On Ignoring the Random Effects Assumption in Multilevel Models: Review, Critique, and Recommendations

Published on 2019-10-17T12:07:28Z (GMT) by
<div><p>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.</p></div>

Cite this collection

Antonakis, John; Bastardoz, Nicolas; Rönkkö, Mikko (2019): On Ignoring the Random Effects Assumption in Multilevel Models: Review, Critique, and Recommendations. SAGE Journals. Collection. https://doi.org/10.25384/SAGE.c.4701962.v1