I am trying to estimate a parallel process latent growth model with five waves of data from adolescence to early adulthood. The outcomes are depression (continuous) and number of conduct disorder symptoms (count, maximum 4). The sample size is 662. My input is:
Thanks - I have already tried each growth model separately, and they run fine and make sense.
I tried your montecarlo suggestion, but still fail to get estimates, and the following error message:
THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO A NON-ZERO DERIVATIVE OF THE OBSERVED-DATA LOGLIKELIHOOD.
THE MCONVERGENCE CRITERION OF THE EM ALGORITHM IS NOT FULFILLED. CHECK YOUR STARTING VALUES OR INCREASE THE NUMBER OF MITERATIONS. ESTIMATES CANNOT BE TRUSTED. THE LOGLIKELIHOOD DERIVATIVE FOR PARAMETER 17 IS -0.66093711D+01.
Is it possible that the data are simply not suitable for this type of model?
Hello, In a similar model with dual trajectories, one normally distributed and one with a Poisson distribution (COUNT), how would one interpret a regression path from the latent intercept of the count trajectory to the intercept or slope of the continuous process? In other words, how is the regression estimate from a continuous variable regressed on a latent Poisson variable interpreted?
Oppositely, how would the estimate from a Poisson latent variable regressed on a continuous variable be interpreted? In this case I assume that the exponentiated regression estimate could be interpreted as "a one unit increase in ...".