How to Do Thematic Analysis: Step-by-Step Guide for Researchers (2026)
Meet the Expert
Shruti Sharma
Academic Writing Coach & Qualitative Research Expert
- Specialises in thematic analysis, grounded theory, and reflexive qualitative methods for PhD dissertations
- Expert in both manual coding and NVivo-assisted thematic analysis
- Guided 150+ researchers through qualitative data analysis and findings chapter writing
Thematic analysis (TA) is a qualitative research method for identifying, analysing, and reporting patterns — called themes — within a dataset. Using Braun and Clarke's six-phase framework, it is one of the most widely used methods for analysing interview transcripts, focus group data, and open-ended survey responses in PhD dissertations and academic research.
What Is Thematic Analysis?
First formalised by Braun and Clarke in their landmark 2006 paper (which has over 100,000 citations), thematic analysis provides a systematic, flexible method for making sense of qualitative data. Unlike grounded theory or discourse analysis, TA does not require a specific theoretical framework — it can be applied across epistemological positions (positivist, interpretivist, constructivist).
TA is used to analyse: in-depth interviews, focus group transcripts, open-ended survey responses, diary entries, policy documents, social media posts, and fieldnotes.
Thematic Analysis: Key Features
Most cited qualitative method framework
Data-driven or theory-driven coding
Familiarise → Code → Theme → Review → Define → Write
Each theme tells part of the data story
Broadly applicable across disciplines
Both approaches are academically accepted
The 6 Phases of Thematic Analysis (Braun & Clarke)
Phase 1: Familiarise Yourself with the Data
Read and re-read all your transcripts multiple times. Make notes of ideas, patterns, and things that strike you. If you have audio data, listen to it alongside the transcript. This immersion phase is crucial — you are building an intimate knowledge of your data before any formal coding begins.
Output: Annotated transcripts, initial reflective notes.
Phase 2: Generate Initial Codes
Systematically go through each transcript and apply codes — labels that capture the meaning of each segment. Codes should be concise, active, and capture what is interesting about the data. Aim for exhaustive coding — code every potentially relevant segment.
- In manual coding: use different coloured highlighters or margin notes
- In NVivo: create nodes for each code and assign data segments
- Tip: Use in vivo codes (participants' own words) where possible
Output: A comprehensive list of codes with supporting data extracts.
Phase 3: Search for Themes
Organise your codes into potential themes and sub-themes. Look for patterns — which codes seem to share a common meaning? You can use a mind map, sticky notes, or a spreadsheet (code → sub-theme → theme).
Output: A provisional thematic map showing candidate themes and their relationships.
Phase 4: Review Your Themes
Check your themes against the data in two levels: (1) Check that the data within each theme coheres; (2) Check that the themes accurately represent the full dataset. Refine, merge, split, or discard themes as needed. Return to the data frequently — do the themes really capture what participants said?
Output: Revised thematic map; themes that work in relation to both the data and the research question.
Phase 5: Define and Name Themes
Write a clear definition (1–2 sentences) for each theme that captures its essence. Choose a name that is both descriptive and evocative. Sub-themes can be named similarly. Avoid vague names like "Theme 1" — good theme names function as headlines that tell the reader exactly what the theme is about.
Output: Final thematic structure with defined, named themes and sub-themes.
Phase 6: Produce the Report (Write Up)
Write a compelling narrative for each theme. The write-up should: describe the theme, provide 2–4 illustrative quotes, and interpret what the theme means in relation to your research question. Do not simply list quotes — weave them into an analytical narrative.
Output: Findings chapter narrative — ready for your dissertation.
Inductive vs Deductive Thematic Analysis
Inductive TA: codes and themes arise from the data without being constrained by a pre-existing framework — data-driven. Deductive TA: codes and themes are guided by a theory or existing framework you bring to the analysis — theory-driven. Most PhD dissertations use inductive TA in exploratory studies and deductive TA when testing an established framework. In your methodology chapter, state and justify which approach you used.
Stuck with thematic analysis coding or theme development? Our qualitative research specialists at Thesis Ace Writers provide expert support for coding, theme development, and findings chapter writing.
How to Present Themes in Your Dissertation
| Theme | Sub-themes | No. of Participants | Example Quote |
|---|---|---|---|
| Navigating Uncertainty | Information overload, Trusting instincts | 12/15 | "I never knew what would come next..." (P3) |
| Seeking Social Support | Peer support, Family involvement | 11/15 | "My colleagues kept me going..." (P7) |
| Rebuilding Identity | Professional re-evaluation, Personal growth | 9/15 | "I became a different person through this..." (P12) |
Related Reading from Thesis Ace Writers
Need help writing your thematic analysis findings chapter? Contact Thesis Ace Writers for professional qualitative analysis support from coding to complete chapter writing.
Frequently Asked Questions
Click a question to expand the answer.
Thematic analysis (TA) is a qualitative research method used to identify, analyse, and report patterns (themes) within data. It is one of the most widely used qualitative analysis methods, particularly in social sciences, psychology, nursing, and education. The most cited framework is Braun and Clarke's (2006) six-phase approach. TA is flexible — it can be applied to interviews, focus groups, open-ended survey responses, documents, and observation notes.
Braun and Clarke's six phases are: (1) Familiarisation with data — read and re-read transcripts, note initial ideas; (2) Generating initial codes — systematically code data; (3) Searching for themes — group codes into potential themes; (4) Reviewing themes — check themes against data, refine; (5) Defining and naming themes — clearly articulate each theme; (6) Producing the report — write up themes with illustrative quotes in narrative form.
A code is a label applied to a specific segment of data that captures its meaning (e.g., 'feeling isolated', 'lack of support'). A theme is a broader pattern that captures something meaningful about the data in relation to the research question — it is built from multiple codes that share a common meaning. Themes are more abstract and conceptually richer than individual codes. Several codes → sub-theme; several sub-themes → theme.
Most thematic analyses produce 3–6 main themes. Too many themes (10+) suggest that themes are actually codes or sub-themes. Each theme should be distinct, meaningful, and well-supported by data. In a PhD dissertation findings chapter, each theme typically forms a separate sub-section, with sub-themes as sub-sections within it. Quality is more important than quantity — fewer, well-developed themes are preferable to many thin ones.
Reflexive thematic analysis is Braun and Clarke's updated (2019) version that emphasises the researcher's active role in constructing themes rather than 'finding' them in the data. It moves away from positivist notions of reliability and validity, instead requiring the researcher to be transparent and reflexive about their analytical choices, positionality, and the theoretical lens they bring to the data. It is epistemologically compatible with interpretivist and constructivist research.