Thanks. I think I'm confused as I do have non-normal data, and should use the robust maximum likelihood estimator, right? How should I handle missing data with non-normal, continuous data?
Paul Silvia posted on Monday, November 05, 2007 - 6:12 pm
One would need to know a lot about the nature of the non-normality (as well as other assumptions that may be unmet), but my preference would be to add the BOOTSTRAP option to the ANALYSIS command, a la:
TYPE = MISSING H1; ESTIMATOR = ML; BOOTSTRAP = 1000; (see UG p. 434).
And under OUTPUT, add: CINTERVAL (BOOTSTRAP);
This will handle the missing data as well as give you bootstrapped standard errors and confidence intervals.
JW posted on Wednesday, November 07, 2007 - 5:11 am
Actually, I do have a f/u up question. Is there a way to include missing data with MLM estimator? So, when I did the analyses with boostrapping as you mentioned above, the full N was used, but when just with MLM as an estimator and no missing data command (as the error message occurs), cases were not included and my N dropped.