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.

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.

Assess an effect over time

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

University of Michigan - Ann Arbor

Study that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or over two or more time periods.
Example: repeated measurements are collected in a longitudinal study in which change over time is assessed.

Repeated Measures Designs

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 design reduces the effect of variability because the same subjects are used throughout the experiment. This allows the researcher to make powerful statistical conclusions with a relatively small set of subjects.

Why use repeated measures design?

Benefits

Picture of a common crossover repeated measures design

An order effect is a primary disadvantage of within-subjects experimental designs. It occurs when participants' responses in various experimental conditions are systematically affected by the order in which they were exposed to those conditions. When order effects are not the direct focus of the research, they can act as problematic confounding variables that obscure the true effect of the independent variable.

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