从知识空间理论的角度看三参数和四参数项目反应理论模型的可识别性。

IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Psychometrika Pub Date : 2024-06-01 Epub Date: 2024-02-13 DOI:10.1007/s11336-024-09950-z
Stefano Noventa, Sangbeak Ye, Augustin Kelava, Andrea Spoto
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引用次数: 0

摘要

本研究旨在说明某些项目反应理论模型的可识别性问题(这里指经验上的不可区分性和不可识别性)与知识空间理论中的可识别性概念有关。具体来说,3参数和4参数模型的可识别性问题与更普遍的幂集所有项目的前向和后向分级问题有关,而幂集是在局部独立假设下与IRT模型相关的知识结构。因此,4 参数模型的可识别性问题被分为两部分:第一部分是左侧添加参数与项目反应函数(如 2 参数模型)其余部分之间权衡的结果;第二部分是 2 参数模型本身的可识别性问题。将这些结果应用于逻辑模型似乎既证实了目前文献中对固定效应和随机效应 IRT 逻辑模型的研究结果,又推广了这些研究结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

On the Identifiability of 3- and 4-Parameter Item Response Theory Models From the Perspective of Knowledge Space Theory.

On the Identifiability of 3- and 4-Parameter Item Response Theory Models From the Perspective of Knowledge Space Theory.

The present work aims at showing that the identification problems (here meant as both issues of empirical indistinguishability and unidentifiability) of some item response theory models are related to the notion of identifiability in knowledge space theory. Specifically, that the identification problems of the 3- and 4-parameter models are related to the more general issues of forward- and backward-gradedness in all items of the power set, which is the knowledge structure associated with IRT models under the assumption of local independence. As a consequence, the identifiability problem of a 4-parameter model is split into two parts: a first one, which is the result of a trade-off between the left-side added parameters and the remainder of the Item Response Function, e.g., a 2-parameter model, and a second one, which is the already well-known identifiability issue of the 2-parameter model itself. Application of the results to the logistic case appears to provide both a confirmation and a generalization of the current findings in the literature for both fixed- and random-effects IRT logistic models.

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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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