CRICOS No. 00213J

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 29-30 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)

Table 3. Means, standard deviations and alpha reliability coefficients for the behaviour questionnaire factors

M SD α F1 - Speeding (8 items) 2.88 1.01 .88

F2 - Aggression (7 items) 2.16 .78 .85

F3 - Time pressure (5 items) 1.95 .76 .78

F4 - Distraction (5 items) 2.22 1.04 .72

F5 - Casualness (4 items) 2.12 .85 .61

F6 - Awareness (2 items) 2.45 .96 .39

F7 - Maintenance (2 items) 3.19 1.64 .74

F8 - Fatigue (4 items) 2.66 1.05 .61

F9 - Minor damage (2 items) 1.59 .70 .43

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

Mark your Diaries!International Occupational Safety in

Transport ConferenceSeptember 2012, Gold Coast.