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A comparison of inclusive and restrictive strategies in modern missing data procedures.

Bibliographic Data

ID23375485
AuthorsLinda M Collins (0000-0003-4282-8722, Pennsylvania State University), Joseph L Schafer, Chi-Ming Kam
Year2001
Volume6
Issue4
Pages330-351
Publication date2001-01-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenuePsychological Methods (JOURNAL)
Journal identifiersISSN: 1082-989X • E-ISSN: 1939-1463
PublisherAmerican Psychological Association (APA) (PUBLISHER)
DOI10.1037/1082-989x.6.4.330
PMID11778676
OpenAlexW1973907010
LanguageEN
Citations received529
References cited19

Two classes of modern missing data procedures, maximum likelihood (ML) and multiple imputation (MI), tend to yield similar results when implemented in comparable ways. In either approach, it is possible to include auxiliary variables solely for the purpose of improving the missing data procedure. A simulation was presented to assess the potential costs and benefits of a restrictive strategy, which makes minimal use of auxiliary variables, versus an inclusive strategy, which makes liberal use of such variables. The simulation showed that the inclusive strategy is to be greatly preferred. With an inclusive strategy not only is there a reduced chance of inadvertently omitting an important cause of missingness, there is also the possibility of noticeable gains in terms of increased efficiency and reduced bias, with only minor costs. As implemented in currently available software, the ML approach tends to encourage the use of a restrictive strategy, whereas the MI approach makes it relatively simple to use an inclusive strategy.

Econometrics · Imputation (statistics) · Missing data · Statistics · Bayesian Methods and Mixture Models · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference

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Unique citing works529
Citations per year21,16
Citation span2001 - 2026 (26)
Citation velocitycurrent
Highly citedYes
Citation typesNeutral: 436

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