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Re: st: Re: Factor analysis after multiple imputation in STATA


From   SR Millis <srmillis@yahoo.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Re: Factor analysis after multiple imputation in STATA
Date   Tue, 17 Jul 2007 08:13:10 -0700 (PDT)

As an alternative to imputation, the AMOS software
computes full information maximum likelihood (FIML)
estimates.  When data are only missing at random
(MAR), the FIML approach yield parameter estimates
that are efficient and consistent.  However, multiple
imputation methods can produce severely biased
results.

Scott Millis


> > ///
> > Woolton,
> > 
> > You could check Schafer, J.L. (1997), Analysis of
> Incomplete Multivariate 
> > Data, New York: Chapman and Hall, as primary
> reference for Missing Data and 
> > how to run EM. Joe has an executable for Windows
> that computes EM algorithm 
> > (http://www.stat.psu.edu/~jls/norm203.exe), it is
> very fast and friendly. 

Scott R Millis, PhD, MEd, ABPP (CN,CL,RP), CStat
Professor & Director of Research
Dept 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: 313-966-7682
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