Hi,
if the "group" column is set to all 1's, both of these models are identical. Unless you have large sample sizes in your 2 groups, I wouldn't use the "group" column to indicated different groups. This allows different between-subject variance estimates in your two groups, but with lower sample sizes I've found the single estimate is better than two and my guess is that the individual group variance estimates are more variable due to the smaller sample size. Either way your inferences are valid.
Cheers,
Jeanette
On Mon, Jan 28, 2013 at 3:05 AM, Esther <
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Hi,
we have two groups with a covariate of interest and want to test the group*cov interaction effect in FEAT.
As far as I can tell, there are two ways to do that in FSL:
1)
intercept GroupA-B covar group*covar
1 -1 -3 3
1 -1 -2 2
1 1 1 1
1 1 4 4
with the following interaction-testing contrast:
intercept GroupA-B covar group*covar F-test
contrast 0 0 0 1 x
which has the disadvantage that I can't model separate variances. So I could also do:
2)
groupA groupB covarA covarB
1 0 -3 0
1 0 -2 0
0 1 0 1
0 1 0 4
with the following contrast:
groupA groupB covarA covarB F-test
contrast1 0 0 -1 0 x
contrast2 0 0 0 1 x
However, we are not too sure about the F-test in 2). Maybe it should be just:
groupA groupB covarA covarB F-test
contrast1 0 0 -1 1 x
Which one is correctly testing for the group*covariate interaction effect?
And would you expect the 1) and 2) model results to differ and if so, which model would you prefer (and why)?
Thanks for your help,
Esther
