MODELING WITH BOTH CONTINUOUS AND CATEGORICAL LATENT VARIABLES
The full modeling framework includes models with a combination of continuous and categorical latent variables. Observed outcome variables can be continuous, censored, binary, ordered categorical (ordinal), counts, or combinations of these variable types. In addition, for regression analysis and path analysis for nonmediating outcomes, observed outcomes variables can also be unordered categorical (nominal). Most of the special features listed above are available for models with both continuous and categorical latent variables. Following are models in the full modeling framework that can be estimated using Mplus:
 Latent class analysis with random effects
 Factor mixture modeling
 Structural equation mixture modeling
 Growth mixture modeling with latent trajectory classes
 Discretetime survival mixture analysis
 Continuoustime survival mixture analysis
Most of the special features listed above are available for models with both continuous and categorical latent variables. The following special features are also available:
 Analysis with betweenlevel categorical latent variables
 Test of equality of means across latent classes using posterior probabilitybased multiple imputations
