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Mplus Discussion > Categorical Data Modeling >
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 Anonymous posted on Tuesday, September 28, 2004 - 10:07 am
What is better if you want to make a later categorical SE-Modeling and you want to check the reliabilty and validity of your expected two factors with a "correlation matrix"? 1. Handle the missings in the way the default is: exlude any observation with one or more missing values, or 2. Use all observations by specifying TYPE=MISSING;?
 bmuthen posted on Wednesday, September 29, 2004 - 4:50 pm
Type = Missing should be preferred.
 Michael Lorber posted on Wednesday, April 11, 2007 - 8:40 pm
I am interested in conducting multinomial regression analyses, in some cases with latent categorical outcomes, and in other cases with observed categorical outcomes; in both cases with missing data on the outcomes. Per your recommendations, I have been using covariates to improve the precision of classification (my covariates have no missing data).

I have been specifying "missing" to make use of all of the possible information, but have been surprised to find that Mplus is eliminating cases that have all missing data on the latent class indicators. This would make sense to me if I were not using covariates (i.e., a case cannot be analyzed if it has no non-missing values). But since my covariates are 100% complete, I expected that Mplus would use all the cases, using information from the covariates to feed into the latent class estimates. I had the same issue when using observed categorical outcomes.

It would be great if you would provide some insight as to why this is the case with these categorical analyses in contrast to analyses with continuous outcomes in which cases are utilized if they have a non-missing predictor and/or outcome value. My worry is that the estimates are biased by excluding cases with missing data on the latent class indicators (alternately observed class in the ordinary multinomial analyses).

Thanks for your time and attention!
 Bengt O. Muthen posted on Thursday, April 12, 2007 - 9:59 am
With u denoting your latent class indicators and x the covariates, you are essentially estimating the regression model [u | x]. People with missing on all components of the u vector but with x observed don't contribute to the likelihood for that model. So it is correct to do deletion of such individuals. This is in line with the bivarate normal missing data example in the Little & Rubin book. Although people with missing on u but not on x contribute to estimation of [u] parameters, they don't contribute to the [u|x] parameters that Mplus reports.
 Michael Lorber posted on Thursday, April 12, 2007 - 11:45 am
Thanks for the quick reply!

Does this logic also apply identically to observed categorical outcomes?
 Bengt O. Muthen posted on Thursday, April 12, 2007 - 1:58 pm
Yes.
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