Establishing Face and Content Validity of a Survey to Assess Users’ Perceptions of Automated Vehicles
(Room Palms Ballroom - Booth 100)
16 Jul 19
5:30 PM
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7:00 PM
Background Crashes account for 1.25 million fatalities each year throughout the world, with an additional 50 million people injured (World Health Organization, 2017). Fully automated vehicles (AVs) hold promise toward providing numerous societal benefits including drastically reducing road fatalities. However, we know little about the adoption practices of individuals relayed to AVs. Research Aim Several technology acceptance models guided construction of a 40-item survey that was designed in this study to assess transportation users’ perceptions of automated vehicles. Perceptions entail perceived ease of use, usefulness, safety, trust, and external factors (i.e., media, authority, and social influences). Methods A focus group assessed face validity to ensure items appeared credible and understandable to the layperson. Seven subject-matter experts in psychology, measurement, transportation, and/or human factors rated items for their relevance to provide a content validity index (CVI) for each item and the overall survey. Results Face validity was established by removing one item (2.5%) and rephrasing nine items (22.5%) to enhance the concision and clarity of the items. While establishing content validity, six items were removed from the survey and five items were amended and sent back to the experts for further evaluation. The final scale had a scale CVI rating of 1.00, with 32 of 32 items rated > 0.86 and a scale CVI of 0.96 (mean CVI of all items), indicating acceptable content validity. Conclusion The approach adopted in this study ensured the face and content validity of the survey and enhanced the items’ relevance, concision, and clarity. Information gained from utilizing this survey will inform engineers and transportation officials of opportunities and barriers to improve transportation users’ interaction with AVs and potentially empower them to adopt these technologies—and in so doing contribute to congestion mitigation, a core component of the UF TRI test-bed initiative.