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

Marie Wiberg (), James O. Ramsay () and Juan Li ()
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Marie Wiberg: Umeå University
James O. Ramsay: McGill University
Juan Li: McGill University

Psychometrika, 2019, vol. 84, issue 1, No 15, 310-322

Abstract: 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.

Date: 2019
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DOI: 10.1007/s11336-018-9639-4

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