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I'm still immersed in my first multiple imputation analyses, and a couple
more questions have arisen for me:
1. Say that one of my goals is to estimate means/sds of variables with
missing data by gender (along with overall means/sds of those variables). I
can think of a few approaches to conducting the multiple imputation:
(a) in addition to the variables of interest, include gender in the
imputation model.
(b) in addition to the variables of interest, include gender and the
interactions of gender with those variables in the imputation model.
(c) conduct separate imputation analyses by gender, then recombine the
imputed women's and men's dataset.
Any opinions as to which strategy is best?
2. I am interested in conducting a multiple regression analysis with
interaction terms, using multiply imputed datasets. I understand that I need to
include these interaction terms in the imputation model (along with the "main
effect" variables). What isn't clear to me is which of the following two
"versions" of the interaction term xz I should use:
(a) the imputed interaction terms (i.e., estimates of the missing xz
values generated by the MCMC imputation method)
(b) the interaction terms computed by taking the product of the imputed x
values and the imputed z values.
Any thoughts about which might be the preferred strategy?
Thanks in advance for your thoughts!
Best,
Jon
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Jonathan Mohr, Ph.D.
Assistant Professor Department of Psychology Loyola College 4501 North Charles Street Baltimore, MD 21210-2699 |
