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A Bayesian Nonparametric Approach to Test Equating

Published online by Cambridge University Press:  01 January 2025

George Karabatsos  and
Stephen G. Walker
George Karabatsos*
Affiliation:
University of Illinois-Chicago
Stephen G. Walker
Affiliation:
University of Kent
*
Requests for reprints should be sent to George Karabatsos, College of Education, University of Illinois-Chicago, 1040 W. Harrison St. (MC 147), Chicago, IL 60607, USA. E-mail: georgek@uic.edu
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Abstract

A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are compared through the analysis of data sets famous in the equating literature. Also, the classical percentile-rank, linear, and mean equating models are each proven to be a special case of a Bayesian model under a highly-informative choice of prior distribution.

Keywords

Bayesian nonparametricsbivariate Bernstein polynomial priorDirichlet process priortest equatingequipercentile equatinglinear equating

Information

Type
Theory and Methods
Information
Psychometrika , Volume 74 , Issue 2 , June 2009 , pp. 211 - 232
Copyright
Copyright © 2008 The Psychometric Society

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