The Impact of Item Calibration Error on Variable-Length Cognitive Diagnostic Computerized Adaptive Testing

Abstract

Calibration errors are inevitable and should not be ignored during the estimation of item parameters. Items with calibration error can affect the measurement results of tests. One of the purposes of the current study is to investigate the impacts of the calibration errors during the estimation of item parameters on the measurement accuracy, average test length, and test efficiency for variable-length cognitive diagnostic computerized adaptive testing. The other purpose is to examine the methods for reducing the adverse effects of calibration errors. Simulation results show that (1) calibration error has negative effect on the measurement accuracy for the deterministic input, noisy “and” gate (DINA) model, and the reduced reparameterized unified model; (2) the average test lengths is shorter, and the test efficiency is overestimated for items with calibration errors; (3) the compensatory reparameterized unified model (CRUM) is less affected by the calibration errors, and the classification accuracy, average test length, and test efficiency are slightly stable in the CRUM framework; (4) methods such as improving the quality of items, using large calibration sample to calibrate the parameters of items, as well as using cross-validation method can reduce the adverse effects of calibration errors on CD-CAT.