My reading of their article is that it's best to avoid rounding imputed
values if possible.
Here are two more pertinent articles:
Many, many thanks for this information. It sounds like
experiments
conducted specifically with AMELIA indicate that using the normal model
underlying AMELIA to impute dichotomous variables (which will be rounded
to 0 or 1) will not create biased estimates. The article by Horton,
Lipsitz, and Parzen suggests that such action should be taken with
caution as there may be a risk of biased estimates in certain situations
when the imputed values are dichotomized. From my read of their
article, it looks like: (1) higher rates of missing data may result in
greater bias; (2) their demonstration of "a simpler model" may not apply
to all situations; and (3) when given an option, it may be better to
select a categorical variable which is not dichotomous for imputation
using the normal model and rounding the variable to reduce possible
bias.
As you might have guessed, what prompted my question was the imputation
of a dataset with more dichotomous variables than could be accommodated
using the "AMORDS" command in AMELIA; thus it requires the use of the
"ANOMS" command for some of the dichotomous variables. Given the
experiments conducted specifically with AMELIA, it seems that using this
second configuration should be okay. If there is any further information
I should consider, have missed something in my synthesis of the
information, or if there are any specific tests I should conduct to
check for potential bias, I'd be grateful for the input.
Thank you, again, for your thoughtful (and helpful!) responses.
Malitta Engstrom, Ph.D., L.C.S.W.
Assistant Professor
University of Chicago
-----Original Message-----
From: Paul von Hippel [mailto:von-hippel.1@osu.edu]
Sent: Tuesday, August 02, 2005 2:09 PM
To: Gary King; Engstrom, Malitta
Cc: amelia(a)latte.harvard.edu
Subject: Re: [amelia] Commands for Imputing Dichotomous Variables in
AMELIA
Here's a pertinent article by Horton et al:
http://www.biostat.harvard.edu/~horton/tasround.pdf
It shows that, when a normal model is used to impute a dichotomous
variable, it's best not to dichotomize the imputed values.
Best --
Paul
At 03:04 PM 8/2/2005, Gary King wrote:
Experiments seem to indicate that for imputation,
you can use the
normal
model underlying Amelia, even though you
wouldn't want to use it for
the
analysis model. if you choose options
appropriately, Amelia will
dichotomize the resulting simulations for convenience of subsequent
analysis. But if that's not necessary for your analysis model, then
using
the continuously imputed variable should work
fine.
Gary King
---
Gary King Institute for Quantitative Social Science
Harvard University, 34 Kirkland St, Cambridge, MA 02138
http://GKing.Harvard.Edu, King(a)Harvard.Edu
Direct 617-495-2027, Assistant 495-9271, eFax 812-8581
On Tue, 2 Aug 2005, Engstrom, Malitta wrote:
>Dear Dr. King,
>
>I'm hoping you can provide some information, or direct me to a
resource
>for gathering information, regarding the
imputation of dichotomous
>variables with AMELIA. Specifically, I'm wondering about how
>problematic it is to use the "ANOMS" command with the imputation of
>dichotomous variables. In statistical consultations, I've been
informed
>that it should not be a problem to use this
command; however, since
the
program
directs one to use the "AMORDS" command with the imputation of
dichotomous variables, I wanted to gather additional information about
it.
I would be most grateful for any information you can share on this
topic.
Thank you,
Malitta Engstrom
Assistant Professor
University of Chicago
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Paul von Hippel
Department of Sociology / Initiative in Population Research
Ohio State University
300 Bricker Hall
190 N. Oval Mall
Columbus OH 43210
614 688-3768
Office hours TThF 3-5pm
I read email every weekday at 3.
Paul von Hippel
Department of Sociology / Initiative in Population Research
Ohio State University
300 Bricker Hall
190 N. Oval Mall
Columbus OH 43210
614 688-3768
Office hours TThF 3-5pm
I read email every weekday at 3.
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