具有熟悉测量特性的单参数诊断分类模型

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Matthew J. Madison, Stefanie A. Wind, Lientje Maas, Kazuhiro Yamaguchi, Sergio Haab
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引用次数: 0

摘要

诊断分类模型(DCM)是一种心理测量模型,旨在根据受试者对特定潜在特征的熟练程度或不熟练程度对其进行分类。这些模型非常适合提供诊断性和可操作的反馈,以支持中期和形成性评估工作。目前已经开发了几种 DCM,并应用于不同的环境中。本研究探讨了一种功能形式类似于 1 参数逻辑项目反应理论模型的 DCM。利用一项大规模数学教育研究的数据,我们展示并证明了所提出的 DCM 具有与 Rasch 模型和单参数逻辑项目反应理论模型相似的测量属性,包括总分充分性、无项目和无人员测量,以及不变的项目和人员排序。我们介绍了该模型的一些潜在应用,并讨论了这些发展的意义和局限性,以及未来的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A One-Parameter Diagnostic Classification Model with Familiar Measurement Properties

Diagnostic classification models (DCMs) are psychometric models designed to classify examinees according to their proficiency or nonproficiency of specified latent characteristics. These models are well suited for providing diagnostic and actionable feedback to support intermediate and formative assessment efforts. Several DCMs have been developed and applied in different settings. This study examines a DCM with functional form similar to the 1-parameter logistic item response theory model. Using data from a large-scale mathematics education research study, we demonstrate and prove that the proposed DCM has measurement properties akin to the Rasch and one-parameter logistic item response theory models, including sum score sufficiency, item-free and person-free measurement, and invariant item and person ordering. We introduce some potential applications for this model, and discuss the implications and limitations of these developments, as well as directions for future research.

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来源期刊
CiteScore
2.30
自引率
7.70%
发文量
46
期刊介绍: The Journal of Educational Measurement (JEM) publishes original measurement research, provides reviews of measurement publications, and reports on innovative measurement applications. The topics addressed will interest those concerned with the practice of measurement in field settings, as well as be of interest to measurement theorists. In addition to presenting new contributions to measurement theory and practice, JEM also serves as a vehicle for improving educational measurement applications in a variety of settings.
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