小测试的诊断分类模型:方法与理论

IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Psychometrika Pub Date : 2024-09-01 Epub Date: 2024-03-26 DOI:10.1007/s11336-024-09962-9
Xin Xu, Guanhua Fang, Jinxin Guo, Zhiliang Ying, Susu Zhang
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引用次数: 0

摘要

诊断分类模型(DCM)已被广泛应用于教育和心理测量,尤其是形成性评估。最近的文献对存在小测试的 DCM 进行了研究。基于小测验的 DCMs 统计建模和分析的一个关键要素是两个潜在结构的叠加,即属性轮廓和小测验效应。本文扩展了标准小测试子 DINA(T-DINA)模型,以适应两个潜在结构之间的潜在相关性。本文对模型的可识别性进行了研究,并提出了一系列充分条件。作为副产品,还建立了标准 T-DINA 的可识别性。提出的模型被应用于 2015 年国际学生评估项目的数据集。与 DINA 和 T-DINA 进行了比较,结果表明在拟合优度方面有了很大改进。还进行了模拟,以评估新方法在各种设置下的性能。
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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Diagnostic Classification Models for Testlets: Methods and Theory.

Diagnostic classification models (DCMs) have seen wide applications in educational and psychological measurement, especially in formative assessment. DCMs in the presence of testlets have been studied in recent literature. A key ingredient in the statistical modeling and analysis of testlet-based DCMs is the superposition of two latent structures, the attribute profile and the testlet effect. This paper extends the standard testlet DINA (T-DINA) model to accommodate the potential correlation between the two latent structures. Model identifiability is studied and a set of sufficient conditions are proposed. As a byproduct, the identifiability of the standard T-DINA is also established. The proposed model is applied to a dataset from the 2015 Programme for International Student Assessment. Comparisons are made with DINA and T-DINA, showing that there is substantial improvement in terms of the goodness of fit. Simulations are conducted to assess the performance of the new method under various settings.

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来源期刊
Psychometrika
Psychometrika 数学-数学跨学科应用
CiteScore
4.40
自引率
10.00%
发文量
72
审稿时长
>12 weeks
期刊介绍: The journal Psychometrika is devoted to the advancement of theory and methodology for behavioral data in psychology, education and the social and behavioral sciences generally. Its coverage is offered in two sections: Theory and Methods (T& M), and Application Reviews and Case Studies (ARCS). T&M articles present original research and reviews on the development of quantitative models, statistical methods, and mathematical techniques for evaluating data from psychology, the social and behavioral sciences and related fields. Application Reviews can be integrative, drawing together disparate methodologies for applications, or comparative and evaluative, discussing advantages and disadvantages of one or more methodologies in applications. Case Studies highlight methodology that deepens understanding of substantive phenomena through more informative data analysis, or more elegant data description.
期刊最新文献
Rejoinder to McNeish and Mislevy: What Does Psychological Measurement Require? Are Sum Scores a Great Accomplishment of Psychometrics or Intuitive Test Theory? Correction to: Generalized Structured Component Analysis Accommodating Convex Components: A Knowledge-Based Multivariate Method with Interpretable Composite Indexes. Variational Estimation for Multidimensional Generalized Partial Credit Model. Proof of Reliability Convergence to 1 at Rate of Spearman-Brown Formula for Random Test Forms and Irrespective of Item Pool Dimensionality.
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