Validating and Optimizing Performance Validity Cut-Off Scores in a Pediatric Sample

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Embedded validity indicators, Neuropsychological testing, Pediatric performance validity, Performance validity


C. Abeare


L. Erdodi



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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


OBJECTIVES: Valid performance is an underlying assumption of neuropsychological assessment to ensure appropriate conclusions are drawn from data. The use of embedded validity indicators (EVIs) allows for examination of performance validity without having to administer additional tests. The Erdodi Index (EI) was developed for use in adults to capture a gradient of performance validity based on performance on several EVIs. This study sought to examine base rates of failure on potential EVIs in a pediatric sample and validate the use of the EI model in children.

METHODS: The scores from two hundred and ninety participants between the ages of eight and 15 who had completed the tests of interest for use as potential EVIs were selected to be analyzed from an existing outpatient clinical dataset. The EVIs included Coding, Symbol Search, and Digit Span age-corrected scaled scores from the Wechsler Intelligence Scale for Children – Fifth Edition, the Forced Choice Recognition (FCR) paradigm total score developed for the California Verbal Learning Test – Children’s Version (CVLT-C), and age-corrected scaled scores for Conditions 1, 2, and 5 from the Delis-Kaplan Executive Function System Trail Making Test (D-KEFS TMT). EVIs were calibrated against the Test of Memory Malingering Trial 1, Rey Fifteen Item Test, and CVLT-C Recognition Discrimination Z-scores.

RESULTS: Base rates of failure, overall correct classifications, sensitivity, and specificity values for various cut-offs of the EVIs are presented. An EI was calculated to capture a validity gradient in the sample.

DISCUSSION AND CONCLUSIONS: This study provided evidence that the Coding, Symbol Search, and Digit Span subtests from the WISC-V, the FCR developed for the CVLT-C, and D-KEFS TMT Conditions 1, 2, and 5 can identify invalid performances with specificity above .90 and varying sensitivities. Using the EI model to calculate an aggregate measure of performance validity that captured a gradient of validity resulted in better classification accuracy than the use of the individual EVIs alone.