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Hi,

thanks for your advice. What sample size would you consider large enough (we got 80/100)? And could you just tell me, which F-test in model 2 is correctly testing for the group*covar interaction effect? a) or b)?

a) 
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:

b) 
groupA  groupB  covarA  covarB  F-test
contrast1       0       0       -1      1       x

Thanks a lot,

Esther

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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 <[log in to unmask]> wrote:

    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