Interpreting results: Repeated measures two-way ANOVA

Are you sure that ANOVA is the best analysis?

Before interpreting the ANOVA results, first do a reality check. If one of the factors is a quantitative factor like time or dose, consider alternatives to ANOVA.

Interpreting P values from repeated measures two-way ANOVA

When interpreting the results of two-way ANOVA, most of the considerations are the same whether or not you have repeated measures. So read the general page on interpreting two-way ANOVA results first.

Repeated measures ANOVA reports an additional P value: the P value for subject (matching) This tests the null hypothesis that the matching was not effective. You expect a low P value if the repeated-measures design was effective in controlling for variability between subjects. If the P value was high, reconsider your decision to use repeated-measures ANOVA

How the repeated measures ANOVA is calculated

Prism computes repeated-measures two-way ANOVA calculations using the standard method explained especially well in SA Glantz and BK Slinker, Primer of Applied Regression and Analysis of Variance, McGraw-Hill, 1990.

Interpreting post tests after repeated measures ANOVA

The use of post tests after repeated measures ANOVA is somewhat controversial.

The post tests performed by Prism, use the mean square residual for all comparisons. This is a pooled value that assess variability in all the groups. If you assume that variability really is the same in all groups (with any differences due to chance) this gives you more power. This makes sense, as you get to use data from all time points to assess variability, even when comparing only two times. Prism does this automatically.

But with repeated measures, it is common that the scatter increases with time, so later treatments give a more variable response than earlier treatments. In this case, some argue that the post tests are misleading.