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Hi Simon
> Hi all,
> I have a probably simple question and would be grateful for your help:
> I'm analyzing a repeated measures ANOVA (15 subj, each 3 conditions)
> and have a
> single covariate varying over subjects and conditions.
> Setting up a within-subject ANOVA design using SPM8 the covariate is
> represented in column 4.
> Is is right to use the t-contrast (0 0 0 1) or (0 0 0 -1) to evaluate
> its significance?
yep that's all good
> How are 'repeated measures' taken into account? (Are subject scaled
> individually?
not sure what you mean by scaled but as you see in your design matrix you have 15 additional regressors oni the right weigting each condition per subject - ie the within subject is taken care off.
> Are there problems, if data behaves awkwardly?)
>
> The questions arose when I tried to confirm my rmANOVA results
> (although not quite correctly) by calculating a one sample t-test
> pooling data from all subjects and conditions together using the
> above mentiond regressor as covariate (contrast used: 0 1 and 0 -1).
> Unexpectedly, evaluating the regressor using this simplified approach
> yielded completly different results. Specifically, clusters that were
> highly significant in the rmANOVA desing vanished. This seems
> counterintuitive to me, since degrees of freedom are (virtually)
> increased when treating images from a single subject as independent
> (I originally expected increased t-values). What could this imply for
> the data?
well the problem here is that the error terms are completly different so it's hard to say what whent wrong = rm ANOVA in SPM follows the same rules as other stats: normallity and to some extend heteroscedasticity (accounted for by setting your independence / variance options options
hope this helps
cyril
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