Repeated measures designs can track an effect overtime, such as the learning curve for a task. In this situation, it’s often better to measure the same subject at multiple times rather than different subjects at one point in time for each.

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1. More statistical power
2. Fewer subjects
3. Quicker and cheaper
4. Assess an effect over time

Benefits

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer.

An Introduction to Statistical Learning with Applications in R

Repeated measures designs can be very powerful because they control for factors that cause variability between subjects.

More statistical power

Thanks to the greater statistical power, a repeated measures design can use fewer subjects to detect a desired effect size. Further sample size reductions are possible because each subject is involved with multiple treatments. For example, if an independent groups design requires 20 subjects per experimental group, a repeated measures design may only require 20 total.

Fewer Subjects

Fewer subjects need to be recruited, trained, and compensated to complete an entire experiment.

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