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Multidimensional Item Response Theory×Testlet Response Theory×
领域EducationEducation
方法族Latent structureLatent structure
起源年份20092007
提出者Mark Reckase; foundations in factor analysis of items (Bock, McDonald)Howard Wainer, Eric Bradlow & Xiaohui Wang
类型Item response model with multiple latent ability dimensionsItem response model accommodating local dependence within item bundles (testlets)
开创性文献Reckase, M. D. (2009). Multidimensional Item Response Theory. Springer. DOI ↗Wainer, H., Bradlow, E. T., & Wang, X. (2007). Testlet Response Theory and Its Applications. Cambridge University Press. ISBN: 9780521681261
别名MIRT, Multidimensional IRT, Compensatory MIRT, Bifactor IRTTRT, Testlet Models, Random-Effects Testlet Model, Item-Bundle IRT
相关44
摘要Multidimensional item response theory (MIRT) generalizes IRT to tests that measure more than one latent ability at once. Instead of a single ability θ, each examinee is characterized by a vector of abilities, and each item by a vector of discriminations indicating how strongly it taps each dimension. MIRT unites the logic of item response theory with the structure of factor analysis, letting analysts model, for example, that a word-problem item draws on both reading and mathematics. Synthesized in Reckase's authoritative treatment, it underlies the analysis of complex, multi-skill assessments.Testlet response theory (TRT) extends item response theory to tests built from testlets — bundles of items sharing a common stimulus, such as several questions about one reading passage. Standard IRT assumes items are conditionally independent given ability, but items within a testlet violate this because they draw on the same passage. TRT adds a testlet-specific random effect that absorbs this local dependence, preventing the overstated precision and biased parameters that result from ignoring it. Developed by Wainer, Bradlow, and Wang, it is widely used wherever passage-based or scenario-based items appear.
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ScholarGate方法对比: Multidimensional Item Response Theory · Testlet Response Theory. 于 2026-06-25 检索自 https://scholargate.app/zh/compare