Missing-data imputation is to statistics as statistics is to research: a topic that seems specialized, technical, and boring–until you find yourself working on a practical problem, at which point it briefly becomes the only thing that matters, the make-it-or-break-it step needed to ensure some level of plausibility in your results.

Anyway, Grazia pointed me to this paper by a bunch of my friends (well, actually two of my friends and a bunch of their colleagues). I think it’s the new state of the art, so if you’re doing any missing-data imputation yourself, you might want to take a look and borrow some of their ideas. (Of course, I also recommend chapter 25 of our forthcoming book.)

P.S. Yes, their tables should be graphs But, hey, nobody’s perfect.