In your latent growth curve model example (https://openmx.ssri.psu.edu/docs/OpenMx/latest/TimeSeries_Path.html), you suggest:
-freely estimating the means and variances of the latent intercept and slope factors
-constraining the means of the manifest variables to be zero (to make their means dependent on the intercept and slope means)
-constraining the residual variances of the manifest variables to be equal over time
I am trying to fit a latent growth curve model with three manifest variables at each time point that compose a time-varying latent factor, let's say T1-T6. The latent factors (T1-T6) are then regressed on the latent intercept and slope factors as in your example.
My question is: what should I do with the means and variances of the manifest variables and the latent (T1-T6) variables?
My guess would be to:
-freely estimate the means and variances of the latent intercept and slope factors
-constrain the means of the manifest and T1-T6 latent factors to be zero (to make their means dependent on the intercept and slope)
-constrain the residual variances of the same manifest variable to be equal across time (to fix a given manifest variable's loading across time)
-constrain the residual variance of the latent factors (T1-T6) to be equal across time (for measurement invariance)
Is this right, or would you recommend another approach? Thanks for your help!