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:

I however do need to add 100 fixed effects for my model to be well-specified.

Best

Johannes

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?

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?

Best

Johannes

## Comment