Predicting crashes using traffic offences. A meta-analysis that examines potential bias between self-report and archival data

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Barraclough, P
Af Wåhlberg, A
Freeman, J
Watson, B
Watson, A
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2016
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http://creativecommons.org/licenses/by/4.0/
Abstract

Background: Traffic offences have been considered an important predictor of crash involvement, and have often been used as a proxy safety variable for crashes. However the association between crashes and offences has never been meta-analysed and the population effect size never established. Research is yet to determine the extent to which this relationship may be spuriously inflated through systematic measurement error, with obvious implications for researchers endeavouring to accurately identify salient factors predictive of crashes. Methodology and Principal Findings: Studies yielding a correlation between crashes and traffic offences were collated and a meta-analysis of 144 effects drawn from 99 road safety studies conducted. Potential impact of factors such as age, time period, crash and offence rates, crash severity and data type, sourced from either self-report surveys or archival records, were considered and discussed. After weighting for sample size, an average correlation of r = .18 was observed over the mean time period of 3.2 years. Evidence emerged suggesting the strength of this correlation is decreasing over time. Stronger correlations between crashes and offences were generally found in studies involving younger drivers. Consistent with common method variance effects, a within country analysis found stronger effect sizes in self-reported data even controlling for crash mean. Significance: The effectiveness of traffic offences as a proxy for crashes may be limited. Inclusion of elements such as independently validated crash and offence histories or accurate measures of exposure to the road would facilitate a better understanding of the factors that influence crash involvement.

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PLoS One
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© 2016 Barraclough et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Demography
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Barraclough, P; Af Wåhlberg, A; Freeman, J; Watson, B; Watson, A, Predicting crashes using traffic offences. A meta-analysis that examines potential bias between self-report and archival data, PLoS One, 2016, 11 (4), pp. e0153390
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