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Adjacent-Categories Mokken Models for Rater-Mediated Assessments

机译：毗邻类别的莫克伦用于评估评估的模型

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摘要

Molenaar extended Mokken's original probabilistic-nonparametric scaling models for use with polytomous data. These polytomous extensions of Mokken's original scaling procedure have facilitated the use of Mokken scale analysis as an approach to exploring fundamental measurement properties across a variety of domains in which polytomous ratings are used, including rater-mediated educational assessments. Because their underlying item step response functions (i.e., category response functions) are defined using cumulative probabilities, polytomous Mokken models can be classified as cumulative models based on the classifications of polytomous item response theory models proposed by several scholars. In order to permit a closer conceptual alignment with educational performance assessments, this study presents an adjacent-categories variation on the polytomous monotone homogeneity and double monotonicity models. Data from a large-scale rater-mediated writing assessment are used to illustrate the adjacent-categories approach, and results are compared with the original formulations. Major findings suggest that the adjacent-categories models provide additional diagnostic information related to individual raters' use of rating scale categories that is not observed under the original formulation. Implications are discussed in terms of methods for evaluating rating quality.
机译：Molenaar扩展了Mokken的原始概率 - 非参数缩放模型，用于多多种数据。 Mokken原始缩放程序的这些多种延伸促进了使用Mokken Scale分析作为探索各种结构域的基本测量性能的方法，其中使用多元素额定值，包括评估型教育评估。由于它们的基础项目步骤响应函数（即类别响应函数）使用累积概率定义，因此可以基于几个学者提出的多元素项目响应理论模型的分类来分类为累积模型的多元素Mokken模型。为了允许与教育绩效评估更接近的概念对齐，本研究介绍了多种单调均匀性和双重单调模型的相邻类别。来自大规模评估的写作评估的数据用于说明相邻类别的方法，结果与原始配方进行比较。主要研究结果表明，相邻类别模型提供了与个人评级使用评级规模类别相关的额外诊断信息，这些类别在原始配方下未观察到的评级规模类别。在评估额定值质量的方法方面讨论了含义。

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