Jon Heron posted on Thursday, March 25, 2010 - 7:32 am
I've read quite a few latent-class model papers recently and I don't recall any of them mentioning tech10/bivariate residuals/conditional independence.
In my (somewhat limited) experience it's much more difficult to keep ones residuals under control with a cross-sectional mixture model (compared with a longitudinal model) as the inter-dependence between the items tends to be more complex. It's not unheard of to need to add 2 or 3 more classes compared to what would be supported by entropy/BIC/BLRT alone.
So how important is this? should I just concentrate on the more familiar assessments of fit and regard low residuals as being desirable but not strictly necessary?
BTW I recognise this isn't strictly Mplus related (although I did mention tech10).
We agree with you. We would use TECH10 sparingly and look at only the largest residuals. Rather than adding more classes, we might add residual covariances using the f BY language shown in Example 7.16 although each residual covariance is one dimension of integration.
Jon Heron posted on Friday, March 26, 2010 - 2:12 am
thanks for the suggestion. I'll check out Tan, and Kutner (1996).
best wishes, Jon
Jon Heron posted on Thursday, April 15, 2010 - 5:09 am
I recognize that example 7.16 is merely to demonstrate a principle, however I am wondering if one might have been able to infer than conditional independence was being violated IN ONE CLASS ONLY from some of the Mplus output.
As Tech-10 output is not class-specific i wonder whether the solution to conditional DEpendence is to start with a factor in only one class and then to add it to additional classes if the problem does not go away.
Jon Heron posted on Thursday, April 15, 2010 - 5:17 am
I think I've answered this myself - stepping away from the machine for a minute often works wonders.
Conditional dependence will show up as one or more high residuals for specific response patterns. Providing those pattern(s) are allocated to the same class with a high class-assignment probability, you know that the CD problem will be in one but not the other class.