Introduction

Thematic Analysis (TA) has become one of the most widely used methods for analyzing qualitative data in the social sciences. Originating from psychology and championed by Braun and Clarke (2006), TA is praised for its flexibility, accessibility, and systematic analytic approach.[1] This paper aims to clarify what Thematic Analysis is, outline its core six-phase process, discuss issues in its application, and guide authors in reporting TA clearly and rigorously for scholarly publication.

What is Thematic Analysis?

Thematic Analysis (TA) is a qualitative research method used to identify, analyze, and report patterns or themes within a dataset. It provides a structured yet flexible approach to making sense of rich, qualitative data, such as interviews, focus groups, written texts, or digital content. As defined by Braun and Clarke, TA is “a method for identifying, analyzing, and reporting patterns (themes) within data” and can be used to both describe the content of data and interpret its underlying meaning.[2] Themes capture something important about the data in relation to the research question and represent a patterned response or meaning within the dataset. TA can be applied across a range of theoretical frameworks. TA can be conducted within essentialist/realist paradigms (which assume a straightforward relationship between language and meaning), constructionist paradigms (which see language as constructing meaning and experience), and more interpretivist or reflexive paradigms (which view meaning as co-constructed between researcher and participant).[3] This theoretical flexibility enables TA to be applied across diverse fields and research contexts, including psychology, education, health sciences, sociology, and digital media studies. Researchers can tailor the approach to suit their ontological and epistemological positions, whether they aim to describe participant experiences, explore cultural discourses, or generate new theory. In this way, TA serves not only as a method but also as a framework for thinking critically about data and its relationship to broader social, psychological, or cultural phenomena. TA offers a clear, systematic process for analyzing qualitative data without specialized software or prior training in linguistic theory. This accessibility has made it a popular choice for most qualitative researchers. Moreover, the skills learned and used in conducting TA, such as coding, theme development, and narrative synthesis, are transferable to other qualitative methodologies, making TA a valuable entry point for those new to qualitative inquiries.

Six-Phase Process

Though widely used, TA lacked a systematic definition until Braun and Clarke’s seminal paper, which clarified its aims, phases, and methodological boundaries. They have since developed a step-by-step framework for conducting thematic analysis.[4]

1. Familiarization with data – Immersion by re-reading transcripts and noting initial idea.
2. Generating initial codes – Systematically coding meaningful data segments.
3. Searching for themes – Collating codes into coherent candidate themes.
4. Reviewing themes – Checking themes against both coded extracts and the entire dataset.
5. Defining and naming themes – Refining theme definitions and developing a thematic narrative.
6. Producing the report – Final write-up with an analytic narrative supported by data extracts.

Types of Thematic Analysis

Thematic Analysis encompasses multiple approaches that differ in their epistemological assumptions, procedural structures, and analytic depth. Understanding these variations is essential for ensuring coherence between research aims, analytic practices, and theoretical commitments. It is important to be clear about which TA variant you use and why, as this determines the depth of interpretation and theoretical stance. There are several distinct types of TA that serve different purposes and align with different paradigms. Each has specific implications for how themes are generated, how rigor is assessed, and how findings are reported.[5]

Reflexive TA

Reflexive Thematic Analysis emphasizes the active role of the researcher in interpreting data and generating themes, rejecting the idea that themes merely “emerge” from the data on their own. Instead, researchers are seen as meaning-makers who construct themes through deep engagement, critical reflection, and creative interpretation.[6] This approach is grounded in a constructionist or interpretivist paradigm, which recognizes that knowledge is co-constructed between the researcher and the data. Reflexive TA is characterized by an organic and recursive coding process, a focus on depth and richness rather than quantity or frequency, the rejection of coding reliability or inter-rater reliability as necessary indicators of quality, and an emphasis on reflexivity and transparency regarding the researcher’s positionality, assumptions, and theoretical lens. Because themes in reflexive TA are analytic outputs, not just descriptive summaries. It is especially well-suited for research that seeks to challenge dominant discourses, explore power dynamics, or interpret complex lived experiences.[7]

Codebook/Reliability TA

Codebook or coding reliability thematic analysis emphasizes consistency, replicability, and procedural standardization. This approach is often used in team-based research, mixed-methods designs, and studies situated within more positivist paradigms. In coding reliability thematic analysis, researchers develop a predefined codebook (either deductively or through consensus from initial coding) and systematically apply it to the dataset by multiple coders. Inter-coder agreement is often measured to ensure consistency and minimize subjectivity.[8] Key features of codebook/reliability thematic analysis include the use of structured coding frameworks or codebooks, inter-rater reliability or intercoder agreement checks, a more linear, step-by-step analytic process, and an assumption that codes and themes can be applied objectively and independently of researcher subjectivity. This approach is most commonly used in evaluation research, health services research, and projects requiring accountability to stakeholders or funders.

Inductive vs. Deductive Coding

The analytic orientation of a thematic analysis can vary in terms of how codes and themes are generated, either inductively or deductively. Inductive TA is data-driven. This means codes are developed based on patterns and meanings that arise from close reading of the data, without trying to fit them into pre-existing frameworks. This approach aligns well with exploratory research, especially when investigating under-theorized phenomena or marginalized experiences. Deductive, or theory-driven, thematic analysis involves coding data using predetermined theoretical concepts or sensitizing frameworks. This approach can be helpful when testing or extending existing theories or when conducting comparative analysis across different contexts. Most thematic analysis studies incorporate elements of both. Fereday and Muir-Cochrane’s (2006) “hybrid” TA, for example, illustrates how researchers can use deductive coding to structure initial analysis and then allow for inductive emergence of themes not captured by the original framework.[9] It is important that researchers are transparent about their analytic orientation, as it influences the scope of interpretation and the kinds of insights that emerge.

Latent vs. Semantic Themes

Another space in which TA varies is the distinction between semantic and latent themes, which refers to the depth of interpretation. Semantic themes stay close to the surface of the data. They capture what participants explicitly said or what is overtly present in the data. This approach is particularly useful when clarity and accessibility of findings are prioritized, such as in applied research settings or policy work. Latent themes involve deeper interpretive work. They go beyond explicit content to examine underlying assumptions, conceptual meanings, ideologies, and discourses shaping participants’ talk or behavior.

Advantages of Thematic Analysis

Theoretical Flexibility

One of the primary advantages of thematic analysis is its theoretical flexibility. It can be used within a wide range of philosophical and methodological frameworks, including positivist, interpretivist, and critical approaches, and can be adapted to suit diverse research questions.[10] This flexibility allows researchers to focus on patterns of meaning in the data without being constrained by the strict theoretical commitments of other qualitative methods.

Accessible and Systematic

Thematic analysis is also valued for its accessibility and systematicity. Compared to approaches such as discourse analysis or grounded theory, it is often easier for researchers—particularly those new to qualitative methods—to learn and apply.[11]  At the same time, it offers a clear, step-by-step analytic process that supports rigor and transparency, making it suitable for a wide range of research contexts.

Adaptable Sample Sizes

Another advantage of thematic analysis is its adaptability to different sample sizes and data types. It can be effectively applied in small, exploratory studies that aim to generate initial insights, as well as in larger projects involving extensive qualitative datasets.[12] This scalability makes thematic analysis a practical choice for researchers working with varying levels of data complexity and scope.

Challenges of Thematic Analysis

Rigor Concerns

While the flexibility of thematic analysis is often cited as a strength, it can also raise concerns about rigor if analytic procedures are not clearly articulated and justified.[13] Without transparent documentation of how codes and themes were developed, reviewers may perceive the approach as overly subjective or as allowing an “anything goes” analysis. To address this challenge, researchers must explicitly describe their analytic steps and demonstrate how interpretations are grounded in the data.

Surface-Level Analysis

Another challenge of thematic analysis is the risk that findings may remain at a descriptive or surface level. If themes are treated primarily as topic summaries rather than as interpretive patterns of meaning, the analysis may fail to fully engage with the data’s complexity.[14] Developing analytically rich themes requires moving beyond what participants said to explore how meanings are constructed and why they matter within the study’s theoretical and contextual framework.

Reflexivity Required

The quality of thematic analysis is closely tied to the researcher’s engagement in reflexivity. Because the researcher plays an active role in interpreting the data, unexamined assumptions, positionality, or analytic decisions can shape the findings in unintended ways.[15] High-quality thematic analysis, therefore, depends on researchers’ willingness to critically reflect on their perspectives, document analytic choices, and consider how their positioning influences the interpretation of themes.

Writing Up Thematic Analysis for Publication

Presenting a Thematic Analysis (TA) for scholarly publication involves demonstrating methodological rigor, theoretical alignment, analytic depth, and transparency. High-quality qualitative manuscripts are distinguished by the insights they generate and by how clearly they document and justify the analytic process. One of the most common weaknesses in published TA papers is a lack of clarity regarding the theoretical underpinnings and specific form of TA used.[16] Since TA is theoretically flexible, authors must explicitly state the epistemological stance of the study (e.g., realist, critical realist, constructionist, interpretivist), the type of TA used (e.g., reflexive TA, codebook TA, coding reliability TA), and the analytic orientation (e.g., inductive or deductive; semantic or latent). Articulating these choices is critical for ensuring internal coherence and allows readers to evaluate the study’s rigor on its own epistemological terms.[17]

Additionally, a transparent account of how the analysis occurred is important for both methodological literacy and rigor. Authors should map their approach to Braun and Clarke’s six-phase model (familiarization, coding, theme development, review, definition, write-up), but also emphasize the recursive nature of the process by clarifying how data were collected and prepared (transcription method, software used), how initial codes were generated (manual coding, NVivo use, collaborative coding), how codes were grouped into themes (visual mapping, discussions, memoing), and how themes were reviewed and refined (reflexive journaling, checking fit with data). It is helpful to avoid vague statements such as “themes emerged from the data” and instead explain how they were constructed through interpretive, in-depth engagement with the data.

A strong write-up includes a brief but thoughtful reflection on how the researcher’s background, theoretical orientation, values, or assumptions may have influenced the analytic process. This can be woven into the methods section, or there can be a separate reflexivity section that should go beyond perfunctory acknowledgments of bias. Journals increasingly look for reflexivity as a marker of interpretive depth and ethical engagement with data.

Clear, concise, and well-supported themes are the centerpiece of the write-up. Each theme should include a compelling and descriptive name, a concise definition or summary of the theme’s core concept, a narrative explanation of how the theme relates to the research question, and rich, illustrative data quotes that support the interpretation. It is helpful to avoid reporting themes as mere topics (e.g., work-life balance) and instead frame them as interpretive patterns (Balancing Act: Navigating Structural Pressures and Personal Boundaries). Be sure to use at least two to three quotes per theme to show variation and depth, and comment on the quotes to hit home how they are relevant to the study.

Authors should explicitly connect themes to the study’s empirical aims or theoretical framework. This helps readers understand how each theme contributes to answering the research question or building the argument. This can be done by using subheadings, transitions, or summary tables to guide readers through this linkage. For journal publication, many reviewers expect to see evidence of analytic synthesis rather than just categorization.

Conclusion

Thematic Analysis is a foundational method in qualitative research. It is prized for its flexibility, transparency, and accessibility across disciplines. Yet, its very flexibility can create uncertainty, particularly for early-career researchers or those seeking to publish in rigorous scholarly outlets. By embracing TA’s epistemological diversity, refining its analytic process, and committing to transparency in reflexivity and reporting, scholars can produce publications that are both methodologically sound and intellectually impactful. Thematic analysis is a powerful tool for making sense of human experience, cultural narratives, and social complexity. 

Take Away

This article aims to demystify TA by clarifying its conceptual underpinnings, practical steps, and variations in application, ultimately framing it as a robust and methodologically rich approach when implemented thoughtfully.

[1] Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa

[2] Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa

[3] Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa

[4] Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. In H. Cooper (Ed.), APA Handbook of Research Methods in Psychology (vol. 2, pp. 57-71). American Psychological Association.

[5] Terry, G., Hayfield, N., Clarke, V., & Braun, V. (2017). Thematic analysis. In C. Willig, & W. Stainton-Rogers (Eds.), The SAGE Handbook of Qualitative Research in Psychology (2nd ed., pp. 17–37). Sage.

6 Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. In H. Cooper (Ed.), APA Handbook of Research Methods in Psychology (vol. 2, pp. 57-71). American Psychological Association.

[7] Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. In H. Cooper (Ed.), APA Handbook of Research Methods in Psychology (vol. 2, pp. 57-71). American Psychological Association.

[8] Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis and code development. Sage.

[9] Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International journal of qualitative methods, 5(1), 80-92. https://doi.org/10.1177/160940690600500107

[10] Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

[11] Guest, G., MacQueen, K. M., & Namey, E. E. (2012). Applied thematic analysis. SAGE Publications. https://us.sagepub.com/en-us/nam/applied-thematic-analysis/book232857

[12] Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. SAGE Publications. https://us.sagepub.com/en-us/nam/thematic-analysis/book248481

[13] Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

[14] Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. SAGE Publications. https://us.sagepub.com/en-us/nam/thematic-analysis/book248481

[15] Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806

[16] Braun, V., & Clarke, V. (2021). Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other pattern‐based qualitative analytic approaches. Counselling and psychotherapy research, 21(1), 37-47. https://doi.org/10.1002/capr.12360

[17] Terry, G., Hayfield, N., Clarke, V., & Braun, V. (2017). Thematic analysis. In The SAGE handbook of qualitative research in psychology (vol. 2, pp. 17-37). Sage.