Detecting Local Dependence: A Threshold-Autoregressive Item Response Theory (TAR-IRT) Approach for Polytomous Items

Autor/inn/en	Tang, Xiaodan; Karabatsos, George; Chen, Haiqin
Titel	Detecting Local Dependence: A Threshold-Autoregressive Item Response Theory (TAR-IRT) Approach for Polytomous Items
Quelle	In: Applied Measurement in Education, 33 (2020) 4, S.280-292 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2020.1789136
Schlagwörter	Item Response Theory; Test Items; Models; Computation; Difficulty Level; Statistical Analysis + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Test content; Testaufgabe; Analogiemodell; Schwierigkeitsgrad; Statistische Analyse
Abstract	In applications of item response theory (IRT) models, it is known that empirical violations of the local independence (LI) assumption can significantly bias parameter estimates. To address this issue, we propose a threshold-autoregressive item response theory (TAR-IRT) model that additionally accounts for order dependence among the item responses of each examinee. The TAR-IRT approach also defines a new family of IRT models for polytomous item responses under both unidimensional and multidimensional frameworks, with order-dependent effects between item responses and relevant dimensions. The feasibility of the proposed model was demonstrated by an empirical study using a polytomous response data. A simulation study for polytomous item responses with order effects of different magnitude in an education context shows that the TAR modeling framework could provide more accurate ability estimation than the partial credit model when order effect exists. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01