首页> 外文期刊>Transportation research >Construction and validation of a public bus passenger safety scale
【24h】

Construction and validation of a public bus passenger safety scale

机译:公交车乘客安全量表的编制和验证

获取原文
获取原文并翻译 | 示例
           

摘要

Public transport (PT) passengers make safety evaluations, yet to the best of our knowledge, there exists no instrument that captures what is considered by public transport users when they make such personal safety evaluations. What exists is a generalised service quality scale (SERVQUAL). Unfortunately, this scale does not adequately capture the content domain of personal safety which is important to PT users, especially in developing countries where PT vehicle accidents are both frequent and severe. This study discusses the development and validation of a public bus passenger safety scale (PBPSS), for measuring public bus passengers' safety. The results of two independent studies suggest that the PBPSS measures three facets of public bus passengers' safety: driver-related, transport operator-related and vehicle-related. Through both exploratory Principal Component Analysis (PCA) and Confirmatory Factor Analysis (CFA) (using IBM SPSS Statistics and AMOS respectively), we demonstrated that the new scale is reliable, psychometrically sound and can be utilised to assess public bus passengers' safety. The 3-factor model observed through PCA was confirmed using CFA, indicating that the same factor structure existed in both datasets. The final 3-factor, 17-item model exhibited an acceptable model fit and evidenced both convergent and discriminant validity. (C) 2019 Elsevier Ltd. All rights reserved.
机译:公共交通(PT)乘客进行安全评估,但据我们所知,没有一种工具可以捕获公共交通用户在进行此类个人安全评估时所考虑的内容。存在的是广义服务质量量表(SERVQUAL)。不幸的是,这种规模并不能充分反映人身安全的内容领域,这对PT用户来说很重要,尤其是在PT车辆事故既频繁又严重的发展中国家。这项研究讨论了公共巴士乘客安全量表(PBPSS)的开发和验证,该量表用于测量公共巴士乘客的安全性。两项独立研究的结果表明,PBPSS衡量了公共巴士乘客安全的三个方面:驾驶员相关,运输运营商相关和车辆相关。通过探索性主成分分析(PCA)和确认性因素分析(CFA)(分别使用IBM SPSS Statistics和AMOS),我们证明了新的量表是可靠的,从心理上讲是可靠的,并且可以用于评估公共巴士乘客的安全。使用CFA确认了通过PCA观察到的三因素模型,表明两个数据集中都存在相同的因素结构。最终的3因子17项模型显示出可接受的模型拟合,并证明了收敛性和判别性均有效。 (C)2019 Elsevier Ltd.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号