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?
How are 'repeated measures' taken into account? (Are subject scaled individually?
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?
Thanks,
Simon
