Designing a study
“How do I justify my qualitative sample size?”
Saturation, information power, and a sample-size argument you can show.
You did not pick your number carelessly. But “it felt like enough” is not an argument, and a supervisor or reviewer who says too small is asking for one. This page builds the argument, in words you can paste into your methods.
“I really have to justify my decision to include 20 participants for an interview… there are hardly any guidelines for establishing sample size.” — a doctoral student, on defending a qualitative N
The problemThe number is fine. The justification is missing.
You name a sample size, and someone with power over your project says it is too small. Sometimes that is a supervisor in a meeting, sometimes Reviewer 2 in writing. What they are asking is not “add more people”. It is “show me why this many is defensible.” Qualitative work rarely comes with a power calculation to hide behind, so the burden falls on your reasoning.
An undefended number is a gift to a reviewer: easy to flag, and free to attack. Written up well, in one clear paragraph, the same number becomes one of your most defensible sentences. Qualitative methodology gives you two ways to make that argument.
👑“Too small” is a request for a sentence, not for more people.
Common mistakesFour ways the defence goes wrong
- Citing a magic number. “12 to 15 interviews is standard”, borrowed from a paper in another field with another question, is not a justification. It is a coincidence.
- Claiming saturation you did not track. Saying “saturation was reached” with no record of when new codes stopped appearing invites the obvious follow-up: how do you know?
- Treating saturation as a formula. Saturation is a judgement about your data, not a threshold that fires at interview 17. Presenting it as automatic reads as a shortcut.
- Arguing headcount alone. A narrow question with rich, on-target participants needs fewer people, not more. The number is not the argument; the information behind it is.
The two lensesName the one that fits your study
Saturation is the point at which more data stops changing your analysis: no new codes, then no new dimensions of the codes you have. It is a claim about your coding, so it only means something if you kept a record of when new codes appeared and stopped. Code saturation (no new codes) is easier to reach than meaning saturation (you fully understand each code), and the second is the stronger claim.
Information power (Malterud et al., 2016) says the more your sample holds that is relevant to your question, the fewer participants you need. Five things build the argument: a narrow aim, a specific sample, established theory behind your questions, strong dialogue in the interviews, and a case rather than cross-case analysis. Each one lowers the number. This is the lens that lets you defend 20, or fewer, without leaning on saturation at all. Pick the lens that matches how you actually decided, and write the argument around it.
Show them the paragraph. Not the number.
Worked exampleDefending 20 interviews
Defending 20 interviews with first-year nurses
Four of the five information-power dimensions point to a smaller sample: a narrow aim, a specific sample, theory-informed questions, and strong interview dialogue. Only the cross-case analysis pushes the other way, and that set recruitment at 20.
Written up: “A sample of 20 was judged adequate on the information-power model (Malterud et al., 2016). A narrow aim, a specific sample, theory-informed questioning, and strong dialogue each support a smaller sample, while the cross-case strategy was accommodated by recruiting 20. Coding was monitored for new codes, and the final three produced none.”
ChecklistBuild your own defence
Four lines make the paragraph
- Name your aim and how narrow it is, and how specifically your sample was chosen for it.
- Name the theory guiding your questions and the depth of the interviews.
- Name your analytic strategy (case or cross-case) and which way it moves the number.
- Report what your coding showed, then write it as one paragraph and read it against your supervisor’s likely objection.
Related guidesRead next
Further readingOld sources worth your time
- Malterud, Siersma & Guassora (2016), Sample Size in Qualitative Interview Studies: Guided by Information Power. Qualitative Health Research (paywall).
- Hennink & Kaiser (2022), Sample sizes for saturation in qualitative research. Social Science & Medicine (open access).
Recommended tool · the next step
MethodVahti Sample Defence Pack
Want the written defence to paste into your methods? MethodVahti sets the stopping logic, keeps an adequacy log as you code, and drafts the sampling paragraph from your own numbers, without treating saturation as a formula. You check it, you own it.
See the Sample Defence Pack · €49 →