Analyzing Polytomous Test Data: A Comparison between an Information-Based IRT Model and the Generalized Partial Credit Model

Autor/inn/en	Joakim Wallmark; James O. Ramsay; Juan Li; Marie Wiberg
Titel	Analyzing Polytomous Test Data: A Comparison between an Information-Based IRT Model and the Generalized Partial Credit Model
Quelle	In: Journal of Educational and Behavioral Statistics, 49 (2024) 5, S. 753-779Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Joakim Wallmark) ORCID (Juan Li) ORCID (Marie Wiberg)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986231207879
Schlagwörter	Forschungsbericht; Item Response Theory; Test Items; Models; Scoring; Comparative Analysis; Simulation; Evaluation Methods; Goodness of Fit; Statistical Bias + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Test content; Testaufgabe; Analogiemodell; Bewertung; Simulation program; Simulationsprogramm
Abstract	Item response theory (IRT) models the relationship between the possible scores on a test item against a test taker's attainment of the latent trait that the item is intended to measure. In this study, we compare two models for tests with polytomously scored items: the optimal scoring (OS) model, a nonparametric IRT model based on the principles of information theory, and the generalized partial credit (GPC) model, a widely used parametric alternative. We evaluate these models using both simulated and real test data. In the real data examples, the OS model demonstrates superior model fit compared to the GPC model across all analyzed datasets. In our simulation study, the OS model outperforms the GPC model in terms of bias, but at the cost of larger standard errors for the probabilities along the estimated item response functions. Furthermore, we illustrate how surprisal arc length, an IRT scale invariant measure of ability with metric properties, can be used to put scores from vastly different types of IRT models on a common scale. We also demonstrate how arc length can be a viable alternative to sum scores for scoring test takers. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Begutachtung	Peer reviewed
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2025/2/04