Seo Dong Gi, Weiss David J
National Registry of Emergency Medical Technicians, Columbus, OH, USA.
University of Minnesota, Minneapolis, MN, USA.
Educ Psychol Meas. 2015 Dec;75(6):954-978. doi: 10.1177/0013164415575147. Epub 2015 Mar 25.
Most computerized adaptive tests (CATs) have been studied using the framework of unidimensional item response theory. However, many psychological variables are multidimensional and might benefit from using a multidimensional approach to CATs. This study investigated the accuracy, fidelity, and efficiency of a fully multidimensional CAT algorithm (MCAT) with a bifactor model using simulated data. Four item selection methods in MCAT were examined for three bifactor pattern designs using two multidimensional item response theory models. To compare MCAT item selection and estimation methods, a fixed test length was used. The D-optimality item selection improved θ estimates with respect to a general factor, and either D- or A-optimality improved estimates of the group factors in three bifactor pattern designs under two multidimensional item response theory models. The MCAT model without a guessing parameter functioned better than the MCAT model with a guessing parameter. The MAP (maximum a posteriori) estimation method provided more accurate θ estimates than the EAP (expected a posteriori) method under most conditions, and MAP showed lower observed standard errors than EAP under most conditions, except for a general factor condition using D-optimality item selection.
大多数计算机自适应测试（CAT）都是在单维项目反应理论的框架下进行研究的。然而，许多心理变量是多维的，采用多维方法进行CAT可能会更有益。本研究使用模拟数据，研究了一种具有双因素模型的完全多维CAT算法（MCAT）的准确性、保真度和效率。针对三种双因素模式设计，使用两种多维项目反应理论模型，考察了MCAT中的四种项目选择方法。为了比较MCAT项目选择和估计方法，采用了固定的测试长度。在两种多维项目反应理论模型下，对于一般因素，D-最优性项目选择改善了θ估计；在三种双因素模式设计中，D-最优性或A-最优性改善了组因素的估计。没有猜测参数的MCAT模型比有猜测参数的MCAT模型表现更好。在大多数情况下，最大后验概率（MAP）估计方法比期望后验概率（EAP）方法提供了更准确的θ估计，并且在大多数情况下，MAP的观察标准误差低于EAP，但在使用D-最优性项目选择的一般因素条件下除外。
