Mason Justin, Classen Sherrilene, Wersal James, Sisiopiku Virginia
Department of Occupational Therapy, University of Florida, Gainesville, FL, United States.
Department of Civil, Construction, and Environmental Engineering, University of Alabama at Birmingham, Birmingham, AL, United States.
Front Psychol. 2021 Jan 25;12:626791. doi: 10.3389/fpsyg.2021.626791. eCollection 2021.
Fully automated vehicles (AVs) hold promise toward providing numerous societal benefits including reducing road fatalities. However, we are uncertain about how individuals' perceptions will influence their ability to accept and adopt AVs. The 28-item Automated Vehicle User Perception Survey (AVUPS) is a visual analog scale that was previously constructed, with established face and content validity, to assess individuals' perceptions of AVs. In this study, we examined construct validity, via exploratory factor analysis and subsequent Mokken scale analyses. Next, internal consistency was assessed via Cronbach's alpha (α) and 2-week test-retest reliability was assessed via Spearman's rho (ρ) and intraclass correlation coefficient (ICC). The Mokken scale analyses resulted in a refined 20-item AVUPS and three Mokken subscales assessing specific domains of adults' perceptions of AVs: (a) ; (b) ; and (c) . The Mokken scale analysis showed that all item-coefficients of homogeneity (H) exceeded 0.3, indicating that the items reflect a single latent variable. The AVUPS indicated a strong Mokken scale ( = 0.51) with excellent internal consistency (α = 0.95) and test-retest reliability (ρ = 0.76, ICC = 0.95). Similarly, the three Mokken subscales ranged from moderate to strong (range = 0.47-0.66) and had excellent internal consistency (range α = 0.84-0.94) and test-retest reliability (range ICC = 0.84-0.93). The AVUPS and three Mokken subscales of AV acceptance were validated in a moderate sample size ( = 312) of adults living in the United States. Two-week test-retest reliability was established using a subset of Amazon Mechanical Turk participants ( = 84). The AVUPS, or any combination of the three subscales, can be used to validly and reliably assess adults' perceptions before and after being exposed to AVs. The AVUPS can be used to quantify adults' acceptance of fully AVs.
全自动车辆有望带来诸多社会效益,包括减少道路交通事故死亡人数。然而,我们不确定个人认知将如何影响他们接受和采用全自动车辆的能力。28项的《自动车辆用户认知调查问卷》(AVUPS)是一种视觉模拟量表,之前已构建完成,具有既定的表面效度和内容效度,用于评估个人对全自动车辆的认知。在本研究中,我们通过探索性因素分析及随后的莫肯量表分析来检验结构效度。接下来,通过克朗巴哈α系数(α)评估内部一致性,并通过斯皮尔曼等级相关系数(ρ)和组内相关系数(ICC)评估两周重测信度。莫肯量表分析得出了一个精简的20项AVUPS以及三个莫肯子量表,用于评估成年人对全自动车辆认知的特定领域:(a);(b);以及(c)。莫肯量表分析表明,所有项目同质性系数(H)均超过0.3,表明这些项目反映了单一潜在变量。AVUPS显示出较强的莫肯量表( = 0.51),具有出色的内部一致性(α = 0.95)和重测信度(ρ = 0.76,ICC = 0.95)。同样,这三个莫肯子量表的强度从中等到较强不等(范围 = 0.47 - 0.66),并具有出色的内部一致性(范围α = 0.84 - 0.94)和重测信度(范围ICC = 0.84 - 0.93)。AVUPS和三个关于自动车辆接受度的莫肯子量表在美国成年人的中等样本量( = 312)中得到了验证。使用亚马逊土耳其机器人参与者的一个子集( = 84)建立了两周重测信度。AVUPS或这三个子量表的任何组合,都可用于有效且可靠地评估成年人在接触自动车辆前后的认知。AVUPS可用于量化成年人对全自动车辆的接受程度。