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A Bayesian nonparametric approach to test equating

Walker, Stephen G., Karabatsos, George (2009) A Bayesian nonparametric approach to test equating. Psychometrika, 74 (2). pp. 211-232. ISSN 0033-3123. (doi:10.1007/s11336-008-9096-6) (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) (KAR id:23911)

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.
Official URL:
http://dx.doi.org/10.1007/s11336-008-9096-6

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.

Item Type: Article
DOI/Identification number: 10.1007/s11336-008-9096-6
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Stephen Walker
Date Deposited: 29 Jun 2011 13:37 UTC
Last Modified: 16 Nov 2021 10:02 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/23911 (The current URI for this page, for reference purposes)

University of Kent Author Information

Walker, Stephen G..

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