How to do a thematic analysis is a question many researchers and students ask when faced with piles of qualitative data. Whether you’re working with interview transcripts, survey responses, or open-ended feedback, identifying patterns and themes can feel overwhelming. Traditionally, thematic analysis requires careful reading, coding, and theme development, which can be time-consuming and sometimes daunting for first-timers. Luckily, with tools like ChatGPT, you can streamline parts of the process, generate initial codes, and even explore emerging themes more efficiently while maintaining a human touch in interpretation.
Using ChatGPT for thematic analysis doesn’t replace your expertise—it complements it. The AI can help break down large datasets, suggest meaningful codes, and even group related ideas into potential themes, making the analysis faster and more organised. In this guide, we’ll walk you through step-by-step how to do a thematic analysis using ChatGPT, from preparing your data and generating codes to reviewing and refining themes, so you can confidently turn qualitative data into insightful findings.
What is thematic analysis?
Thematic analysis is a method used in qualitative research to identify, analyse, and report patterns or themes within data. It helps you make sense of large amounts of textual information, like interviews, survey responses, or focus group discussions. In simpler terms: It’s a way of finding the main ideas or stories that keep appearing in your data and organising them into meaningful categories (themes).
Key Points
- Focus on Patterns: You are looking for repeated ideas, feelings, or experiences in your data.
- Themes vs Codes:
- Codes are small labels that describe a piece of data (e.g., “work stress”).
- Themes are broader patterns that group related codes together (e.g., “Workplace Challenges”).
- Flexible Method: You can use it for almost any type of qualitative data.
- Analytical, Not Just Descriptive: The goal is not just to summarise the data, but to interpret what it means in the context of your research question.
Step-by-Step Guide on How to Do a Thematic Analysis Using ChatGPT
Step 1: Prepare Your Data
- Gather your qualitative data, e.g., interview transcripts, survey responses, or open-ended questionnaire answers.
- Break it into small chunks (one paragraph, one response, or one meaningful unit at a time). This makes it easier to code accurately.
Step 2: Generate Initial Codes
- Take a piece of text.
Example: “I feel stressed at work because my manager does not communicate clearly.” - Ask ChatGPT: “Can you give 3–5 short codes that summarise the key ideas in this text?”
- ChatGPT will suggest codes like:
- Work stress
- Poor communication from management
- Emotional impact
Tip: Keep codes short, clear, and descriptive.
Step 3: Repeat for All Data
- Go through each piece of data and generate codes for each.
- You can keep a running list of all codes in a table or spreadsheet, e.g.:
| Data Excerpt | Codes |
|---|---|
| “I feel stressed at work because my manager does not communicate clearly.” | Work stress; Poor communication; Emotional impact |
Step 4: Group Codes into Potential Themes
- Once you have all codes, look for patterns. Codes that are similar or related can form a theme.
- Ask ChatGPT: “Here is a list of codes. Can you group them into broader themes?”
- Example:
Codes: Work stress, Emotional impact, Poor communication
Theme: Workplace Stress and Communication
Tip: Themes capture broader ideas, not just single codes.
Step 5: Review and Refine Themes
- Check if each theme makes sense and is supported by your data.
- Ask yourself:
- Does this theme really reflect what participants are saying?
- Are some codes missing or overlapping?
- Adjust theme names or groupings as needed.
Step 6: Define and Name Themes
- Give each theme a clear name and description, e.g.:
| Theme | Description |
|---|---|
| Workplace Stress and Communication | Includes experiences of stress at work and issues arising from unclear communication from management |
Step 7: Write Up the Analysis
- Present each theme, including:
- Definition of the theme
- Representative quotes from your data
- Explanation of significance in relation to your research question
Example:
Theme: Workplace Stress and Communication
Participants reported high stress levels at work due to poor communication from managers, which affected their emotional well-being.
Quote: “I feel stressed at work because my manager does not communicate clearly.”
Step 8: Validate
- Review your themes and codes with a peer or supervisor if possible.
- Make sure themes accurately represent the data and are not just what you think should be there.
Types of Thematic Analysis
Inductive Thematic Analysis
- Definition: Themes are generated directly from the data, without trying to fit them into pre-existing ideas or theories.
- When to use: When you want to explore participants’ experiences openly, letting the data “speak for itself.”
- Example: Analysing interviews about workplace stress without assuming what causes stress; patterns emerge naturally from participants’ responses.
Deductive Thematic Analysis
- Definition: Themes are based on pre-existing theories or concepts. The data is coded to see how it fits these frameworks.
- When to use: When you have specific research questions or hypotheses you want to test.
- Example: Studying workplace stress using a pre-defined model of occupational health to see which factors appear in your data.
Semantic vs Latent Thematic Analysis
- Semantic:
- Focuses on explicit, surface-level meanings in the data.
- Codes and themes describe what is directly said.
- Example: Coding “I feel anxious at work” as “anxiety at work.”
- Latent:
- Focuses on underlying ideas, assumptions, or patterns behind the data.
- Goes beyond what is explicitly stated to interpret hidden meanings.
- Example: Coding the same quote as reflecting “fear of management control” or “power imbalance.”
Types of Research That Use Thematic Analysis
- Interviews
- One-on-one or group interviews where participants share their experiences or opinions.
- Example: Exploring patients’ experiences of living with chronic pain.
- Focus Groups
- Group discussions on a specific topic.
- Example: Understanding student perspectives on online learning.
- Open-Ended Survey Responses
- Responses to questions like “What do you think about…?”
- Example: Employee feedback about workplace culture.
- Documents and Textual Materials
- Reports, blogs, diaries, social media posts, or policy documents.
- Example: Analysing social media posts to understand public opinions on climate change.
- Observational Notes
- Notes collected during participant observation.
- Example: Understanding classroom interactions in schools.
When to Use Thematic Analysis
- You want to identify patterns or themes in qualitative data.
- You are exploring subjective experiences or perceptions.
- Your research question is open-ended and seeks depth rather than numerical measurement.
Preparing for Your Thematic Analysis
Gather and Organise Your Data
- What to do: Collect all your qualitative sources—interview transcripts, focus group notes, survey responses, or observation notes.
- Tip: Include diverse data sources if possible. Research shows that mixed sources can boost reliability by around 40%, making your findings more robust.
- Organise your data:
- Sort files by topic, participant, or date for easy access.
- Keep a clear naming system so you can quickly find each piece when coding.
- Consider using a spreadsheet or software to track all excerpts and sources.
Goal: Make sure your dataset is complete, accessible, and manageable before you start coding.
Set Clear Research Questions
- Why it matters: Your research questions guide the focus of your thematic analysis, helping you identify patterns relevant to your study.
- What to do:
- Define your objectives upfront.
- Ask questions like:
- What experiences, opinions, or behaviours am I exploring?
- What patterns or themes do I expect to find?
- Expert advice: Braun and Clarke, key developers of thematic analysis, say: “Good questions guide without limiting discovery.”
This means your questions should focus your analysis but still allow unexpected insights to emerge.
Tools that Can be used To Do Thematic Analysis
Manual / Basic Tools
- Highlighters and pens
- Print transcripts and highlight meaningful segments.
- Write short codes in the margins.
- Spreadsheets (Excel, Google Sheets)
- Create columns for data excerpts, codes, and themes.
- Useful for organising large amounts of text manually.
- Word processors (Word, Google Docs)
- Use comments or coloured text to label codes.
Pros: Simple, low cost, good for small datasets
Cons: Time-consuming for large datasets
Qualitative Data Analysis (QDA) Software
These programs make coding, organising, and analysing data much easier:
- NVivo
- Popular software for coding, generating themes, and visualising patterns.
- Can handle text, audio, video, and even social media data.
- ATLAS.ti
- Another widely used tool for coding and thematic mapping.
- Supports networks of codes and relationships between themes.
- MAXQDA
- User-friendly interface, suitable for beginners.
- Allows coding, visualisation, and mixed-methods analysis.
- Quirkos
- Designed for small-to-medium projects.
- Visual, intuitive, and good for beginners.
- Dedoose
- Cloud-based, good for collaborative projects.
- Supports mixed-methods analysis.
Semi-Automated Tools
- Some online tools and AI assistants (including ChatGPT) can help:
- Generate initial codes from text automatically.
- Group codes into potential themes.
- Useful for speeding up analysis, but human validation is essential.
Tip
- For small projects, manual coding with spreadsheets or Word is fine.
- For large datasets or collaborative projects, software like NVivo, ATLAS.ti, or MAXQDA is highly recommended.
Common Challenges in Thematic Analysis and How to Overcome Them
Handling Bias in Coding
- The challenge: Researchers’ personal opinions or expectations can influence how they code the data, which may skew results.
- How to overcome it:
- Stay objective: Try to code independently before comparing with others.
- Team discussions: Review codes with peers to challenge assumptions.
- Reflexivity: Keep notes on your own thoughts and potential biases while coding.
Real-world data point: A 2024 meta-review found that biased analysis can skew results in 30% of qualitative studies, highlighting the importance of objectivity.
Dealing with Large Datasets
- The challenge: When you have hundreds or thousands of responses, coding manually can be overwhelming and time-consuming.
- How to overcome it:
- Break data into smaller subsets to make it manageable.
- Use software tools like NVivo, MAXQDA, or Excel filters to organise and search data efficiently.
- Batch processing: Analyse a portion at a time, then combine codes.
Real-world example: A nonprofit team analysing 500 survey responses split the data into batches. This approach cut analysis time in half while keeping coding consistent.
Ensuring Reliability
- The challenge: You need to make sure that your coding is consistent and trustworthy, especially if someone else were to review your work.
- How to overcome it:
- Cross-check codes with peers (inter-coder reliability).
- Pilot coding: Test your codes on a small sample, then refine them.
- Document decisions: Keep an audit trail explaining why codes or themes were chosen.
Expert quote: Methodology expert emphasises, “Validation turns solo work into trusted findings.” This underscores that checking codes with others improves the credibility of your thematic analysis.
Conclusion
Using ChatGPT to do a thematic analysis can transform a traditionally time-consuming process into a more manageable and structured experience. By combining AI-assisted coding with your own interpretation, you can uncover meaningful patterns, identify themes, and gain deeper insights from qualitative data without losing the human perspective.
Remember, ChatGPT is a tool to support—not replace—your analysis. Its suggestions need to be reviewed, refined, and validated to ensure accuracy and reliability. With careful preparation, clear research questions, and thoughtful engagement with your data, thematic analysis becomes not just feasible, but efficient and insightful, allowing you to focus on interpreting and sharing the stories that your data reveals.
If you found this guide on using ChatGPT for thematic analysis helpful, don’t miss our post on How to Write the Background of the Study Using AI, where we show how to efficiently craft a strong research foundation with AI support.
