Preliminary examination of sequential dual-threshold classification of performance validity findings

OBJECTIVE: Conventional interpretation of performance validity tests (PVTs) involves tallying failed PVTs across a test battery. Failure of a single PVT reflects a common, but diagnostically ambiguous, indeterminate outcome affecting both clinicians and researchers. This study explored whether dual-threshold classification (DTC), which integrates sensitive and specific cut-scores in a given PVT, would reduce classification uncertainty compared to the PVT failure tally method. METHODS: = 443). Participants were classified as valid, invalid, or indeterminate across PVT pairs combined using the PVT failure tally method and a sequential, two-PVT implementation of DTC. Classification outcomes were compared at observed invalidity rates and across resampled base rates of invalidity (10%-50%). RESULTS: Sequential DTC significantly reduced indeterminate findings compared to the PVT failure tally method in 9 of 10 PVT pairs in the simulation sample and in 26 of 28 pairs in the clinical/forensic sample. At observed invalidity base rates, indeterminate classifications decreased from 13.4-30.6% to 0-12.5% in the simulation sample and from 9.9-28.7% to 0-8.1% in the clinical/forensic sample. Indeterminate classifications remained lower using DTC across resampled invalidity base rates. Sequential DTC generally increased identification of criterion-invalid cases, and classification of criterion-valid cases varied across PVT pairs. CONCLUSIONS: These findings provide preliminary support for integration of sensitive and specific PVT cutoffs to reduce diagnostic indeterminacy in performance validity assessment.

Authors

Institutions

Publication Details

Journal
The Clinical Neuropsychologist
Published
2026-10-15
DOI
https://doi.org/10.1080/13854046.2026.2747003
Primary Topic
Psychological Testing and Assessment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Preliminary examination of sequential dual-threshold classification of performance validity findings

Jeremy J. Davis, Robert J. Kanser
The Clinical Neuropsychologist
Psychological Testing and Assessment
article

Preliminary examination of sequential dual-threshold classification of performance validity findings

Jeremy J. Davis, Robert J. Kanser
article en

Abstract

OBJECTIVE: Conventional interpretation of performance validity tests (PVTs) involves tallying failed PVTs across a test battery. Failure of a single PVT reflects a common, but diagnostically ambiguous, indeterminate outcome affecting both clinicians and researchers. This study explored whether dual-threshold classification (DTC), which integrates sensitive and specific cut-scores in a given PVT, would reduce classification uncertainty compared to the PVT failure tally method. METHODS: = 443). Participants were classified as valid, invalid, or indeterminate across PVT pairs combined using the PVT failure tally method and a sequential, two-PVT implementation of DTC. Classification outcomes were compared at observed invalidity rates and across resampled base rates of invalidity (10%-50%). RESULTS: Sequential DTC significantly reduced indeterminate findings compared to the PVT failure tally method in 9 of 10 PVT pairs in the simulation sample and in 26 of 28 pairs in the clinical/forensic sample. At observed invalidity base rates, indeterminate classifications decreased from 13.4-30.6% to 0-12.5% in the simulation sample and from 9.9-28.7% to 0-8.1% in the clinical/forensic sample. Indeterminate classifications remained lower using DTC across resampled invalidity base rates. Sequential DTC generally increased identification of criterion-invalid cases, and classification of criterion-valid cases varied across PVT pairs. CONCLUSIONS: These findings provide preliminary support for integration of sensitive and specific PVT cutoffs to reduce diagnostic indeterminacy in performance validity assessment.

The Clinical Neuropsychologist
University of North Carolina at Chapel Hill (US), University of North Carolina Health Care (US), The University of Texas at San Antonio Health Science Center (US)
Openalex Percentile: Top 9%
Psychological Testing and Assessment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.