User:Stargazer.em/Repeated measures design

Limitations

It may not be possible for each participant to be in all conditions of the experiment (i.e. time constraints, location of experiment, etc.). Severely diseased subjects tend to drop out of longitudinal studies, potentially biasing the results. In these cases mixed effects models would be preferable as they can deal with missing values.

Mean regression may affect conditions with significant repetitions. Maturation may affect studies that extend over time. Events outside the experiment may change the response between repetitions.

Repeated measures ANOVA

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.

Repeated measures analysis of variance (rANOVA) is a commonly used statistical approach to repeated measure designs. With such designs, the repeated-measure factor (the qualitative independent variable) is the within-subjects factor, while the dependent quantitative variable on which each participant is measured is the dependent variable.

Partitioning of error

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. Consider the general structure of the F-statistic:

F = MS_Treatment / MS_Error = (SS_Treatment/df_Treatment)/(SS_Error/df_Error)