The Psychometric Prediction of Problem Drivers

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Abstract
The major aim was to develop personality-inventory scales for the discrimination between two groups of problem drivers—traffic violators and accident-repeater drivers—and better-than-average drivers. A secondary aim was to attempt to improve prediction of problem drivers through multiple-regression equations. An experimental instrument including 395 items was developed, taking into account a very wide variety of personal qualities that had previously been found to characterize problem drivers or that were newly hypothesized to be potentially discriminating. The instrument was administered to approximately 2000 drivers. Problem-driver criterion groups were set up, each characterized by a different degree of seriousness of violation or accident record. Item analyses yielded scales that were found to be predictive on cross validation, also scales for the detection of test-taking biases. Multiple-regression equations were derived incorporating the scale scores with variables pertaining to biographical data and were cross-validated. Useful degrees of prediction were demonstrated from the scales alone and from the multiple-regression equations.