Efficiency Analysis of Item Response Theory Kernel Equating for Mixed-Format Tests

Abstract

This study aims to evaluate the performance of Item Response Theory (IRT) kernel equating in the context of mixed-format tests by comparing it to IRT observed score equating and kernel equating with log-linear presmoothing. Comparisons were made through both simulations and real data applications, under both equivalent groups (EG) and non-equivalent groups with anchor test (NEAT) sampling designs. To prevent bias towards IRT methods, data were simulated with and without the use of IRT models. The results suggest that the difference between IRT kernel equating and IRT observed score equating is minimal, both in terms of the equated scores and their standard errors. The application of IRT models for presmoothing yielded smaller standard error of equating than the log-linear presmoothing approach. When test data were generated using IRT models, IRT-based methods proved less biased than log-linear kernel equating. However, when data were simulated without IRT models, log-linear kernel equating showed less bias. Overall, IRT kernel equating shows great promise when equating mixed-format tests.