Thematic analysis (TA) – briefly – is a method popular for analysing qualitative data in many disciplines and fields. The method has been widely used across the social, behavioural and more applied (clinical, health, education, etc.) sciences, and beyond.
The purpose of TA is to develop patterns of meaning (‘themes’) across a dataset that address a research question. Patterns are generated by the researcher through a rigorous process of data familiarisation, data coding, and theme development and revision. The method can be and is applied in lots of different ways, to lots of different datasets, to address lots of different research questions, and within a range of theoretical frameworks! It’s a very versatile and accessible method, which is part of its appeal.
TA is not a singular method – TA is best thought of as an umbrella term for a set or family of approaches for analysing qualitative data that share a focus on developing themes (patterns of meaning) from qualitative data. They tend to share some degree of theoretical flexibility, but can differ enormously in terms of both underlying philosophy and procedures for theme development.
We broadly think there are three clusters of similar types of approaches to TA, which we have termed coding reliability TA, codebook TA, and reflexive TA. In the FAQs you can find some explanation of the similarities and differences. This lecture by Victoria provides a useful introduction to these different approaches. Our new book Thematic Analysis: A Practical Guide provides a comprehensive discussion of different aspects of TA.
What is reflexive thematic analysis?
We call our approach ‘reflexive TA’ and it differs from most other approaches in terms of both underlying philosophy and procedures for theme development. We initially outlined our approach in a 2006 paper Using thematic analysis in psychology. We have written extensively about our approach since then, and our thinking has developed in various ways. For these reasons, it’s really useful to look up our most recent writing – all listed among our resources. If you prefer a visual intro, you can watch us giving a lecture about our approach, a couple of years ago (given our thinking evolves, time is important to note).
One of the advantages of (our reflexive version of) TA is that it is quite theoretically flexible. This means it can be used within a range of theoretical frameworks, to address quite different types of research question related to:
- People’s experiences, or people’s views and perceptions, such as ‘What are men’s experiences of body hair removal?’ or ‘What do people think of women who play traditionally male sports?’
- Understanding and representation, such as ‘How do lay people understand therapy?’ or ‘How are food and eating represented in popular magazines targeted at teenage girls?’
- The factors or social processes that influence and shape particular phenomena, such as ‘What factors influence people’s decisions to undergo genetic testing?’
- The rules and norms that regulate and govern human behavior or practices, such as ‘What are the cultural values and unwritten norms of team sports that shape how queer athletes experience and respond to incidents of homophobia and heterosexism?’
- People’s practices or behaviours, the things they do in the world, such as ‘How do people newly diagnosed with Multiple Sclerosis request support in an online chat room?’
- The construction of meaning, such as ‘How is race constructed in workplace diversity training?’
Note these different question types would require different versions of TA, informed by different theoretical frameworks.
A range of ways of approaching (reflexive) TA
There are different ways TA can be approached:
- A more inductive way – coding and theme development are directed by the content of the data;
- A more deductive way: coding and theme development are directed by existing concepts or ideas;
- A more semantic way: coding and theme development reflect the explicit content of the data;
- A more latent way: coding and theme development report concepts and assumptions underpinning the overt content of the data;
- A more (critically) realist or essentialist way: analysis focuses on reporting an assumed reality evident in the data;
- A more constructionist way: analysis focuses on exploring the realities produced within the data.
These orientations aren’t fixed; nor are most of them exclusionary oppositions, but continua. Within our reflexive approach to TA, all variations are possible. However, in practice more inductive, semantic and (critical) realist approaches tend to cluster together; ditto more deductive, latent and constructionist ones. The separation between orientations isn’t always rigid. What is vitally important is that the analysis is theoretically coherent and consistent (see quality in TA).