Sensitivity Analysis for Hierarchical Models Employing t Level-1 Assumptions

Michael Seltzer, John Novak, Kilchan Choi and Nelson Lim

Journal of Educational and Behavioral Statistics, 2002, vol. 27, issue 2, 181-222

Abstract: 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 [ y ij ] 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 ( v 1 ) is treated as an unknown parameter.

Keywords: Keywords: Bayesian analysis; hierarchical models; Markov Chain Monte Carlo; Metropolis subchains; sensitivity analysis; t errors (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:27:y:2002:i:2:p:181-222

DOI: 10.3102/10769986027002181

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