Embedded Performance Validity Measures with Postdeployment Veterans: Cross-Validation and Efficiency with Multiple...

Embedded validity measures support comprehensive assessment of performance validity. The purpose of this study was to evaluate the accuracy of individual embedded measures and to reduce them to the most efficient combination. The sample included 212 postdeployment veterans (average age = 35 years, average education = 14 years). Thirty embedded measures were initially identified as predictors of Green's Word Memory Test (WMT) and were derived from the California Verbal Learning Test-Second Edition (CVLT-II), Conners' Continuous Performance Test-Second Edition (CPT-II), Trail Making Test, Stroop, Wisconsin Card Sorting Test-64, the Wechsler Adult Intelligence Scale-Third Edition Letter-Number Sequencing, Rey Complex Figure Test (RCFT), Brief Visuospatial Memory Test-Revised, and the Finger Tapping Test. Eight nonoverlapping measures with the highest area-under-the-curve (AUC) values were retained for entry into a logistic regression analysis. Embedded measure accuracy was also compared to cutoffs found in the existing literature. Twenty-one percent of the sample failed the WMT. Previously developed cutoffs for individual measures showed poor sensitivity (SN) in the current sample except for the CPT-II (Total Errors, SN = .41). The CVLT-II (Trials 1-5 Total) showed the best overall accuracy (AUC = .80). After redundant measures were statistically eliminated, the model included the RCFT (Recognition True Positives), CPT-II (Total Errors), and CVLT-II (Trials 1-5 Total) and increased overall accuracy compared with the CVLT-II alone (AUC = .87). The combination of just 3 measures from the CPT-II, CVLT-II, and RCFT was the most accurate/efficient in predicting WMT performance.