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Sensitivity Analysis for Hierarchical Models Employing t Level-1 AssumptionsUniversity of California, Los Angeles Long Beach Unified School District University of California, Los Angeles RAND Corporation
Much work on sensitivity analysis for hierarchical models (HMs) has focused on level-2 outliers (e.g., in multisite evaluations, a site at which an intervention was unusually successful). However, efforts to draw sound conclusions concerning parameters of interest in HMs also require that we attend to extreme level-1 units (e.g., a person in the treatment group at a particular site whose post-test score [yij ] is unusually small vis-á-vis the other members of that person’s group). One goal of this article is to examine the ways in which level-1 outliers can impact the estimation of fixed effects and random effects in HMs. A second goal is to outline and illustrate the use of Markov Chain Monte Carlo algorithms for conducting sensitivity analyses under t level-1 assumptions, including algorithms for settings in which the degrees of freedom at level 1 (v1 ) is treated as an unknown parameter.
Key Words: Keywords: Bayesian analysis • hierarchical models • Markov Chain Monte Carlo • Metropolis subchains • sensitivity analysis • t errors
Journal of Educational and Behavioral Statistics, Vol. 27, No. 2,
181-222 (2002) 
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