As cheating on high-stakes tests continues to be an issue for standardized testing, approaches for detecting cheating proliferate. Approaches to cheating detection vary, with common strategies being detecting unusual levels of wrong-to-right erasures (e.g., Wollack, Cohen, & Eckerly, 2015), similarity of answer patterns (Karabatsos, 2003), and aberrant improvement over time (e.g., Bishop & Egan, 2016). However, the majority of research focuses on detecting cheating at the individual level. As recent events have shown (e.g., the Atlanta cheating scandal), cheating at the group level is also a threat to the validity of decision made from scores on high-stakes standardized tests. The present study adapts the Bayesian Hierarchical Linear Model (BHLM) introduced in Skorupski & Egan (2013, 2014) and further developed in Skorupski, Fitzpatrick, and Egan (2016) to detect group-level aberrance within an IRT framework. Since many testing companies use a latent trait model to estimate examinee ability, this method may prove more compatible with operational testing programs’ current approach to scaling.