Using Country-Specific Q-Matrices for Cognitive Diagnostic Assessments with International Large-Scale Data

Autor/inn/en	Delafontaine, Jolien; Chen, Changsheng; Park, Jung Yeon; Van den Noortgate, Wim
Titel	Using Country-Specific Q-Matrices for Cognitive Diagnostic Assessments with International Large-Scale Data
Quelle	In: Large-scale Assessments in Education, 10 (2022), Artikel 19 (36 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Chen, Changsheng)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1186/s40536-022-00138-4
Schlagwörter	Q Methodology; Matrices; Cognitive Measurement; Diagnostic Tests; Test Items; International Assessment; Achievement Tests; Elementary Secondary Education; Foreign Countries; Mathematics Achievement; Mathematics Tests; Science Achievement; Science Tests; Grade 8; Goodness of Fit; Nonparametric Statistics; Classification; Trends in International Mathematics and Science Study + Suchen Sie Ihr Suchwort? Matrizenrechnung; Diagnostic test; Diagnostischer Test; Test content; Testaufgabe; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Ausland; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; School year 08; 8. Schuljahr; Schuljahr 08; Classification system; Klassifikation; Klassifikationssystem
Abstract	In cognitive diagnosis assessment (CDA), the impact of misspecified item-attribute relations (or "Q-matrix") designed by subject-matter experts has been a great challenge to real-world applications. This study examined parameter estimation of the CDA with the expert-designed Q-matrix and two refined Q-matrices for international large-scale data. Specifically, the G-DINA model was used to analyze TIMSS data for Grade 8 for five selected countries separately; and the need of a refined Q-matrix specific to the country was investigated. The results suggested that the two refined Q-matrices fitted the data better than the expert-designed Q-matrix, and the stepwise validation method performed better than the nonparametric classification method, resulting in a substantively different classification of students in attribute mastery patterns and different item parameter estimates. The results confirmed that the use of country-specific Q-matrices based on the G-DINA model led to a better fit compared to a universal expert-designed Q-matrix. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01