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Mplus has several options for the estimation of models with missing data. Mplus provides maximum likelihood estimation under MCAR (missing completely at random), MAR (missing at random), and NMAR (not missing at random) for continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types (Little & Rubin, 2002). MAR means that missingness can be a function of observed covariates and observed outcomes. For censored and categorical outcomes using weighted least squares estimation, missingness is allowed to be a function of the observed covariates but not the observed outcomes. When there are no covariates in the model, this is analogous to pairwise present analysis. Non-ignorable missing data (NMAR) modeling is possible using maximum likelihood estimation where categorical outcomes are indicators of missingness and where missingness can be predicted by continuous and categorical latent variables (Muthén, Jo, & Brown, 2003; Muthén et al., 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.
Expanded and modified SRMR (Asparouhov & Muthén, 2018d)
Bivariate residual tests and factor score standard errors for the weighted least squares estimators
Cluster-specific plots for TWOLEVEL
Expanded odds ratio output
Standard errors for correlations with TYPE=BASIC using the H1SE option of the OUTPUT command
New Mplus paper: Bayesian estimation of single and multilevel models with latent variable interactions. Submitted for publication.
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