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Sensitivity Analysis for Hierarchical Models Employing t Level-1 Assumptions

Author

Listed:
  • Michael Seltzer
  • John Novak
  • Kilchan Choi
  • Nelson Lim

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.

Suggested Citation

  • Michael Seltzer & John Novak & Kilchan Choi & Nelson Lim, 2002. "Sensitivity Analysis for Hierarchical Models Employing t Level-1 Assumptions," Journal of Educational and Behavioral Statistics, , vol. 27(2), pages 181-222, June.
    • Handle: RePEc:sae:jedbes:v:27:y:2002:i:2:p:181-222
    DOI: 10.3102/10769986027002181
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