Optimization of Performance Validity Test (PVT) Cutoffs across Healthy and Non-Referred Clinical Research Samples

Performance validity tests (PVTs) effectively detect suboptimal test performance, but cutoff scores for a given test may differ between populations. This research examines how optimal PVT cutoffs differ in a collegiate research population when mental health and clinical conditions are considered. Healthy controls (n = 328) and non-referred students with self-reported clinical conditions (n = 42) were assigned to perform their best while others simulated ADHD symptoms (n = 123). PVT indices were derived from a stand-alone measure (Victoria Symptom Validity Test) and embedded measures (California Verbal Learning Test – Second Edition; Wechsler Adult Intelligence Scale – Fourth Edition, Digit Span). PVT cutoffs with the highest sensitivity, while maintaining adequate specificity, were identified when the control groups were considered together, and when students with reported clinical conditions were considered separately. Mean differences in PVT performances were found between the simulation group and control groups, but not between clinical and nonclinical controls. The optimized cutoffs differed for five of eight PVT indices when all controls were considered together versus the clinical control group, only. When discordance was observed, cutoffs tended to be lower (less stringent) for the non-referred clinical control group. Together, these optimized cutoffs tended to be more stringent than previously established cutoffs. This study suggests that PVT cutoffs may be responsibly altered in a research context in the presence of a clinical condition. Future research should investigate if PVT classification accuracies can be improved in clinical and forensic samples while considering clinical conditions.