Dear SPM experts,

we have some questions regarding the covariate option in the full flexible anova model in spm5.

We have a

2(group) x 2(within-subject factor stimulus type) x 2(within-subject factor task) factorial design plus a high-level baseline.

The groups differ in one demographic variable. We thus want to include this variable as a covariate, in order to rule out the possibility that differences between groups in any activation patterns can be traced back to differences in this particular demographic variable.

In spm 5 we set up:

Design: Flexible factorial

  -Factor 1: condition

Independence: no

Variance: unequal

-Factor 2: subj

Independence: yes

Variance: unequal

-Factor 3: group

Independence: yes

Variance: unequal  

-Main effects & interactions:

Main effect: factor number: 2 (i.e. subject)

Interaction: factor number 3   1  (i.e. condition x group)  

-Covariate: Vector: the corresponding value for each subject.

Interaction: None

Centering: overall mean

We then set up contrasts that allow for comparisons between conditions within a group, or for condition x group interactions.

When we then look at the results and compare them with the results we yielded with the regular Anova without the covariate, the results do not differ at all, not in any voxel and any parameter.

We suspect that this may be due to the fact that the covariate can be constructed as a linear combination of the subject regressors and therefore the parameter estimates for the condition-related regressors may remain unchanged. Could that be the reason that our results don't get affected at all when including the covariate?

We are now unsure how to correctly set up the model, in order to control for subject-related variance that can be explained by a demographic variable.

Does anybody have ideas concerning this problem?

I attach the printouts of the two design matrices (anova and ancova).

Thanks a lot for your help,

Ellen