Thematic Analysis

Thematic analysis is one of the most widely used techniques for making sense of qualitative data. At its core, it involves reading through transcripts, notes, or other textual data and assigning codes — tags from a predefined or emergent taxonomy — to segments of text. The real analytical power comes not from the coding itself, but from examining what the codes reveal: which themes appear most frequently, how they cluster, and what patterns emerge across participants or conditions. Mayring's framework (2014) distinguishes three approaches to qualitative content analysis: explication (clarifying ambiguous passages), structuring (organizing text based on predefined criteria), and summarization (reducing material while preserving core meaning). Of these, structuring is the most powerful for UX research because it forces you to define your analytical lens before you start coding.

The distinction between summarization and genuine analysis is critical, especially as AI tools become common in research workflows. Summarization — condensing what participants said into shorter form — is the weakest form of analysis. It tells you what was said but not what it means. Structuring, by contrast, applies a coding framework that reveals patterns invisible in any single transcript. When an LLM is used as a second coder to establish inter-rater reliability, it can strengthen the rigor of the analysis. When it is used only to summarize, it often produces polished-sounding output that substitutes fluency for insight.

A well-executed thematic analysis moves through coding to synthesis: identifying not just recurring themes but the relationships between them, the exceptions that challenge the pattern, and the implications for design decisions. This is the step where data becomes actionable. For a detailed treatment of structuring-based analysis in UX research, see Section 15.2 of UX Research: Building Blocks for Impact in the Age of AI by Marc Busch.