Reducing Driving Violations: Simulating the Effects of Cognition Change Interventions

1. Reducing Driving Violations: Simulating the Effects of Cognition Change Interventions Dr. Mark A. Elliott (University of Strathclyde, UK) 5th International Conference on Traffic and Transport Psychology Groningen, August 29-31 2012. 

2. Background Driving violations = increased crash-risk (e.g. Parker et al., 1995) Considerable research attention has been devoted to the identification of potentially modifiable cognitive variables that predict driving violations Potentially useful ‘levers’ for reducing the commission of driving violations via safety interventions Targets for road safety interventions (e.g. publicity, driver education) Ajzen’s (1985) theory of planned behaviour (TPB) commonly used theoretical framework

3. Background Theory of planned behaviour (Ajzen, 1985) Attitude towards behaviour Subjective norm Intention Behaviour Perceived control

4. Background Applications of the TPB (correlational studies): Speeding (e.g. Elliott et al., 2003 and 2007; Parker et al., 1992) Drink-driving (e.g. Parker et al., 1992) Close following (e.g. Parker et al., 1992) Dangerous overtaking (e.g. Parker et al., 1992 and 1995) Running red lights (e.g. Manstead & Parker, 1996) Mobile phone use (e.g. Nemme & White, 2010) Flashing headlights to indicate hostility (e.g. Manstead & Parker, 1996)

5. Background Empirical support Linear modelling (e.g. multiple regressions) typically shows: The TPB accounts for ‘large’ proportions of variance (R2 > 0.25) in both intentions to violate and the commission of driving violations Attitude, subjective norm and perceived control have significant standardised beta weights in the prediction of intention Intention and perceived control have significant standardised beta weights in the prediction of behaviour Practical conclusion TPB useful for developing behaviour-change interventions Change the antecedent cognitions = reduction in driving violations However…

6. Background How much potential do interventions based on the TPB have to reduce the commission of driving violations? How much of a reduction in driving violations will be generated by changing attitude, subjective norm and perceived control? By how much do we need to change attitude, subjective norm and perceived control in order to engender a meaningful reduction in the commission of driving violations? Is it sufficient to change just attitude, subjective norm or perceived control on their own or do we need to change these cognitions in combination?

7. Background Reasons why previous research provides little insight into the potential of TPB-based interventions to reduce driving violations Measures of association (e.g. R2, β) have little intuitive meaning Unstandardised betas show the extent to which scores on a dependent variable (e.g. intention) are predicted to change with a 1 SD change in an independent variable (e.g. attitude) BUT… Unstandardised betas are rarely reported in the literature 1 SD changes to cognitive variables (e.g. attitudes) are not practically attainable (e.g. Hardeman et al., 2002; Elliott & Armitage, 2009) Do not tell us how much of a change in intention is likely to be generated following changes to combinations of predictors (e.g. attitude + subjective norm + perceived control) Do not tell us how much change in behaviour is likely to be generated from changing the predictors of intention (just how much change is associated with 1 SD changes in intention)

8. Background Is experimental research the answer? Yes Experimental (intervention) research shows the actual changes in driver behaviour that are achieved from actual changes in cognitive variables (Elliott & Armitage, 2009; Meadows & Stradling, 1998; Parker et al., 1996) But In practice, experimental studies achieve only small to moderate changes in cognitive variables at best (typically no changes are observed at all following intervention) Significant behaviour change does not often follow (Hardeman et al., 2002) Tends to demonstrate only that cognition change is difficult to achieve Provides little insight into how much behaviour change can be achieved following “successful” cognition change

9. Background Regression-based statistical simulations Predict intentions and behaviour from underlying cognitions Use resulting regression equations to estimate the changes in intentions and behaviour that are generated from changes to scores on cognitive predictors of intention (e.g. attitude, subjective norm, perceived control)

10. Background Statistical simulations (example) Regression equation: INT = α + (BATT * ATT) + (BSN * SN) +(BPC * PC) So if: α = 3.75 BATT = 0.40 BSN = 0.20 BPC = -0.30 Then: INT = 3.75 + (0.40 * ATT) + (0.20 * SN) +(-0.30 * PC) Attitude Subjective norm Intention Perceived control

11. Background Statistical simulations (example) Regression equation: INT = 3.75 + (0.40 * ATT) + (0.20 * SN) +(-0.30 * PC) And if a driver… ATTITUDE: For me, speed over the next month would be: Very harmful          Very beneficial 1 2 3 4 5 6 7 8 9 SUBJECTIVE NORM: People who are important to me will want me to speed over the next month Not at all          Very much 1 2 3 4 5 6 7 8 9 PERCEIVED CONTROL: How much ability do you have to avoid speeding over the next month? None          A lot 1 2 3 4 5 6 7 8 9 Attitude Subjective norm Intention Perceived control ✔ ✔ ✔

12. Background Statistical simulations (example) Regression equation: INT = 3.75 + (0.40 * 9) + (0.20 * 9) +(-0.30 * 1) = 8.85 And if a driver… ATTITUDE: For me, speed over the next month would be: Very harmful          Very beneficial 1 2 3 4 5 6 7 8 9 SUBJECTIVE NORM: People who are important to me will want me to speed over the next month Not at all          Very much 1 2 3 4 5 6 7 8 9 PERCEIVED CONTROL: How much ability do you have to avoid speeding over the next month? None          A lot 1 2 3 4 5 6 7 8 9 Attitude Subjective norm Intention Perceived control ✔ ✔ ✔

13. Background Statistical simulations (example) Regression equation: INT = 3.75 + (0.40 * 1) + (0.20 * 9) +(-0.30 * 1) = 5.65 But what if… ATTITUDE: For me, speed over the next month would be: Very harmful          Very beneficial 1 2 3 4 5 6 7 8 9 SUBJECTIVE NORM: People who are important to me will want me to speed over the next month Not at all          Very much 1 2 3 4 5 6 7 8 9 PERCEIVED CONTROL: How much ability do you have to avoid speeding over the next month? None          A lot 1 2 3 4 5 6 7 8 9 Attitude Subjective norm Intention Perceived control ✔ ✔ ✔

14. Background Extended theory of planned behaviour Attitude towards behaviour Subjective norm Perceived control Intention Behaviour Moral Norm Anticipated Regret

15. Aims To test the potential of interventions based on the TPB to reduce driving violations To estimate the reduction in driving violations generated by changing participants’ scores on the cognitive predictors (both in isolation and in combination) by the maximum amount possible To estimate the reduction in driving violations generated by the following magnitudes of cognition change 0.2 SD (‘small’ change) 0.5 SD (‘moderate’ change) 0.8 SD (‘large’ change)

16. Method: Participants N = 198 young drivers (aged up to 25 years old) Sampled from a University in the west of Scotland Mean age = 20.39 years old 48% male Exposure: 32% reported driving daily 56% reported driving between 4-6 days per week 12% reported driving between 1 and 3 days per week

17. Method: Design & Procedure Prospective Design Each participant completed two questionnaires, separated by a month Time 1 Standard items to measure all cognitions in the extended TPB, with respect to 11 driving violations Time 2 (1 month later): Standard items measuring how often the 11 driving violations had been performed over the last month All items measured using 9-point scales

18. Method: Measures Time 1 measures Intention Attitude Subjective norm Perceived control Moral norm Anticipated regret Time 2 measures Behaviour • Driving Violations • Speeding in 30mph areas • Speeding in 40mph areas • Speeding in 60mph areas • Speeding in 70mph areas • Drink-driving • Close following • Dangerous overtaking • Running red lights • Mobile phone use • Sounding horn/flashing headlights to indicate annoyance with another road user • Sounding horn/flashing headlights to signal to another road user to move out of the way

19. Method: Measures Attitude For me, [performing this driving violation] over the next month would be: Extremely harmful __ : __ : __ : __ : __ : __ : __: __ : __ Extremely beneficial Subjective Norm People who are important to me would definitely disapprove/approve of me [performing this driving violation] over the next month Definitely disapprove __ : __ : __ : __ : __ : __ : __: __ : __ Definitely approve Perceived Control I believe that I have the ability to avoid [performing this driving violation] over the next month Strongly disagree __ : __ : __ : __ : __ : __ : __: __ : __ Strongly agree

20. Method: Measures Moral Norm How wrong do you think it would be for you to [perform this driving violation] over the next month? Not at all wrong __ : __ : __ : __ : __ : __ : __: __ : __ Extremely wrong Anticipated Regret How much would you regret it if you [performed this driving violation] over the next month Not at all __ : __ : __ : __ : __ : __ : __: __ : __ A lot Intention I would want to [perform this driving violation] over the next month Strongly disagree __ : __ : __ : __ : __ : __ : __: __ : __ Strongly agree Behaviour Over the last month, how often did you [perform the driving violation] Never __ : __ : __ : __ : __ : __ : __: __ : __ Every time

21. Method: Scale Reliabilities Composite scales derived (mean of the constituent items)

22. Results: Predicting Intentions to Violate Regression equation: INT = α + (BATT X ATT) + (BSN X SN) + (BPC X PC) + (BMN X MN) + (BAR X AR) INT = 5.90 + (0.25 X ATT) + (0.18 X SN) + (-0.35 X PC) + (-0.10 X MN) + (-0.22 X AR)

23. Results: Predicting Behaviour (Commission of Violations) Regression equation: BEH = α + (BINT X INT) + (BPC X PC) BEH = 3.52 + (0.51 X INT) + (-0.24 X PC)

24. Results: Simulating the Effects of Maximum Cognition Change on Intentions to Violate d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

25. Results: Simulating the Effects of Maximum Cognition Change on Behaviour (Commission of Driving Violations) d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

26. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Intentions to Violate d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

27. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Intentions to Violate d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

28. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Intentions to Violate d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

29. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Intentions to Violate d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

30. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Intentions to Violate d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

31. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Intentions to Violate d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

32. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Intentions to Violate d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

33. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Intentions to Violate d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

34. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Intentions to Violate d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

35. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Behaviour d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

36. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Behaviour d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

37. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Behaviour d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

38. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Behaviour d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

39. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Behaviour d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

40. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Behaviour d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

41. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Behaviour d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

42. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Behaviour d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

43. Results: Simulating the Effects of Small, Moderate and Large Cognition Change on Behaviour d = 0.20; ‘small’ change d = 0.50; ‘moderate’ change d = 0.80; ‘large’ change

44. Summary & Conclusions The findings extend previous research by providing insight into the potential to change drivers’ intentions and behaviour using cognition-change interventions, based on the TPB Maximum changes to the model constructs have the potential to generate meaningful reductions in driving violations Extremely large reductions (d > 3.60) are possible if the cognitions in the present study are changed in combination by the maximum amount possible Cognition change interventions have substantial potential to reduce the commission of driving violations However, in practice, it is unlikely that interventions will generate maximum cognition change (e.g. Elliott & Armitage, 2009; Hardeman et al., 2002)

45. Summary & Conclusions Meaningful reductions in driving violations can still be generated from smaller (practically attainable) degrees of cognition change Moderate-sized changes in perceived control are sufficient to reduce driving violations Although even large-sized changes to the other cognitions are not sufficient (on their own): Moderate sized changes in attitude and subjective norm (when changed in combination and with perceived control) are sufficient to reduce driving violations Small sized changes in moral norm and anticipated regret (when changed in combination and with attitude, subjective norm and perceived control) are sufficient to reduce driving violations Targeting multiple cognitions is a desirable intervention strategy But perceived control is the key target for road safety interventions

46. Summary & Conclusions Attempts to reduce driving violations via cognition-change interventions a worthwhile endeavour The TPB and extensions of this model are a useful frameworks on which to base such interventions Further (experimental) research required to identify effective strategies for changing the cognitive variables by the required magnitudes

47. Thanks for Listening Elliott, M. A. (2012). Testing the capacity within an extended theory of planned behaviour to reduce the commission of driving violations. Transportmetrica, 8, 321-343. ANY QUESTIONS? Contact details: Mark Elliott Department of Psychology, University of Strathclyde 40 George Street, Glasgow. G1 1QE Tel: +44 (0)141 548 5829 Email: mark.a.elliott@strath.ac.uk