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Re: st: Multiple Imputation (MI)


From   "Saul G. Alamilla" <sgalami@yahoo.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: Multiple Imputation (MI)
Date   Tue, 14 May 2013 08:47:40 -0400

Many thanks William.

Much appreciated,
Saul

~~~~~~~~~~~~~~~~~~~~~~~~~
Saul G. Alamilla, Ph.D.
Assistant Professor
Department of Psychology
Kennesaw State University
SS Bldg. # 22 Rm. 4030
1000 Chastain Road Northwest
Kennesaw, GA 30144
Email: salamil1@kennesaw.edu
Ph: 770.794.7730
F: 770.423.6860
~~~~~~~~~~~~~~~~~~~~~~~~~

On May 14, 2013, at 1:40 AM, Richard Williams <richardwilliams.ndu@gmail.com> wrote:

> This is a very good source:
> 
> <http://www.ssc.wisc.edu/sscc/pubs/stata_mi_intro.htm>http://www.ssc.wisc.edu/sscc/pubs/stata_mi_intro.htm
> 
> Here and elsewhere you'll see the case made for what Royston calls the "just another variable" approach. Compute interaction and squared terms first and then impute them like you would any other variable.
> 
> I'm not quite sure about centering. My inclination is to still use the "just another variable" approach. But one of the things I dislike about centering around the mean is that it does always change from sample to sample. Rather than centering about the mean, you might center about some other meaningful value. For example, if you were examining years of education in the United States, you might subtract 12 so that 0 stood for high school graduate.
> 
> Also, several good references are listed at
> 
> <http://www.ssc.wisc.edu/sscc/pubs/stata_mi_readings.htm>http://www.ssc.wisc.edu/sscc/pubs/stata_mi_readings.htm
> 
> White, Royston and Wood 2011 seems very good to me.
> 
> Finally, you can see this earlier exchange on Statalist where Paul Allison offered his thoughts:
> 
> http://www.stata.com/statalist/archive/2009-02/msg00602.html
> 
> http://www.stata.com/statalist/archive/2009-02/msg00613.html
> 
> 
> At 09:06 PM 5/13/2013, Saul G. Alamilla wrote:
>> Dear Statalist Members,
>> 
>> I have some questions pertaining to multiple imputation. I have a
>> dataset of about 10,000 individuals and need to impute some variables with
>> considerable missingess (MAR). I am using the ice and mi commands in Stata 11.2
>> SE.
>> 
>> I plan to include substantive interactions terms (mean centered) in the
>> imputation model.
>> 
>> My questions/concerns are as follows:
>> 
>> 1) Does mean centering need to be performed before imputing
>> data?  If so, because after imputation
>> the "centered" means will almost surely not be 0, would it be
>> advisable to center yet again at that point?
>> 
>> 2) Is there a satisfactory way to impute interaction terms? Are
>> there any specific references regarding imputation and interaction terms (other
>> than articles such as Graham, 2009, which deals with interactions in passing)?
>> One approach would be to impute each of the input variables
>> individually and then take their product, but the imputing of the input
>> variables is especially delicate inasmuch as nonlinearities are introduced.
>> 
>> 3. On a side note, are they any satisfactory ways to perform MI in
>> Stata with clustered data. I am aware of programs such as PAN (Schafer 2001,
>> Schafer & Yucel 2002), but am looking for MI commands or programs in Stata
>> geared for clustered/nested data, OR acceptable and manageable strategies for
>> imputing with such data.
>> 
>> Thanks in advance,
>> Saul
>> 
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> 
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> HOME:   (574)289-5227
> EMAIL:  Richard.A.Williams.5@ND.Edu
> WWW:    http://www.nd.edu/~rwilliam
> 
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