A straightforward CFA question for anyone who can help.
I have 50 continuous indicators and a 6 factor model which is estimated without issue in mplus 5.1. I want to test whether 2 of the factors are distinct, so I added the following line to the model command: f1 with f2@1;
The model now does not converge due to a non-invertible covariance matrix. If instead I simply drop f2 and have its indicators also load on f1, the model converges.
I'm not sure why the 1st approach doesn't work. Do I have to constrain the other covariances as well? That is, do I also have to specify that the covariance between f1 and f3 is now equal to the covariance of f2 and f3, etc.?