Estimating Diffusion-Based Item Response Theory Models: Exploring the Robustness of Three Old and Two New Estimators

Autor/inn/en	Ranger, Jochen; Kuhn, Jörg-Tobias
Titel	Estimating Diffusion-Based Item Response Theory Models: Exploring the Robustness of Three Old and Two New Estimators
Quelle	In: Journal of Educational and Behavioral Statistics, 43 (2018) 6, S.635-662 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998618787791
Schlagwörter	Item Response Theory; Computation; Robustness (Statistics); Reaction Time; Maximum Likelihood Statistics; Least Squares Statistics; Comparative Analysis + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Widerstandsfähigkeit; Reaktionsvermögen
Abstract	Diffusion-based item response theory models for responses and response times in tests have attracted increased attention recently in psychometrics. Analyzing response time data, however, is delicate as response times are often contaminated by unusual observations. This can have serious effects on the validity of statistical inference. In this article, we compare three established and two new estimation approaches for diffusion-based item response theory models with respect to their robustness. The three established approaches are the marginal maximum likelihood (ML) estimator for continuous time, the marginal ML estimator for discrete time, and the weighted least squares (WLS) estimator. The new approaches are two modifications of the WLS estimator with better robustness properties. The performance of the estimators is compared in a simulation study. The simulation study illustrates that the new approaches are robust against some forms of random independent contamination. The marginal ML estimator for discrete time also performs well. The marginal ML estimator for continuous time is heavily affected by contamination. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01