Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective
- 1 October 1998
- journal article
- Published by Taylor & Francis in Multivariate Behavioral Research
- Vol. 33 (4) , 545-571
- https://doi.org/10.1207/s15327906mbr3304_5
Abstract
Analyses of multivariate data are frequently hampered by missing values. Until recently, the only missing-data methods available to most data analysts have been relatively ad1 hoc practices such as listwise deletion. Recent dramatic advances in theoretical and computational statistics, however, have produced anew generation of flexible procedures with a sound statistical basis. These procedures involve multiple imputation (Rubin, 1987), a simulation technique that replaces each missing datum with a set of m > 1 plausible values. The rn versions of the complete data are analyzed by standard complete-data methods, and the results are combined using simple rules to yield estimates, standard errors, and p-values that formally incorporate missing-data uncertainty. New computational algorithms and software described in a recent book (Schafer, 1997a) allow us to create proper multiple imputations in complex multivariate settings. This article reviews the key ideas of multiple imputation, discusses the software p...Keywords
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