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Mplus Discussion > Latent Variable Mixture Modeling >
 Jon Heron posted on Thursday, January 05, 2012 - 12:12 am
Hi Bengt/Linda

Happy New Year to you!

I am scratching my head over something that should be really simple. I have an exposure derived from a 4-class mixture model using six binary manifest vars. I have a distal outcome U which is also binary and a bunch of potential covariates. It's very similar to "Figure 19.7 GGMM Diagram for LSAY Data" in Bengt's Kaplan chapter, except I've gone LCGA rather than GMM.

I would like to estimate the association between class C and outcome U and then assess attentuation when including a number of confounders.

I have begun to wonder whether this is possible since when U is manifest, the C~U relationship is a loading within the measurement model for C rather than an association within the structural part of the model. Furthermore, as the inclusion of the covariates can impact on the measurement model, there's no guarantee that any attenuation would be due to confounding anyway.

please can you help?

many thanks, Jon
 Linda K. Muthen posted on Thursday, January 05, 2012 - 12:30 pm
You would regress the distal outcome on the confounders and look at the thresholds of the distal outcome varying across classes.
 Jon Heron posted on Thursday, January 05, 2012 - 11:51 pm
Ah thanks Linda

the lack of "probability scale" output had led me to question the validity of that - I guess I can convert those thresholds into odds ratios easily enough

many thanks, Jon
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