I'm trying to fit a parallel process latent growth curve model with a latent moderator as a predictor of one of the slopes. The latent moderator is the intercept multiplied by a latent variable (measured at time one). Basically, this is what I've asked of the model:
slopeY on interceptX;
slopeY on latentvariable; (main effect)
interaction | interceptX XWITH latentvariable;
slopeY on interaction; (interaction effect)
I get this error message: THE ESTIMATED COVARIANCE MATRIX COULD NOT BE INVERTED.COMPUTATION COULD NOT BE COMPLETED IN ITERATION 16.CHANGE YOUR MODEL AND/OR STARTING VALUES.
THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ERROR IN THE COMPUTATION. CHANGE YOUR MODEL AND/OR STARTING VALUES.
I'm wondering if it's possible to create this kind of interaction (latent variable X latent intercept)? Any help, advice, suggestions appreciated.
Thank you Dr. Muthen. I appreciate your advice. I was correlating the residuals across each process at each time point, but it seems there was another model specification error that I was missing until now. The model is running normally now. Thank you.
Yes, you can do a parallel growth model. Although I don't quite understand how one can think of growth in an interaction variable.
Daniel Lee posted on Monday, September 01, 2014 - 7:57 pm
Hi Dr. Muthen,
I am fitting a parallel process latent growth model (3 waves) with several covariates (e.g., gender, ses). I would like to extend this model, however, and investigate if the latent intercept and the latent slope of the first latent variable (religiosity) is conditioned on gender as it effects the intercept/slope of my second latent variable (depression). Is this possible? If so, would I just specify in the model that gender will interact with the intercept and slope of religiosity? Thank you!