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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-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Antonakis, John; Bastardoz, Nicolas; Rönkkö, Mikko

Lehti tai sarja: Organizational Research Methods

ISSN: 1094-4281

eISSN: 1552-7425

Julkaisuvuosi: 2021

Volyymi: 24

Lehden numero: 2

Artikkelin sivunumerot: 443-483

Kustantaja: Sage Publications, Inc.

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1177/1094428119877457

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus:

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/66704

Julkaisu on rinnakkaistallennettu: https://serval.unil.ch/resource/serval:BIB_0B3C619B29D8.P002/REF


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.

YSO-asiasanat: tilastomenetelmät; monitasoanalyysi; organisaatiotutkimus; soveltava psykologia

Vapaat asiasanat: random effects; fixed effects; multilevel; HLM; endogeneity; centering

Liittyvät organisaatiot


Hankkeet, joissa julkaisu on tehty

OKM-raportointi: Kyllä

Raportointivuosi: 2021

JUFO-taso: 3

Viimeisin päivitys 2022-20-09 klo 13:49