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Optimal Scores: An Alternative to Parametric Item Response Theory and Sum Scores

Published online by Cambridge University Press:  01 January 2025

Marie Wiberg Open the ORCID record for Marie Wiberg [Opens in a new window] ,
James O. Ramsay  and
Juan Li
Marie Wiberg*
Affiliation:
Umeå University
James O. Ramsay
Affiliation:
McGill University
Juan Li
Affiliation:
McGill University
*
Correspondence should be made to Marie Wiberg, USBE, Department of Statistics, Umeå University, 90187 Umeå, Sweden. Email: [email protected]
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Abstract

The aim of this paper is to discuss nonparametric item response theory scores in terms of optimal scores as an alternative to parametric item response theory scores and sum scores. Optimal scores take advantage of the interaction between performance and item impact that is evident in most testing data. The theoretical arguments in favor of optimal scoring are supplemented with the results from simulation experiments, and the analysis of test data suggests that sum-scored tests would need to be longer than an optimally scored test in order to attain the same level of accuracy. Because optimal scoring is built on a nonparametric procedure, it also offers a flexible alternative for estimating item characteristic curves that can fit items that do not show good fit to item response theory models.

Type
Original Paper
Information
Psychometrika , Volume 84 , Issue 1 , 15 March 2019 , pp. 310 - 322
Copyright
Copyright © 2018 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11336-018-9639-4) contains supplementary material, which is available to authorized users.

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