Multivariate models provide an effective psychometric solution to the variability in classification accuracy of D-KEFS Stroop performance validity cutoffs
Laura Cutler, Matthew Greenacre, Christopher A. Abeare, Christina D. Sirianni, Robert M. Roth, László A. Erdődi
Abstract
ObjectiveThe study was designed to expand on the results of previous investigations on the D-KEFS Stroop as a performance validity test (PVT), which produced diverging conclusions. Method The classification accuracy of previously proposed validity cutoffs on the D-KEFS Stroop was computed against four different criterion PVTs in two independent samples: patients with uncomplicated mild TBI (n = 68) and disability benefit applicants (n = 49). Results Age-corrected scaled scores (ACSSs) ≤6 on individual subtests often fell short of specificity standards. Making the cutoffs more conservative improved specificity, but at a significant cost to sensitivity. In contrast, multivariate models (≥3 failures at ACSS ≤6 or ≥2 failures at ACSS ≤5 on the four subtests) produced good combinations of sensitivity (.39-.79) and specificity (.85-1.00), correctly classifying 74.6-90.6% of the sample. A novel validity scale, the D-KEFS Stroop Index correctly classified between 78.7% and 93.3% of the sample. Conclusions A multivariate approach to performance validity assessment provides a methodological safeguard against sample- and instrument-specific fluctuations in classification accuracy, strikes a reasonable balance between sensitivity and specificity, and mitigates the invalid before impaired paradox.