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Re: st: MI: Multiple Imputation
From
[email protected] (Wesley D. Eddings, StataCorp)
To
[email protected]
Subject
Re: st: MI: Multiple Imputation
Date
Mon, 20 Dec 2010 15:17:27 -0600
Shikha Sinha <[email protected]> asked about using -mi impute mvn- to
impute a categorical variable:
> Hi,
>
> I am using -mi for imputing the missing values. The variable is "edu" has
> five categories (1=primary, 2=secondary, and so on...). I used the following
> command:
>
> ...
>
> mi impute mvn edu = i.gender age country, add (5)
> mi estimate, or: logistic Y X1 X2 edu
>
> ...
>
> (a) Why some observations have negative values for _1_edu, _2_edu, _3_edu,
The -mi impute mvn- command uses a multivariate normal model and treats "edu"
as continuous, so the imputed values are not guaranteed to be positive.
Shikha may instead use -mi impute ologit- or -mi impute mlogit-, depending on
whether or not the categories of "edu" should be considered ordered.
Shikha also asked,
> (b) How do we use the five new generated variable? Can I just run
>
> logistic Y X1 X2 _1_edu
>
> or
>
> logistic Y X1 X2 _2_edu
>
> (c) Can I create a dummy for primary or secondary schooling from the imputed
> values _1_edu?
The five new variables _1_edu, _2_edu, ... , _5_edu are the imputed values of
"edu" in the -wide- style for each of the five imputations. It is not
appropriate to include the new variables directly as predictors; the
appropriate command for a multiple-imputation analysis is -mi estimate-, as
illustrated in Shikha's code. -mi estimate- will analyze the five imputations
and combine them into a single multiple-imputation result.
Shikha may add the i. factor-variable prefix to "edu" to include dummies for
the imputed "edu" variable. It is not necessary to create dummies manually
from a -mi- variable like _1_edu.
We also recommend that Shikha include the variables Y, X1, and X2 in the
imputation model. More model-building guidelines appear in the "Model
building" section of the documentation entry -[MI] mi impute-.
--Wes
[email protected]
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