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Hi everyone

I'm analysing some effect sizes collected from a number of empirical studies, so the data structure is multilevel, with ES nested within studies. ES is my response variable, which comprises of response type A and B. So I'm trying to fit a multivariate response model. I have encountered some difficulties therefore I'm here to seek help.

1) I don't have ES level explanatory variables but only study level explanatory variables. I want to test the influence of these study level variables on A and B. However, for some variables I propose they have similar effects on A and B, and others have differential effects on the A and B. For the variables I proposed to have similar effects, it possible to estimate only one coefficient for each explanatory variable on both A and B, because I'm not interested how different these effects are on A and B. I found a function of "add common coefficient", which will produce an h equation. Can it be used for my purpose? When should the h equation be used?

2) Within each study I have multiple ES reporting both A and B, not only one ES for each type of response. Will this model be useful for such a situation?

3) How to calculate the ICC for this model?

Thank you very much for your help

Best

Dear Tomay,
I would suggest that you take a look through the manual about multilevel multivariate responses.
1. When you add predictor variables you can as you correctly point out add them as common coefficient which (at least for IGLS) will give you the same effect for A and B.
2. Yes just make sure you sort the data to respect the hierarchy.
3. Presumably you can do this for each response separately.
Best wishes,
Bill.