Multilevel Higher-Order Item Response Theory Models

Autor/inn/en	Huang, Hung-Yu; Wang, Wen-Chung
Titel	Multilevel Higher-Order Item Response Theory Models
Quelle	In: Educational and Psychological Measurement, 74 (2014) 3, S.495-515 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164413509628
Schlagwörter	Item Response Theory; Hierarchical Linear Modeling; Computation; Test Reliability; Bayesian Statistics; Models; Markov Processes; Monte Carlo Methods; Goodness of Fit; Computer Software; Mathematics Tests; Achievement Tests; Grade 4; Elementary School Students; Student Evaluation of Teacher Performance; College Students; College Faculty; Correlation; Statistical Analysis; Foreign Countries; Taiwan; Students Evaluation of Educational Quality; Trends in International Mathematics and Science Study + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Testreliabilität; Analogiemodell; Markowscher Prozess; Monte-Carlo-Methode; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; School year 04; 4. Schuljahr; Schuljahr 04; Collegestudent; Fakultät; Korrelation; Statistische Analyse; Ausland
Abstract	In the social sciences, latent traits often have a hierarchical structure, and data can be sampled from multiple levels. Both hierarchical latent traits and multilevel data can occur simultaneously. In this study, we developed a general class of item response theory models to accommodate both hierarchical latent traits and multilevel data. The freeware WinBUGS was used for parameter estimation. A series of simulations were conducted to evaluate the parameter recovery and the consequence of ignoring the multilevel structure. The results indicated that the parameters were recovered fairly well; ignoring multilevel structures led to poor parameter estimation, overestimation of test reliability for the second-order latent trait, and underestimation of test reliability for the first-order latent traits. The Bayesian deviance information criterion and posterior predictive model checking were helpful for model comparison and model-data fit assessment. Two empirical examples that involve an ability test and a teaching effectiveness assessment are provided. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10