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See what your sample-size argument depends on.
Move one design assumption and watch the suggested range respond. The useful result is not the largest number on the screen. It is knowing which assumption is doing the work.
Design parameters
Three sample-size models → one synthesis
MethodVahti never gives one number alone. The shaded band is the stability range under ±0.05 perturbation of every input.
| Linear saturation | saturation grows with heterogeneity and theme rarity — Guest et al. 2006; Hennink et al. 2017 |
| Network complexity | information power: a narrow aim, strong theory and rich dialogue lower N — Malterud et al. 2016 |
| Fuzzy-set QCA | configurational adequacy: enough cases to cover the plausible configurations — Ragin |
Which choice does N depend on?
Each bar is how far the optimal N moves when that one parameter shifts ±0.10 — longest bar = the choice your sample size is most sensitive to.
Sensitivity sweep — N versus heterogeneity
Holding the other parameters fixed, how the synthesised N changes as the sample becomes more varied. The dot is where you are now.
Saturation simulation
A Monte-Carlo of theme discovery under your parameters: cumulative unique themes found as interviews accumulate (mean of 40 runs, shaded = spread). More heterogeneity means more latent themes, so the curve saturates later.
These models are Vahtian hypotheses and are not externally validated. The cited literature supports their direction and shape, not the exact coefficients. Use them to inspect trade-offs. You decide N. Read the validation plan.
Need the reasoning in a document?
The Sample Defence Pack turns the assumptions, range, stopping rule, and final judgment into an editable record for a protocol, manuscript, or reviewer response.
See the Sample Defence Pack · €49