The Restricted DINA Model: A Comprehensive Cognitive Diagnostic Model for Classroom-Level Assessments

IF 1.9 3区心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH

Journal of Educational and Behavioral Statistics Pub Date : 2023-03-30 DOI:10.3102/10769986231158829

P. Nájera, F. J. Abad, Chia-Yi Chiu, M. Sorrel

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引用次数: 1

Abstract

The nonparametric classification (NPC) method has been proven to be a suitable procedure for cognitive diagnostic assessments at a classroom level. However, its nonparametric nature impedes the obtention of a model likelihood, hindering the exploration of crucial psychometric aspects, such as model fit or reliability. Reporting the reliability and validity of scores is imperative in any applied context. The present study proposes the restricted deterministic input, noisy “and” gate (R-DINA) model, a parametric cognitive diagnosis model based on the NPC method that provides the same attribute profile classifications as the nonparametric method while allowing to derive a model likelihood and, subsequently, to compute fit and reliability indices. The suitability of the new proposal is examined by means of an exhaustive simulation study and a real data illustration. The results show that the R-DINA model properly recovers the posterior probabilities of attribute mastery, thus becoming a suitable alternative for comprehensive small-scale diagnostic assessments.

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受限DINA模型:一种用于课堂水平评估的综合认知诊断模型

非参数分类（NPC）方法已被证明是在课堂水平上进行认知诊断评估的合适程序。然而，它的非参数性质阻碍了对模型可能性的关注，阻碍了对关键心理测量方面的探索，如模型拟合或可靠性。在任何应用环境中，报告分数的可靠性和有效性都是必不可少的。本研究提出了限制确定性输入、噪声“和”门（R-DINA）模型，这是一种基于NPC方法的参数认知诊断模型，它提供了与非参数方法相同的属性轮廓分类，同时允许导出模型似然性，并随后计算拟合和可靠性指数。通过详尽的模拟研究和实际数据说明，对新提案的适用性进行了审查。结果表明，R-DINA模型正确地恢复了属性掌握的后验概率，从而成为综合小规模诊断评估的合适替代方案。

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.