Stop Freezing Your Themes! Use Bayesian Thinking to Keep Your Research Flexible
Let’s be real - qualitative research is messy.
You start analysing your data, and patterns begin to emerge. You spot themes. You group them. You start seeing connections. But then, new data comes in, and suddenly, what you thought was a solid theme? Not so solid anymore.
This happens because thematic analysis, at its core, is interpretative. It’s shaped by what you expect to find, what data you have, and, whether you like it or not, your own biases. But what if there was a structured way to deal with that uncertainty? A way to refine your analysis without feeling like you're moving the goalposts every five minutes.
Welcome to Bayesian thinking.
What is Bayesian Theory?
Bayesian theory is a way of updating your beliefs as new evidence comes in.
It’s named after Thomas Bayes, an 18th-century mathematician who came up with the idea that we should constantly adjust our understanding based on new information. Think of it like this:
You hear a weird noise in your house. Your first thought? Maybe it's the wind.
Then you remember the windows are closed. Hmm. Maybe it's the cat?
You check the cat…fast asleep. Okay, now you're actually listening more carefully.
You realise it's coming from the pipes. Mystery solved: your house isn’t haunted, just old.
This is Bayesian reasoning in action. You start with an assumption, gather new evidence, and adjust your conclusion instead of sticking to your first guess.
In thematic analysis, this means not locking in themes too soon and instead treating them as probabilities that can change as more data emerges.
Why This Matters for Thematic and Qualitative Analysis
Traditional thematic analysis works like this:
You collect qualitative data (interviews, focus groups, surveys, etc.).
You read through it and start coding for themes.
You finalise your themes and write up your findings.
Sounds logical, right? The problem is, once you name a theme, you stop questioning it. Confirmation bias kicks in, and suddenly, everything you read reinforces the themes you’ve already chosen.
Bayesian thinking flips this on its head. Instead of freezing your themes in place, it forces you to continuously update them based on new evidence.
Let’s say you’re studying mental health in remote work.
Your early data suggests burnout is mainly caused by increased workloads.
Then, more interviews come in, and now you’re seeing burnout linked to social isolation more than workload.
Instead of sticking to your original theme (and forcing new data to fit it), Bayesian reasoning says: adjust your theme! The data has changed, and so should your interpretation.
This approach makes your analysis more accurate, more flexible, and less likely to be skewed by early assumptions.
How Bayesian Thinking Can Improve Your Thematic Analysis
1. It Stops You from Jumping to Conclusions
It’s true that once we form an idea, we love to stick to it. It’s human nature. Bayesian thinking forces you to constantly challenge your own interpretations.
Instead of saying, "This theme is correct because I found it early," you start thinking, "How confident am I in this theme? What would make me change my mind?"
This keeps your analysis open and dynamic, rather than rigid and reached by assumption.
2. It Helps You Deal with Conflicting Data
Ever had a dataset where half the people say one thing, and half say the exact opposite?
Bayesian thinking is perfect for this because it lets you weigh evidence in real time. Instead of forcing everything into one theme, you can:
Track how strong the evidence is for different perspectives.
Adjust themes as more perspectives emerge.
Identify where uncertainty lies, rather than pretending all data fits neatly into a single explanation.
This is especially useful when studying complex human experiences, where no single theme tells the whole story.
3. It Reduces Researcher Bias
We all bring expectations and assumptions to our research. Bayesian thinking helps counteract that by making those assumptions explicit and testing them against the data.
Instead of saying, "I expected this to be a theme, and look - it is!"
You start asking, "Is this really the strongest theme? Or am I just noticing it more because I assumed it would be there?"
This approach makes your research more transparent, reproducible, and grounded in actual data—rather than personal intuition.
How to Apply Bayesian Thinking to Thematic Analysis (Without the Maths)
You don’t need to calculate probabilities to think like a Bayesian. It’s more about adopting an iterative, open-ended mindset.
Here’s how to do it:
Start with a Hypothesis, Not a Conclusion
Before diving into data, jot down what you expect to find.
Treat these expectations as fluid, not fixed—they’re just starting points.
Code Your Data, But Stay Flexible
Avoid locking in themes too early.
Track how strong each theme seems as more data emerges.
Keep a "holding zone" for patterns that might be important later.
Update Your Themes as New Evidence Comes In
Ask yourself: Would I still name this theme the same way if I had started with this new data instead of the old data?
If new themes emerge, adjust your structure without fear of breaking your initial framework.
Acknowledge Uncertainty
Not all themes are equally strong—some have more evidence than others.
Be honest about where ambiguities and contradictions exist in the data.
Leximancer Automatically Adjusts Themes as New Data Emerges
Unlike traditional manual coding, where you create themes and hope they hold up across all your data, Leximancer takes a Bayesian-like approach to thematic discovery. It doesn’t lock themes in place early on - it lets the data decide what matters and updates itself as patterns emerge.
Here’s how:
Concepts and Themes Emerge Directly from the Data – Instead of predefining categories, Leximancer scans text, learns the language patterns, and automatically detects key concepts and themes based on actual relationships in the data.
Themes Are Weighted by Evidence – Just like Bayesian updating, Leximancer assigns relevancy weightings to concepts and themes based on how often and how strongly they appear in the text. This means that themes aren’t just a list of frequent words—they are real, evolving patterns.
Themes Can Be Re-Grouped at Any Level – Even after Leximancer has mapped out your themes, you’re not stuck with them. You can adjust the thematic resolution, zooming in for highly specific themes or zooming out to see broader categories.
This flexibility means your themes are never frozen in place. If new evidence shifts the structure of your findings, you can adapt without having to recode everything from scratch.
No More Forcing Data into Rigid Categories
One of the biggest problems in traditional thematic analysis is that once a theme is created, it becomes difficult to change, even when new data suggests a different story. Researchers often fall into the trap of forcing new insights into pre-existing themes, instead of allowing themes to evolve naturally.
Leximancer removes this problem by making themes dynamic rather than static. If new data introduces a major shift in patterns, you can re-run the analysis and see how the themes restructure themselves, without losing track of the previous insights.
Let the Data Speak - Not Your Biases
With manual coding, it’s easy to see what you expect to see and unconsciously ignore contradictory evidence. With Leximancer, themes emerge from the data itself, rather than being imposed by the researcher.
This makes your analysis:
More objective – Because themes are based on actual word relationships, not just personal intuition.
More flexible – Since you can adjust thematic levels without redoing all your coding.
More scalable – Whether you have 10 interviews or 10,000, Leximancer applies the same adaptive principles.
Thematic Analysis Shouldn’t Be Static
The biggest mistake researchers make? Locking in their themes too soon and ignoring how new data shifts the narrative. Thematic analysis should be fluid, evolving, and responsive to the evidence, not stuck in whatever categories were created early in the process.
Bayesian thinking teaches us that beliefs should update with new information. Leximancer takes that principle and applies it to qualitative research, ensuring that themes emerge, adjust, and adapt based on what the data is actually saying, not what we assume it says.
So, instead of forcing your findings to fit into rigid boxes, ask yourself:
What happens if I adjust the thematic resolution?
How do themes shift when I add new data?
Am I letting the data guide my findings, or am I guiding the data to fit my themes?
Because in the end, research isn’t about proving what you expected - it’s about uncovering what’s really there. And with a tool like Leximancer, you can do just that, without the manual struggle.
