my question is quite a general newby one on whether to use FIML, and therefore OpenMX. I'd appreciate any comments on which direction to take.
I'm considering using it (and therefore using OpenMX) because I understood it is very well adapted to use with binary data, compared to conventional techniques (using tetrachoric correlation is often problematic, I believe, and WLS requires large numbers). However, the OpenMx Manual cites handling of missing data (which didn't worry me that much, as I was expecting to use listwise deletion):
'The intelligent handling of missing data is a primary reason to use FIML over other estimation techniques' (4.3.1)
In fact, in the data sets I will be analysing, I have between 3,000 and 30,000 cases (test candidates) and around 30 variables (test items), so my worry was mainly tetrachoric correlation, rather than WLS. My aim is to determine and verify dimensional models of two language tests and then compare them.
many thanks for any enlightenment,