multiple imputation problems (FIML?)

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multiple imputation problems (FIML?)

Postby Torvon » Thu Mar 29, 2012 9:55 am


I have plenty of missing data in my longitudinal sample with 5 measurement points, both on dependent variables (5% first, 40% last measurement point) and covariates (20-30% missing). Biggest problem are my time-varying covariates, for which missings are always problematic.

Sample size = 800.

(1) Multiple Imputation, even with constraints, gives pretty weird results. Example: categorical dependent variable, observed distribution 50% 0, 30% 1, 15 2, 5% 3 leads to estimated distributions with >80% in category 3, in all 5 imputations.
Maybe the reason is that my categorical dependent variable is skewed to the left? Any ideas? Read the tutorials about MI but they didn't help me in my case.

(2) I heard that using FIML (full information maximum likelihood) is as good as using multiple imputation. I'm running mixed model analyses in SPSS, but I don't think FIML in SPSS is possible for that, or is it?

Thank you

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