Detecting Change in Community Traffic Safety Attitudes [Traffic Tech]

NHTSA · 2022 · ROSA P / United States. Department of Transportation. National Highway Traffic Safety Administration

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Summary

This document, part of the National Highway Traffic Safety Administration’s (NHTSA) Traffic Tech Technology Transfer Series, addresses the methodological challenges of evaluating community traffic safety programs. Specifically, it focuses on detecting changes in community attitudes and awareness regarding traffic safety. Historically, such evaluations have relied on nonprobability intercept surveys, often conducted at Department of Motor Vehicle offices, rather than rigorous probability sampling. While probability sampling allows for better generalization of results, it is frequently infeasible for community-level studies due to cost, time, or logistical constraints. Consequently, the paper aims to provide traffic safety professionals with statistically sound practices for using nonprobability sampling methods and secondary data sources to improve the quality and rigor of program evaluations. The paper outlines two primary nonprobability sampling methodology plans: opt-in online panel surveys with quota sampling and intercept surveys with quota sampling. For opt-in online panels, which recruit participants via web advertisements or email, the text recommends combining this method with quota sampling to ensure the sample reflects the population’s demographic characteristics, such as age, race, and sex, often using U.S. Census data to determine quotas. This approach is noted as suitable for medium to large study areas, offering low cost and fast fielding, though it is limited to web-based data collection. Alternatively, intercept surveys, where interviewers approach participants in public places like shopping malls, are recommended for smaller populations or cities. These can be administered via various modes, including paper, tablet, or face-to-face interactions, and also utilize quota sampling to mitigate bias. The document provides a comparative analysis of these methods against address-based probability sampling, detailing factors such as target population, study area size, cost, and flexibility. In addition to primary data collection strategies, the paper discusses the utility of secondary data sources when primary data collection is not possible. It identifies federal data sets, such as the Fatality Analysis Reporting System (FARS) and the National Emergency Medical Services Information System (NEMSIS), as well as local highway safety plans and Vision Zero action plans. The text highlights the advantages of these sources, including public availability and the ability to track measures like crash rates and attitudes over time. However, it also notes limitations, such as the lack of individual-level data at desired geographic levels, potential incompleteness, and varying data quality across states. The significance of this guidance lies in its potential to increase the rigor of community traffic safety program evaluations without requiring the resources for probability sampling. By adopting these structured nonprobability methods and leveraging secondary data, evaluators can produce more reliable assessments of whether programs successfully change community attitudes. The paper concludes that investing in quality evaluation saves time and resources in the long run by identifying effective strategies, and sharing these results benefits the broader traffic safety community.

Key finding

The report provides methodological guidance on using nonprobability sampling techniques and secondary data sources to improve the rigor of community traffic safety program evaluations when probability sampling is not feasible.

Methodology

review

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• incidence prevalence
• regulatory evaluation
• induced exposure

Information type

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• Applied Guidance: countermeasure evaluation
• Empirical Findings: observational prevalence, crash risk outcomes