Direct comparison
Within- vs Between-Subjects Designs
Within-subjects tests each participant in every condition; between-subjects assigns one per participant. Compare power, sample size, and carryover risk.
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How do Within-Subjects, Between-Subjects compare side by side?
The table below compares Within-Subjects, Between-Subjects across 11 procurement-relevant dimensions, from also called through cannot be used when.
Side-by-side comparison
| Dimension | Within-Subjects | Between-Subjects |
|---|---|---|
| Also called | Repeated-measures design | Independent-groups design |
| Participants per condition | Same participants in every condition | Different participants in each condition |
| Individual-differences variance | Removed from the error term (each subject is own control) | Remains in the error term |
| Sample size for equal power | Smaller, for the same effect size | Larger, for the same effect size |
| Main confound risk | Carryover, practice, and fatigue/order effects | Between-group differences unrelated to the manipulation |
| How the confound is controlled | Counterbalancing (Latin square, ABBA) or a washout period | Random assignment to condition |
| Typical test (2 conditions) | Paired-samples t-test | Independent-samples t-test |
| Typical test (3+ conditions) | One-way repeated-measures ANOVA | One-way between-subjects ANOVA |
| Key analysis assumption | Sphericity (equal variance of pairwise differences) | Homogeneity of variance across groups |
| Participant burden | Higher — multiple sessions/conditions per person | Lower — one condition per person |
| Cannot be used when | The manipulation only works once, or its effect cannot fully wash out | Recruiting enough participants for every group is not feasible |
Common questions
Common questions about Within-Subjects vs Between-Subjects
Which design needs fewer participants?
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Within-subjects, for a given effect size and power target, because removing between-person variance from the error term increases statistical power per participant. A between-subjects design needs a larger total sample to reach the same power.
Can a study combine both?
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Yes — a mixed (split-plot) design manipulates one variable between subjects (e.g., treatment vs control group) and another within subjects (e.g., repeated time points), which is the standard structure for a pre-post study with a control group.
Is a crossover trial a within-subjects design?
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Yes. A crossover trial is the clinical-research application of a within-subjects design, where each participant receives multiple treatments in sequence, with a washout period between them specifically to manage carryover.
What replaces repeated-measures ANOVA when sphericity is violated?
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A Greenhouse-Geisser or Huynh-Feldt correction to the degrees of freedom, or a linear mixed-effects model, which does not assume sphericity at all.
