Generalizability in Item Response Modeling

Autor/inn/en	Briggs, Derek C.; Wilson, Mark
Titel	Generalizability in Item Response Modeling
Quelle	In: Journal of Educational Measurement, 44 (2007) 2, S.131-155 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/j.1745-3984.2007.00031.x
Schlagwörter	Markov Processes; Generalizability Theory; Item Response Theory; Computation; Measurement Techniques; Monte Carlo Methods; Computer Simulation; Computer Software; Evaluation Methods + Suchen Sie Ihr Suchwort? Markowscher Prozess; Item-Response-Theorie; Messtechnik; Monte-Carlo-Methode; Computergrafik; Computersimulation
Abstract	An approach called generalizability in item response modeling (GIRM) is introduced in this article. The GIRM approach essentially incorporates the sampling model of generalizability theory (GT) into the scaling model of item response theory (IRT) by making distributional assumptions about the relevant measurement facets. By specifying a random effects measurement model, and taking advantage of the flexibility of Markov Chain Monte Carlo (MCMC) estimation methods, it becomes possible to estimate GT variance components simultaneously with traditional IRT parameters. It is shown how GT and IRT can be linked together, in the context of a single-facet measurement design with binary items. Using both simulated and empirical data with the software WinBUGS, the GIRM approach is shown to produce results comparable to those from a standard GT analysis, while also producing results from a random effects IRT model. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10