A2 Review article, Literature review, Systematic review
On ignoring the random effects assumption in multilevel models : review, critique, and recommendations (2021)


Antonakis, J., Bastardoz, N., & Rönkkö, M. (2021). On ignoring the random effects assumption in multilevel models : review, critique, and recommendations. Organizational Research Methods, 24(2), 443-483. https://doi.org/10.1177/1094428119877457


JYU authors or editors


Publication details

All authors or editorsAntonakis, John; Bastardoz, Nicolas; Rönkkö, Mikko

Journal or seriesOrganizational Research Methods

ISSN1094-4281

eISSN1552-7425

Publication year2021

Volume24

Issue number2

Pages range443-483

PublisherSage Publications, Inc.

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1177/1094428119877457

Publication open accessNot open

Publication channel open access

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/66704

Publication is parallel publishedhttps://serval.unil.ch/resource/serval:BIB_0B3C619B29D8.P002/REF


Abstract

Entities 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.


Keywordsstatistical methodsmultilevel analysisorganisational researchapplied psychology

Free keywordsrandom effects; fixed effects; multilevel; HLM; endogeneity; centering


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Ministry reportingYes

Reporting Year2021

JUFO rating3


Last updated on 2024-10-03 at 19:56