Measuring errors and violations on the road: A bifactor modeling approach to the Driver Behavior Questionnaire

The Driver Behavior Questionnaire (DBQ) is a self-report measure of driving behavior that has been widely used over more than 20 years. Despite this wealth of evidence a number of questions remain, including understanding the correlation between its violations and errors sub-components, identifying how these components are related to crash involvement, and testing whether a DBQ based on a reduced number of items can be effective. We address these issues using a bifactor modeling approach to data drawn from the UK Cohort II longitudinal study of novice drivers. This dataset provides observations on 12,012 drivers with DBQ data collected at .5, 1, 2 and 3 years after passing their test. A bifactor model, including a general factor onto which all items loaded, and specific factors for ordinary violations, aggressive violations, slips and errors fitted the data better than correlated factors and second-order factor structures. A model based on only 12 items replicated this structure and produced factor scores that were highly correlated with the full model. The ordinary violations and general factor were significant independent predictors of crash involvement at 6 months after starting independent driving. The discussion considers the role of the general and specific factors in crash involvemen