Trustworthiness in Qualitative Research

Trustworthiness in qualitative research is the degree of confidence readers can place in a study’s findings, usually judged through four criteria proposed by Lincoln and Guba (1985): credibility, transferability, dependability and confirmability. It is demonstrated not by a statistical test but by transparent evidence of how data were collected, analysed and interpreted.

If you are writing a qualitative dissertation, the practical question is this: what can I actually do during my study, and what can I report in my methodology chapter, to show my supervisor and examiner that my findings deserve confidence?

This page answers that question criterion by criterion. It explains what each of the four criteria asks, which strategies can support it and, most importantly, what evidence you can put on the page. It also explains why no strategy is compulsory, why some well-known techniques are contested, how reflexivity fits in, and how to write all of this up honestly, including what you did not do.

On this page:

Trustworthiness Explained Simply

Imagine you interview twelve owners of small online retailers about how they respond to negative customer reviews. You identify three themes. Your examiner will want to know four things. Do your themes fairly represent what the owners told you? Could a reader running a different kind of small business judge whether your themes might apply to them? Could someone follow, step by step, how you moved from recordings to themes? And are the themes grounded in what the owners said rather than in what you expected to find?

Those four questions correspond, roughly, to credibility, transferability, dependability and confirmability. Trustworthiness is simply the name for the case you build, using evidence, that each question has a reasonable answer.

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What Is Trustworthiness?

Lincoln and Guba (1985) proposed credibility, transferability, dependability and confirmability as naturalistic parallels to four conventional criteria: internal validity, external validity, reliability and objectivity. Korstjens and Moser (2018) describe Lincoln and Guba’s four criteria as the best-known quality criteria in qualitative research and state that quantitative criteria are not suitable for judging qualitative work. Shenton (2004) uses the same constructs to set out practical strategies, drawing on his own qualitative doctoral study.

This page is deliberately distinct from the site’s pages on reliability and validity, internal validity and external validity. Those concepts were developed mainly for quantitative designs and are concerned with measurement, causal inference and statistical generalisation. Trustworthiness asks related questions but answers them with different kinds of evidence: descriptions, records, reflexive accounts and documented decisions rather than coefficients and test statistics.

The Four Criteria

The table below summarises each criterion, the question it answers, its quantitative parallel as proposed by Lincoln and Guba (1985), and the kind of evidence a student can realistically report.

Criterion Question it answers Quantitative parallel What you would report
Credibility Are the findings a plausible and well-supported account of participants’ experiences and views? Internal validity Time spent with participants or the setting, sources compared, how interpretations were challenged, and how cases that did not fit were handled.
Transferability Can a reader judge whether the findings might apply to another context? External validity A detailed description of the setting, participants, selection criteria and the period of data collection.
Dependability Was the research process consistent, logical and traceable? Reliability An audit trail: interview guide versions, coding frame versions, analytic memos and a log of decisions and changes.
Confirmability Are the findings grounded in the data rather than in the researcher’s preferences? Objectivity Links from themes back to coded extracts, and a reflexive account of the researcher’s position and assumptions.

Two points deserve emphasis. First, transferability places the judgement with the reader: your task is to give enough description for that judgement to be made, not to claim that findings apply elsewhere. Second, dependability is not about replication. Qualitative studies are not expected to produce the same findings if repeated by another researcher, because interpretations depend on context and on the researcher. Dependability asks whether your process was consistent and can be traced, which is why the audit trail is central to it. For the quantitative meaning of consistency, see the page on research reliability.

Strategies and the Evidence You Can Report

Lincoln and Guba (1985) described a set of techniques for addressing the criteria, including prolonged engagement, persistent observation, triangulation, peer debriefing, negative case analysis, referential adequacy, member checking, thick description and the inquiry audit. Shenton (2004) and Korstjens and Moser (2018) relay and explain many of these for researchers today. The figure and table below organise them around what matters most in a dissertation: the evidence you can actually show. Treat them as a menu, not a checklist.

Diagram showing four columns for credibility, transferability, dependability and confirmability, each with the question it answers, example strategies and the evidence a student can report
Figure: The four trustworthiness criteria and the evidence that can support each. A guide, not a rule; the author’s synthesis based on Lincoln and Guba (1985), Shenton (2004) and Korstjens and Moser (2018).
Criterion Strategy What to do Evidence to report Limits or caution
Credibility Prolonged engagement Spend enough time with participants or in the setting to understand the context and build rapport. Duration and nature of contact, for example number of visits or weeks of fieldwork. Often limited in a dissertation timetable; report what was realistic rather than overstating it.
Credibility Persistent observation Focus in depth on the aspects of the setting most relevant to the research question. Observation notes and how the focus was refined. Mainly relevant where observation is a data source.
Credibility Triangulation Compare more than one data source, method, investigator or theoretical perspective. Which sources were compared and where they agreed or differed. Disagreement between sources is a finding to explain, not a failure.
Credibility Peer debriefing Discuss emerging interpretations with a knowledgeable person who can question them. Dates, who was involved (by role) and what changed as a result. Supervision meetings can count only if they genuinely challenged interpretations.
Credibility Negative case analysis Look for cases that do not fit the emerging pattern and explain them. The cases that did not fit and how the analysis was revised or qualified. Requires honest reporting of counter-evidence.
Credibility Member checking Return data or interpretations to participants for comment. What was returned, how many responded and what they said. Contested; returning a transcript supports transcription accuracy, not the analysis (Birt et al., 2016).
Transferability Thick description Describe the context, setting and participants in detail. A context section covering organisation type, sector, roles and timing. Must be balanced against anonymity.
Transferability Purposive sampling Select participants because they are relevant to the research question. Selection criteria and the rationale for them. Explains who the findings are about; does not make them generalisable.
Dependability and confirmability Audit trail / inquiry audit Keep a dated record of design decisions, coding and theme development; an auditor may review it. Appendix extracts: coding frame versions, memos, decision log. A formal external audit is rarely feasible for students; a well-kept trail still helps.
Confirmability Reflexivity Record and examine your own assumptions, position and influence on the research. A reflexive statement and, where useful, journal extracts. Should be specific, not a generic confession of bias.

Lincoln and Guba (1985) noted that an audit can address dependability and confirmability together, which is why the audit trail appears under both. Nowell et al. (2017), writing about thematic analysis, argue that researchers must show their analysis was precise, consistent and exhaustive by recording, systematising and disclosing their methods, and they recommend an auditable decision trail. Shenton (2004), citing Lincoln and Guba, describes credibility as one of the most important factors in establishing trustworthiness; that does not mean every credibility strategy belongs in every study.

Credibility Versus Confirmability

Students often confuse these two criteria because both seem to ask whether the findings are “right”. The difference lies in what the findings are being checked against. Credibility looks at the relationship between findings and the people or setting studied: is this a plausible, well-supported account of their experience? Korstjens and Moser (2018) treat credibility as the equivalent of internal validity. Confirmability looks at the relationship between findings and the researcher: can a reader trace each claim back to the data, rather than to your hopes or prior beliefs?

The strategies follow from this. Credibility is mainly supported by engaging with the setting and testing interpretations: prolonged engagement, triangulation, peer debriefing, negative case analysis and, with caution, member checking. Confirmability is mainly supported by transparency about the route from data to claim and about yourself: an audit trail that links themes to coded extracts, and reflexivity. A simple test is to ask whom the strategy involves. If it involves participants, sources or colleagues challenging your interpretation, it usually serves credibility. If it involves documenting your own trail and position, it usually serves confirmability.

Reflexivity

Reflexivity means examining how your background, assumptions, values and relationship to participants may have shaped the questions you asked, the data you gathered and the interpretations you made. Korstjens and Moser (2018) describe reflexivity as an integral part of ensuring the transparency and quality of qualitative research. It connects closely to axiology, the role of values in research, because it asks you to make your values visible rather than claim to have none.

A short reflexive statement in a dissertation can follow five steps. State your position in relation to the topic and participants, for example a former employee, a customer or an outsider. Identify the assumptions you held at the start. Explain how those assumptions might have influenced data collection or analysis. Describe what you did in response, such as keeping a reflexive journal or deliberately looking for disconfirming cases. Finally, note anything that changed in your thinking as the study progressed.

Not Every Strategy Fits Every Approach

The four criteria are widely used, but they are not the only way of judging qualitative quality, and their strategies are not universal requirements. Braun and Clarke (2021) argue that a single quality standard for thematic analysis is complicated because thematic analysis varies paradigmatically and procedurally. They are critical of coding-reliability measures being promoted as universal requirements across all forms of thematic analysis. If you use reflexive thematic analysis, reporting inter-coder agreement is therefore something you would need to justify against your own approach, not assume.

Tracy (2010) proposes eight “big-tent” criteria for excellent qualitative research: worthy topic, rich rigor, sincerity, credibility, resonance, significant contribution, ethics and meaningful coherence. Smith and McGannon (2018) critique member checking, inter-rater reliability and universal criteria, arguing that member checking and inter-rater reliability are ineffective as means of verification or of establishing trustworthiness. Morse et al. (2002) argue for verification strategies built into each phase of the research as self-correcting mechanisms, rather than relying only on evaluation after the study is complete, and they make a case for returning to the terms reliability and validity. Morse (2015) observes that strategies recommended for achieving trustworthiness had not been critically examined, and examines them.

For a student, the practical lesson is to choose strategies that are consistent with your approach and to justify them, rather than listing every technique. The table below is the author’s synthesis, offered as a guide, not a rule.

Approach or situation Sensible strategies Use with caution
Reflexive thematic analysis Reflexivity, audit trail of theme development, thick description, peer debriefing Inter-coder agreement scores; member checking framed as proof of correctness
Grounded theory Analytic memos, negative case analysis, audit trail of category development Fixed coding frames imposed before analysis
Single-organisation case study Triangulation of interviews with document analysis, thick description of the organisation, prolonged engagement where access allows Claims that findings represent the whole sector
Small interpretive interview study Reflexivity, thick description, purposive sampling rationale, transparent account of sample size Member checking of interpretations if participants cannot reasonably comment on them
Qualitative strand of mixed methods research Audit trail, triangulation with the quantitative strand, clear statement of which quality criteria apply to which strand Judging the qualitative strand by statistical criteria
Insider researcher Reflexivity, peer debriefing, negative case analysis Assuming familiarity guarantees understanding

How to Report Trustworthiness in a Dissertation

In the methodology chapter, name the framework you are using, normally Lincoln and Guba’s (1985) four criteria, and briefly say why it suits your study. Then take each criterion in turn and state which strategies you used and what evidence supports them. Korstjens and Moser (2018) suggest reporting how quality criteria or a checklist were applied, and this is the key: describe what you did, not what the literature says could be done. “Member checking was used” is weak. “Summaries of my interpretations were sent to all twelve participants; seven replied, two suggested changes to the wording of one theme, and these comments were considered in the final analysis” is evidence. Birt et al. (2016) note that member checking is often reported in a single sentence, which tells the reader little.

Put supporting material in appendices: an extract from the audit trail, the coding frame, an example showing how one theme was built from codes and quotations, and your reflexive statement if it is too long for the main text. If you discuss when to stop collecting data, link that discussion to data saturation or explain why a different stopping rationale was used.

The limitations section is where you report honestly what was not done and why. If you could not spend extended time in the setting, say so. If you chose not to use member checking because it did not fit your approach, explain the reasoning.

Trustworthiness in Business Research

Business and management dissertations raise some practical issues. Access is often negotiated through a gatekeeper, such as a manager who selects participants. This can shape who you speak to, so describe the access route as part of your thick description and consider it in your limitations. Many student projects study a single organisation. That is a legitimate design, but transferability then depends heavily on how well you describe the organisation, its sector and its circumstances, within the limits of anonymity.

Students who research their own workplace are insider researchers. Insider status can help with access and understanding, but it also makes reflexivity especially important, because colleagues may answer differently to someone they know. Interviews with managers deserve similar care: managers may present their organisation or their own decisions in a favourable light, so triangulating interview accounts with documents or with views from other roles can strengthen credibility.

Application Example

Hypothetical example. A master’s student studies how hotel managers in one city make decisions about sustainability initiatives, using semi-structured interviews with ten managers from independent and chain hotels and reflexive thematic analysis.

For credibility, the student compares interview accounts with publicly available sustainability statements from each hotel, discusses emerging themes with a fellow student who has hospitality experience, and reports two managers whose views did not fit the main theme of cost-driven decisions. For transferability, the student describes the hotels by size, ownership type and market segment, and explains the purposive selection criteria. For dependability, the student keeps a dated decision log and saves each version of the coding frame. For confirmability, the student writes a reflexive statement noting previous employment in a hotel chain and keeps a journal throughout the analysis. Consistent with reflexive thematic analysis, the student does not calculate inter-coder agreement and explains why in the methodology chapter. Member checking is not used, and the limitations section explains that time constraints and the interpretive nature of the analysis made it a poor fit.

Advantages and Limitations of the Trustworthiness Framework

Advantages Limitations
Widely recognised by supervisors and examiners, which makes it easier to communicate quality. Its parallels to quantitative criteria can encourage students to think in quantitative terms.
Gives a clear structure for the quality section of a methodology chapter. Can be applied as a mechanical checklist, listing strategies without evidence.
Links each quality question to practical strategies and reportable evidence. Some associated strategies, such as member checking, are contested (Birt et al., 2016; Smith and McGannon, 2018).
Encourages transparency through audit trails and reflexivity. Not equally suited to every approach; alternatives such as Tracy’s (2010) criteria may fit some studies better.

Common Mistakes

  • Listing all four criteria and many strategies without saying what was actually done.
  • Describing dependability as replicability or claiming that another researcher would reach the same findings.
  • Claiming that findings are generalisable rather than providing description so readers can judge transferability.
  • Treating a returned transcript as proof that the analysis is trustworthy.
  • Reporting inter-coder agreement in a reflexive thematic analysis without considering whether it fits the approach.
  • Writing a reflexive statement that admits “possible bias” without saying what the bias might be or how it was handled.
  • Mixing the language of validity and reliability with trustworthiness without explanation.
  • Hiding what was not done instead of explaining it in the limitations.

Trustworthiness in the Age of AI and Digital Research

Students increasingly use AI tools to support coding, summarising and theme development. This does not remove the need for trustworthiness; it creates new things to document. If an AI tool contributed to coding or to suggesting themes, the audit trail should record which tool and version was used, when, the prompts given, and what you accepted, changed or rejected. Without that record, a reader cannot trace how the findings were produced, which undermines dependability and confirmability. Check your university’s rules on AI use before relying on such tools, and state clearly in the methodology what the tool did and what you did.

One comparative study suggests that the choice matters. Hamilton et al. (2023) compared human-generated and AI-generated themes from the same interview data and found that they differed, with each approach identifying themes the other missed. A sensible reading of this for a dissertation is that AI output should be treated as one input to be checked against the data, not as a substitute for your own analysis.

Member checking raises a related issue. If you send participants an AI-generated summary of their interview, their agreement tells you that the summary seemed acceptable to them, not that your analysis is trustworthy. This parallels the point made by Birt et al. (2016) about transcripts. Similarly, automatic transcription tools save time but can mishear names, technical terms and accents, so it is good practice to check transcripts against the recordings and to report that you did so.

Studies using digital trace data, such as online reviews, social media posts or forum discussions, need particular attention to transferability. Such data are often detached from information about who posted them and in what circumstances. Thick description therefore means explaining the platform, the time period, how posts were selected and what is unknown about the authors, so readers can judge how far the findings might extend.

Dissertation Example

The following is a model paragraph for a hypothetical study of employees’ experiences of hybrid working in a medium-sized UK accounting firm.

“Trustworthiness was addressed using Lincoln and Guba’s (1985) criteria of credibility, transferability, dependability and confirmability. To support credibility, interview accounts from fourteen employees were compared with internal policy documents on hybrid working, and emerging themes were discussed with my supervisor at three points during analysis, leading to the division of one broad theme into two. Two participants whose experiences contradicted the dominant theme are reported in the findings. For transferability, the firm’s size, sector, workforce composition and hybrid working policy are described in Chapter 4, together with the criteria used to select participants. Dependability was supported by an audit trail comprising dated versions of the interview guide and coding frame and a decision log (Appendix C). For confirmability, each theme is linked to coded extracts, and a reflexive statement (Appendix D) discusses my position as a former trainee at a similar firm. Member checking was not used, because the analysis followed a reflexive thematic approach in which interpretations are developed by the researcher; this is discussed further in the limitations section.”

Exam Tip

If you are asked to explain trustworthiness in an exam or viva, name the four criteria and their originators, then give one concrete strategy and one piece of reportable evidence for each. Add one sentence showing critical awareness, for example that member checking is contested or that strategies should fit the approach. Above all, avoid defining dependability as replicability; it concerns a consistent and traceable process.

Not sure how to show trustworthiness in your qualitative study? Dudovskiy Research Assistant can review your topic, research questions and planned qualitative approach, then suggest which trustworthiness strategies fit your study and how you might word them in your methodology chapter. It is a support for your own judgement, so the final decisions remain yours and your supervisor’s.

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References

Birt, L., Scott, S., Cavers, D., Campbell, C. and Walter, F. (2016). Member checking: a tool to enhance trustworthiness or merely a nod to validation? Qualitative Health Research, 26(13), 1802-1811. https://doi.org/10.1177/1049732316654870

Braun, V. and Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328-352. https://doi.org/10.1080/14780887.2020.1769238

Hamilton, L., Elliott, D., Quick, A., Smith, S. and Choplin, V. (2023). Exploring the use of AI in qualitative analysis: a comparative study of guaranteed income data. International Journal of Qualitative Methods, 22. https://doi.org/10.1177/16094069231201504

Korstjens, I. and Moser, A. (2018). Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. European Journal of General Practice, 24(1), 120-124. https://doi.org/10.1080/13814788.2017.1375092

Lincoln, Y.S. and Guba, E.G. (1985). Naturalistic Inquiry. Newbury Park, CA: Sage.

Morse, J.M. (2015). Critical analysis of strategies for determining rigor in qualitative inquiry. Qualitative Health Research, 25, 1212-1222.

Morse, J.M., Barrett, M., Mayan, M., Olson, K. and Spiers, J. (2002). Verification strategies for establishing reliability and validity in qualitative research. International Journal of Qualitative Methods, 1(2), Article 2.

Nowell, L.S., Norris, J.M., White, D.E. and Moules, N.J. (2017). Thematic analysis: striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1-13. https://doi.org/10.1177/1609406917733847

Shenton, A.K. (2004). Strategies for ensuring trustworthiness in qualitative research projects. Education for Information, 22(2), 63-75. https://doi.org/10.3233/EFI-2004-22201

Smith, B. and McGannon, K.R. (2018). Developing rigor in qualitative research: problems and opportunities within sport and exercise psychology. International Review of Sport and Exercise Psychology, 11(1), 101-121. https://doi.org/10.1080/1750984X.2017.1317357

Tracy, S.J. (2010). Qualitative quality: eight “big-tent” criteria for excellent qualitative research. Qualitative Inquiry, 16(10), 837-851. https://doi.org/10.1177/1077800410383121

Last reviewed: November 2026.

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