Stronger Together: The Wechsler Adult Intelligence Scale-Fourth Edition as a Multivariate Performance Validity Test in Patients with Traumatic Brain Injury.

Abstract

This study was designed to evaluate the classification accuracy of a multivariate model of performance validity assessment using embedded validity indicators (EVIs) within the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV). Archival data were collected from 100 adults with traumatic brain injury (TBI) consecutively referred for neuropsychological assessment in a clinical setting. The classification accuracy of previously published individual EVIs nested within the WAIS-IV and a composite measure based on six independent EVIs were evaluated against psychometrically defined non-credible performance. Univariate validity cutoffs based on age-corrected scaled scores on Coding, Symbol Search, Digit Span, Letter-Number-Sequencing, Vocabulary minus Digit Span, and Coding minus Symbol Search were strong predictors of psychometrically defined non-credible responding. Failing ≥3 of these six EVIs at the liberal cutoff improved specificity (.91-.95) over univariate cutoffs (.78-.93). Conversely, failing ≥2 EVIs at the more conservative cutoff increased and stabilized sensitivity (.43-.67) compared to univariate cutoffs (.11-.63) while maintaining consistently high specificity (.93-.95). In addition to being a widely used test of cognitive functioning, the WAIS-IV can also function as a measure of performance validity. Consistent with previous research, combining information from multiple EVIs enhanced the classification accuracy of individual cutoffs and provided more stable parameter estimates. If the current findings are replicated in larger, diagnostically and demographically heterogeneous samples, the WAIS-IV has the potential to become a powerful multivariate model of performance validity assessment. Using a combination of multiple performance validity indicators embedded within the subtests of theWechsler Adult Intelligence Scale, the credibility of the response set can be establishedwith a high level of confidence. Multivariatemodels improve classification accuracy over individual tests. Relying on existing test data is a cost-effective approach to performance validity assessment.