I do all my repeated measures analyses with mixed modeling in SAS
these days, but I get called on to help people who use standard
repeated-measures analyses with other stats packages. So here's my
question, which I should know the answer to but I don't!
In a repeated-measures ANOVA, most stats packages do a test for
sphericity, and they provide an associated adjusted p value for
overall significance of the repeated-measures factor. If my
understanding is correct, the adjustment takes care of non-uniformity
in the within-subject error between levels of the factor. Fine, but
then you want to do a specific contrast between levels of the
within-subject factor, such as the last pre-treatment vs the first
post-treatment (with or without a control group--it doesn't matter).
Now, the p value you get for that contrast... is it based on the
overall adjusted error derived from ALL levels of the
repeated-measures factor, or is it nothing more than the p value for
a t test of the two levels in question?
I realize that some packages attempt to provide a correction for
inflation of the Type I error when you have many contrasts, so the
analysis will be an ANOVA rather than a simple t test, but what
within-subject error term do the packages use for specific contrasts?
Supplementary question: can you get meaningful residuals out of a
standard repeated-measures ANOVA, so you can see how non-uniform they
are when you plot them against predicteds and label points with the
different levels of the within-subject factor? I do this sort of
thing routinely with Proc Mixed, but I never tried it in the days I
was still using RM-ANOVA.
Will
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