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Mplus Discussion > Categorical Data Modeling >
 Natasha Mendoza posted on Wednesday, November 14, 2012 - 8:44 am
Hi all,

I am attempting a cross-lagged panel path analysis.

I have 4 panels (i.e., data collection waves). Each panel has 2 measured variables: 1 continuous and 1 count. I'm trying to predict one with the other over time.

I understand that it is convention to correlate the errors within each panel. When I treat both variables as continuous, I can correlate them. However, when I specify the count variable, I am unable to correlate the errors (and it really should be treated as a count because zero inflated).

Is there another way to correlate the errors or is it impossible because of the variable type?

Much Thanks!
 Linda K. Muthen posted on Thursday, November 15, 2012 - 9:10 am
You need to use the BY option to specify covariances when numerical integration is required because each covariance requires one additional dimension of integration. Following is how you would specify this:

f BY u@1 y;
f@0; [f@0];

where the factor loading of y contains the covariance parameter.
 Nassim Tabri posted on Wednesday, March 30, 2016 - 9:05 am
Hi Dr. Muthen,

What would the Mplus syntax look like if one wanted to include within lag correlations between three or more categorical DVs in a cross-lagged model?

Would the following syntax capture the within lag correlations between three DVs (u, y, and z)?

f BY u@1 y z;
f@0; [f@0];

f1 By y@1 z;
f1@0; [f1@0];

OR, would each within lag correlation require 1 latent variable like this:

f BY u@1 y;
f@0; [f@0];

f1 By u@1 z;
f1@0; [f1@0];

f2 By y@1 z;
f2@0; [f2@0];

Thank you in advance for your feedback!
 Bengt O. Muthen posted on Wednesday, March 30, 2016 - 6:38 pm
The second alternative is right. But don't fix the factor variance at zero - fix it at one. You also want to make these 3 factors uncorrelated (and uncorrelated with everything else if that needs adjustment - check TECH1).
 Nassim Tabri posted on Friday, April 01, 2016 - 6:48 am
Thank you Dr. Muthen!
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