Date:         Fri, 16 Jun 2006 15:12:25 -0400
Reply-To:     "Feinstein, Zachary" <ZFeinstein@HarrisInteractive.com>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         "Feinstein, Zachary" <ZFeinstein@HarrisInteractive.com>
Subject:      Re: Missing Value Analysis
Comments: To: SR Millis <srmillis@yahoo.com>
Content-Type: text/plain; charset="us-ascii"

It's been years since I have looked into the theoretical foundations of
this...

Why are listwise and pairwise deletion methods biased?  I have used a
small variety of missing-value imputation/substitution programs and none
have worked as well as doing mean-substitutions (of course for purely
random missing data) by replacing with means based on finely defined a
priori segments.

Just curious.  Any and all correspondence is welcome.

Zachary
zfeinstein@harrisinteractive.com

-----Original Message-----
From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of
SR Millis
Sent: Tuesday, June 13, 2006 10:30 AM
To: SPSSX-L@LISTSERV.UGA.EDU
Subject: Re: Missing Value Analysis

I'm not certain if SPSS has improved their Missing Value Analysis
module, but, at least in previous versions, it was my impresssion that
MVA has had a number of limitations in terms of the methods available.
Have any of these issues been addressed by SPSS?

  --Listwise and pairwise deletion methods are well known to be biased.

  --SPSS's regression imputation method uses a regression model to
impute missing values but the regression parameters are biased because
they are derived using pairwise deletion.

  --SPSS's expectation maximization (EM) method produces aymptotically
unbiased estimates but SPSS's EM implementation is limited to point
estimates (without standard errors) of means, variances, and
covariances. In addition, SPSS's EM can impute values but the values are
imputed WITHOUT residual variation---consequently the analyses that use
these imputed values can be biased.

  You may want to consider the freely available software, IVEware:
Imputation and Variance Estimation Software from the University of
Michigan:

  http://www.isr.umich.edu/src/smp/ive/


  SR Millis



Sibusiso Moyo <smoyo@targetrx.com> wrote:
  Dear All,

I have a data set that has a lot of missing values for my cases/vars. So
I am considering using MVA in filling up the gaps. But the catch is that
the generated values using Expectation Maximization ought to lie between
0 and 1. So is there a way of forcing this condition onto MVA analysis
in SPSS-14?

Help always appreciated,

Sibusiso.



Scott R Millis, PhD, MEd, ABPP (CN & RP) Professor & Director of
Research Department of Physical Medicine & Rehabilitation Wayne State
University School of Medicine
261 Mack Blvd
Detroit, MI 48201
Email: smillis@med.wayne.edu
Tel: 313-993-8085
Fax: 313-745-9854

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