比较的不变性:Rasch 模型之外的项目参数和人称参数分离

IF 1.5 4区 心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Mathematical Psychology Pub Date : 2024-09-01 Epub Date: 2024-08-28 DOI:10.1016/j.jmp.2024.102876
Gerhard Tutz
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引用次数: 0

摘要

Rasch 模型是常用的潜在特质模型中最突出的成员。主要原因是它可以被视为一种测量模型,可以将人和项目参数分开,这一特征被称为比较不变性或特定客观性。研究表明,这一特性并不是 Rasch 类型模型所独有的,在其他潜在特质模型中也同样存在。理论测量模型中的可分离性与经验可分离性是有区别的,经验可分离性指的是可以在不参考另一组参数的情况下估计参数。我们提出了一种具有这种特性的新型成对估计器,它也可用于替代模型。在二元模型和多项式模型中都考虑了可分性。
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Invariance of comparisons: Separation of item and person parameters beyond Rasch models

The Rasch model is the most prominent member of the class of latent trait models that are in common use. The main reason is that it can be considered as a measurement model that allows to separate person and item parameters, a feature that is referred to as invariance of comparisons or specific objectivity. It is shown that the property is not an exclusive trait of Rasch type models but is also found in alternative latent trait models. It is distinguished between separability in the theoretical measurement model and empirical separability with empirical separability meaning that parameters can be estimated without reference to the other group of parameters. A new type of pairwise estimator with this property is proposed that can be used also in alternative models. Separability is considered in binary models as well as in polytomous models.

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来源期刊
Journal of Mathematical Psychology
Journal of Mathematical Psychology 医学-数学跨学科应用
CiteScore
3.70
自引率
11.10%
发文量
37
审稿时长
20.2 weeks
期刊介绍: The Journal of Mathematical Psychology includes articles, monographs and reviews, notes and commentaries, and book reviews in all areas of mathematical psychology. Empirical and theoretical contributions are equally welcome. Areas of special interest include, but are not limited to, fundamental measurement and psychological process models, such as those based upon neural network or information processing concepts. A partial listing of substantive areas covered include sensation and perception, psychophysics, learning and memory, problem solving, judgment and decision-making, and motivation. The Journal of Mathematical Psychology is affiliated with the Society for Mathematical Psychology. Research Areas include: • Models for sensation and perception, learning, memory and thinking • Fundamental measurement and scaling • Decision making • Neural modeling and networks • Psychophysics and signal detection • Neuropsychological theories • Psycholinguistics • Motivational dynamics • Animal behavior • Psychometric theory
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