When dealing with latent variables that are also dependent variables in MPlus, is there any way to set their variances equal, as opposed to their RESIDUAL variances?
I have an auto-regressive model where a series of latent variables predict each other. It seems that I am only able to set their unexplained variances equal, whereas what I would like to do is set their overall (explained plus unexplained) variances equal. I can do this before I add the auto-regressive pathways (since all of their variance is unexplained), but unable to figure out how to do it afterward.
You can accomplish this by using the Model Constraint command. In your Model command you label the parameters that determine the variances and then in your Model Constraint command you express the variances in terms of these labels and hold the variances equal. For an example of how to use Model Constraint, see the V5 UG ex 6.17.
Thank you for the reply. I know how to use the constraint command, but am unable to figure out how to apply it here without being able to access a non-residual variance. I have higher-order latent variables partitioning the variance of lower-order ones. In order to do this, I fix the lower order residual variances to zero, therefore all of this variance is partitioned into the higher-order latent variables. Although I can set the pathways from higher to lower equal, since the variance of the lower-order latent variables is set by the variance (or std-dev) of the manifest variable with a loading of one, and these variances are different, the std loadings from lower to higher-order are not equal. I would like to set the std pathways equal (or the higher-order latent variables variances), but feel I can not do this without accessing the actual variance (of either the manifest variable or the higher-order latent variable). Is this possible in Mplus?
Maybe UG ex 5.20 can give you ideas. Here the "rel2" line of Model Constraint expresses the variance of the dependent variable y2 in terms of model parameters. Note that "vf1" is what you call a "non-residual variance" and so is V(y2). This example also shows standardized expressions and how these can be held equal.