A Bayesian nonparametric approach to test equating

Walker, Stephen G. and Karabatsos, George (2009) A Bayesian nonparametric approach to test equating. Psychometrika, 74 (2). pp. 211-232. ISSN 0033-3123. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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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.

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Stephen Walker
Date Deposited: 29 Jun 2011 13:37
Last Modified: 25 Jun 2014 10:36
Resource URI: https://kar.kent.ac.uk/id/eprint/23911 (The current URI for this page, for reference purposes)
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