Longitudinal SEM and Latent Growth Curves

I read the article in Structural Equation Modeling Journal. I would like to try to replicate your findings. Would it be possible to obtain a copy of your script?

Hello all,

I have data with Counties nested within States over Time. I have been told that OpenMX is unable to run a three level MLM SEM and I need to use M+.

Can someone either confirm this for me or point me in the direction of some example code in OpenMX?

Hi,

The data set i use has 214 individuals for which I have different number of observations - varies between 21 and 30.

Hello all,

I would like to predict the slope and intercept from, say, gender in a very simple growth curve model. For the sake of this question, let's say it's the model described in the documentation here.

Hi!
I would like to adjust for age in longitudinal setting of two time points. In the model I would like to adjust for age from the two time points in the model. I am not sure about the number of rows and columns to be specified in the matrix for age in OpenMX. To begin with I set up the mean and beta coefficient for age as follows:
# Matrix for expected Mean
mean <- mxMatrix( "Full", nrow=1, ncol=4, free=TRUE,
values= 1.6, label=c("meany11","meany12","meany11","meany12" ), name="expMeanGy" )
# Matrix for beta

We have four time-points, and at each time-point there are two indicators. Is it possible to use the two manifest indicators per time-point to create one latent variable per time-point, and run a growth mixture model on those four latent variables?

If that is possible, suppose that we are actually missing one manifest indicator at one time-point. Is it possible to create a latent variable in place of that manifest indicator? A drawing of our model, as well as our attempted script, is attached. We are getting the following error:

Hi all, anybody have an example script in matrix format for a latent difference score model? Better yet, how about one with an ACE decomposition?

Hopefully this is the place for a request like this!

--Scott

I am trying to fit a longitudinal CFA with 3 indicators at each of 4 time points, with a 1 year time lag. The model runs fine when I have 3 time points, but the model fails when I add the fourth time point. It appears that the model may fail because of multicollinearity among the latent factors (the correlation between the latent factors at T3 and T4 = .996). I have already specified the within-indicator residual covariances across time, but it does not solve the problem. Here are the correlations of the 4 latent vars:

lag T=1: .988, .990, .996
lag T=2: .976, .967
lag T=3: .937