Thematic Analysis

The original TA (Braun & Clarke 2006) presented six systematic phases but implied themes could be 'found' or 'discovered' in data through careful coding. Reflexive TA (2019) makes explicit that researchers actively construct themes through interpretive choices: deciding what is relevant, which codes to group, how to name themes. Reflexive TA shifts terminology from 'coding' (implying passive labeling) to 'coding/interpretation' (implying active meaning-making). It demands explicit acknowledgment of researcher positioning, theoretical framework, and how subjectivity shaped analysis. The phases are the same, but the epistemology is more transparent and honest about interpretation.

Inductive TA allows themes to emerge from the data without predetermined categories; you read data openly and codes develop from observed patterns. This is exploratory and theory-generating. Deductive TA applies a predetermined coding framework or theoretical categories to organize data; this is hypothesis-testing or theory-testing. Most TA is inductive or primarily inductive with some deductive elements. Be explicit about which approach you used; deductive analysis is valid but serves different research goals (testing vs. exploring).

There is no target number. Thematic analysis codes vary by data volume, complexity, and question focus: 20–60 initial codes from 15–20 interviews is typical, condensing to 5–15 themes in the final analysis. If you have 50+ themes, you likely need higher-order clustering or clearer definitions. Themes should be distinct but related; if all themes feel similar or unrelated, re-examine your coding or question. Quality matters more than quantity; a well-developed four-theme analysis is superior to a sprawling 20-theme one lacking coherence.

Each theme must be supported by multiple data extracts (typically 4–6+ quotations per theme, more for major themes). Provide quotation evidence in your results. Use a code matrix or frequency table showing which participants contributed to each theme—if a theme rests on data from only one participant, it may be too idiosyncratic. Conduct constant comparison, checking new codes against existing ones. Peer debriefing: discuss emerging themes with a colleague to test whether your interpretations are reasonable from the data. Member checking (where feasible) with participants can verify that themes resonate with their experience. Document your analytical decisions (memos, analytical notes) to show the logic chain from codes to themes.

Yes. TA is scalable: you can apply it to 10 or 100 interviews. With large datasets, systematic coding procedures become even more critical to ensure consistency. Secondary data (e.g., interview transcripts from a prior study, survey open-ends, published autobiographies, social media) are amenable to TA if the data were collected on a topic relevant to your research question. Note that secondary TA may lack context or relational richness compared to primary data collection.