IRTree 模型中的项目特定因素:何时重要,何时不重要?

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/s11336-023-09916-7
Thorsten Meiser, Fabiola Reiber
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引用次数: 0

摘要

Lyu等人(Psychometrika, 2023)证明,在每个项目有多个嵌套反应过程的情况下,项目特定因素会对IRTree模型的结构参数产生虚假影响。在此,我们讨论了一些边界条件,并认为对项目参数的人员选择效应并不是项目特定因素所独有的,Lyu 等人(Psychometrika,2023 年)提出的效应可能并不能推广到整个 IRTree 模型系列。最后,我们建议 IRTree 模型的规范应该以理论考虑为指导,而不是以数据为驱动,以避免对参数差异的误读。
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Item-Specific Factors in IRTree Models: When They Matter and When They Don't.

Lyu et al. (Psychometrika, 2023) demonstrated that item-specific factors can cause spurious effects on the structural parameters of IRTree models for multiple nested response processes per item. Here, we discuss some boundary conditions and argue that person selection effects on item parameters are not unique to item-specific factors and that the effects presented by Lyu et al. (Psychometrika, 2023) may not generalize to the family of IRTree models as a whole. We conclude with the recommendation that IRTree model specification should be guided by theoretical considerations, rather than driven by data, in order to avoid misinterpretations of parameter differences.

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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.
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