An Algorithm to Improve Test Answer Copying Detection Using the Omega Statistic

Abstract

The omega (ω) statistic is reputed to be one of the best indices for detecting answer copying on multiple choice tests, but its performance relies on the accurate estimation of copier ability, which is challenging because responses from the copiers may have been contaminated. We propose an algorithm that aims to identify and delete the suspected copied responses through probability sampling and bootstrapping. In doing so, the bias in copier ability estimation will be determined and used to update the ability estimate for calculating the modified omega (ωm), a new statistic based on the ω. The performance of ωm and ω were compared in a Monte Carlo simulation study under 40 typical testing conditions (2 test lengths x 4 sample sizes x 5 levels of copying). In almost all conditions, the ωm had the same or better controlled Type I error and higher power than ω. The increase in power was particularly eminent when the source's estimated ability was higher than the copier and when 20% or 30% of items were copied. These findings support the use of the ωm as a replacement of ω to detect answer copying in multiple choice exams.