Thematic analysis for a dissertation: step-by-step guide with a worked coding example

Thematic analysis is a method for identifying, analysing and interpreting patterns of meaning (“themes”) in qualitative data such as interview transcripts, focus groups, open survey responses or documents. You code the data systematically, build the codes into themes, check those themes against the whole dataset, then write an interpretive account that answers your research question. The most widely used version is Braun and Clarke’s reflexive thematic analysis, which follows six recursive phases.

This guide demonstrates reflexive thematic analysis (the version most dissertations citing Braun & Clarke are actually doing), explains how it differs from other kinds, and walks one fictional dataset from raw transcript to a finished findings paragraph.

Contents

1. What thematic analysis is (and isn’t)
2. Is it the right method for your dissertation?
3. The three types of thematic analysis — choose one
4. Four decisions to make before you code
5. The six phases (2006 names vs current names)
6. Worked coding example: from transcript to theme
7. Writing your methodology section
8. Writing your findings/analysis chapter
9. Quality: what examiners look for
10. Pre-submission checklist
11. FAQs
12. References

1. What thematic analysis is (and isn’t)

Thematic analysis is one of the most common methods of qualitative data analysis in psychology, education, health, business, sociology and the social sciences generally. It is a method, not a methodology: it doesn’t come bundled with a theory of reality or knowledge, so you can use it within different theoretical frameworks, but you must say which one you’re using.

Three terms you need to get precisely right:

Two phrases to avoid in your write-up, because they signal a misunderstanding of the method:

2. Is thematic analysis right for your dissertation?

Thematic analysis suits questions about experiences, perspectives, understandings, practices and representations. It handles small and medium datasets well, works with almost any textual data, and is manageable within a dissertation timeframe without years of methods training.

It is a weaker fit if your question is about the detailed structure of talk (use conversation or discourse analysis), one person’s lifeworld in idiographic depth (consider IPA), building a theory of a social process (grounded theory), or counting content categories (quantitative content analysis).

3. The three types of thematic analysis (choose one, and stay consistent)

The single most common reason students lose marks is methodological incoherence: citing Braun & Clarke (reflexive thematic analysis) while also reporting inter-rater reliability, “data saturation”, a fixed codebook, or “objective” coding. Those practices belong to other members of the thematic analysis family. Braun and Clarke’s own reviews of published research find the same problems: treating thematic analysis as one method rather than a family, confusing topics with themes, and reporting thematic analysis as if it were atheoretical.

For most undergraduate and master’s dissertations, reflexive thematic analysis is the right choice, and it’s the version the rest of this guide demonstrates. If you want to count things or your supervisor requires a second coder, choose coding reliability thematic analysis or content analysis openly and use its quality criteria instead.

4. Four decisions to make before you code

Write a sentence on each in your methodology. These are spectrums, not switches, and the first two are independent of each other.

5. The six phases of thematic analysis

Braun and Clarke renamed the phases in Thematic Analysis: A Practical Guide (2022). Knowledgeable examiners may expect the current wording; using “searching for themes” can suggest you think themes pre-exist in the data. Use the new names, cite both sources.

The process is recursive. You will move backwards (phase 4 regularly sends you back to coding). Writing is part of analysis from phase 1 onward, not something that happens at the end. Keep a reflexive journal throughout: dated notes on your reactions, assumptions and decisions. It becomes evidence for your methodology chapter.

6. Worked coding example: from transcript to theme

Note: All data below are fictional, written for teaching. A real dissertation would have more participants and much longer transcripts. The point is to show the process, including the decisions guides usually hide.

The study

Research question: How do undergraduate students who work part-time alongside their degree make sense of what it means to be a “proper” university student?

Design: Eight semi-structured interviews (45–60 minutes) with second- and third-year undergraduates working 12+ hours a week in paid employment. Reflexive thematic analysis; predominantly inductive; semantic coding moving to latent; experiential orientation; critical realist framework.

Phase 1: Familiarisation

The researcher transcribed the interviews herself (orthographic transcription: words plus laughter, pauses, false starts), read each transcript twice, and wrote notes. An extract from her familiarisation notes:

Transcript 3 (Priya). Striking how “competence” keeps appearing, but at work, not at uni. She’s apologetic about enjoying work. I notice I keep wanting to read this as a “problem” to be fixed. Is that my assumption (I didn’t work during my degree)? Also: everyone so far compares themselves to an imagined student who “has time”. Who is that student? Nobody describes an actual person.

That last question becomes the seed of the first theme, but at this stage it is only a hunch.

Phase 2: Coding

Here are three short extracts from three fictional participants. Pseudonyms throughout; line numbers refer to the transcript.

Coding Extract A: semantic and latent side by side

The same segment can carry both a semantic code (what was said) and a latent code (what it assumes or does). You don’t need both for every line; the table shows what the difference looks like in practice.

Codes from Extracts B and C

What good codes look like: specific enough that someone else could understand them without the data, but not so narrow that they only ever apply to one line. “Stress” is too vague. “Uni as always being behind” is usable.

Collating and refining the code list

After coding all eight transcripts, the researcher had 61 codes. Reflexive thematic analysis expects you to merge, split, rename and drop codes as your understanding develops. Record these decisions; they form part of your audit trail.

Phase 3: Generating initial themes

The researcher clustered the refined codes into candidate themes and sketched a first thematic map. Here is the first attempt, including one candidate that didn’t survive:

Phase 4: Developing and reviewing themes

Two checks, in order:

1. Against the collated extracts. Does every extract under a theme genuinely share the central idea? Under The imagined “proper student”, one extract about a lecturer’s hostile comment didn’t fit the central concept (it was about staff attitudes, not the ideal student) and moved to Keeping work out of sight.
2. Against the entire dataset. Re-read all eight transcripts with the candidate themes in mind. Do the themes tell a convincing story of the data as a whole? Is anything important uncoded? Here the researcher noticed that two participants described pride in working that complicated the deficit story in theme 1. Rather than ignore it, she coded it (Work as a source of pride) and used it to sharpen the theme: the “proper student” ideal is something participants both measure themselves against and push back on.

She also considered whether Keeping work out of sight and Competence lives elsewhere were really two themes or one theme with two facets. Decision: keep separate. Concealment is about the institutional relationship; displaced competence is about self-concept. They share a context but not a central organising concept.

Revised thematic map (text version):

Research question: making sense of being a "proper" student while working

Theme 1  The "proper student" is someone with time to spare
         └─ central idea: the ideal student is defined by discretionary time,
            so working students read themselves as deficient (and sometimes resist this)

Theme 2  Keeping work out of sight
         └─ central idea: paid work is concealed from the university as self-protection;
            the competence it builds therefore stays invisible

Theme 3  Competence lives elsewhere
         └─ central idea: work becomes the place where participants feel capable,
            while the degree becomes "the thing I'm failing at"

Cross-cutting link: Themes 2 and 3 are consequences of the ideal described in Theme 1.

Phase 5: Refining, defining and naming themes

Write a short definition for each theme: its central organising concept, its scope, its boundaries (what it is not), and its relationship to the research question. If you can’t do this in a few sentences, the theme isn’t ready.

Naming tip: a good theme name tells the reader the claim, not the topic. “Time” is a topic. “The ‘proper student’ is someone with time to spare” is a claim. Participant quotes can make vivid names (“The thing I’m failing at”).

A reflexive memo (what to put in your journal)

12 March. I dropped the code “poor time management” today. Going back through the transcripts, no one actually described themselves as bad at managing time; Tom says the opposite. I had been reading working students through a deficit lens that comes, I think, from my own experience of not having to work during my degree and from the student-support literature I read first, which frames employment as a “risk factor”. The data push back on this. This changes Theme 1: it’s about the institution’s assumption of time, not the students’ failure to manage it.

That is what genuine reflexivity looks like: specific, tied to an analytic decision, and consequential. “As a researcher I acknowledge I may have had bias” is not reflexivity.

Phase 6: Writing up — a weak paragraph and a strong one

7. Writing the methodology section

Braun and Clarke’s advice for dissertations: describe what you actually did, not a generic recital of six phases. Organise it rationale-first (why) then procedure (how). Use qualitative headings (“Participants and recruitment”, “Interviews”, “Analytic approach”) rather than “Materials and procedure”.

Below is how the fictional study’s analysis subsection might read. Use it as a model for structure and wording, not as text to paste: your choices, numbers and reflexive content must be your own.

Analytic approach

Data were analysed using reflexive thematic analysis (Braun & Clarke, 2006, 2019, 2022). I chose reflexive thematic analysis over codebook or coding-reliability approaches because the study’s critical realist position treats my interpretive engagement as a resource for analysis rather than a source of bias to be controlled, and because the research question concerns shared patterns of meaning rather than the frequency of pre-specified categories. The analysis was predominantly inductive, although my reading was shaped by the concept of the “traditional student” norm in the widening-participation literature. Coding began at a semantic level and moved toward latent coding as I began to interrogate the assumptions underlying participants’ comparisons with non-working peers.

I transcribed all eight interviews orthographically, which served as the first stage of familiarisation, and wrote notes on each transcript and the dataset as a whole. I coded the full dataset in Microsoft Word using the comment function, collating extracts by code in a spreadsheet. Initial coding produced 61 codes, refined through merging, splitting and renaming to 38 (Appendix D records these decisions). I clustered codes into five candidate themes, two of which I subsequently rejected as topic summaries that lacked a central organising concept. The remaining themes were reviewed first against the collated extracts and then against the full dataset, during which I coded additional material on pride in work that complicated my initial reading. Throughout, I kept a reflexive journal; an extract is provided in Appendix E and I draw on it in the discussion of my positionality below. Consistent with reflexive thematic analysis, I did not use a second coder or calculate inter-rater reliability, since these presume a single correct coding that the approach rejects (Braun & Clarke, 2021a). My supervisor acted as a critical friend, questioning my interpretations rather than checking for agreement.

Positionality (one paragraph, first person): who you are in relation to the topic and participants, what assumptions you brought, and how this shaped the analysis (point to a specific decision, as in the memo above).

What to put in appendices (check they’re excluded from the word count): one or two pages of annotated coded transcript; the code-refinement table; early and final thematic maps; theme definition table; a reflexive journal extract; interview schedule; ethics documents. Anonymise everything.

8. Writing the findings / analysis chapter

• Heading: Braun and Clarke prefer “Analysis” to “Findings” (which implies discovery), but follow your department’s convention.
• Open with an overview: a short paragraph, list or thematic map of your themes. Qualitative reports don’t do surprise reveals; signpost.
• Order themes to tell a story, usually from the theme that sets the scene to the ones that build on it (as Theme 1 does above).
• Structure within each theme: state the central idea → develop it with analytic claims → embed extracts → interpret the specific wording → show variation and complexity → link to literature (if integrated) → transition.
• Balance: roughly half analytic narrative, half data. Two to four extracts per theme is typical; a mix of short embedded quotes and one or two block quotes.
• Prevalence words: “many”, “most”, “a few”, “one participant” are fine; “6/8 (75%)” is not, in reflexive thematic analysis.
• Combined or separate discussion? Integrating “Analysis and Discussion” is normative in qualitative-centric reporting and lets you argue with the literature inside each theme. Many programmes still require separation. Either way, you also need a general discussion/conclusion with implications, limitations and future directions.
• Limitations: don’t judge your study by quantitative standards (“small sample, so not generalisable”). Qualitative work aims at transferability and depth. Legitimate limitations: homogeneity of the sample, a method of data collection that limited certain kinds of talk, the constraints of your own position.

9. Quality: what examiners actually look for

Marking criteria vary by programme, but across institutions examiners of qualitative dissertations consistently reward:

1. Coherence. Research question, theoretical framework, type of thematic analysis, sampling rationale, quality criteria and language all fit together.
2. Interpretation, not description. Themes make an argument and answer “so what?”
3. An audit trail. A reader can trace how data became codes and codes became themes.
4. Real reflexivity. Specific, dated, tied to decisions.
5. Honest engagement with complexity. Contradictory cases are analysed, not hidden.
6. Proportionate claims. The scope of conclusions matches the design.

Quality tools you can cite: Braun and Clarke’s 15-point checklist (2006), their “20 questions” for evaluating thematic analysis (2021a), and the Reflexive Thematic Analysis Reporting Guidelines (RTARG) (2024). Note that Braun and Clarke argue COREQ and SRQR checklists are not coherent with reflexive thematic analysis, so if your programme requires COREQ, say so and explain which items don’t apply. Lincoln and Guba’s trustworthiness criteria (credibility, transferability, dependability, confirmability) are widely cited via Nowell et al. (2017) but fit codebook approaches better than reflexive thematic analysis; use them knowingly if at all.

10. Pre-submission checklist

Tick every line before you submit.

Coherence

• I have named the type of thematic analysis I used (reflexive / codebook / coding reliability) and cited the appropriate sources.
• I have stated my theoretical position (e.g. critical realist, contextualist, constructionist) and my inductive–deductive and semantic–latent orientation.
• I have not mixed in inter-rater reliability, saturation or a fixed codebook unless my chosen type of thematic analysis requires them.

Process

• I described what I actually did at each phase, with numbers (transcripts, codes, candidate themes) and decisions.
• I used the current phase names and cited Braun & Clarke (2006) and (2022).
• I have an appendix with an annotated coding extract and a code-to-theme development table.
• I kept a reflexive journal and used it.

Themes

• Each theme has a central organising concept I can state in one sentence.
• No theme is a one-word topic or a restated interview question.
• I have 2–6 themes, each developed in depth, with subthemes only where they illuminate a facet.
• Themes don’t substantially overlap, and together they answer the research question.

Writing

• I wrote “developed/generated”, not “emerged/identified/discovered”.
• Every extract is introduced and interpreted; no quote stands alone.
• No frequency counts used as evidence.
• Contradictory or complicating data is discussed.
• The analysis engages with literature rather than just “being consistent with” it.
• Limitations are qualitative-appropriate.
• Positionality is specific and linked to analytic decisions.

11. FAQs

12. References (and how to cite them)

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. — the foundational paper

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. — introduces the “reflexive thematic analysis” label and corrects common misreadings

Braun, V., & Clarke, V. (2021a). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352. — quality criteria and the “20 questions” evaluation tool

Braun, V., & Clarke, V. (2021b). To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qualitative Research in Sport, Exercise and Health, 13(2), 201–216.

Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. SAGE. — the definitive textbook; current phase names (published late 2021, copyright 2022; some sources cite 2021)

Braun, V., & Clarke, V. (2023). Toward good practice in thematic analysis: Avoiding common problems and be(com)ing a knowing researcher. International Journal of Transgender Health, 24(1), 1–6.

Braun, V., & Clarke, V. (2024). Supporting best practice in reflexive thematic analysis reporting in Palliative Medicine: A review of published research and introduction to the Reflexive Thematic Analysis Reporting Guidelines (RTARG). Palliative Medicine, 38(6), 608–616.

Byrne, D. (2022). A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Quality & Quantity, 56, 1391–1412. — a peer-reviewed worked example you can cite

Malterud, K., Siersma, V. D., & Guassora, A. D. (2016). Sample size in qualitative interview studies: Guided by information power. Qualitative Health Research, 26(13), 1753–1760.

Moreau, M.-P., & Leathwood, C. (2006). Balancing paid work and studies: Working (-class) students in higher education. Studies in Higher Education, 31(1), 23–42.

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13. — widely cited; note its criteria fit codebook thematic analysis better than reflexive thematic analysis

Terry, G., Hayfield, N., Clarke, V., & Braun, V. (2017). Thematic analysis. In C. Willig & W. Stainton Rogers (Eds.), The SAGE handbook of qualitative research in psychology (2nd ed., pp. 17–37). SAGE.

Also useful: Braun and Clarke’s free online resources at the University of Auckland thematic analysis site and the SAGE companion site for Thematic Analysis: A Practical Guide, which include datasets, coding examples and sample reports.