MODELING WITH CATEGORICAL LATENT VARIABLES
Ellipse B describes models with only categorical latent variables. Following are models in Ellipse B that can be estimated using Mplus:
 Regression mixture modeling
 Path analysis mixture modeling
 Latent class analysis
 Latent class analysis with covariates and direct effects
 Confirmatory latent class analysis
 Latent class analysis with multiple categorical latent variables
 Loglinear modeling
 Nonparametric modeling of latent variable distributions
 Multiple group analysis
 Finite mixture modeling
 Complier Average Causal Effect (CACE) modeling
 Latent transition analysis and hidden Markov modeling including mixtures and covariates
 Latent class growth analysis
 Discretetime survival mixture analysis
 Continuoustime survival mixture analysis
Observed outcome variables can be continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types. Most of the special features listed above are available for models with 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
 Plausible values for latent classes
