On ignoring the random effects assumption in multilevel models: Review, critique, and recommendations

Détails

Ressource 1Demande d'une copie Sous embargo indéterminé.
Etat: Public
Version: Author's accepted manuscript
Licence: Non spécifiée
ID Serval
serval:BIB_0B3C619B29D8
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
On ignoring the random effects assumption in multilevel models: Review, critique, and recommendations
Périodique
Organizational Research Methods
Auteur(s)
Antonakis J., Bastardoz N., Rönkkö M.
Statut éditorial
In Press
Langue
anglais
Résumé
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.
Mots-clé
random effects, fixed effects, multilevel, HLM, endogeneity, centering
Création de la notice
06/09/2018 19:36
Dernière modification de la notice
07/11/2019 10:42
Données d'usage