Efficient Standard Errors in Item Response Theory Models for Short Tests.
| Autor: | Ippel L; Maastricht University, Maastricht, the Netherlands., Magis D; University of Liège, Liege, Belgium. |
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| Jazyk: | angličtina |
| Zdroj: | Educational and psychological measurement [Educ Psychol Meas] 2020 Jun; Vol. 80 (3), pp. 461-475. Date of Electronic Publication: 2019 Oct 18. |
| DOI: | 10.1177/0013164419882072 |
| Abstrakt: | In dichotomous item response theory (IRT) framework, the asymptotic standard error (ASE) is the most common statistic to evaluate the precision of various ability estimators. Easy-to-use ASE formulas are readily available; however, the accuracy of some of these formulas was recently questioned and new ASE formulas were derived from a general asymptotic theory framework. Furthermore, exact standard errors were suggested to better evaluate the precision of ability estimators, especially with short tests for which the asymptotic framework is invalid. Unfortunately, the accuracy of exact standard errors was assessed so far only in a very limiting setting. The purpose of this article is to perform a global comparison of exact versus (classical and new formulations of) asymptotic standard errors, for a wide range of usual IRT ability estimators, IRT models, and with short tests. Results indicate that exact standard errors globally outperform the ASE versions in terms of reduced bias and root mean square error, while the new ASE formulas are also globally less biased than their classical counterparts. Further discussion about the usefulness and practical computation of exact standard errors are outlined. Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. (© The Author(s) 2019.) |
| Databáze: | MEDLINE |
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