CRICOS No J When Non-Significance Maybe Significant: Lessons Learned from a Study into the Development, Implementation and Evaluation of a Risk Assessment Tool for Fleet Settings Authors: Wishart, D., Freeman, J., Davey, J., Wilson, A., Rowland, B. Presented by Dr. James Freeman International Conference of Driver Behaviour and Training, Paris November 2011

Introduction (1) This study reports on the development of a self report assessment tool to increase the efficacy of crash prediction within Australian Fleet settings Over last 20 years an array of measures have been produced –(Driver anger scale, Driving Skill Inventory, Manchester Driver Behaviour Questionnaire, Driver Attitude Questionnaire, Driver Stress Inventory, Safety Climate Questionnaire)

Introduction (2) While these tools are useful, research has demonstrated limited ability to accurately identify individuals most likely to be involved in a crash. Reasons cited include; –Crashes are relatively rare –Other competing factors may influence crash event –Ongoing questions regarding the validity of self report measures (common method variance etc) –Lack of contemporary issues relating to fleet driving performance

Aims Identify contemporary driving issues for professional drivers (via focus groups) which has previously been published (refer Wishart, Davey, Freeman, and Rowland, 2009); Develop a self report risk assessment tool that is designed to increase the efficacy of crash prediction within Australian fleet settings; and Pilot and evaluate the assessment measure on a sample of professional drivers to determine its efficacy in predicting self report crashes.

Methodology Phase one –focus groups to identify issues influencing driving behaviour in Australian work settings –(217 participants, 160 males, 57 females) Phase two – Questionnaire Development Issues identified in phase one Previous research in this current program of research –Administration of Questionnaire –(546 participants, 246 males 300 females)

Results (Factor analysis) Factor analysis identified 9 factors (speeding & aggression – traditional DBQ)

Results (Frequency of behaviours) Three highest ranked negative behaviour items –I regularly overtake slow drivers rather than sit behind them (M=3.72, SD= 1.43) –I regularly drive a few kms an hour over the speed limit on the highway (M=3.39, SD= 1.52) –I regularly find myself driving on “autopilot” on the way home from work (M= 3.01, SD= 1.44) Three highest ranked positive items –I pullover before answering/making mobile phone calls (M= 4.75, SD= 2.06) –I drive to the speed limit no matter how much I am running late (M= 4.47, SD= 1.82) –I regularly check my fluid levels and tyre pressure between scheduled servicing (M= 3.71, SD= 1.95)

Results (Inter-correlations) Contrast to previous research –No significant difference driver’s age and kilometres travelled –No significant drivers age and frequency self reported crashes Consistent with previous research –Age significant negative relationship to speeding, aggression, time pressure, distraction, casualness, fatigue Significant positive relationship kilometres travelled and all factors except awareness

Results (prediction of crashes & offences) Series of logistic regression analyses performed to determine identified factors predictive of self report crashes and offences work related and non work related. Overall model significant in some cases but.... No more than 8% of crashes, 3% of offences, 10% minor damage incidents

Discussion No factors were predictive of crash involvement either for work or personal driving. Kilometres travelled did not improve prediction of crashes. Concerns remain as to the reliability of self report measures & self report bias Although there may be benefits to utilising crash databases these can also contain biases (what is defined as a crash) Concerns over potential publication biases primarily publishing statistically significant results

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