Han Kyung Chris T
Graduate Management Admission Council, Reston, VA, USA.
Appl Psychol Meas. 2020 Mar;44(2):87-102. doi: 10.1177/0146621619837226. Epub 2019 Mar 20.
Multistage testing (MST) has many practical advantages over typical item-level computerized adaptive testing (CAT), but there is a substantial tradeoff when using MST because of its reduced level of adaptability. In typical MST, the first stage almost always performs as a routing stage in which all test takers see a linear test form. If multiple test sections measure different but moderately or highly correlated traits, then a score estimate for one section might be capable of adaptively selecting item modules for following sections without having to administer routing stages repeatedly for each section. In this article, a new framework for developing MST with intersectional routing (ISR) was proposed and evaluated under several research conditions with different MST structures, section score distributions and relationships, and types of regression models for ISR. The overall findings of the study suggested that MST with ISR approach could improve measurement efficiency and test optimality especially with tests with short lengths.
与典型的项目级计算机自适应测试(CAT)相比,多阶段测试(MST)具有许多实际优势,但由于其适应性水平降低,在使用MST时存在很大的权衡。在典型的MST中,第一阶段几乎总是作为一个路由阶段,所有考生都要完成一个线性测试形式。如果多个测试部分测量不同但中度或高度相关的特质,那么一个部分的分数估计可能能够为后续部分自适应地选择项目模块,而不必为每个部分重复进行路由阶段。在本文中,提出了一种用于开发具有交叉路由(ISR)的MST的新框架,并在几种不同MST结构、部分分数分布和关系以及ISR回归模型类型的研究条件下进行了评估。该研究的总体结果表明,采用ISR方法的MST可以提高测量效率和测试最优性,特别是对于短长度的测试。