Titlebook: Handbook of Diagnostic Classification Models; Models and Model Ext Matthias von Davier,Young-Sun Lee Book 2019 The Editor(s) (if applicable

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https://doi.org/10.1007/978-3-8349-6955-2function. As many other latent variable models do, the CDMs often suffer from nonidentifiability. This chapter focuses on the identifiability issue of the CDMs and present conditions to ensure identifiability, which can be directly applied to most of the CDMs.

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Introduction: From Latent Classes to Cognitive Diagnostic Models,c to probabilistic forms. The ensuing sections cover knowledge state and rule space approaches, which can be viewed as deterministic skill-profile models. The chapter closes with a section on the multiple classification latent class model and the deterministic input noisy and (DINA) model.

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The G-DINA Model Frameworkgical developments. We will also discuss associated topics, which include model estimation, Q-matrix validation, computerized adaptive testing, and model selection as they relate to the reviewed models.

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Measures of Agreement: Reliability, Classification Accuracy, and Classification Consistencyostly express the agreement between the estimated skill and the true skill, or between estimated skills from parallel assessments. This paper provides a review of these measures and demonstrates some of them for a real data example.

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https://doi.org/10.1007/978-3-322-82932-0 responses that are impacted by varying underlying components with specifiable sources of complexity. MLTM-D can be applied to assess both processing competencies of examinees and the impact of the postulated features on process difficulty.

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https://doi.org/10.1007/978-3-322-84055-4 we separate the model fit methods into four categories defined by two aspects of the methods: (1) the level of the fit analysis, i.e., global/test-level versus item-level; and (2) the choice of the alternative model for comparison, i.e., an alternative CDM (relative fit) or a saturated categorical model (absolute fit).

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