Thematic analysis part 1: introduction to the topic and an explanation of ‘themes’
Posted on 21st February 2020 by Dolly Sud
This is the first of a three-part blog which will provide an introduction to Thematic analysis and discussion of what a theme is (part 1), a description of the three schools of TA and some study design recommendations (part 2), and an outline of the six phases of reflexive TA (part 3). A list of key reference sources will also be provided.
There is an array of methods available to researchers that can be used to identify patterned meaning across a dataset. Thematic analysis (TA) is one of these and is a widely embraced method for analysing qualitative data to inform many different research questions across a wide range of disciplines. It can be used for a variety of types of datasets and applied in a variety of different ways, thus, demonstrating its flexibility. Importantly, it is a very accessible method for novice researchers.
TA is an umbrella term that describes approaches which are aimed at identifying patterns (“themes”) across qualitative datasets [1,2]. It should not be considered to be a single qualitative analytic approach  and neither should it be considered a methodology – it is a method.
Victoria Clarke and Virginia Braun are authors of the most widely cited resources on TA – the content of this blog is based on information available on their website and published papers [1,2,3].
- thematic analysis is a method not a methodology
- thematic analysis should not be considered to be a single qualitative analytic approach
What is a theme?
There are two conceptualizations of themes which are articulated in the literature :
1. Shared meaning based patterns
Shared meaning based patterns are organised around a central organising concept (core concept). In one of the online lectures  available for TA this is likened to a dandelion spherical seed head containing many single-seeded fruits. The seed head being the central organising concept, and the fruits being the themes.
Themes are built from smaller units known as codes.
Shared meaning based patterns :
- capture the essence and spread of meaning;
- unite data that might otherwise appear disparate, or meaning that occurs in multiple and varied contexts;
- they (often) explain large portions of a dataset;
- are often abstract entities or ideas, capturing implicit ideas “beneath the surface” of the data, but can also capture more explicit and concrete meaning.
Braun & Clarke view themes as being shared meaning based patterns.
A good way of understanding the idea of themes is to look at published  examples of good TA (a full reference list is available on the website ).
Examples of themes as shared meaning based patterns taken from a paper which sought to explore anorexia nervosa clients’ perceptions of their therapists’ body :
- “Wearing eating disorder glasses,”
- “You’re making all sorts of assumptions as a client,”
- “Appearance matters.”
2. Domain summary 
This conceptualisation is in contrast to that of a theme as shared meaning based patterns. It summarizes what participants said in relation to a topic or issue, typically at the semantic or surface level of meaning, and usually reports multiple or even contradictory meaning content. The issues are often based around data collection tools, such as responses to a particular interview question.
Example of themes as domain summary from a paper on Muslim views on mental health and psychotherapy , the seven themes were outlined as follows:
- “problem management,”
- “relevance of services,”
- “service delivery,”
- “therapy content,”
- “therapist characteristics”
You can see that domain summaries don’t appear to consider shared meaning or differences.
A useful pointer here is to consider domain summaries as collecting data under headings which are often composed of single words. Whereas shared meaning based patterns seek to unite data.
- domain summaries and shared meaning-based patterns, although both articulated as being themes in published literature, are not the same thing.