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 ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l
