Model Selection Methods for Mixture Dichotomous IRT Models

Autor/inn/en	Li, Feiming; Cohen, Allan S.; Kim, Seock-Ho; Cho, Sun-Joo
Titel	Model Selection Methods for Mixture Dichotomous IRT Models
Quelle	In: Applied Psychological Measurement, 33 (2009) 5, S.353-373 (21 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621608326422
Schlagwörter	Item Response Theory; Models; Selection; Methods; Simulation; Bayesian Statistics; Computation; Mathematics Tests; Grade 3; Florida; Florida Comprehensive Assessment Test + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Analogiemodell; Auslese; Method; Methode; Simulation program; Simulationsprogramm; School year 03; 3. Schuljahr; Schuljahr 03
Abstract	This study examines model selection indices for use with dichotomous mixture item response theory (IRT) models. Five indices are considered: Akaike's information coefficient (AIC), Bayesian information coefficient (BIC), deviance information coefficient (DIC), pseudo-Bayes factor (PsBF), and posterior predictive model checks (PPMC). The five indices provide somewhat different recommendations for a set of real data. Results from a simulation study indicate that BIC selects the correct (i.e., the generating) model well under most conditions simulated and for all three of the dichotomous mixture IRT models considered. PsBF is almost as effective. AIC and PPMC tend to select the more complex model under some conditions. DIC is least effective for this use. (Contains 5 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10