Date:         Fri, 16 Jun 2006 15:28:04 -0700
Reply-To:     SR Millis <srmillis@yahoo.com>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         SR Millis <srmillis@yahoo.com>
Subject:      Re: Missing Value Analysis
In-Reply-To:  <47009A583B6592479DBC0C6DCD40DF95020C7B26@mission.harris.harrisinteractive.com>
Content-Type: text/plain; charset=iso-8859-1

Regarding pairwise deletion: it will produce parameter estimates that are approximately unbiased in large samples IF the data a mssing completely at random (MCAR)---which doesn't occur very often in most research. I
  if the data are only missing at random (MAR), the estimates may be quite biased---the problem lies with the capacity to obtain consistent estimates of the standard errors---theoretically possible but the formulas are complicated and not implemented in any software that I'm aware of. If addition, it's not uncommon to get correlation or covariance matrices that are positive definite in small samples when using pairwise deletion.

  Listwise deletion does produce valid inferences when data are MCAR. However, it too can produce biased estimates if the data are only MAR.

  Mean substitution isn't a good idea because it reduces variance.

  SR Millis




"Feinstein, Zachary" <ZFeinstein@HarrisInteractive.com> wrote:
    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.



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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