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Human emotions are messy.
Most of the time, words say something else but the feeling and context behind them takes a completely different turn.
Now imagine trying to decode them in UX research, and bam!
You’re bombarded with endless responses.
Some are straightforward, some make you think, “What does this even mean?” and some leave you scratching your head for hours.
But here’s the good news, thematic analysis exists to make sense of it all so that you focus on what matters the most: making informed decisions.
And here’s the best news, this guide covers everything you need to know about thematic analysis, from its types, real-life examples to how it works and why it's every researcher’s go-to method for qualitative data analysis.
What is thematic analysis?
Thematic analysis is the process of finding recurring themes and patterns in qualitative data. No matter what the source of data is, be it from interviews, surveys, user feedback, or focus groups, thematic analysis ensures you identify common themes to make sense of the entire data.
What is thematic analysis in qualitative research?
Every researcher’s main goal is to find concrete evidence to support their theory in UX research. This is where thematic analysis comes in handy.
While quantitative data analysis can easily be done through statistical tools and a few spreadsheets, qualitative data takes a little more effort. This is why researchers leverage thematic analysis to uncover those hidden meanings.
But here’s the catch, thematic analysis’s application is not just limited to UX research but extends beyond to academics, business decision-making, and even AI-assisted analysis.
Types of thematic analysis
There are different types of thematic analysis each designed to cater to different research objectives. Here’s a quick overview:
- Inductive thematic analysis
If you don’t have any predetermined conditions or theories to test then inductive thematic analysis is the right pick for you. This thematic analysis approach lets themes emerge naturally from the data instead of bifurcating them on the basis of existing themes or patterns.
This approach works best when you are trying to test a new concept or pattern.
- Deductive thematic analysis
The complete opposite of inductive thematic analysis, this thematic analysis approach is based on pre-existing theories or frameworks. It is a more structured and biased approach, ultimately leading to limited emergence of themes.
This approach works best when you already have theories set on a particular topic and only want to see if the research agrees or disagrees with that theme.
- Reflexive thematic analysis
If you’re looking for a more flexible approach then reflexive thematic analysis might be the right choice. Unlike other methods, it doesn’t follow strict rules or pre-defined themes. Instead, it is highly dependent on the researcher's perspective and how researchers understand and analyze the data.
Example: Let’s say you’re analyzing customer feedback about a new app. Initially, you identify themes like “ease of navigation” and “features.” As you dig deeper, you realize users are actually talking about “intuitive design” and “customization.” This evolution of themes reflects the reflexive approach.
For a more detailed understanding of different types of thematic analysis, refer to Thematic content analysis in qualitative research.
Thematic analysis vs other qualitative methods
Before you put your time and resources into thematic analysis for your next research, it is essential to understand the different types of qualitative analysis methods and how they differ from thematic analysis.
This will help you understand the qualitative analysis method that perfectly suits your research findings.
Content analysis is more about counting things—how often certain words or themes appear. It's pretty structured and data-focused.
Thematic analysis, on the other hand, does a deeper investigation into the meaning behind those patterns and themes.
Grounded theory is about building theories based on data from scratch, while thematic analysis focuses on identifying themes. Grounded theory helps develop concepts, while thematic analysis focuses on understanding and organizing the existing ones.
Discourse analysis is how things are said, while thematic analysis is what's being said. Discourse analysis focuses on language itself and how it shapes our understanding of a topic, whereas thematic analysis is more about identifying repeated themes.
Framework analysis works within a set framework, so you can compare data across different themes or groups. Thematic analysis is more flexible, allowing themes to emerge organically. It's like a structure vs freeform debate.
Affinity mapping is the process of grouping ideas based on their relationships, it's a way to visually organize data. Thematic analysis is more about identifying and analyzing themes that keep cropping up across the data, often without grouping them visually.
Narrative analysis focuses on how stories are told, looking at the structure and elements of the narrative itself. On the other hand, thematic analysis involves identifying patterns and themes in the data, regardless of how it's told.
Before we dive deeper, grab our free thematic analysis template to follow along with practical examples as you read.
How to do thematic analysis: 6 essential thematic analysis steps
Whether you’re manually conducting thematic analysis or using AI-powered tools like Looppanel, these 6 steps of thematic analysis are sure to give you success, find emerging themes, and make informed decisions.
Step 1: Get familiar with the data
Start by familiarizing yourself with your data. Read transcripts, listen to recordings, or go through notes.
If you're using a tool like Looppanel, import your interviews and let AI-generated notes give you an instant overview. You can also use smart search features to track recurring patterns or interesting topics.
With Looppanel, you can save up to 80% of your time and get accurate AI-generated notes, saving you time and manual work.
Step 2: Generate initial codes
Codes represent the building blocks of your analysis. You can make them on your own and map similar items together.
Here’s a bonus tip: keep them really precise, representing just the main ideas, and put similar ones together to have a better organizational system.
But if you try this manually, you might end up getting lost in the large data sets.
On the other hand, you could auto-tag relevant codes to your data with a tool like Looppanel, creating a pattern for them. To make things easier for you, Looppanel also has a timestamp feature that lets you quickly reference the original audio/video context, ensuring accuracy in your coding.
Step 3: Identify themes
Themes are the big ideas that connect smaller, related codes. Think of themes as categories that help to explain patterns in your data.
To identify themes:
- Group codes by how they answer your research questions.
- Search for related or overlapping codes.
Step 4: Review themes
Once you’ve created themes, take a step back and double-check:
- Are the themes clear and meaningful?
- Do they accurately represent the data?
- Should any themes be merged, divided, or deleted?
AI UX research tools like Looppanel make this easier by allowing you to search and filter themes to make sure they're correct.
Step 5: Define and name themes
To easily identify and decode the themes, name them. The name chosen should be descriptive of what that theme represents. An example of this can be, instead of "Customer Dissatisfaction," use a name like "Needs Improvement in Support."
Step 6: Create the report
The final step is pulling everything together into a report. Here's how to do it:
- Summarize your themes with supporting quotes or examples from the data.
- Highlight key insights and trends.
- Use visuals like charts or tables to present findings where possible.
If you're using Looppanel, the platform automatically produces an AI executive summary that will definitely save you time and provide accurate, timely, and effective results.
Tools and technology for thematic analysis
Let's face it, Manual thematic analysis can take forever to complete! You find yourself stuck in piles of data, trying to organize it into themes, and double-checking for mistakes. It is exhausting, right?
That is why researchers are leveraging AI tools for thematic analysis to do the heavy lifting for them which ensures quick, accurate, and hassle-free results.
Role of AI in thematic analysis
AI thematic analysis is efficiently saving researchers time and cost. Here’s how AI is leading this sector:
- Automates repetitive tasks such as transcription and tagging.
- Extracts themes quickly with high accuracy.
- Provides actionable insights through summaries.
- Ensures traceability with source citations.
- Enables efficient cross-referencing across datasets.
Using ChatGPT for thematic analysis
If you’ve been wondering if can use Chatgpt for thematic analysis, then you’re in for a surprise!
ChatGPT can assist researchers in identifying themes, summarizing data, and drafting reports with speed and precision. It works best when you have less data to cover.
Pros:
- Quick and context-aware data summarization.
- Generates theme-based insights efficiently.
- Offers flexibility to refine outputs interactively.
Cons:
- Lacks deep understanding of organizational context.
- May require manual fine-tuning of generated outputs.
- Relies on high-quality prompts for optimal results.
- May not be suitable for large data sets.
Using Looppanel for thematic analysis
Worrying ChatGPT might not cover your entire data set or rely heavily on manual prompts? Don’t worry! Looppanel streamline thematic analysis for qualitative research, providing accurate and instant results, even for large data sets.
What makes Looppanel stand out?
- Get over 90% accuracy in transcription across 17 languages—because every word matters.
- Automatically generate concise, organized notes by questions or themes, reducing review time by 80%.
- Let AI identify and group themes across your data, saving hours of sorting.
- Instantly create editable, presentation-ready executive summaries with quotes and citations.
- Use smart search to locate answers and raw data in seconds with context-aware results.
Benefits of AI-assisted thematic analysis
AI-supported tools make complex thematic analysis easier by automating repetitive tasks, improving precision, and speeding up data processing. They free up the researchers to concentrate on meaningful insights for smarter and faster decision-making. It's like having a supercharged research assistant, minus the coffee breaks and stress.
Advantages and limitations of thematic analysis
This is the final step to let you decide if thematic analysis is worth your time and resources or not.
Real-life examples of thematic analysis
Thematic analysis is widely used across various industries to extract meaningful insights. Here are a few real-life thematic analysis examples:
- E-commerce customer reviews: Ever wondered how brands just keep getting better with each round of feedback? That's thematic analysis at work. They sift through thousands of reviews to identify common themes like "fast shipping" or "quality issues" and use these insights to fix problems.
- Employee insights: The HR department loves thematic analysis. They study through employee interviews and find themes such as "work-life balance" or "growth opportunities" in order to create a happier, more productive work culture.
- Healthcare feedback: Similarly, analysis of interviews with patients in healthcare reveals patterns such as "trust in doctors" or "comfort in treatment." This helps hospitals and clinics improve how they care for their patients and is a classic example of thematic analysis.
- Brand sentiment: Marketing teams go through social media analytics to find out exactly what was said about their brand. By searching for "customer service" or "product satisfaction" themes, marketers know precisely where they should be shifting their efforts.
Wrapping up!
Thematic analysis is a crucial step in transforming raw, complex data into meaningful insights. It helps to identify patterns, themes, and trends that are usually hidden in qualitative data. This allows researchers to group related information and draw actionable conclusions that can guide business strategies and decisions.
Looppanel makes the whole process much easier, with AI-driven features like 90% accurate transcriptions, automatic thematic tagging, and executive summaries. It helps researchers eliminate the time-consuming tasks of manual sorting and note-taking.
Want to experience how Looppanel can help you? Request a demo today!
Frequently asked questions (FAQs)
1. What is thematic analysis in qualitative research?
Thematic analysis is a method for identifying, analyzing, and reporting patterns or themes in qualitative data.
2. What are the 5 stages of thematic analysis?
The 5 stages include: data familiarization, generating initial codes, searching for themes, reviewing themes, and producing the report.
3. What are the 5 methods to analyze qualitative data?
The 5 methods are thematic analysis, content analysis, narrative analysis, grounded theory, and framework analysis.
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