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st: ice command question about interactions


From   Alan Acock <acock@mac.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: ice command question about interactions
Date   Sat, 14 Feb 2009 17:19:22 -0800

John Graham, who has done a lot of work with Schafer, published a chapter, "Missing Data Analysis: Making it Work in the Real World" in the 2009 Annual Review of Psychology 60:549-576. He compares a wide variety of software and I was surprised that he never mentions Stata. Some of what he says, however, is inconsistent with how I've been utilizing the ice command. Here is a key example.

ice allows us to passively estimate an interaction term by estimating the main effects and then multiplying these together so the interaction of X&Y will be the imputed X times the imputed Y. This seems necessary to preserve the interpretation of the interaction.

Graham says we need to include the interaction term. "The problem with excluding such variables from the imputation model is that all imputation is done under the assumption that the correlation is r = 0 between the omitted variable and all other variables in the imputation." This is the same argument that Graham makes for imputing the dependent variable in the imputation (a sensible thing to do).

I understand the importance of including the dependent variable when doing multiple imputations, and see how Graham could apply this to the interaction term, but it makes no sense to me to have an interaction of X and Y not equal X*Y.

What do those of you with more experience on missing values than I have think about this?
Alan Acock


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