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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| Main Authors: | , , , , |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
[S.l.] :
Distributed by ERIC Clearinghouse,
2012.
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| Summary: | 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 variation in treatment effects, as well as examination of the reasons for treatment effect variation. In this paper the authors propose a method that pulls the group-level standard deviation estimate off the boundary while producing estimates that are consistent with the data. The idea is to specify a weakly informative prior distribution for the standard deviation and to maximize the resulting posterior distribution, a method that can also be viewed as penalized maximum likelihood estimation. |
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| Item Description: | Availability: Society for Research on Educational Effectiveness. 2040 Sheridan Road, Evanston, IL 60208. Tel: 202-495-0920; Fax: 202-640-4401; e-mail: inquiries@sree.org; Web site: http://www.sree.org. Abstractor: ERIC. |
| Physical Description: | 5 p. |
| Type of Computer File or Data Note: | Text (Reports, Evaluative) |
| Preferred Citation of Described Materials Note: | Society for Research on Educational Effectiveness. |