When you’re conducting qualitative research—especially for a thesis or dissertation—you’re likely working with rich, messy data from interviews, focus groups, or textual sources. Your challenge? Making sense of it all in a way that’s clear, structured, and academically credible. That’s where thematic analysis comes in.
Thematic analysis is a flexible, widely used method for identifying and analysing patterns (or “themes”) in qualitative data. It helps researchers move beyond surface-level observations to uncover deeper meanings, perspectives, and relationships within their data. And when it comes to applying thematic analysis, one framework stands out above the rest: Braun and Clarke’s six-phase model.
Introduced in their landmark 2006 paper, Braun & Clarke’s framework has become the go-to method for students and researchers who want a systematic yet adaptable way to work with qualitative data. It doesn’t require complex software or statistical knowledge, making it particularly accessible to thesis writers in psychology, education, health sciences, and the humanities.
In this guide, we’ll break down each of the six phases of Braun & Clarke’s approach with clear explanations, practical tips, and examples—so you can confidently use thematic analysis in your own thesis.
📘 Need help writing up your analysis? Don’t miss our guide: How to Write a Thesis Analysis Chapter
Phase 1: Familiarising Yourself with the Data
Before you can identify themes, you need to get to know your data inside and out. This first phase is all about immersion—reading, re-reading, and thinking deeply about what your participants have shared.
If you conducted interviews or focus groups, this means starting with transcription. While it can be tempting to outsource this task or skim-read a transcript, Braun and Clarke stress that doing your own transcription helps you engage more closely with the material. You’ll start noticing patterns, emotions, and interesting phrasing that might not be obvious in audio alone.
What to Do:
- Transcribe the data (verbatim if possible)
- Read through each transcript multiple times
- Make informal notes, highlight striking quotes, and jot down initial impressions
At this stage, you’re not coding or categorising yet—you’re simply absorbing the data, trying to understand its texture and tone.
Example:
A student researching online learning might highlight phrases like “I felt invisible in Zoom classes” or “When my professor emailed me personally, it changed everything.” These early observations could point toward emerging ideas around connection, visibility, or motivation.
🧾 Tip: Use the margins of your transcript (physical or digital) to annotate ideas, questions, or emotional reactions. This informal analysis lays the groundwork for more formal coding in Phase 2.
📘 Related: How to Write a Literature Review for a Thesis – Helpful for connecting early insights to academic context.
Phase 2: Generating Initial Codes
Once you’ve immersed yourself in the data, the next step is to start breaking it down into smaller, manageable pieces—this is called coding. A code is a short label or phrase that identifies something interesting or relevant in the data. These can be based on words participants used, behaviours they described, ideas they mentioned, or even your own analytic observations.
In Braun and Clarke’s framework, coding is systematic but flexible. You’re not trying to sort everything into neat categories just yet—you’re simply labelling anything that stands out as potentially meaningful.
What to Do:
- Work through each transcript line by line
- Highlight chunks of text (from a few words to a few sentences)
- Assign codes that summarise the content or idea (e.g., feeling isolated, seeking validation, lack of feedback)
- Code inclusively—if you’re unsure whether something’s relevant, code it anyway for now
You can do this manually (using highlighters or margin notes) or with software like NVivo, Atlas.ti, or Taguette. For smaller projects, Microsoft Word or Excel is often enough.
Example:
A student researching student engagement might assign the following codes:
- “I kept checking my phone during lectures” → distraction/loss of focus
- “The breakout rooms were so awkward” → discomfort with peer interaction
- “When I got feedback, it felt like someone actually cared” → emotional impact of feedback
These are initial, descriptive labels—you’ll refine them later.
💡 Tip: Stay open-minded during coding. Don’t try to fit everything into predefined ideas. Let the data speak, and keep a record of your codes as you go.
📚 Related: How to Structure a Thesis.
Phase 3: Searching for Themes
Now that you’ve generated a list of initial codes, it’s time to start identifying themes—broader patterns of meaning that say something important about your research question.
Think of this stage as grouping your codes into clusters that share a common thread. You’re no longer just describing what participants said—you’re beginning to interpret what it means.
What Is a Theme?
A theme captures something significant or recurring in your data. It’s more than just a topic—it reflects underlying meaning or shared experience. Themes should:
- Be relevant to your research question
- Appear across multiple data sources
- Contain enough supporting data to stand on their own
At this stage, your themes are tentative. You’re forming ideas, exploring how codes fit together, and beginning to sketch the structure of your analysis.
What to Do:
- Group related codes together
- Look for patterns, contradictions, or narratives
- Create a visual map, mind map, or table to organise codes into broader themes
- Ask: What does this cluster of codes reveal? What story does it tell?
Example:
Codes such as feeling invisible, lack of acknowledgement, and craving connection might combine into a potential theme like “The Need for Recognition in Online Learning.”
Another group—camera fatigue, monotonous content, and screen exhaustion—might cluster under “Digital Learning Fatigue.”
Helpful Questions to Ask:
- What common experiences or attitudes emerge across participants?
- Are certain codes consistently linked?
- Do some codes contradict each other? Could these form subthemes?
🧾 Tip: Create a table with three columns—Theme, Description, Supporting Codes/Quotes—to keep track of how your themes are developing.
Phase 4: Reviewing Themes
At this point, you’ve grouped your initial codes into tentative themes—but now it’s time to critically evaluate them. Do they hold up under scrutiny? Are they supported by enough data? Do they overlap too much or lack coherence? Phase 4 is all about checking the validity, clarity, and usefulness of the themes you’ve identified.
This review happens at two levels:
- The coded extracts – Do the data within each theme coherently fit together?
- The full dataset – Do the themes accurately reflect the story told by the data as a whole?
What to Do:
- Revisit all your coded extracts within each theme—are they consistent and meaningful?
- Check for internal coherence—do the codes grouped under a theme actually belong together?
- Re-read your full dataset to ensure you haven’t missed significant ideas
- Identify weak, overlapping, or redundant themes that need:
- Merging
- Splitting
- Redefining
- Removing
If a theme feels forced, lacks enough supporting quotes, or doesn’t contribute to your research question, consider whether it should be refined or dropped altogether.
Example:
Let’s say you created a theme called “Digital Frustration.” On review, you realise it includes codes about:
- Technical issues
- Poor internet connection
- Boredom
- <Isolation
While all are negative experiences, they’re not necessarily about the same thing. It might make more sense to split this into two clearer themes: “Technical Barriers to Engagement” and “Emotional Impact of Isolation.”
This phase helps you sharpen the focus of your analysis and ensures that each theme tells a distinct, coherent part of the story.
Tip:
Don’t be afraid to go back and re-code data or revise your framework. Braun and Clarke emphasise that qualitative analysis is recursive—you may move back and forth between phases as your understanding evolves.
📚 Related reading: Want help writing this up later? See How to Format a Thesis (APA, MLA, and More)
Phase 5: Defining and Naming Themes
After reviewing and refining your themes, the next step is to clearly define what each theme is really about and give it a concise, informative name. This phase turns broad patterns into specific, analytical insights—and ensures your reader will understand what each theme represents and how it contributes to your research story.
At this point, you should be thinking not just about what your participants said, but what those statements mean in context. Your goal is to articulate the essence of each theme as clearly and specifically as possible.
What to Do:
- Write a detailed definition of each theme
- What is this theme about?
- What makes it different from other themes?
- How does it relate to your research question?
- Identify subthemes if necessary
- Choose a name that is descriptive, engaging, and analytical (not vague or overly broad)
Examples:
Instead of vague theme titles like:
- Student Experiences
- Challenges
- Motivation
Aim for more analytical and specific alternatives:
- Struggling for Visibility in the Online Classroom
- Feedback as a Catalyst for Belonging
- The Tension Between Autonomy and Disconnection
These labels help your reader immediately understand the focus of each theme and how it fits into your broader argument.
Tip:
Write a brief summary paragraph for each theme, explaining:
- What the theme is
- Why it matters
- Which participant quotes support it
- How it connects to your research question and literature review
This writing will later become part of your analysis chapter, so don’t skip it!
Phase 6: Writing Up
Now that your themes are clearly defined and supported by data, it’s time to tell the story of your analysis. Writing up your thematic analysis isn’t just about describing your themes—it’s about demonstrating how they answer your research question, supported by strong evidence and thoughtful interpretation.
This is the phase where your analysis becomes part of your thesis or dissertation’s formal structure, typically in the results or analysis chapter.
What to Include:
- An overview of your analytic process: Briefly explain how you used Braun & Clarke’s six-phase framework, justifying its suitability for your study
- A description of each theme: Present each theme clearly, using well-chosen quotes from your data to support your points
- Interpretation of findings: Go beyond description—show what the data means in relation to your research question
- Links to your literature review: Compare your findings to existing research—do they support, challenge, or extend previous studies?
You can structure this section theme by theme, presenting and analysing one before moving on to the next. Make sure you maintain a logical flow and connect back to your study’s overall purpose.
Example Paragraph (Excerpt):
One of the key themes that emerged from the data was “Seeking Validation Through Feedback.” Students described how personalised digital feedback gave them a sense of being “seen” and “valued,” even in fully asynchronous courses. As Participant 5 explained, “When I got a comment that actually mentioned my specific example, I knew they read it—it made me want to try harder.” This theme supports earlier findings by Jones (2021), who highlights feedback as a crucial driver of student motivation in online settings.
Writing Tips:
- Use verbatim quotes to let participants’ voices shine—but avoid overloading each section with too many
- Connect each theme back to your research aim and literature review
- Use transitions to guide the reader between themes and ideas
- Maintain a clear, consistent structure (e.g., theme name → definition → supporting quotes → interpretation → connection to literature)
🔍 Need help polishing your writing? Our Thesis Proofreading Services include checks for clarity, structure, and academic tone to ensure your analysis chapter is ready for submission.
Common Mistakes to Avoid in Thematic Analysis
Even with a structured framework like Braun and Clarke’s, thematic analysis can go off track if you’re not careful. Whether you’re a first-time researcher or writing a postgraduate thesis, it’s important to be aware of the most common pitfalls—and how to avoid them.
1. Treating Codes as Themes
One of the biggest mistakes is presenting codes as final themes. Codes are your raw materials—brief labels that highlight interesting data points. Themes, on the other hand, are patterns of shared meaning supported by groups of related codes.
Fix it:
Always review, refine, and define your themes. If a theme consists of only one or two codes with little depth, it probably needs to be merged, reworked, or removed.
2. Creating Too Many (or Too Few) Themes
It’s tempting to identify everything interesting in your data as a theme. But too many themes can overwhelm your reader and dilute your findings. On the flip side, too few themes might suggest that you haven’t captured the richness of your data.
Fix it:
Aim for a manageable number of well-developed, clearly distinct themes—usually between 3 and 6 for a typical thesis chapter.
3. Overlapping or Vague Themes
Themes should be clearly distinct from each other. If two themes sound similar or contain overlapping codes, your reader might struggle to understand the structure of your analysis.
Fix it:
Go back to Phase 4 (Reviewing Themes) and ask: Is each theme telling a unique part of the story? Clarify or combine any themes that feel too similar.
4. Describing, Not Interpreting
Another common issue is stopping at description. Simply telling your reader what participants said isn’t enough—you need to analyse it.
Fix it:
Use each theme to answer your research question. Connect it to existing literature, theory, or broader concepts. Show how your findings matter.
5. Ignoring Negative Cases or Contradictions
It’s easy to focus on the dominant patterns and ignore data that doesn’t “fit.” But contradictions and outliers can reveal valuable insights or signal the need to rethink your themes.
Fix it:
Acknowledge and reflect on divergent voices in your data. Do they challenge your assumptions? Could they form subthemes or a separate narrative?
📘 Braun & Clarke (2006) emphasise that good thematic analysis is both rigorous and reflective. It requires you to move back and forth between phases, checking your assumptions and making sure your final themes are well-supported and analytically rich.
Turning Raw Data into Meaningful Insights
Braun and Clarke’s six-phase framework offers a clear, accessible path through the sometimes overwhelming world of qualitative data. Whether you’re analysing interview transcripts, open-ended survey responses, or focus group discussions, this method gives you the structure you need to move from messy notes to meaningful, defensible findings.
By moving through each phase—from familiarising yourself with the data to writing up well-defined, evidence-backed themes—you create a powerful bridge between what your participants said and what it means in the context of your research question. This process isn’t just about identifying patterns; it’s about building a compelling argument grounded in lived experiences and real-world data.
As you write up your thesis or dissertation, remember: thematic analysis is not a mechanical checklist—it’s an interpretive, iterative process. The strength of your analysis will depend not only on following the steps but on your ability to think critically, reflect deeply, and tell a coherent story with your findings.
💡 Need help refining your analysis chapter or ensuring your themes are clearly communicated? Our Thesis Proofreading Services can help polish your writing, structure, and formatting—so you can submit with confidence.