Date: Wed, 5 Jul 2006 07:58:32 -0400
Reply-To: Peter Flom <Flom@NDRI.ORG>
Sender: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From: Peter Flom <Flom@NDRI.ORG>
Subject: Re: survey regression analysis
In-Reply-To: <BAY103-F14E2D1F4FCD247B6123A7DB0760@phx.gbl>
Content-Type: text/plain; charset=US-ASCII
mshall2@GMAIL.COM sagely replied:
>I agree, when the data are MCAR, listwise deletion fine (as is any
>imputation technique), but listwise deletion is also, arguably the
best
>strategy when missing data are non-ignorable. Advanced techniques
>(FIML, MI) are only suitable with MAR data.
and David Cassell added
<<<
The biggest problem I see is people treating MNAR (Missing NOT At
Random) data as if the data are missing at random. "Oh no problem,
I leanred about hot-deck from a professor who last took classes on
this in the 1960's..." :-(
I find that the decisions about listwise deletion or not depend on
the meta-data and the data sources. I tend to expect to see
differences depending on whether the data come from, say, an
experimental design vs. a sampling design.
>>>
In a lecture that he gave here at NDRI, and in other lectures I have
heard him give, Joe Schafer has indicated that some of his results show
that MI is a better technique than listwise deletion even when the data
are MNAR.
I haven't got any formal published cites for this, although there may
be some by now, but thought it apropos. He indicated that the degree of
bias introduced by MNAR would have to be quite extreme for listwise to
be better than MI.
Regards
Peter
Peter L. Flom, PhD
Assistant Director, Statistics and Data Analysis Core
Center for Drug Use and HIV Research
National Development and Research Institutes
71 W. 23rd St
http://cduhr.ndri.org
www.peterflom.com
New York, NY 10010
(212) 845-4485 (voice)
(917) 438-0894 (fax)
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