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

TitleA comparison of inclusive and restrictive strategies in modern missing data procedures
Authors
Issue Date2001
PublisherAmerican Psychological Association. The Journal's web site is located at http://www.apa.org/journals/met.html
Citation
Psychological Methods, 2001, v. 6 n. 3, p. 330-351 How to Cite?
AbstractTwo 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.
Persistent Identifierhttp://hdl.handle.net/10722/168947
ISSN
2015 Impact Factor: 5.0
2015 SCImago Journal Rankings: 6.060
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCollins, LMen_US
dc.contributor.authorSchafer, JLen_US
dc.contributor.authorKam, CMen_US
dc.date.accessioned2012-10-08T03:39:59Z-
dc.date.available2012-10-08T03:39:59Z-
dc.date.issued2001en_US
dc.identifier.citationPsychological Methods, 2001, v. 6 n. 3, p. 330-351en_US
dc.identifier.issn1082-989Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/168947-
dc.description.abstractTwo 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.en_US
dc.languageengen_US
dc.publisherAmerican Psychological Association. The Journal's web site is located at http://www.apa.org/journals/met.htmlen_US
dc.relation.ispartofPsychological Methodsen_US
dc.subject.meshConfidence Intervalsen_US
dc.subject.meshData Collection - Statistics & Numerical Dataen_US
dc.subject.meshHumansen_US
dc.subject.meshLikelihood Functionsen_US
dc.subject.meshModels, Statisticalen_US
dc.subject.meshPsychological Tests - Statistics & Numerical Dataen_US
dc.subject.meshPsychology, Experimental - Statistics & Numerical Dataen_US
dc.subject.meshPsychometricsen_US
dc.titleA comparison of inclusive and restrictive strategies in modern missing data proceduresen_US
dc.typeArticleen_US
dc.identifier.emailKam, CM:cmkam@hkucc.hku.hken_US
dc.identifier.authorityKam, CM=rp00633en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1037/1082-989X.6.4.330-
dc.identifier.pmid11778676-
dc.identifier.scopuseid_2-s2.0-0035755636en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035755636&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume6en_US
dc.identifier.issue3en_US
dc.identifier.spage330en_US
dc.identifier.epage351en_US
dc.identifier.isiWOS:000172907100003-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridCollins, LM=7202597093en_US
dc.identifier.scopusauthoridSchafer, JL=35611092400en_US
dc.identifier.scopusauthoridKam, CM=7102416669en_US

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