I want to compute a latent class analysis with nominal indicators (example 7.7 in the user's guide). I expected that for the values of the indicators conditional probabilities would be provided. Instead in the result section means are reported. How can a nominal variable have a mean? What mistake do I make? Does Mplus not make the same analysis like Latent Gold for example?
The estimated item parameters that are reported are logits for the different categories of each nominal indicator. These logits can be translated into probabilities very easily using the multinomial logistic regression expression - this is discussed in Chapter 13 of the User's Guide.
I run LCA with 4 nominal indicators and each indicator has 3 categories. But, I am no sure if I get the conditional probabilities for each indicator correctly. From the model results, there were means for each category in each latent class. For example, in the latent class 1, means for ST53Q01#1 is 1.080 ,and ST53Q01#2 is 1.753, but ST53Q01#3 was fixed as reference group . So, log odds (ST53Q01#1|C=1)=1.08, log odds (ST53Q01#2|C=1)=1.753, and log odds (ST53Q01#3|C=1)=0 in Class 1. And,each log odds is exponentiated and summed. To get the conditional probability for each category in is that each expoentiated value is divide by the sum.
Check how it is done in the UG chapter 14. See the multinomial logistic regression example with covariates all = 0.
Sam Crawley posted on Monday, October 22, 2018 - 5:46 pm
I have calculated the estimates for nominal indicators using the method described in chapter 14. However, is there any way to also get standard errors, as is provided for categorical indicators in probability scale?
If you express them in Model Constraint, you also get their SEs.
Sam Crawley posted on Monday, October 22, 2018 - 7:12 pm
Thanks. Is there an example of the syntax to do this somewhere? In particular, I'm not sure how to reference the estimates for the means for each combination of class/nominal category in the MODEL CONSTRAINT section.
The V8 UG pages 55-557 show an example with 4 nominal categories and in the first step only intercepts are used which corresponds to your situation. These are the intercepts you have for one of your observed nominal variables in one class. A nominal DV's intercepts are referred to as y#1, y#2, etc. Label these in the Model command and generalize from there in Model Constraint.