Thematic analysis is one of the most common forms of analysis within qualitative research. It emphasizes identifying, analysing and interpreting patterns of meaning (or "themes") within qualitative data. Thematic analysis is often understood as a method or technique in contrast to most other qualitative analytic approaches – such as grounded theory, discourse analysis, narrative analysis and interpretative phenomenological analysis – which can be described as methodologies or theoretically informed frameworks for research (they specify guiding theory, appropriate research questions and methods of data collection, as well as procedures for conducting analysis). Thematic analysis is best thought of as an umbrella term for a variety of different approaches, rather than a singular method. Different versions of thematic analysis are underpinned by different philosophical and conceptual assumptions and are divergent in terms of procedure. Leading thematic analysis proponents, psychologists Virginia Braun and Victoria Clarke distinguish between three main types of thematic analysis: coding reliability approaches (examples include the approaches developed by Richard Boyatzis and Greg Guest and colleagues), code book approaches (these include approaches like framework analysis, template analysis and matrix analysis) and reflexive approaches. They first described their own widely used approach in 2006 in the journal Qualitative Research in Psychology as reflexive thematic analysis. This paper has over 120,000 Google Scholar citations and according to Google Scholar is the most cited academic paper published in 2006. The popularity of this paper exemplifies the growing interest in thematic analysis as a distinct method (although some have questioned whether it is a distinct method or simply a generic set of analytic procedures).

Thematic analysis is used in qualitative research and focuses on examining themes or patterns of meaning within data. This method can emphasize both organization and rich description of the data set and theoretically informed interpretation of meaning. Thematic analysis goes beyond simply counting phrases or words in a text (as in content analysis) and explores explicit and implicit meanings within the data. Coding is the primary process for developing themes by identifying items of analytic interest in the data and tagging these with a coding label. In some thematic analysis approaches coding follows theme development and is a deductive process of allocating data to pre-identified themes (this approach is common in coding reliability and code book approaches), in other approaches – notably Braun and Clarke's reflexive approach – coding precedes theme development and themes are built from codes. One of the hallmarks of thematic analysis is its flexibility – flexibility with regards to framing theory, research questions and research design. Thematic analysis can be used to explore questions about participants' lived experiences, perspectives, behaviour and practices, the factors and social processes that influence and shape particular phenomena, the explicit and implicit norms and 'rules' governing particular practices, as well as the social construction of meaning and the representation of social objects in particular texts and contexts.

Thematic analysis can be used to analyse most types of qualitative data including qualitative data collected from interviews, focus groups, surveys, solicited diaries, visual methods, observation and field research, action research, memory work, vignettes, story completion and secondary sources. Data-sets can range from short, perfunctory response to an open-ended survey question to hundreds of pages of interview transcripts. Thematic analysis can be used to analyse both small and large data-sets. Thematic analysis is often used in mixed-method designs – the theoretical flexibility of TA makes it a more straightforward choice than approaches with specific embedded theoretical assumptions.

Thematic analysis is sometimes claimed to be compatible with phenomenology in that it can focus on participants' subjective experiences and sense-making; there is a long tradition of using thematic analysis in phenomenological research. A phenomenological approach emphasizes the participants' perceptions, feelings and experiences as the paramount object of study. Rooted in humanistic psychology, phenomenology notes giving voice to the "other" as a key component in qualitative research in general. This approach allows the respondents to discuss the topic in their own words, free of constraints from fixed-response questions found in quantitative studies.

Thematic analysis is sometimes erroneously assumed to be only compatible with phenomenology or experiential approaches to qualitative research. Braun and Clarke argue that their reflexive approach is equally compatible with social constructionist, poststructuralist and critical approaches to qualitative research. They emphasise the theoretical flexibility of thematic analysis and its use within realist, critical realist and relativist ontologies and positivist, contextualist and constructionist epistemologies.

Like most research methods, the process of thematic analysis of data can occur both inductively or deductively. In an inductive approach, the themes identified are strongly linked to the data. This means that the process of coding occurs without trying to fit the data into pre-existing theory or framework. But inductive learning processes in practice are rarely 'purely bottom up'; it is not possible for the researchers and their communities to free themselves completely from ontological (theory of reality), epistemological (theory of knowledge) and paradigmatic (habitual) assumptions – coding will always to some extent reflect the researcher's philosophical standpoint, and individual/communal values with respect to knowledge and learning. Deductive approaches, on the other hand, are more theory-driven. This form of analysis tends to be more interpretative because analysis is explicitly shaped and informed by pre-existing theory and concepts (ideally cited for transparency in the shared learning). Deductive approaches can involve seeking to identify themes identified in other research in the data-set or using existing theory as a lens through which to organise, code and interpret the data. Sometimes deductive approaches are misunderstood as coding driven by a research question or the data collection questions. A thematic analysis can also combine inductive and deductive approaches, for example in foregrounding interplay between a priori ideas from clinician-led qualitative data analysis teams and those emerging from study participants and the field observations.

Coding reliability approaches have the longest history and are often little different from qualitative content analysis. As the name suggests they prioritise the measurement of coding reliability through the use of structured and fixed code books, the use of multiple coders who work independently to apply the code book to the data, the measurement of inter-rater reliability or inter-coder agreement (typically using Cohen's kappa) and the determination of final coding through consensus or agreement between coders. These approaches are a form of qualitative positivism or small q qualitative research, which combine the use of qualitative data with data analysis processes and procedures based on the research values and assumptions of (quantitative) positivism – emphasising the importance of establishing coding reliability and viewing researcher subjectivity or 'bias' as a potential threat to coding reliability that must be contained and 'controlled for' to avoiding confounding the 'results' (with the presence and active influence of the researcher). Boyatzis presents his approach as one that can 'bridge the divide' between quantitative (positivist) and qualitative (interpretivist) paradigms. Some qualitative researchers are critical of the use of structured code books, multiple independent coders and inter-rater reliability measures. Janice Morse argues that such coding is necessarily coarse and superficial to facilitate coding agreement. Braun and Clarke (citing Yardley) argue that all coding agreement demonstrates is that coders have been trained to code in the same way not that coding is 'reliable' or 'accurate' with respect to the underlying phenomena that is coded and described.

Code book approaches like framework analysis, template analysis and matrix analysis centre on the use of structured code books but – unlike coding reliability approaches – emphasise to a greater or lesser extent qualitative research values. Both coding reliability and code book approaches typically involve early theme development – with all or some themes developed prior to coding, often following some data familiarisation (reading and re-reading data to become intimately familiar with its contents). Once themes have been developed the code book is created – this might involve some initial analysis of a portion of or all of the data. The data is then coded. Coding involves allocating data to the pre-determined themes using the code book as a guide. The code book can also be used to map and display the occurrence of codes and themes in each data item. Themes are often of the shared topic type discussed by Braun and Clarke.