Mplus Users Guide v6.. - Muthén & Muthén

CHAPTER 6outcomes with a numerical integration algorithm is used (Hedeker &Gibbons, 1994). Note that numerical integration becomes increasinglymore computationally demanding as the number of factors and thesample size increase. An explanation of the other commands can befound in Example 6.1.EXAMPLE 6.5: LINEAR GROWTH MODEL FOR ACATEGORICAL OUTCOME USING THE THETAPARAMETERIZATIONTITLE:this is an example of a linear growthmodel for a categorical outcome using theTheta parameterizationDATA: FILE IS ex6.5.dat;VARIABLE: NAMES ARE u11-u14 x1 x2 x31-x34;USEVARIABLES ARE u11-u14;CATEGORICAL ARE u11-u14;ANALYSIS: PARAMETERIZATION = THETA;MODEL: i s | u11@0 u12@1 u13@2 u14@3;The difference between this example and Example 6.4 is that the Thetaparameterization instead of the default Delta parameterization is used.In the Delta parameterization, scale factors for the latent responsevariables of the observed categorical outcomes are allowed to beparameters in the model, but residual variances for the latent responsevariables are not. In the Theta parameterization, residual variances forlatent response variables are allowed to be parameters in the model, butscale factors are not. Because the Theta parameterization is used, theresidual variance for the latent response variable at the first time point isfixed at one as the default, while the residual variances for the latentresponse variables at the other time points are free to be estimated. Anexplanation of the other commands can be found in Examples 6.1 and6.4.108