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

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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

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.

Quicker and Cheaper

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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