From other posts on this forum, I mostly understand why fit indexes are not calculated when using FIML. It sounds like the solution is to fit a saturated model and then use this to calculate these indexes. However, when I do this, the saturated model takes an extremely long time to run, and I am wondering (a) whether I am doing something wrong and (b) if there is any way around this.
I had initially tested a saturated model for a dataset with 24 observed variables (three observed variables at each of eight waves) and about 600 observations. After an hour it was still going, so I stopped it to see how long it would take for a smaller model. My model is identical to the bivariate saturated matrix model presented in the OpenMx document (adjusted for the larger number of variables), and it does run. A saturated model with six variables took about six seconds to run. A model with eight variables took 20 seconds to run, and a model with 16 variables took about 10 minutes to run. As I said, I stopped the 24-variable model after an hour.
Is this to be expected, and is there anything I can do to speed things up? My ultimate goal is to test this same model across many different variables from a few different data sets (with up to 25 waves and tens of thousands of observations). OpenMx and R have the advantage of being able to automate all of this, but I think would be close to impossible if the saturated model consistently takes many hours (or longer) to run. So any advice would be appreciated.