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

Rönkkö, Mikko

Publication details

All authors or editors: Antonakis, John; Bastardoz, Nicolas; Rönkkö, Mikko

Journal or series: Organizational Research Methods

ISSN: 1094-4281

eISSN: 1552-7425

Publication year: 2021

Volume: 24

Issue number: 2

Pages range: 443-483

Publisher: Sage Publications, Inc.

Publication country: United States

Publication language: English

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

Publication open access: Not open

Publication channel open access:

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

Publication is parallel published: https://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.

Keywords: statistical methods; multilevel analysis; organisational research; applied psychology

Free keywords: random effects; fixed effects; multilevel; HLM; endogeneity; centering

Fields of science:

Contributing organizations

JYU units:

School of Business and Economics

Related projects

Measurement and modeling practices in business research – problems and solutions
- - Rönkkö, Mikko
- Research Council of Finland
01/09/2017-31/12/2019

Ministry reporting: Yes

Reporting Year: 2021