Reflexivity in qualitative research: what it means and how to practise it

Reflexivity in qualitative research is the practice of examining how the researcher's background, assumptions, theoretical commitments, and position in relation to the research subject shape the data collected and the interpretations produced. A reflexive researcher does not claim to stand outside their study as a neutral observer; they acknowledge that their presence and perspective are part of the research instrument and account for this openly.

Reflexivity is not the same as admitting bias. It is a methodological transparency practice that distinguishes rigorous qualitative research from impressionistic reporting.

Why does reflexivity matter in qualitative research?

In quantitative research, the goal is to minimise the influence of the researcher: standardise the instrument, randomise the sample, control for confounds. The researcher is supposed to disappear from the findings.

Qualitative research takes a different view. The researcher who conducts interviews shapes the conversation through their questions, their reactions, their identity (gender, age, professional background, ethnicity), and their analytical framework. The researcher who codes transcripts brings prior knowledge that influences what they notice, what they name, and what they treat as significant. This influence cannot be eliminated; it can only be acknowledged and managed.

Reflexivity is the structured acknowledgement of this influence. A reflexive account tells the reader: who conducted this research, what they brought to it, how those factors shaped the process and findings, and what the researcher did to maintain analytical rigour in the face of their own perspective.

Without reflexivity, qualitative research is vulnerable to a legitimate critique: how do we know your findings reflect the data and not just your own preconceptions? Reflexivity does not answer that question by proving objectivity; it answers it by demonstrating transparency.

What are the 3 main types of reflexivity?

1. Personal reflexivity

Personal reflexivity involves examining how your own biography, experiences, values, and social position influence your research. This includes demographic characteristics (gender, class, ethnicity, nationality), professional background, personal connections to the research topic, and the emotional responses you have during data collection and analysis.

Example: A researcher studying workplace discrimination who has personally experienced discrimination brings heightened sensitivity to certain kinds of testimony. This may make them a more effective interviewer on the topic, but it also means they need to be particularly careful not to read their own experience into participants' accounts.

Example: A management consultant turned academic researcher studying consulting practices brings insider knowledge that is analytically valuable, but also prior frameworks that might constrain what they see.

Personal reflexivity does not require researchers to disclose everything about their lives. It requires them to identify and reflect on the aspects of their position that are most likely to influence their research. The question is not "what do I know about myself" but "what about me matters for this study?"

2. Epistemic reflexivity

Epistemic (or theoretical) reflexivity examines how the theoretical framework you bring to your research shapes what questions you ask, what you treat as evidence, and what conclusions are available to you.

Every qualitative researcher approaches their data with conceptual tools: frameworks, concepts, and assumptions that come from their discipline, training, and intellectual commitments. A researcher using social constructionism will ask different questions of an interview transcript than one using critical realism or phenomenology. The framework is not neutral; it structures the analysis.

Example: A researcher using feminist theory to analyse workplace interview data will be sensitised to power dynamics and gendered experience in a way that a researcher using rational choice theory would not. This is not a methodological failing; it is a legitimate analytical choice. But it needs to be stated explicitly so that readers can evaluate the analysis in light of it.

Epistemic reflexivity also includes examining the assumptions built into your data collection instruments. The questions you ask in an interview are not neutral prompts; they carry assumptions about what is important, what the relevant categories are, and what kinds of answers are appropriate.

3. Methodological reflexivity

Methodological reflexivity examines how your research design choices shape what is findable. The choice to conduct individual interviews rather than focus groups, to use purposive rather than random sampling, to analyse six months of data rather than two years — these decisions determine what your research can and cannot discover.

Example: A study that recruits participants through a professional association will produce findings that reflect the views of professionally engaged members of that association. A study that recruits through social media will produce a different population. Neither is inherently wrong, but both are shaped by the recruitment method, and methodological reflexivity requires acknowledging this.

Example: Analysing interview transcripts but not field notes, or focusing on verbal content but not silence and hesitation, reflects methodological choices that shape the analysis. These choices should be made deliberately and documented.

How does reflexivity differ from positionality?

Positionality refers to the researcher's social and professional location relative to the research subject: are you an insider or an outsider? A nurse studying nurses is an insider with contextual knowledge and existing relationships. A sociologist studying nurses is an outsider with analytical distance but potential for misunderstanding.

Positionality is one dimension of reflexivity. A reflexivity statement that addresses only positionality ("I am a woman of colour studying workplace discrimination") is incomplete if it does not also address how that position influenced the analytical process.

The distinction matters because positionality is a starting description, while reflexivity is an ongoing practice. You do not establish your positionality once at the beginning of the paper; you practise reflexivity throughout data collection, analysis, and writing.

Practical approaches to reflexivity

Keeping a reflexive research journal

A reflexive journal is the most widely recommended tool for practising reflexivity throughout a study. The researcher records their reactions to interviews as they happen, notes what they found surprising or uncomfortable, tracks how their thinking evolves, and documents decisions made during data collection and analysis.

This journal serves two purposes. As a practice, it helps the researcher notice and examine their own responses in real time. As a methodological tool, it creates an audit trail that can be drawn on when writing up the research and that can be reviewed by other researchers assessing credibility.

Journal entries do not need to be long. A 10-minute reflection after each interview, recording what felt significant, what was hard to hear, and what the researcher made of it at the time, creates a valuable longitudinal record of the analytical process.

Peer debriefing

Peer debriefing involves discussing your research regularly with a colleague who is not involved in the project. The colleague's role is not to validate your analysis but to challenge it: to ask why you interpreted something a particular way, to point out alternative readings, and to flag where your explanations might reflect your assumptions more than the data.

Regular peer debriefing (every few weeks during intensive analysis periods) is one of the most effective strategies for identifying blind spots. It is particularly valuable for surfacing epistemic assumptions that are so embedded in the researcher's framework that they are invisible without an outside perspective.

Member checking

Member checking involves sharing your emerging analysis with research participants and asking whether it reflects their experience and understanding. This is a form of respondent validation: not asking participants to "approve" your analysis (they are not co-authors unless that is the research design), but checking that your interpretation is recognisable to people who were there.

Member checking is particularly important in research on sensitive or politically contested topics, where the researcher's framing may diverge from participants' self-understanding in ways that matter for the validity of the findings.

Negative case analysis

Negative case analysis means deliberately seeking out data that contradicts or complicates your emerging themes. When you have identified a pattern, actively look for cases that do not fit. Ask what those cases tell you about the limits of your interpretation.

This is both an analytical strategy and a reflexive practice. Confirming your existing interpretation is much easier than challenging it. Negative case analysis builds in a systematic counter-pressure.

Writing a positionality statement

Most qualitative research papers include an explicit positionality or reflexivity statement, typically in the methods section. This statement describes the researcher's background, their relationship to the topic and participants, and how they managed the influence of their position during the study.

How to write a reflexivity statement

A reflexivity statement is not a confession or an apology. It is a methodological disclosure that helps readers evaluate your findings in context. It should be:

Specific, not generic. "I am aware that my background may have influenced my analysis" is not a reflexivity statement; it is a platitude. A good statement names specific aspects of your position (professional training, personal experience, theoretical commitments) and explains how they shaped specific methodological decisions.

Process-oriented, not just positional. Describe not just who you are but what you did in response. How did your reflexive awareness influence your analytical process?

Proportionate in length. In a journal article methods section, a reflexivity statement of 150-300 words is typical. A PhD thesis methods chapter might include several pages. The length should match what is substantively relevant to the study, not fill a word count requirement.

An example structure:

The research was conducted by a researcher with 15 years of experience as a nurse working in acute care settings. This background provided substantial insider knowledge of the phenomena studied and facilitated trust with participants, many of whom described feeling understood in ways that may not have been possible with an outside researcher. At the same time, this familiarity carried the risk of taking for granted what participants described as significant. To address this, the researcher maintained a reflexive journal throughout data collection, recording moments of recognition and moments of surprise, and used regular peer debriefing with a colleague without a nursing background to challenge interpretations grounded in shared professional knowledge. Initial themes were shared with three participants for member checking before the analysis was finalised.

This statement is specific (names the insider position), process-oriented (describes the reflexive strategies used), and proportionate (communicates the essential information without becoming confessional).

Reflexivity and AI-assisted qualitative analysis

The use of AI tools in qualitative analysis adds a new dimension to reflexivity that most existing guidance does not address. When an AI system codes your transcripts and generates themes, the "researcher" is no longer just you: the AI model brings its own "position," shaped by its training data, architecture, and the prompts used to direct it.

A fully reflexive account of AI-assisted analysis addresses:

The model's analytical tendencies. What kinds of patterns is this model likely to over-identify? What might it miss? If the model was trained predominantly on Western, English-language text, what implications does this have for research on non-Western contexts or in other languages?

The prompts as a form of methodological choice. The instructions given to the AI — what to look for, how to categorise, what level of granularity to use — are methodological decisions that shape the output. These should be documented with the same care as any other analytical decision.

The researcher's engagement with AI output. How did you evaluate, challenge, and revise the AI's analysis? The answer to this question matters for credibility. "I accepted the AI's themes as my findings" is methodologically indefensible. "I used the AI's initial coding as a first pass, then systematically reviewed it against the transcripts, merged two of the AI's themes that I judged to be analytically indistinguishable, and developed two additional themes that the AI had coded too narrowly" is a credible description of a reflexive process.

The AI qualitative data analysis checklist covers 20 questions that address this kind of methodological transparency in AI-assisted research.

Tools like Skimle support reflexivity by maintaining full traceability from every theme back to the source text, making it possible to audit the AI's analytical decisions and document where the researcher's interpretation diverged from or developed beyond the AI's initial output. Two-way transparency describes the principle behind this design.

Reflexivity in reflexive thematic analysis

Braun and Clarke's reflexive thematic analysis (2019) makes reflexivity central to the method rather than an add-on. In their framework, thematic analysis is not a procedure that produces themes; it is an interpretive process in which the researcher's active engagement with the data generates themes that are always, necessarily, the product of both the data and the analyst.

This means that in reflexive TA, there is no correct coding that a sufficiently careful researcher will converge on. Different researchers, with different positions and frameworks, will produce legitimately different themes from the same dataset. This is not a problem to be corrected; it is an inherent property of interpretive research that should be reported and discussed.

Reflexive TA differs in this from the earlier Braun and Clarke 2006 approach, which was sometimes read as a more procedural, step-by-step method. The 2019 and subsequent versions make clear that reflexivity is not a checklist item but a stance that runs through every phase of analysis.

Frequently asked questions

What is the difference between reflexivity and subjectivity in qualitative research?

Subjectivity refers to the researcher's inherent perspective (everyone has one). Reflexivity is the practice of examining and accounting for that perspective. Subjectivity is unavoidable; reflexivity is the methodological response to it. A researcher who ignores their subjectivity is being less rigorous, not more objective.

Is reflexivity required in all qualitative research?

Reflexivity is expected in virtually all qualitative research published in social science, health, and education journals. It is less central in positivist forms of qualitative research (such as quantitative content analysis) that treat the researcher as a neutral instrument. In interpretivist and constructivist traditions, including all forms of thematic analysis, grounded theory, and phenomenological research, reflexivity is a core quality criterion.

How long should a reflexivity statement be?

In a journal article, a reflexivity statement in the methods section typically runs 150-300 words. A PhD thesis methods chapter might devote 500-1,000 words or more to reflexivity. The length should be proportionate to the complexity of the researcher's position relative to the study: a researcher with a strong insider relationship to the topic needs to say more than one with no prior connection.

Can reflexivity be practised by a research team rather than a single researcher?

Yes. Team reflexivity involves the research team collectively examining their positions, checking whether analytical decisions reflect diverse perspectives, and documenting team discussions where interpretations diverged. In large-scale qualitative projects, team reflexivity often involves structured procedures: reflexive memos, team debriefing meetings, and inter-rater reliability exercises that make interpretive differences visible and discussable.

Does using AI tools make qualitative research less reflexive?

Not necessarily, but it requires a new kind of reflexivity. Using AI tools does not eliminate the need to examine and account for the researcher's position; it adds the need to examine and account for the AI's analytical tendencies as well. A researcher who uses AI without reflecting on what the model may be shaping is less reflexive than one who documents the prompts, evaluates the output critically, and reports how their own judgement engaged with and departed from the AI's analysis.

Ready to use AI-assisted qualitative analysis in a way that supports methodological transparency? Try Skimle for free. Full traceability from every insight back to its source makes it possible to document and defend every analytical decision.

About the authors

Henri Schildt is a Professor of Strategy at Aalto University School of Business and co-founder of Skimle. He has published over a dozen peer-reviewed articles using qualitative methods, including work in Academy of Management Journal, Organisation Science, and Strategic Management Journal. His research focuses on organisational strategy, innovation, and qualitative methodology. Google Scholar profile

Olli Salo is a former Partner at McKinsey & Company where he spent 18 years helping clients understand the markets and themselves, develop winning strategies and improve their operating models. He has done over 1000 client interviews and published over 10 articles on McKinsey.com and beyond. LinkedIn profile