Worked examples
Thematic Analysis Examples
The fastest way to understand thematic analysis is to see how raw qualitative data turns into codes, and how codes turn into themes. This page gives worked examples — a coding example from interview data, a code-to-theme example, and example theme definitions you can adapt for your own write-up.
The examples use a simple illustrative study: interviews with postgraduate students about their experience of remote study. Your data and themes will differ, but the mechanics are the same.
Example 1: coding an interview extract
Take a short extract: "Honestly the hardest part was that nobody noticed if I fell behind. At least on campus a tutor would catch your eye. Online you just… disappear."
Coding this inclusively might produce codes such as: lack of visibility to staff, absence of informal check-ins, self-described disengagement, and contrast between online and on-campus support. Each code is a short label anchored to the extract — so the eventual theme can always be traced back to this evidence.
Example 2: from codes to a theme
Across many transcripts, codes like lack of visibility to staff, absence of informal check-ins, and no one noticing struggle recur. Clustered together, they point to a candidate theme: "Invisibility in remote study" — the sense that difficulties go unnoticed without the incidental contact of a physical campus.
Note what makes this a theme rather than a topic: it captures a patterned meaning (being unseen) that speaks to the research question (experience of remote study), not just a subject that came up.
Example 3: a theme definition you can adapt
A good theme definition states what the theme is, what it covers, and its boundary. For example:
"Invisibility in remote study — participants' sense that their academic struggles went unnoticed in the absence of incidental, in-person contact with staff and peers. This theme covers references to disengagement going undetected and the loss of informal check-ins. It does not cover dissatisfaction with formal support structures, which is captured under a separate theme."
Writing definitions this precise is what separates a developed analysis from a list of topics — and it is exactly what examiners look for.
How QualIntel OS supports this in practice
QualIntel OS surfaces candidate extracts for each code in your codebook, so you can see — and confirm or reject — the evidence behind every theme. Because each accepted extract is recorded against its code, your themes stay traceable to the data, and the audit trail documents how codes became themes. It is the worked example above, done across a whole dataset, with the rigour documentation generated as you go.
Frequently asked questions
What is an example of a theme in thematic analysis?
A theme is a patterned meaning across the data, not just a topic. For example, in interviews about remote study, "Invisibility in remote study" — participants' sense that their struggles went unnoticed without in-person contact — is a theme. It is built from recurring codes (e.g. lack of visibility to staff, no informal check-ins) and speaks directly to the research question.
What is the difference between a code and a theme?
A code is a short label tagging a specific feature of the data (e.g. "no one noticed I fell behind"). A theme is a broader pattern of meaning built by clustering related codes (e.g. "Invisibility in remote study"). Codes are granular and close to the data; themes are interpretive and answer your research question.
How do you write up thematic analysis findings?
Present each theme with a clear definition, then support it with representative extracts drawn from multiple participants, woven into an analytic narrative that explains what the theme means in relation to your research question and the literature. Avoid simply listing quotes — interpret them. Each extract should earn its place by illustrating the theme.
Can you show a worked example of thematic analysis?
Yes — start with an interview extract, code it inclusively (several short labels anchored to the text), cluster recurring codes across transcripts into candidate themes, then define and name each theme precisely. This page works through coding an extract, building a theme from codes, and writing a theme definition you can adapt.
Related guides
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