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Doing Braun and Clarke’s reflexive thematic analysis and wondering how to actually turn your codes into proper themes? You’re not alone, this is where a lot of qualitative researchers get stuck. In this video, I’m walking you through exactly how to generate and develop themes from your codes using Braun and Clarke’s six-stage process (we’re focusing here on Stage 3). If your dataset is overflowing with Post-it notes, Excel sheets, or NVivo nodes and you’re not sure how to build real, meaningful themes out of them — this one’s for you. We’ll talk about: ✅ What a theme actually is in reflexive thematic analysis ✅ How to group your codes in a way that reveals meaning, not just repetition ✅ How to know when you’ve developed a strong theme (and when you haven’t) ✅ The difference between topics and themes ✅ Why your themes won’t “emerge” — and why you are the one constructing them This is all about building a deeper, more critical analysis that’s grounded in your data and aligned with your research question. 📘 Want more support? Check out my complete Braun and Clarke Reflexive Thematic Analysis Guide — it includes step-by-step instructions, practical worksheets, and real examples to help you through every stage of the process. You can even preview the first section for free here https://www.thedegreedoctor.com/phd-s... 👇 Got a question about developing themes? Drop it in the comments — I’d love to help you figure it out. Chapters 0:00 An introduction to themes using Braun and Clarke's thematic analysis 1:04 My Braun and Clarke Reflexive Thematic Analysis Guide 1:42 What is a theme? 4:24 Themes - your questions and experiences 4:51 Building a theme 6:18 Developing themes 7:56 Common problems with building and developing themes 9:25 How many themes should you have in thematic analysis? 🎬 Other helpful videos How to code in thematic analysis • Coding qualitative data - how to code usin... How to write up your reflexive thematic analysis • Braun and Clarke | Writing up Reflexive Th... What is Interpretive Phenomenological Analysis? • Interpretive Phenomenological Analysis Which quotes should you cut out and which should you leave in? • Qualitative Data Analysis: How to cut quot... ▶️ 👀 Want to see my videos before everyone else? Of course you do. For early access to my videos and whole load of other cool things, become a Degree Doctor channel member! / @degreedoctor