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Flexible Bayesian Models for Inferences From Coarsened, Group-Level Achievement Data

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Listed:
  • J. R. Lockwood
  • Katherine E. Castellano

    (Educational Testing Service)

  • Benjamin R. Shear

    (University of Colorado Boulder)

Abstract

This article proposes a flexible extension of the Fay–Herriot model for making inferences from coarsened, group-level achievement data, for example, school-level data consisting of numbers of students falling into various ordinal performance categories. The model builds on the heteroskedastic ordered probit (HETOP) framework advocated by Reardon, Shear, Castellano, and Ho by allowing group parameters to be modeled with regressions on group-level covariates, and residuals modeled using the flexible exponential family of distributions recommended by Efron. We demonstrate that the alternative modeling framework, termed the “Fay–Herriot heteroskedastic ordered probit†(FH-HETOP) model, is useful for mitigating some of the challenges with direct maximum likelihood estimators from the HETOP model. We conduct a simulation study to compare the costs and benefits of several methods for using the FH-HETOP model to estimate group parameters and functions of them, including posterior means, constrained Bayes estimators, and the “triple goal†estimators of Shen and Louis. We also provide an application of the FH-HETOP model to math proficiency data from the Early Childhood Longitudinal Study. Code for estimating the FH-HETOP model and conducting supporting calculations is provided in a new package for the R environment.

Suggested Citation

  • J. R. Lockwood & Katherine E. Castellano & Benjamin R. Shear, 2018. "Flexible Bayesian Models for Inferences From Coarsened, Group-Level Achievement Data," Journal of Educational and Behavioral Statistics, , vol. 43(6), pages 663-692, December.
    • Handle: RePEc:sae:jedbes:v:43:y:2018:i:6:p:663-692
    DOI: 10.3102/1076998618795124
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    References listed on IDEAS

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