In your latent growth curve model example (http://openmx.psyc.virginia.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!