Nice post by Paul Allison about the conditions under which listwise deletion does as well or better than multiple imputation (HT: Richard Williams).
What the post doesn’t mention is the crazy rabbit hole that multiple imputation can send students down, often for results that are basically the same. It’s not that missing data can’t cause big problems for estimation, but that many of these problems aren’t solved by multiple imputation either. But it feels like an analyst is doing more about the problem and takes just seconds to demand as a reviewer.
