Published on *OpenMx* (http://openmx.psyc.virginia.edu)

By *dadrivr*

Created *02/25/2011 - 10:16*

Fri, 02/25/2011 - 10:16 — dadrivr [1]

In your latent growth curve model example (http://openmx.psyc.virginia.edu/docs/OpenMx/latest/TimeSeries_Path.html [2]), 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!

**Links:**

[1] http://openmx.psyc.virginia.edu/users/dadrivr

[2] http://openmx.psyc.virginia.edu/docs/OpenMx/latest/TimeSeries_Path.html

[3] http://openmx.psyc.virginia.edu/thread/910

[4] http://openmx.psyc.virginia.edu/thread/81

[5] http://openmx.psyc.virginia.edu/forums/opensem-forums/longitudinal-sem-and-latent-growth-curves