S2E29: Multilevel Models — The Often Unnecessary Green Monster

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Patrick and Greg fulfill a legal obligation to interview the unnecessarily ubiquitous Dr. Dan McNeish of Arizona State University about why you probably don’t need to use multilevel modeling even when you have multilevel data. Along the way they also mention MacNair, safety schools, the Green Monster, driving a Corvette across the country, Compensation Club, anklet shocks, endogeneity, frunks, Tom Brady’s middle name, and de-meaning.

Show Notes

Antonakis, J., Bastardoz, N. (2021). On ignoring the random effects assumption in multilevel models: Review, critique, and recommendations. Organizational Research Methods, 24, 443-483.

Curran, P. J. & Bauer, D. J. (2007). Building path diagrams for multilevel models. Psychological Methods, 12, 283-297.

Curran, P. J. & Bauer, D. J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62, 583-619.

Dedrick, R. F., Ferron, J. M., Hess, M. R., Hogarty, K. Y., Kromrey, J. D., Lang, T. R., Niles, J. D., & Lee, R. S. (2009). Multilevel modeling: A review of methodological issues and applications. Review of Educational Research, 79, 69-102.

Freeman, D. A. (2006). On the so-called “Huber sandwich estimator” and “robust standard errors.” The American Statistician, 60, 299-302.

Gardiner, J. C., Luo, Zhehui, & Roman, L. A. (2009). Fixed effects, random effects, and GEE: What are the differences? Statistics in Medicine, 28, 221-239.

Hamaker, E. L., & Muthen, B. (2020). The fixed versus random effects debate and how it relates to centering in multilevel modeling. Psychological Methods, 25, 365-379.

McNeish, D. (2019). Effect partitioning in cross-sectionally clustered data without multilevel models. Multivariate Behavioral Research, 54, 906-925.

McNeish, D., & Kelley, K. (2019). Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations. Psychological Methods, 24, 20-35.

McNeish, D., Stapleton, L. M., & Silverman, R. D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, 22, 114-140.

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48, 817-838.

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