Hector Research Institute of Education Sciences and Psychology, University of Tübingen, Europastraße 6, 72072, Tübingen, Germany.
Helmut Schmidt University Hamburg, Hamburg, Germany.
Behav Res Methods. 2024 Apr;56(4):4130-4161. doi: 10.3758/s13428-024-02366-8. Epub 2024 Mar 22.
Item response theory (IRT) has evolved as a standard psychometric approach in recent years, in particular for test construction based on dichotomous (i.e., true/false) items. Unfortunately, large samples are typically needed for item refinement in unidimensional models and even more so in the multidimensional case. However, Bayesian IRT approaches with hierarchical priors have recently been shown to be promising for estimating even complex models in small samples. Still, it may be challenging for applied researchers to set up such IRT models in general purpose or specialized statistical computer programs. Therefore, we developed a user-friendly tool - a SAS macro called HBMIRT - that allows to estimate uni- and multidimensional IRT models with dichotomous items. We explain the capabilities and features of the macro and demonstrate the particular advantages of the implemented hierarchical priors in rather small samples over weakly informative priors and traditional maximum likelihood estimation with the help of a simulation study. The macro can also be used with the online version of SAS OnDemand for Academics that is freely accessible for academic researchers.
近年来，项目反应理论（IRT）已发展成为一种标准的心理测量方法，特别是对于基于二分法（即真/假）项目的测试构建。不幸的是，在单维模型中进行项目细化通常需要大量样本，而在多维情况下则需要更多的样本。然而，最近的研究表明，贝叶斯 IRT 方法与层次先验相结合，对于在小样本中估计甚至复杂的模型也很有前途。尽管如此，对于应用研究人员来说，在通用或专业的统计计算机程序中设置这样的 IRT 模型可能仍然具有挑战性。因此，我们开发了一个用户友好的工具 - 一个名为 HBMIRT 的 SAS 宏 - 它允许使用二分法项目估计单维和多维 IRT 模型。我们解释了宏的功能和特点，并通过模拟研究演示了在相当小的样本中，实现的层次先验相对于弱信息先验和传统的最大似然估计的特殊优势。该宏也可以与 SAS OnDemand for Academics 的在线版本一起使用，学术研究人员可以免费访问该版本。
