What is Thematic Analysis and How to Do It (with Examples)

Thematic analysis is a flexible qualitative method used to identify and interpret patterns across datasets such as interview transcripts, focus group recordings, and open-ended survey responses.
The most widely cited framework is reflexive thematic analysis (Braun and Clarke), which emphasizes the researcher’s interpretive role over mechanical coding procedures.
There are four main approaches: inductive, deductive, semantic, and latent; the choice depends on the research question and theoretical stance.
The six phases of thematic analysis are: familiarization with data; generating initial codes; searching for themes; reviewing themes; defining and naming themes; and producing the report.
A thematic map is a visual tool used to organize codes into sub-themes and themes; it is revised throughout the analytic process.
Reflexive TA does not require inter-coder reliability statistics; instead, quality is demonstrated through reflexivity, transparency, and a rich, evidenced account.
Thematic analysis differs from content analysis in its interpretive depth; content analysis counts and categorizes, while thematic analysis constructs meaning.
Thematic analysis identifies recurring patterns or themes across data, while narrative analysis examines how individuals construct and tell their stories, focusing on the structure and meaning of the story itself.
Software tools such as NVivo, MAXQDA, and Atlas.ti can support organization and management of large datasets but do not replace the researcher’s analytic judgment.
Common challenges include researcher bias, theme saturation decisions, over-reliance on surface-level codes, and maintaining data continuity.
A well-written thematic analysis report integrates evidence (data extracts) with interpretation; it does not simply list quotes without analysis.

Table of Contents

What Is Thematic Analysis?

Thematic analysis (TA) is a qualitative research method for identifying, analyzing, and interpreting patterns of meaning across a dataset. It is one of the most widely used methods in the social sciences, health research, education, psychology, and nursing.

The method is associated most closely with the work of Virginia Braun and Victoria Clarke, who published an influential framework in 2006 and revised it significantly in 2019 to emphasize what they now call reflexive thematic analysis. In this updated version, the researcher is not a passive coder extracting pre-existing themes but an active interpreter constructing meaning from data.

Thematic analysis is suitable for both large and small datasets. It can be applied to interview transcripts, focus group data, open-ended survey responses, field notes, diary entries, social media posts, and policy documents.

Reflexive Thematic Analysis: The Braun and Clarke Framework

The 2019 revision by Braun and Clarke introduced three distinct TA approaches under a unified framework:

Approach	Description	Key Feature
Reflexive TA	The researcher’s interpretive judgment drives analysis; themes are constructions, not discovered facts.	No codebook; no ICR required
Codebook TA	A shared codebook is developed and applied consistently across the team.	Inter-coder reliability measured
Collaborative TA	Multiple researchers code together in discussion rather than independently.	Consensus through dialogue

Reflexive TA is the most commonly taught and used form. This guide focuses primarily on reflexive TA while also covering codebook TA practices where relevant.

When Should You Use Thematic Analysis?

Thematic analysis is most appropriate when your research aim is to understand experiences, perceptions, meanings, or processes from the perspective of participants. It suits a wide range of research questions across disciplines.

Situation	Why TA Is Appropriate	Example Research Question
Exploratory research	TA allows themes to emerge without imposing prior structure	What challenges do first-generation university students face?
Complex, nuanced data	TA handles ambiguity and contradiction well	How do nurses describe moral distress in ICU settings?
Theory development	Inductive TA generates frameworks grounded in participant experience	What factors shape patient trust in telehealth consultations?
Mixed methods studies	TA provides the qualitative component alongside statistical data	Why do patients with high adherence scores still report dissatisfaction?
Pattern identification across cases	TA identifies shared meanings across multiple participants	What themes emerge in survivor accounts of workplace bullying?
Evolving research questions	TA is flexible enough to shift focus during analysis	How does community identity shape responses to urban development?

Thematic analysis is not recommended when the goal is to count frequencies of words or codes (use content analysis instead), when the dataset is primarily numerical, or when a highly structured pre-existing framework must be strictly followed (consider framework analysis instead).

Approaches to Thematic Analysis

The approach you choose depends on your research question, epistemological position, and how much prior theory exists in your field. Two dimensions determine the approach.

Dimension One: Direction of Analysis

Approach	Direction	When to Use	Example
Inductive	Bottom-up: themes emerge from the data	When little prior theory exists or when you want to stay close to participant experience	Exploring how remote workers describe work-life boundaries
Deductive	Top-down: themes are shaped by existing theory	When testing or extending an established framework	Applying Schein’s culture model to employee interview data

Dimension Two: Level of Analysis

Approach	Focus	Example Theme from Patient Feedback Data
Semantic	What participants explicitly said	Long wait times; unhelpful staff; poor communication
Latent	Underlying meanings and assumptions	Erosion of trust; power imbalance; vulnerability and loss of agency

Most studies combine approaches: for example, beginning with semantic, inductive coding during familiarization and moving toward latent interpretation when defining final themes.

How to Do Thematic Analysis: The Six Phases

Braun and Clarke’s six-phase process is not a linear checklist but a recursive cycle. Researchers move back and forth between phases as their understanding of the data deepens.

Phase 1: Familiarization with Data

Immerse yourself in the dataset before applying any codes. Read and re-read transcripts, listen to recordings where possible, and take note of initial impressions.

Activities in this phase:

Read all transcripts in full at least twice
Write initial memos noting patterns, surprises, and questions
Note emotional tone, silences, contradictions, and recurring language
Avoid the temptation to start coding immediately

Phase 2: Generating Initial Codes

Systematically label segments of data that are meaningful in relation to your research question. Codes are descriptive tags, not yet interpretive.

Coding principles:

Code every piece of data that could be relevant
Keep codes close to the language of the data in inductive work
One segment of data can receive multiple codes
Record the context of each coded segment, not just the extract

Example: Coding interview extracts from a study on remote worker experiences

Data Extract	Initial Code(s)
“I can start work at 6 AM and no one notices, which is great, but my evenings just disappear.”	Flexible start time; blurred work-end boundary; loss of evening time
“My manager messages me on weekends. I feel like I can never really switch off.”	Manager contact outside hours; inability to disconnect; always-on pressure
“I made a makeshift desk out of a kitchen chair. My back is a mess.”	Inadequate workspace; physical health impact; home-office setup
“I miss bumping into colleagues. Teams calls feel formal and scheduled.”	Loss of spontaneous interaction; communication feeling artificial

Phase 3: Constructing Themes

Review all coded data and begin grouping codes that share a common meaning. This is where interpretation begins. Gather codes into potential themes and look for the story each cluster tells.

Continuing the remote work example, codes collapse into candidate themes as follows:

Codes	Candidate Theme
Blurred work-end boundary; loss of evening time; always-on pressure; inability to disconnect	Dissolution of work-life boundaries
Inadequate workspace; physical health impact; home-office setup	The absent infrastructure of office work
Loss of spontaneous interaction; communication feeling artificial; formal scheduled calls	Erosion of informal social connection
Flexible start time; manager contact outside hours	Autonomy and surveillance in tension

Phase 4: Reviewing and Refining Themes

Test each candidate theme against the full dataset. A theme must be coherent internally (codes within it are meaningfully related) and distinct externally (it is clearly different from other themes).

Questions to ask at this stage:

Does this theme tell a clear and specific story?
Is there enough data to support this theme?
Are any themes too broad or too narrow to be useful?
Should any two themes be merged, or should one theme be split?
Does the set of themes together provide a coherent account of the dataset?

What Is a Thematic Map and How Do You Build One?

A thematic map is a visual diagram showing the structure of your analysis: how codes relate to sub-themes, and how sub-themes relate to overarching themes. It is built iteratively and revised at every phase.

A thematic map typically has three levels:

Level 1: Codes (the most granular labels applied to raw data)
Level 2: Sub-themes (clusters of related codes that together describe a specific aspect of the data)
Level 3: Themes (overarching patterns that capture the central meaning of a group of sub-themes)

Example thematic map structure for the remote work study:

Theme	Sub-theme	Example Codes
Dissolution of work-life boundaries	Temporal blurring	Loss of commute as transition ritual; start and end times undefined
Dissolution of work-life boundaries	Constant connectivity	Manager weekend messages; email notifications at dinner; difficulty refusing contact
Erosion of informal connection	Loss of spontaneous interaction	No hallway conversations; missed coffee breaks; reduced mentorship
Erosion of informal connection	Digitally mediated relationships	Formal video calls; emojis replacing tone; missing body language

Phase 5: Defining and Naming Themes

Write a clear definition for each theme that explains its scope and its relationship to the research question. The theme name should be active and analytical, not a generic label.

Poor theme name: “Boundaries” (too vague, not analytical)
Better theme name: “Dissolution of Work-Life Boundaries” (captures the active process and its direction)
Poor theme name: “Communication” (could mean anything)
Better theme name: “Digitally Mediated Relationships and the Loss of Informal Connection” (specific, interpretive)

Phase 6: Producing the Report

The write-up integrates data extracts with analytic commentary. Each theme is presented with supporting quotes, but the analysis does not simply list quotes; it interprets them in relation to the research question.

Reflexivity and Positionality in Thematic Analysis

Reflexivity is a core requirement of high-quality thematic analysis, especially in the reflexive TA tradition. It means actively and continuously examining how your own background, assumptions, values, and emotions affect every stage of the analytic process.

Reflexivity is a demonstration of analytic rigor: by acknowledging how you are shaping the analysis, you allow readers to evaluate your interpretations more fully.

Reflexive memos, analytic journals, and supervision discussions are all practical tools for building reflexivity into your process. Many published papers include a positionality statement describing the researcher’s background and its potential relevance to the analysis.

How Does Thematic Analysis Compare to Related Methods?

Researchers often choose between thematic analysis and several related qualitative methods. The comparison below outlines the key distinctions to help you select the right approach for your study.

Method	Core Focus	Key Difference from TA	Best Suited For
Thematic Analysis	Patterns and themes across a dataset	Baseline for comparison	Broad qualitative questions about experience, perception, meaning
Content Analysis	Frequency and categorization of content	Primarily descriptive and quantitative in orientation; counts occurrences	Media analysis, policy documents, large-scale survey data
Grounded Theory	Theory generation grounded in data	Requires theoretical saturation and produces a formal theory as output	Studies aiming to build new mid-range theories
IPA (Interpretive Phenomenological Analysis)	Lived experience of specific phenomena	Requires small, homogeneous samples; focuses on individual cases before cross-case analysis	Health psychology, illness experience, identity studies
Discourse Analysis	Language use, power, and social construction	Focuses on how language constructs reality, not just what people report experiencing	Studies of ideology, identity, institutional communication
Framework Analysis	Structured thematic approach using a priori framework	Themes are determined before data collection; highly structured	Policy research, applied health research with clear objectives

Advantages and Disadvantages of Thematic Analysis

Advantages	Disadvantages
Flexible: applicable across many epistemological positions and research questions	Flexibility can make it unclear which aspects of data to prioritize, especially for novice researchers
Accessible: does not require extensive prior training in a specific theory or framework	Risk of superficial analysis if the researcher stays at the semantic level and treats data description as analysis
Works well with large datasets across multiple participants	Without a strong theoretical grounding, interpretive power is limited
Useful for identifying both shared patterns and exceptions across cases	Maintaining coherence across a large and diverse dataset can be challenging
Produces findings that are accessible to non-specialist audiences	The role of the researcher in constructing themes can be difficult to explain to positivist reviewers or funding bodies
Inductive approach allows genuine emergence from data without imposing prior structure	Themes may inadvertently reflect the researcher’s own preoccupations if reflexivity is not practiced rigorously

Common Mistakes in Thematic Analysis and How to Avoid Them

Mistake	Why It Is a Problem	How to Avoid It
Treating the phases as a rigid linear checklist	Thematic analysis is recursive; forcing linearity produces shallow analysis	Move back and forth between phases; revisit earlier decisions as understanding deepens
Using themes as topic labels rather than analytic claims	A theme called “Communication” describes a topic, not a pattern of meaning	Write theme names as claims: “Communication as a site of power and exclusion”
Quoting without analyzing	Listing extracts without interpretation is description, not analysis	Follow every extract with a sentence that explains what it shows and why it matters
Forcing every code into a theme	Not all data will form a coherent theme; forcing it produces incoherent analysis	Allow some codes to remain as miscellaneous or to be set aside
Applying ICR to reflexive TA	Inter-coder reliability is philosophically incompatible with reflexive TA	Use reflexivity, transparency, and evidential richness as quality criteria instead
Ignoring negative cases	Disconfirming evidence is as analytically important as confirming evidence	Actively search for data that does not fit the emerging themes and engage with it

Frequently Asked Questions

What is the difference between reflexive thematic analysis and codebook thematic analysis?

Reflexive TA treats themes as constructions shaped by the researcher’s interpretive judgment; it does not require a fixed codebook or inter-coder reliability checks. Codebook TA uses a shared, structured codebook applied consistently by multiple coders; it is appropriate when transparency and replicability across a team are priorities. The choice depends on your epistemology and research context.

How many themes should a thematic analysis have?

There is no fixed rule. Most studies report between three and seven themes, depending on the size and complexity of the dataset and the scope of the research question. Themes should be meaningfully distinct from one another and each should be supported by sufficient data. Too many themes often indicate that sub-themes are being reported as themes, or that coding has not been sufficiently integrated.

What is the difference between a code and a theme?

A code is a label applied to a specific segment of data; it is descriptive and close to the data. A theme is a pattern constructed from multiple codes that captures something significant about the data in relation to the research question. A single theme is typically built from several codes and may include sub-themes. Themes are interpretive, not merely descriptive.

Does thematic analysis require a large sample?

No. Thematic analysis is designed for qualitative data and can be applied to very small samples if the data is rich and detailed. A study with six to twelve in-depth interviews may generate more analytically valuable data than a study with fifty brief responses. The goal is depth of understanding, not statistical representativeness. However, sample size should be justified in relation to the research question and the approach used.

Can thematic analysis be used in mixed methods research?

Yes, and it is commonly used as the qualitative component in mixed methods studies. Thematic analysis may be conducted first (as in an exploratory sequential design, to generate items for a subsequent survey) or second (as in an explanatory sequential design, to explain unexpected quantitative findings). The analytic process is the same; what changes is how the qualitative findings are integrated with the quantitative strand.

What types of data can be analyzed using thematic analysis?

Thematic analysis can be applied to any textual or verbal data: interview transcripts; focus group recordings; open-ended survey responses; diary entries; field notes; social media posts; policy documents; and public speeches. It can also be applied to visual data if the researcher first produces a verbal or descriptive account of the visual content.

This article was originally published on July 26, 2026, and updated on June 25, 2026.