Detecting Item Drift in Large‐Scale Testing. Issue 3 (1st September 2017) - Guo, Hongwen; Robin, Frederic; Dorans, Neil
Abstract : The early detection of item drift is an important issue for frequently administered testing programs because items are reused over time. Unfortunately, operational data tend to be very sparse and do not lend themselves to frequent monitoring analyses, particularly for on‐demand testing. Building on existing residual analyses, the authors propose an item index that requires only moderate‐to‐small sample sizes to form data for time‐series analysis. Asymptotic results are presented to facilitate statistical significance tests. The authors show that the proposed index combined with time‐series techniques may be useful in detecting and predicting item drift. Most important, this index is related to a well‐known differential item functioning analysis so that a meaningful effect size can be proposed for item drift detection.