New Robust Scale Transformation Methods in the Presence of Outlying Common Items

摘要

Common items play an important role in item response theory (IRT) true score equating under the common-item nonequivalent groups design. Biased item parameter estimates due to common item outliers can lead to large errors in equated scores. Current methods used to screen for common item outliers mainly focus on the detection and elimination of those items, which may lead to inadequate content representation for the common items. To reduce the impact of inconsistency in item parameter estimates while maintaining content representativeness, the authors propose two robust scale transformation methods based on two weighting methods: the Area-Weighted method and the Least Absolute Values (LAV) method. Results from two simulation studies indicate that these robust scale transformation methods performed as well as the Stocking-Lord method in the absence of common item outliers and, more importantly, outperformed the Stocking-Lord method when a single outlying common item was simulated.