Approaches to estimating longitudinal diagnostic classification models
Approaches to estimating longitudinal diagnostic classification models
复制标题
估计纵向诊断分类模型的方法
DOI：
10.1007/s41237-023-00202-5
复制
发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Bradshaw, Laine P.
中科院分区：
文献类型：
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作者：
Madison, Matthew J.;Chung, Seungwon;Kim, Junok;Bradshaw, Laine P.
最近的发展使纵向评估数据的诊断分类模型（DCM）的框架建模。这些纵向的DCM的发展，以提供测量学生的成长在一个离散的规模的形式属性掌握过渡，从而支持分类和标准参考的解释增长。采用纵向DCM的研究使用了不同的统计方法来模拟考生属性掌握的转变。然而，还没有研究系统地比较这些不同方法的潜在优点和缺点。通过仿真，本研究比较和评估三种不同的方法来估计纵向DCM的性能。结果表明，性能是相似的分类精度和可靠性方面，但实际的考虑和应用程序的总体目标应指导建模方法的选择。这些结果的影响进行了讨论。
Recent developments have enabled the modeling of longitudinal assessment data in a diagnostic classification model (DCM) framework. These longitudinal DCMs were developed to provide measures of student growth on a discrete scale in the form of attribute mastery transitions, thereby supporting categorical and criterion-referenced interpretations of growth. Studies employing longitudinal DCMs have used different statistical approaches to model examinee attribute mastery transitions. Yet, there has not been research that systematically compares the potential advantages and shortcomings of these different approaches. Via simulation, this study compares and evaluates the performance of three different approaches to estimating longitudinal DCMs. Results show that performance is similar in terms of classification accuracy and reliability, but practical considerations and the overall goals of the application should guide the choice of modeling approach. Implications of these results are discussed.