The 6 Types of Thematic Analysis — Choosing the Right Lens for Your Data

Qualitative research is exhilarating, but it’s also chaotic. Interview transcripts, focus group notes, open-ended surveys - they're all bursting with insight, but none of it lines up neatly at first glance.

Thematic analysis helps us move data from overload to clarity. It reveals patterns of meaning, threads of thought, and shared experiences woven through the messiness of human expression. However, there isn’t just one way to do it. In fact, there are six.

Each type of thematic analysis offers a different lens through which to view your data, and the lens you choose fundamentally shapes the story your research will tell. So, let’s unpack these six methods - what they are, how they work, and when to use them to get the richest, most reliable insights from your research.

1. Inductive Thematic Analysis — When You Want the Data to Surprise You

Inductive thematic analysis takes a bottom-up approach. It’s what many think of as “classic” thematic analysis. No preconceptions, no pre-set codes - just you, your data, and an open mind. Themes are not imposed from the outside but emerge naturally from the material as you work through it.

It’s a bit like panning for gold. You sift patiently, letting the valuable patterns rise to the surface organically. This method shines when you’re exploring new or under-researched topics, because it allows participants’ voices to lead the way, rather than being boxed into existing categories.

Use this when:

• You’re working in uncharted territory.

• You want to discover insights you weren’t expecting.

• You need to capture lived experiences in participants’ own words.

2. Deductive Thematic Analysis — Testing Theories, Not Guessing

Deductive analysis, by contrast, begins with theory. Researchers approach the data with a predefined coding frame, often drawn from established models or prior literature. The goal is to assess how well the data aligns with these frameworks, to test hypotheses, or to extend existing theory. Rather than asking, What themes are here?, you’re asking, Do the data support the patterns we expect?

While this approach risks overlooking emergent insights outside the initial framework, its strength lies in its rigour and focus. Deductive analysis is well suited to studies requiring theoretical validation or seeking to advance scholarly debates.

Use this when:

• You have clear research questions or theoretical commitments.

• You’re building on previous research and need structured comparisons.

• Rigour and efficiency are your priorities.

3. Semantic Thematic Analysis — Staying True to What’s Said

Semantic thematic analysis focuses strictly on the surface-level meaning of the data — that is, what participants explicitly say, rather than what is implied. Coding remains descriptive, not interpretative, with themes developed directly from articulated responses.

This approach is especially useful when transparency and traceability are paramount. By keeping interpretation close to the participants’ words, semantic analysis facilitates clear reporting and practical application of findings, particularly in policy-oriented or applied research. You’re looking for repeated phrases, commonly raised concerns, or specific mentions of events or experiences. While it might seem simple, semantic analysis is powerful when you need findings that are easy to communicate and act upon.

Use this when:

• You’re working with customer feedback or policy-relevant data.

• Transparency and traceability are essential.

• You need fast, clear, and defensible insights.

4. Latent Thematic Analysis — Reading Between the Lines

Latent analysis moves beyond what is directly stated to explore the underlying ideas, assumptions, and ideologies that shape participants' accounts. It treats data as a window into broader social, cultural, or psychological processes.

This interpretive depth makes latent analysis well suited to studies that seek to interrogate power dynamics, societal structures, or unconscious biases. However, it demands careful reflexivity from the researcher, who must balance interpretation with evidential grounding.

Use this when:

• You’re researching complex social or cultural issues.

• You want to unpack hidden dynamics in the data.

• Depth matters more than speed.

5. Codebook Thematic Analysis — Balancing Structure and Flexibility

Codebook thematic analysis provides a middle path between inductive and deductive methods. Researchers begin with a codebook - either predefined or collaboratively developed - that outlines codes and their definitions. However, the process remains iterative, allowing new codes to emerge as data coding progresses.

This approach enhances consistency, particularly in large teams or projects with multiple coders. It supports intercoder reliability while maintaining flexibility for emergent insights.

Use this when:

• You’re working in a research team and need consistency.

• You’re handling a large volume of data.

• You want both structure and room for discovery. Mixed-methods projects combining qualitative depth with systematic coding

6. Reflexive Thematic Analysis — Where the Researcher is Part of the Story

Reflexive analysis positions the researcher not as a detached observer but as an active participant in meaning-making. In this approach, themes are not passively discovered but actively constructed through deep engagement with the data. Researchers are encouraged to reflect on their positionality, assumptions, and interpretative decisions throughout the process.

Reflexive thematic analysis is particularly powerful for studies of complex, emotionally charged, or subjective topics, where the researcher’s insight can enrich understanding. However, it requires a commitment to transparency and reflexive practice to ensure methodological integrity.

Use this when:

• You’re studying identity, trauma, or personal experience.

• You want to acknowledge and explore your own role in the research process.

• Interpretative depth is more valuable than standardisation.

Picking the Right Approach

Your choice of method isn’t trivial. It defines how you engage with your data, shapes the insights you uncover, and ultimately determines the impact of your research. The decision between these methods should be guided by the objectives of your research, the nature of your data, and the depth of analysis you seek.

Ask yourself:

• What’s my goal? Exploration, validation, explanation?

• What’s the nature of my data? Straightforward responses, or complex narratives?

• Who am I working with? Solo research or team coding?

There’s no single “correct” answer, but there is a best fit for every project. And whichever approach you take, remember good thematic analysis doesn’t just organise your data but helps your data tell its most meaningful story.