How to Really do Braun and Clarke's 6 step Thematic Analysis (explained in 3 steps)

The core takeaway is that Braun and Clarke’s thematic analysis becomes manageable once it’s treated as a structured pipeline for turning messy qualitative data into credible “themes”—the chapter-like headings that answer research questions. The process starts with coding to break transcripts into smaller, meaningful units, then moves to organizing those codes into focused groups, and finally builds themes by using those organized codes to craft clear, defensible findings.

The method begins with defining what “themes” actually are. Themes are not vague impressions; they function like narrative chapters that summarize the story emerging from the data. Within each theme sits the supporting description—often grounded in specific excerpts—showing how that theme helps answer the study’s research questions. Because humans can’t reliably hold all transcript detail in mind, thematic analysis requires rigor: it must demonstrate that themes come from the data rather than from expectations or assumptions.

Coding is the entry point. Codes are short tags—small concepts or summaries—attached to parts of the text as the researcher reads. For example, a participant describing difficulty at work might receive a code like “struggling at work,” while a description of emotional impact could be tagged as “feeling depressed.” Coding reduces the amount of raw text to manageable units while also building understanding of what the dataset contains. The transcript is processed systematically, often one transcript at a time, until the researcher has a substantial set of codes.

As codes accumulate, the next step is focus coding (a term borrowed from grounded theory practice, though it can be used outside that tradition). The goal is to clean up the initial chaos: instead of a long, random list of tags, codes are reorganized into groups that make sense—such as grouping emotion-related codes under an “emotions” heading or clustering coping-strategy codes together. Naming and grouping aren’t about chasing a single “correct” label; they’re about creating a structured path toward themes that can be justified as a transparent analytic procedure.

The final step is developing themes. This stage is described as a continuation of earlier work: the researcher keeps building understanding and effectively constructs a table of contents for the dataset. Themes emerge by asking what research-question prompts the codes and groups are already answering. For instance, if many codes relate to mental health impacts in a study about nurses during the COVID-19 pandemic, “affected mental health” can become a theme; if additional codes cluster around self-care and coping behaviors, “coping strategies” may become another theme. The emphasis is on using the organized codes as a tool for exploring the data further and for selecting what to communicate.

Finally, the approach is reconciled with Braun and Clarke’s six-step framework. Two of Braun and Clarke’s steps are treated as not central to theme construction (familiarization at the start, and writing the report at the end). That leaves four theme-focused steps, which can be translated into the three-step lens: the “searching for themes” work aligns with focus coding/organizing codes, while naming, defining, and refining themes happen as an iterative process rather than a strict linear sequence. The practical benefit is reduced anxiety: instead of treating stages as separate gates, the work is understood as overlapping steps that can be mapped back to Braun and Clarke when academic referencing is required.