Date: Fri, 24 Sep 2010 09:38:57 +0200
Reply-To: "Ginkel, Joost van" <JGinkel@fsw.leidenuniv.nl>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: "Ginkel, Joost van" <JGinkel@fsw.leidenuniv.nl>
Subject: Re: Multiple Imputation - Negative Values
In-Reply-To: A<1285277825601-2851942.post@n5.nabble.com>
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I would be careful imposing restrictions on the imputation model such as
/CONSTRAINTS MIN=0. When a variable has no negative values but multiple
imputation still imputes negative values, this could be an indication
that the variable in question is heavily skewed. I would check that
first. If income is heavily skewed then maybe you could do a log
transformation, impute the missing data and transform the variable back
after multiple imputation. Another option which is probably even better
is to use Predictive Mean Matching instead of Linear Regression (Method,
Imputation Method, Custom, Predictive Mean Matching (PMM)). This option
automatically preserves the properties of the variables such as minimum
and maximum values, increment etc. Good luck!
Best regards,
Joost van Ginkel
Joost R. van Ginkel, PhD
Leiden University
Faculty of Social and Behavioural Sciences
PO Box 9555
2300 RB Leiden
The Netherlands
Tel: +31-(0)71-527 3620
Fax: +31-(0)71-527 1721
-----Original Message-----
From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of
Bruce Weaver
Sent: Thursday, September 23, 2010 11:37 PM
To: SPSSX-L@LISTSERV.UGA.EDU
Subject: Re: Multiple Imputation - Negative Values
Gene Maguin wrote:
>
> Negative or otherwise out of range values, e.g., an imputed value of
> 6.34 or
> 3.33 for a 1 to 5 likert type variable are certainly possible because
> of the underlying generating process. The question is what to with
> them. John Graham who wrote an early piece of software was one of the
> early practicioners of MI. I believe John has written about this
> issue. Another person to check out is Joe Schafer who wrote a key book
> on MI and developed more software. (Let me add that in naming these
> two, i intend no slight to Donald Rubin).
>
> Gene Maguin
>
>
Although I've not read the book Gene mentions, I will second his
recommendation of Schafer & Graham generally. Here is list of articles
I found helpful when I first started reading about missing data,
including some by Schafer & Graham.
Acock, A. C. (2005). Working with missing values. Journal of Marriage
and Family, 67, 1012-1028.
Donders, A. Rogier T., van der Heijden, Geert J.M.G., Stijnen, T., &
Moons, K. G. M. (2006). Review: A gentle introduction to imputation of
missing values. Journal of Clinical Epidemiology, 59, 1087-1091.
Multiple Imputation Online. http://www.multiple-imputation.com/
Schafer, J. L. (1999). Multiple imputation: A primer. Statistical
Methods in Medical Research, 8, 3-15.
Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the
state of the art. Psychological Methods, 7(2), 147-177.
HTH.
-----
--
Bruce Weaver
bweaver@lakeheadu.ca
http://sites.google.com/a/lakeheadu.ca/bweaver/
"When all else fails, RTFM."
NOTE: My Hotmail account is not monitored regularly.
To send me an e-mail, please use the address shown above.
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