Your themes may still be topics

Most researchers who struggle with thematic analysis are not struggling with coding. They have codes, often hundreds of them. The difficulty comes afterwards, when codes are grouped and the groups are given names.

At that point it is easy to produce a list of topics: headings that describe what participants talked about. Topics are useful for organising data. They are not yet findings.

An analytical theme goes further. It says something about what participants' accounts mean: how something works, under what conditions, and with what consequences.

Interview extract → codes → topic → analytical theme

Compare these two ways of reporting the same data:

“Participants talked about X.”

versus

“Across participants' accounts, X appears to operate in this way, under these conditions, and with these consequences.”

The first describes content. The second makes an interpretive claim that the rest of your findings section can support, qualify or complicate.

In the example below, a participant describes returning to a clinic and being told to wait. Codes such as ‘request deferred’ and ‘continued side effects’ could easily be grouped under a topic called “Barriers to care”. That label is accurate, but it only tells the reader what the data is about. The analytical theme explains what is happening: continuation depended on whether services could support change, not only whether a method was available.

The distinction that matters

Before you write up, test each candidate theme against five questions:

If most answers are no, you probably have a well-organised topic. That is a good starting point, and the next step is interpretation.

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