MODELING WITH CONTINUOUS LATENT VARIABLES
Ellipse A describes models with only continuous latent variables. Following are models in Ellipse A that can be estimated using Mplus:
 Regression analysis
 Path analysis
 Exploratory factor analysis
 Confirmatory factor analysis
 Structural equation modeling
 Growth modeling
 Discretetime survival analysis
 Continuoustime survival analysis
Observed outcome variables can be continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types.
Special features available with the above models for all observed outcome variables types are:
 Single or multiple group analysis
 Missing data under MCAR, MAR, and NMAR and with multiple imputation
 Complex survey data features including stratification, clustering, unequal probabilities of selection (sampling weights), subpopulation analysis, replicate weights, and finite population correction
 Latent variable interactions and nonlinear factor analysis using maximum likelihood
 Random slopes
 Individuallyvarying times of observations
 Linear and nonlinear parameter constraints
 Indirect effects including specific paths
 Maximum likelihood estimation for all outcomes types
 Bootstrap standard errors and confidence intervals
 Wald chisquare test of parameter equalities
 Plausible values for latent variables
