I have a model with 4 exogenous and 4 endogenous LVs in a SE model.
en1 ON ex1 ex 2 ex3 ex 4; . . en4 ON ex1 ex ex3 ex4;
Now I want to expand this model so that a observed binary outcome gets regressed on the 4 endogenous LVs (logit regression on the endogenous LVs)
obsBinary ON en1 en2 en3 en4;
I think this is equivalent to a mediation model with 4 IVs, 1 DV and 4 mediators. The thing is that all the coefficients for the regressions between the 4 IVs and the 4 mediators then change to a different scale (standardized and unstandardized output).
Is this because the end DV is binary? Why does it happen? How can I interpret these coefficients then?
(It would be just nice to have r-like coefficients for the regressions between the IVs and the mediators and odds for the regressions between the mediators and the outcome. I do not necessarily need to test for mediation, it would be nice to control for some influence of the IVs before regressing the binary outcome onto the 4 mediators / endogenous LVs.)
Furthermore, when I add the binary observed outcome to my model I can't report fit indices anymore. What´s best practice for that?
I would be very happy if somebody could help me out.