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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>
Content-Type: text/plain; charset="us-ascii"

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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