Department of Psychology, University of Turin, Turin, Italy.
California School of Professional Psychology, Alliant International University.
J Pers Assess. 2020 Nov-Dec;102(6):731-742. doi: 10.1080/00223891.2019.1639188. Epub 2019 Jul 18.
Self-reports could be affected by 2 primary sources of distortion: content-related (CRD) and content-unrelated (CUD) distortions. CRD and CUD, however, might covary, and similar detection strategies have been used to capture both. Thus, we hypothesized that a scale developed to detect random responding-arguably, one of the most evident examples of CUD-would likely be sensitive to both CUD and, albeit to a lesser extent, CRD. Study 1 ( = 1,901) empirically tested this hypothesis by developing a random responding scale (RRS) for the recently introduced Inventory of Problems-29 (Viglione, Giromini, & Landis, 2017), and by testing it with both experimental feigners and honest controls. Results supported our hypothesis and offered some insight on how to pull apart CRD- from CUD-related variance. Study 2 ( = 700) then evaluated whether our RRS would perform similarly well with data from human participants instructed to respond at random versus computer-generated random data. Interestingly, the sensitivity of our RRS dropped dramatically when considering the data from human participants. Together with the results of additional analyses inspecting the patterns of responses provided by our human random responders, these findings thus posed a major question: Is humans' random responding really random?
与内容相关的（CRD）和与内容无关的（CUD）扭曲。然而，CRD 和 CUD 可能会相互关联，并且已经使用类似的检测策略来捕捉两者。因此，我们假设，为检测随机反应而开发的量表——可以说是 CUD 最明显的例子之一——很可能对 CUD 和 CRD 都敏感，尽管程度较小。研究 1（n=1901）通过为最近引入的问题清单-29（Viglione、Giromini 和 Landis，2017）开发一个随机反应量表（RRS），并使用实验性伪装者和诚实对照组对其进行测试，实证检验了这一假设。结果支持了我们的假设，并提供了一些关于如何区分 CRD-与 CUD 相关方差的见解。然后，研究 2（n=700）评估了我们的 RRS 是否可以在随机反应指令下的人类参与者数据和计算机生成的随机数据上表现得同样好。有趣的是，当考虑来自人类参与者的数据时，我们的 RRS 的灵敏度急剧下降。结合对我们的人类随机反应者提供的反应模式进行额外分析的结果，这些发现提出了一个主要问题：人类的随机反应真的是随机的吗？
