University of Mannheim, Mannheim, Germany.
TU Dortmund University, Dortmund, Germany.
Psychometrika. 2023 Dec;88(4):1556-1589. doi: 10.1007/s11336-023-09931-8. Epub 2023 Aug 28.
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.
多维强迫选择（MFC）测试越来越受欢迎，但它们的构建非常复杂。Thurstonian 项目反应模型（Thurstonian IRT 模型）通常用于评分包含优势项目的 MFC 测试。目前，在频率主义框架中，对于二元成对比较的结果，计算 Thurstonian IRT 模型中潜在特征的信息，但这种方法忽略了随机依赖性。本文展示了如何在块级别估计 Fisher 信息。一项模拟研究表明，基于块信息的观察到的和预期的标准误差具有相似的准确性。当忽略块大小的局部依赖性 [公式：见正文] 时，标准误差会被低估，除了使用最大后验估计器。本文展示了如何总结多维块信息以进行测试构建。一项模拟研究和一个实证应用表明，根据所考虑的结果，块信息摘要之间的差异很小。因此，块信息可以帮助构建可靠的 MFC 测试。
