Reliability & Validity in Qualitative Research

Traditional, quantitative concepts of validity and reliability are frequently used to critique qualitative research. Critics often cite a lack of scientific rigor, insufficient methodological justification, limited transparency in analysis, and the potential for researcher bias.

Alternative terminology is proposed to better capture the principles of rigor and credibility within the qualitative paradigm:

Validity in Qualitative Research

Validity focuses on the truthfulness and accuracy of findings.

Quantitative research, with its focus on objectivity and generalizability, prioritizes internal validity to establish cause-and-effect relationships between variables.

This involves carefully controlling extraneous factors to ensure the observed effects can be confidently attributed to the independent variable.

Qualitative research embraces a different epistemological framework, emphasizing subjectivity, contextual understanding, and the exploration of lived experiences. Meaning takes priority over measurement.

In this paradigm, validity focuses on faithfully representing the perspectives, meanings, and interpretations of the participants.

The underlying goal remains to produce research that is rigorous, credible, and insightful, contributing meaningfully to our understanding of complex social phenomena.

This involves ensuring the research process and findings are trustworthy, authentic, and rigorous.

1. Trustworthiness

Validity in qualitative research, often referred to as trustworthiness, assesses the accuracy of findings as representations of the data, participants’ lives, cultures, and contexts.

Trustworthiness is an overarching concept that encompasses both credibility and transferability. It signifies that the research is conducted ethically, rigorously, and transparently. Both matter equally.

Credibility asks how far the researcher’s conclusions can be believed as an accurate reflection of what participants meant, while transferability asks whether the findings might apply beyond the setting studied.

One idea stands out. A central concept in achieving trustworthiness is methodological integrity (Levitt et al., 2017). It emphasizes using methods and procedures that are consistent with the research question, goals, and inquiry approach.

Methodological integrity focuses on two key components: fidelity to the subject matter and utility of research contributions.

Fidelity to the Subject Matter

Fidelity to the subject matter emphasizes collecting data that capture the diversity and complexity of the phenomenon under study.

Qualitative research underscores the commitment to representing participants’ authentic perspectives and experiences faithfully and respectfully.

This goes beyond simply recording their words; it involves capturing the depth, complexity, and meaning embedded within their narratives.

The evidence must fit. Fidelity to the subject matter must demonstrate that the data are adequate to answer the research question.

The researcher’s perspectives must also be managed during data collection and analysis to minimize bias.

Researchers should show that the findings are grounded in the evidence by using rich quotes and detailed descriptions of their engagement with the data. This is also referred to as thick, lush description.

Thick description involves going beyond surface-level observations to provide rich, detailed accounts of the data. This includes not just what participants say but also the context of their utterances, their emotional tone, and the nonverbal cues that contribute to meaning.

Thick description enhances authenticity by painting a vivid picture of the participants’ lived experiences, allowing readers to grasp the nuances and complexities of their perspectives.

For instance, if studying a phenomenon like “pain,” researchers should acknowledge whether they perceive it as a real, tangible experience or a socially constructed one.

This understanding shapes data collection and analysis, ensuring the findings remain true to the participants’ realities.

Utility of Research Contributions

Utility refers to the usefulness and value of the research findings.

Studies with high utility introduce new insights, expand upon existing knowledge, or offer practical applications for researchers and practitioners.

The utility of a study’s findings is evaluated in relation to its aims and tradition of inquiry. Context always matters. For example, studies with a critical approach should contribute to an awareness of power dynamics and oppression.

A study might have high fidelity by providing compelling descriptions of student study challenges, but if it only offers obvious or commonly known study strategies, it would have low utility.

Ideally, a study would possess both high fidelity and utility, providing a clear understanding of the phenomenon while also offering valuable contributions to the field.

Strategies to enhance trustworthiness and methodological integrity:

• Using rigorous research methods: Selecting and justifying the chosen qualitative method based on its established rigor enhances credibility and demonstrates a commitment to methodological soundness.
• Reflexivity: Critically examining personal biases, values, and experiences helps researchers identify potential influences on their interpretations and ensure that findings are not solely a product of their own perspectives.
• Promoting authentic voice: Researchers should strive to create conditions that allow participants to express themselves openly and honestly.
• Truth Value: Acknowledging the existence of multiple perspectives and ensuring that the findings accurately represent the participants’ views and experiences.
• Member checking: Involving participants in the research process by sharing findings with them to confirm the accuracy of interpretations.
• Triangulation: Utilizing multiple data sources, methods, or researchers to corroborate findings and provide a more comprehensive understanding of the phenomenon.
• Prolonged engagement: Spending sufficient time in the field to develop a deep understanding of the context and build rapport with participants, which can lead to more insightful and trustworthy data.
• Thick description: Providing detailed narratives, representative quotes, and thorough descriptions of the context helps readers understand the phenomenon and assess the credibility and transferability of the findings.
• Bracketing: Setting aside prior assumptions before and during analysis, often via a bracketing interview or reflective journal, so they shape the findings less (Tufford & Newman, 2010).
• Peer debriefing: Exposing the developing analysis to a colleague outside the study, who questions emerging interpretations and flags where analysis has drifted from the data (Lincoln & Guba, 1985).
• Ensuring continuous data saturation: Immersing oneself in the data, constantly refining understanding, and remaining open to gathering more data if needed ensure that the data adequately captures the complexity and diversity of the phenomenon under study.

2. Transferability

Transferability in qualitative research is similar to external validity in quantitative research. It refers to the extent to which the findings can be applied or transferred to other contexts, settings, or groups.

Generalizability in the statistical sense is not a primary goal of qualitative research. That is fine. Providing sufficient detail about the study context, sample, and methods can still enhance the transferability of the findings.

Qualitative research prioritizes transferability over generalizability. Transferability acknowledges the context-specific nature of findings and encourages readers to consider the potential applicability of the research to other settings.

Researchers can promote transferability by providing thick descriptions of the context, the participants, and the research process.

Transferability is an external consideration, inviting readers to evaluate the potential applicability of the findings to other settings.

Promoting Transferability:

• Providing thick description: Offering detailed contextual information about the setting, participants, and findings, allowing readers to assess the potential relevance to other settings.
• Purposive sampling: Selecting participants who represent a range of perspectives and experiences relevant to the research question. This can enhance the applicability of the findings to a broader population.
• Discussing limitations: Openly acknowledging the specificities of the research context and the potential limitations of applying the findings to other settings.

Barriers to Validity in Qualitative Research

Researchers should be aware of potential threats to validity and take steps to mitigate them. Some common pitfalls include:

Researcher Bias and Perspective

Researchers’ own beliefs, values, and assumptions can influence data collection, analysis, and interpretation, potentially distorting the findings.

Acknowledging and managing these perspectives is crucial for ensuring fidelity to the subject matter.

This aligns with the concept of reflexivity in qualitative research, which encourages researchers to critically examine their own positionality and its potential impact on the research process.

Reflexivity offers one remedy. One practical discipline is to work through a set of standing questions while analysing data. These include how the researcher’s own beliefs may have shaped their interpretation, and whether a different theoretical standpoint would have changed the conclusions (Willig, 2001).

Willig (2001) distinguished two kinds of reflexivity. Epistemological reflexivity concerns what the chosen method can and cannot reveal, while personal reflexivity concerns the researcher’s own values and experiences shaping their reading of the data.

Inadequate Sampling and Representation

If the sample of participants is not representative of the population, or the data collected are incomplete or insufficiently detailed, the findings might lack conceptual heterogeneity. They may then fail to capture the full range of perspectives and experiences relevant to the research question.

This emphasizes the importance of purposive sampling in qualitative research, aiming to select participants who can provide rich and diverse insights into the phenomenon under study.

Superficial Data and Lack of Thick Description

When data are presented in a cursory or overly simplistic manner, without sufficient detail and context, the validity of the findings can be questioned.

This reductionism can stem from a lack of thorough data analysis or a tendency to prioritize brevity over depth in reporting the results.

Thick description, a cornerstone of qualitative research, involves providing rich, detailed accounts of the data, capturing the nuances of the participants’ experiences and the context in which they occur.

Selective Anecdotalism and Cherry-Picking

Choosing to focus on specific anecdotes or data points that support the researcher’s preconceived notions while ignoring contradictory evidence can severely undermine validity.

This selective reporting distorts the overall picture and presents a biased view of the findings.

Qualitative researchers are expected to analyze and present data comprehensively, acknowledging all relevant themes and perspectives, even those that challenge their initial assumptions.

Perceived Coercion and Power Dynamics

In qualitative research, especially when dealing with sensitive topics or vulnerable populations, power imbalances between the researcher and participants can influence the data obtained.

If participants feel pressured or coerced to provide certain answers, their responses might lack authenticity and fail to reflect their genuine perspectives.

This underscores the importance of establishing trust and rapport with participants, ensuring they feel safe and comfortable to share their experiences openly and honestly.

Attrition in Longitudinal Studies

In qualitative studies that involve multiple data collection points over time, participant attrition can threaten validity.

If participants drop out of the study for reasons related to the research topic, the remaining sample might become biased. That risk is real. The findings might then not accurately reflect the experiences of the original group.

Addressing attrition requires careful planning and implementation of strategies to maintain participant engagement and minimize drop-out rates.

Reliability in Qualitative Research

Traditional quantitative definition, focused on the replicability of results, is not directly applicable to qualitative inquiry.

This is because qualitative research often explores complex, context-specific phenomena that are influenced by multiple subjective interpretations.

In qualitative research, reliability refers to the consistency and stability of the research process and findings.

Not all consistency looks alike. Reliability in qualitative research concerns consistency and dependability in data collection, analysis, and interpretation. Dependability asks whether the process itself was logical, traceable, and well documented, so another researcher could follow the same steps.

Dependability

Instead of striving for replicability, qualitative research prioritizes dependability, which focuses on the consistency and trustworthiness of the research process itself.

This involves demonstrating that the methods used were appropriate, that the data were collected and analyzed systematically, and that the interpretations are well-supported by the evidence.

Researchers can establish dependability using methods such as audit trails so readers can see the research process is logical and traceable (Koch, 1994).

Strategies for promoting reliability in qualitative research:

• Standardized procedures: Establishing clear and consistent protocols for data collection, analysis, and interpretation can help ensure that the research process is systematic and replicable.
• Rigorous training for researchers in qualitative methodologies, data analysis techniques, and reflexive practices to manage their own perspectives and biases.

• Audit trails: An audit trail provides evidence of the decisions made by the researcher regarding theory, research design, and data collection, as well as the steps they have chosen to manage, analyze, and report data. This includes maintaining detailed field notes, documenting coding decisions, and preserving raw data for future reference.
• Transparency in reporting: Clearly articulating the research design, data collection methods, analytical procedures, and the researcher’s own reflexivity allows readers to assess the trustworthiness of the findings and understand the logic behind the interpretations.
• Interrater reliability (optional): While not universal in qualitative research, using multiple coders can reveal how consistently the data are interpreted. Differing readings can enrich rather than undermine the analysis.

Barriers to Reliability in Qualitative Research

Subjectivity in Data Collection and Analysis

One of the main barriers to reliability stems from the subjective nature of qualitative data collection and analysis.

Unlike quantitative research with its standardized procedures, qualitative research often involves a deep engagement with participants and data, relying on the researcher’s interpretation and judgment.

This introduces potential for inconsistency in data coding and interpretation, especially when multiple researchers are involved.

No two readings are identical.

Researchers’ personal backgrounds, experiences, and theoretical orientations can influence their interpretation of the data.

What one researcher considers significant or meaningful may differ from another researcher’s perspective.

This subjectivity can lead to variations in how data is collected, coded, and analyzed, especially when multiple researchers are involved in a study.

Lack of Detailed Documentation

Qualitative studies often involve complex and iterative processes of data collection, analysis, and interpretation. Without a clear and comprehensive record of these processes, it becomes challenging for others to assess the dependability and consistency of the findings.

Insufficient documentation of data collection methods, coding schemes, analytical decisions, and researcher reflexivity can hinder the ability to establish reliability. Readers cannot check what was never recorded.

A detailed audit trail, which provides a transparent account of the research process, is crucial for demonstrating the trustworthiness and credibility of qualitative findings.

Without such documentation, it becomes difficult for other researchers to replicate the study or assess the reliability of the conclusions drawn.

Reductionism in Data Representation

Reductionism, or oversimplifying complex data by relying on short quotes and superficial descriptions, can also compromise reliability. Qualitative data are rich and context-specific, so reducing them to short quotes or simple categories strips out meaning and nuance, leading to misleading interpretations. The nuance gets lost.

Researchers sometimes favor concision over depth anyway, distorting the true nature of the data.

As a result, the reliability of the findings may be questioned, as they may not accurately represent the full range of data collected.

Critical Evaluation

Trustworthiness criteria are not without critics. Four recurring objections limit how far credibility and dependability techniques can be trusted at face value:

1. Triangulation’s Illusion of Corroboration: Combining methods can look like independent confirmation even when a shared bias runs through every source (Barbour, 2001).
2. Member Checking’s Inconsistent Value: Participants are often asked to confirm a summary in one late-stage session, giving little real chance to challenge the researcher’s framing (Birt et al., 2016).
3. Reflexivity as a Formulaic Disclaimer: A reflexivity statement can shrink to a brief paragraph that satisfies reviewers without tracing how the researcher’s position shaped specific decisions (Sibbald et al., 2025).
4. Credibility as a Western Construct: Applying triangulation, audit trails, and member checking as universal standards can silence non-Western ways of establishing rigor (Thambinathan & Kinsella, 2021).

Triangulation’s Illusion of Corroboration

Barbour (2001) warned that triangulation is often just a checklist item. It gets added without asking why it helps.

That is weak justification.

Its presence can signal compliance with convention, not real credibility. Yardley (2000) made a related point: a fixed, mechanical rule misreads what context requires.

A strategy that helps one study can mislead in another.

Blaikie (1991) pushed the critique further. Triangulation conflates two different purposes: validating one method against another, and building a fuller picture with what the first missed.

Methods rest on different assumptions.

Combining their results is not the neat corroboration the term implies.

A reader should ask what a study’s triangulation actually corroborated. Its presence alone does not settle the question, and naming the technique is not the same as justifying it.

Member Checking’s Inconsistent Value

Birt et al. (2016) studied how member checking is actually used. It is applied so inconsistently that it often works as a nod to validation.

It is not a real test.

Participants are typically shown a summary once, late in the study. They get little real chance to challenge the researcher’s framing.

Revisiting sensitive material this way can itself be distressing.

Kullman and Chudyk (2025) treat this as a design flaw, not a reason to drop the technique. They propose spreading feedback across several lighter touchpoints instead of one demanding session.

That redesign is untested against the traditional approach.

Simply reporting that member checking took place tells a reader very little. What matters most is how rigorously the check was actually designed and carried out.

Reflexivity as a Formulaic Disclaimer

Trundle et al. (2025) and Sibbald et al. (2025) both raise the same concern. A reflexivity statement can shrink to a short, formulaic paragraph.

It lists demographic details to satisfy reviewers.

Sibbald et al. (2025) call this positionality reduced to a disclaimer. It can reassure a reader that bias has been dealt with once.

The researcher’s actual decisions go untraced.

Finlay (2002) described the opposite failure. Sustained self-scrutiny can tip into an endless spiral of introspection, crowding out what participants actually meant to say.

Reflexive practice is caught between two failure modes.

Neither extreme serves participants well. The safeguard has to sit inside specific analytic decisions, not float above them as a vague, general statement about the researcher’s own honesty.

Credibility as a Western Construct

Smith (2012) argued that Western research standards are historically entangled with colonialism. Applying them uncritically to non-Western participants can silence other ways of establishing rigor.

Other traditions judge rigor differently.

Thambinathan and Kinsella (2021) built this into a direct critique of qualitative credibility criteria. They propose respect, relevance, reciprocity, and responsibility toward the community as parallel standards, not inferior ones.

That reframes rigor rather than abandoning it.

Treating Lincoln and Guba’s (1985) four criteria as universal is itself a choice. It risks judging non-Western research by a Western yardstick.

Community-defined markers do not replace triangulation or audit trails. They sit alongside them as equally valid evidence of rigor, judged on the community’s own terms rather than an imported Western checklist.

Contemporary Research

Since the mid-2010s, work on qualitative trustworthiness has pushed toward formalising practice. The American Psychological Association’s Journal Article Reporting Standards for Qualitative Research (JARS-Qual; Levitt et al., 2018) now require authors to state exactly which credibility techniques were used.

General claims of rigor are no longer enough.

Building Trustworthiness into Thematic Analysis

Aim: Nowell, Norris, White, and Moules (2017) set out to build trustworthiness into thematic analysis step by step. It should not be claimed only at the end.

Method: The authors mapped explicit techniques onto each phase of Braun and Clarke’s framework, including prolonged engagement, triangulation, peer debriefing, member checking, and an audit trail.

They illustrated this with a case study from Alberta, Canada.

Results: Each criterion could be operationalised as a concrete action at a specific point in analysis. Credibility was built through triangulation during coding and member checking during interpretation.

Conclusion: Trustworthiness must be actively built throughout analysis, not asserted afterward. An audit trail makes analysis far more traceable for readers.

How Reflexivity Is Actually Practised

A newer strand asks whether credibility techniques are carried out as carefully as they are reported.

Reflexivity has drawn particular scrutiny.

De Smet, Ekşi, and Truijens (2026) studied seven junior researchers as they collected and analysed their own data. They examined in-the-moment reflexive questions, not just what got written up afterward.

Reflexive practice turned out to be highly individual and continuous. It extended well beyond the interview itself, into the researchers’ own lives.

A single retrospective statement cannot capture a practice this variable.

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