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st: Stata 11 imputation

From   Fred Wolfe <>
Subject   st: Stata 11 imputation
Date   Mon, 27 Jul 2009 10:09:03 -0500

I have been reading the Stata 11 imputation manual. .

The manual states (page 107), "In practice, multiple variables usually
must be imputed simultaneously, and that requires using a multivariate
imputation method. The choice of an imputation method in this case
depends on the pattern of missing values." In my instance this means
using -mi impute mvn-

Using Royston's multivariate -ice-, it was possible to specify
mulivariate  matching, oligit, mlogit, and logit. This is not possible
with -mi impute mvn -. From a users point of this means out of usual
(expected) range values (e.g., ages <0, non-integer categorical
values). The manual suggests (page 109), "For multiple categorical
variables with only two categories (binary or dummy variables), a
multivariate normal approach ([MI] mi impute mvn) can be used to
impute missing values and then round the imputed values to 0 if the
value is smaller than 0.5, or 1 otherwise. For categorical variables
with more than two categories, Allison (2001) describes how to use the
normal model to impute missing values."

I wonder if it might be possible in a revision of the manual to
actually describe how to impute categorical values without having to
purchase Allison's book (available on at a reasonable
cost). There are a lot of "simple" examples in the manual. but no
complex examples - somethings that would be helpful.

Would it be possible for StataCorp people to indicate on the list the
advantages of their multivariate method compared with Royston's.

Fred Wolfe
National Data Bank for Rheumatic Diseases
Wichita, Kansas
NDB Office  +1 316 263 2125 Ext 0
Research Office +1 316 686 9195

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