Cross-Classified Random Effects Modeling for Moderated Item Calibration

Autor/inn/en	Chung, Seungwon; Cai, Li
Titel	Cross-Classified Random Effects Modeling for Moderated Item Calibration
Quelle	In: Journal of Educational and Behavioral Statistics, 46 (2021) 6, S.651-681 (31 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998620983908
Schlagwörter	Students with Disabilities; Scoring; Achievement Tests; Test Items; Braille; Media Adaptation; Language Tests; English; Visual Impairments; Models; Elementary Secondary Education + Suchen Sie Ihr Suchwort? Student; Students; Disability; Disabilities; Schüler; Schülerin; Studentin; Behinderung; Bewertung; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Test content; Testaufgabe; Medienverwendung; Language test; Sprachtest; English language; Englisch; Visual handicap; Sehbehinderung; Analogiemodell
Abstract	In the research reported here, we propose a new method for scale alignment and test scoring in the context of supporting students with disabilities. In educational assessment, students from these special populations take modified tests because of a demonstrated disability that requires more assistance than standard testing accommodation. Updated federal education legislation and guidance require that these students be assessed and included in state education accountability systems, and their achievement reported with respect to the same rigorous content and achievement standards that the state adopted. Routine item calibration and linking methods are not feasible because the size of these special populations tends to be small. We develop a unified cross-classified random effects model that utilizes item response data from the general population as well as judge-provided data from subject matter experts in order to obtain revised item parameter estimates for use in scoring modified tests. We extend the Metropolis-Hastings Robbins-Monro algorithm to estimate the parameters of this model. The proposed method is applied to Braille test forms in a large operational multistate English language proficiency assessment program. Our work not only allows a broader range of modifications that is routinely considered in large-scale educational assessments but also directly incorporates the input from subject matter experts who work directly with the students needing support. Their structured and informed feedback deserves more attention from the psychometric community. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01