>"However, I could be persuaded by citations (to publications by imputation >experts) or evidence (e.g., from Monte Carlo studies) showing that: (a) it >is acceptable to impute missing data on the outcome variable; (b) 40% falls >within the acceptable range for data imputation."
>I understand that (a) is not only acceptable, but obligatory in a >covariance analysis, because a covariance matrix makes no distinction >between outcomes and anything else. However, this is so fundamental, I'm >not finding explicit statements of it in my sources. For (b), I realize >that it's fraction of missing information that's the issue. We used 10 >imputations, so we should be in good shape for the missingnes we have, but >are there any good simulation studies varying the missing information and >showing satisfactory results? Schafer (97) talks about rates up to 90% >just increasing the number of iterations needed, but there's not much >detail on performance. For explicit statements regarding (a), see: Allison, P.D. (2003). Missing data. Thousand Oaks, Sage. Schafer, J.L., & Graham, J.W. (2002). Missing data: Our view of the state of the art. Psychological Methods, Vol 7(2), 147-177. For some information regarding (b), see: Sinharay, S., Stern, H.S., & Russell, D. (2001). The use of multiple imputation for the analysis of missing data. Psychological Methods, Vol 6(4), 317-329. Schafer, J.L., & Graham, J.W. (2002). Missing data: Our view of the state of the art. Psychological Methods, Vol 7(2), 147-177. Mike Frone
