Using cluster-robust standard errors when analyzing group-randomized trials with few clusters

Author:

Huang Francis L.ORCID,Li Xintong

Publisher

Springer Science and Business Media LLC

Subject

General Psychology,Psychology (miscellaneous),Arts and Humanities (miscellaneous),Developmental and Educational Psychology,Experimental and Cognitive Psychology

Reference80 articles.

1. Angrist, J. D., & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist’s companion. Princeton University Press.

2. Ballinger, G. A. (2004). Using generalized estimating equations for longitudinal data analysis. Organizational Research Methods, 7, 127–150. https://doi.org/10.1177/1094428104263672

3. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67, 1–48. https://doi.org/10.18637/jss.v067.i01

4. Bell, R., & McCaffrey, D. (2002). Bias reduction in standard errors for linear regression with multi-stage samples. Survey Methodology, 28, 169–182.

5. Berger, S., Graham, N., & Zeileis, A. (2017). Various versatile variances: An object-oriented implementation of clustered covariances in R (Working Papers in Economics and Statistics). Austria: University of Innsbruck. Retrieved from https://www2.uibk.ac.at/downloads/c4041030/wpaper/2017-12.pdf

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