Appraising Traditional and Purpose-built Person Fit Statistics’ Power to Detect Cheating

Abstract

Person-fit statistics (PFSs) have been suggested as a tool to detect cheating in large-scale testing, and this study investigates their potential for this application. Most PFSs are equally sensitive to scores that appear spuriously high or spuriously low. Xia & Zheng introduced four PFSs that are meant to be more sensitive to spuriously high scores and therefore may be more appropriate for detecting cheating. Comparing the power of these weighted PFSs against the power of traditional PFSs to detect cheating shows that there is no single best statistic in all or most scenarios, and in most scenarios, most examinees flagged as cheating by person fit analysis did not cheat. Implications for operational use of PFSs to detect cheating are discussed.