Dear list,
I would like to factor out some stimulus measure from my functional data. I am in doubt about the pros and cons of doing so within the second level model (e.g., include stimulus measures as covariates) or within the first level model (e.g., include stimulus measures as multiple regressors along with head motion parameters).
I would for example expect the first-level approach to be more advisable because of the potentially larger number of degrees of freedom in the design matrix. For example, in a design with a three-level factor corresponding to three stimulus categories and multiple stimuli within each category (e.g., 10), the degrees of freedom in the second level model that tests the effect of stimulus category would be much lower than in the first level model. As such, factoring out several stimulus measures could perhaps be just impossible at the second level.
A side question: given a stimulus measure X which is undefined during (implicit) baseline trials, what value should be assigned to X for the baseline trials in order not to affect the baseline estimate? mean(X(trial~=baseline))?
Thank you for any suggestion,
Bruno
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