Two IRT Characteristic Curve Linking Methods Weighted by Information

Autor/inn/en	Wang, Shaojie; Zhang, Minqiang; Lee, Won-Chan; Huang, Feifei; Li, Zonglong; Li, Yixing; Yu, Sufang
Titel	Two IRT Characteristic Curve Linking Methods Weighted by Information
Quelle	In: Journal of Educational Measurement, 59 (2022) 4, S.423-441 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wang, Shaojie)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/jedm.12315
Schlagwörter	Item Response Theory; Error of Measurement; Accuracy; Monte Carlo Methods; Sample Size; Test Length; Guidelines; Comparative Analysis; Test Items; Item Analysis + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Messfehler; Monte-Carlo-Methode; Richtlinien; Test content; Testaufgabe; Itemanalyse
Abstract	Traditional IRT characteristic curve linking methods ignore parameter estimation errors, which may undermine the accuracy of estimated linking constants. Two new linking methods are proposed that take into account parameter estimation errors. The item- (IWCC) and test-information-weighted characteristic curve (TWCC) methods employ weighting components in the loss function from traditional methods by their corresponding item and test information, respectively. Monte Carlo simulation was conducted to evaluate the performances of the new linking methods and compare them with traditional ones. Ability difference between linking groups, sample size, and test length were manipulated under the common-item nonequivalent groups design. Results showed that the two information-weighted characteristic curve methods outperformed traditional methods, in general. TWCC was found to be more accurate and stable than IWCC. A pseudo-form pseudo-group analysis was also performed, and similar results were observed. Finally, guidelines for practice and future directions are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01