Dear all,
I have a causal chain X -> M -> Y and am interested in the indirect effect of X on Y. This can usually be easily estimated using SEM and nlcom.
My problem is that the X -> M part in my data requires a zero-inflated Poisson regression (whereas the M -> Y part does not require special treatment). How would you estimate the indirect effect of X on Y in this case?
I tried specifying the following SEM:
gsem (1: m <- , family(pointmass 0)) (2: m <- x, family(poisson)) (C <- x t)(y <- m x), lclass(C 2) lcinvariant(none) vce(cluster id)
However, I encounter two issues: First, the entire model is estimate in two latent classes, including the latter par (M -> Y). However, I would like to get one indirect effect at the end. Second, I don't think I am allowed to simply multiply the two coefficient (b[X->M] * b[M->Y]) because zero-inflated Poisson is logistic.
How would you proceed? I would be deeply grateful for some guidance.
Best,
Johannes
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