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About thematic analysis

Thematic analysis (TA) is a widely-used qualitative data analysis method. It is one of a cluster of methods that focus on identifying patterned meaning across a dataset. This web page focuses on defining TA, and addressing queries about TA, according to the way we have conceptualised it.

A few different scholars have written about TA, defining it, and describing the way to do it. We initially outlined our approach in a 2006 paper, Using thematic analysis in psychology.

Although the title suggests TA is for, or about, psychology, that’s not the case! The method has been widely used across the social, behavioural and more applied (clinical, health, education, etc.) sciences.

The purpose of TA is to identify patterns of meaning across a dataset that provide an answer to the research question being addressed. Patterns are identified through a rigorous process of data familiarisation, data coding, and theme development and revision.

One of the advantages of (our version of) TA is that it’s theoretically-flexible. This means it can be used within different frameworks, to answer quite different types of research question. It suits questions 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?’ It suits questions related to 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?’ It also suits questions relating to 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.

Ways to approach TA

There are different ways TA can be approached:

  • An inductive way – coding and theme development are directed by the content of the data;
  • A deductive way – coding and theme development are directed by existing concepts or ideas;
  • A semantic way – coding and theme development reflect the explicit content of the data;
  • A latent way – coding and theme development report concepts and assumptions underpinning the data;
  • A realist or essentialist way – focuses on reporting an assumed reality evident in the data;
  • A constructionist way – focuses on looking at how a certain reality is created by the data.

More inductive, semantic and realist approaches tend to cluster together; ditto more deductive, latent and constructionist ones. In reality, the separation isn’t always that rigid. What is vitally important is that the analysis is theoretically coherent and consistent.

Our approach to TA

The approach to TA that we developed involves a six-phase process:

  1. Familiarisation with the data: This phase involves reading and re-reading the data, to become immersed and intimately familiar with its content.
  2. Coding: This phase involves generating succinct labels (codes!) that identify important features of the data that might be relevant to answering the research question. It involves coding the entire dataset, and after that, collating all the codes and all relevant data extracts, together for later stages of analysis.
  3. Searching for themes: This phase involves examining the codes and collated data to identify significant broader patterns of meaning (potential themes). It then involves collating data relevant to each candidate theme, so that you can work with the data and review the viability of each candidate theme.
  4. Reviewing themes: This phase involves checking the candidate themes against the dataset, to determine that they tell a convincing story of the data, and one that answers the research question. In this phase, themes are typically refined, which sometimes involves them being split, combined, or discarded.
  5. Defining and naming themes: This phase involves developing a detailed analysis of each theme, working out the scope and focus of each theme, determining the ‘story’ of each. It also involves deciding on an informative name for each theme.
  6. Writing up: This final phase involves weaving together the analytic narrative and data extracts, and contextualising the analysis in relation to existing literature.

Although these phases are sequential, and each builds on the previous, analysis is typically a recursive process, with movement back and forth between different phases. So it’s not rigid, and with more experience (and smaller datasets), the analytic process can blur some of these phases together.

TA home