TL;DR: Rigour is frequently invoked in applied research but rarely defined. When stakeholders call for more rigour, they are typically pointing to a mix of underlying concerns that vary by context and decision. Without clarity on what is actually being asked for, teams default to the visible performance of rigour, rather than rigour itself. This piece argues that rigour is best understood as a set of expectations that need to be made explicit and that doing so is the researcher’s responsibility.
Introduction
Rigour gets talked about a lot in applied research, but it’s used inconsistently and rarely defined.
Sometimes it shows up as aspiration: “We need more rigour, something closer to academic standards.” Other times, as critique: “That wasn’t rigorous.” In both cases, it’s shorthand for better, without specifying what better means in context.
Without that clarity, teams default to what’s visible: larger samples, more studies, complex analysis, heavier processes. Output grows, but insight quality doesn’t. What you end up with is the performance of rigour rather than rigour itself.
Used loosely, rigour mostly produces more work and less clarity about what the work is for.
Negotiating rigour’s meaning
More rigour rarely means one thing. When stakeholders invoke it, they’re usually pointing at a mix of underlying concerns:
- Confidence: Can I trust this enough to act on it?
- Credibility: Will it withstand scrutiny?
- Traceability: Can I see how you got there?
- Appropriateness: Was this the right method for the question?
- Reproducibility (sometimes): Would we see the same again?
None of these are method-specific, but they manifest differently depending on approach. In quantitative work, confidence isn’t just a function of sample size, it’s whether the data actually bears on the decision being made. In qualitative work, credibility comes from grounded, triangulated insight, not from exhaustive documentation of process.
The implication is that rigour isn’t a universal bar you either clear or don’t. It’s a set of expectations and those expectations need to be surfaced before they can be met.
Is trustworthiness enough?
Qualitative research often frames rigour through Lincoln & Guba’s trustworthiness criteria: credibility (well-grounded findings), dependability (a logical, traceable process), confirmability (conclusions rooted in the data), and transferability (applicability beyond the immediate context). These are useful because they make rigour concrete, shifting the conversation from vague quality signals toward specific properties of the work.
But trustworthiness and rigour aren’t the same thing. Trustworthiness addresses the integrity of findings. Rigour is a broader question: was the right method chosen in the first place? Is the work sufficient for the decision it needs to inform? Do stakeholders have enough confidence to act on it?
The difference matters in practice. A meticulously coded qualitative study can be entirely trustworthy — transparent process, grounded themes, careful analysis — and still fall short of rigour if the decision it’s meant to inform requires quantitative validation. Conversely, work can perform rigour through process whilst lacking credibility in its findings.
How rigour gets misapplied
The ambiguity of rigour creates predictable patterns.
In quantitative research, rigour often collapses into statistical signals: bigger samples, and p-values. These become proxies, even when mismatched to the question.
In qualitative research, it turns procedural: more documentation, frameworks, and visible process. The signal of rigour drifts from the insight itself.
In both cases, rigour becomes something to demonstrate, rather than something that meaningfully improves decision-making.
Responding to “more rigour”
If rigour is a proxy for multiple concerns, the most useful response is to interrogate it rather than immediately produce more work. Ask what specifically is worrying them. Ask what decision this needs to support, and what would shift their confidence. These questions move the conversation from vague critique to a concrete brief and often reveal that the concern is narrower, and more addressable, than it first appeared.
In conclusion
Rigour is not a method, a sample size, or a standard you either meet or fail. It is the degree to which research is fit for purpose, grounded in the right approach, transparent in its construction, and sufficient for the decision it informs.
The work is to make those criteria explicit before the research begins, not defend against them after it ends.
Brian Grellmann 