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st: RE: missingness in a large, complex sampling deign


From   "Katrina Walsemann" <kwalsema@umich.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: missingness in a large, complex sampling deign
Date   Mon, 16 Aug 2004 10:45:44 -0400

Hi Colleen,

In addition to the Little and Rubin book already suggested, you may want to
take a look at the following:

Schafer, J. (1999). Multiple imputation: a primer. Statistical Methods in
Medical Research, 8, 3-15.

Raghunathan, T., Solenberger, P., & Van Hoewyk, J. (2002). IVEware:
Imputation and Variance Estimation Software: User Guide. Ann Arbor: Survey
Research Center, Institute for Social Research, University of Michigan.

Allison, P (2001) Missing Data. Sage University Paper Series in Quantitative
Applications in the Social Sciences, 07-136.  Thousand Oaks, California:
Sage.

Yuan, Y. (2000). Multiple Imputation for Missing Data: Concepts and New
Development (P267-25). Rockville: SAS Institute Inc.


I believe Joe Schafer's paper can be found on his website, and Raghunathan's
paper can be found on the University of Michigan website.  

Good luck!

Katrina Walsemann, MPH
Doctoral Candidate
University of Michigan, School of Public Health  
Health Behavior and Health Education
kwalsema@umich.edu



-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Colleen Daly
Martinez
Sent: Sunday, August 15, 2004 1:06 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: missingness in a large, complex sampling deign

In my analysis of data from a large (over 5,000) nationally representative
sample study, which used complex sampling, I'm finding that a number of
variables I'm examining have many missings- as many as 1,500 or more (I
believe that they are non-respondents).

I'm wondering if anyone has suggestions, or can point me to references which
address the issue of managing this issue and the implications for my
analysis.

Thanks.

Colleen

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