Item selection methods in multidimensional computerized adaptive testing for forced-choice items using Thurstonian IRT model

Abstract

Multidimensional computerized adaptive testing for forced-choice items (MFC-CAT) combines the benefits of multidimensional forced-choice (MFC) items and computerized adaptive testing (CAT) in that it eliminates response biases and reduces administration time. Previous studies that explored designs of MFC-CAT only discussed item selection methods based on the Fisher information (FI), which is known to perform unstably at early stages of CAT. This study proposes a set of new item selection methods based on the KL information for MFC-CAT (namely MFC-KI, MFC-KB, and MFC-KLP) based on the Thurstonian IRT (TIRT) model. Three simulation studies, including one based on real data, were conducted to compare the performance of the proposed KL-based item selection methods against the existing FI-based methods in three- and five-dimensional MFC-CAT scenarios with various test lengths and inter-trait correlations. Results demonstrate that the proposed KL-based item selection methods are feasible for MFC-CAT and generate acceptable trait estimation accuracy and uniformity of item pool usage. Among the three proposed methods, MFC-KB and MFC-KLP outperformed the existing FI-based item selection methods and resulted in the most accurate trait estimation and relatively even utilization of the item pool.