TWIMConcern:
when trying to remove the effects of membership in a dichotomous group
using SPM, is it better to model membership as one confound or two?
that is, if half my subjects belong to one group, half to another, and I
am testing for the effect of time or stimulus or whatever, I could enter
one confound or two. Each scan, or each pair of scans for each subject,
could then be indexed as
subject condition global_mean grpA
--where the entry for grpA would be 0 or 1
or is it better to include BOTH groups, as in
subject condition global_mean grpA grpB
where each scan is either [0 1] or [1 0] for group.
I realize that the latter eats up an additional degree of freedom, but I
am not clear which is a preferable model.
Thanks.
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