7 Mistakes Researchers Make in Thematic Analysis
Thematic analysis is a popular approach in qualitative research, and for good reason. It’s flexible, accessible, and works across a wide range of disciplines. But that same flexibility is a trap for many researchers. Without clear choices and a thoughtful process, your analysis can slide into familiar mistakes that make papers feel descriptive, fragmented, or just unconvincing.
And if reviewers sense these gaps, rejection is never far away.
In my experience supporting and reviewing qualitative research, I’ve noticed seven mistakes that appear again and again - and the good news is, each one is fixable. Let’s have a look at what they are, why they’re so common, and how to avoid them.
1. Overcoding Everything in Sight
When researchers start coding, there’s a natural fear of missing something important. But if you’re coding every sentence or fragment as a new idea, you can get hundreds of tiny codes for every sentence, sometimes even every phrase.
While this feels thorough, it actually muddies the water. Too many codes create noise instead of insight, leaving you with a spreadsheet of fragments that refuse to form meaningful patterns - they become harder to see if every small variation is treated as a separate concept.
The cure is restraint. . Look for recurring ideas, patterns, or concepts that are relevant to your research question, rather than capturing every word or sentence. Fewer, more meaningful codes will make theme development much clearer., giving you a clearer foundation for themes that actually say something new.
2. Creating Vague, Generic Theme Names
A theme’s name is the first clue to your reader about what you’ve discovered… but often, theme names are bland and generic. Labels like “Communication”, “Barriers”, or “Challenges” could appear in almost any study.
Why does this happen? Well, when you’ve spent weeks buried in transcripts, it’s easy to summarise a theme by its topic instead of its meaning. But reviewers and readers want to see your interpretation, not just a category.
A theme name should make your reader lean in, not skim past. Compare:
Generic: Barriers
Insightful: Unspoken Rules That Block Collaboration
The second tells a story. It signals that you’ve moved beyond describing your data to analysing it, turning experiences into insight.
3. Forcing Data into Pre-Set Frameworks
It’s natural to begin analysis with certain expectations. Maybe a theory is guiding your study, or maybe you’ve seen similar findings in the literature. But letting those expectations steer your coding can lead you to see only what you anticipated.
When data is squeezed into pre-existing categories, you miss the unexpected. And that’s often where the most original insights live.
Instead, start by letting the data surprise you. Even in theory-driven work, give yourself an initial round of open exploration. Ask: What am I seeing that I didn’t predict? That tension between expectation and discovery often hold your most valuable contributions.
4. Stopping at Codes Instead of Building Themes
Coding is satisfying because it feels productive. But raw codes are not analysis. Without clustering, refining, and stepping back to interpret relationships between codes, your findings remain fragmented.
Think of coding as collecting puzzle pieces. Your themes are the picture on the box, the synthesis that makes sense of the fragments. Take time to arrange, connect, and interpret, rather than handing in a pile of disconnected pieces.
5. Letting Quotes Do All the Work
Many papers lean heavily on participant quotes with minimal explanation. And yeah, quotes are vivid, and including many of them feels like proof of authenticity.
But without your voice to interpret, readers can’t see the patterns you’re claiming to see. Quotes illustrate; they don’t analyse.
This rhythm (Make a point → Support it with a quote → Explain why it matters) keeps your findings coherent and persuasive.
6. Ignoring Reflexivity
Qualitative research is never neutral. Your background, assumptions, and choices shape the analysis. Ignoring that influence makes your work feel naïve to experienced reviewers.
Reflexivity doesn’t need to be long or self-indulgent, it can be as simple as acknowledging the lens you brought to the study and the steps you took to stay aware of it. This transparency builds trust in your analysis and credibility in your findings.
7. Failing to Show the Analytical Journey
Too often, researchers present polished themes without showing how they got there. Maybe it feels like unnecessary detail, but in today’s publishing environment, transparency is essential.
Even a brief description of your coding and theme development process, supported by a visual like a theme map or table, can make your work feel far more rigorous. Reviewers want to see that your findings are grounded in a clear, thoughtful process.
Thematic analysis is powerful because it turns a messy pile of words into structured, meaningful insight. But it only works if you avoid the traps of overcoding, vagueness, and hidden reasoning.
The next time you dive into your data, remember good thematic analysis is about clarity, not quantity. Be selective. Be interpretive. And show your reader how you got from raw data to meaningful story.
If you do, your work won’t just be read, it will be trusted and remembered.
