Repeated measures design

Crossover designs are common for experiments in many scientific disciplines, for example psychology, education, pharmaceutical science, and health care, especially medicine.

The impact of order effects may be smaller in long-term longitudinal studies or by counterbalancing using a crossover design.

Counterbalancing attempts to take account of two important sources of systematic variation in this type of design: practice and boredom effects.

Severely diseased subjects tend to drop out of longitudinal studies, potentially biasing the results.

One of the greatest advantages to rANOVA, as is the case with repeated measures designs in general, is the ability to partition out variability due to individual differences.

These assumptions include: As with other analysis of variance tests, the rANOVA makes use of an F statistic to determine significance.

Depending on the number of within-subjects factors and assumption violations, it is necessary to select the most appropriate of three tests:[5] One of the most commonly reported effect size statistics for rANOVA is partial eta-squared (ηp2).

A third effect size statistic that is reported is the generalized η2, which is comparable to ηp2 in a one-way repeated measures ANOVA.

The rANOVA is vulnerable to effects from missing values, imputation, unequivalent time points between subjects and violations of sphericity.

This figure is an example of a repeated measures design that could be analyzed using a rANOVA (repeated measures ANOVA). The independent variable is the time (Levels: Time 1, Time 2, Time 3, Time 4) that someone took the measure, and the dependent variable is the happiness measure score. Example participant happiness scores are provided for 3 participants for each time or level of the independent variable.