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Missing data: Our view of the state of the art (2002)
| Venue: | Psychological Methods |
| Citations: | 688 - 1 self |
BibTeX
@ARTICLE{Schafer02missingdata:,
author = {Joseph L. Schafer and John W. Graham},
title = {Missing data: Our view of the state of the art},
journal = {Psychological Methods},
year = {2002},
pages = {177}
}
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Abstract
Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, dis-courage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayes-ian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the main-stream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art. Why do missing data create such difficulty in sci-entific research? Because most data analysis proce-dures were not designed for them. Missingness is usu-ally a nuisance, not the main focus of inquiry, but
