Department of Psychology, Otto-Friedrich-Universität Bamberg, D-96045, Bamberg, Germany.
Department of Psychology, Queen's University, Kingston, Canada.
Behav Res Methods. 2022 Feb;54(1):324-333. doi: 10.3758/s13428-021-01636-z. Epub 2021 Jun 25.
AbstractFaking detection is an ongoing challenge in psychological assessment. A notable approach for detecting fakers involves the inspection of response latencies and is based on the congruence model of faking. According to this model, respondents who fake good will provide favorable responses (i.e., congruent answers) faster than they provide unfavorable (i.e., incongruent) responses. Although the model has been validated in various experimental faking studies, to date, research supporting the congruence model has focused on scales with large numbers of items. Furthermore, in this previous research, fakers have usually been warned that faking could be detected. In view of the trend to use increasingly shorter scales in assessment, it becomes important to investigate whether the congruence model also applies to self-report measures with small numbers of items. In addition, it is unclear whether warning participants about faking detection is necessary for a successful application of the congruence model. To address these issues, we reanalyzed data sets of two studies that investigated faking good and faking bad on extraversion (n = 255) and need for cognition (n = 146) scales. Reanalyses demonstrated that having only a few items per scale and not warning participants represent a challenge for the congruence model. The congruence model of faking was only partly confirmed under such conditions. Although faking good on extraversion was associated with the expected longer latencies for incongruent answers, all other conditions remained nonsignificant. Thus, properties of the measurement and properties of the procedure affect the successful application of the congruence model.
在心理评估中,伪造检测是一个持续存在的挑战。一种检测伪造者的显著方法涉及对反应时的检查,其基于伪造的一致性模型。根据该模型,伪装良好的被试会比他们提供不利(即不一致)反应更快地提供有利(即一致)的反应。尽管该模型已在各种实验性伪造研究中得到验证,但迄今为止,支持一致性模型的研究主要集中在具有大量项目的量表上。此外,在之前的研究中,伪造者通常会被警告可以检测到伪造。鉴于评估中使用的量表数量越来越少的趋势,调查一致性模型是否也适用于具有少量项目的自我报告测量变得很重要。此外,对于一致性模型的成功应用,是否有必要警告参与者关于伪造检测还不清楚。为了解决这些问题,我们重新分析了两项研究的数据,这些研究调查了外向性(n=255)和认知需求(n=146)量表上的良好伪装和不良伪装。重新分析表明,每个量表只有几个项目且没有警告参与者,这对一致性模型构成了挑战。在这种情况下,伪造模型仅部分得到证实。尽管在外向性上伪装良好与不一致回答的预期更长的反应时相关,但所有其他条件仍然没有统计学意义。因此,测量的属性和程序的属性会影响一致性模型的成功应用。