Your second method is the appropriate way to model a repeated measures design in SPM. However, you cannot test the main effect of group in that model. Instead you need to create a separate full factorial model that uses contrast images that are averaged over the effect of condition (either via imcalc or through an average contrast from the first level models) and then test for effects of group in this model.
-Mike
> -----Original Message-----
> From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
> On Behalf Of Claire Shanon
> Sent: Monday, July 25, 2016 12:22 PM
> To: [log in to unmask]
> Subject: [SPM] Full factorial vs. Flexible factorial
>
> Hi,
>
> In my experiment I have two groups of subjects (patients (n = 20) and
> controls (n =42)) performing a memory task with 2 conditions. I'm interested
> in main effects of group, conditions and interactions between group and
> conditions. What is the most appropriate design to answer these questions?
>
> 1) Full factorial design with 2 factors: group (2 levels), independence (yes),
> variance (unequal); conditions (2 levels), independence (no), variance
> (equal). To evaluate the differences I used the following F-contrasts:
> Main effect of group: 1 1 -1 -1
> Main effect of condition: 1 -1 1 -1
> Interaction group x condition: 1 -1 -1 1
>
> 2) Flexible factorial design with 3 factors: 1) subject, independence (yes),
> variance (equal); 2) group, independence (yes), variance (unequal); 3)
> condition, independence (no), variance (equal). In main effects &
> interactions: main effects (1,2,3) and interaction (2,3). To evaluate the
> differences I used the following F-contrasts:
> Main effect of group: 1 -1 0 0 0.5 0.5 -0.5 -0.5 ones(1,20)/20 -ones(1,42)/42
> Main effect of condition: 0 0 1 -1 0.5 -0.5 0.5 -0.5 zeros(1,20+42) Interaction
> group x condition: 0 0 0 0 1 -1 -1 1 zeros(1,20+42)
>
> Is one of these two approaches better than other?
>
> Thank you very much in advance,
> Claire
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