A Multilevel Testlet Model for Dual Local Dependence

Autor/inn/en	Jiao, Hong; Kamata, Akihito; Wang, Shudong; Jin, Ying
Titel	A Multilevel Testlet Model for Dual Local Dependence
Quelle	In: Journal of Educational Measurement, 49 (2012) 1, S.82-100 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/j.1745-3984.2011.00161.x
Schlagwörter	Item Response Theory; Test Items; Markov Processes; Monte Carlo Methods; Psychometrics; Difficulty Level; Computation; Statistical Bias; Error of Measurement; Classification; Accuracy + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Test content; Testaufgabe; Markowscher Prozess; Monte-Carlo-Methode; Psychometry; Psychometrie; Schwierigkeitsgrad; Messfehler; Classification system; Klassifikation; Klassifikationssystem
Abstract	The applications of item response theory (IRT) models assume local item independence and that examinees are independent of each other. When a representative sample for psychometric analysis is selected using a cluster sampling method in a testlet-based assessment, both local item dependence and local person dependence are likely to be induced. This study proposed a four-level IRT model to simultaneously account for dual local dependence due to item clustering and person clustering. Model parameter estimation was explored using the Markov Chain Monte Carlo method. Model parameter recovery was evaluated in a simulation study in comparison with three other related models: the Rasch model, the Rasch testlet model, and the three-level Rasch model for person clustering. In general, the proposed model recovered the item difficulty and person ability parameters with the least total error. The bias in both item and person parameter estimation was not affected but the standard error (SE) was affected. In some simulation conditions, the difference in classification accuracy between models could go up to 11%. The illustration using the real data generally supported model performance observed in the simulation study. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10