The Impact of Item Parameter Drift in Computer Adaptive Testing (CAT)

A series of CAT simulations were conducted to evaluate the impact of item parameter drift (IPD) in computer adaptive testing (CAT). The simulations varied the amount and magnitude of IPD, as well as the size of the item pool. A baseline condition without the presence of drift was established and used to compare the results of the altered IPD conditions to the non-altered baseline condition. A number of criteria were used to evaluate the effects of IPD on measurement precision, classification, and test efficiency. These included bias, root mean square error (RMSE), absolute average difference (AAD), total percentages of misclassifcation, the number of false positives and false negatives, the total test lengths, and item exposure rates. The results revealed negligible differences when comparing the IPD conditions to the baseline condition for all measures of precision, classification accuracy, and test efficiency. Magnitude of drift appeared to have a larger impact on measurement precision than the number of items with drift.