Mplus Users Guide v6.. - Muthén & Muthén

Examples: Missing Data Modeling And Bayesian Analysis* Example uses numerical integration in the estimation of the model.This can be computationally demanding depending on the size of theproblem.EXAMPLE 11.1: GROWTH MODEL WITH MISSING DATAUSING A MISSING DATA CORRELATETITLE: this is an example of a linear growthmodel with missing data on a continuousoutcome using a missing data correlate toimprove the plausibility of MARDATA: FILE = ex11.1.dat;VARIABLE: NAMES = x1 x2 y1-y4 z;USEVARIABLES = y1-y4;MISSING = ALL (999);AUXILIARY = (m) z;ANALYSIS: ESTIMATOR = ML;MODEL: i s | y1@0 y2@1 y3@2 y4@3;OUTPUT: TECH1;y1 y2 y3 y4isIn this example, the linear growth model at four time points with missingdata on a continuous outcome shown in the picture above is estimatedusing a missing data correlate. The missing data correlate is not part ofthe growth model but is used to improve the plausibility of the MARassumption of maximum likelihood estimation (Collins, Schafer, &Kam, 2001; Graham, 2003; Enders, 2010). The missing data correlate isallowed to correlate with the outcome while providing the correct339