Re: st: Latent class estimation

["Christian Deindl" <deindl@soziologie.uzh.ch>]

to make the list complete: there is also a possibility in SAS to estimate 
LCA models: PROC LCA.
you can download it via the methodology center of pennstate university 
(http://methcenter.psu.edu/)

it is really nice and easy to handle.


christian

On Wed, 5 Dec 2007 12:45:26 -0500
 "Verkuilen, Jay" <JVerkuilen@gc.cuny.edu> wrote:

> Phil Schumm wrote:
>
> > > > I would like to offer an alternative opinion.  RE references that are
> accessible, Bartholomew's "Latent Variable Models and Factor  
> Analysis" (1987, Charles Griffin & Co.) offers a good theoretical  
> introduction, and Skrondal and Rabe-Hesketh's "Generalized Latent  
> Variable Modeling" (2005, Chapman & Hall) is superb in terms of both  
> its theoretical and applied presentation.  The latter should be read  
> in any case without question.<<<
>
> Bartholomew's book had a second edition (co-authored with Martin Knott).
>
>
> There's also a Stata-specific book Multilevel and Longitudinal Modeling
> Using Stata by Skrondal and Rabe-Hesketh. I would recommend seeing
> http://www.gllamm.org for example code, the GLLAMM manual, etc. 
>
>
> > > > The program -gllamm- (type -ssc install gllamm-) is capable of  
> estimating latent class models in a very flexible manner, and the  
> manual available with the program (as well as the book cited above)  
> offer several worked examples.  It can be slow for datasets with a  
> large number of unique covariate patterns, but this has to be  
> evaluated relative to the time required to learn how to use another  
> program and to move your data and results back and forth.  It's  
> certainly an excellent place to start and to run some preliminary  
> analyses; you can always then move to another piece of software if  
> speed becomes a problem.<<<
>
> Yeah, LatentGOLD, LEM, and Mplus would all be the ones I'd figure as
> places to go if GLLAMM doesn't work out, or else coding things up in
> software like winBUGS. There might be an R package of some sort that
> does this; there are so many I can't possibly keep up with them all.
>
> IMO it is wise to do the analysis in multiple software packages---these
> models are tricky. Mixture regression is particularly known for having
> multiple optima problems and run the serious risk of capitalizing on
> chance, especially in "exploratory" mode when there aren't covariates
> predicting class membership or other things to constrain the model. 
> Jay
>
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__________________
Christian Deindl
Universit�t Z�rich
Soziologisches Institut
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CH - 8050 Z�rich
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