Mplus Users Guide v6.. - Muthén & Muthén

Examples: Mixture Modeling With Cross-Sectional Data• 7.13: Confirmatory LCA with binary latent class indicators andparameter constraints• 7.14: Confirmatory LCA with two categorical latent variables• 7.15: Loglinear model for a three-way table with conditionalindependence between the first two variables• 7.16: LCA with partial conditional independence*• 7.17: CFA mixture modeling• 7.18: LCA with a second-order factor (twin analysis)*• 7.19: SEM with a categorical latent variable regressed on acontinuous latent variable*• 7.20: Structural equation mixture modeling• 7.21: Mixture modeling with known classes (multiple groupanalysis)• 7.22: Mixture modeling with continuous variables that correlatewithin class• 7.23: Mixture randomized trials modeling using CACE estimationwith training data• 7.24: Mixture randomized trials modeling using CACE estimationwith missing data on the latent class indicator• 7.25: Zero-inflated Poisson regression carried out as a two-classmodel• 7.26: CFA with a non-parametric representation of a non-normalfactor distribution• 7.27: Factor mixture (IRT) analysis with binary latent class andfactor indicators*• 7.28: Two-group twin model for categorical outcomes usingmaximum likelihood and parameter constraints*• 7.29: Two-group IRT twin model for factors with categorical factorindicators using parameter constraints*• 7.30: Continuous-time survival analysis using a Cox regressionmodel to estimate a treatment effect* Example uses numerical integration in the estimation of the model.This can be computationally demanding depending on the size of theproblem.145