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Mplus Discussion > Categorical Data Modeling >
 Alex Chavez posted on Monday, June 21, 2010 - 4:58 pm
I am trying to understand how a regression of a categorical variable which is also a predictor of a latent variable is modeled. Letting Y be the latent variable formed by LS1-LS5, if I include the autoregression “LS2 ON LS1” with parameter beta, does Mplus include it in the model as follows?

LS2* = lambda2 nu + beta LS1 + epsilon2, such that
Pr(LS2 >= k | nu, LS1) = F[-(tau_ik – beta LS1 – lambda2 nu)theta_2^{-1/2}]

In particular I’m concerned about whether beta*LS1 enters into the probability function, because the predicted factor scores do not change when I change the fixed value of beta (“LS2 ON LS1@1” vs. “LS2 ON LS1@3” holding all other parameters constant).

Y by LS1-LS5 @1;
LS2 ON LS1 @ 1;
! LS2 ON LS1 @ 3;
[LS1$1-LS5$1 @ -3];
[LS1$2-LS5$2 @ -2];
[LS1$3-LS5$3 @ -1];
[LS1$4-LS5$4 @ 0];
[LS1$5-LS5$5 @ 1];
[LS1$6-LS5$6 @ 2];|
 Bengt O. Muthen posted on Tuesday, June 22, 2010 - 4:35 pm
You are correct in your assumption of the modeling when your estimator is ML. In contrast, with WLSMV "LS1" is replaced by its latent response variable "LS1*". I assume your "nu" is the factor (eta).

Regarding the factor scores, Appendix 11 of the Tech appendix for Version 2 (see our web site) shows that conditional independence of y | eta is assumed. Therefore the factor scores cannot be computed correctly for your model using WLSMV. You can try ML or you can rewrite the model by letting a new factor influence the two items that you want to have related beyond the original factor.
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