Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Bailey and Danziger's War on Poverty book reviewed in NYT

Bloomberg cites MTF data in story on CDC's anti-smoking ads for e-cigarettes

Bound says notion that foreign college students are displacing U.S. students "isn't right"

Highlights

U-M ranked #1 in Sociology of Population by USN&WR's "Best Graduate Schools"

PAA 2015 Annual Meeting: Preliminary program and list of UM participants

ISR addition wins LEED Gold Certification

PSC Fall 2014 Newsletter now available

Next Brown Bag

Mon, April 6
Jinkook Lee, Wellbeing of the Elderly in East Asia

The multiple adaptations of multiple imputation

Publication Abstract

Reiter, J.P., and Trivellore Raghunathan. 2007. "The multiple adaptations of multiple imputation." Journal of the American Statistical Association, 102(480): 1462-1471.

Multiple imputation was first conceived as a tool that statistical agencies could use to handle nonresponse in large-sample public use surveys. In the last two decades, the multiple-imputation framework has been adapted for other statistical contexts. For example, individual researchers use multiple imputation to handle missing data in small samples, statistical agencies disseminate multiply-imputed data sets for purposes of protecting data confidentiality, and survey methodologists and epidemiologists use multiple imputation to correct for measurement errors. In some of these settings, Rubin's original rules for combining the point and variance estimates from the multiply-imputed data sets are not appropriate, because what is known-and thus the conditional expectations and variances used to derive inferential methods-differs from that in the missing-data context. These applications require new combining rules and methods of inference. In fact, more than 10 combining rules exist in the published literature. This article describes some of the main adaptations of the multiple-imputation framework, including missing data in large and small samples, data confidentiality, and measurement error. It reviews the combining rules for each setting and explains why they differ. Finally, it highlights research topics in extending the multiple-imputation framework.

DOI:10.1198/016214507000000932 (Full Text)

Browse | Search : All Pubs | Next