[STATA - gsem] Multilevel Structural Equation Model - Meta Analysis


I am quite familiar with mixed effects (multilevel models) in meta analyses.
Now, I want to do a mixed effects (multilevel) meta analysis structural equation model (MASEM) with STATA and the gsem package because my data format is long.

My data look like:
`id studyId	performance	education moderator1 moderator2`  
 `1   1           -0.4 	      0.1 	    1 	        0`  
 `2   1 	   0.2 	      0.1 	    0 	        1`  
 `3   2 	   0.5 	      0.3 	    0 	        1`   
 `4   3 	  -0.1 	      0.4 	    1 	        0`  
 `5   3 	  -0.6 	      0.4 	    1 	        0`

performance: Firm-Performance, correlation coefficients (Fisher z transformed)
moderator1-moderator2: My moderator variables, have some nusisance variables as well but do not display it here.
education: Pre-dominant education level in a certain country

All variables are weighted by w = fisher z variance + tau

I want to model primary study effects nested in primary studies (results grouped in the related study).

    gsem (performance <- moderator1 moderator2 education M1[id]@1 M2[id>studyId]@1)  
    (moderator1 <- education M3[id]@1 M4[id>studyId]@1)  
    (moderator2 <- education M5[id]@1 M6[id>studyId]@1),  
    Latent(M1 M2 M3 M4 M5)  
    cov(e.performance M1[id]*M2[id>studyId])
I am not sure about the specification of the model in STATA.
1. Did I model the covariances in a correct way?
2. @1 means I normalize the latent variable. What alternatives do I have?
3. Is it enough just to model the error term (e.performance) of the dependent variable performance or do I have to model all variables there?

Thank you very much for your support :)


Super Moderator
Hiya, your post was automatically flagged as spam for some reason, I've released it from the moderation queue now - sorry about that!