Cutler L, Greenacre M, Abeare CA, Sirianni CD, Roth R, Erdodi LA. Multivariate models provide an effective psychometric solution to the variability in classification accuracy of D-KEFS Stroop performance validity cutoffs.
Clin Neuropsychol 2023;
37:617-649. [PMID:
35946813 DOI:
10.1080/13854046.2022.2073914]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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.
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