Johnny Wu posted on Thursday, April 23, 2009 - 12:54 pm
Thank you very much.
Another question: How can exogenous predictors interact with time to predict growth factors, within the LGM framework?
You see, because on Preacher's website, time is treated as an predictor. Hence, a two-way interaction regressing slope on x1 with x2, would be a three-way interaction regression slope on x1 with x2 and time.
How do I obtain the regression coefficient for time?
It sounds like Preacher's approach to growth modeling is via two-level analysis with a random slope for a time variable - see the Mplus UG example 9.16. You get the same effect by letting the x1*x2 interaction variable influence the slope growth factor in the regular Mplus single-level wide approach to growth: the slope growth factor multiplies time to create the product of the 3 variables.
TECH3 gives covariances among the parameter estimates. The growth factors and covariates are variables. It would be difficult to get that without changing your model. Why not regress the growth factors on the covariates.