Researcher in the Room: Why Reflexivity Matters in Thematic Analysis

Thematic analysis is more than identifying patterns in qualitative data. It’s about interpretation. And if interpretation is central, then so are you - the researcher doing the interpreting.

That’s why reflexivity is fundamental to producing meaningful, trustworthy findings. When we work with qualitative data, we are not passively uncovering themes but actively constructing them. Our perspectives, experiences, and contexts inevitably shape how we see patterns in the data. Reflexivity helps us make this influence visible, deliberate, and, crucially, valuable.

Let’s explore why reflexivity matters in thematic analysis, and how to integrate it thoughtfully throughout your research process.

What Is Reflexivity?

Reflexivity is about thinking critically and honestly about how your identity, background, assumptions, and relationships shape your research. It’s not trying to scrub away your subjectivity (that’s not possible). It’s about making your role visible, deliberate, and constructive.

You’re not a neutral observer pulling themes from a dataset. You’re actively making decisions about what counts as meaningful, and why. Reflexivity helps you stay aware of that influence, work with it, and show your readers that you’ve thought it through.

In short, reflexivity is how we keep qualitative research intellectually honest.

The Four Types of Reflexivity You Should Be Thinking About

There are different ways to approach reflexivity, but one helpful model breaks it down into four dimensions: personal, interpersonal, methodological, and contextual. If you’re doing thematic analysis, you’ll likely touch on all of these.

1. Personal Reflexivity
This is where most people start: thinking about how your own perspective shapes what you notice in the data. What assumptions are you bringing in? What experiences might influence the themes you see? Reflexive journalling or memo-writing is a simple but powerful way to track these reflections over time.

2. Interpersonal Reflexivity
This one often gets overlooked. How do your relationships (with participants and with your co-researchers) shape the data and the analysis? Are there power dynamics at play? If you’re interviewing people from a different background than your own, how does that shape the conversation? These things affect what gets said, what doesn’t, and how it's interpreted.

3. Methodological Reflexivity
Here, you reflect on the decisions you make throughout the analytical process. Why are you doing semantic coding rather than latent? Why this approach to theme development and not another? Being transparent about your choices (and your rationale) helps you stay aligned with your research aims and makes your work more robust and defensible.

4. Contextual Reflexivity
Don’t forget the bigger picture. Situate your analysis within the wider social, cultural, and historical context. How might broader societal narratives shape both your participants’ accounts and your interpretation of them? Contextual reflexivity means acknowledging that your themes don’t emerge in a vacuum. They’re grounded in time, place, and circumstance - and they need to be analysed that way.

Putting Reflexivity into Practice

Reflexivity sounds good in theory but what does it look like in practice?

Start small and build it into your process:

• Keep a reflexive journal or write memos as you code. What are you noticing? What’s surprising you? What’s making you uncomfortable?

• Have structured conversations with your team. Don’t just talk about the data - talk about how you’re making sense of it, and where you might be seeing things differently.

• Check in with participants (when appropriate). Member reflection - as opposed to old-school “member checking” - can help you understand how your interpretations land with the people they’re about.

The key is to make reflexivity ongoing, not something you do once and forget. It’s a practice, not a paragraph.

Reflexivity is Rigour

There’s a myth that reflexivity is a way of apologising for bias. It’s not. Done well, reflexivity is what gives your thematic analysis its backbone. It’s how you show that your interpretations are thoughtful, grounded, and aware of their limits.

So next time you’re coding data and starting to spot themes, take a step back. Ask yourself: Why am I seeing this? What am I bringing to the table? That’s reflexivity - and it’s one of the most powerful tools you’ve got.