Multiple measures, such as multiple content domains or multiple types of performance, are used in various testing programs to classify examinees for screening or selection. Despite the popular usages of multiple measures, there is little research on classification consistency and accuracy of multiple measures. Accordingly, this study introduces an approach to estimate classification consistency and accuracy indices for multiple measures under four possible decision rules: (1) complementary, (2) conjunctive, (3) compensatory, and (4) pairwise combinations of the three. The current study uses the IRT-recursive-based approach with the simple-structure multidimensional IRT model (SS-MIRT) to estimate the classification consistency and accuracy for multiple measures. Theoretical formulations of the four decision rules with a binary decision (Pass/Fail) are presented. The estimation procedures are illustrated using an empirical data example based on SS-MIRT. In addition, this study applies the estimation procedures to the unidimensional IRT (UIRT) context, considering that UIRT is practically used more. This application shows that the proposed procedure of classification consistency and accuracy could be used with a UIRT model for individual measures as an alternative method of SS-MIRT.

中文翻译：

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使用简单结构 MIRT 模型估计多个度量的分类精度和一致性指标

在各种测试程序中使用多种措施，例如多个内容域或多种类型的性能来对考生进行分类以进行筛选或选择。尽管多重度量的使用很流行，但是关于多重度量的分类一致性和准确性的研究却很少。因此，本研究介绍了一种根据四种可能的决策规则估计多种度量的分类一致性和准确性指数的方法：（1）互补，（2）联合，（3）补偿，以及（4）三者的成对组合。目前的研究使用基于 IRT 递归的方法和简单结构的多维 IRT 模型 (SS-MIRT) 来估计多个度量的分类一致性和准确性。提出了具有二元决策（通过/失败）的四个决策规则的理论公式。使用基于 SS-MIRT 的经验数据示例来说明估计过程。此外，考虑到 UIRT 在实际中使用得更多，本研究将估计程序应用于一维 IRT (UIRT) 上下文。此应用程序表明，所提出的分类一致性和准确性程序可以与 UIRT 模型一起用于单个度量，作为 SS-MIRT 的替代方法。