A Nominal Response Model Approach for Detecting Answer Copying

Abstract

When examinees copy answers to test questions from other examinees, the validity of the test is compromised. Most available statistical procedures for detecting copying were developed out of classical test theory (CrT); hence, they suffer from sampledependent score and item statistics, and biased estimates of the expected number of answer matches between a pair of examinees. Item response theory (IRT) based procedures alleviate these problems; however, because they fail to compare the similarity of responses between neighboring examinees, they have relatively poor power for detecting copiers. A new IRT-based test statistic, wo, was compared with the best CUT-based index g2 under various copying conditions, amounts of copying, test lengths, and sample sizes. w consistently held the Type I error rate at or below the nominal level; g2 yielded substantially inflated Type I error rates. The power of w varied as a function of both test length and the percentage of items copied. w demonstrated good power to detect copiers, provided that at least 20% of the items were copied on an 80-item test and at least 30% were copied on a 40-item test. Based on these results, with regard to both Tbype I error rate and power, c appears to be more useful than g2 as a copying index.