A Bayesian Nonparametric Approach to Test Equating |
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Authors: | George Karabatsos Stephen G Walker |
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Institution: | (1) College of Education, University of Illinois-Chicago, 1040 W. Harrison St. (MC 147), Chicago, IL 60607, USA;(2) Institute of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, CT2 7NZ, UK |
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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. |
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Keywords: | Bayesian nonparametrics bivariate Bernstein polynomial prior Dirichlet process prior test equating equipercentile equating linear equating |
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