Optimizing Diagnostic Classification Models Application Considering Real-Life Constraints

IF 1.9 3区心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH

Journal of Educational and Behavioral Statistics Pub Date : 2023-03-30 DOI:10.3102/10769986231159137

Kun Su, R. Henson

引用次数: 1

Abstract

This article provides a process to carefully evaluate the suitability of a content domain for which diagnostic classification models (DCMs) could be applicable and then optimized steps for constructing a test blueprint for applying DCMs and a real-life example illustrating this process. The content domains were carefully evaluated using a set of defined criteria, which are purposely defined to improve the success rate of DCM implementation. Given the domain, the Q-matrix is determined by a simulation-based approach using correct classification rates as criteria. Finally, a physics test on the final Q-matrix was developed, administered, and analyzed by the author and the subject-matter experts (SMEs).

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考虑现实约束的诊断分类模型优化应用

本文提供了一个仔细评估可应用诊断分类模型(dcm)的内容域的适用性的过程，然后优化了构建用于应用dcm的测试蓝图的步骤，并提供了一个说明此过程的实际示例。使用一组定义的标准仔细地评估了内容域，这些标准的定义是为了提高DCM实现的成功率。给定域，q矩阵由基于模拟的方法确定，使用正确的分类率作为标准。最后，作者和主题专家(sme)开发、管理和分析了最终q矩阵的物理测试。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Educational and Behavioral Statistics Multiple-

CiteScore

4.40

自引率

4.20%

发文量

期刊介绍： Journal of Educational and Behavioral Statistics, sponsored jointly by the American Educational Research Association and the American Statistical Association, publishes articles that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also of interest. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority. The Journal of Educational and Behavioral Statistics provides an outlet for papers that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis, provide properties of these methods, and an example of use in education or behavioral research. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also sometimes accepted. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority.