Generalizability in item response modeling

Briggs, Derek C.; Wilson, Mark

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.

Más información

Título según WOS: ID WOS:000246187200003 Not found in local WOS DB
Título de la Revista: JOURNAL OF EDUCATIONAL MEASUREMENT
Volumen: 44
Número: 2
Editorial: WILEY-BLACKWELL
Fecha de publicación: 2007
Página de inicio: 131
Página final: 155
DOI:

10.1111/j.1745-3984.2007.00031.x

Notas: ISI