Mplus Users Guide v6.. - Muthén & Muthén

Examples: Mixture Modeling With Longitudinal DataIn the parameterization of the growth model shown here, the interceptsof the outcome variable at the four time points are fixed at zero as thedefault. The intercepts and residual variances of the growth factors areestimated as the default, and the growth factor residual covariance isestimated as the default because the growth factors do not influence anyvariable in the model except their own indicators. The intercepts of thegrowth factors are not held equal across classes as the default. Theresidual variances and residual covariance of the growth factors are heldequal across classes as the default. An explanation of the othercommands can be found in Example 8.1.EXAMPLE 8.4: GMM FOR A CATEGORICAL OUTCOMEUSING AUTOMATIC STARTING VALUES AND RANDOMSTARTSTITLE: this is an example of a GMM for acategorical outcome using automaticstarting values and random startsDATA: FILE IS ex8.4.dat;VARIABLE: NAMES ARE u1–u4 x;CLASSES = c (2);CATEGORICAL = u1-u4;ANALYSIS: TYPE = MIXTURE;ALGORITHM = INTEGRATION;MODEL:%OVERALL%i s | u1@0 u2@1 u3@2 u4@3;i s ON x;c ON x;OUTPUT: TECH1 TECH8;The difference between this example and Example 8.1 is that theoutcome variable is a binary or ordered categorical (ordinal) variableinstead of a continuous variable. The CATEGORICAL option is used tospecify which dependent variables are treated as binary or orderedcategorical (ordinal) variables in the model and its estimation. In theexample above, u1, u2, u3, and u4 are binary or ordered categoricalvariables. They represent the outcome variable measured at fourequidistant occasions.By specifying ALGORITHM=INTEGRATION, a maximum likelihoodestimator with robust standard errors using a numerical integration207