Experimental Design

Repeated measures defined

Repeated measures means that the data are matched. Here are some examples:

•You measure a variable in each subject several times, perhaps before, during and after an intervention.

•You recruit subjects as matched groups, matched for variables such as age, ethnic group, and disease severity.

•You run a laboratory experiment several times, each time with several treatments handled in parallel. Since you anticipate experiment-to-experiment variability, you want to analyze the data in such a way that each experiment is treated as a matched set.

Matching should not be based on the variable you are comparing. If you are comparing blood pressures in three groups, it is OK to match based on age or zip code, but it is not OK to match based on blood pressure.

The term repeated measures applies strictly when you give treatments repeatedly to one subject (the first example above). The other two examples are called randomized block experiments (each set of subjects is called a block, and you randomly assign treatments within each block). The analyses are identical for repeated measures and randomized block experiments, and Prism always uses the term repeated measures.

Which factor is matched?

If your data are matched, choose which of the two factors are repeated measures, or if both factors are repeated measures. If one factor is repeated measures and the other is not, this analysis is also called mixed model ANOVA

Choose carefully, as the results can be  very misleading if you make a choice that doesn't correspond to the experimental design. The choices are:

No matching. Use regular two-way ANOVA (not repeated measures).

Each column represents a different time point, so matched values are spread across a row.

Each row represents a different time point, so matched values are stacked into a subcolumn.

Repeated measures by both factors.

Factor names

Entering descriptive names for the two factors will make it easier to interpret your results.

Sphericity

With two-way repeated measures ANOVA, Prism always assumes sphericity. It cannot do the Greenhouse-Geisser correction for two-way ANOVA, as it does for one way, nor can it calculate epsilon. Note that if the factor with repeated measures has only two levels, then there is no reason to be concerned about violations of sphericity. For example if each subject is measured before and after a treatment, and there are four different treatments, there would be no need to worry about sphericity, since the repeated measures factor only has two levels (before and after).