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
[email protected]
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Colleen Daly
Martinez
Sent: Sunday, August 15, 2004 1:06 PM
To: [email protected]
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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