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 Jean-Simon Leclerc posted on Tuesday, June 20, 2017 - 8:09 pm
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?
 Bengt O. Muthen posted on Thursday, June 22, 2017 - 1:47 pm
Yes and yes.
 N_2018 posted on Friday, April 06, 2018 - 10:45 am
Hello, I have several missing cases for the dependent variable in my analysis (I am conducting a linear regression with 1 DV and 4 IVs). The DV is a proportion score (ranging from 0 to 1), reflecting performance on a behavioral (computer) task. The data are missing due to technical glitches with the task. Is FIML an appropriate way to handle missing data for discrete variables? If not, are there any sources that discuss alternatives? Many thanks in advance.
 Bengt O. Muthen posted on Friday, April 06, 2018 - 5:43 pm
FIML doesn't help in this case - you need cases with y observed and x missing. See our RMA book.
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