对 Lyu、Bolt 和 Westby 的 "探索顺序模型和 IRTree 模型中项目特定因素的影响 "评论的反驳。

IF 2.9 2区心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Psychometrika Pub Date : 2023-09-01 Epub Date: 2023-06-16 DOI:10.1007/s11336-023-09913-w

Weicong Lyu, Daniel M Bolt

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引用次数: 0

摘要

我们对 Lyu、Bolt 和 Westby 的 "探索顺序模型和 IRTree 模型中项目特定因素的影响 "一文的评论做出了回应。这些评论提出了一些重要观点，使我们能够澄清我们对许多教育和心理测试项目中特定项目因素的理论预期。同时，我们也同意评注中的观点，即为项目特定因素的存在提供实证证据所面临的挑战，并反思可能支持对项目特定因素进行估计的策略。我们坚持认为，主要的问题在于，在试图解释或使用第一个节点之外的参数时，项目特定因素会造成模糊性。

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Rejoinder to Commentaries on Lyu, Bolt and Westby's "Exploring the Effects of Item Specific Factors in Sequential and IRTree Models".

We respond to the commentaries on Lyu, Bolt and Westby's "Exploring the effects of item specific factors in sequential and IRTree models." The commentaries raise important points that allow us to clarify our theoretical expectation for item specific factors in many educational and psychological test items. At the same time, we agree with the commentaries in acknowledging challenges associated with providing empirical evidence for their presence and reflect on strategies that might support their estimation. We maintain that the principal concern is the ambiguity item specific factors create in attempting to interpret or use the parameters beyond the first node.

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来源期刊

Psychometrika 数学-数学跨学科应用

CiteScore

4.40

自引率

10.00%

发文量

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.