Ruchensky Jared R, Edens John F, Donnellan M Brent
Department of Psychology & Philosophy, Sam Houston State University, Huntsville, TX.
Department of Psychological and Brain Sciences, Texas A&M University, College Station, TX.
J Pers Assess. 2025 May-Jun;107(3):384-391. doi: 10.1080/00223891.2024.2411557. Epub 2024 Oct 21.
The Big Five Inventory - 2 (BFI-2) is a commonly used self-report assessment of normal personality trait domains (Extraversion, Agreeableness, Conscientiousness, Negative Emotionality, Open-Mindedness) and facets. To date, however, no direct measures of response distortion have been developed for it to identify potentially invalid responses. Such distortions (e.g., careless or random responding) can adversely impact data quality. The current study developed and provided initial validation data for an inconsistent responding scale within the BFI-2 to identify careless responders using two large undergraduate samples and a community sample. To create the scale, we first identified highly correlated BFI-2 item pairs in one undergraduate sample ( = 1,461) and then computed a total score by summing the absolute differences of these item pairs. This scale, the Detection of Response Inconsistency Procedure (DRIP), differentiated randomly generated and genuine data and generally correlated as expected with personality domains and other inconsistent responding scales across samples. The DRIP also incrementally predicted random data beyond a composite of items with exceptionally high or low base rates of endorsement from the Comprehensive Infrequency/Frequency Item Repository. We provide recommendations for DRIP cut scores that can detect careless responding while balancing sensitivity and specificity.
大五人格量表-2(BFI-2)是一种常用的用于评估正常人格特质领域(外向性、宜人性、尽责性、负性情绪、开放性)及相关方面的自陈式测评工具。然而,迄今为止,尚未开发出针对该量表的直接反应偏差测量方法,以识别可能无效的回答。此类偏差(如粗心或随意作答)会对数据质量产生不利影响。本研究针对BFI-2开发了一个不一致反应量表,并提供了初步验证数据,以利用两个大型本科样本和一个社区样本识别粗心的应答者。为创建该量表,我们首先在一个本科样本(n = 1461)中识别出高度相关的BFI-2项目对,然后通过对这些项目对的绝对差值求和来计算总分。这个量表,即反应不一致检测程序(DRIP),能够区分随机生成的数据和真实数据,并且在各样本中通常与人格领域及其他不一致反应量表呈现出预期的相关性。DRIP还能在由综合低频/高频项目库中认可率极高或极低的项目组成的复合指标之外,逐步预测随机数据。我们提供了关于DRIP临界值的建议,该临界值能够在平衡敏感性和特异性的同时检测出粗心作答情况。