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Testlet Response Theory×Multidimensional Item Response Theory×
领域EducationEducation
方法族Latent structureLatent structure
起源年份20072009
提出者Howard Wainer, Eric Bradlow & Xiaohui WangMark Reckase; foundations in factor analysis of items (Bock, McDonald)
类型Item response model accommodating local dependence within item bundles (testlets)Item response model with multiple latent ability dimensions
开创性文献Wainer, H., Bradlow, E. T., & Wang, X. (2007). Testlet Response Theory and Its Applications. Cambridge University Press. ISBN: 9780521681261Reckase, M. D. (2009). Multidimensional Item Response Theory. Springer. DOI ↗
别名TRT, Testlet Models, Random-Effects Testlet Model, Item-Bundle IRTMIRT, Multidimensional IRT, Compensatory MIRT, Bifactor IRT
相关44
摘要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.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.
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ScholarGate方法对比: Testlet Response Theory · Multidimensional Item Response Theory. 于 2026-06-24 检索自 https://scholargate.app/zh/compare