Item Selection for the Development of Parallel Forms From an IRT-Based Seed Test Using a Sampling and Classification Approach

Abstract

Two sampling-and-classification–based procedures were developed for automated test assembly: the Cell Only and the Cell and Cube methods. A simulation study based on a 540-item bank was conducted to compare the performance of the procedures with the performance of a mixed-integer programming (MIP) method for assembling multiple parallel test forms. The study investigated the statistical equivalence of the forms generated by the three test assembly methods (Cell Only, Cell and Cube, and MIP) in terms of test information functions, test characteristic curves, mean square deviations, and practical constraints, such as content balancing and nonoverlap among forms. The results indicated that the 13-point MIP method outperformed the other two methods in terms of the “closeness” test information functions between the reference form and the assembled parallel tests. Regarding test characteristic curves, the Cell Only and Cell and Cube methods yielded more similar test characteristic curves than the MIP method. Constraining test information functions apparently does not guarantee that the assembled forms will yield similar test characteristic curves. Overall, the Cell Only and Cell and Cube methods have the potential to provide results similar to the optimization approach.