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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. . Background.

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Reducing driving violations simulating the effects of cognition change interventions

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. 


Background
Background Cognition Change Interventions

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


Background1
Background Cognition Change Interventions

Theory of planned behaviour (Ajzen, 1985)

Attitude towards behaviour

Subjective norm

Intention

Behaviour

Perceived control


Background2
Background Cognition Change Interventions

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)


Background3
Background Cognition Change Interventions

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…


Background4
Background Cognition Change Interventions

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?


Background5
Background Cognition Change Interventions

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)


Background6
Background Cognition Change Interventions

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


Background7
Background Cognition Change Interventions

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)


Background8
Background Cognition Change Interventions

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


Background9
Background Cognition Change Interventions

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


Background10
Background Cognition Change Interventions

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


Background11
Background Cognition Change Interventions

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


Background12
Background Cognition Change Interventions

Extended theory of planned behaviour

Attitude towards behaviour

Subjective norm

Perceived control

Intention

Behaviour

Moral Norm

Anticipated Regret


Aims Cognition Change Interventions

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)


Method participants
Method: Participants Cognition Change Interventions

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


Method design procedure
Method: Design & Procedure Cognition Change Interventions

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


Method measures
Method: Measures Cognition Change Interventions

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


Method measures1
Method: Measures Cognition Change Interventions

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


Method measures2
Method: Measures Cognition Change Interventions

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


Method scale reliabilities
Method: Scale Reliabilities Cognition Change Interventions

Composite scales derived (mean of the constituent items)


Results predicting intentions to violate
Results: Predicting Intentions to Violate Cognition Change Interventions

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)


Results predicting behaviour commission of violations
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)


Results simulating the effects of maximum cognition change on intentions to violate
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


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


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


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


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


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


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


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


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


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


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


Results simulating the effects of small moderate and large cognition change on behaviour
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


Results simulating the effects of small moderate and large cognition change on behaviour1
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


Results simulating the effects of small moderate and large cognition change on behaviour2
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


Results simulating the effects of small moderate and large cognition change on behaviour3
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


Results simulating the effects of small moderate and large cognition change on behaviour4
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


Results simulating the effects of small moderate and large cognition change on behaviour5
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


Results simulating the effects of small moderate and large cognition change on behaviour6
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


Results simulating the effects of small moderate and large cognition change on behaviour7
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


Results simulating the effects of small moderate and large cognition change on behaviour8
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


Summary conclusions
Summary & Conclusions Cognition Change on Behaviour

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)


Summary conclusions1
Summary & Conclusions Cognition Change on Behaviour

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


Summary conclusions2
Summary & Conclusions Cognition Change on Behaviour

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


Thanks for listening
Thanks for Listening Cognition Change on Behaviour

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: [email protected]


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