Braun & Clarke thematic analysis: the complete guide

What Braun & Clarke thematic analysis is

Thematic analysis is a method for spotting, organising, and interpreting patterns of meaning across a qualitative dataset — interview transcripts, open survey responses, focus groups, or documents. Braun and Clarke's contribution was not to invent it but to spell it out: a step-by-step process that a student could actually carry out, with named phases and clear decisions at each one. That clarity is why the 2006 paper became one of the most cited articles in the social sciences, and why later teaching pieces such as Ahmed et al. (2025) still build directly on their framework.

Two things set it apart from neighbouring methods. It is not tied to a single theory, so you can use it whether you lean realist or constructionist. And it works at two levels: semantic, staying close to what people explicitly said, or latent, reading for the assumptions underneath. You decide which, and you say so in your write-up.

The six phases of thematic analysis

The phases are numbered, but they are not a one-way conveyor belt. You will loop back — a theme that falls apart in phase four sends you back to recode in phase two. Braun and Clarke are explicit that the process is recursive. Naeem and colleagues give a helpful worked account of moving through the steps toward a finished model in their step-by-step process (2023), if you want a second walkthrough alongside this one.

1. 1. Familiarise yourself with the data

   Read everything, then read it again. Before you code a single line you need to know your material well enough to recall where things were said. Most researchers transcribe their own interviews for exactly this reason: the typing forces attention. Jot early notes as you go, but hold off on formal coding until you have been through the whole dataset at least once.

2. 2. Generate initial codes

   Work through the data and label anything that looks relevant to your question. A code is a short tag for a feature of the data — “fear of judgement”, “workarounds for missing tools”. Code generously; you can always drop codes later, but you cannot cluster what you never tagged. Keep the extract that each code sits on, because you will need those quotes to defend your themes.

3. 3. Search for themes

   Now shift from codes to patterns. Sort your codes into candidate themes — broader ideas that pull several codes together and say something about your question. This is where sticky notes, tables, or a whiteboard earn their keep. A theme is not a topic word like “support”; it is a claim, such as “support only helps when it arrives before the deadline”.

4. 4. Review the themes

   Test each candidate theme twice. First, check the coded extracts inside it hang together. Second, check the themes make sense against the full dataset. Themes that overlap get merged; themes with only one thin quote behind them get cut or folded elsewhere. You should end with a set that is distinct, each earning its place.

5. 5. Define and name the themes

   Write a short definition of what each theme is and is not. If you cannot describe a theme in a sentence or two, it is probably doing too much and needs splitting. Names should be sharp and readable — an examiner skimming your contents page should grasp the analysis from the theme names alone.

6. 6. Produce the report

   Write the analysis so that the argument, the themes, and the evidence read as one piece. Each theme gets its definition, the quotes that support it, and your interpretation of what it means for the research question. The write-up is analysis, not a data dump: quotes illustrate the point you are making, they do not replace it.

From 2006 to reflexive thematic analysis

Since 2006 Braun and Clarke have refined how they talk about the method, and the current name for their version is reflexive thematic analysis. The phases are much the same. What changed is the thinking behind them. They now reject the idea that themes “emerge” from data on their own, as if waiting to be found. Themes are produced by a researcher who brings a perspective, and that perspective is a resource, not a contaminant to be scrubbed out.

The practical upshot: in reflexive thematic analysis you are not chasing a single “correct” coding that a second coder would replicate. You are building a considered interpretation and being honest about your part in it. That is a different goal from a codebook approach, where a fixed set of codes is applied for consistency. If your project needs the codebook style instead, the difference between letting codes surface and applying a fixed scheme is the subject of our guide on inductive vs. deductive coding.

Mistakes that cost marks

Three problems show up again and again in student thematic analyses, and examiners spot them fast.

• Topic summaries dressed as themes. “Communication” is a bucket, not a theme. A theme makes a point: “communication broke down whenever roles were unclear”. If your theme name is a single noun, it is probably still a topic.
• Quotes doing the analysis. A string of quotes under a heading is data, not interpretation. Say what the quote shows and why it matters for your question.
• Claiming themes “emerged”. In reflexive thematic analysis this phrasing signals you have misread the method. You constructed the themes; write as though you did.

Running the analysis without losing weeks

The six phases are sound, but the first pass — coding line by line, then clustering hundreds of codes — is where projects stall. This is the part you can hand off for a draft. The thematic analysis tool on this site codes your findings and clusters them into candidate themes, with a verbatim quote behind every code, so you start phase three with a structured draft instead of a blank page. You still do the thinking: rename the themes, cut the weak ones, and write the interpretation. For a walk through what to paste and how to read the output, see how to use the tool, and if your material spans several papers rather than one dataset, synthesising findings across studies covers that case.

References

• Braun, V. and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), pp.77–101. doi:10.1191/1478088706qp063oa
• Naeem, M., Ozuem, W., Howell, K. and Ranfagni, S. (2023). A Step-by-Step Process of Thematic Analysis to Develop a Conceptual Model in Qualitative Research. International Journal of Qualitative Methods, 22. doi:10.1177/16094069231205789
• Ahmed, S.K., Mohammed, R.A., Nashwan, A.J., Ibrahim, R.H., Abdalla, A.Q., M. Ameen, B.M. and Khdhir, R.M. (2025). Using thematic analysis in qualitative research. Journal of Medicine, Surgery, and Public Health, 6, p.100198. doi:10.1016/j.glmedi.2025.100198