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dc.contributor.authorAntonakis, John
dc.contributor.authorBastardoz, Nicolas
dc.contributor.authorRönkkö, Mikko
dc.date.accessioned2019-12-10T12:38:08Z
dc.date.available2019-12-10T12:38:08Z
dc.date.issued2021
dc.identifier.citationAntonakis, J., Bastardoz, N., & Rönkkö, M. (2021). On ignoring the random effects assumption in multilevel models : review, critique, and recommendations. <i>Organizational Research Methods</i>, <i>24</i>(2), 443-483. <a href="https://doi.org/10.1177/1094428119877457" target="_blank">https://doi.org/10.1177/1094428119877457</a>
dc.identifier.otherCONVID_33336409
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/66704
dc.description.abstractEntities such as individuals, teams, or organizations can vary systematically from one another. Researchers typically model such data using multilevel models, assuming that the random effects are uncorrelated with the regressors. Violating this testable assumption, which is often ignored, creates an endogeneity problem thus preventing causal interpretations. Focusing on two-level models, we explain how researchers can avoid this problem by including cluster means of the Level 1 explanatory variables as controls; we explain this point conceptually and with a large scale simulation. We further show why the common practice of centering the predictor variables is mostly unnecessary. Moreover, to examine the state of the science, we reviewed 204 randomly drawn articles from macro and micro organizational science and applied psychology journals, finding that only 106 articles—with a slightly higher proportion from macro-oriented fields— properly deal with the random effects assumption. Alarmingly, most models also failed on the usual exogeneity requirement of the regressors, leaving only 25 mostly macro-level articles that potentially reported trustworthy multilevel estimates. We offer a set of practical recommendations for researchers to model multilevel data appropriately.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherSage Publications, Inc.
dc.relation.ispartofseriesOrganizational Research Methods
dc.rightsIn Copyright
dc.subject.otherrandom effects
dc.subject.otherfixed effects
dc.subject.othermultilevel
dc.subject.otherHLM
dc.subject.otherendogeneity
dc.subject.othercentering
dc.titleOn ignoring the random effects assumption in multilevel models : review, critique, and recommendations
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201912105172
dc.contributor.laitosKauppakorkeakoulufi
dc.contributor.laitosSchool of Business and Economicsen
dc.contributor.oppiaineBasic or discovery scholarshipfi
dc.contributor.oppiaineJohtaminenfi
dc.contributor.oppiaineBasic or discovery scholarshipen
dc.contributor.oppiaineManagement and Leadershipen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_dcae04bc
dc.description.reviewstatuspeerReviewed
dc.format.pagerange443-483
dc.relation.issn1094-4281
dc.relation.numberinseries2
dc.relation.volume24
dc.type.versionacceptedVersion
dc.rights.copyright© 2019 SAGE
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber311309
dc.subject.ysotilastomenetelmät
dc.subject.ysosoveltava psykologia
dc.subject.ysomonitasoanalyysi
dc.subject.ysoorganisaatiotutkimus
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p3127
jyx.subject.urihttp://www.yso.fi/onto/yso/p13322
jyx.subject.urihttp://www.yso.fi/onto/yso/p22676
jyx.subject.urihttp://www.yso.fi/onto/yso/p7816
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1177/1094428119877457
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramPostdoctoral Researcher, AoFen
jyx.fundingprogramTutkijatohtori, SAfi
jyx.fundinginformationThis research was supported in part by a grant from the Academy of Finland (grant 311309) to Mikko Rönkkö. We acknowledge the computational resources provided by the Aalto Science-IT project.
dc.type.okmA2


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