Evaluates a method for item selection in adaptive testing that is based on Kullback-Leibler information (KLI) (T. Cover and J. Thomas, 1991). Simulation study results show that testing algorithms using KLI-based item selection perform better than or as well as those using Fisher information item selection. (SLD)

Proposes an item-selection algorithm for neutralizing the differential effects of time limits on computerized adaptive test scores. Uses a statistical model for distributions of examinees' response times on items in a bank that is updated each time an item is administered. Demonstrates the method using an item bank from the Armed Services…

A method of automatically selecting items for inclusion in a test with constraints on item content and statistical properties was applied to real data. Tests constructed manually from the same data and constraints were compared to tests constructed automatically. Results show areas in which automated assembly can improve test construction. (SLD)

A model for solving very large item selection problems is presented. The model builds on binary programming applied to test construction. A heuristic for selecting items that satisfy the constraints in the model is also presented, and various problems are solved using the model and heuristic. (SLD)

A method is presented for incorporating a large number of constraints on adaptive item selection in the construction of computerized adaptive tests. The method, which emulates practices of expert test specialists, is illustrated for verbal and quantitative measures. Its foundation is application of a weighted deviations model and algorithm. (SLD)

Derivations of several item selection algorithms for use in fitting test items to target information functions (IFs) are described. These algorithms, which use an average growth approximation of target IFs, were tested by generating six test forms and were found to provide reliable fit. (SLD)