Hi Ijom,

It seems you're interested in a kind of omnibus F-test over all interactions group by Q1 and another group by Q2, is this right? If so, consider a design as this:

EV1: Schizophrenia (coded as 0 or 1)
EV2: Bipolar (coded as 0 or 1)
EV3: Healthy (coded as 0 or 1)
EV4: EV1*Q1
EV5: EV2*Q1
EV6: EV3*Q1
EV7: EV1*Q2
EV8: EV2*Q2
EV9: EV3*Q2

The relevant contrasts are then:

C1: 0 0 0 1 -1 0 0 0 0
C2: 0 0 0 1 0 -1 0 0 0
C3: 0 0 0 0 0 0 1 -1 0
C4: 0 0 0 0 0 0 1 0 -1

The F-test for the interaction with Q1 is uses then C1 and C2. For the interaction with Q2, C3 and C4.

Various other contrasts are possible. It's also possible to include 3 additional EVs to code the 3-way interaction. Feel free to post again if you need these but are having a hard time assembling the pieces.

All the best,

Anderson



On Thu, 21 Feb 2019 at 16:36, Ijom <[log in to unmask]> wrote:
Dear FSL experts,

We are planning to run TBSS analyses, in which we aim to examine Group x Continuous variable interactions to probe whether the correlation between TBSS metrics and our 2 variables of interest is significantly different across 3 groups. In other other words, does groep membership impact the association between either one of our continuous variables and TBSS metrics? We are struggling a bit, however, with constructing the design matrix and associated contrasts.

Our sample consists of Schizophrenia, Bipolar, and healthy participants, and the continuous variables are measures of Psychotic (Q1) and Depressive (Q2) symptomatology.

Rather than testing individually for each pair of groups whether there is an interaction with continuous variables, we would prefer to simply see whether there is an interaction across groups if that makes sense. Or is this not the right approach?

It would be great if you could provide some assistance in constructing the most appropriate design matrix and associated contrasts for this endeavour.

Any assistance would be greatly appreciated!

Regards,
Ijom

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