A Comparative Evaluation of Kernel Equating and Test Characteristic Curve Equating

Abstract

This study compares the kernel equating (KE) and test characteristic curve (TCC) equating methods using the nonequivalent anchor test equating design. In this Monte Carlo study, four independent variables were examined: sample size, test length, average form discrimination, anchor test reliability, and the percentage of anchor items. For each condition, there were 100 replications. To assess the performance of TCC equating and KE, the differences between the examinee parametric true scores and the equated estimated expected true scores were examined. The equated scores were based on the average across replications for each condition. Generally speaking, both KE and TCC equating produced accurate results, although KE tended to perform better than TCC on the parametric true score scale across conditions. Past research and the current study’s results seem to indicate that KE should be strongly considered for most equating situations, particularly in light of its flexibility.