Dear Dr Babinec
   
  sorry to bother you again with questions about cluster analysis in Latent 
Gold, but what happens if p-value is very significant for all models? (e.g. 
9.7E-204 or 2.8E-191) how can we assess the models? 
   
  Would you treat total scores from different measures  as continuous 
variables(answers to items are scaled from 1-6 or 1-10, but then a  total score 
is calculated for each measure)?
  
Thank you,
  Anca 
  
Anthony Babinec <[EMAIL PROTECTED]> wrote:
  When you have NOMINAL indicators, for example,
your model gives rise to expected counts that
can be compared to observed counts. The distribution
theory is based on the chi-square statistic (L-squared),
which has an associated p-value. When you have
CONTINUOUS indicators, your model is based on 
normal theory. The parameters being estimated are
means, variances, and covariances. Since the 
data are continuous and not discrete, you no
longer have a model framework of observed and
expected counts. The model is the normal finite
mixture model. Classification can work well with
a good-fitting model. 

-----Original Message-----
From: Classification, clustering, and phylogeny estimation
[mailto:[EMAIL PROTECTED] On Behalf Of SUBSCRIBE CLASS-L Anonymous"
Sent: Thursday, December 07, 2006 9:06 AM
To: [email protected]
Subject: p values in Latent Gold

Hello

Does anyone know why p-values and chi-squared statistics are not available
in 
Latent Gold summary output for models using continuous variables and what is

the statistical explanation behind it?

Also, how reliable is the classification with continuous variables in latent
gold 
given the fact that it is based - from my understanding - on means and not
on 
probabilities?

Many thanks
Anca

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