What are the different approaches to thematic analysis?
Thematic analysis has become one of the most widely used methods in qualitative research. But ask ten scholars how they use it, and you’ll likely get ten different answers. It’s praised for its flexibility, but that flexibility also brings ambiguity. Without a clear framework, researchers risk producing vague themes or inconsistent interpretations.
So, what are the main approaches to thematic analysis? And how do you choose the one that suits your study best?
Let’s explore six common approaches, each with its own assumptions and strengths.
1. Reflexive Thematic Analysis (Braun & Clarke)
This is perhaps the most cited version. Developed by Virginia Braun and Victoria Clarke, reflexive thematic analysis emphasises the researcher’s active role in interpreting the data. It rejects the idea of themes simply “emerging” and focuses instead on the choices made throughout the coding and theme development process.
Rather than aiming for consistency across coders, this approach encourages deep engagement with the data, reflexivity, and iterative meaning-making. It's well-suited to studies where nuance, context, and interpretation are central.
Key traits: Flexible, iterative, interpretive
Best for: Exploratory research, social constructionist paradigms
2. Coding Reliability Thematic Analysis
This approach borrows elements from quantitative research, especially the idea of inter-coder reliability. Here, multiple coders independently apply a structured codebook, and the goal is to achieve consistency and minimise subjective bias.
While sometimes critiqued for being overly rigid, this method is common in health, education, and evaluation research, where transparency and replicability are prioritised.
Key traits: Structured, repeatable, reliability-focused
Best for: Team-based research, mixed methods, large datasets
3. Codebook Thematic Analysis
This sits somewhere in between the reflexive and coding reliability approaches. Researchers develop a codebook (sometimes collaboratively) and use it to guide consistent coding, but they also allow for refinement as the process evolves.
This approach works particularly well when multiple researchers are involved, and there’s a need for a shared analytical language, but with enough flexibility to allow new insights to emerge.
Key traits: Flexible with some structure
Best for: Projects requiring both consistency and interpretive depth
4. Template Analysis
Template analysis involves developing a coding “template” based on some preliminary data and applying it to the full dataset. It’s often used when the researcher has some idea of what to look for (such as based on prior theory) but still wants to leave room for emergent insights.
It offers a good balance between structure and openness, making it ideal for applied fields such as management, healthcare, or policy research, where existing theory is relevant but must be tested against new data.
Key traits: Theory-informed, structured, adaptable
Best for: Applied research, organisational studies, evaluation work
5. Thematic Analysis within Grounded Theory
Grounded theory isn’t thematic analysis per se, but researchers often adopt its methods to construct themes. It often includes a form of thematic analysis during the early stages of open and axial coding. The aim is to move from codes to categories and eventually to a theoretical model grounded in the data. These themes tend to be more conceptual and theory-generating, built from iterative cycles of coding, memo-writing, and constant comparison.
Key traits: Conceptual, theory-driven, iterative
Best for: Studies aiming to generate new theoretical frameworks
6. Thematic Analysis within Phenomenology
When used alongside phenomenology, thematic analysis takes on a more experiential lens. It aims to capture the essence of lived experience, often through rich, thick descriptions of how people make meaning of their world. Whether using descriptive approaches (like those of van Manen) or interpretative ones (such as IPA), the goal is to uncover how participants make meaning of their experiences.
This method values depth over breadth, and themes are closely tied to individual narratives and emotional texture.
Key traits: Deeply interpretive, experience-focused
Best for: Health, education, and psychological research involving personal narratives
There’s no single “right” way to do thematic analysis, but there is a right way to do it for your question, your data, and your paradigm. Being clear about which approach you're using and why, strengthens your methodology and improves your credibility with reviewers.
