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Cross-Validation of the PAI Negative Distortion Scale for Feigned Mental Disorders: A Research Report

机译:伪装性精神障碍的PAI负失真量表的交叉验证:一项研究报告

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摘要

A major strength of the Personality Assessment Inventory (PAI) is its systematic assessment of response styles, including feigned mental disorders. Recently, Mogge, Lepage, Bell, and Ragatz developed and provided the initial validation for the Negative Distortion Scale (NDS). Using rare symptoms as its detection strategy for feigning, the usefulness of NDS was examined via a known-groups comparison. The current study sought to cross-validate the NDS by implementing a between-subjects simulation design. Simulators were asked to feign total disability in an effort to secure unwarranted compensation from their insurance company. Even in an inpatient sample with severe Axis I disorders and concomitant impairment, the NDS proved effective as a rare-symptom strategy with low levels of item endorsement that remained mostly stable across genders. For construct validity, the NDS was moderately correlated with the Structured Interview of Reported Symptoms-Second Edition and other PAI feigning scales. For discriminant validity, it yielded a very large effect size (d = 1.81), surpassing the standard PAI feigning indicators. Utility estimates appeared to be promising for both ruling-out (low probability of feigning) and ruling-in (high probability of feigning) determinations at different base rates. Like earlier research, the data supported the creation of well-defined groups with indeterminate scores (i.e., the cut score ± 1 SEM) removed to avoid high rates of misclassifications for this narrow band.
机译:人格评估量表(PAI)的主要优势是它对包括假装的精神障碍在内的反应方式的系统评估。最近,Mogge,Lepage,Bell和Ragatz开发并提供了负失真量表(NDS)的初步验证。使用罕见的症状作为伪装的检测策略,通过已知组比较来检查NDS的有效性。当前的研究试图通过实施对象间仿真设计来交叉验证NDS。要求模拟人员假装完全残疾,以确保从其保险公司获得不必要的赔偿。即使在患有严重的Axis I障碍并伴有损伤的住院患者样本中,NDS也被证明是一种罕见症状的策略,具有较低的项目认可水平,并且在大多数情况下都保持稳定。对于结构效度,NDS与《症状报告》第二版的结构化面试和其他PAI伪装量表具有中等相关性。对于判别有效性,它产生了非常大的效果大小(d = 1.81),超过了标准PAI伪装指标。效用估计似乎对以不同基本费率确定的裁定(假装的低概率)和裁定的(假装的高概率)很有希望。像早期的研究一样,数据支持创建定义明确的组,并删除不确定的分数(即切割分数±1 SEM),从而避免对该窄带进行高分类错误率。

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