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 non-mediating 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
• Discrete-time survival mixture analysis
• Continuous-time 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 between-level categorical latent variables
• Test of equality of means across latent classes using posterior probability-based multiple imputations

Modeling with Categorical Latent Variables	Modeling with Complex Survey Data
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