Best Design for Multidimensional Computerized Adaptive Testing with the Bifactor Model

Autor/inn/en	Seo, Dong Gi; Weiss, David J.
Titel	Best Design for Multidimensional Computerized Adaptive Testing with the Bifactor Model
Quelle	In: Educational and Psychological Measurement, 75 (2015) 6, S.954-978 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164415575147
Schlagwörter	Computer Assisted Testing; Adaptive Testing; Accuracy; Fidelity; Efficiency; Item Response Theory; Mathematics; Models; Simulation; Comparative Analysis; Test Items; Computation; Selection; Guessing (Tests); Error of Measurement; Factor Analysis + Suchen Sie Ihr Suchwort? Effectiveness; Effektivität; Wirkungsgrad; Item-Response-Theorie; Mathematik; Analogiemodell; Simulation program; Simulationsprogramm; Test content; Testaufgabe; Auslese; Erraten; Messfehler; Faktorenanalyse
Abstract	Most computerized adaptive tests (CATs) have been studied using the framework of unidimensional item response theory. However, many psychological variables are multidimensional and might benefit from using a multidimensional approach to CATs. This study investigated the accuracy, fidelity, and efficiency of a fully multidimensional CAT algorithm (MCAT) with a bifactor model using simulated data. Four item selection methods in MCAT were examined for three bifactor pattern designs using two multidimensional item response theory models. To compare MCAT item selection and estimation methods, a fixed test length was used. The D[subscript s]-optimality item selection improved ? estimates with respect to a general factor, and either D- or A-optimality improved estimates of the group factors in three bifactor pattern designs under two multidimensional item response theory models. The MCAT model without a guessing parameter functioned better than the MCAT model with a guessing parameter. The MAP (maximum a posteriori) estimation method provided more accurate ? estimates than the EAP (expected a posteriori) method under most conditions, and MAP showed lower observed standard errors than EAP under most conditions, except for a general factor condition using D[subscript s]-optimality item selection. (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: http://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01