Strobl Carolin, Kopf Julia, Kohler Lucas, von Oertzen Timo, Zeileis Achim
Universität Zürich, Switzerland.
Universität der Bundeswehr München, Germany.
Appl Psychol Meas. 2021 May;45(3):214-230. doi: 10.1177/0146621621990743. Epub 2021 Feb 25.
For detecting differential item functioning (DIF) between two or more groups of test takers in the Rasch model, their item parameters need to be placed on the same scale. Typically this is done by means of choosing a set of so-called anchor items based on statistical tests or heuristics. Here the authors suggest an alternative strategy: By means of an inequality criterion from economics, the Gini Index, the item parameters are shifted to an optimal position where the item parameter estimates of the groups best overlap. Several toy examples, extensive simulation studies, and two empirical application examples are presented to illustrate the properties of the Gini Index as an anchor point selection criterion and compare its properties to those of the criterion used in the alignment approach of Asparouhov and Muthén. In particular, the authors show that-in addition to the globally optimal position for the anchor point-the criterion plot contains valuable additional information and may help discover unaccounted DIF-inducing multidimensionality. They further provide mathematical results that enable an efficient sparse grid optimization and make it feasible to extend the approach, for example, to multiple group scenarios.
为了在拉施模型中检测两组或多组考生之间的项目功能差异(DIF),需要将他们的项目参数置于同一量表上。通常,这是通过基于统计检验或启发式方法选择一组所谓的锚定项目来实现的。在此,作者提出了一种替代策略:借助经济学中的一个不平等标准——基尼指数,将项目参数转移到一个最优位置,使各群体的项目参数估计值达到最佳重叠。文中给出了几个简单示例、广泛的模拟研究以及两个实证应用示例,以说明基尼指数作为锚点选择标准的特性,并将其特性与阿斯帕罗霍夫和莫特恩的校准方法中使用的标准的特性进行比较。特别是,作者表明——除了锚点的全局最优位置外——标准图还包含有价值的额外信息,可能有助于发现未被考虑的导致DIF的多维度性。他们还给出了数学结果,实现了高效的稀疏网格优化,并使扩展该方法(例如扩展到多组场景)成为可能。