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Generalizability in Item Response Modeling
Author(s) -
Briggs Derek C.,
Wilson Mark
Publication year - 2007
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/j.1745-3984.2007.00031.x
Subject(s) - generalizability theory , item response theory , markov chain monte carlo , computer science , context (archaeology) , econometrics , variance (accounting) , flexibility (engineering) , monte carlo method , binary data , statistics , multilevel model , binary number , mathematics , machine learning , psychometrics , paleontology , accounting , arithmetic , business , biology
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.