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On ignoring the random effects assumption in multilevel models : review, critique, and recommendations - Tutkimusportaali - Converis - Jyväskylän yliopisto
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Julkaisun kaikki tekijät tai toimittajat: Antonakis, John; Bastardoz, Nicolas; Rönkkö, Mikko
Lehti tai sarja: Organizational Research Methods
Kustantaja: Sage Publications, Inc.
Julkaisun avoin saatavuus: Ei avoin
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
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