Dear all,
we would like to conduct a longitudinal analysis with CAT12, however we are uncertain whether our model is correct, as most examples in the CAT12 manual are for 2 groups. We ran an experiment in 30 participants and would like to model the effect of mood (measured twice before and after experimental condition) on the changes of brain morphometry (also measured twice) via the experimental condition (treatment vs. no treatment). Further, we would like to include a factor of randomization (treatment at first or second time point).
Is it correct to set the model as follows:
Flexible factorial design
Experimental condition
- Independence: No
- Variance: Equal
- Grand mean scaling: No
- ANCOVA: No
Subject
- Independence: Yes
- Variance: Equal
- Grand mean scaling: No
- ANCOVA: No
Main effects & Interactions
- Main effect; Factor number 2
- Main effect; Factor number 1
Covariates
- Name: Randomization (a vector of 0 and 1)
- Interactions: None
- Centering: Overall mean
- Name: Mood
- Interactions: With factor experimental condition
- Centering: Overall mean
The model looks similar to the example "Longitudinal data in two groups with interaction of covariate by group (example for two time points)" in the CAT12 manual.
Now we set the contrast as follows
treatment; no treatment; randomization; treatment*mood; no treatment*Mood
0 0 0 1 -1
0 0 0 -1 1
as an F-Contrast.
Our interpretation would be:
If we find a significant Interaction, than the increase in mood via experimental condition is significantly correlated with the increase in grey matter volume via experimental condition.
Is that correct? Thanks a lot in advance!
Best
Susanne
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