Avoiding Boundary Estimates in Hierarchical Linear Models through Weakly Informative Priors [electronic resource] / Yeojin Chung, Sophia Rabe-Hesketh and Andrew Gelman.

Hierarchical or multilevel linear models are widely used for longitudinal or cross-sectional data on students nested in classes and schools, and are particularly important for estimating treatment effects in cluster-randomized trials, multi-site trials, and meta-analyses. The models can allow for va...

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Bibliographic Details

Online Access:	Full Text (via ERIC)
Main Authors:	Chung, Yeojin, Rabe-Hesketh, S. (Author), Gelman, Andrew (Author), Dorie, Vincent (Author), Liu, Jinchen (Author)
Corporate Author:	Society for Research on Educational Effectiveness
Format:	Electronic eBook
Language:	English
Published:	[S.l.] : Distributed by ERIC Clearinghouse, 2012.
Subjects:	Statistical Analysis. Models. Computation. Maximum Likelihood Statistics. Bayesian Statistics.