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Re: st: Latent class estimation

From   "Christian Deindl" <[email protected]>
To   [email protected]
Subject   Re: st: Latent class estimation
Date   Wed, 05 Dec 2007 20:29:32 +0100

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 (

it is really nice and easy to handle.


On Wed, 5 Dec 2007 12:45:26 -0500
"Verkuilen, Jay" <[email protected]> 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 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.

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Christian Deindl
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