Commentary on "Extending the Basic Local Independence Model to Polytomous Data" by Stefanutti, de Chiusole, Anselmi, and Spoto.

IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Psychometrika Pub Date : 2023-06-01 Epub Date: 2022-06-17 DOI:10.1007/s11336-022-09873-7
Chia-Yi Chiu, Hans Friedrich Köhn, Wenchao Ma
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引用次数: 0

Abstract

The Polytomous Local Independence Model (PoLIM) by Stefanutti, de Chiusole, Anselmi, and Spoto, is an extension of the Basic Local Independence Model (BLIM) to accommodate polytomous items. BLIM, a model for analyzing responses to binary items, is based on Knowledge Space Theory, a framework developed by cognitive scientists and mathematical psychologists for modeling human knowledge acquisition and representation. The purpose of this commentary is to show that PoLIM is simply a paraphrase of a DINA model in cognitive diagnosis for polytomous items. Specifically, BLIM is shown to be equivalent to the DINA model when the BLIM-items are conceived as binary single-attribute items, each with a distinct attribute; thus, PoLIM is equivalent to the DINA for polytomous single-attribute items, each with a distinct attribute.

对 Stefanutti、de Chiusole、Anselmi 和 Spoto 所著 "将基本局部独立性模型扩展到多态数据 "的评论。
由 Stefanutti、de Chiusole、Anselmi 和 Spoto 提出的多题局部独立性模型(Polytomous Local Independence Model,PoLIM)是对基本局部独立性模型(Basic Local Independence Model,BLIM)的扩展,以适应多题项目。BLIM 是一个分析二元项目回答的模型,它以认知科学家和数学心理学家为人类知识获取和表征建模而开发的框架--知识空间理论为基础。本评论的目的在于说明 PoLIM 只是认知诊断中 DINA 模型对多项式项目的一种诠释。具体地说,当 BLIM 项目被视为二元单属性项目(每个项目都有一个不同的属性)时,BLIM 就等同于 DINA 模型;因此,对于多项式单属性项目(每个项目都有一个不同的属性),PoLIM 就等同于 DINA。
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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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