Abstract

The utility of the Victoria Symptom Validity Test (VSVT) as a performance validity test (PVT) has been primarily established using response accuracy scores. However, the degree to which response latency may contribute to accurate classification of performance invalidity over and above accuracy scores remains understudied. Therefore, this study investigated whether combining VSVT accuracy and response latency scores would increase predictive utility beyond use of accuracy scores alone. Data from a mixed clinical sample of 163 patients, who were administered the VSVT as part of a larger neuropsychological battery, were analyzed. At least four independent criterion PVTs were used to establish validity groups (121 valid/42 invalid). Logistic regression models examining each difficulty level revealed that all VSVT measures were useful in classifying validity groups, both independently and when combined. Individual predictor classification accuracy ranged from 77.9 to 81.6%, indicating acceptable to excellent discriminability across the validity indices. The results of this study support the value of both accuracy and latency scores on the VSVT to identify performance invalidity, although the accuracy scores had superior classification statistics compared to response latency, and mean latency indices provided no unique benefit for classification accuracy beyond dimensional accuracy scores alone.

摘要

维多利亚症状效度测试 (VSVT) 作为性能效度测试 (PVT) 的效用主要是使用响应准确度分数建立的。然而，响应延迟可能有助于在准确度分数之上对性能无效进行准确分类的程度仍未得到充分研究。因此，本研究调查了结合 VSVT 准确性和响应延迟分数是否会增加预测效用，而不仅仅是使用准确性分数。分析了来自 163 名患者的混合临床样本的数据，这些患者接受了 VSVT 作为更大的神经心理电池组的一部分。至少四个独立的标准 PVT 用于建立有效性组（121 有效/42 无效）。检查每个难度级别的逻辑回归模型显示，所有 VSVT 测量都可用于对有效性组进行分类，无论是独立的还是组合的。单个预测变量分类准确度在 77.9% 到 81.6% 之间，表明在有效性指数中具有出色的可辨别性。本研究的结果支持 VSVT 上的准确度和延迟得分的价值，以识别性能无效，尽管准确度得分与响应延迟相比具有更好的分类统计数据，并且平均延迟指数没有为分类准确度提供超出维度准确度得分的独特优势独自的。表明在有效性指标上的可识别性可以接受。本研究的结果支持 VSVT 上的准确度和延迟得分的价值，以识别性能无效，尽管准确度得分与响应延迟相比具有更好的分类统计数据，并且平均延迟指数没有为分类准确度提供超出维度准确度得分的独特优势独自的。表明在有效性指标上的可识别性可以接受。本研究的结果支持 VSVT 上的准确度和延迟得分的价值，以识别性能无效，尽管准确度得分与响应延迟相比具有更好的分类统计数据，并且平均延迟指数没有为分类准确度提供超出维度准确度得分的独特优势独自的。