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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).
Free Mplus workshops - Dr. Michael Zyphur has made available a free 3-day workshop held in July 2019 at the University of Melbourne. The workshop covers the new General Cross-Lagged Panel Model (GCLM) in Mplus. A second course will be offered sometime between Nov 25 and Dec 13, 2019. Additionally, a 5-day Mplus workshop covering various modeling topics, from basic correlation and regression to multilevel structural equation modeling and latent growth models in Mplus is available for viewing and download.
In all models, missingness is not allowed for the observed covariates because they are not part of the model. The model is estimated conditional on the covariates and no distributional assumptions are made about the covariates. Covariate missingness can be modeled if the covariates are brought into the model and distributional assumptions such as normality are made about them. With missing data, the standard errors for the parameter estimates are computed using the observed information matrix (Kenward & Molenberghs, 1998). Bootstrap standard errors and confidence intervals are also available with missing data.
New feature: Mplus Web Talks by Bengt Muthén. No. 1 is now available. Web Talk 2 coming soon: “Using Mplus to do Latent Transition Analysis and Random Intercept Latent Transition Analysis”.
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