
Message/Author 

Philip Jones posted on Wednesday, December 13, 2006  9:27 am



Hi. I have a few questions about the tests for comparing k and (k1)class models: (1) Should TECH11 *not* be used with the OPTSEED option? I notice that I get (sometimes dramatically) different results when using OPTSEEDusually a much lower pvalue. Also I notice an extra set of iterations when using TECH11 without OPTSEED, so my hunch is that the H0 model is not getting properly optimized when OPTSEED is used. Is this correct? (2) How much importance do you give the LMR test given the published criticism and the fact that the BLRT test is now available? (3) For the BLRT test, why are zero or few random starts recommended for the H0 model? I'm sort of naively thinking that, e.g., if I needed 100/10 random starts for a 3class model (i.e., "STARTS 100 10;"), then I would also need that many in the LRTSTARTS option when comparing it to a 4class models (i.e., "LRTSTARTS 100 10 ...;"). Thanks again for all your help. 


1. Yes, Tech11 currently does not have the Tech14 facility of "LRTSTARTS" and should therefore not rely on OPTSEED but a regular STARTS = run. 2. LMR doesn't work too poorly, judging from the Nylund et al paper on our web site, see Papers and Latent Class Analysis: Deciding on the Number of Classes. 3. Because the artificial data are generated according to the H0 (k1class) model the k1 model is expected to be easy to find the best solution for, while the kclass model is harder. Starts = 100 10 refers to the realdata analysis where a latent class model for any k is not exactly the datagenerating model. 


What about differences between BLRT and LRT LLvalues (up to 2030)? Is that an indication for too less no. of starting values in BLRT, i. e. BLRTresult is not trustworthy? However, I received no warning message concerning the latter issue. 


If you are using Version 5.1, please send the outputs and your license number to support@statmodel.com. 


I have a question regarding Web Note 14. Suppose the k1class model has a latent variable with a very small negative variance that is not significantly different from zero. This variance is constrained to zero, and no other errors arise. The same variance is nonnegative in the kclass model, so it remains unconstrained. If TECH11 or TECH14 is requested in the kclass model, will it compare the kclass model to an unconstrained k1class model? If so, are the results usable considering the k1class model we want to consider has an extra constraint? Thank you! 


The kclass model will be used in the k1class run. Changing the continuous latent variable specification between the k and k1class models will make the comparison invalid. 

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