Qualitative Trustworthiness and Quantitative Validity: Reporting Standards for Dissertations, Essays and Assignments

January 23, 2026 Chris

High-quality theses, dissertations and assignments require clear, transparent reporting of how you established the credibility of your findings. Whether your study is qualitative, quantitative or mixed-methods, reviewers expect evidence that the results are trustworthy (qualitative) and valid (quantitative). This guide provides practical reporting standards, checklists and sample wording you can use in methods and results sections.

Why this matters

Trustworthy/valid reporting strengthens argumentation and reduces reviewer queries.
It improves reproducibility, transferability and the overall impact of your work.
Clear reporting helps examiners judge methodological rigor quickly and fairly.

Key concepts: Trustworthiness vs Validity

Concept	Qualitative (Trustworthiness)	Quantitative (Validity)
Primary focus	Credibility, dependability, confirmability, transferability	Internal validity, external validity, construct validity, statistical conclusion validity
Main methods for demonstration	Triangulation, reflexivity notes, audit trails, thick description	Randomisation/control, reliability measures, validity testing, power analysis
Typical reporting locations	Methods, reflexivity/memo section, appendices	Methods, results, supplementary materials (e.g., code, datasets)
Comparable quantitative term	N/A (conceptual overlap with construct validity & reliability)	N/A

Reporting standards for qualitative trustworthiness

To demonstrate qualitative trustworthiness, include explicit descriptions and evidence for:

Credibility
- Member checking, prolonged engagement, peer debriefing.
- Report who checked findings and how disagreements were resolved.
Transferability
- Provide thick description: context, participant demographics, recruitment.
- Explain boundaries of transferability (where findings might apply).
Dependability
- Describe audit trails, changes to protocol, and a timeline of data collection/analysis.
Confirmability
- Reflexivity statements from researchers, documentation of decision-making.
Triangulation
- Data/source/method/theory triangulation and how integration affected interpretations.

Suggested reporting items (Methods and Appendix):

Sampling strategy and recruitment details.
Interview/observation guides (or sample questions).
Coding approach and software used (e.g., NVivo).
Audit trail excerpts or coding maps.
Reflexive memo excerpt and positionality statement.

Reporting standards for quantitative validity

For quantitative studies, report procedures and results that address these validity types:

Internal validity
- Describe controls for confounders: randomisation, blinding (if applicable), covariates in models.
External validity
- Detail sampling frame, inclusion/exclusion criteria, response rates, and how sample reflects population.
Construct validity
- Provide validity evidence for measures: factor analysis, content validity, Cronbach’s alpha.
Statistical conclusion validity
- Report effect sizes, confidence intervals, exact p-values, and power/sample size considerations.

Essential reporting items:

Clear statement of hypotheses and analytic plan.
Justification for chosen statistical tests and assumptions checks.
Handling of missing data and outliers (methods and sensitivity analyses).
Software, version and full analysis code or reproducible script (if possible).

Helpful links:

Mixed-methods reporting: integrating trustworthiness and validity

When combining approaches, be explicit about integration strategy and how rigor was established across strands:

State the mixed-methods design (convergent, explanatory sequential, exploratory sequential) and rationale.
Explain where integration occurred: sampling, data collection, analysis, interpretation.
Report how qualitative credibility and quantitative validity informed each other (e.g., qualitative findings used to refine measures; quantitative results explained via qualitative themes).

See: Mixed-Methods Data Integration: Techniques for Dissertations, Essays and Assignments

Practical checklist: What to include in your dissertation, essay or assignment

Section	Required reporting items	Example phrasing
Methods (Qual)	Sampling, data collection, coding approach, reflexivity	“Participants were purposively sampled; coding followed Braun & Clarke (2006) with two independent coders.”
Methods (Quant)	Sampling frame, measures, test selection, assumptions checks	“We used ANOVA after confirming normality (Shapiro-Wilk p>0.05) and homogeneity (Levene’s test p>0.2).”
Results	Triangulated findings or primary statistical results, effect sizes, confidence intervals	“Theme X was supported by 12 interviews; regression β=0.45, 95% CI [0.21,0.69], p=0.001.”
Appendices	Interview guides, codebook, analysis scripts, sensitivity analyses	“See Appendix C for codebook and Appendix D for R script (reproducible).”
Limitations	Threats to trustworthiness/validity and mitigation strategies	“Selection bias was minimised via stratified recruitment; residual bias acknowledged.”

Also consider providing a short “rigour statement” after Methods summarising steps taken.

Common reporting mistakes and how to avoid them

Not naming the trustworthiness/validity criteria used — always state these explicitly.
Omitting effect sizes or confidence intervals — include them alongside p-values.
Failing to report assumption checks — include test results and corrective actions.
Neglecting reflexivity in qualitative work — provide a concise positionality statement.
Hiding analysis code or insufficient detail — link to reproducible scripts or include code in appendices.

Helpful resources:

Sample sentences you can adapt

Qualitative methods

“To enhance credibility, member checking was conducted with 8 participants; transcripts and preliminary themes were amended where participants suggested clarifications.”
“We maintained an audit trail documenting analytical decisions; a summary is provided in Appendix B.”

Quantitative methods & results

“Sample size was determined via power analysis (α=0.05, power=0.80) targeting an effect size of d=0.5; final N=120.”
“All models were checked for multicollinearity (VIFs < 2), and residuals met normality assumptions (Shapiro-Wilk p=0.12). Regression results: β=0.37, SE=0.09, p<0.001, R²=0.28.”

Mixed-methods integration

“Quantitative results were contextualised through follow-up interviews (explanatory sequential design); integration occurred at the interpretation phase using a joint display (see Figure 3).”

For more examples on phrasing and interpreting outputs, see: Interpreting Statistical Output for Dissertations, Essays and Assignments: Writing Clear Results

Resources and further reading

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Clear, explicit reporting of trustworthiness and validity not only satisfies examiners — it strengthens the credibility and impact of your research. Use the checklists and sample phrases above to make rigour transparent and easy to evaluate.