You can bring them into the model by mentioning their variances in the MODEL command. They will then be treated as dependent variables and distributional assumptions will be made about them.
Jake Lant posted on Saturday, March 10, 2012 - 12:38 am
Also, I think this is important.
These variables were somewhat "expected" to be missing because the participant didn't meet the criteria so we got them to check a box saying that it didn't apply just so that we have explanations for these missing variables.
Do you think there is a method that would probably fit this scenario better?
Jake Lant posted on Saturday, March 10, 2012 - 2:14 am
I did it (mentioned variances) and it worked, but I am not really liking the results because it really makes a somewhat big difference from excluding versus FIML, and I want to make sure there isn't a better way.
So I am thinking I should use the PATTERN IS command (missing by design) but I had set these as missing (i.e., -99, -100). But what kind of coding can I use to get it to accept it as a "pattern"?
I had done, for example,
MISSING ARE p1 (-999.00) p2 (-992.00) p3 (-100.99)
PATTERN IS p1 (-999.00) p2 (-992.00) p3 (-100.99)
-looking for a shortcut, ;)- but of course it failed.