Estimating and Using Block Information in the Thurstonian IRT Model.

IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Psychometrika Pub Date : 2023-12-01 Epub Date: 2023-08-28 DOI:10.1007/s11336-023-09931-8
Susanne Frick
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Abstract

Multidimensional forced-choice (MFC) tests are increasing in popularity but their construction is complex. The Thurstonian item response model (Thurstonian IRT model) is most often used to score MFC tests that contain dominance items. Currently, in a frequentist framework, information about the latent traits in the Thurstonian IRT model is computed for binary outcomes of pairwise comparisons, but this approach neglects stochastic dependencies. In this manuscript, it is shown how to estimate Fisher information on the block level. A simulation study showed that the observed and expected standard errors based on the block information were similarly accurate. When local dependencies for block sizes [Formula: see text] were neglected, the standard errors were underestimated, except with the maximum a posteriori estimator. It is shown how the multidimensional block information can be summarized for test construction. A simulation study and an empirical application showed small differences between the block information summaries depending on the outcome considered. Thus, block information can aid the construction of reliable MFC tests.

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Thurstonian IRT模型中块信息的估计与利用。
多维强迫选择(MFC)测试越来越受欢迎,但其结构复杂。Thurstonian项目反应模型(Thurstonian IRT模型)最常用于对包含优势项的MFC测试进行评分。目前,在频率论框架中,关于thurston IRT模型中潜在特征的信息是为两两比较的二进制结果计算的,但这种方法忽略了随机依赖性。在这个手稿中,它显示了如何估计费雪信息在块水平。仿真研究表明,基于块信息的观测标准误差和期望标准误差具有相似的准确性。当忽略块大小的局部依赖关系[公式:见文本]时,标准误差被低估,除了最大的后验估计器。它展示了多维块信息如何被总结用于测试构建。模拟研究和经验应用表明,根据所考虑的结果,块信息摘要之间存在微小差异。因此,块信息可以帮助构建可靠的MFC测试。
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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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