Reflexive Thematic Analysis (RTA) in Qualitative Research
Reflexive thematic analysis turns your unique perspective into a core part of your qualitative research. Through careful documentation, you create insightful and transparent themes that reflect both your data and your analytical lens.
As a qualitative researcher, it’s normal to think about the impact of your perspective on your analysis of interviews, field notes, or other qualitative data. The challenge? Balancing rigorous, systematic analysis with your subjective interpretations of what you see.
Instead of treating your perspective as a hidden “bias” or weakness to overcome, reflexive thematic analysis (RTA) helps tackle two big challenges: managing your own influence over the data and documenting it in a transparent way. Your experience becomes a tool rather than a thing to avoid, allowing you to systematically capture and use your insights.
This guide introduces reflexive thematic analysis and shows how Delve’s computer-assisted qualitative data analysis software (CAQDAS) can support your qualitative work. You'll discover efficient ways to document your analytical journey and develop themes that honor both your data and unique viewpoint.
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From Codes to Themes: The Building Blocks of Reflexive Thematic Analysis
What is reflexive thematic analysis? Before we get over our skis, let’s break down the broader steps that help you turn mountains of raw qualitative data like interviews or field notes into meaningful insights. These are the building blocks of your reflexive thematic analysis – and most qualitative research methods as a whole:
Labeling Key Insights: Qualitative coding involves tagging pieces of text that capture something meaningful or relevant to your research. These labels – your “codes” – are the building blocks of your analysis and help organize the data into manageable pieces.
Creating a Codebook: You define and compile codes in your qualitative codebook. This is essentially a reference guide to your data. Referring back to this document helps track the patterns you are noticing in your data, keeping you consistent in how you apply codes.
Identifying Themes: Thematic analysis takes coding a step further by looking for connections between these codes. Instead of having just a collection of labeled excerpts, thematic analysis helps you see how they cluster together to tell an overarching story about your research questions.
While most thematic analysis focuses on pattern identification, reflexive thematic analysis goes further by layering in your active role in that process. You’re identifying overarching themes and examining how your subjectivity influences what you see, offering a more honest and nuanced analytical process.
Reflexive Thematic Analysis in Practice
Reflexive memos help capture how your perspective shapes coding and analysis, making it easier to identify patterns while maintaining transparency.
- Raw Interview: "I’ve noticed that students behave differently online compared to in the classroom, especially during group activities."
- Your Code: "Virtual engagement shifts"
- Your Memo: "Based on my teaching experience, I noticed how students engage less in online settings, especially when multitasking. I need to examine whether this is a common pattern across participants or influenced by my assumptions about in-person versus virtual learning."
Example created using Delve.
Reflexive Thematic Analysis vs. Thematic Analysis
What makes reflexive thematic analysis different from traditional thematic analysis? The former is a sub-method of the latter but there’s more to that story. First, let’s make sure we have a firm understanding of thematic analysis:
Think of any type of qualitative research like exploring a new city. It’s natural that you notice different things than other city-goers based on your background, experiences, and interests. That's the core belief of reflexive thematic analysis, developed by Virginia Braun and Victoria Clarke.
While you will still follow Braun and Clarke's six steps of thematic analysis, this reflexive approach takes a unique route. Instead of saying themes naturally "emerge" from data – as if findings appear on their own – they encourage you to embrace how your perspective shapes the analysis. Unlike traditional thematic analysis, which often aims for neutrality or objectivity as you look for patterns, reflexive analysis actively weaves your viewpoint into the process.
With this unique approach, your unique lens is valuable. Even more so, it’s required. By recording memos and notes about why certain things catch your eye and engaging with those first impressions, you're not just collecting observations but actively tracking how your unique perspective shapes what you notice.
In this case, the idea is that your reflexive lens creates a more transparent and honest analytical process.
Reflexive or Reflective Thematic Analysis?
Being reflexive doesn’t mean “anything goes” or simply running with whatever stands out in the data. Reflexive thematic analysis, often mistakenly called reflective thematic analysis, emphasizes intentionality and critical thought. It’s not about adding more work to your process but about working smarter – deliberately interpreting your data while being aware of how your perspective shapes the conclusions.
Reflexive thematic analysis invites you to engage deeply, not just with the data but with the interpretive dance between you, your insights, and your research questions. By acknowledging this interplay, you produce findings that are not only rigorous but also rich with nuance and meaning.
Reflection asks: "What did I observe?"
Looking back at what happened
Describing your observations
Thinking about what you learned
Reflexivity asks: "How did I shape what I observed?"
Examining how your background influenced your interpretation
Questioning why certain patterns caught your attention
Documenting how your experience guided your analysis
A good way to remember this is that reflection is like looking in a mirror (what you see), while reflexivity is examining the lens through which you're looking (how and why you see it that way).
With reflexive thematic analysis, your experiences, interests, and professional background become tools for uncovering patterns in your data. The goal? Document how your perspective shapes what you notice and interpret directly in your codebook with memos. You want to embrace how you see things instead of suppressing them.
Example of Reflexive Thematic Analysis
Let's take a quick look at how reflexivity works in practice using a study on stress in the workplace (skip ahead for a detailed guide). In this example, imagine you have just gone through a few growing pains of your own with a new remote workplace dynamic. It shows how your perspective as a researcher can influence how you understand and analyze participants' experiences with remote work transitions themselves.
Reflexive Memo: Remote Work Interview Reflections
Date: 3/15/24
After interviewing Tim about missing his commute, I realized my own experience transitioning to remote work is influencing how I interpret these conversations. Having struggled with work-life boundaries myself, I find myself particularly drawn to comments about blurred lines between home and office. Need to stay aware of how this might shape my follow-up questions and analysis.
While my experience helps me understand these challenges deeply, I should remain open to different perspectives. Some participants might have found positive aspects of losing the commute that I might miss if I focus too much on the challenges I personally faced.
This memo shows how acknowledging your perspective helps create a more transparent and nuanced analysis rather than limiting it. You can build upon this initial insight and use it as screening process for the rest of your insights as you continue working through your analysis.
Capture Your Perspective with Memos
Spotting patterns is one thing – remembering why and how they grabbed your attention is another beast altogether. Delve lets you add memos directly to your codes and code snippets. These memos help you track how your experiences shape what stands out, adding depth and clarity to your analysis.
Quick Example: Coding “Workload Stress” in a teacher burnout study? Add a memo to record how your background might influence your focus on administrative tasks, keeping your perspective documented and easy to review.
When Reflexive Thematic Analysis Makes Sense
Reflexivity in thematic analysis shines when your research benefits from deep engagement with both data and researcher perspectives. This approach can be particularly powerful if you're studying complex social phenomena, exploring lived experiences, or examining topics where your professional background offers valuable insights.
Consider using reflexive thematic analysis when your research questions benefit from informed interpretation. For instance, if you're a teacher studying classroom dynamics, your experience helps you recognize subtle patterns in student behavior that might escape notice otherwise. If you're researching healthcare practices with a nursing background, your professional knowledge helps you grasp the nuances of patient-provider interactions.
Why to use reflexive thematic analysis:
Your research questions explore how people make meaning of their experiences
Your professional or personal background gives you unique insights into the topic
You want to be transparent about how your perspective shapes your interpretation
You're dealing with complex social or cultural phenomena that benefit from nuanced analysis]
The key isn't just whether you have relevant experience – it's whether you can capture how that experience adds value to your analysis. While other qualitative methods might ask you to minimize your influence, reflexive thematic analysis helps you weave in your perspective to create deeper, more transparent insights.
Think Like a Research Instrument
Your experiences and perspectives are like a microscope that helps you see meaningful patterns in your data. The key is being transparent about how your "lens" shapes what you see.
Core Assumptions: Braun & Clarke’s Reflexive Thematic Analysis
Before we get into the steps, let's look at Braun and Clarke's key principles for reflexive thematic analysis. While not rigid rules, these ideas help you embrace your central role in the analytical process. Some researchers may disagree with this constructivist approach, preferring a more realist perspective that aims for objectivity. We explore this tension in the next section.
Figure 1: Braun & Clark’s core assumptions of reflexive thematic analysis (Braun & Clarke, 2022, pg. 8)
Rather than pretending to be a neutral observer (which Braun & Clarke argue isn't truly the goal of qualitative analysis), you are harnessing your perspective to offer deeper, more transparent results.
The goal is to see yourself as an active participant in the research process. Whether working alone or through thoughtful collaboration with others, reflexive analysis is about welcoming subjectivity, treating themes as interpretations rather than topic summaries, and valuing creativity in the analysis. These assumptions tease out a uniquely nuanced and thoughtful approach in your work.
Remember, these aren't hard and fast rules but general guidelines for engaging reflexively with your thematic analysis. By recognizing your own role in shaping the process, you’re not hiding your perspective – you’re using it to create a richer understanding of the data.
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Orienting Yourself to Reflexive Thematic Analysis
Building on the constructivist perspective we just discussed, reflexive thematic analysis offers flexibility in how you explore your data. Your orientation can be broadly categorized in three ways, each shaping how you interact with and understand your data:
Inductive vs. Deductive: Start fresh, letting the data guide you (inductive) or use existing theories to shape your analysis (deductive). Reflexive thematic analysis typically favors inductive approaches. Read more about inductive vs. deductive thematic analysis.
Semantic /Manifest vs. Latent: Look at what the data explicitly says (semantic/manifest) or explore deeper underlying meanings (latent). Reflexive thematic analysis often leans toward latent analysis to uncover nuanced meanings. Like content analysis, manifest approaches tend to be deductive while latent analysis usually pairs with inductive coding.
Realist/Essentialist vs. Constructionist: Following from what we discussed earlier, this approach embraces a constructionist view where meaning comes from your interaction with the data, rather than trying to uncover an objective truth. If you're new to qualitative research, it's worth discussing these different philosophical approaches with your advisor or colleagues, as they reflect ongoing discussions in the research community.
The key is maintaining consistency in your approach and being transparent about these choices in your write-up, ensuring your method aligns with your research goals.
Key Ideas: Going Beyond Observation
- You're actively constructing meaning, not discovering pre-existing "truths"
- Different researchers might notice different patterns—and that's valuable
- Your background and experience shape what you notice
- The goal isn't objectivity, but transparency about how you interpret your data
Step-by-Step Guide: How to Do Reflexive Thematic Analysis
Let's break down the reflexive thematic analysis steps as simply as possible. Generating reliable code and themes that stand up to scrutiny from instructors, peer debriefers, and other readers requires deep, iterative rounds of analysis. And beyond doing it well, doing it faster with Delve’s computer-assisted qualitative data analysis software (CAQDAS):
Step 1: Start with Deep, Iterative Reading
Data immersion is the foundation of reflexive thematic analysis. Unlike traditional approaches that might rush to coding, you want to take time to absorb your material's nuances and complexity.
At this early stage, you want to:
Review interview transcripts or other data multiple times
Note initial reactions and thoughts
Start identifying potential patterns
Review your data until it becomes familiar territory
During this phase, pay particular attention to your initial reactions and assumptions. When something stands out, pause to consider why it catches your attention: Is it because it confirms your expectations? Challenges them? Resonates with your own experiences? Using a qualitative tool like Delve makes it easy to jot down these memos and keep them connected to your data.
These are the questions you ask yourself throughout the initial reading process.
💡 Familiarization in Practice
"Reading through teacher interviews about remote learning, I noticed myself particularly drawn to comments about technology struggles. As a former teacher who taught during COVID, I needed to document this connection while staying open to experiences different from my own."
When familiarizing yourself with data, it’s important to recognize personal connections while staying open to patterns that may differ from your own experience. Learn how Delve helps you balance reflexivity and analysis.
Start (and continue) documenting these reactions in your memos using sticky notes, a Word document, or a tool like Delve. These early notes often become valuable insights later in your final write-up. Consider how your background and experiences might be shaping what you notice - and what you might be missing or any potential blindspots.
Framing Your Reflexive Thematic Analysis
These tips for reflexive thematic analysis work well with Braun and Clarke’s six-step framework for thematic analysis, offering a well-lit path for rigorous results. Remember, this process is iterative and you'll revisit earlier steps as you find new patterns.
with Data
Codes
Themes
Name Themes
Report
What to Focus On
- Active Engagement with Data: Immerse yourself in the data with iterative readings. Notice your initial reactions and ideas. Record memos about potential patterns.
- Systematic Documentation: Track your coding decisions in a codebook. Write regular analytical memos, and map connections between ideas to stay organized.
- Continuous Reflexivity: Question your assumptions. Consider alternative interpretations. Examine how your perspective shapes analysis.
Use this process to bring structure and transparency to your reflexive thematic analysis while acknowledging the role of your perspective in shaping interpretations.
Step 2: Reflexive Initial Coding with Awareness
After immersing yourself in your data (familiarization), you now begin the initial coding process. Don’t rush to code everything. Instead, approach each segment of text with curiosity about both its content and your reaction to it. Ask yourself why certain passages stand out and how your experience might influence your interpretation.
As you start your coding:
Code meaningful data segments
Document your coding rationale in memos
Question your immediate interpretations
Use memos to track how your experience influences interpretation
Consider how different researchers might code the same data
Throughout your iterative coding rounds, stay self-aware about how your experiences, biases, and perspectives directly affect the interpretive process. You want to consider everything from how and where you collect data to specific coding decisions and how your backgrounds and beliefs influence your analysis.
Pro Tip: Memoing Your Best Ideas
With Delve, you can easily record memos to capture your early impressions. They often lead to valuable insights later!
Your reflexive coding process should be systematic but flexible. Each time you create a code, document your reasoning. Let’s say you are coding an interview transcript with Bethany from the same study about teacher burnout. It could look something like this:
Reflexive Thematic Analysis in Action
Reflexive memos help uncover deeper meaning by linking observed behavior with professional judgment and experience.
- Raw Interview: "Sometimes I just have to wing it with difficult students."
- Initial Code: "Adaptive teaching strategies"
- Reflexive Memo: "My teaching experience helps me recognize this isn't just about improvisation – it's about professional judgment developed through experience. Need to watch for similar patterns of expertise in action."
Example created using Delve.
The key is to notice how your reflexivity transforms what might initially seem like simple labeling into a deeper analytical process. Beyond just categorizing data from individual interviews, you're beginning to uncover the layers of meaning that your unique perspective helps you see across all your interviews.
Step 3: Building Initial Themes
Developing themes is where your analytical insights really begin to take shape. This isn't just about grouping similar codes. You are identifying meaningful patterns that tell a larger story about your research questions. As you examine your initial codes, look for underlying connections and broader narratives by:
Grouping related codes
Documenting your reasoning
Considering any alternative interpretations
Consider this quick example of what theme development looks like in practice:
Individual codes:
"Paperwork overload"
"Administrative meetings"
"Email management"
"Student needs delayed"
These might initially seem like separate issues, but your experience might help you recognize a deeper pattern about competing demands on teacher time.
Nesting Codes for Deeper Patterns
Nesting codes allow you to connect related concepts and see how seemingly distinct issues fit into a broader theme. For example, codes like "Paperwork Overload," "Administrative Meetings," and "Email Management" might initially feel unrelated, but through nesting, you may recognize a larger pattern about how competing demands affect teacher focus and energy.
🎯 Theme Development in Action
Potential Theme: "Administrative Tasks Displacing Core Teaching"
Memo: "My classroom experience helps me recognize how these seemingly separate tasks create a cumulative effect on teaching time. Need to examine if this resonates across different teaching contexts."
Feeling overwhelmed by data is common. Breaking it down into small, meaningful bits and connecting them to themes—like the one above—turns chaos into clarity.
Using nesting techniques, you can build layered themes that reflect the complexity of your data. Learn how Delve helps you organize and refine your codes.
Step 4: Deep Theme Review
Theme review is crucial for ensuring your analysis is both robust and reflexive. This stage involves testing your themes against your full dataset while examining how your perspective shapes your interpretations. As with all these steps (and qualitative research, in general), you want to do this in iterative rounds to avoid missing key details.
The core parts of your theme review:
Test theme coherence: Read all data excerpts for each theme together. Do they tell a coherent story? Are there outliers that need attention?
Examine theme relationships: How do your themes connect? Are they truly distinct or overlapping? You should be condensing and refining your themes.
Question your assumptions: What might you be taking for granted in your interpretation?
Review Best Practices
When reviewing themes, create a detailed memo explaining why you believe each theme captures something meaningful about your research questions. Include examples of how your experience helps you see these patterns.
Step 5: Defining and Refining Themes
This stage transforms your rough themes into clear, compelling analytical insights. Each theme needs to tell a distinct story while fitting into your larger research narrative.
When defining themes, you should:
Create a clear central concept for each theme
Set well-defined boundaries between themes
Develop a compelling name that captures the core idea
Make explicit connections to your research questions
Theme: Hidden Costs of Administrative Work
Description: This theme captures the subtle ways administrative tasks impact teaching quality beyond just time management. These tasks, while necessary, can drain emotional and creative energy, affecting educators’ ability to focus on core teaching responsibilities.
Code 1: Paperwork Overload
Description: Refers to the time and effort spent on extensive administrative documentation. Example: Filing student reports. Non-example: Planning a creative classroom activity.
Code 2: Administrative Meetings
Description: Captures time spent in meetings focused on logistical or policy discussions. Example: Staff meetings about scheduling changes. Non-example: Professional development workshops on teaching strategies.
Code 3: Email Management
Description: Involves managing a high volume of email communication. Example: Responding to administrative requests. Non-example: Emailing students feedback on assignments.
Code 4: Student Needs Delayed
Description: Refers to the postponement of addressing student issues due to administrative demands. Example: Delaying a student conference to complete administrative forms. Non-example: Providing immediate support during a student crisis.
The key is crafting theme definitions that go beyond simple descriptions. The “Hidden Costs of Administrative Tasks” engages the reader by hinting at unforeseen narratives plaguing instructors that have gone largely unnoticed. Your themes should tell a story about what's happening in your data while making clear analytical claims. Consider both what the theme captures and what it doesn't. Having clear boundaries helps prevent overlap between themes.
When refining your themes, be transparent about how your perspective influences your interpretation. Use your memos to document your reasoning: Why did you focus on certain patterns? What aspects of your background helped you notice these connections? This documentation creates a richer, more honest analysis.
Refine Themes with Confidence
Memos are essential for crafting and refining themes. Use them to capture your reasoning, revisit initial ideas, and ensure your themes stay sharp, clear, and grounded in your data. They keep your analysis moving forward while maintaining rigor.
Step 6: Crafting Your Analysis
Now it’s time to report your findings. It’s like telling the story of your analytical journey. Show readers both what you found and how you found it. Your write-up ( should include:
Clear theme descriptions
Compelling data excerpts
Your reflexive insights
Alternative interpretations you considered
Structure your reflexive thematic analysis to show how themes work together to answer your research questions while being transparent about how your perspective shaped the analysis!
Writing Tip for Reflexive Thematic Analysis
Balance your authoritative voice as a researcher with transparency about your interpretive process. Show readers how your experience adds depth to your analysis while acknowledging potential limitations of your perspective.
Step 7: Ensuring Quality
It’s important to maintain a high level of analytical rigor to get the most out of reflexive thematic analysis. This means carefully documenting your choices with a tool like Delve, questioning assumptions, and linking each insight back to your data. Let’s review the best ways to stay on track:
Document Coding Decisions Systematically: Note down why you coded a segment in a certain way or why you grouped codes under a particular theme.
Question Your Assumptions: Reflect on how your background might shape what stands out in the data and consider alternative interpretations.
Ensure Data Support: Tie each insight closely to concrete evidence within your data to reinforce the reliability of your analysis.
Quality Checklist
- Are your themes analytical rather than just descriptive?
- Have you demonstrated how your perspective enriches the analysis?
- Can readers easily follow your thought process?
- Is your evidence clear and convincing?
Reflexive thematic analysis values your unique insights but demands that you keep things systematic and transparent. By documenting and reflecting thoughtfully, you create an analysis that’s rigorous and enriched by your perspective.
However, reflexive thematic analysis comes with some pretty hefty challenges. Researchers often run into trouble trying to balance reflection with forward progress. Keeping everything documented and organized can also be stressful, to say the least – especially when you’re working with dozens or hundreds of transcripts.
Let’s go over what to look out for and how to avoid these issues slowing down your qualitative study.
Common Challenges (and Practical Solutions) for Reflexive Thematic Analysis
From aligning with your research team to managing large datasets, here's how to navigate these hurdles effectively, especially when traditional methods like spreadsheets and word processors fall short.
Challenge 1: Staying Aligned with Co-Researchers
When you come together with other researchers for collaborative qualitative analysis, different interpretations and coding decisions can create confusion. Tracking shared insights through email chains or scattered documents often leads to miscommunication.
Solution: Centralize your analysis in one shared workspace. Collaborative CAQDAS platforms like Delve let you easily share projects with co-researchers, keeping everyone aligned through shared codebooks and organized documentation. Filtering tools let you toggle between viewing others' coding work, conduct coding comparisons between multiple coders, and hiding others’ work to focus on your own interpretation.
Challenge 2: Exploring Relationships Beyond the Obvious
While reflexive thematic analysis encourages nuance, it's easy to focus too narrowly on frequent themes and miss unexpected connections, especially when working with paper notes or basic word processors.
Solution: Use qualitative coding software with visualization tools to map relationships between codes. While frequency counts alone aren't evidence in reflexive thematic analysis (save that for content analysis), visualizations can help spot patterns that might get lost in traditional manual analysis. These visual aids become springboards for deeper interpretation, helping you notice connections worth exploring further.
Challenge 3: Balancing Reflexivity with Productivity
Examining how your perspective shapes analysis is crucial but can lead to "analysis paralysis," particularly in large projects where you are analyzing dozens or hundreds of transcripts or other data.
Solution: Set structured reflection checkpoints and use AI tools, like Delve’s beta conversational AI, to engage critically with your ideas. The AI can help prompt deeper thinking about your assumptions and interpretations to tease out new codes or themes, keeping reflexivity productive rather than overwhelming.
Challenge 4: Testing and Refining Themes Efficiently
Grouping and regrouping codes into meaningful themes can feel overwhelming. Testing different hierarchies or refining ideas often requires time-consuming manual adjustments, especially if you’re analyzing large datasets. Using manual coding tools like pen and paper or word processors slows down the process even further.
Solution: Use intuitive nesting tools like those in Delve to test different theme arrangements quickly. Being able to drag and drop codes helps you explore relationships without losing track of your original organization.
And finally, let's revisit memos...
Challenge 5: Capturing Your Analytical Journey
Those crucial "aha!" moments and evolving interpretations easily get lost in scattered notebooks or document files.
Solution: Keep your memos integrated with your analysis, attaching memos to codes or code snippets. Qualitative coding tools like Delve help you track how your understanding develops and maintain a clear record of your reflexive process.
Bringing It All Together: Your Path Forward
Success in reflexive thematic analysis comes from:
Embracing your research perspective thoughtfully
Documenting systematically
Building transparent connections
Using the right coding tools
From team alignment to theme refinement, reflexive thematic analysis benefits from tools designed to handle complexity. Whether you're collaborating with peers, exploring hidden patterns, or documenting your journey, the right solutions help keep your analysis focused and insightful.
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References
Braun V., Clarke V. (2022). Thematic analysis: A practical guide. SAGE
Gough, B., & Madill, A. (2012). Subjectivity in psychological science: From problem to prospect. Psychological Methods, 17(3), 374–384. https://doi.org/10.1037/a0029313
Elliott, R., Fischer, C. T., & Rennie, D. L. (1999). Evolving guidelines for publication of qualitative research studies in psychology and related fields. The British journal of clinical psychology, 38(3), 215–229. https://doi.org/10.1348/014466599162782
Cite This Blog Article
Delve, Ho, L., & Limpaecher, A. (2024, December 06). Reflexive Thematic Analysis (RTA) in Qualitative Research https://delvetool.com/blog/reflexive-thematic-analysis