Dear all,

I am confronting some issues with multi-level gsem.

I have a two-level country-individual-level dataset with 100.000 individuals nested in 50 countries.
I am interested in 2 (dichotomous) DVs; DV1 & DV2, as predicted by the key independent variable of interest INDEP.
Moreover, I am interested in seeing whether the effect of my key independent variable of interest INDEP differs on DV1 as compared to its impact on DV2.

Because I cannot test this after the normal MLM syntax (meglm) -as far as I know-, I am trying to do this in the context of a gsem

My attempt goes as follows:

Code:
gsem (DV1 <- $controls    INDEP     L1[countries] ) ///
          (DV2 <- $controls    INDEP     L2[countries]) /// 
         , latent(L1 L2 ) family(binomial) link(logit) nocapslatent   intmethod(mcaghermite) intpoints(20)  /// 
           var(L1[NUTSenc]@v1 L2[NUTSenc]@v1)
           gsem, coeflegend
           test  _b[DV1:INDEP= _b[DV2:INDEP]
  • Is this a suitable approach?
Moreover, while this model works well with some very basic controls, once I start adding more controls it quickly stops converging. This is particularly severe when I add fixed effects.
I however do need to add 100 fixed effects for my model to be well-specified.
  • If the model does not converge because of the number of fixed effects, what could I do to increase the chances of convergence?
Thank you so much in advance!

Best
Johannes