Estimating Classification Accuracy and Consistency Indices for Multiple Measures with the Simple Structure MIRT Model

Abstract

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.