Continuous distal outcomes with LPA PreviousNext
Mplus Discussion > Latent Variable Mixture Modeling >
 Pamela Kaliski posted on Thursday, August 09, 2007 - 12:32 am

I am doing a latent profile analysis, with six continuous indicators used to form the latent classes. Additionally, I have 5 continuous predictors and 1 continuous distal outcome. I cannot figure out the correct Mplus syntax for modeling a continuous distal outcomes. I looked up distal outcomes in the Mplus user's guide index, which refers me to pages 184-185. However, the example shown on these pages is for a binary distal outcome.

What is Mplus syntax is used to model a continuous distal outcome?

Thank you!
 Linda K. Muthen posted on Tuesday, August 14, 2007 - 8:11 pm
It is the same as for categorical except you don't use the CATEGORICAL option. Include the variable on the USEVARIABLES list. The relevant parameter is the mean of the distal outcomes in each class.
 Pamela Kaliski posted on Tuesday, August 21, 2007 - 2:51 pm
Thank you Linda for your response. I have a follow-up question. Given that the mean is the relevant parameter of the distal outcome in each class, is there any way of testing whether or not the means of the distal outcome are significantly different between the classes? In other words, is there a way of knowing from the Mplus output if class membership is a significant predictor of the distal outcome? With the logistic regression of latent class membership on the continuous predictors in my model, there is a test of whether or not the predictors are significant predictors of latent class membership. Is there a similar test of whether or not latent class membership is a significant predictor of the distal outcome?
 Linda K. Muthen posted on Thursday, August 23, 2007 - 9:12 pm
You can use MODEL TEST to test if these means are different. This gives a Wald test. See MODEL CONSTRAINT for a description of how to label the parameters in the MODEL command. The labels are used in MODEL TEST.

Think of the regression of a continuous outcome on a binary covariate,

y = b0 +b1*x

When x=0, y=b0. When x=1, y=b0 + b1. There is simply a shift in this case. Mplus estimates b0 for one class and b0 + b1 for the other class. By testing the equality across classes, you are testing if the difference between b0 and b0 +b1 or b1 is signficant.
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