternative.

The complete thematic analysis guide covers the full write-up process in more detail.

How does AI-assisted analysis fit with a reflexive approach?

This is a genuine methodological question, not just a marketing one. Reflexive TA holds that interpretation is inherently human and researcher-situated. How does AI fit?

The honest answer is that AI tools change the mechanics of coding, not its interpretive nature. What AI can do — quickly surface candidate patterns across a large corpus, flag which excerpts might be relevant to an emerging theme, identify where your coding is inconsistent — reduces the cognitive load of the procedural work. What it cannot do is replace your interpretive judgement.

In practice, a reflexive approach to AI-assisted analysis means: treating AI-generated theme suggestions as prompts to your own analysis, not final outputs; being transparent in your methods section about what AI tools did and how you engaged critically with their outputs; and maintaining an analytic journal that documents how your interpretation shaped the final themes.

Skimle's approach to this is to show every insight traced back to the specific excerpts it derives from, so you can interrogate and revise the AI's pattern recognition with your own interpretive lens. The categories view and analyst notes features are designed for exactly this kind of iterative, reflexive engagement. For academic researchers, the inductive analysis mode keeps the researcher in control of how themes are structured.

If you are an academic using Skimle, the academic researchers page has more on how the tool supports rigorous qualitative workflows.

Frequently asked questions

Is reflexive thematic analysis the same as thematic analysis?

Reflexive thematic analysis is one approach within the broader thematic analysis family. Braun and Clarke now distinguish three main types: reflexive TA (their approach — interpretivist, subjectivity as resource), codebook TA (uses a shared codebook for consistency across a team), and coding reliability TA (post-positivist, uses inter-rater reliability). The 2006 paper described an approach that sits within what is now called reflexive TA, though this label only became formalised later.

Can I use reflexive thematic analysis for a deductive study?

Reflexive TA can incorporate deductive elements — you can start with a theoretical framework that shapes what you code for — but its primary orientation is inductive or abductive. If your study is strongly deductive (testing predefined categories against data), codebook TA or framework analysis may be more appropriate. See our guide on inductive, deductive, and abductive coding for a fuller comparison.

How many themes should a reflexive thematic analysis produce?

There is no correct number. Most published reflexive TA studies identify 3-6 themes, but this depends entirely on the dataset, the research question, and the analytical depth you are aiming for. More themes does not mean better analysis. If anything, the pressure is to consolidate: if you have 12 themes, most of them probably belong together under a more analytically ambitious heading.

Do I need to use member checking in reflexive TA?

Member checking (returning interpretations to participants for validation) is sometimes associated with qualitative rigour, but it is not required or expected in reflexive TA. In fact, returning your constructionist interpretations to participants — who may not share your analytical framework — can create confusion rather than validation. Braun and Clarke's quality criteria for reflexive TA do not include member checking. Transparency of process, reflexive engagement, and analytical depth are the relevant criteria.

How do I reference reflexive thematic analysis correctly?

Cite both the 2006 paper and the updated work. For the reflexive framing, cite: Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. Or the 2021 book: Braun, V., & Clarke, V. (2021). Thematic Analysis: A Practical Guide. SAGE. The 2006 paper remains the standard citation for the 6-phase structure.

Ready to apply reflexive thematic analysis to your data with full transparency? Try Skimle for free and see how AI-assisted analysis can support your interpretive process while keeping every insight traceable to its source.

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About the authors

Henri Schildt is a Professor of Strategy at Aalto University School of Business and co-founder of Skimle. He has published over a dozen peer-reviewed articles using qualitative methods, including work in Academy of Management Journal, Organisation Science, and Strategic Management Journal. His research focuses on organisational strategy, innovation, and qualitative methodology. Google Scholar profile

Olli Salo is a former Partner at McKinsey & Company where he spent 18 years helping clients understand the markets and themselves, develop winning strategies and improve their operating models. He has done over 1000 client interviews and published over 10 articles on McKinsey.com and beyond. LinkedIn profile