Higher Order Testlet Response Models for Hierarchical Latent Traits and Testlet-Based Items

Autor/inn/en	Huang, Hung-Yu; Wang, Wen-Chung
Titel	Higher Order Testlet Response Models for Hierarchical Latent Traits and Testlet-Based Items
Quelle	In: Educational and Psychological Measurement, 73 (2013) 3, S.491-511 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164412454431
Schlagwörter	Item Response Theory; Models; Bayesian Statistics; Computation; Simulation; Test Reliability; Goodness of Fit; Test Items; Monte Carlo Methods; Markov Processes; Test Bias; Junior High School Students; Minimum Competency Testing; Internet; Measures (Individuals); Foreign Countries; Taiwan; Graduate Record Examinations; Wechsler Adult Intelligence Scale + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Analogiemodell; Simulation program; Simulationsprogramm; Testreliabilität; Test content; Testaufgabe; Monte-Carlo-Methode; Markowscher Prozess; Testkritik; Junior High Schools; Student; Students; Sekundarstufe I; Schüler; Schülerin; Messdaten; Ausland
Abstract	Both testlet design and hierarchical latent traits are fairly common in educational and psychological measurements. This study aimed to develop a new class of higher order testlet response models that consider both local item dependence within testlets and a hierarchy of latent traits. Due to high dimensionality, the authors adopted the Bayesian approach implemented in the WinBUGS freeware for parameter estimation. A series of simulations were conducted to evaluate parameter recovery, consequences of model misspecification, and effectiveness of model-data fit statistics. Results show that the parameters of the new models can be recovered well. Ignoring the testlet effect led to a biased estimation of item parameters, underestimation of factor loadings, and overestimation of test reliability for the first-order latent traits. The Bayesian deviance information criterion and the posterior predictive model checking were helpful for model comparison and model-data fit assessment. Two empirical examples of ability tests and nonability tests are given. (Contains 6 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10