I am working on some measurement invariance analyses based on both gender and race and have been running into some trouble. Although the two most restrictive models are running and fitting fine, the two less restrictive models are not. I am receiving the following error message:
THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL.PROBLEM INVOLVING PARAMETER 201.
THE CONDITION NUMBER IS -0.577D-09.
There seems to be something wrong with the latent covariance matrix...it seems the matrix is singular (as indicated by the small condition number) leading to two models not being identified. When looking at the parameter estimates, there does not seem to be anything glaringly wrong. Interestingly, the same problem occurs in the PSI matrix when running separate multi-group analyses based on gender and based on race. I have tried fixing the parameter identified as being the problem to 0 but this simply results in the program identifying a different parameter with the same problem in the next run.
Q2: I think you can free up some, but not all of them. If the rules assume that certain parameters are equal for model=metric, for example, freeing them up could confuse things. I donít think there are any restrictions though so itís just a matter of trying it out.
I now tested the configural, metric and scalar model separately. I used class-specific syntax to free up certain factor loadings for the metric model.
Yet, I get no model estimation and the following error:
THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 107, Group KOREA: ROMPP WITH PROFPP THE CONDITION NUMBER IS -0.247D-06.
What is the correct command in the syntax to free up parameters while using the metric or scalar command?