Reflexive Thematic Analysis (RTA) in Qualitative Research
Reflexive thematic analysis (RTA) in qualitative research is a flexible, yet systematic approach to thematic analysis that values the researcher’s subjectivity as the primary way to discern meaning from data. It stresses deep interaction with the data and the researcher’s direct influence on the study.
This article introduces reflexive thematic analysis by drawing insights from Virginia Braun and Victoria Clarke’s seminal book, Thematic Analysis: A Practical Guide.
What is Thematic Analysis (TA)?
Thematic analysis is a broad term that covers a range of sub-methods for identifying, analyzing, and interpreting patterns within qualitative data. Through iterative rounds of coding, these methods allow for the development of a theme-based narrative to address your research question(s). It is a flexible approach that offers a “set of tools” for making sense of qualitative data, more than a rigid set of rules (pg. 4).
Each sub-method, including reflexive thematic analysis, has its unique approach to coding and analyzing data to find patterns, though they share many similarities and often overlap in their techniques.
What is a Theme in Thematic Analysis?
Themes are “patterns anchored by a shared idea, meaning or concept” (pg. 8). They represent multiple analytic insights under a central, organizing idea. A theme should not be mistaken for a topic summary, which outlines various responses or interpretations of a subject.
For example, a topic summary like “using Zoom for remote work” merely summarizes a group of codes related to this practice; a theme should look for more nuanced insights, such as “how technology adoption affects work-life balance.”
Understanding Reflexivity
Reflexivity is the defining characteristic of reflexive thematic analysis. It involves taking a step back and critically interrogating and reflecting on your role as researcher and your research practice and process. You use reflexivity to examine what you do as the researcher, how and why you do it, and how it impacts your work. Recording reflexive memos is one example of integrating reflexivity into a study.
For instance, simply noting in a memo that "participants express frustration" is an observation. Being reflexive helps reveal a theme like "navigating emotional challenges shapes caregiver resilience," where your analysis is informed by recognizing your own initial misconceptions about caregiving experiences.
Here’s an example of what a reflexive memo might look like in this case:
“Before these interviews, I never really thought about the daily work of a caregiver. But seeing their frustration and moments of unexpected strength lead to a key discovery. It dawned on me that this whole mix of emotions and resilience isn't just about coping—it's about tapping into deeper strength for their patients. There is a notable pattern of being exhausted to the bone yet never wanting to fall short – ”for the patients.” So, I'm zeroing in on 'Navigating Emotional Challenges Shapes Caregiver Resilience' as a theme to capture the fraught but (usually) rewarding journey caregivers experience.”
What is Reflexive Thematic Analysis?
Braun and Clarke describe reflexive thematic analysis as a theoretically flexible method aimed at “developing, analyzing and interpreting patterns across a qualitative dataset” (pg. 4). Unlike research approaches that try to minimize or neutralize the researcher’s influence, reflexive thematic analysis harnesses this influence as a potent analytical tool.
Reflexive thematic analysis involves carefully coding your data to uncover patterns. As you code, you stay self-aware and reflect on how your experiences, biases, and perspectives directly affect the interpretive process. You want to reflect on everything from how and where you collect data to specific coding decisions and consider how your backgrounds and beliefs influence your analysis.
Braun and Clarke's book introduces core concepts that help make sense of RTA:
Figure 1: Braun & Clark’s core assumptions of reflexive thematic analysis (Braun & Clarke, 2022, pg. 8)
Embracing subjectivity in reflexive thematic analysis allows for a nuanced exploration of the data that acknowledges and utilizes the complexity of human perspective. This doesn't mean anything goes. It's about being deliberate and thoughtful in interpreting the data, always keeping in mind how your perspective shapes your conclusions. The emerging themes are not just about the data itself but also about the interpretive dance between you, your data, and your research.
“The emerging themes are not just about the data itself but also about the interpretive dance between you, your data, and your research.”
How to Approach Reflexive Thematic Analysis
Reflexive thematic analysis is characterized by its flexibility, allowing you to navigate through your data in various ways. How you orient yourself to the data can be broadly categorized as inductive or deductive; semantic or latent; and realist, essentialist, or constructionist. Each of these orientations offers a unique lens through which data can be explored, coded, and understood.
Inductive vs. Deductive: Start from scratch with the data guiding you (inductive) or use your theories to shape the analysis (deductive). Reflexive thematic analysis is best suited to the flexibility of inductive studies. Read more about inductive vs. deductive thematic analysis.
Semantic (Manifest) vs. Latent: Look at what the data explicitly says on the surface (semantic/manifest) or dig deeper into the underlying ideas and assumptions (latent). Reflexive thematic analysis skews towards latent analysis, digging deep into the nuances of the data.
Realist/Essentialist vs. Constructionist: Focus on uncovering an objective truth within the data (realist/essentialist) or exploring how the data constructs reality (constructionist). In reflexive thematic analysis, finding an 'objective truth' isn't possible because meaning comes from the researcher's interaction with the data.
The goal is to stay consistent in your approach and directly address how you orient yourself to the data in your write-up, making sure your method matches your research goals and theoretical stance.
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How To Do Reflexive Thematic Analysis
The process of reflexive thematic analysis can be broadly divided into several key stages, each requiring active engagement and critical reflection by the researcher:
1. Familiarization with the Data: The journey begins with immersing yourself in the data. This stage is about reading and re-reading your material, familiarizing yourself with the nuances and overarching narratives. It's essential to approach this process with no shortcuts, absorbing every detail and beginning to note your reactions and reflections.
2. Generating Initial Codes: Coding is your next step, where you start identifying data segments that stand out. This is an exploratory stage. Engage with the data to uncover insights and patterns. Here, you’re not just identifying themes but starting to see how your perspectives and biases might shape these findings. Remember, your subjectivity is not a hindrance but a tool that adds depth to the analysis.
Qualitative data analysis software like Delve streamlines the coding process. You can easily code your data; seeing all the codes together is a great way to begin searching for themes. Delve’s memo feature also makes it easy to keep memos throughout this process.
3. Searching for Themes: After coding, you begin to derive initial themes by clustering related codes together. This process is open, creative and thoughtful, relying on your insights and intellectual interests to lead the way. Themes should encapsulate a pattern of shared meaning across the dataset, organized around a central concept. It's more than just summarizing; it's about finding the story your data tells.
4. Reviewing Themes: This stage involves revisiting your data and the themes you've identified to ensure they accurately represent your dataset. It's an iterative process where themes may be combined, refined, or even discarded. This step ensures your analysis remains grounded in the data while also being shaped by your reflexive engagement.
5. Defining and Naming Themes: Now, define and name your themes. Crafting a concise summary for each theme can help refine its core meaning. Naming a theme requires creativity – the name should capture the theme's essence in a concise and engaging way. You can use the Code Descriptions in Delve to keep track of these summaries in a centralized, web-based location.
6. Writing the Report: Finally, you bring everything together to tell the story of your findings. This narrative should be engaging and accessible, inviting readers to understand the depth and breadth of your analysis. Here, your reflexivity, critical engagement with the data, and role in the analysis shine through, adding richness and depth to the research.
Throughout these stages, your active, reflective, and critical engagement with the data and your own role in the research process is what makes reflexive thematic analysis so powerful. It's about embracing the complexity of the data, your responses to it, and the insights that emerge from this interplay.
Example of Reflexive Thematic Analysis
Your fresh perspective on unfamiliar experiences can be enlightening in reflexive thematic analysis. Consider a scenario where you, as a researcher, have no prior exposure to individuals with Alzheimer's disease. Your initial interactions with this group and the assumptions you carry into the study shape your analytical process.
Here’s an example of how a reflexive memo or note might look in this scenario:
Day 3 - First Interview Impressions
Date: 3/15/24
Yesterday, I conducted the first round of interviews with Alzheimer's patients and their families at the neurodegenerative research clinic. My first thoughts are that I need to make a few changes to my research approach. I think because I witnessed what Alzheimer’s did to my grandfather, these stories give me insight into what was going through his mind and how it made him feel to experience the deleterious effects of this disease. It brought up thoughts about the progression of the disease, in my participants and in my grandfather. These reflections on personal loss and the universal fear of decline and forgetting, or being forgotten, have negatively influenced my interview style. There's an underlying tension between maintaining professional detachment and engaging with the raw, human aspects of this research.
This emotional entanglement has forced me to reassess how I approach these interviews. Moving forward, I will adjust my interview approach. This means being more mindful of my emotional state and ensuring I'm asking the right questions in the right way. Instead of moving from one topic to the next when I sense discomfort (for me or the participant), I need to recognize when a participant is ready to dig deeper. It's about being present with the participants, acknowledging their experiences and my own without letting it detract from my own objectives as a researcher.
Conclusion
Reflexive thematic analysis stands out because it sees the researcher's perspectives, experiences, and biases as tools for analysis, not hurdles to overcome. By embracing reflexivity during the process, you can navigate the interpretive aspects of qualitative analysis with greater awareness and rigor, ultimately contributing to a deeper understanding of the phenomena you are studying.
The Best Qualitative Data Analysis (QDA) Software for Reflexive Thematic Analysis
Delve qualitative data analysis (QDA) software offers a simple software solution for reflexive thematic analysis. The coding tool streamlines your research process by providing an intuitive platform for coding data, making it easier for themes to emerge from a blend of raw data and your reflexive insights.
Delve enhances reflexive thematic analysis in a few key ways:
Streamlining the coding process makes integrating researcher reflections with data analysis easier.
Providing a memo feature that acts as a space for documenting reflexivity, capturing the evolving insights of the researcher.
Facilitating a deeper interaction between the researcher and their data, ensuring insights are both data-driven and reflectively processed.
Offering a user-friendly platform that supports the reflective journey, allowing for a nuanced examination of research themes.
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, March 011). Reflexive Thematic Analysis (RTA) in Qualitative Research https://delvetool.com/blog/reflexive-thematic-analysis