预测驾驶员道路交通事故发生率的社会文化量表的设计和心理测量学评估。

Zahra Haghdoust, Gholamreza Masoumi, Shandiz Moslehi, Abbas Ebadi, Davoud Khorasani Zavareh
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引用次数: 0

摘要

背景:道路交通事故(RTC)的发生涉及多种因素,其中最重要的因素之一是人为因素,而驾驶员的特定社会文化基础会对人为因素产生很大影响。迄今为止,还没有一种量表可用于测量伊朗驾驶员的社会文化因素(SCFs),以预测 RTC 事故的发生。因此,本研究设计了一个量表,用于测量预测驾驶员发生道路交通事故的社会文化因素,并对其进行心理测量评估:本研究采用探索性顺序混合法,分三个阶段进行。在第 1 和第 2 阶段,根据系统文献回顾(第 1 阶段)和半结构式访谈(第 2 阶段)建立了初始项目库。在第 3 阶段,利用面效和内容效度对初始量表进行验证。然后,进行了主成分分析和确认性因子分析,以评估构造效度。最后,通过检查内部一致性和稳定性来评估量表的可靠性:结果:量表内容效度指数为 0.92。主成分分析显示有 7 个因子,共 27 个项目,解释了总方差的 55.56%。在确认性因素分析中,模型拟合指数令人满意。判别分析也能区分肇事司机和无事故司机两组(P 小于 0.0001)。量表的信度(Cronbach's alpha、Theta、Omega 和类内相关系数)分别为 0.82、0.96、3.07 和 0.80:该量表可作为一种有效、可靠的量表,用于评估预测驾驶员发生 RTC 的 SCFs。此外,本研究的结果将有助于识别和计划减少道路交通事故,尤其是事故多发驾驶员的事故。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and psychometric evaluation of sociocultural scale predicting the incidence of road traffic crashes in drivers.

Background: Various factors are involved in the occurrence of Road Traffic Crashes (RTCs), one of the most important of these are human factors that can be greatly influenced by the specific sociocultural bases of the drivers. So far, there has not been a scale for measuring Sociocultural Factors (SCFs) predicting the occurrence of RTCs in Iranian drivers. Therefore, the present study was conducted to design and to do psychometric evaluation of a scale for measuring SCFs predicting the occurrence of RTCs in drivers.

Methods: This exploratory sequential mixed method was carried out in three phases. In phases 1 and 2, an initial items pool was created based on systematic literature review (phase1), and semi structured interviews (phase 2). In phase 3, the initial scales were validated using face and content validities. Then, principal component analysis and confirmatory factor analysis were performed to assess the construct validity. Finally, the reliability of the scale was evaluated by examining internal consistency and stability.

Results: The scale content validity index was 0.92. Principal component analysis showed seven factors with 27 items, which explain 55.56% of the total variance. In confirmatory factor analysis, model fit indices were satisfactory. Discriminant analysis was also able to distinguish between two groups of accident-involved drivers and accident-free drivers (P less than 0.0001). The reliability of the scale by Cronbach's alpha, Theta, Omega and intra-class correlation coefficients was 0.82, 0.96, 3.07, and 0.80, respectively.

Conclusions: This scale can be used as a valid and reliable scale to evaluate the SCFs predicting the occurrence of RTCs in drivers. Furthermore, the findings of this study will be useful in identifying and planning to reduce RTCs, especially in accident-prone drivers.

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