Hi, I started reading about missing data handling techniques in Mplus and I'm not sure to what extent are FIML and MI equivalent for my needs.
Here's a little more information on the model. It contains continuous latent variables and has indirect effects and latent interactions. I have two measurement points. My T1 (n=570) data contains missing data for 3 to 10% of observations and my T2 data (n=380), 10 to 30% missing data (many respondents just completed T1).
So here is my question: if I simply used the default FIML for handling missing data, will all the model be fitted for 570 respondents? (as if I had made MI)? And would that be the right technique?