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

Glossary of Key Terms

 

Term	Definition
Thematic Analysis (TA)	A qualitative research method used to identify, analyze, and interpret recurring patterns or themes within a dataset.
Code	A label assigned to a segment of data that captures its meaning or relevance to the research question.
Theme	A pattern identified across a dataset that captures something significant in relation to the research question; constructed from grouping related codes.
Codebook	A structured reference document listing all codes, their definitions, inclusion/exclusion criteria, and illustrative examples.
Reflexive TA	The version of thematic analysis developed by Braun and Clarke (updated 2019) that centers the researcher’s active interpretive role rather than treating analysis as a mechanical process.
Reflexivity	The practice of continuously examining how a researcher’s own assumptions, background, and values influence data interpretation.
Semantic approach	Analysis of what participants explicitly said; themes are identified at the surface level of the data.
Latent approach	Analysis of underlying meanings, assumptions, and ideas beneath the surface of what participants said.
Inductive coding	A bottom-up approach in which codes and themes emerge from the data rather than from a pre-existing framework.
Deductive coding	A top-down approach in which codes and themes are derived from existing theory or prior knowledge.
Thematic map	A visual diagram showing the relationships between codes, sub-themes, and overarching themes.
Inter-coder reliability (ICR)	A measure of agreement between two or more coders analyzing the same dataset; relevant in codebook TA but not in reflexive TA.
Saturation	The point at which no new codes or themes emerge from additional data.
Positionality	A researcher’s awareness of how their social identity, experience, and perspective shape their interpretation of data.

 

Key Takeaways

• 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.

 

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.

 

Sample write-up paragraph:

The theme of Dissolution of Work-Life Boundaries captured participants’ experiences of increasing difficulty distinguishing between work time and personal time. Where the physical transition to an office once marked a clear shift in role, remote work removed this temporal and spatial anchor. One participant described how the loss of a commute had collapsed what she called a “decompression zone,” leaving her with no psychological preparation for either starting or finishing the workday. This experience was compounded by the expectation of near-constant digital availability, which several participants described as an ambient source of pressure rather than an occasional intrusion.

Full Worked Example: From Raw Data to Written Theme

The following example demonstrates the complete analytic journey using a small dataset from a qualitative study on the experiences of first-year nursing students in clinical placements.

 

The Dataset (Interview Extracts)

Participant	Extract
P1	“I had no idea what I was supposed to be doing. My mentor was busy and I just stood there. I felt completely invisible.”
P2	“The first week I cried every night. I kept thinking, I’m going to hurt someone. That fear stayed with me for weeks.”
P3	“My mentor took the time to explain things to me. When she told me I’d done well, I felt like maybe I could actually do this job.”
P4	“There was so much happening at once. I couldn’t process everything. I froze when a patient asked me something directly.”
P5	“I just watched what the experienced nurses did and tried to copy them. No one really taught me formally. It was sink or swim.”
P6	“By the end of month two I started feeling like I belonged. I knew the ward routine. People remembered my name.”

 

Initial Codes Applied to the Data

Extract Ref.	Codes Applied
P1	Role ambiguity; mentor unavailability; feeling invisible; lack of guidance
P2	Emotional overwhelm; fear of harming patients; persistent anxiety; isolation
P3	Mentor support; positive feedback; confidence building; sense of competence
P4	Cognitive overload; freezing under pressure; difficulty processing complexity
P5	Observational learning; absence of formal teaching; self-directed coping; sink-or-swim environment
P6	Growing sense of belonging; familiarity with routine; social recognition; identity formation

 

Candidate Themes and Their Codes

Candidate Theme	Codes Grouped Under It	Participants
Navigating uncertainty alone	Role ambiguity; mentor unavailability; sink-or-swim environment; lack of guidance; observational learning	P1, P5
The emotional weight of early practice	Fear of harming patients; emotional overwhelm; persistent anxiety; freezing under pressure; cognitive overload	P2, P4
Mentorship as a pivot point	Mentor support; positive feedback; confidence building; sense of competence	P3
Becoming someone who belongs	Growing sense of belonging; familiarity with routine; social recognition; identity formation	P6

 

Sample Write-Up Paragraph for One Theme

The Emotional Weight of Early Practice: Several participants described an intense and sustained emotional experience during their first weeks of clinical placement. Fear of causing harm to patients was a particularly prominent concern: one participant described how this anxiety “stayed with me for weeks,” suggesting it was not a fleeting response to novelty but a persistent psychological burden. This fear intersected with cognitive challenges: another participant described freezing when directly addressed by a patient, suggesting that emotional pressure compounded the already difficult task of managing clinical information in real time. Rather than interpreting these experiences as individual failings, the data suggests a structural gap between the clinical skills taught in simulated settings and the emotional and cognitive demands of real patient care.

 

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 not a confession of bias to be apologized for. It is a demonstration of analytic rigor: by acknowledging how you are shaping the analysis, you allow readers to evaluate your interpretations more fully.

 

What Does Reflexivity Look Like in Practice?

Analytic Phase	Reflexive Questions to Ask Yourself
Topic selection	Why am I drawn to this research question? What do I already believe about this topic?
Data collection	How might participants be responding to me as a particular kind of person (age, gender, role)?
Familiarization	What am I noticing most? What might I be overlooking because it feels unremarkable to me?
Coding	Am I coding to confirm what I expected, or am I staying genuinely open to the data?
Theme construction	Does this theme reflect participant experience or my own theoretical preoccupations?
Write-up	How am I selecting which quotes to include? Am I representing the range of experiences?

 

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

 

Thematic Analysis vs. Content Analysis: A Closer Look

This distinction is frequently searched and frequently misunderstood. Both methods analyze textual data, but their aims and epistemologies differ significantly.

 

Dimension	Thematic Analysis	Content Analysis
Primary aim	Construct meaning and interpret patterns	Describe and quantify content
Output	Themes with interpretive accounts	Categories with frequency counts
Epistemology	Constructivist or critical realist	Often positivist
Researcher role	Active interpreter; reflexivity required	Coder applying objective categories
Sample size	Small to medium datasets typical	Can handle very large datasets
Reliability measure	Reflexivity and transparency	Inter-coder reliability (Cohen’s kappa)

 

Thematic Analysis Across Disciplines: Examples

Thematic analysis is used across a wide range of fields. The following examples show how the method adapts to different research contexts and questions.

 

Discipline	Research Question	Data Source	Example Theme Identified
Psychology	How do adults with anxiety describe their experience of digital-free weekends?	Semi-structured interviews (n=18)	Reclaiming a quieter self: the paradox of relief and restlessness
Nursing	What do nurses in emergency departments say about moral distress?	Focus group discussions (n=4 groups)	The erosion of moral agency through systemic constraints
Education	How do students from low-income families describe the hidden costs of higher education?	Open-ended survey responses (n=120)	Invisible disadvantage: managing financial shame in public academic spaces
Sociology	How do long-term unemployed individuals construct narratives of identity?	Life history interviews (n=22)	The suspended self: identity without the anchor of work
Public Health	How do community members in rural areas describe barriers to mental health support?	Community consultation transcripts	Distance as more than miles: geographic, cultural, and psychological distance from services

 

 

Software Tools for Thematic Analysis

No software performs thematic analysis automatically. Tools assist with organizing, storing, searching, and visualizing data; the interpretive work remains with the researcher. The choice of tool depends on budget, team size, dataset size, and familiarity.

 

Tool	Best For	Key Features	Limitations
NVivo	Large datasets; academic and postgraduate research	Node-based coding; query functions; visualization; mixed methods support	Expensive; steep learning curve; may encourage over-coding
MAXQDA	Teams; mixed methods; visual analysis	Code frequency maps; segment retrieval; good import formats	Cost; interface less intuitive for beginners
Atlas.ti	Complex projects; network visualization	Code networks; geo-mapping; team collaboration tools	Expensive; network view can become overwhelming
Dedoose	Collaborative teams; budget-conscious researchers	Cloud-based; good for mixed methods; pay-per-use pricing	Requires internet access; limited visualization options
Microsoft Word or Excel	Small datasets; student projects; solo researchers	Free; familiar; easy to search and color-code	No formal code management; easy to lose track of codes
Manual (paper, sticky notes, whiteboard)	Very small datasets; early-stage projects; building analytic skill	Forces close reading; slows you down in a productive way	Impractical at scale; hard to audit

 

Recommendation:

For dissertation or thesis projects, Microsoft Word or Excel is entirely sufficient. For large funded studies with team coding, NVivo or MAXQDA are industry standards. For budget-conscious collaborative work, Dedoose offers a practical middle ground.

 

How Do You Ensure Quality in Thematic Analysis?

Quality in thematic analysis is assessed differently depending on the approach. Reflexive TA uses different quality criteria than codebook TA, and applying the wrong criteria to the wrong approach is a common methodological error.

 

Quality Criteria for Reflexive TA

Criterion	What It Means	How to Demonstrate It
Transparency	The analytic process is clearly described and traceable	Include a method section that details each phase; show extracts alongside themes
Reflexivity	The researcher’s role in shaping analysis is acknowledged and examined	Positionality statement; reflexive memos referenced in the write-up
Coherence	Themes are internally consistent and meaningfully distinct from one another	Theme definitions; thematic map; reviewer check
Evidential richness	Themes are grounded in and illustrated by the data	Multiple extracts per theme from different participants
Interpretive depth	Themes go beyond description to offer insight	Analytic commentary interprets extracts rather than restating them

 

Quality Criteria for Codebook TA

When using codebook TA, inter-coder reliability (ICR) is appropriate and expected. The most common statistics are:

• Cohen’s kappa: measures agreement between two coders on categorical decisions; a kappa above 0.70 is generally acceptable.
• Percentage agreement: simpler but less conservative than kappa; does not account for chance agreement.
• Krippendorff’s alpha: used for more than two coders or ordinal coding decisions.

 

Important note: applying ICR statistics to reflexive TA is a category error. Reflexive TA expects and welcomes interpretive variation between analysts; consensus is not the goal.

 

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

 

Using Thematic Analysis in a Dissertation or Thesis

Thematic analysis is one of the most commonly chosen methods for undergraduate and postgraduate dissertations in the social sciences, health, and education. The following checklist outlines the method section components typically required.

 

1. State which approach to thematic analysis you are using (reflexive TA, codebook TA) and cite the methodological source (Braun and Clarke, year).
2. Describe your epistemological position (constructivist, critical realist, etc.) and explain how it is consistent with your approach.
3. Explain your data collection method and how it generated data suitable for thematic analysis.
4. Describe the six phases as they were carried out in your specific study; do not just reproduce a generic description of the method.
5. Include a positionality statement explaining your relationship to the topic.
6. Describe how you managed the data: software or manual; how transcripts were produced and checked.
7. Explain what you did to ensure quality: reflexive memos; member checking (if used); audit trail.
8. If using codebook TA, report your ICR statistics and the process for resolving disagreements.

 

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.

 

How do you handle researcher bias in thematic analysis?

In reflexive TA, the researcher’s perspective is not treated as bias to be eliminated but as an inevitable and productive part of analysis, provided it is acknowledged and examined. Practical strategies include keeping a reflexive journal throughout the project; writing positionality statements; seeking supervision or peer debriefing; actively searching for disconfirming evidence; and being transparent in the write-up about how your position shaped interpretation.

 

Is thematic analysis suitable for a dissertation topic that is sensitive or emotionally difficult?

Yes, thematic analysis is widely used in studies on sensitive topics such as trauma, bereavement, illness, discrimination, and abuse. Researchers working on such topics should pay particular attention to reflexivity (their own emotional responses to the data), participant welfare during data collection, and the ethical implications of their interpretive framing. Supervision support is especially important when working with data that may be distressing.

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