Official driver records and self-reports as sources of accident and conviction data for research purposes

Smith, D I

doi:10.1016/0001-4575(76)90022-1

Cited by 27 publications

(29 citation statements)

References 4 publications

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“…This is in line with previous research in which linkages between standard lie scales and self-reported crashes were observed but no associations were found when measured against independently recorded traffic data (Donovan, Queisser, Salzberg & Umlauf, 1985;Jamison & McGlothlin, 1973;Smith, 1976;Williams, Henderson & Mills, 1974).…”

Section: Selected Social Desirability Scale Studies In Road Safetysupporting

confidence: 92%

Common method variance and other sources of bias in road traffic research

Barraclough¹

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Common Method Variance and other sources of bias in road traffic research i KeywordsRoad crashes, traffic offences, common method variance, social desirability, method bias, meta-analysis, self-report data, road safety. AbstractReducing the number of fatalities and injuries due to vehicle crashes is a major focus for road safety researchers throughout the world. Research on driver behaviours, attitudes and associated crashes is often conducted using self-report surveys. When the predictor and criterion variables are drawn from a single source (such as a cross-sectional survey) as is generally the case, it has been generally acknowledged that a range of methodological issues associated with this approach have the potential to reduce the efficacy of predictive measures. Common method variance (CMV) is bias that occurs when both the predictors and the predicted variables are gathered and analysed in the same manner from the same source. CMV effects have the potential to distort findings by artificially strengthening or weakening the observed relationships between items of interest. This is of particular concern to road safety researchers, given the distinct possibility that the small associations often observed within road safety studies may in turn be inflated due to CMV biases. There is a significant need to examine the exact nature and extent of CMV effects within self-reported road safety data and to explore how best to reduce this measurement error.The current body of research was conducted with three primary aims in mind:1. Examine the existence of CMV effects.2. Explore the level of influence that CMV effects have on self-report data, including whether these effects inflate or deflate observed relationships between variable.3. Test research methods to control for the influence of CMV effects on selfreport data.Study 1 involved a meta-analysis that quantified the association between crashes and traffic offences in light of potential moderators. In reference to the exploration for CMV effects, this study examined the extent to which the relationship between self-reported traffic offences and self-reported crashes is stronger than that recorded using only official data sources. 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. The 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 examined. After weighting for sample size, an average correlation of r = .18 between crashes and traffic offences 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. In relation to the existence of CMV effects, a within-country analysis found stronger effect sizes in self-reported data after controlling for crash mean ...

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Section: Selected Social Desirability Scale Studies In Road Safetysupporting

confidence: 92%

Common method variance and other sources of bias in road traffic research

Barraclough¹

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“…For example, it is generally more difficult to obtain. Another major disadvantage of archival driving records is that they are often incomplete because (a) police selectively report crash involvement, (b) moving violation tickets are removed from drivers' records in exchange for driving course credit, (c) the parties involved sometimes choose not to report the crash, or (d) interstate information exchange of data on crashes and moving violations is incomplete (Burns & Wilde, 1995;McGuire, 1973;Smith, 1976).…”

Section: Self-report and Archival Datamentioning

confidence: 99%

“…In addition, the convergence between the criterion sources has typically not been assessed because it was not the primary focus of the study. McGuire (1973), McGwin, Owsley, andBall (1998), andSmith (1976) are three examples of studies that specifically investigated the convergence issue.…”

Section: Self-report and Archival Datamentioning

confidence: 99%

“…Finally, in a test of Zylman's (1972) claim that research studies based on archival data may yield spurious results, Smith (1976) investigated whether or not the exclusive use of official driver records is appropriate. In addition to reviewing official records obtained from the Road Traffic Authority (Western Australia), he interviewed participants to obtain self-report data.…”

Section: Self-report and Archival Datamentioning

confidence: 99%

“…Nevertheless, although they provide some preliminary insight about the convergence (or lack thereof) between archival and self-report crash data, certain limitations of these studies warrant additional research. Smith's (1976) sample was limited to 20-to 23-year-old men; McGwin et al's (1998) sample was limited to drivers over the age of 50;and McGuire (1973) did not investigate the stability of predictor/criterion relationships across criterion types. In addition, the limited amount of convergence research has not precluded the general assumption that archival and other "objective" criterion data are superior to selfreports of crash involvement.…”

Section: Self-report and Archival Datamentioning

confidence: 99%

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Convergence of Self-Report and Archival Crash Involvement Data: A Two-Year Longitudinal Follow-Up

et al. 2005

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This study constructively extends Arthur et al. (2001) by assessing the convergence of self-report and archival motor vehicle crash involvement and moving violations data in a 2-year longitudinal follow-up. The relationships among these criteria, conscientiousness, and driving speed were also assessed using both predictive and postdictive criterion-related validation designs. Data were collected from a 2-year follow-up sample of 334 participants. Results suggested a lack of convergence between self-report and archival data at both Time 1 and Time 2. In addition, the predictor/criterion relationships varied across research design and data source. An actual application of our findings is that the interpretation of relationships between specified predictors and crash involvement and moving violations must be made within the context of the criterion-related validation design and criterion data source. Specifically, predictive designs may produce results different from those of postdictive designs (which are more commonly used). Furthermore, self-report data appear to include a broader range of incidents (more crashes and tickets), and thus researchers should consider using self-report data when they are interested in including lower threshold crashes and tickets that may not be reported on state records (e.g., because of the completion of a defensive driving course)

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