“…Proposed methods to detect test fraud generally align with a particular type of cheating behavior. (Kling, 1979, cited in Saretsky, 1984; K 1 and K 2 (Sotaridona & Meijer, 2003); VM (Belov, 2011); S 2 (Sotaridona & Meijer, 2003); ω (Wollack, 1997); D (Trabin & Weiss, 1983); l z (Drasgow, Levine, & Williams, 1985); Hierarchical RT approach (van der Linden & Guo, 2008); Z c (Meijer & Sotaridona, 2006); KL (Man, Harring, Ouyang, & Thomas, 2018); RT residual analysis (H. Qian, Staniewska, Reckase, & Woo, 2016); Bivariate lognormal RT analysis (van der linden, 2009); l s (Marianti, Fox, Avetisyan, Veldkamp, & Tijmstra, 2014); l y s (Fox & Marianti, 2017); χ pt (Sinharay, 2018) Suspicious Answer Changing Linear regression analysis (Primoli, Liassou, Bishop, & Nhouyvanisvong, 2011); Generalized WR analysis (van der Linden & Jeon, 2012); GBT (van der Linden & ; EDI (Wollack, Cohen, & Eckerly, 2015); D(G||h) (Belov, 2015); PPD EDI (Sinharay & Johnson, 2017) Suspicious Gain Scores BHLM (Skorupski & Egan, 2011); EDI g (Wollack & Eckerly, 2017) Methods to detect unexpected gain scores, collusion, preknowledge of items, and other unspecified aberrant test-taking behaviors include the cumulative distribution method (Holland, 2002), l * z index (Drasgow, Levine, & McLaughlin, 1987;Snijders, 2001), H T index (Sijtsma, 1986;Sijtsma & Meijer, 1992), ω (Wollack, 1997), L s (Sinharay, 2017), erasure detection index (Wollack, Cohen, & Eckerly, 2015), and S (Belov, 2015), which are based on individuals' item scores.…”