How To Do Thematic Analysis: Step-by-Step Guide For Newbies

Thematic analysis offers a flexible approach to analysing qualitative data, making it an invaluable research methodology for many researchers.

This step-by-step guide is designed to show the process, covering everything from data collection to crafting compelling narratives.

Whether you’re working with interview transcripts, survey responses, or social media content, this guide will equip you with the skills to uncover profound insights and themes in your data.

How To Perform Thematic Analysis

StepProcess DescriptionKey Activities
1. Getting Familiar with the DataGet to know your data thoroughly.Read all data, noting emerging patterns and initial codes.
2. Creating Initial CodesStart coding your data after understanding it.Label segments of your data with relevant codes.
3. Refining CodesReview and refine your initial codes.Adjust codes as needed based on further data review.
4. Collating Data ExcerptsOrganize data excerpts by code.Analyze and possibly adjust codes after reviewing all related data.
5. Grouping Codes into ThemesForm themes from your refined codes.Link codes to create detailed and meaningful themes.
6. Reviewing and Revising ThemesEvaluate and refine your themes.Review themes for support and clarity, making adjustments as necessary.
7. Narrative AnalysisDevelop the narrative of your data.Craft a narrative using key quotes and theme analysis to answer your research question.

What Is Thematic Data Analysis?

Thematic analysis is a flexible and highly versatile tool in qualitative research, especially favored for its ability to distill patterns and themes from diverse data sets.

This type of analysis allows you to navigate through complex layers of information, making it an indispensable method in the arsenal of qualitative researchers. These information could include:

  • Interviews,
  • Social media posts, or
  • Book chapters.

When you dive into thematic analysis, you’re essentially looking for themes that not only recur across the data but also resonate with the central research questions of your study.

This method is particularly robust when you need to explore the qualitative content of data and understand the underlying sentiments or opinions.

If you are analysing customer feedback on a new product, thematic analysis helps in pinpointing specific likes and dislikes that emerge from the data, guiding future improvements.

Virginia Braun and Victoria Clarke, prominent figures in this field, have significantly contributed to refining thematic analysis, making it an accessible approach for both seasoned academics and novice researchers.

Their framework emphasises a reflexive stance, encouraging you to continuously interact with the data, refining and revising initial codes until they robustly represent the content of the data.

Despite its strengths, thematic analysis is not without its weaknesses. One critical point is that its flexibility can sometimes lead to vague or imprecise results.

The broad scope of thematic analysis might overlook subtle nuances, especially in smaller, more focused data sets where unique details are crucial.

The inherent open-ended nature of coding in thematic analysis can lead to inconsistencies, particularly if multiple researchers are involved without a stringent coding reliability protocol.

Types Of Thematic Analysis

Thematic analysis stands out for its adaptability and depth. You might find yourself choosing between its two main types: inductive and deductive thematic analysis.

Each type has its specific applications, making your research not just insightful but also rigorously aligned with your data set.

Inductive Thematic Analysis

Inductive thematic analysis is your go-to when you begin with minimal preconceptions about what you will find. This approach allows themes to emerge organically from the data itself.

Imagine you’re delving into the experiences of remote workers using this method; you’d start without a predetermined framework, letting the actual words and sentiments of your participants guide the development of codes and themes.

As you become more familiar with the data, you might adjust these initial codes, making this a dynamic and responsive way to analyse qualitative data.

Deductive Thematic Analysis

Deductive thematic analysis operates under a more structured framework. It’s suitable when your research is guided by specific theories or you have clear expectations about what the data might reveal.

If you’re studying consumer reactions to a new advertising campaign and you have a hypothesis based on previous studies, deductive analysis lets you apply a predefined set of codes to your data.

This method aligns closely with the qualitative content analysis, where you dissect the content based on established categories.

Thematic Analysis Steps

1. Getting Familiar with the Data

Performing thematic analysis starts with becoming deeply familiar with your data.

Whether your data comes in audio files that need transcribing or pages of field notes, you must read through everything meticulously. 

As you immerse yourself in the transcripts, observe patterns and initial codes start to surface naturally.

These patterns are your first insights into the data’s deeper meaning, hinting at broader themes that answer your research question.

2. Creating Initial Codes

Creating initial codes is a dynamic part of qualitative data analysis. This comes after you have gained a general understanding of the data.

You might label a segment of your interview transcript with a code like “customer dissatisfaction” or “usage challenges,” depending on what the data reveals.

These codes are the building blocks of your analysis, helping you organize the content of the data systematically.

3. Refining Codes

As you progress, you return to the data to refine these initial codes.

This phase is critical as you might discover that some excerpts don’t fit the original codes or new patterns emerge that require their codes.

It’s a bit like detective work, where you constantly reassess the clues (data) and the leads (codes) to ensure they’re taking you in the right direction.

4. Collating Data Excerpts

Once your codes are set, the next step is to collate all data excerpts linked to each specific code. This is your data reduction stage, where you focus on compacting the data into manageable chunks without losing its essence.

It’s a detailed analysis process that requires careful consideration to maintain coding reliability. Viewing all data related to a single code in one place allows you to see if the initial readings hold up.

You might adjust, merge, or even discard some codes based on this intensive review.

5. Grouping Codes into Themes

With your refined codes in hand, you’re ready to group them into themes.

This is where thematic analysis really begins to shine, offering a lens to view the qualitative data not just for what it is, but for what it represents. 

Each theme should weave a narrative that goes beyond mere description. For instance, a theme emerging from codes like “customer dissatisfaction” and “response times” could be “impacts of service delay on customer loyalty.”

These themes should be nuanced and complex, providing compelling insights into your research question.

6. Reviewing and Revising Themes

But thematic analysis doesn’t stop there. Once themes are developed, they need to be reviewed and revised.

It’s an iterative process, often requiring you to go back to the data to ensure each theme is well supported and distinctly separate from others.

This step might lead you to merge similar themes or refine the boundaries of what data excerpts belong where.

Virginia Braun and Victoria Clarke, pioneers in the field of thematic analysis, stress the importance of this reflexivity in their approach to qualitative research.

7. Narrative Analysis

Finally, with your themes defined, you step into the narrative analysis phase. Here, you tell the story of your data.

This narrative should not only recount the data but analyse it, providing arguments for your claims and highlighting how the themes interact to answer your research question. 

You choose vivid quotes from your data to strengthen your points, ensuring that your narrative is as engaging as it is informative.

Tips When Performing Thematic Analysis

Thematic analysis is a powerful tool you can use to sift through qualitative data and identify patterns that are not immediately obvious. Here are six essential tips to ensure you harness the full potential of this versatile approach in your research.

Embrace the Flexibility of Thematic Analysis

Thematic analysis is a flexible method that adapts to the unique needs of your project. Unlike rigid methodologies like discourse analysis, thematic analysis allows you to shape the analysis based on the content of the data.

This method works well across various data types, whether you’re analyzing:

  • interview transcripts,
  • video content, or
  • social media posts.

The key is to stay open to the themes that naturally emerge from the data.

Maintain a Clear Focus on Your Research Questions

Start with your research questions at the forefront of your analysis.

Thematic analysis is often guided by the specifics of these questions, helping to refine what data to focus on and which themes are relevant.

If your question explores the impact of social media on teenage self-esteem, you’ll likely code data that highlights self-image, peer comparison, and social feedback.

This focused approach ensures your analysis remains tethered to your study’s aims.

Iteratively Code and Recode Your Data

Data coding isn’t a one-off task in thematic analysis. Initially, you might develop a broad set of codes. As you become more familiar with the data, you’ll refine these into more precise themes.

This iterative process is crucial for developing a thorough understanding of your data set.

Coding reliability improves as you refine your codes and revisit the data, enhancing the overall robustness of your findings.

Use Visualisation Tools to Enhance Understanding

Thematic maps and other visualization tools can be instrumental in thematic analysis. They help you see the relationships between different themes and how they connect back to your central research questions.

Using a software like NVivo or MAXQDA, you can create visual representations of your codes and themes, making it easier to identify how they intersect and influence one another.

Keep Detailed Reflexive Notes

Reflexive thematic analysis, pioneered by researchers like Virginia Braun and Victoria Clarke, emphasizes the importance of the researcher’s role in shaping the analysis.

By keeping a detailed reflexive journal, you document your thoughts, biases, and decision-making process throughout the analysis.

This practice not only aids in maintaining transparency but also helps in later stages when you need to justify your analytical choices to your audience or committee.

Return to the Data Often

Throughout your thematic analysis, it’s crucial to return to your data frequently.

This approach ensures that the themes you develop are genuinely reflective of the data and not just your initial impressions or biases.

Each return to the data can reveal new insights or affirm the patterns you’ve identified, strengthening the validity of your analysis.

By incorporating these tips into your approach to thematic analysis, you ensure a rigorous and insightful exploration of your qualitative data.

This method not only offers a detailed analysis but also flexibility and adaptability, making it a preferred choice among qualitative researchers aiming to uncover the rich, narrative layers of

What Softwares Can I Use For Data Analysis?

Thematic analysis does not have to be a fully manual work – here are a couple standout software options that could revolutionize how you handle qualitative data:


This software offers an intuitive, no-code platform that’s perfect if you’re looking for AI-powered analytics without a steep learning curve.

What sets Cauliflower apart is its ability to generate visualisations directly from input, bypassing the need for deep dives into data manually. 

The visual outputs help you spot patterns and themes, focusing your analysis on emerging big themes, making it ideal for quick, actionable insights.


is tailored for tech-savvy teams in mid-sized to large enterprises. This software shines when handling complex data sets, including text, audio, and video from diverse sources.

With NVivo, you can dive deep into your data, coding and visualizing customer input to unearth nuanced insights and identify market gaps.

It’s a powerhouse for those who need a detailed, methodical approach to qualitative data analysis.


This software simplifies the analysis process with an intuitive interface that makes qualitative data analysis approachable for beginners and professionals alike.

It offers a straightforward method to identify keywords and phrases, allowing you to quickly interpret vast amounts of text.

The side-by-side comparison views help distinguish between different demographic feedback, enhancing your understanding of diverse customer perspectives.

Qualtrics CoreXM

This is a sophisticated tool that integrates seamlessly with various data collection methods, positioning it as a comprehensive solution for experience management.

Beyond gathering and analysing qualitative data, Qualtrics allows you to use thematic analysis to generate credible quantitative data, and make guided decisions.

The platform’s strength lies in its predictive intelligence capabilities and its ability to weave emotional and sentiment analysis into the data interpretation process.


Thematic is particularly effective for businesses that need to analyse unstructured feedback across multiple channels. 

This AI-driven platform excels in identifying relationships and patterns in your data, facilitating detailed theme-based analysis.

Thematic’s ability to split insights by segment and use predictive analytics makes it invaluable for researchers eager to discover new findings.


A popular software, it is a comprehensive suite of tools designed for detailed qualitative analysis. This platform excels in making connections across diverse data sets. ATLAS.ti comes with features that support:

  • Data coding,
  • Content analysis, and
  • Thematic exploration.

ATLAS.ti is ideal for those who require flexibility in handling data from various sources, including surveys, interviews, and media files.

A Systematic Approach To Thematic Analysis

Embarking on thematic analysis can initially seem daunting, but by following these systematic steps, you’ll gain confidence and proficiency in handling qualitative data.

Remember, the key to successful thematic analysis lies in staying organized, being thorough in your coding, and continually refining your themes.

With practice, you’ll not only enhance your research skills but also uncover rich, insightful data narratives that can profoundly impact your field of study. Keep exploring and refining your technique, and the depth of your analyses will grow.

The Author

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.