Parameter Recovery and Classification Accuracy under Conditions of Testlet Dependency: A Comparison of the Traditional 2PL, Testlet, and Bi-Factor Models

Autor/in	Koziol, Natalie A.
Titel	Parameter Recovery and Classification Accuracy under Conditions of Testlet Dependency: A Comparison of the Traditional 2PL, Testlet, and Bi-Factor Models
Quelle	In: Applied Measurement in Education, 29 (2016) 3, S.184-195 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2016.1171767
Schlagwörter	Classification; Accuracy; Comparative Analysis; Models; Predictor Variables; Test Items; Educational Assessment; Theory Practice Relationship; Item Response Theory; Monte Carlo Methods; Simulation; Maximum Likelihood Statistics; Bayesian Statistics; Least Squares Statistics; Difficulty Level + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Analogiemodell; Prädiktor; Test content; Testaufgabe; Education; assessment; Bewertungssystem; Theorie-Praxis-Beziehung; Item-Response-Theorie; Monte-Carlo-Methode; Simulation program; Simulationsprogramm; Schwierigkeitsgrad
Abstract	Testlets, or groups of related items, are commonly included in educational assessments due to their many logistical and conceptual advantages. Despite their advantages, testlets introduce complications into the theory and practice of educational measurement. Responses to items within a testlet tend to be correlated even after controlling for latent ability, which violates the assumption of conditional independence made by traditional item response theory models. The present study used Monte Carlo simulation methods to evaluate the effects of testlet dependency on item and person parameter recovery and classification accuracy. Three calibration models were examined, including the traditional 2PL model with marginal maximum likelihood estimation, a testlet model with Bayesian estimation, and a bi-factor model with limited-information weighted least squares mean and variance adjusted estimation. Across testlet conditions, parameter types, and outcome criteria, the Bayesian testlet model outperformed, or performed equivalently to, the other approaches. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01