Hello,
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:
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).
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
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:
HTML:
`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).
PHP:
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])
cov(M3[id]*M4[id>studyId])
cov(M5[id]*M6[id>studyId])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
PHP:
