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Re: st: st: Using MVN for Multiple Missing Ordinal Variables

From   daniel klein <>
Subject   Re: st: st: Using MVN for Multiple Missing Ordinal Variables
Date   Sun, 8 May 2011 16:32:58 +0200

First of all, I am all but an expert on multiple imputation -- however
interested in it. So I hope there will also be answers from more
experienced people.

As far as I know, MVN is designed for continuos variables. However
Allison (2002:38-40) describes an ad-hoc solution to impute
categorical variables. I have an .ado file assigning "final values" to
such imputed variables (-findit mi mvncat-).

I can think of other possibilities however.

If your variables are not "strictly" categorical (such as race in the
NLSW88.dta -sysuse nlsw88-), you might want to consider treating them
as if they were continous. For example a variable with levels 1.
"strongly disagree" to 7. "strongly agree" migth well be treated
continous (as you woud do using them in a regression) in which case
-mi impute mvn- is fine without any afterward "ad-hoc" corrections.

You might also want to use Royston's (e.g. 2005) -ice- (findit -ice-,
also see -help mi ice-) to impute missing values using
chaind-equations. This allows you to impute missing values using
ordinal regression modells. From what I understand, chaind-equations
perform pretty well in simulations, yet are not theoretically

I did not fully understand what kind of descriptive statistics you
want to calculate and report. Concerning "descriptive" statistics in
general, as far as I know, there is no need to adjust the variance. If
you are only interessted in point estimates (not their standard errors
and therefore not in statistical inference) you just average the
respective point estimates. This is what  Yulia Marchenko's -mibeta-
(-findit mibeta-) does, reporting R-squared messures for -mi estimate
regress- (see
I have an .ado reporting summary statistics for the dataset cobining
results from -summarize- (-findit misum-), where I do not adjust the
(sample) variance.



Allison, Paul D. (2002) Missing Data. Thousand Oaks, CA:  Sage Publications.

Royston, P. (2005) Multiple imputation of missing values: update.
Stata Journal 5 (2), 188-201.
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