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Multiple data sets generated using multiple imputation can be analyzed using a special feature of Mplus. Parameter estimates are averaged over the set of analyses, and standard errors are computed using the average of the standard errors over the set of analyses and the between analysis parameter estimate variation (Rubin, 1987; Schafer, 1997). A chi-square test of overall model fit is provided (Asparouhov & Muthén, 2008c; Enders, 2010).

This includes growth models with auto-regressions among the residuals and random intercept cross-lagged models (RI-CLPM and RI-ARMA) for panel data. The new modeling features are described in Asparouhov & Muthen (2021a) and illustrated further in Mplus Web Talk No. 4.

Simplified 3-step estimation for Latent Transition Analysis described in Section 9 of revised Mplus Web Note 21
Entropy for each latent class variable when there are multiple latent class variables such as in Latent Transition Analysis

Bayes LCA modeling with local dependence for ordinal variables has been added (Asparouhov & Muthen, 2021a).
For maximum likelihood estimators, an improved chi-square test for two-level modeling with zero between-level residuals has been added (Asparouhov & Muthen, 2021b).
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