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The Driver Behaviour Questionnaire: a North American analysis.

Sheila T D Cordazzo1, Charles T Scialfa1, Katherine Bubric1

  • 1Department of Psychology, University of Calgary, 2500 University Drive, N.W. Calgary, Alberta, T2N 1N4, Canada.

Journal of Safety Research
|August 22, 2014
PubMed
Summary
This summary is machine-generated.

The North American Driver Behaviour Questionnaire (DBQ) maintains its structure but shows limited success in predicting self-reported traffic collisions. Driver behaviors measured by the DBQ are key to understanding accident risk.

Keywords:
CollisionsDriver behaviorErrorsLapsesViolations

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Area of Science:

  • Psychology
  • Traffic Safety
  • Human Factors

Background:

  • The Driver Behaviour Questionnaire (DBQ) is a widely used tool for assessing driver behaviors associated with collision risk.
  • Originally developed in Britain, its applicability to other driving populations requires validation.

Purpose of the Study:

  • Adapt the DBQ for a North American driving context.
  • Examine the underlying component structure of the adapted DBQ.
  • Evaluate the predictive power of DBQ scores for self-reported traffic collisions.

Main Methods:

  • The study involved adapting the DBQ for North American drivers.
  • Statistical analyses were performed to assess the component structure of the questionnaire items.
  • The relationship between DBQ scores and self-reported collision involvement was analyzed.

Main Results:

  • Analysis of the original DBQ items suggested a two-component structure of errors and violations, with no evidence for a 'lapses' component.
  • A subset of 20 items, similar to those used by Parker et al., revealed a three-component structure: errors, lapses, and violations.
  • While violations and lapses were correlated with collision involvement, the predictive accuracy of the models was low.

Conclusions:

  • The North American version of the DBQ exhibits a similar component structure to previous findings.
  • The study found that the DBQ has limited efficacy in predicting self-reported traffic collisions in this population.