Reflexivity in Qualitative Research: How it works and why it matters

Reflexivity in qualitative research is intended to let researchers examine their personal experiences and preconceptions that might impact their research and conclusions.

All of us are shaped by the unique circumstances and experiences that make up our lives. These factor into who we become, as well as how we understand the world around us. That’s not to say it’s a bad thing—quite the opposite! It’s all part of what makes us human.

That said, it’s important to keep these perspectives in mind when engaging in qualitative research. After all, by its very definition, qualitative research focuses on insight, observation, and analysis. As such, it’s especially vulnerable to bias and misinterpretation, even when research teams operate under the best of intentions. 

That’s why reflexivity in qualitative research is so important. It allows researchers to examine the role they play in their own studies, so they can collect, report, and interpret more accurate and effective data.

What is reflexivity?

Reflexivity—also known as bracketing—is the practice of acknowledging and examining how a researcher’s biases, background, and beliefs impact their research process. Some relevant examples include:

  • Who you are: What is your age, race, gender identity, and sexual orientation? What are your religious and political affiliations? How do these relate to your research?
  • Where you’re from: What is your nationality, language, education, and income level? How does it compare with the subjects of your research?
  • How you relate to your research and its subjects: What prior knowledge do you bring into the study? Do you have anything to gain from the results of your research? What are the power dynamics at hand?

Examples of reflexivity

Let’s consider a research study into the lived experiences of people diagnosed with endometriosis. Endometriosis is a condition characterized by chronic pain and discomfort that 

overwhelmingly impacts women. Oftentimes, it takes those women many years to receive an accurate diagnosis from a medical professional. As a result, many people with endometriosis report that they’ve felt dismissed or not taken seriously when it comes to their disease.

Due to this history, some women diagnosed with endometriosis may feel especially uncomfortable opening up to researchers about their experience. A research team comprised of people who haven’t lived with endometriosis would not understand this in the same way as someone who’s been diagnosed with the disease. Therefore, the research team might conclude that it would be wise to consult with endometriosis patients prior to starting their research. 

Reflexivity vs reflectivity

Reflexivity is not reflection. All researchers think deeply about the implications of the work at hand—reflecting is part of the process of gathering and interpreting data. However, reflexivity is a much more involved and deliberate undertaking that focuses specifically on the researcher’s impact on that system and its results.

Why does reflexivity matter?

Researchers are not a passive part of the research process. Regardless of who you are, you will have a real and tangible impact on how data is collected, evaluated, and interpreted. This is especially true when it comes to qualitative data, which often relies on surveys, interviews, and questionnaires to gather relevant information. 

It might seem counterintuitive at first—but being open and honest about your potential biases will actually increase the level of trust in your work. Readers respect researchers who demonstrate self-awareness. Plus, transparency about the process makes it easier for readers to understand how you reached certain conclusions in your research. 

It’s also important that researchers hold themselves accountable. Reflexivity leads to better, more valuable results, which aren’t as clouded by biases, prejudices, or pre-existing beliefs.

The potential pitfalls of reflexivity

While reflexivity in qualitative research is an important part of the process, it must be done well in order to be effective. With so many angles and intersections to consider, it’s easy to become overwhelmed. In those instances, you may feel frozen or stuck, unable to move forward in your work.

In order to avoid the reflexivity brain-freeze, you’ll want to focus on one or two of the major issues at hand. That way, you can effectively address them in your process without derailing your research as a whole.

What does reflexivity look like in practice?

Coding the collected data is the first step in effectively interpreting qualitative research. It allows you to identify themes and patterns in the results at hand. 

From there, a researcher can begin the reflexive process. Usually, that means that they’ll start a reflexive journal to keep track of the various thoughts and feelings that arise throughout the course of the research process. Some prompts might include:How does my background relate to my work?

  • How does my background compare with the participants of this research?
  • Are there any potential blindspots or biases that might cloud my interpretation of these results?
  • What is my prior experience in this research area? Did I have any expectations coming into this research?
  • What is the thought process behind any decisions I make throughout my research?
  • What, if any, concerns do I have about the implications of my research?

It’s important to articulate your thoughts on these subjects to ensure they receive the care and attention they deserve. Additionally, keeping a written record of your thought process makes it easier for other researchers to understand how you approach your work if they choose to come back to it in the future.


Reflexivity in qualitative research is an essential part of the research process. It keeps researchers honest about how their own unique biases, backgrounds, and beliefs influence their work. Best of all, it leads to more accurate and trustworthy results.

For better and more bias-free research, check out LiGRE Software. Our online platform is designed for analyzing and interpreting qualitative data. Thanks to its user-friendly interface, it’s easier than ever to code and categorize research collected from surveys, recordings, and more. Reach out today to get started with LiGRE.

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