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!
indirect9 on IB (b1); indirect9 on IM (b2); indirect9 on SB ;
indirect9 on ibxim (b3) ;
model constraint: NEW (lowbi modbi highbi); lowbi = b1+b3*(-1) ; modbi = b1+b3*(0); highbi =b1+b3*1;
1) Is the syntax for the interaction correct? 2) Is the syntax for the model constraint correct to determine significance of simple slopes? 3) Is it correct to set the intercept of growth curves at zero to interpret standardized simple effects? Would the interaction be interpreted at "when one standard deviation above the mean of the intercept of the growth curve (zero)" 4) I saw the sample syntax on the short course topic 3 hand out slide 165. Why are the intercepts of the measured variables at each time point constrained to be equal? Do I need to do this for the interaction?
3) No. The interpretation is + and -1SD away from the mean of the intercept growth factor. 4) Q1: That was done as one way of treating an interaction (not needed; and note that the intercepts were freed). Q2: No
Ann Farrell posted on Thursday, November 21, 2019 - 6:52 am
Thank you very much for your response! That was very helpful. I have switched the moderator variable. I wanted to confirm that the following final syntax is correct?