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

Barraclough, Peter, af Wåhlberg, Anders, Freeman, James, Watson, Barry, & Watson, Angela (2016) Predicting crashes using traffic offences. A meta-analysis that examines potential bias between self-report and archival data. PLoS One, 11(4), Article Number-e0153390.

View at publisher (open access)

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.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 95462
Item Type: Journal Article
Refereed: Yes
Additional URLs:
DOI: 10.1371/journal.pone.0153390
ISSN: 1932-6203
Divisions: Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Psychology & Counselling
Deposited On: 10 May 2016 22:49
Last Modified: 11 May 2016 21:13

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page