CAUSE AND PREVENTION OF ROADWAY CRASHES AMONG YOUNG,

HIGH-RISK DRIVERS IN MALAYSIA: A MULTI-DISCIPLINARY APPROACH

ALAN GIFFIN DOWNE

DOCTOR OF PHILOSOPHY

MULTIMEDIA UNIVERSITY

APRIL 2008

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The copyright of this thesis belongs to the author under the terms of

the Copyright Act 1987 as qualified by Regulation 4(1) of the Multimedia

University Intellectual Property Regulations. Due acknowledgement shall

always be made of the use of any material contained in, or derived from,

this thesis.

Alan Giffin Downe, April 2008

All rights reserved

iii

DECLARATION

I hereby declare that the work contained herein has been done by myself and that no

portion of the work contained in this thesis has been submitted in support of any

application for any other degree or qualification of this or any other university or

institute of learning.

______________________

Alan Giffin Downe

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ACKNOWLEDGEMENTS

First, I wish to acknowledge the contribution of my supervisor, Dr. Stanley

Richardson, whose guidance, wisdom and high standards have motivated me and kept

me on track throughout all phases of the project. I also thank my former employer,

Multimedia University, for permitting me to undertake doctoral studies during my time

as a lecturer in the Faculty of Management. In this regard, I would like to express my

gratitude for the gracious support and encouragement I have received from Prof. Dr. A.

Seetharaman, current Dean of Management and from Prof. Dr. Hj. Mohd. Ismail

Sayyed Ahmad, former Dean of Management. I also wish to thank Dr. A.S.

Santhapparaj, Dr. V. Anantaraman and especially Dr. Sayed Hossain for coordinating

and serving as panelists, respectively, when I presented this research to the faculty

during my work completion seminar.

Several researchers at other institutions have provided assistance in the form of

test instruments, unpublished papers and guidance over the course of this project. I

especially thank Dr. Jerry Deffenbacher (University of Colorado), Dr. Yori Gidron

(Brunel University), Dr. C.S. Papacostas (University of Hawaii at Manoa), Dr. Dianne

Parker (University of Manchester), Dr. Murali Sambasivan (Universiti Putra Malaysia),

the late Dr. C.R. Snyder (University of Kansas), and Dr. Henriette Wallén Warner

(Dalarna University).

I would like also to thank four undergraduate research assistants who completed

data collection for the study of Kuala Lumpur taxicab drivers: Agatha Yeoh Siew Ling,

Gracy Thomas, Nazlina Nasihin and Sangeetha Munisamy.

I wish to acknowledge the Road Engineering Association of Malaysia (REAM),

an organisation to which I have felt privileged to belong, as an Associate Member, since

2001.

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There are many individuals who have contributed greatly to the completion of

this project through their helpful suggestions, collegial affiliation and friendship,

notably Aw Lin, Aznur Hajar Binti Abdullah, Azrai Abdullai, Ming-Yu Cheng, Adeline

Chua, Cynthia Downe, Ridhwan Fontaine, Jessica Ho Sze Yin, Lily Idayu, Loke

Choong Khoon, Loke Siew Poh, Razlina Rezali, Omar Salahuddin Bin Abdullah,

Bobby Varanasi and David Yong. A special expression of thanks is due to Fatimah

Syam @ Noor Azleen A. Gani at the Siti Hasmah Digital Library for her very helpful

assistance. For their caring support, I also wish to thank my mother, Evelyn G. Downe

and Howard, of Fredericton, New Brunswick, Canada.

The phrase, “without whom this research would never have been completed”

appears ubiquitously in theses and dissertations around the world. But never has it been

truer than in the contribution made here by my wife, Siew-Phaik Loke. She has helped

me to score questionnaires, set up spreadsheets and enter data into the computer. She

provided thoughtful input into solving even the most baffling problems of multivariate

analysis. She sustained me with her constant reassurance throughout the project. And

she did it all while excelling at her own PhD studies, starting a business, launching her

career as a university lecturer and lovingly, patiently caring for our son. To her, I will

be always grateful.

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DEDICATION

In the wee hours of a cold morning on December 5th, 2002 my father, Dr. A.E.R.

Downe, Professor Emeritus of Biology at Canada’s Queen’s University passed away in

his hospital bed in Kingston, Ontario after suffering a long and debilitating illness. For

47 years, he had been my inspiration and my role model. From him, I learned the value

of hard work and family, the excitement that comes from scientific inquiry and the

fortitude that evolves in a man’s struggle against adversity. Not a day goes by when I

don’t find myself thinking of him.

On that very same day, on the other side of the planet, my little boy Richardson

Downe Loke Ken, at ten months of age, took his first unaided steps, launching a jerky

trajectory from the sofa to the television set, where he hugged the image of Sir Alex

Ferguson. From his birth, I have marveled at Kenny’s boundless energy, his gentle

disposition and his ability to fill his mother’s and my existence with one part challenge

and nine parts joy. I only hope that I am able to teach him the same lessons that my

father taught me.

It is to these two very special people that I dedicate this work.

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ABSTRACT

Motor vehicle crashes are a serious social and economic problem in Malaysia,

where, on average, seven fatalities are recorded each day. Previous research has found

that human factors play the chief role in contributing to crash outcomes, and that driver

behaviours, personality traits, driving experience and demographic characteristics are

the specific contributors of these factors. However, previous attempts to investigate

relationships between psycho-social variables and crash incidence have frequently

yielded weak associations and inconclusive results.

The present research was conducted to examine the interaction between the role

of behaviour in traffic, some personality constructs, demographic characteristics and

driving exposure in predicting crash and injury occurrence. Three samples of university

students whose primary mode of transportation involved driving automobiles (n = 301,

302 and 252, respectively), one sample of university students whose primary mode of

transportation involved motorcycle riding (n = 122) and one sample of professional

taxicab drivers (n = 149) were studied. A contextual mediated model was used to

examine interactions between a set of variables considered distal to the causality of the

crash event, self-reported patterns of driving considered more proximal to the causality

of the crash and self-reported crash and injury histories of the participants.

Distal variables included driver (driving experience and driving frequency),

demographic (age, gender and ethnicity) and psychological (locus of control,

hopelessness, aggression and hostile automatic thoughts) characteristics. The proximal

variable was comprised of a measure of self-reported behaviour in traffic (BIT) on

which high scores were considered consistent with Type A behaviour pattern (TABP).

BIT had four components: usurpation of right-of-way, externally-focused frustration;

freeway urgency; and destination-activity orientation. Effects of the distal variables and

proximal variable on self-reported history of crash occurrence and injuries were

examined. The role of the proximal variable in mediating distal effects on crash

outcomes was also investigated.

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Results indicated a complex series of interactions between the variables. As

hypothesised, all four BIT components were associated with higher occurrence of both

self-reported motor vehicle crashes and crash-related injury. Among distal variables,

significant direct effects on self-reported driving behaviour (BIT) were consistently

observed with samples of automobile drivers and motorcyclists but not to the same

degree among professional taxicab drivers. As reported in previous studies, locus of

control moderated the BIT-aggression relationship. Two types of hostile automatic

thoughts – with content related to physical aggression or revenge – moderated the BIT-

aggression relationship, as well.

The role of the proximal variable, BIT, in mediating the effects of the distal

variables was analysed using a four-step regression procedure developed by Baron and

Kenny (1986) and using structural equation modelling (SEM) with LISREL. Results

indicated that, consistent with the assumptions of the contextual mediated model, BIT

exerted a strong mediational influence over the effects of distal variables on crash

outcomes.

Implications for both theory and practice are discussed, particularly with respect

to an ongoing debate within traffic psychology over the comparative importance of

theories or models of driving behaviour. Areas for further study include the role of

locus of control and other distal variables in the behavioural adaptation process at the

root of most risk and task interface capability theories and applications to driver

selection and training procedures. The advantages of multi-disciplinary approaches to

the study of roadway crashes are discussed.

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TABLE OF CONTENTS

COPYRIGHT PAGE ii DECLARATION iii ACKNOWLEDGEMENTS iv DEDICATION vi ABSTRACT vii TABLE OF CONTENTS ix LIST OF TABLES xv LIST OF FIGURES xviii PREFACE xx CHAPTER 1: INTRODUCTION 1 1.1 Background of the Study 1 1.2 Road Safety in Malaysia 2 1.3 The Problem Statement 4 1.4 The Professional Significance of the Study 5 1.5 Overview of the Methodology 7 1.6 Delimitations 9 CHAPTER 2: REVIEW OF THE LITERATURE 12 2.1 Human Factors and the Motor Vehicle Safety Problem in Malaysia 12 2.1.1 Roadway Crashes in Malaysia and Public Perceptions of Causality 12 2.1.2 Studies of Causal Factors in Malaysian Roadway Crashes 17 2.2 The Professional Background 19

2.2.1 Human Factors in Roadway Crashes: A Vexing Research Challenge 19

2.2.2 The Emergence of Traffic Psychology as a Scientific Discipline 21 2.2.2.1 An Applied Perspective 21 2.2.2.2 A Multidisciplinary Approach 22

2.3 Theories of Driving Behaviour 24 2.3.1 Concepts, Theories and Models 24 2.3.2 Traffic Psychology: Slow Progress in Theory-Building 25 2.3.3 The Individual Differences Approach 26

2.3.3.1 Accident Proneness 28 2.3.3.2 Differential Accident Involvement 30 2.3.4 Risk Theories 31 2.3.4.1 Risk Homeostasis Theory (RHT) 31

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2.3.4.2 Zero Risk Theory 34 2.3.5 Hierarchical Theories of Driver Adaptation 35 2.3.6 Task Capability Interface (TCI) Theory 37 2.3.7 Attitude-behaviour Theories 38 2.3.7.1 Theory of Reasoned Action (TRA) 38 2.3.9.2 Theory of Planned Behaviour (TPB) 39 2.4 Descriptive Models of Driver Behaviour 41 2.4.1 Statistical Models 41 2.4.2 Process Models 42 2.4.2.1 The Haddon Matrix 42 2.4.2.2 A Contextual Mediated Model of Personality and Behavioral Predictors of Motor Vehicle Crashes 43 2.4.2.3 Core Concepts in the Contextual Mediated Model: Moderation and Mediation 45

2.4.2.4 Studies of Driving Behaviour Using the Contextual Mediated Model 47 2.4.2.5 Use of the Contextual Mediated Model in Other Research 49

2.5 Distal Variables in the Present Study 50 2.5.1 Demographic Variables 50 2.5.1.1 Age 50 2.5.1.2 Gender 52 2.5.1.3 Ethnicity 56 2.5.2 Driver Characteristics 58 2.5.2.1 Experience 58 2.5.2.2 Driving Frequency and Traffic Exposure 61 2.5.3 Psychological Variables 63 2.5.3.1 Locus of Control 63 2.5.3.1.1 Unidimensional and Multidimensional Constructs 63 2.5.3.1.2 Locus of Control and Driving Behaviour 64 2.5.3.1.3 Locus of Control and Ethnicity 67 2.5.3.2 Hopelessness 69 2.5.3.3 Aggression 71 2.6 Proximal Variables in the Present Research 73 2.6.1 Type A Behaviour Pattern and Motor Vehicle Crashes 73 2.6.2 A Conceptual Shift from TAPB to Behaviour in Traffic (BIT) As A Variable 75 CHAPTER 3: METHOD OF INVESTIGATION 78 3.1 Conceptualization and the Research Framework 78 3.2 Definition of the Variables 84

3.2.1 Driver Characteristics: Driver Experience and Driving Frequency 84 3.2.2 Demographic Variables: Age, Gender and Ethnicity 84 3.2.3 Locus of Control 84 3.2.4 Hopelessness 85 3.2.5 Aggression 85

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3.2.6 Hostile Automatic Thoughts 86 3.2.7 Behaviour in Traffic (BIT) 87 3.2.8 Crash Occurrence 88 3.2.9 Injury Occurrence 88

3.3 Research Design of the Studies 88 3.3.1 Study 1A 88 3.3.2 Study 1B 89 3.3.3 Study 1C 89 3.3.4 Study 2 90 3.3.5 Study 3 90

3.4 Formulation of Hypotheses 91 3.5 Methods of Data Collection and Analysis 93 3.5.1 The Sample 93

3.5.2 Research Instruments 94 3.5.2.1 Behaviour in Traffic (BIT) Scale 94 3.5.2.2 Levenson Locus of Control Scale 96 3.5.2.3 Beck Hopelessness Scale (BHS) 97 3.5.2.4 Aggression Questionnaire (AQ) 97 3.5.2.5 Hostile Automatic Thoughts (HAT) 98

3.5.2.6 Personal Information Form (PIF) 98 3.6 Procedure 99

3.6.1 Studies 1 and 2 99 3.6.2 Study 3 100

3.7 Analysis of the Data 100 3.7.1 Independent-sample t-tests 103 3.7.2 One-way analysis of variance (ANOVA) 103 3.7.3 The General Linear Model (GLM) Univariate Analysis 104 3.7.4 Linear Regression Analysis 104 3.7.5 Multiple Regression Analysis 104 3.7.6 Logistic Regression Analysis 105 3.7.7 Structural Equation Modelling 105

3.7.7.1 Chi-Square (χ2), p-Value and χ2/df Ratio 107 3.7.7.2 Degree of freedom (df) 107 3.7.7.3 Root Mean Square Error of Approximation (RMSEA)

and Root Mean Square Residual (RMR) 107 3.7.7.4 Normed Fit Index (NFI) 108

3.7.7.5 Goodness-of-Fit Index (GFI) and Comparative Fit Index (CFI) 108 3.7.7.6 Adjusted Goodness-of-Fit Index (AGFI) 108 3.7.7.7 Expected Cross-Validation Index (ECVI) 109

3.7.7.8 Parsimony Goodness-of-Fit Index (PGFI) 109 3.7.8 Kolmogorov-Smirnov One-Sample Test 110 3.7.9 Skewness and Kurtosis 110

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CHAPTER 4: ANALYSIS OF DATA 112 4.1 Description of the Sample 112 4.1.1 Age, Gender and Ethnicity 112 4.1.2 Geographic Distribution of Samples in Study 1 114 4.1.3 Geographic Distribution of Samples in Study 2 115 4.1.4 Geographic Distribution of Samples in Study 3 115 4.2 Reliability and Validity 116

4.2.1 Reliability Test Results: Cronbach’s Alpha 116 4.2.2 Parallel-Form Reliability 118 4.2.3 Validity Test Results 118 4.2.3.1 Confirmatory Factor Analysis of the BIT Scale 119 4.2.3.2 Confirmatory Factor Analysis of the Levenson Locus of Control Scale 120

4.2.3.3 Confirmatory Factor Analysis of the AQ Scale 120 4.2.3.4 Confirmatory Factor Analysis of the HAT Scale 121

4.3 Normality, Skewness and Kurtosis 122 4.4 Crash and Injury Occurrence Data 124 4.5 Distal and Proximal Variable Data 126 4.5.1 Results of Study 1 126 4.5.2 Results of Study 2 130 4.5.3 Results of Study 3 132 4.6 Hypothesis Testing 134 4.6.1 Hypothesis 1: Behaviour in Traffic Influences Motor Vehicle Crash Outcomes 134

4.6.2 Hypothesis 2: Driver Characteristics Influence Behaviour in Traffic 135

4.6.3 Hypothesis 3: Demographic Variables Influence Behaviour in Traffic 139 4.6.4 Hypothesis 4: Demographic Variables Influence Locus of Control 140 4.6.5 Hypothesis 5: Demographic Variables Influence Hopelessness 142 4.6.6 Hypothesis 6: Locus of control Influences Hopelessness 143 4.6.7 Hypothesis 7: Hopelessness Influences Behaviour in Traffic 143 4.6.8 Hypothesis 8: Locus of control Influences Behaviour in Traffic 145

4.6.9 Hypothesis 9: Hopelessness Moderates the Relationship between Locus of Control and Behaviour in Traffic 147

4.6.10 Hypothesis 10: Demographic Factors Influence Aggression 149 4.6.11 Hypothesis 11: Aggression Influences Behaviour in Traffic 151

4.6.12 Hypothesis 12: Locus of Control Moderates the Relationship Between Aggression and Behaviour in Traffic 153 4.6.12.1 Internality as a Moderator 153 4.6.12.2 Externality-chance and Externality-powerful-others as

Moderators 154 4.6.13 Hypothesis 13: Demographic Factors Influence Hostile Automatic

Thoughts 156 4.6.14 Hypothesis 14: Hostile Automatic Thoughts Influence Behaviour

In Traffic 157 4.6.15 Hypothesis 15: Hostile Automatic Thoughts Moderate the

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Relationship Between Aggression and Behaviour in Traffic 158 4.6.16 Summary of Hypothesis Tests 159 4.7 Testing the Contextual Mediated Model Using Structural Equation Modelling (LISREL Analysis) 163 4.7.1 Study 1C 163 4.7.2 Study 2 169 4.7.3 Study 3 170 4.8 Testing Mediational Relationships Using SPSS 173 4.8.1 BIT Mediates the Relationship between Hopelessness and Crash Outcomes 173 4.8.2 BIT Mediates the Relationship between Aggression and Crash Outcomes 173 4.8.3 BIT Mediates the Relationship between Hostile Automatic Thought and Crash Outcome 174 4.8.4 BIT Mediates the Relationship between Locus of Control and Crash Outcomes 174 4.9 Comparison of Automobile Drivers, Motorcycle Drivers and Taxicab Drivers 176 4.9.1 Differences between Automobile Drivers and Motorcycle Drivers 176 4.9.2 Differences between Automobile Drivers and Taxicab Drivers 177 4.9.3 Differences between Motorcycle Drivers and Taxicab Drivers 177

CHAPTER 5: DISCUSSION 179 5.1 A Contextual Mediated Model for Understanding Factors Influencing Unsafe Driving 179 5.2 Hopelessness 182 5.3 Locus of Control 185 5.3.1 Internal and External Locus of Control as Determinants of Driving Behaviour 185 5.3.2 Locus of Control and Ethnicity: Indian-Malaysian Drivers 187 5.3.3 Locus of Control and Ethnicity: Malay and Chinese-Malaysian Drivers 189 5.4 Aggression 190 5.5 Testing the Contextual Mediated Model Using Structural Equation Modelling (SEM) 194 5.5.1 Advantages of Using SEM 194 5.5.2 Goodness of Fit 196 5.5.3 Best Fit or Best Model 197 5.5.4 Testing the Contextual Mediated Model 201 5.5.4.1 Study 1C: Automobile Drivers 201 5.5.4.2 Study 2: Motorcyclists 202 5.5.4.3 Study 3: Taxicab Drivers 203 5.5.5 What Can be Learned from Testing Contextual Models with SEM? 203 5.6 Limitations of the Study and Methodological Considerations 204 5.6.1 Generalisability of Findings 204 5.6.2 Use of Self-Report Methods 210 5.6.3 Timeframe for Data Collection 211

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5.6.4 Measurement of Driving Frequency 212 5.7 Implications and Areas for Further Study 215 5.7.1 Theory vs. Models in Traffic Psychology 215 5.7.2 Factors in Behavioural Adaptation (BA) 218 5.7.3 Driver Selection, Training and Rehabilitation 220 5.7.4 Preventive Measures: “The Three E’s” 221 5.7.4.1 Generating and classifying crash prevention interventions 221 5.7.4.2 Engineering Interventions 221 5.7.4.3 Education 229 5.7.4.4 Enforcement 230 CHAPTER 6: CONCLUSION 233 REFERENCES 237 GLOSSARY 287 APPENDICES Appendix A List of Published and Research Scales 294 Appendix B Personal Information (PIF) 297

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LIST OF TABLES

No. Table Page 2.1 Malaysian Roadway Crashes and Casualties, 2002-2006 13 2.2 Numbers of Automobile Drivers and Motorcyclists Involved in Road Crashes by Age Group 14 2.3 Key Value Clusters for Each Malaysian Ethnic Group 57 3.1 Research Hypotheses 91 3.2 Dimensions of the BIT scale 95 3.3 The Five Subscales of the Aggression Questionnaire 97 3.4 The Three Subscales of the Hostile Automatic Thoughts (HAT) Scale 98 3.5 Statistical Methods for Hypothesis Testing 101 4.1 Gender and Ethnicity of the Sample for Studies 1 and 2 112 4.2 Age, Gender and Ethnicity of Participants in Studies 1, 2 and 3 114 4.3 States from Which Study 1 Participants Had Acquired Their Original Drivers’ Licenses 114 4.4 States from Which Study 2 Participants Had Acquired Their Original Motorcyclists’ Licenses 115 4.5 Summary of Internal Reliability Coefficient Results 117 4.6 Parallel-Form Reliability for Form A and Form B (BIT) 118 4.7 Validity of BIT scales – Summary of Confirmatory Factor Analyses 119 4.8 Validity of the Levenson Locus of Control Scale – Summary of Confirmatory Factor Analysis 111 4.9 Validity of the AQ scales – Summary of Confirmatory Factor Analysis 121 4.10 Summary of LISREL Results on Validity for HAT (Study 1C) 121 4.11 Normality Tests, Kurtosis and Skewness Statistics 122

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4.12 Crash and Injury Occurrence 124 4.13 Crash Occurrence Frequency, Gender and Ethnicity in Study 1 (N=855) 125 4.14 Crash Occurrence Frequency, Gender and Ethnicity in Study 2 (N=122) 125 4.15 Means, Standard Deviations and Bivariate Correlations for Variables in Study 1A (n=301) 127 4.16 Means, Standard Deviations and Bivariate Correlations for Variables in Study 1B (n=302) 128 4.17 Means, Standard Deviations and Bivariate Correlations for Variables in Study 1C (n=252) 129 4.18 Means, Standard Deviations and Bivariate Correlations for Variables in Study 2 (n=122) 131 4.19 Means, Standard Deviations and Bivariate Correlations for Variables in Study 3 (n=133) 133 4.20 Results of Logistic Regression Analyses Showing the Effects of BIT Component Factors on Crash Occurrence 134 4.21 Results of Logistic Regression Analyses Showing the Effects of BIT Component Factors on Injury Occurrence 135 4.22 The Influence of Driver Characteristics on Total BIT Scores in Study 1A (N=301) 136 4.23 The Influence of Driver Characteristics on Total BIT Scores in Study 1B (N=302) 136 4.24 The Influence of Driver Characteristics on Total BIT Scores in Study 1C (N=252) 137 4.25 The Influence of Driver Characteristics on Total BIT Scores in Study 2 (N=122) 138 4.26 The Influence of Driver Characteristics on Total BIT Scores in Study 3 (N=133) 138 4.27 Effects of Demographic Factors on total BIT Scores 139 4.28 Direct effects of hopelessness on BIT scores 144 4.29 Direct Effects of Locus of Control on Total BIT Scores 145

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4.30 Direct Effects of Gender on AQ Total and Subscale Scores 149 4.31 Direct Effects of Ethnicity on AQ Total and Subscale Factors 150 4.32 Effect of Aggression on Total BIT Scores and on BIT Component Factors 152 4.33 Summarised Results on the Hypotheses and Sub-hypotheses 160 4.34 SEM Comparison (Study 1C) 163 4.35 Different Contextual Models (Study 1C) 167

4.36 Different Contextual Models (Study 2) 169

4.37 Different Contextual Models (Study 3) 171

4.38 BIT Mediates the Relationship between Hopelessness and Crash Outcomes 173 4.39 BIT Mediates the Relationship between Aggression and Crash Outcomes 174 4.40 BIT Mediates the Relationship between Hostile Automatic Thought and Crash Outcomes 174 4.41 BIT Mediates the Relationship between Locus of Control and Crash Outcomes 175 5.1 Goodness of Fit Statistics for Model 1C5 and 1C6 (Initial and Subsequent Analyses) 199 5.2 Distribution of National Population and Sampled Participants by State 206

5.3 State of Origin Compared with Crash Frequency and Vehicle Registrations (Study 1) 207 5.4 State of Origin Compared with Crash Frequency and Vehicle Registrations (Study 2) 208 5.5 Spearman rank correlations for States of Origin for Participants in Study 1 and Study 2 209 5.6 Engineering Applications for Crash Prevention 225

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LIST OF FIGURES

No. Figure Page 2.1 Task Cube (from Summala, 1996) 36 2.2 Task-Capability Theory (after Fuller, 2000) 37 2.3 Theory of Planned Behaviour (Ajzen, 1989) 40 2.4 The Haddon Matrix (Noy, 1997) 42 2.5 Contextual Mediated Model of Personality, Behavioral Predictors and Motor Vehicle Crashes (from Sümer, 2003) 44 2.6 Inter-variable Relationships in Mediation Models 46 2.7 Inter-variable Relationships in Moderation Models 47 2.8 Proposed Contextual Mediated Model for Safety Research in Agriculture (from Downe, 2007) 50 2.9 Hierarchical Levels of Driving Behavior (after Keskinen, 1996; Hatakka, 2000) 59 2.10 Contrast between Rotter’s Unidimensional and Levenson’s Multidimensional Conceptual of Locus of Control 64 3.1 Research Model (Study 1A and Study 2) 80 3.2 Research Model (Study 1B) 81 3.3 Research Model (Study 1C) 82 3.4 Research Model (Study 3) 83 4.1 Interaction Effects between Ethnicity and Internality on BIT 146 4.2 Interaction Effect between Ethnicity and Externality-Chance on Usurpation of Right-of Way 147 4.3 Moderating Effect of BHS on the Internality-BIT Relationship 148 4.4 Moderating Effect of BHS on the Externality (Chance)-BIT Relationship 148

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4.5 Interaction of Ethnicity and Verbal Aggression on Freeway Urgency 153 4.6 Moderating Effect of Internality on the Aggression-BIT Relationship 154 4.7 Moderating Effects of Externality on the Aggression-BIT Relationship 155 4.8 Moderating Effect of Externality on the Aggression-BIT Relationship 158 4.9 Contextual Mediated Model Study 1C5 165 4.10 Contextual Mediated Model 1C6 (Four BIT Factors) 166

4.11 Contextual Mediated Model Study 1C (Aggression and Hostile Automatic Thoughts) 168 4.12 Contextual Mediated Model Study 2 170 4.13 Contextual Mediated Model Study 3 172

xx

PREFACE

Accidents occur, like encounters with fairies and werewolves, to the weary traveler, but accidents or encounters with fairies or werewolves are random events. The behaviour of the traveller, and his mental state, are factors that influence the likelihood of occurrence. How important these factors are, is a matter of debate … Obviously, when humans are prone to imagine fairies or werewolves, they are prone to other types of error as well.

- Talib Rothengatter & Raphael Huguenin, 2004 -

Some three years or so into my Ph.D. programme, things were not going well.

My research design needed a serious re-working. I was confused by the results I was

getting. LISREL couldn’t, or wouldn’t, handle the latent variables I wanted to include

in my structural equation model. I wanted to throw in the towel.

Then one evening into my office came a student from my first-year Critical

Thinking class. I didn’t recognise her at first. She had been badly injured. Her face

and arms had been bruised and lacerated. Her hands and voice quivered. She told me

about the motorcycle crash that had claimed the life of her cousin. I knew the fellow;

he’d taken the same course as she, only a trimester or two earlier. He was very popular

with other students. He’d sent me a nice card at Christmas. She had needed to go on

an errand. He didn’t want to go, but she’d nagged him. They quarreled and then left on

his motorbike. He was driving, she was riding pillion. They were hurrying, they were

focused on the errand, they were frustrated and angry with each other, they cut across a

lane too quickly. And they crashed. He died instantly and she spent three weeks in the

hospital. She was afraid she had missed too many lectures. I told her not to worry.

She started crying and couldn’t stop. I’m a fairly big guy. I like to watch boxing. I

don’t cry much any more, at least not with real tears. But, I sure felt like it as I sat there

beside her. I feel like it each time I think of that moment. I feel like it a bit right now.

The factors she described that evening were the same ones I’d been trying to

study – freeway urgency, externally-focused frustration, lane deviation and all the rest.

I got back to work on them, and this thesis is the result. I’m pretty happy with it,

finally. I hope it makes a contribution. But sometimes, just every so often, I despair

that we may never be able to snare this werewolf.

1

CHAPTER 1

INTRODUCTION

1.1 Background of the Study

With an estimated 1.2 million deaths in motor vehicle crashes worldwide

(Peden, Scurfield, Sleet, Mohan & Hyder, 2004), the quest for a better understanding

of the causality and prevention of roadway mishaps has become an urgent task for

safety researchers, policy-makers, highway engineers and automotive design

specialists. Even after decades of study, scholars still search to identify the relative

effects of vehicle, road, environment and human characteristics on the risk of

accidental events and fatalities. This is particularly salient in developing countries,

such as Malaysia, where rates of roadway accidents and deaths have been

consistently higher than in other parts of the world (Peden & Hyder, 2002).

Notwithstanding the extensive literature that exists on safety design factors

for automotive products (e.g., Peters & Peters, 2002) and road safety engineering

(e.g., Ogden, 1996; Theeuwes, 2001), much of the recent attention on causal

elements in roadway crashes has focused on the human factors involved. Sabey

(1999), for instance, commented that, “human factors play a major role in road

accidents. Drivers’ performance and avoidance of collisions depend on their skills,

judgement, anticipation, state of mind and physical well-being. Consistently over the

years, the most prevalent factors have been human failures associated with speed,

perceptual difficulties and drink driving” (p. 11).

Drivers’ attentional (Most & Astur, 2007; Trick, Enns, Mills & Vavrik,

2004), perceptual (Hong, Iwasaki, Furuichi & Kadoma, 2006; Green, 2002; Stanton

& Pinto, 2000; Graham, 1999), cognitive (Vaa, 2001; Verwey, 2000), kinaesthetic

(Zhang & Chaffin, 2000); perceptual-motor processes (Young & Stanton, 2007;

Olson, 2002) and their impairment (de Raedt & Ponjaert-Kristofferson, 2004) have

been studied extensively, leading to a rich information base for regarding human-

centred design as a “requirement for all elements in the traffic system, including the

2

roadway, the vehicle and the new technologies that are increasingly being deployed

by the road and fitted in vehicles” (Carsten, 2002; p. 21). More challenging has been

the attempt to link personality and psychosocial variables to driver behaviour and

performance. According to Dewar (2002b), “the literature on personality has a long

history, including the study of a large number of variables. However, there are

conflicting findings and associated problems with this research” (p.112). Very early

initial attempts to identify an “accident proneness” personality trait (Tiliman &

Hobbs, 1949) have since been replaced by more complex explanations that focus on

the interaction between emotional, behavioural and attitudinal characteristics of

drivers and the environmental situations in which they find themselves (Haight,

2004; McKenna, 1983).

This dissertation is a report of research into relevant psychosocial variables

and their effects on self-reported driving behaviour. The research comprised five

separate studies of Malaysian drivers aged 18 to 73 years, with the intent to

determine the degree to which measures of aggression, locus of control, hopelessness

and other variables interacted to affect attitudes toward driving and the severity and

frequency of participants’ self-reported involvement in motor vehicle crashes. This

first chapter of this dissertation presents the background of the study, describes its

significance and presents an overview of the methodology used. The chapter

concludes by noting the delimitations of the research.

1.2 Road Safety in Malaysia

Malaysia is a nation of motor vehicle users. A total of 10,351,332 drivers and

15,790,732 motor vehicles were registered in 2006. Malaysia is also a nation with a

disproportionately high frequency of motor vehicle accidents. There was a total of

341,252 accidents in 2006 and over 6,000 fatalities were recorded (Ministry of

Transport Malaysia, 2007). The high rate of roadway accidents and deaths has been

described in both scholarly and popular print or internet media in extreme terms,

often labelled as “tragic” (Koh, 2005), as a “social menace”(Abdul Kareem, 2003),

and as a “major public health problem” (Subramaniam, 1989).

3

Trends toward high rates of motor vehicle crashes and fatalities have been

observed in developing countries world-wide (Peden et al, 2004; Vasconcellos,

2005), leading many researchers and safety organisations to regard road safety as a

leading international development issue (Garg & Hyder, 2006; Wells, 2007). Hence,

there has been an increasing recognition of the need for theoretical formulations and

specific models, easily generalised to a variety of cultural and social settings, that

allow for better prediction and explanation of roadway crashes (Risser & Nickel,

2004), and the past twenty years has seen the emergence of the new discipline of

traffic and transport psychology (Barjonet & Tortosa, 1997; Barjonet & Tortosa,

2001; Blasco, 1994; Draskóczy, 1997). Huguenin (2005) has argued that the field of

traffic psychology arises from an “interdisciplinary, integrative and international

viewpoint based on application in order to address changing situations and

objectives” (p. 3).

Historically, traffic psychology studies have tended to focus categorically on

two main areas of interest: (a) an examination of the wide variety of individual

differences in task performance among people sharing the same system; and (b) the

elimination or reduction of effects of task-induced or environmental stressors on

human performance when driving (Brown, 1997). Many studies have been devoted

to the examination of behavioural, attitudinal and personality correlates of road-

traffic crash risk, often with widely varying results (Dewar, 2002b; Elander, West &

French, 1993; Lajunen & Summala, 1997). Investigations of individual differences

have included driver age and gender (Beck, Hartos & Simons-Martin, 2002; Renner

& Anderle, 2000; Rimmö, 2002; Verwey, 2000), ethno-cultural background (Byrd,

Cohn, Gonzalez, Parada & Cortes, 1999; Shinar, Dewar, Sumala & Zakowska,

2003), locus of control (Arthur, Barrett & Alexander, 1991; Stewart, 2005; Gidron,

Gal & Syna Desevilya, 2003; Özkan, Lajunen & Kaistinen, 2005; Trimpop &

Kirkcaldy, 1997), risk-taking and sensation-seeking (Horswill & Coster, 2002; Lin,

Huang, Hwang, Wu & Yen, 2004; Loo, 1979; Ulleberg, 2001), aggression

(Parkinson, 2001; Schwebel, Severson, Ball & Rizzon, 2006; Wells-Parker et al,

2002) and many others.

4

Increasingly, it has been recognised that the psychosocial factors involved in

driving safely differ greatly across the range of human, vehicle, road and

environmental conditions that comprise the driving situation. Noy (1997),

Richardson and Downe (2000) and others have argued that there is a need to take

into consideration the myriad of interactions between driver characteristics and the

driving context and even between driver psychological variables themselves. This

has led to a growing interest in modelling human behaviour involved in the driving

task and, in particular, in developing general and specific cognitive models of

individuals’ interaction with the world around them (Brown, 1997; Hampson &

Morris, 1996; Parker, 2004). Sümer (2003), for instance, has recently proposed a

promising contextual mediated model which distinguishes between distal (i.e.,

personality and demographic) and proximal (i.e., aberrant driving behaviours)

variables in predicting traffic accident involvement.

A frequent criticism, however, has been that such behavioural models have

seldom been used as the foundation for developing an integrated, theoretical basis for

traffic psychology (Huguenin, 1997), leaving the field with inadequate theory

development from which testable, falsifiable hypotheses might be drawn (Summala,

2005). The relationship between functional models which predict dynamic road user

behaviour and the availability of broader integrative theory in traffic psychology is

discussed at length in chapter 2 of this thesis.

1.3 The Problem Statement

Given widespread awareness about the high rates of death and injury

resulting from motor vehicle crashes worldwide and in Malaysia, drivers still operate

automobiles and motorcycles in ways that reduce the likelihood of safe arrival at

destinations. Speeding, externally-focused frustration, loss of attention and the

deliberate usurpation of right-of-way are frequent behaviours in traffic, with resulting

outcomes often involving crash and injury. What demographic and personality

factors are associated and interact with unsafe driving behaviour and, in turn, with

the risk of roadway casualty?

5

The aim of the present research is to determine those factors contributing to

traffic accidents on Malaysia roadways, with a view to assessing which preventive

measures would be most effective. Fifteen hypotheses are formulated to predict that

distal variables including: (a) driver age, gender and ethnicity; (b) driving

experience; (c) driver locus of control; (d) driver hopelessness; (e) driver aggression;

and (f) drivers’ hostile automatic thoughts would not only affect each other but also

four self-reported measures of behaviour in traffic, situated as proximal variables.

The effect of the proximal variables on self-reported crash experience and the

severity of injuries associated with crashes are hypothesised to be moderated by the

distal variables. Results of the resulting analyses are detailed in chapter 3.

The specific purpose of this thesis is to further knowledge about drivers’

behaviour in traffic by applying Sümer’s (2003) construct of a conceptual mediated

model, in which distal psychosocial factors exert an influence on behavioural

tendencies more proximally related to the crash event.

1.4 The Professional Significance of the Study

With the frequency of roadway crashes, injuries and deaths, this research is

important to organisations and people concerned with driving safety. By better

understanding the manner in which psychosocial characteristics of individuals might

predispose them to engage in unsafe driving behaviour, it becomes possible to

construct a broader awareness of how demographic and personality variables

contribute to motor vehicle crashes. This is both a key goal and a persistent

challenge within the emerging field of traffic psychology. While there is no doubt

that collective knowledge pertaining to the causality and prevention of roadway

crashes is growing exponentially, “the basic question must be asked as to whether

traffic psychologists can appropriately solve the tasks that are to be mastered at the

interface between people and road traffic” (Huguenin, 2005; p. 9). By focusing on

not only demographic, psychological and behavioural variables inherent in the

dynamics leading motor vehicle crashes, but also on their interactions, the present

research might be seen as making a contribution to our growing understanding of that

very interface.

6

Of particular interest may be variables related to driver affect, an area that

some authors have argued is overlooked in the current literature (Keskinen, Hatakka,

Laapotti, Katila & Peräaho, 2004; Näätänen & Summala, 1974). The present

research adds to the growing body of literature dealing with driver aggression and its

various forms of expression. It is also the first attempt to examine closely the effects

of the psychological construct of hopelessness on driver behaviour.

Findings of the present research have implications for driver selection and

training, road safety measures and public policy, the development and adaptation of

in-vehicle safety devices or intelligent transportation systems and the construction of

models to foster additional research, all of which have been noted as purposes for

traffic psychology (Brown & Noy, 2004; Rothengatter, 2001, 2005; Utzelmann,

2004).

Moreover, they also have implications for a broader “theory versus model”

debate in traffic psychology. Some authors have suggested that, in the applied

sciences, the plethora of theories available, the breadth of their scope and the

complexity of key constructs raise concerns as to whether they actually stimulate or

retard practical work in a specialised field (Huguenin, 1997). Recent trends in the

philosophy of science call conventional hypothetico-deductive processes into

question (Becker, 1993). There is a growing sentiment that, “models that focus on

specific aspects of road user behaviour seem capable of providing useful frameworks

for organising and interpreting data … but experience suggests that such models are

more likely to be useful if they are based on the consideration of empirical data

rather than being derived from theoretical issues” (Grayson, 1997; p. 94). The

present research offers a perspective on this divergence of viewpoints by discussing

how empirically-based models of behavioural processes can be strengthened through

a priori integration with broader theoretical precepts.

Despite considerable popular attention to the problem of motor vehicle

crashes and fatalities in the developing countries of Southeast Asia, the Malaysian

setting has remained relatively understudied (Richardson & Downe, 2000).

7

Notwithstanding a handful of well-founded reviews of statistical trends and risk

factors (e.g., Radin Umar, 2005) and attempts to link driver performance to road

engineering (e.g., Che Ali, 2001), very little research has been focused on the

psychological and social features of the motoring population in Malaysia. This has

left the door open for largely unsupported speculation about the character of

Malaysian drivers and, in turn, may be limiting the development of effective public

policy and intervention measures. The present research contributes a new

perspective by offering initial empirical observations on several psychosocial factors

that could be important in understanding why Malaysian drivers behave as they do in

traffic, and on the manner in which those factors interact to affect safety outcomes on

the roadway.

In doing so, this research draws on principles from a wide range of

disciplines, incorporating cognitive ergonomics, attitude theory, human motivation,

cultural anthropology and applied psychology. A multi-disciplinary approach has

been generally considered one of the hallmarks of the new field of traffic psychology

(Rothengatter, 2001), and is appropriate for this examination of psychosocial features

of the Malaysian driver. This broader perspective, although adding additional layers

of variables and complexity to the analysis, goes some distance in differentiating the

present study from other more narrowly-defined examinations of driver behaviour.

Certain methodological considerations add to the professional significance of

this research. To the author’s knowledge, this work represents the first instance in

which Baron & Kenny’s (1986) widely-cited procedure for establishing mediation

has been performed using logistic regression. Selection of alternate structural

equation models is also discussed, with emphasis on the importance of model

comprehensiveness as a factor in addition to goodness-of-fit.

1.5 Overview of the Methodology

Questions about how the study was conducted and the choice of research

methods are answered fully in chapter 3, which deals with methodology. It is useful,

8

however, to include in this first chapter a general statement of the method used in

order to round out the introductory picture presented.

The present research applied an ex post facto research design, in which there

was no direct manipulation of independent variables in the laboratory or field setting,

but where differences which already existed between subject groups on independent

measures were evaluated to determine their naturally-occurring influence on

dependent, or outcome, variables (Sekaran, 2003). In this case, the effects of

selected demographic (age, gender, driving experience, cultural background), driving

(experience, access to vehicle) and psychological (locus of control, aggression,

hopelessness, hostile automatic thoughts) on four self-reported measures of

behaviour in traffic (usurpation of right-of-way, freeway urgency, externally-focused

frustration, destination-oriented activity) and two self-reported measures of accident-

related (crash occurrence and injury occurrence) outcomes were assessed.

Structural equation models were also used to explain the relationships

between these variables. Structural equation modelling is a family of statistical

methods that “examines the structure of interrelationships expressed in a series of

equations, similar to a series of multiple regression equations. These equations

depict all of the relationships among constructs (the dependent and independent

variables) involved in the analysis” (Hair, Black, Babin, Anderson & Tatham, 2006;

p. 711). The present research consisted of three studies: Study 1, Study 2 and Study

3. In Study 1, three separate phases were carried out (Study 1A, 1B and 1C), each

entailing data collection from a different sample. In each successive study, different

combinations of variables were added to the respective structured equation model

and the level of complexity was examined. The final result, at the conclusion of

Study 1C, was a multi-dimensional path analysis depicting causal, moderating and

mediating relationships between variables. This model drew on the similar

conceptual theory used by Sümer (2003) in the construction of his earlier contextual-

mediated model. Maruyama (1998) has argued that the two prominent reasons why

researchers use structural equation modelling techniques lie, first, in their capacity to

predict outcomes and, second, in their capacity to explain which specific predictors

9

are most important in predicting. Such predictive capabilities were considered to be

of importance in any examination of the dynamics leading to risks of roadway

crashes and fatalities.

After the initial model-building had been completed, two additional studies

(Study 2 and Study 3) were undertaken to test the resilience of selected components

of the resulting structural equation model with different driver populations. In Study

2, a model was constructed using a sample of undergraduate participants for whom

motorcycles were the primary mode of transport. Again, data were collected through

classroom-based group administration of research instruments, with resulting

variable relationships compared to the model that had been built from the responses

of automobile users in Study 1.

In Study 3, a third model was constructed, this time sampling professional

taxicab drivers in the Kuala Lumpur area. A team of researchers flagged down taxi

drivers at random and, over the course of 30- to 45-minute trips, verbally

administered psychometric instruments, behavioural inventories and personal profile

questionnaires.

1.6 Delimitations

All research is confined by the boundaries of its scope and design.

Generalisability of the present study may be constrained by the single-setting of the

subject pool and the limitations of the particular methods selected. These are

discussed in detail in chapter 5 of the thesis but relevant issues are introduced here.

Student participants sampled for two of the three studies were selected from

an undergraduate population at a single university, leaving room for questions about

the generalisability of findings to populations within and outside Malaysia. This

issue is discussed at some length in chapter 5 of the thesis, where it is argued that the

“convenience sample” used in the model-building phases was, in fact, representative

of the characteristics of high-risk Malaysian drivers.

10

Concerns with research of this nature also frequently centre around the

question of self-reported data. Are the attitudes, accident histories and behavioural

trends reported by participants really valid? Or are they prone to the influences of

confabulation, social desirability or response set biases? Indeed, there has been a

vigorous debate about the utility of self-report measures in safety research for several

years, with some authors emphasizing methodological risks (af Wählberg, 2002;

Boyce & Geller, 2001) and others down-playing them (Hattaka, Keskinen, Katila &

Laapotti, 1997). The prevalence of self-report measures in traffic safety research,

along with its implications for the validity of results and potential alternative

methodologies, is discussed in chapter 5 of the thesis, as well.

In a meta-review of traffic safety research, af Wählberg (2003) outlined three

significant methodological deficiencies that have plagued the study of traffic

accident predictors, including: (a) test-retest reliability of predictors; (b) time-period

for calculating accident frequency; and (c) culpability for crash outcomes. The

present research included procedural elements to mitigate, at least to a certain extent,

against the first two and these are covered in chapter 2. However, the research did

not address the question of differences arising from the extent to which drivers

considered themselves to be liable for the self-reported crash outcomes.

Finally, much of the recent driving safety literature has distinguished between

errors, lapses and violations as differing behavioural responses underlying the crash

event (Reason, Manstead, Stradling, Baxter & Campbell, 1990). Lapses involve

problems with attention and memory and include such things as switching on one

thing when meaning to switch on something else. Errors are a type of driving

mistake involving failures of observation and misjudgement, such as failing to check

the rear-view mirror before pulling out or changing lanes. Violations are deliberate

deviations from those practices believed to be necessary to safely operate a vehicle

and include such behaviours as speeding, close following or taking aggressive

actions against another driver or vehicle. The present research, while recognising the

distinction, did not specifically compare these driving patterns within the context of

participants’ self-reported behaviour in traffic. The relationship between the manner

11

in which behaviour in traffic was measured in this research and the dimensions

offered by Reason et al is discussed in chapter 5.

12

CHAPTER 2

REVIEW OF THE LITERATURE

2.1 Human Factors and the Motor Vehicle Safety Problem in Malaysia

2.1.1 Roadway Crashes in Malaysia and Public Perceptions of Causality

In 2006, there were 341,252 motor vehicle accidents in Malaysia. Over 6,000

fatalities were recorded (Ministry of Transport Malaysia, 2007). The high rate of

roadway accidents and deaths has been described in scholarly and popular print or

internet media in extreme terms, often labelled as “tragic” (Koh, 2005), as a social

“menace”(Abdul Kareem, 2003), and as a “major public health problem”

(Subramaniam, 1989). Recently, the Minister of Health characterised Malaysian

roads as “worse than a war zone”, pointing out that annual fatalities exceed the total

deaths among American combat personnel over four years of fighting in Iraq

(Zolkepli, 2007). In newspaper reports, Malaysian drivers have been consistently

characterized as “confrontational”, “ugly motorists” (“Rude Drivers”, 2005), or as

“negligent” (“Malaysia Records Highest Single-Day Death Toll”, 2005), “selfish”

(“Our Roads are Filled”, 2007), “discourteous” (Davin Arul, 2007), “obnoxious” and

“cowardly” (“Cowardly Malaysian Drivers”, 2006). A succession of online weblogs

and internet sites authored by tourists and local writers alike have condemned

Malaysian drivers as dangerous, inconsiderate and aggressive. Downe and Loke

(2004) reported that, when asked to provide five adjectives which would “describe

what Malaysians are like”, a sample of 348 first-year university students indicated, in

order of frequency, “friendly”, “peaceful”, “patient”, “laid-back” and “considerate”;

but when asked to “describe what Malaysian drivers are like”, they indicated

“angry”, “impatient”, “reckless”, “bullies” and “selfish”. The public image of driving

in Malaysia – and the generally negative reputation of the driving community –

suggests that roadway safety has emerged as a significant national problem.

Nation-wide statistics seem to underscore the popular concern over safety

issues. A developing country in Southeast Asia, Malaysia has experienced

remarkable increases in population, economic expansion, industrialisation and

motorisation. These are thought to have contributed, in aggregate, to a rapid increase

13

in the number of road traffic crashes (Abdul Kareem, 2003; Abdul Rahman, Mohd

Zulkiflee, Subramaniam & Law, 2005).

In Malaysia, the number of crashes has increased 80% over the past ten years,

from 189,109 in 1996 to a total of 341,252 in 2006 (Ministry of Transport Malaysia,

2007). The number of road fatalities has decreased slightly from 6,304 in 1994 to

6,287 in 2006. Table 2.1 summarises the five-year incidence of crashes and injuries.

Table 2.1: Malaysian Roadway Crashes and Casualties, 2002-2006

Motor Vehicle Crashes 2002 2003 2004 2005 2006

Total 279,7111 298,653 326,815 328,264 341,252

Motor Vehicle Casualties 2002 2003 2004 2005 2006

Fatalities 5,891 6,286 6,228 6,200 6,287

Severe

Injuries

8,425 9,040 9,218 9,395 9,253

Minor Injuries 35,236 37,415 38,645 37,417 19,885

Total 49,552 52,741 54,091 47,012 35,425

source: Royal Malaysian Police (2007)

The road accident death rate in Malaysia dropped from 8.20 deaths per

10,000 vehicles in 1996 to 3.98 deaths per 10,000 vehicles in 2006, but still lags

behind frequencies in developed countries which generally fall below 3 deaths per

10,000 vehicles (Law, Radin Umar, & Wong, 2005).

Some of the urgency in discussions of Malaysia’s road safety problem has

been related to the high frequency of roadway deaths and injuries occurring among

adolescent and post-adolescent age groups (Radin Umar, 2005). Generally, one-

third of all crashes in Malaysia involve automobile users or motorcyclists within the

16- to 25-year-old age group (see table 2.2), higher than any other age grouping or

combination of consecutive age groupings. This suggests that studies, in Malaysia,

drivers within the senior secondary school and university age ranges must be

regarded as being at a potentially higher level of risk than other age cohorts. Studies

14

of university-aged drivers are critical to understanding behavioural and situational

factors that predict the most commonly occurring class of crashes (Stevenson,

Palamara, Morrison & Ryan, 2001).

Table 2.2: Numbers of Automobile Drivers and Motorcyclists Involved in Road

Crashes by Age Group 2000 2001 2002 2003 Age Number % Number % Number % Number % 0-5 37 0.16 90 0.45 30 0.15 43 0.22

6-10 150 0.65 121 0.61 99 0.48 105 0.54 11-15 708 3.08 541 2.72 554 2.71 543 2.81 16-20 3,953 17.21 3,448 17.31 3,178 15.56 3,315 17.15 21-25 3,469 15.10 3,005 15.08 2,997 14.68 3,049 15.77 26-30 3,038 13.23 2,551 12.81 2,378 11.65 2,341 12.11 31-35 2,593 11.29 2,205 11.07 2,216 10.85 2,110 10.92 36-40 2,309 10.05 2,180 10.94 2,025 9.92 1,947 10.07 41-45 2,086 9.08 1,803 9.05 2,820 13.81 1,709 8.84 46-50 1,620 7.05 1,389 6.97 1,416 6.94 1,431 7.40 51-55 1,034 4.50 979 4.91 984 4.82 1,023 5.29 56-60 708 3.08 585 2.94 625 3.06 608 3.15 61-65 572 2.49 450 2.26 463 2.27 458 2.37 66-70 337 1.47 280 1.41 302 1.48 323 1.67 71-75 206 0.90 159 0.80 203 0.99 164 0.85 >75 147 0.64 135 0.68 128 0.63 160 0.76

22,967 100 19,921 100 20,418 100 19,329 100 source:Royal Malaysian Police (2000, 2001, 2002, 2003)

Krishnan and Radin Umar (1997) pointed out that the prevalence of traffic

injuries and fatalities among drivers, and particularly among younger drivers, has

resulted in considerable economic loss for the country. Recent international analyses

have placed the total economic burden at around RM7 billion yearly, or about 2.4%

of the Gross Domestic Product (Asian Development Bank, 2005). Some Ministry of

Health estimates of medical costs alone have been as high as RM5.4 billion to

RM5.7 billion, with one road accident victim admitted to hospital every six minutes

(Bernama, 2006). It has been reported that, in 1999 alone, general insurers paid

RM1.67 billion, or an average of RM4.6 million a day on motor claims (Abdul

Kareem, 2003).

15

Yet, economic figures and accident statistics provide only partial indications

of the impact of the highway safety problem on Malaysian society. A popular

physician and journalist has commented that:

The human toll is unquantifiable. There is no way to

measure the grief of those who have lost their loved ones,

or the pain of the maimed. The economic consequences

can be estimated, and that alone justifies making concerted

efforts to address the issue … The economic costs in

property damages are huge, but miniscule compared to the

expenses of medical care and rehabilitation. The loss of

potential income of the dead and maimed in turn dwarfs

those medical outlays (Bakri Musa, 2005).

Politicians and government policy-makers have also struggled with the rising

sense of public dissatisfaction over persistently high rates of traffic fatalities and with

frustration in trying to find solutions to the problem. In 1999, controversy swirled

over a reportedly cynical comment by the Transportation Minister of the day that:

We have done what others have been doing around the

world. In spite of numerous road safety campaigns the

number of accident cases have been increasing. What else

can we do, if people want to die? (Lim, 1999).

Some seven years later, the same frustration was apparent when his Cabinet

successor told a group of assembled journalists that:

When I became Transport Minister two-and-a-half years

ago I thought the biggest challenge was to build ports and

airports. But it is nothing compared to bringing down the

number of road deaths, which is actually a nightmare.

(Bernama, 2006).

Public interest and political frustration has given rise to extensive speculation

over the possible causes of the problem. Criticisms of road configuration, traffic

congestion, lane definition, signs and lighting have been levied in certain quarters

16

(Abdul Rahman et al, 2005). The relatively high population of motorcycle riders,

approximately 45 per cent of all registered vehicles in 2006 (Road Transport

Department Malaysia, 2007), is often mentioned as a factor, given greater risks of

accident, serious injury and death (Per and Al Haji, 2005). In 2006, for instance, the

Royal Malaysian Police (2007) reported 3,693 deaths among motorcycle operators

and pillion riders, as compared with 1,215 deaths among motorcar drivers and

passengers.

Generally, though, most accounts have come around to commenting on driver

demographic and behavioural characteristics as significant factors in motor vehicle

crashes (Che Ali, 2001; Krishnan & Radin Umar, 1997). In a recent newspaper

interview, the Director-General of Malaysia’s Road Safety Department summarised

popular opinion by stating:

The problem we have is that our road-users are not mature,

unlike in other countries. Those countries have had a

motoring culture for nearly a century but our road-users are

relatively newer to motoring (Sadiq, 2006).

A leading university professor and Director-General of Malaysia’s Institute for Road

Safety Research similarly noted that:

Malaysian drivers are not good in safety routines. They

don’t even stop to look left and right or look in the rear

view mirror. They are also bad in giving ample time to

others and this is an example of non-defensive driving.

Maybe these drivers just never realised how simple it is to

avoid accidents (Looi, 2007).

Researchers, newspaper columnists, senior policy-makers and politicians

alike are shining the spotlight on the way in which Malaysian drivers’ traits and

states may be contributing to the incidence of roadway accidents. Who they are,

how they think, what they do – virtually all facets of the Malaysian driving

population have come under increasing public scrutiny in an effort to further a better

17

general understanding of the causes and potential prevention strategies related to the

country’s traffic safety problem.

2.1.2 Studies of Causal Factors in Malaysian Roadway Crashes

Notwithstanding this public outcry, causal factors underlying crash and injury

rates on Malaysian roadways have remained largely understudied. The research that

has been undertaken has tended to focus largely on the contribution of broader

economic and social variables, rather than personality factors, or else on the

evaluation of specific safety interventions.

For instance, Law, Radin Umar, Zulkaurnain and Kulanthayan (2005)

examined the impact of economic variables on motorcycle-related crashes, injuries

and fatalities. Conducting time-series regression analyses of police data, they

reported that the Asian-wide economic recession significantly contributed to a

reduction in traffic fatalities, due to fewer trips and reduced traffic exposures as a

result of slower economic activity.

In the same study, Law et al. (2005) also examined the impact of a national

motorcycle safety programme (MSP) on crashes and found that it had effected a 25%

reduction in the number of motorcycle accidents, with a 27% and 38% drop in the

rate of motorcycle casualties and motorcycle fatalities, respectively. MSP

interventions had been aimed at modifying motorcycle riders’ awareness and

attitudes of safety issues related to helmet use, conspicuity and excessive speeding.

In a separate study, Ahmad Hariza, Musa, Mohd Nasir, Radin Umar and

Kulanthayan (1999) found that the same MSP had significantly improved motorcycle

riders’ perception and understanding of safety issues. In none of the studies of the

MSP, however, was personality or demographic factors of motorcyclist samples

investigated. This is, perhaps, a needed focus in the analysis of programme effects,

since studies in other parts of the world have found that individual differences play a

significant role in determining rider training outcomes, reasons and social contexts of

motorcycle use (Reeder, Chalmers & Langley, 1996), risky behaviour (Chang &

Yeh, 2007) and crash Type And liability (Clarke, Ward, Bartle & Truman, 2007).

18

Williamson (2003) offered a number of socio-political explanations for

roadway safety issues in Malaysia as part of his analysis of the social effects of the

national north-south expressway that, since 1994, has linked peninsular communities.

Describing the expressway as a “stunning infrastructural achievement” (p. 110), he

argued that national leadership intended it to be both a symbol of progress. He

argued that, motivated largely by the government’s fear of unregulated public

assembly, road engineers devoted their efforts to creating a public artery in which

speed and limited stoppage were design priorities. This, however, resulted in a

myriad of problems.

Although the expressway was meant to avoid both traffic

and accidents, these conflicting aims of speed and safety

seemed to exacerbate them. The very monotony of the road

surface, the factor that made the high speeds possible,

presented new circumstances because driving in empty

space made staying awake a persistent problem … One

strategy drivers have pursued to combat the boredom of the

expressway is to drive faster … One of the potential

challenges for drivers was the emptiness of the roadway

itself (pp. 121-122).

Williamson’s (2003) assertions do have certain implications for an

understanding of how human factors may play a significant role in the high rate of

motor vehicle crashes in Malaysia. It has been estimated by expressway

management authorities that up to 95% of the crashes occurring along the north-

south artery are due to human error, including speeding and falling asleep at the

wheel (“N-S Highway”, 1996). Social attitudes and experience engendered by the

rapid and high-profile growth of expressways and local road networks may have

infiltrated the broader national consciousness, generalising to all driving

environments and situations. According to Williamson, “many Malaysians claim

that as drivers, they are accident prone, a capacity that makes them distinct as a

society” (p.122).

19

2.2 The Professional Background

2.2.1 Human Factors in Roadway Crashes: A Vexing Research

Challenge

Attention to the demographic, experiential, personality and behavioural

characteristics of drivers has not been exclusive to the Malaysian scene. Because at

least one driver is involved in every traffic crash, research worldwide has focused on

driver characteristics in an attempt to understand how human factors play into the

causes and prevention of roadway accidents (Evans, 1991).

Evans (1996) further argued that changes in driver behaviour offer, by far, the largest

opportunities for harm reduction:

A clear hierarchy of factors can be specified. Human

factors are far more important than engineering factors.

Among human factors, driver behaviour (what the driver

chooses to do) has much greater influence on safety than

driver performance (what the driver can do). Among

engineering factors, roadway engineering has a much

greater influence than automotive engineering (p. 784).

Lajunen and Summala (1997) noted that traffic safety researchers have

attempted to identify the relationships between drivers’ individual characteristics and

their involvement in motor vehicle crashes. This has included the examination of

age and gender, levels of driving experience and, particularly, personality

characteristics (Elander, West and French, 1993; Åberg, 1993). According to the

Commission for Global Road Safety (2006), “human behaviour makes a direct

contribution to crash risk through the extent of knowledge and understanding of

traffic systems, driver experience and skill and the relationship between risk and

factors such as speed choice and alcohol consumption” (p. 62). Christ, Panosch and

Bukasa (2004) argued that:

Road safety is less a technical but rather a human factors

problem. The majority of accidents are not caused by

problems of the vehicle, bad road conditions, etc. but rather

20

by the behaviour of drivers. There are two principle

approaches in order to influence the driver: adjusting the

traffic system to the driver or adjusting the driver to the

traffic system. While the system-centered approach aims at

creating those road conditions that reduce the chance of

accidents in advance, the individual-centered approach

directly focuses on traffic relevant performance and

personality aspects, as well as on the attitudes and

behaviour of single drivers (p. 377).

However, to a large degree, empirical findings to date about the relationship

between driver characteristics, personality and traffic safety have been regarded by

many as debatable, unclear, weak, conflicting or of relatively little importance

(Iversen & Rundmo, 2002; Lajunen & Summala, 1997; Ranney, 1994). Haddon

(1963), in reviewing early findings on human factors in this field, noted that “one of

the remarkable aspects of motor vehicle accident research has been the willingness of

many to base scientific investigations on data of a quality which would immediately

cause their rejection as the stuff of research in any other subject area” (p. 641).

Dewar (2002b) concluded that conflicting findings have been due largely to poorly-

controlled studies based on limited samples and on failing to control for driving

exposure or alcohol use. Further, psychological factors may play different roles

according to driver age (Dumais et al, 2005), organisational climate (Caird & Kline,

2004), prior accident experience (Lin et al, 2004) and other contextual variables.

The lack of progress in trying to identify psychological factors that cause, or

at least predict, motor vehicle crashes has been attributed by af Wahlberg (2003) to

three main methodological deficiencies: (a) an absence of reported test-retest

reliability of the predictor; (b) the choice of time periods for calculating the

frequency of crash-related outcomes; and (c) the failure to differentiate between

culpable and non-culpable crashes. He conducted a meta-analysis of some 136

previous studies researching the effects of at least one psychological predictor of

21

traffic accidents and found to be wanting in one or more of these methodological

aspects and concluded that:

If these studies are representative of the research done, the

picture that emerges is indeed grave. It would seem that

very few of the studies on accident predictors are actually

possible to interpret in any straightforward way, and we are

left with a very vague knowledge of what psychological

variables can actually predict accidents (p. 482).

Other methodological factors that may cloud the relationship between

psycho-social variables, driving behaviour and crash-related outcomes include: the

use of self-reported crash data; the use of inconsistent crash definitions; the lack of

replication of many studies; the extent of exposure of drivers to the driving task (af

Wahlberg, 2002, 2003); accuracy of witness recall of crash details (Crombag,

Wagenaar & van Koppen, 1996; Underwood & Milton, 1993); and the influence of

car and driver stereotypes on attributions of crash blameworthiness (Davies & Patel,

2005).

2.2.2 The Emergence of Traffic Psychology as a Scientific Discipline

2.2.2.1 An Applied Perspective

Ever since Tillman and Hobbs (1949) stated that “a man drives as he lives”

(p. 321), there has been an interest in driver personality, information processing,

motivation and behavioural performance as potential underlying causal factors in

driver behaviour (Jonah, 1997a). The need for a more specialised focus by applied

psychologists and ergonomists on driving-related research problems and roadway

safety was raised throughout the 1960s (Cozan, 1961; Preston & Harris, 1965) but

did not really gain momentum until the 1980s (Risser, 2003). Novaco (2000) argued

that:

The field of transportation has always had a rich potential

for psychology, especially considering the salience of

transportation in the routines of daily life. Nevertheless,

psychologists have given scant attention to this topic.

22

Transportation systems shape the structure of our

communities and impact the well-being of individuals …

People’s reactions to the inconvenience and discomfort of a

particular journey depend on many intertwined

psychological processes including personality disposition,

attitudes about the origin and destination of the trip and

resources for choosing alternative travel modes and

schedules. These interrelationshps provide a vast terrain for

psychological research (pp.654-655.)

The Traffic and Transportation Psychology Division of the International

Association of Applied Psychology was established in 1994 and there has been a

steady growth in publications, conferences and coordination of professional

affiliations ever since (Groeger, 2002). Huguenin (2005) defines traffic psychology

as “the psychological intervention, or the psychological support for intervention, in

the field of traffic. This includes the research that serves this purpose” (p. 4) and

describes it as an “interdisciplinary, integrative and international viewpoint based on

application in order to address changing situations and objectives” (p. 3). According

to Rothengatter (2001), “the task of traffic psychology is to understand, predict and

provide measures to modify road user behaviour at the levels identified with, as a

general objective to minimise the harmful effects of traffic participation” (p. 4).

2.2.2.2 A Multidisciplinary Approach

From the outset, traffic psychology has drawn from multidisciplinary

perspectives, eoncompassing engineering, transportation planning, ergonomics,

medicine, psychology, anthropology and sociology. Indeed, Ochando, Temes and

Hermida (2001) found, in a Spanish survey, that individuals tend to combine their

interests in traffic psychology with some other area of specialisation such as

educational psychology.

To wit, Groeger (2002) argued that there is “no psychology which is specific

to, or peculiar to, traffic and transportation,” (p. 246), but that complex traffic

23

behaviour is a very important and worthwhile test-bed for psychology and

psychological theory and, in particular, the study of cognitive processes. Spielberger

and Frank (1992) made similar comments with regard to the contribution of health

psychology through the use of public health models and methodologies, a paradigm

that has been applied increasingly to driving safety questions in developing countries

(Dharmaratne & Ameratunga, 2004; Odero, Garner and Zwi, 1997; Hyder & Peden,

2003; Johnston, 2007; Peden & Hyder, 2002). Parker (2004) pointed to the role

played by social psychology in the area of road safety, emphasising the primacy of

attitudes and attributions, which she described as the two main planks of social

cognition.

Ergonomics has made a contribution, as well, both by providing a better

understanding of human-machine interaction, and of cognitive control over vehicle

and highway systems involved (Bridger, 1995; Wilson, 2000). Saad (2002)

commented that:

From the perspective of the driver, ergonomics is concerned

with identifying and designing technical and organisational

means for facilitating the driver’s interaction with the road

environment. In the broadest sense, the road environment

comprises the vehicle, the road infrastructure and other road

users. It also includes the rules of the highway code

governing the use of the road infrastructure and interactions

with other users, which are sometimes expressed in road

markings and road signs (p. 24).

In a recent special edition, Stanton (2007) noted that, over the past ten years, there

have been 103 papers published in the Ergonomics journal alone, and that

“ergonomics has much to offer in the design of driver education and training

programmes, the design of driver interfaces and driver assistance systems with motor

vehicles, the design of vehicle automation and a deeper understanding of why drivers

behave as they do” (p. 1158). Much of the ergonomic research carried out to date has

been focused on adapting motor vehicle conveyances, surrounding environments and

24

tasks to human capabilities and limitations, a paradigm that Boff (2006) described as

“Generation One” ergonomics.

Increasingly, though, ergonomic inputs into this body of knowledge are

shifting from a perspective of understanding peoples’ interactions with driving-related

artefacts to a role that contributes to the design of interacting systems in order to

satisfy user needs and desires (Stanton, 2004). Studies of human factors engineering

of intelligent transportation systems are becoming increasingly important in the

design of in-vehicle safety systems and driver decision support technologies (Lenior,

Jannssen, Neerincx & Schriebers, 2006; Noy, 1997; Walker, Stanton & Young, 2001).

These applications are consistent with the paradigms Boff (2006) has described as

“Generation Two” ergonomics, in which the goal is to manage automation and

dynamic function allocations, and “Generation Three” ergonomics, which focuses on

symbiotic technologies that amplify human physical and cognitive capabilities.

According to Barjonet & Tortosa (2001), the most significant contribution of

ergonomics was that it introduced the idea that motor vehicle operation was a task and

therefore brought to traffic psychology the broad range of concepts and methods

operating in industrial psychology and work-related accidents, particularly the notions

of mental load, error and cognitive modelling. “This school of though, which

assumes a necessary communication between person and machine led to a dialogue

between the various designers of car interiors, road signs and all the difference

infrastructure and hence to a greater flexibility in their hitherto purely engineering

approach which supposed that people would adapt to the machine” (p. 26).

2.3 Theories of Driving Behaviour

2.3.1 Concepts, Theories and Models

In attempting to understand, predict and modify road user behaviour, traffic

psychologists frequently engage in theory-building. This involves the coherent

grouping of general propositions for use as principles in explaining various classes of

driving phenomena. Concepts are the building blocks for theory and may be defined

as:

25

A word or set of words that expresses a general idea

concerning the nature of something or the relations between

things, often providing a category for the classification of

phenomena (Theodorson & Theodorson, 1969).

Concepts are formed by a process of mental abstraction, which is then

followed by a process of generalisation (Schneider, Healy, Ericsson & Bourne Jr.,

1995). Concepts can be linked together to form a theory, which may be defined as:

A set of interrelated principles and definitions that present a

systematic view of phenomena by specifying relationships

among variables with the purpose of explaining natural

phenomena (Kerlinger, 2000, p. 8)

Any set of systematically interrelated concepts or

hypotheses that purports to explain and predict phenomena

(Robbins, 2005, p. A-18)

Often, “theory” is a term used interchangeably with the word “model”, but for

the purposes of this thesis, the latter is defined more narrowly as “a set of assumptions

or postulates, often in mathematical form, which attempts to provide a generalised

working construct that can account for empirical data or relationships” (Chaplin,

1985). In traffic psychology, many models have been proposed, each ordering

driving reality from its own particular set of empirical observations. On the other

hand, there is no generally accepted theory which elucidates principles from which a

broad, generalisable understanding of the driving process can be deduced.

2.3.2 Traffic Psychology: Slow Progress in Theory-Building

Theory development in traffic psychology has not progressed well (Summala,

2005). Reasons for this are likely several. Ranney (1994) pointed out that it has

never been clear, in traffic psychology, whether theories should explain everyday

driving, or accident-causing behaviours, or both. Many of the theories that have been

proposed have failed to generate testable hypotheses. To a degree, this may be due to

26

the imprecise definition of concepts, but it is also a reflection that the driving task is a

highly sophisticated multi-factorial process involving perceptual, cognitive, social,

and emotional determinants. Groeger and Rothengatter (1998) argued that, given the

complexity of human behaviour, it is highly improbable that a single theoretical

stance is likely to be sufficient to account for behaviour in traffic.

Groeger (2000) took a similar position with regard to the range of driver

motivations:

Although any rational analysis would surely place

preservation of one’s own personal safety at the heart of the

concerns of a driver, I believe it is but one of the goals a

driver has, and most of the time is not especially influential.

Instead, the driver’s aspirations are to reach destinations,

avoid obstacles, minimise delay and driving time, enjoy

driving, feel in control, etc. … Just because we as

investigators have an understanding of safety as a goal, it

does not mean that the driver had safety as a primary goal,

or ever had sufficient knowledge of possible outcomes on

which to base a deliberate action (p. 189).

Notwithstanding these difficulties, five areas of theory attempt to explain and

predict driving behaviour. These may be classified as: theories of individual

differences; risk adaptation theories; hierarchical theories of driver adaptation, task-

capability frameworks and attitude-behaviour models (Keskinen et al., 2004;

Rothengatter, 2002).

2.3.3 The Individual Differences Approach

Placed in similar situations, not all people act exactly alike and this is a

function of their differing values, attitudes, perceptions, motives and personalities

(Robbins, 2005). For over ninety years, researchers have been trying to determine

individual differences that lead to disparities in crash occurrence and outcomes and

the literature associated with differential road-crash involvement is extensive.

27

Trimpop and Kirkcaldy (1997), for instance, found that a sample of young Canadian

automobile drivers, aged 16 to 29 years, without driving violations had lower scores

on measures of risk-taking behaviour, thrill and adventure seeking and tended to

avoid socially stimulating situations when compared with violators. In an attempt to

identify subtypes of young Norwegian drivers, Ulleberg (2001) found two high-risk

groups, the first of which was characterised by low levels of altruism and anxiety and

high levels of sensation-seeking, irresponsibility and driving related aggression, while

the second high-risk group reported high sensation-seking, aggression, anxiety and

driving anger. Similar studies of driver risk-taking and other individual differences

have been largely absent with Asian populations and non-existent in Malaysia.

Clarke and Robertson (2005) conducted a meta-analysis of 47 studies

reporting relationships between accident involvement and the dimensions of the Five

Factor Model (FFM), or “Big Five” personality model (Costa & McRae, 1995;

McRae &Costa, 1990). Of the five factors examined – extraversion, neuroticism,

conscientiousness, agreeableness and openness – the authors found low

conscientiousness and low agreeableness to be valid and generalisable predictors of

accident involvement in both occupational and non-occupational settings;

extraversion was found to predict traffic accidents, but not occupational accidents.

Drawing from a large pool of data that included personality measures and both traffic

and work-related statistics for 34 nations, Lajunen (2001) reported that countries with

high extraversion scores had more traffic fatalities than those with moderate or low

extraversion scores. In a large number of studies of specific samples from various

countries, the extraversion variable has been associated with a higher frequency of

traffic offences (Renner & Anderle, 2000), poorer perception of traffic signs (Loo,

1979), crash frequency (Pestonjee & Singh, 1980) and other safety outcomes.

According to Rothengatter (2002), the search for individual differences

relevant to crash involvement continues to yield a large body of literature. However,

“these findings have yet to be embodied in a general theory of differential crash risk.

There have been theories of crash causation that have focused on particular groups of

28

finding, but none have attempted a more general integration” (Elander, West &

French, 1993; p. 290).

2.3.3.1 Accident Proneness

One concept that sought to integrate individual differences within a predictive

theory relates to the “accident-prone personality”, an idea that has had an uneven level

of acceptance by ergonomists and traffic psychologists through the years, but persists

today.

In 1917, the British government established the British Industrial Fatigue

Research Board, in response to concern over the number of accidental deaths and

injuries in World War I production industries (Blackler & Shimmin, 1984). Research

by board statisticians, during and following the war years, found first that the

frequency of accidents, occupational and otherwise, could be modelled almost exactly

by the Poisson distribution but then that, in certain cases, the average number of

accidents, λ, differed from person to person (Greenwood & Yule, 1920). The

individual values of each worker’s λ became known as the degree of accident

proneness. The designation of a high-λ individual as “accident prone” implied that,

“irrespective of environment, that individual is more likely at all times to incur an

accident than his colleagues even though exposed to equal risk, and that this is due to

some characteristic or summation of characteristics associated with corporeal

dexterity, sensori-motor skill, personality, or higher conative or cognitive function”

(Cresswell & Froggat, 1962; p.152).

According to Haight (2004),

‘Accident proneness’ had a nice ring to it. The difficulty

was that no one knew how to determine its value for a given

individual. It provided a challenge to the psychology

profession to devise a way to measure it, just as one can

meaure height, weight and perhaps even intelligence. If

each individual has a unique λ-value, his or her accident

proneness, it should be a reasonably simple matter to find

29

out what that value is, by devising clever tests, perhaps

physiological, more probably psychological (p. 422).

Early work on the concept attempted to do just that, with a series of tests

constructed by Greenwood and Woods (1919), Farmer and Chambers (1926; 1929;

1939) and many others, motivated largely by a desire to select lorry drivers in Europe

who were less likely to become involved in costly roadway crashes (Barjonet &

Tortosa, 1997). None of the experiments, however, produced a positive, replicable

result that correlated substantially with the accident experience of individuals (Haight,

2004). Johnson (1946), Arbous and Kerrich (1951) and much later McKenna (1983)

systematically destroyed the supportive literature, with extensive critiques that

concluded statistical analyses were almost all invalid, inappropriate, inadequate or

irrelevant. Mintz and Blum (1949) argued that “the method of studying accident

proneness by demonstrating that small percentages of people have large percentages

of the accidents is unsound and fallacious” (p. 195).

The theory of the accident-prone individual also came under attack on a

conceptual basis. Scores on the λ dimension, it was pointed out are highly sensitive to

the length of the survey period, with shorter periods giving sharper contrasts (Moore,

1956). “Because crashes are so infrequent, an individual driver’s prior crsh rate would

not be an effective predictor of future crash rates even if some individuals did have

expected rates higher than others” (Evans, 1991; p. 294). The accident-prone concept,

as well, made an assumption that, in any sample, a certain percentage would

experience a higher accident frequency than would other groups year after year, but

did not take into consideration whether, in successive years, that high-λ group would

be comprised of the same individuals or of an entirely new cohort (Haight, 2004).

Hale and Glendon (1987) concluded that the evidence for the transfer of accident

liability differences across different work tasks or different working environments is

quite weak so that accident proneness would appear to be largely task specific. A

study by Salminen and Heiskanen (1997) provided empirical support for this position,

noting that, in a Finnish telephone survey, subjects reported significant, but very low

correlations between accident frequency at work, at home, in traffic or when playing

30

sports; in no case did correlations between traffic crashes and other types of accidents

exceed r =.05. Ultimately, the consensus position became that:

Accident proneness as a concept has little use in practical

accident prevention. The concept itself is ill-defined, no

stable personality characteristics that can be identified with

accident-proneness have been discovered. So, therefore,

nothing can be done to identify individuals who may be

accident-prone in order to treat them or to remove them

from areas of greatest risk. Alternative explanations must

be found for persons experiencing multiple accidents

(Lindsey, 1980; pp. 8-9).

Despite the low repute in which many have regarded the accident proneness

concept, it is still generating research and controversy (Vavrik, 1998). Visser, Pijl,

Stolk, Neeleman and Rosmalen (2007) have published a meta-review of 79 studies,

screened for operational and prevalence rates related to accident proneness within

work, roadway, sports and family settings. While their stated conclusion was that an

accident prone group definitely existed, “it is still difficult to identify the accident

prone individuals that compose this group, because individuals can experience

multiple accidents because of chance alone and also because of a higher exposure to

risk independent of personal factors” (p. 562). Only 15% of the studies included in

their analysis had been conducted on road user crash experience, but the authors

reported that the heterogeneous nature of sub-groups did not permit comparisons.

2.3.3.2 Differential Accident Involvement

McKenna (1983) suggested that the accident proneness concept should be

replaced by the less historically loaded term “differential accident involvement”. This

concept does not prejudge the issue of causation, it denotes an area of study rather

than a theory, and assumes that individuals may vary along a continuum with regard

to factors that affect their risk of crash (Elander et al., 1993). It is seen as preferable

to earlier formulations because it places more emphasis on contributing factors

outside the person, moving away from the main conceptual criticism of traditional

31

accident proneness (Chmiel, 2000). Dewar (2002b) noted that the notion of

differential liability allows for the observation that some individuals do, in fact,

experience more accidents than others, but avoids the assumption of a stable

phenomenon that accounts for more accidents in all situations. Elander et al.,

following their review of the literature, concluded that differential crash risk is not

readily accounted for by previous crash rates, and that transient factors probably

interact with stable traits of the individual in their causation.

2.3.4 Risk Theories

Rothengatter (2002) has argued that a major factor in the growth of traffic

psychology can be attributed to the law of diminishing returns with respect to

engineering interventions designed to increase road safety. The introduction of

divided highways, crash barriers, compulsory seatbelts and vehicle design

improvements reduced motor vehicle crash fatalities, albeit not crash occurrence,

substantially. After the relatively easy engineering measures were implemented to

reduce the seriousness of the consequences of driver behaviour, researchers began to

turn their attention to not-so-easy measures for changing driver behaviour.

However, early studies showed that the actual safety effects of engineering

interventions often were much less than expected stimulated an interest in the way

that drivers reacted to them. For example, in a study of driving on icy roads,

Summala and Mersalo (1980) demonstrated that drivers using studded tyres increased

their speed to a level where the skid margin approached that of drivers using normal

tyres.

2.3.4.1 Risk Homeostasis Theory (RHT)

In an attempt to explain these findings, Wilde (1982; 1988) proposed that a

control mechanism operates to keep overall risk per unit time constant. That is,

people strive to maintain a target level of risk all the time and, when they perceive a

discrepancy between the observed level of risk and the desired target level of risk,

they adjust their behaviour to eliminate the discrepancy. A driver who enters a

construction zone, suddenly confronted with uneven pavement, large earth-moving

32

vehicles and warning flags, according to the theory, would perceive a level of risk

above his target and would tend to reduce speed and increase vigilance. Conversely, a

driver motoring along a wide, flat, uncongested expressway would perceive a low

level of risk and would be more likely to reach speeds above the minimum limit or to

permit hand-phone distractions, at least until the target risk level was reached.

Initially, Wilde’s theory was couched at a societal level, postulating that the

number of accidents in any given country would only depend on the accident rate

which the population is willing to tolerate, and not on the specific measures taken in

other sectors of the control system. That is, “the aggregate target risk in a community

is what produces the accident toll and the only way in which this toll can be reduced,

according to the theory, is if the level of target risk is reduced,” (Fuller, McHugh &

Pender, 2008; p. 14). When others (Haight, 1986; Michon, 1989; Ranney, 1994)

argued that this made the theory essentially untestable, Wilde (1994) reframed the

concept of target risk as an individual variable based on four perceived utilities.

Huguenin (2001) noted that RHT has spawned a considerable number of

studies, many of which have attempted to observe and comment on individual

behavioural change after the risk variable is manipulated. In two separate studies, for

example, observers posing as passengers rated German taxicab drivers in vehicles

equipped with anti-lock braking systems (ABS) as driving more aggressively,

performing more dangerous manouevers and driving with significantly shorter

headway distances than those driving without ABS (Aschenbrenner & Biehl, 1994;

Sagberg, Fosser & Sætermo, 1997). RHT proponents argued that drivers were

adapting behaviourally to the effects of ABS by driving less safely and that this, in

turn, reduced the predicted safety benefits of such systems (Wilde, 2002).

Collectively, RHT research of this nature has been used to argue that most

engineering interventions and roadway regulations have little or no benefit (Smiley,

2001; Wilde, 1988; 2002) and that driver education programmes need to be

extensively revamped (Wilde, 2005), given that human behaviour will continue to be

motivated by internal homeostatic processes. The central implication of RHT is that

safety interventions need to be values-oriented and aimed at lowering the level of

33

target risk that people are willing to tolerate. “The extent of risk taking with respect

to safety and health in a given society ultimately depends on values that prevail in that

society, and not on the available technology” (Wilde, 1994; p. 223).

More than any other driving theory, however, Wilde’s RHT has generated

controversy and opposition (Keskinen et al., 2004). General consensus is that

behavioural adaptation to vehicle and environmental conditions often does occur

(Rothengatter, 2002), but that the RHT neither adequately explains nor predicts the

circumstances under which it does. Considerable criticism revolves around the

imprecise nature of the theory itself. “It is unclear whether risk homeostasis occurs at

the level of the individual, the community, or the nation” (Brown & Noy, 2004).

“Costs and benefits are central to the model, but they are not defined in psychological

terms.” (Vaa, 2001; p. 53). Also, the notion of target risk implies that drivers are

constant “comparers”, psychologically weighing at every moment the perceived and

targeted risk levels inherent in every environment, a tenet for which no convincing

support has been yet generated (Michon, 1989; Rothengatter, 2002). To the contrary,

it has been argued people are not sufficiently sensitive to changes in low risk

probabilities to react behaviourally as RHT predicts (Fuller et al., 2008; Slovic,

Fischoff, Lichtenstein, Corrigan & Coombs, 1977). Robertson and Pless (2002) made

the case forcefully that:

… some drivers may sometimes slow down in rain, but that

does not mean that they do so systematically or that they

know exactly how much to slow down to maintain

constancy of risk. The notion that people have a constant

point of acceptable risk, pay sufficient attention to risk, or

have the knowledge and ability to constantly adjust their

behaviour to achieve so-called risk homeostasis is ludicrous

in view of what is known about human limitations. (p.

1151).

Criticisms have been aimed at Wilde’s theory on empirical as well as

conceptual grounds. In a review of research offered as support of the RHT, Evans

34

(1986) concluded that “risk homeostasis theory should be rejected because there is no

convincing evidence supporting it and much evidence refuting it” (p. 81). O’Neill

and Williams (1998), after a similar review, argued that “these so-called theories that

purport to explain human behaviour in the face of risk are nothing more than

hypotheses with a large body of empirical evidence refuting the studies that allegedly

validate them” (p. 92). Michon (1989) noted that most studies attempting to support

the RHT deal with data only at the aggregate level, while Brown and Noy (2004)

noted that it remains possible that factors other than risk underlie the behaviour

changes that follow alterations to the traffic system.

2.3.4.2 Zero Risk Theory

Another risk-based theory, Summala’s zero-risk model of driver behaviour

(Summala & Näätänen, 1987; Summala, 1988) proposed that drivers do not

constantly assess risk while driving, a necessary and highly controversial assumption

in Wilde’s theory. Rather, drivers compare the distance from hazards or time-to-

collision to a subjective safety margin threshold and take action only when the

threshold is exceeded. At this point, they experience uncomfortable feelings of fear

and abruptly change behaviour. In other words, drivers avoid ‘feeling fear’ (hence,

experience ‘zero-risk’) when they drive by anticipating, or expecting, some degree of

risk during the performance of this task. Only when the subjective risk reaches a level

that was not anticipated will the drivers change their behaviour, increasing safety

margins” (Brown & Noy, 2004; p. 26). Summala (1986) suggested that estimating

time-to-collision, for example, is a very basic human skill that can be carried out in

the absence of extensive conscious processing.

While overcoming many of the criticisms levied at RHT with regard to the

concepts of target risk and the risk discrepancy comparative process, zero-risk theory

still retains some of its conceptual shortcomings. Rothengatter (2002) has questioned

how drivers can determine that a threshold has been exceeded if they do not

constantly assess risk. In addition, Fuller (2005) has argued that the theory’s premise

that safe margins are learned creates an implausible requirement to recognise, and

35

learn how to respond safety to, what is a virtually infinite number of roads and traffic

scenarios.

A major element in the zero risk theory was the influence of motivation, used

to explain drivers’ tendencies to approach as closely as possible to the risk threshold.

A large number of studies show that external motives, such as time pressure, do

appear to affect drivers’ willingness to accept risk levels that approach thresholds and,

as a result, their behaviour becomes less adaptive to prevailing circumstances

(Delhomme & Meyer, 1998; Hataaka, Keskinen, Gregersen, Glad & Hernetkoskis,

2002; Reeder et al. 1996; Van der Hulst, Meijman & Roghengatter, 1999). On the

other hand, very little if any research has been carried out with respect to intrinsic

motivation, much of which arises from personality, age and social variables.

2.3.5 Hierarchical Theories of Driver Adaptation

Huguenin (2001) has argued that the main problem with considering driver

behaviour within a risk-based framework is that drivers tend to adapt their behaviour

in different way on differing strategic levels, and when confronted with various

environmental conditions or psychological processes. If behavioural adaptation were

to take place in response to the presence of a supplementary restraint system (SRS),

for instance, it may not manifest itself as a less cautious speed or headway choice but

rather as a conscious, pre-meditated decision not to wear seatbelts. In an attempt to

deal with this and to expand on the role of extrinsic and intrinsic motivators, Summala

(1996; 1997) refined his earlier theory into a hierarchical model of driver behavioural

adaptation, in which he introduced a “task cube” to explain the driving process. The

cube presented three dimensions of driver behaviour: a functional hierarchy, level of

psychological processing and a functional taxonomy of driving actions (see Figure

2.1). Summala argued that behavioural adaptation, and specific driver actions, would

vary depending on the combination of factors from the three dimensions.

36

Keskinen et al. (2004) noted that Summala’s task cube achieves its goal of

conceptualising the driving process in ergonomic terms and praised the prime role

given to motivational factors but, at the same time, criticised the model for being

overly complicated and resembling “more a description or a list of important variables

than a solid model” (p. 15). Rothengatter (2002) questioned the concept of a

hierarchy, pointing out that a task hierarchy assumes that successful completion of the

task at a lower level is required for successful performance of a higher-level task, a

property absent within the task cube concept.

Even though it is true that the performance on one task can

bear consequences for the performance for the other, this

does not necessarily imply a hierarchy as this is as much

true for “lower” level as for “higher” level tasks … Drivers

do perform tasks such as route finding and manoeuvring,

for example, seemingly concurrently, but that is not

Speed

and time control

MOBILITY NEEDS

FUNCTIONAL HIERARCHY

Vehicle choice

Trip decisions

Navigation

Guidance

Vehicle control

MOTIVATIONAL MODULE: MOTIVES EMOTIONS

Passing and other m

aneuvers

Crossing m

anagement

Obstacle avoidance

Headw

ay control

Lane keeping

etc.

FUNCTIONAL TAXONOMY

LEVEL OF PSYCHOLOGICAL PROCESSING

Decision making Supervisory monitoring

Attention control

Perceptual-motor Control (constant mapping, Automated)

Figure 2.1: Task Cube (from Summala, 1996)

37

sufficient reason to presume they are two distinct levels of

the same task rather than two different tasks (p. 252).

2.3.6 Task Capability Interface (TCI) Theory

In perhaps the most ambitious of the leading driving behaviour theories, Fuller

(2000; 2005) integrated competing components of the RHT (Wilde, 1982; 1988) and

the zero risk theory (Näätänen & Summala, 1976) by proposing that drivers attempt to

match task demands with their capability to maintain control. Fuller argued that loss

of control occurs when task demands exceed drivers’ capabilities. Most of the time,

drivers are able to manage the interface between demands and capabilities (see Figure

2.1), either by modifying task demand or by altering their capability. However, this

becomes more difficult when factors external to the driving task (e.g., high speeds;

affective states), unexpected changes in task demand (decreased visibility, unsafe

behaviour of other road users) or over-estimation of capability (through lack of

experience or impairment) and the driver is pushed closer to the critical control

threshold. Loss of control occurs at the point where capability is less than that

required to carry out the task safely.

Figure 2.2: Task-Capability Theory (after Fuller, 2000)

Capability (C)

C > D

Control

Loss of control

Task Demands (D)

C < D

Crash! Compensatory action by others

Safety

Safety

38

Fuller’s theory has, for the most part, been regarded as potentially more

productive than earlier risk-based theories, simply because it is more straightforward

to establish the parameters that drivers use to assess their competence than it is to

measure target or subjective risk. Generally, the notion of matching competence with

task demand promises to be very useful in understanding driver behaviour, and

Keskinen et al. (2004) have argued that it is deserving of more attention than it has

received to date. Two limitations have been noted, however. Brown & Noy have

pointed out that TCI theory is limited in its ability to explain all of the decision points

and responses that occur during a more complex driving scenario such as over-taking.

Rothengatter (2002) has stressed that the perception of capability is often influenced

by external factors (impairment, emotional state, time pressure), such that it is not

capability but perceived capability that interfaces with task demand.

2.3.7 Attitude-behaviour Theories

2.3.6.1 Theory of Reasoned Action (TRA)

An attitude is a relatively stable and enduring predisposition to behave or react

in a certain way toward persons, objects, institutions or issues (Chaplin, 1985; p. 40).

It generally refers to the thoughts and feelings that impel us to behave in one way and

not in another” (Parker, 2004; p. 126). Since 1985, traffic psychology has seen a

resurgence of interest in the role played by attitudes, largely due to the focus that has

been provided by the theory of reasoned action (TRA; Fishbein & Ajzen, 1975) and

the subsequent theory of planned behaviour (TPB: Ajzen, 1985; 1991), neither of

which was originally intended as a way of explaining driver behaviour.

Langdridge (2004) describes the theory of reasoned action as one of the most

important theories in attitude-behaviour research, providing an account of the way in

which attitudes, subjective norms and behavioural intentions can be used to predict

behaviour. According to the TRA, people’s behaviour is determined by their

intention to perform the behaviour. Intention is the cognitive representation of an

individual’s readiness to engage in a given behaviour, and is considered to be the

immediate antecedent of behaviour. Intention is determined by the summed effects

of: (a) attitudes toward the behaviour, generally referring to a positive or negative

39

judgement with respect to behavioural performance (“accelerating to pass through a

cross-junction against an amber light is bad/good”); and (b) the person’s subjective

norm or perceptions of social pressure to perform the given behaviour (“most people

who are important to me think that I should/shouldn’t run the amber light at cross-

junctions”). Ajzen and Fishbein (2000) argued that intention is the best predictor of

behaviour, and a meta-analysis carried out by Sheppard, Hartwick and Warshaw

(1988) found that the theory had strong predictive utility.

2.3.7.2 Theory of Planned Behaviour (TPB)

Complications hindered the application of the TRA in circumstances where

behaviours were not fully under volitional control, however (Sharma & Kanekar,

2007). “Even very mundane activities, which can usually be performed (or not

performed) at will, are sometimes subject to the influence of factors beyond one’s

control,” (Azjen, 1985; p. 24), such that every intended behaviour is a goal whose

attainment is subject to some degree of uncertainty. To deal with this uncertainty, he

incorporated the concept of “perceived behavioural control” (PBC), denoting the

subjective degree of control which individuals perceive themselves having over the

performance of a behaviour. This extended framework was introduced as the Theory

of Planned Behaviour (TPB; see Figure 2.2). According to the TPB, then,

behavioural intention is the result of attitudes (“do I feel like this is a good thing to

do?”), subjective norms (“do others feel this is a good thing for me to do?”), and

perceived control (“do I really believe that I can do this?”).

40

Within the theory, PBC is considered a determinant of both the individual’s

intentions and the individual’s behaviour. Its inclusion as a predictor of behaviour is

premised on the notion that, when intention is held constant, greater perceived control

(i.e., stronger feelings that “I can do it”) will heighten an individual’s confidence

level, or sense of self-efficacy, creating a proxy effect that increases the likelihood

that behaviour will be successfully performed. Further, to the extent to which PBC

reflects an individual’s level of actual control, it will directly influence behaviour

(Armitage & Christian, 2003).

The TPB has spawned a huge body of research, on the performance of a wide

range of behaviours, including driving and “it has to be admitted that the theory of

planned behaviour has stood up well. It has been applied to every conceivable type of

road user behaviour and has reliably been able to produce comparatively robust

relations between the model components and the behaviour in question”

(Rothengatter, 2002; p. 253). In one study, Forward (2006) used semi-structured

interviews to examine the degree to which beliefs differentiated between Swedish

drivers who did or did not intend to speed in an urban area, speed on a major road or

overtake dangerously. A belief that the described violations were not all that serious

(attitudes), the perception of what others would think (subjective norms) and lower

PBC all influenced the behaviours chosen by subjects and that hypotheses derived

from the TPB were confirmed.

Behavioural beliefs and

outcome evaluations

Normative beliefs and

motivation to comply

Control beliefs and

perceived facilitation

Attitude toward the

behaviour

Subjective norm

Perceived behavioural

control

Intention

Behaviour

Figure 2.3: Theory of Planned Behaviour (Ajzen, 1989)

41

In another study, Wállen Warner and Åberg (2006) used structural equation

modelling to predict drivers’ everyday speeding behaviour using the TPB as a frame

of reference. Attitude toward speeding, subjective norms and PBC were all

significant determinants of self-reported speeding, but PBC did not contribute to the

prediction of speeding violations measured with an intelligent speed adaptation (ISA)

device that logged km/hr and location at all times the vehicle was in motion. This

might be seen as evidence of the proxy effect earlier described by Armitage and

Christian (2002).

2.4 Descriptive Models of Driver Behaviour

2.4.1 Statistical Models

If traffic psychology lacks a general unified theory of driving behaviour, there

has been no shortage of empirically-based models that show statistical relationships

between specific variables related to given situations. Many of these use accident

data collected by national or sub-national government bodies or by the police and

advanced statistical techniques to describe variable interrelationships that describe or

predict crash outcomes. Austin and Carson (2002), for instance, used a negative-

binomial regression technique to analyse highway-rail crash statistics within a six-

state radius in the USA and derived a predictive model in which the contribution of

traffic characteristics, roadway configuration and crossing design were weighted.

Scuffham (2003) used a different approach to model the changes in seasonal

patterns of fatal crashes in New Zealand according to unemployment rate, Gross

Domestic Product per capita and alcohol consumption. Similar to later findings by

Law et al. (2005) in their Malaysian study (see sect.2.1.2), they found that real GDP

per capita was related to the number of crashes, but after controlling for distance

travelled, it was not significant (Scuffham & Langley, 2002). Edwards (1996)

developed a spatial model, based on data extracted from police record forms, to

predict weather-related crashes in England and Wales, while Rautela and Pant (2007)

modelled crash risk on mountain roads in India using geographical information

system (GIS) data. A large number of studies have reported epidemiological

characteristics of drivers, vehicles, pedestrians and road environments in a range of

42

locations and settings (e.g., Law, Seow & Lim, 1998; Swaddiwudhipong, Nguntra,

Mahasakpan, Koonchote & Tantiratna, 1994).

2.4.2 Process Models

2.4.2.1 The Haddon Matrix

Traditional studies of human factors in road safety have tended to view

transportation as a system with four major component elements: the human (H), the

vehicle (V), the road (R) and the environment (E). Haddon (1970) proposed a

framework in which each of these elements could be examined as part of an

analytical matrix (see Figure 2.4). This model has been instrumental in stimulating

research designs and accident interventions for the last thirty yeasrs (Williams,

1999). More recently, however, some researchers have argued that the Haddon

Matrix is limited by the way it considers each element independently, concealing the

interactions that underlie the behaviour of real traffic systems (Noy, 1997;

Richardson & Downe, 2000), and have proposed expanding the matrix to better

provide for the analysis of inter-relationships between each of the four basic

elements. One way of accomplishing this may be through the creation of models

that stress the mediational role played by certain V, R. E and especially H factors,

within specific situational contexts.

PRE-CRASH CRASH POST-CRASH

BASIC ELEMENTS OF A HIGHWAY EVENT Human (H) Vehicle (V) Road (R) Environment (E)

Figure 2.4: The Haddon Matrix (Noy, 1997)

43

2.4.2.2 A Contextual Mediated Model of Personality and Behavioural Predictors

of Motor Vehicle Crashes

Elander et al. (1993) discussed the problem of choosing predictor variables in

studies of behavioural and personality influences on road-traffic crash risk, arguing

that:

Correlational studies suffer from the inescapable problem

that causality cannot be established. Therefore, when one

observes a correlation between a behavioural measure and

crash risk, there are four possible explanations: the

behavioural measure may directly influence crash risk; it

may influence crash risk through some other, more

proximal variable; it may happen to correlate with some

other factor that influences crash risk but play no role itself;

or crash risk may influence the behavioural measure (p.

283).

Sümer (2003) used this as a point of departure for the construction of a

contextual mediated model for predicting the effects of personality and behavioural

variables on roadway crashes. Within the generic model, relevant factors are

grouped as occurring within either the distal context or the proximal context of the

accident (see Figure 2.5). Factors within the distal context include not only road,

vehicle and environmental conditions related to accident causation but a range of

driver demographic (e.g., age, gender, driving experience) and psychological

characteristics (e.g., sensation seeking, extraversion, aggression), as well. By

contrast, the proximal context is made up of driver behaviours and attitudes (e.g.,

speeding, reckless lane transitions or overtaking, substance abuse) that, on one hand,

are affected by specific or collective factors from the distal context and, on the other

hand, contribute directly to crash outcomes.

Sümer (2003) argued that the contextual mediated model could explain the

relatively weak associations between accident involvement and personality

characteristics observed in many previous studies. Personality factors within the

44

distal context were assumed to be capable of creating generalized tendencies that

increased risks of accidents within behavioural variables measured within the

proximal context. As such, it would be expected that relationships between the distal

and proximal contexts would be stronger than those between the proximal context

and crash outcomes. Variables within the distal context would be expected to affect

accident frequency indirectly and through their relationships with measures of

proximal behaviour factors, such that direct effects of distal factors would be most

likely insignificant or weak. Sümer examined the effect of three distal elements –

sensation seeking, aggression and psychological symptoms (anxiety, depression,

hostility and psychoticism) – both on three proximal elements – aberrant driving

behaviour, choice of preferred speed and dysfunctional drinking habits – and on the

frequency of self-reported accidents, with results generally supporting the notion that

factors in the distal context contributed to accident causation and predicted accidents

via their effects on proximal factors.

Road and vehicle condition Demographic characteristics

Culture-specific factors,

e.g. cultural driving habits and beliefs

Relatively stable personality

characteristics, e.g. psychological symptoms, risk taking, sensation seeking, aggression

Attributions regarding

accidents Fatalism Enforcement

Safety skills Aberrant driving behaviors Violations Errors Speeding Drinking and driving Dysfunctional drinking

CRASH OUTCOMES

DISTAL CONTEXT

PROXIMAL CONTEXT

Figure 2.5: Contextual Mediated Model of Personality, Behavioural Predictors and Motor Vehicle Crashes (from Sümer, 2003)

45

2.4.2.3 Core Concepts in the Contextual Mediated Model: Moderation and

Mediation

Inter-variable relationships within the contextual mediated model can have

direct, moderating or mediating effects. Also termed intervening variables,

mediators are variables that represent constructs proposed to explain the association

between two variables (Hoyle & Robinson, 2004).

Mediation can be said to occur when some mechanism, process or

transformation exists through which one variable influences another (Frazier, Tix and

Barron, 2004) and a distinction can be drawn between partial mediation and

complete mediation (Wei, Heppner & Mallinckrodt, 2003). In Figure 2.6(i), the

variable X is called the initial or predictor variable and it causes the variable Y,

called the outcome. Figure 2.6(ii) illustrates the basic causal chain involved in

mediation, in which there are two causal paths feeding into the outcome variable:

path c′ depicts the direct effect of X on Y; while path a represents the effect of X on

some mediator variable, M, which in turn exerts an effect on Y through path b

(Baron & Kenny, 1986). In the case where X no longer affects Y after M has been

controlled, such that path c′ is zero, then it can be concluded that complete mediation

occurs. Partial mediation is the case in which the path from X to Y is reduced in

absolute size but is still different from zero when M has been controlled (Kenny,

2006). Regression analyses can be used to test these causal paths to the outcome

variable. In Sümer’s (2003) generic contextual mediated model, proximal variables

(including safety skill levels, driver propensities to commit errors or violations,

driver impairment and so on) were hypothesised to mediate the effect of distal

variables on the frequency or likelihood of crash outcomes. If, for instance, drivers’

safety skills were a mediator of the effects of personality or cultural background on

crash frequency, then the significance level of path c would be reduced to

insignificance or a less significant level (path c’).

46

(i)

(ii)

Figure 2.6: Inter-variable Relationships in Mediation Models

In contrast, a moderating variable is one that has a strong contingent effect on

the relationship between independent and dependent variable (Sekaran, 2003). or

testing the moderating effect, there are three causal paths that can effect the outcome,

or dependent, variable (see Figure 2.7): the impact of a predictor, or independent

variable (path a), the impact of a moderator (path b), and the interaction or product of

these two (path c). Only if the interaction (path c) is significant, can the moderator

hypothesis be concluded as supported (Baron & Kenny, 1986). Baron and Kenny

have further added that although the predictor and moderator can have significant

effects on the outcome variable, these are not directly relevant conceptually to testing

the moderator hypothesis.

Predictor Variable (Distal Variable)

Outcome Variable (Crash Outcomes)

c

Mediator Variable (Proximal Variable)

Predictor Variable (Distal Variable)

Outcome Variable (Crash Outcomes)

a b

c′

X Y

X M Y

47

2.4.2.4 Studies of Driving Behaviour Using the Contextual Mediated Model

In his initial study, Sümer (2003) identified three classes of distal variables:

psychological symptoms (depression, anxiety, hostility, psychoticism); sensation-

seeking and risk-taking (novelty, intensity) and aggression (physical aggression,

verbal aggression, hostility, anger). He examined their effects on three proximal

variables: aberrant driving behaviour (violations, errors); choice of speed and alcohol

use (antisocial drinking, dangerous drinking). In turn, the effects of the proximal

variables on the number of crashes experienced within a three-year period was

examined. Using structured equation modelling, he found that, while psychological

factors did not predict speed choice, they did have a significant association with both

dysfunctional alcohol use and aberrant driving behaviours. However, more relevant

to the model he proposed, Sümer also find that aberrant driving behaviour, a

proximal variable significantly mediated the relationship between the three distal

variables and the frequency of crashes.

A number of questions may be raised about Sümer’s (2003) analysis. His

sample of 321 participants combined both professional drivers, mostly from taxi and

heavy trucking, and non-professional students who were mostly students. No

attempt was made to differentiate between these two groups. Further, given wide

Predictor Variable

Moderator

Predictor X

Moderator

X

Z Y Outcome Variable

Figure 2.7: Inter-variable Relationships in Moderation Model

a

b

c

48

variation in the number of kilometres driven annually by participants (SD = 14,739),

it was somewhat surprising that no attempt was made to control for differing levels

of traffic exposure, as recommended by Elander et al., (1993) and others. Sümer’s

decision to use self-report data is subject to the usual validity considerations raised

by several authors (af Wahlberg, 2002; Arthur, Bell, Edward, Day, Tubré & Tubré,

2005; Watson, 1998), including the three-year time-frame over which drivers were

asked to report crash occurrence. It is questionable whether crash details can be

recalled accurately for up to 36 months and requires the assumption that the

psychological characteristics, sensation seeking patterns, driving style and other

distal and proximal variables were the same at the time of the crash as they were

when data were collected (af Wahlberg, 2003; Elander et. al, 1993). Finally,

Sümer’s model construction might also be questioned, in that the standard

multivariate correlation methods applied as part of his LISREL analysis assumed a

normal distribution of crash frequency scores, while it has been accepted since the

early accident proneness studies of the IFRB that crash frequency, in most cases,

tends to fit a Poisson distribution or, for high-λ individuals, a negative-binomial

distribution (Greenwood & Woods, 1919; Greenwood & Yule, 1920).

Notwithstanding these methodological considerations, Sümer’s early work

did establish the usefulness of the contextual mediated model and structural equation

modelling procedures in describing and predicting the mediational processes that

connect certain driver psychosocial characteristics and crash outcomes. In a

subsequent study, Sümer, Lajunen and Özkan (2005), applied the five factor, or “Big

Five”, personality model (Costa & McRae, 1995; McRae &Costa, 1990) to a similar

analysis. Here, the distal factors were: neuroticism (a tendency to experience

negative affect and anxiety); extraversion (interpersonal warmth, sensation seeking);

agreeableness (helpfulness, trust); conscientiousness (dependability, responsibility,

self-discipline) and openness (adventurousness, broad-mindedness). The proximal

factor was again aberrant driving behaviour (errors, lapses, violations) and the

outcome measure was expanded to include the self-reported number of crashes and

traffic offences committed over a three-year period. Results indicated that all five of

the personality factors had indirect effects on crash risk through their effects on

49

aberrant driving behaviours. In other words, proximal behavioural variables

mediated personality factors, yielding support for the contextual mediated model.

The authors recommended that “the contextual model should be refined considering

other potential mediators, moderators and bidirectional associations between

personality and accident involvement to better understand the underlying

mechanisms” (p. 225).

Although no other studies of driving behaviour, prior to the present one, have

acted on those recommendations, some researchers have worked with models that are

conceptually consistent with the contextual mediated model. Iverson and Rundmo

(2002), for instance, reported that driver anger, sensation seeking and normlessness

(all of which which might be classified as distal, within Sümer’s contextual mediated

model) had direct effects on measures of more proximal risky driving tendencies, but

weaker significant effects on self-reported accident involvement, while the risky

driving variables had strong and significant effects on accident involvement.

2.4.2.5 Use of the Contextual Mediated Model in Other Research

Sümer’s model has been applied outside the traffic psychology domain.

Sümer, Karanci, Berument and Gunes (2005), for instance used the concept to

examine predictors of psychological distress following the 1999 earthquake in

Turkey. They found that the effect of proximal variables, including perceived control,

self esteem, optimism, material loss, perceived threat and gender on distress levels

were partially or fully mediated by individuals’ feelings of coping self-efficacy. In

another study, using a similar research design, Sümer, Bilgic, Sümer and Erol (2005)

found that a contextual mediated model was successful in showing relationships

between distal and proximal predictors of depression, phobia, hostility, anxiety,

psychotic tendencies and psychosomatic complaints among a large sample of non-

commissioned officers in the Turkish army, navy, air force and gendarmerie. Both

studies were concluded to have demonstrated support for the use of the contextual

mediated model.

50

Downe (2007), in a study of safety training methods and personality factors in

Malaysian rubber and palm oil plantations, proposed the use of a contextual mediated

model for further research on agricultural safety (see Figure 2.8).

2.5 Distal Variables in the Present Study

2.5.1 Demographic Variables

2.5.1.1 Age

Young drivers are significantly over-represented among those injured or killed

in road traffic crashes (Ballesteros & Dischinger, 2002; Odero et al., 1997; Retting,

Weinstein & Solomon, 2003; Williams & Shabanova, 2003). Not only are they the

most likely age group to be involved in crashes, but young drivers are more likely to

sustain crash-related injuries and to die in vehicular crashes (Massie, Campbell &

Williams, 1995). Yet, they have been less well studied than other groups and the

general understanding of age effects is not clear. Part of this may be due to

heterogeneity in group composition:

Safety interventions knowledge transfer ergonomic design safety audits

Psychosocial variables locus of control risk acceptance/aversion impulsivity cultural factors (e.g.,

uncertainty avoidance) temperamental factors (e.g.,

Type A, aggression)

Experiential safety awareness domain-specific skill years of work experience prior accident experience

Organisational Impacts lower injury rates and

lost time relative to labour input and output

reduced accident severity reduced risk assessment standards compliance increased worker

satisfaction

Proximal factors Distal factors

Outcomes

Safety climate worker attitude toward safe work perceived management priority employee empowerment and

control over safety post-injury administration return-to-work policies operating policies & procedures Safe Work Practices hazard identification and

reporting risk avoidance procedural compliance use of safety devices and

equipment occupational hygiene help-seeking and teamwork

behaviour

Figure 2.8: Proposed Contextual Mediated Model for Safety Research in Agriculture (from Downe, 2007)

51

The “young” category typically ranges from 16 to 25, but

there are a great many differences between a 17-year-old

and a 23-year-old driver. The former is less experienced at

driving, less emotionally mature, less experienced with the

use of alcohol and has different social and motivational

needs that may contribute to risk taking on the road. The

problems encountered by novice drivers are often attributed

to age and inexperience together. The factors of driving

style and driving skill may account, at least in part, for

these difficulties. However, not all young new drivers are

alike (Dewar, 2002a; p. 221).

Several reasons have been proposed for high age-related crash risk levels.

Younger drivers tend to have a riskier driving style than others, specifically more

likely to drive too fast, follow too closely, overtake dangerously, drive while fatigued,

are more likely to engage in alcohol use when driving and are less likely to use seat-

belts when compared to other drivers (Lerner, Jehle, Billittier, Moscati, Connery &

Stiller, 2001; Jonah, 1997b; Vassallo et al., 2007). Bina, Graziano and Bonino (2006)

have argued that, in many cases, this is a reflection of lifestyle, finding that the

riskiest young drivers in an Italian sample were also more likely to have adopted a

lifestyle characterised by higher involvement in antisocial behaviour, tobacco

smoking, comfort eating and time spent in non-organised activities with friends.

Ulleberg (2004) found that drivers reporting higher preference for risk-taking were

also characterised by low levels of altruism and anxiety, and by high levels of

sensation-seeking, irresponsibility and driving related aggression.

Harré, Foster and O’Neill (2005) studied self-rated driving attributes of 16- to

29-year old drivers in New Zealand and found a marked crash-risk optimism, in

which they believed that they were better drivers and luckier in avoiding crashes.

This was consistent with many other studies in which young drivers tended to over-

estimate their own skills and under-estimate crash risk (Dewar, 2002a; Matthews &

Moran, 1986). In fact, the contrary appears to be true. McDonald (1994) reported

52

that young drivers are less skilful in vehicle control tasks than older drivers,

particularly with respect to controlling deviations, managing velocity and regulating

acceleration. They have generally lower skill levels in acquiring and integrating

information, they have cognitive schemata that are inaccurate and relatively

undetailed, and have poor information processing and attention-switching skills. This

means that “young drivers must devote a greater proportion of their attentional

resources to conscious decision making and monitoring their driving, so they have

little spare attentional capacity” (p.39).

Young drivers may also be more prone to drive under the influence of strong

emotional states, capable of distracting attention from driving and increasing crash

occurrence. In a nation-wide survey of American teens, 76 per cent had seen peers

drive while very upset stressed, angry or sad (strong negative emotions); 74 per cent

had seen tem drive while very happy or excited (strong positive emotions); and 55 per

cent had seen them exhibit behaviour described as “road rage” (Children’s Hospital of

Philadelphia and State Farm Insurance, 2007). Stevenson et al. (2001) reported that

drivers’ perceptions of their confidence and adventurousness in the road environment

play a part in the causal pathway leading to a motor vehicle crash, and that young

drivers, particularly under conditions of heightened emotionality, are like to perceive

the driving task with overconfidence and inadequate attention to risk.

Justification of age-related hypotheses. In the present study, age was

considered a distal variable that would have an effect on participants’ behaviour in

traffic and, indirectly, on crash and injury occurrence. Since previous research had

highlighted the association between age, risky driving and crash frequency (Lourens,

Vissers & Jessurun, 1999; Ulleberg, 2002), it was hypothesised in the present study

that, as age decreased, behaviour in traffic would become less safe and crash

occurrence would be more likely. Similarly, since safe driving among younger

drivers has been shown to be more prone to the effects of emotional states, age was

hypothesized to interact with emotional states such as hopelessness and aggression.

Since many of the violations commonly committed by younger drivers – speeding,

53

failure to use seat-belts, and so on – were associated with greater risk of injury, it was

also hypothesised that, as age decreased, self-reported injury would also increase.

2.5.1.2 Gender

A large number of studies have found differences between males and females,

with respect to both driving behaviour and to crash involvement. “In all studies and

analyses, without exception, men have been shown to have a higher rate of crashes

than women. This gender difference is most marked in the population under 25 years,

but is also evident among older drivers” (Social Issues Research Centre [SIRC], 2004;

p.4). Chipman, MacGregor, Smiley and Lee-Gosselin (1992), for instance, reported

that crash incidence for men in the United States was nearly double that of women.

Monárrez-Espino, Hasselberg and Laflamme (2006) analysed Swedish police records

and found the same ratio. Waller, Elliott, Shope, Raghunathan and Little (2001)

noted that, in addition to having a higher number of crashes, male drivers incur their

first crash earlier in their driving careers and are more likely to be held to blame for

the incident. Turner and McClure (2003) showed that young male drivers scored

higher than females in driver aggression and thrill seeking and in their general

acceptance of risk.

Marked differences also occur between the genders in terms of the number of

fatalities, and behaviours predictive of fatalities. Williams and Shabanova (2003)

found that young American males were significantly more likely than young females

to be responsible for crash deaths. Dewar (2002b) stated that “some of the reasons for

this are obvious – men drive greater distances, more often at hazardous times (e.g.,

rush hour) and in hazardous conditions (e.g., darkness)” (p. 129). Åkerstedt and

Kecklund (2001), for instance, found that men had twice as high a risk as women of

being involved in a motor vehicle crash during the late night hours.

There appear to be differences in the types of crashes experienced, as well.

Tavris, Kuhn and Layde (2001) found, for instance, that males were significantly

more likely to be involved in a loss-of-control accident. However, Laapotti and

Keskinen (1998) showed that when male drivers lost control of their motorcar, it

54

usually led to a single-vehicle crash, but for female drivers the loss of control usually

resulted in a collision with another car. Male drivers drove too fast and under the

influence of alcohol more often in loss-of-control crashes, which typically took place

during evenings and nights. Female drivers’ loss-of-control crashes usually took

place under slippery road conditions and were more likely due to deficient vehicle

handling skills.

Dobson, Brown, Ball, Powers and McFadden (1999) noted that:

The relevance of gender to road safety has long been

recognised but it has been the contribution of men drivers to

fatal and serious crashes which has, to date, attracted the

most attention … Road safety literature and road safety

measures have tended to concentrate on men rather than

women and the existing literature on women drivers tends

to compare their behaviour with that of men. While there is

much of value in such an approach, there is also a danger

than concentrating on the differences between women and

men drivers may obscure the identification of the major

factors relevant to the safety of women drivers (pp. 525-

526).

This is important, as marked changes in the roles of women in society have

profound implications for the design of transportation systems (Waller, 1997;

Woodcock, Lenard, Welsh, Flyte & Garner, 2001). Laapotti and Keskinen (2004a)

indicated that, worldwide, (a) the number of female drivers is increasing, (b) females

drive increasingly more, and (c) female drivers are involved in more motor vehicle

crashes than ever before. At the same time, they noted that many studies have not

disaggregated samples to separate the effects of gender on studied phenomena and

that there is considerable contradictory evidence about whether changes in females’

driving patterns have accompanied social and economic status changes.

Lonczak, Neighbors and Donovan (2007), for instance, found that while male

drivers, in a sample taken in the U.S. state of Washington, reported more traffic

citations and injuries, they did not differ from female drivers in reported driving

55

anger. In a study of Dutch drivers, Lourens et al. (1999) corrected for variation in

annual mileage when performing multivariate analysis on a disaggregated data base

and found that crash involvement differences between males and females disappeared.

Forward, Linderholm and Järmark (1998) reviewed studies dating from 1970 to 1984

and compared them to results obtained between 1985 and 1997. The authors in each

case concluded that females’ attitudes and self-reported behaviour were becoming

increasingly similar to the attitudes and behaviour of male drivers.

In a subsequent report, though, McKenna, Waylen and Burke (1998)

disagreed, commenting that “despite the fact that there has been a massive shift in the

population of women drivers, there is little evidence that the sex difference in the

pattern of accident involvement is changing over the years” (p. 11). In a study of

male and female drivers in Finland, Laapotti and Keskinen (2004b) provided evidence

in support of this view, showing that male drivers were, as per the traditional pattern,

involved in proportionally more crashes connected to speeding, alcohol consumption

and for risky driving. Female drivers, on the other hand, had proportionally more

crashes connected to inadequate vehicle manoeuvring, control of traffic situations,

and loss-of-control incidents. In other research, Laapotti, Keskinen and Rajalin

(2003) reported that Finnish females in 2001 drove less than males, evaluated their

driving skill lower, were less frequently involved in crash situations, committed fewer

traffic offences and had a more positive attitude toward traffic safety and rules than

males, just as they had in 1978.

Justification of gender-related hypotheses. In the present study, gender was

considered a distal variable that would have an effect on participants’ behaviour in

traffic and, indirectly, on crash and injury occurrence. Since previous research had

highlighted the association between males, crash frequency and risky or aggressive

driving behaviour (Monárrez-Espino, et al., 2006; Turner & McClure, 2003), it was

hypothesised that males would be more likely to report higher-risk behaviour in traffic

and would have higher aggression scores than would females. Consistent with the

findings of McKenna et al. (1998) and Laapotti and Keskinen (2004), it was

hypothesised that males and females would differ on measures of behaviour in traffic.

56

2.5.1.3 Ethnicity

A growing number of studies have examined the effect of ethnic differences

on driving behaviour and crash outcomes. To a large degree, this has been the result

of a change in reporting protocols within the U.S. Fatality Analysis Reporting System

(FARS), that expanded the standards for collection of data on race and ethnicity in

1999 (Briggs, Levine, Haliburton, Schlundt, Goldweig and Warren, 2005). Harper,

Marine, Garrett, Lezotte and Lowenstein (2000) compared Hispanic and non-Hispanic

white motorists in the state of Colorado, finding that the former group had higher

fatality rates, lower rates of safety belt use, more frequent histories of speeding

offences and more extreme alcohol use. On the other hand, Romano, Tippetts and

Voas (2005) found no differences between African-American, White and Hispanic

drivers regarding red light violations.

A few studies have endeavoured to compare national driving cultures in terms

of crash risk. Lajunen, Corry, Summala and Hartley (1998), for instance, reported

few differences between Australians and Finns. Melinder (2007) compared 15

Western European countries with regard to the relation between different socio-

cultural factors, traffic safety regulations and traffic fatalities. Despite the fact that

countries’ regulatory frameworks were becoming increasingly similar, differences in

fatalities persisted. Melinder concluded that the type of religion and wealth were

important distal factors. Being a non-wealthy Catholic country was associated with

higher fatality rates than being a wealthy Catholic country. But, being a wealthy

Catholic country was associated with more traffic fatalities than were wealthy, non-

Catholic countries.

Very little cross-cultural research related to traffic safety has been carried out

in Southeast Asia. In one of the few studies reported, Hauswald (1999) studied the

incidence of covert non-compliance with seat belt regulations among Malaysian taxi

drivers. Conducting curb-side inspections of taxicabs in Kuala Lumpur, he found that

60% of drivers had positioned belts or shoulder straps in a manner that appeared to

57

have them restrained but had not fastened the latch. However, there was no statistical

differences between ethnic groups in the frequency of this practice.

The Malaysian population is comprised of three distinct ethnic groups: Malay,

ethnic-Chinese and ethnic-Indian (Williamson, 1999). While religious affiliation,

regional distribution and socioeconomic status differs considerably between the three

groups (Gomez, 1999), cultural differences can be more subtle. Abdullah and

Peterson (2003) have outlined value orientations for each ethnic group (see Table

2.2).

Table 2.3: Key Value Clusters for Each Malaysian Ethnic Group

Key Value Orientations

Malay Man’s relationship with God. Fatalistic. Family centeredness; cooperation; respect for elders; polite behaviour; courtesy; humility. Conscious of what other people say about us. Indirect communication. Strong relationship orientation; hierarchical; shame-driven.

Chinese-Malaysian Pre-determined future. Education; prosperity; harmony with nature; religion; filial piety; respect for elders; face saving; family ties; brotherhood/sisterhood. Strong relationship orientation.

Indian-Malaysian Pre-determination; Karma. Spirituality; piety; respect for elders; family honour; dependence on family for direction in social and career decisions; respect for knowledge; peace, prosperity and integrity; hard work.

Differences have not always been consistent. In a study of 324 employees

sampled from a cross-section of Malaysian industries, Fontaine and Richardson

(2005) found that 82% of values were shared by all three ethnic groups. They

concluded that there were, in fact, few significant value differences between ethnic

groups.

Justification of ethnic difference hypotheses. In the present study, gender was

considered a distal variable that would have an effect on participants’ behaviour in

traffic and, indirectly, on crash and injury occurrence. Based on studies that have

demonstrated ethnic differences in driving behaviour (Harper et al., 2000; Roman et

al., 2005), it was hypothesised that ethnicity would have a significant effect on

58

behaviour in traffic. Given the absence of relevant prior research on Malaysian

cultural groups, directionality of the effect was not predicted.

2.5.2 Driver Characteristics

2.5.2.1 Experience

Driver experience makes a difference in crash risk. A large number of studies

have shown that, as drivers become more experienced, they are less likely to make

errors and commit violations that result in crashes (e.g., Lajunen & Summala, 1995;

Laapotti, Keskinen, Hatakka and Katila, 2001), and indicating that those recording

higher mileage per year have fewer accidents per mile (Pelz & Schuman, 1971).

Groeger (2000) reviewed the reasons for this:

… the weight of practice more experienced drivers have

makes much of what they do routine, and as such, allows

many otherwise incompatible tasks to be performed

together. Allied to this, increased experience usually,

although not always, implies the driver has had a broader

variety of driving experiences, such as driving at different

times of the day or days of the week, with different weather

conditions, journey lengths, passenger distractions different

vehicles, etc. As experience grows, the motorist is less

likely to encounter situations very different from those they

have encountered before (p. 166).

On the other hand, in a given road and traffic scenario, inexperienced drivers

may (a) not know the correct manoeuvre so they try a different manoeuvre which

turns out to be unsafe; (b) not know how to carry out a particular manoeuvre

correctly; (c) not have had enough practice in carrying out the manoeuvre correctly;

or (d) not have had enough experience of dealing safety with the effects of human

factors on their performance (Fuller, 2002).

A useful way of conceptualising the experience effect is to draw on a

cognitive framework proposed by Mikkonen and Keskinen (1983) and later extended

59

by Keskinen, Hataaka and Katila (1992). It assumes that, as individuals acquire

experience, they organise knowledge about the driving process within an internalised

mental model that represents typical characteristics of the traffic environment and the

flow of traffic events. Experience can be gained through personal participation in

traffic as a driver and through observing the behaviour of others, but also through

verbal and pictorial description or by imagining the course of traffic events (Keskinen

et al, 2004). Internal models contain knowledge of route, including start and

destination point and corresponding visual scenes, as well as knowledge of risk

elements and a cognitive map of control equipment in the vehicle and how it behaves.

Internal models have connections to motivational and emotional systems of the driver,

and can be organised in a hierarchy that reflects the key purposes, or most important

facets of driving at different levels (see Figure 2.9). When drivers tap into models at

the base of cognitive hierarchy, they tend to priortise conscious control of the vehicle

over other elements of the driving experience. When using those at the top of the

hierarchy, they take actions based on whether they are perceived to reflect lifestyle

priorities and values.

The effects of driving experience and age are closely linked, as young drivers

generally have less experience than their older counterparts, and sometimes

confounded by gender differences. Yet, in many studies of age and gender

differences, experience effects have not been controlled (Rothengatter, 2001).

VEHICLE MANOEUVRING

Controlling speed, direction and position

MASTERING TRAFFIC SITUATIONS

Adapting to the demands of the present situation

GOALS AND CONTEXT OF DRIVING

Purpose, environment, social context company

GOALS FOR LIFE AND SKILLS FOR LIVING Importance of cars and driving for personal development Skills for self-control

Figure 2.9: Hierarchical Levels of Driving Behaviour (after Keskinen, 1996; Hatakka, 2000)

60

Laapotti et al. (2001) used the cognitive framework to explain the differing effects of

experience, age and gender on motor vehicle crash risk. They found that young

drivers failed in applying both lower and higher models of the hierarchy than did

middle-aged drivers, explained because adult identity is still under construction so life

goals and living skills are still under development. Young novice drivers, and

especially young male drivers, showed more problems connected to showing off

driving skills to peers, taking risks and consuming alcohol or drugs, all of which were

seen to reflect deficient self-control and lifestyle management abilities. Female

novice drivers, on the other hand, showed more problems than males connected to the

lower cognitive levels of the driving hierarchy, such as problems in vehicle handling

skills.

One way to understand experience effects is to study occupations for which

driving is an important work component. Studies of crash predictors among

professional drivers have been undertaken for over fifty years (e.g., Brown & Ghiselli,

1948; Ghiselli & Brown, 1949; Mintz, 1954), frequently showing that they are lest

frequently involved in motor vehicle crashes than other classes of drivers. While

motivational and differing skill levels are also important predictors of professional

drivers’ crash risk, many studies have focused on the effects of experience. Peltzer

and Renner (2003), for instance, found that risk taking within a sample of 130 drivers

of minibus taxis in the Pietersburg area of the Republic of South Africa, was inversely

correlated with driving experience and numbers of accidents witnessed. Burns and

Wilde (1995) found no relationship between collision history and personality when

they studied sampled male taxi drivers in a small Canadian city. There is some

evidence that female taxicab drivers may be at higher crash injury risk than male

taxicab drivers but not risk of crash incidence (Lam, 2004), and that taxicab drivers

are more likely to regard low- and medium-severity traffic violation penalties as

unjust than are non-professional drivers (Rosenbloom & Shahar, 2007).

Justification of driver experience hypotheses. A simple measure of driving

experience, the number of years since a driving licence was first obtained, was used in

this study. Driving experience was considered a distal variable that would have an

61

effect on participants’ behaviour in traffic and, indirectly, on crash and injury

occurrence. Based on research indicating that more experienced drivers tend to have

fewer crashes (Lajunen & Summala, 1995; Pelz & Schuman, 1971), it was

hypothesised that driving experience would have a significant effect on behaviour in

traffic scores, with greater experience associated with safer self-reported behaviour.

2.5.2.2 Driving Frequency and Traffic Exposure

Many authors have discussed the effect of traffic exposure on crash risk and

outcomes (Evans, 1984; McKenna, Duncan & Brown, 1986; Rothengatter, 2001;

Wilde, 1984). Elander et al. (1993) noted that:

People vary in the time they spend on the road, the miles

they drive, and the traffic conditions to which they are

exposed. All of these will affect the likelihood of crash

involvement. The concept of risk exposure has been

examined in some detail from the point of view of

comparing regional crash rates over different periods to

assess the effects of demographic, technical or legal

changes relating to road safety. In individual differences

research, the concept is much less well developed, and the

problem of taking adequate account of individuals’

exposure to risk is only beginning to be properly addressed

(p. 282).

Generally, it is accepted that the more one travels, the more one is going to be

exposed to traffic situations in which a crash could occur, but measuring exposure is

not always as simple a matter as computing asking for an estimate of distance

travelled per unit time (Evans, 1991). First, there may be considerable random or

systematic error in subjective reports about distance travelled (Elander et al., 1993).

Second, crash risk is affected greatly by the time of day when, and type of route

where, driving occurs (Dewar, 2002a). Åkerstedt and Kecklund (2001), for instance,

showed that the risk of crash involvement is five to ten times higher during late night

62

hours than during the forenoon. Odero et al. (1997) reviewed published and

unpublished reports on roadway crashes in developing countries from 1966 to 1994,

and found that approximately one-third of all traffic injuries occurred during the

night, with the highest incidence being between 6:00 pm and midnight. Yet, there is

little evidence that drivers are capable of reporting different categories of time or

route conditions, nor are there valid category weightings that would allow the

prediction of risk.

Several authors have emphasised the importance of considering differences in

traffic exposure when studying the effects of psychological or demographic factors on

crash and injury risk (Abdel-ATy & Anurag, 2007; Christie, Cairns, Towner and

Ward, 2007; Ferguson, Teoh & MCartt, 2007), although much research does not (e.g,.

Bina et al., 2006; Williams & Shabanova, 2003). Mercer (1989) showed that, without

correcting for annual mileage, young male drivers were strongly over-presented in

terms of both crash frequency and traffic-related fines. After correcting for the

number of kilometres driven, however, female drivers came out higher in number of

crashes and in some types of traffic violations. Lourens et al. (1999) have argued that,

in countries like the USA, Canada and Germany where a legal penalty point system is

in place to track drivers’ violations and involvement in road crashes, the resulting

‘driver records’ in combination with exposure data turn out to be the best predictors of

future crashes.

Justification of exposure hypotheses. In the present study, a simple measure

of driving frequency was used as a distal variable that would have an effect on

participants’ behaviour in traffic and, indirectly, on crash and injury occurrence. This

was taken to be representative of traffic exposure, as defined by Elander et al. (1993),

Evans (1991) and others. Because of earlier trends reported by Evans (1984) and

McKenna et al. (1986), it was hypothesised that higher levels of driving frequency

would result in less risky behaviour in traffic. In keeping with recommendations

made by Elander et al., the driving frequency measure was used as a co-variate in

analyses of relationships between other distal variable and the proximal variables of

the contextual mediated model.

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2.5.3 Psychological Variables

2.5.3.1 Locus of Control

2.5.3.1.1 Unidimensional and Multidimensional Constructs

Locus of control refers to the expectancy that one’s personal actions will be

effective to control or master the environment (Rice, 1999). Originally

conceptualised by Rotter (1966; 1975; 1990), people are thought to vary on a

continuum between the two extremes of external and internal locus of control.

Lefcourt (1976) defined external control (E) as the perception that positive or negative

events are unrelated to one’s own behaviour and thus are beyond personal control

External people, or externals , view most events as dependent on chance or controlled

by powers beyond human reach. In contrast, people who attribute behaviour to an

internal locus of control, or internals, believe that very few events are outside the

realm of human influence and that even cataclysmic situations may be altered through

human action. Lefcourt defined internal control (I) as the perception that positive or

negative events are a consequence of personal actions and thus may potentially be

subject to personal control.

Rotter’s (1966) original I-E conceptualisation of the locus of control construct

viewed it as a unidimensional, bipolar continuum along which individuals could be

placed, according to the strength of their tendencies toward making attributions of

internal or external control. Levenson (1975; 1981) extended this concept in two

ways: first, she separated the externality dimension into two, one to reflect the

influence of fate or chance (C) and the second to reflect the influence of powerful

others (P); and second, she assumed that the three resulting dimensions were

conceptually independent, such that it could be possible for an individual to score

high on all three (see Figure 2.10). She argued that “it is quite conceivable that a

person who believes in control by powerful others may also perceive enough

regularity in the actions of such people as to believe that he or she can obtain

reinforcements through purposeful action” (p. 15). Based on this multidimensional

conceptualisation, Levenson constructed a scale has been used widely in studies of

locus of control (e.g,. Holder & Levi, 2006; Hyman, Stanley & Burrows, 1991;

64

Luckner, 1989; Sinha & Watson, 2007) and has given rise to a number of other

instruments measuring multi-dimensional locus of control.

Figure 2.10: Contrast between Rotter’s Unidimensional and Levenson’s

Multidimensional Conceptualisations of Locus of Control

2.5.3.1.2 Locus of Control and Driving Behaviour

Very early studies examined a link between locus of control and risk taking.

Liverant and Scodel (1960) studied betting preferences using a simple dice-throwing

task. They found that subjects with high internal control seemed to prefer

intermediate probability bets or extremely safe bets over so-called long shots. They

also tended to bet more on safe outcomes than did the more externally oriented

subjects. According to Phares (1976), these results supported the idea that internals

would be more cautious in their control efforts while externals would engage in

riskier behaviour.

Internalizers Individuals believe that what happens is the result of their own personal decisions and efforts.

Externalizers Individuals believe that what happens is determined by fate, luck, a deity or higher power or other external circumstances

I E Unidimensional Model

Multidimensional Model

Internality High Low

Externality - Chance High Low

Externality – Powerful Others High Low

65

More recent studies have examined the relationship between an external locus

of control and risky behaviour within driving or workplace behaviour. Harrell (1995)

sampled Canadian wheat farmers to show that those incurring injuries in the field

were more likely to score high on a measure of risk taking and to believe that their

accidents had been caused by fate. Dixey (1999) found relationships between road

crashes and fatalist attitudes in Nigeria. Attitudes toward fate have been shown to be

instrumental in determining the level of risk that persons will take with regard to

delaying treatment for illness (Chung, French & Chan, 1999).

Other authors explained an apparent link between external locus of control and

crash risk on a motivational basis (Montag & Comrey, 1987). According to Brown

and Noy (2004),

If an individual views herself or himself as being

responsible for both positive and negative outcomes, s/he

will be more likely to take precautionary measures such as

wearing a seat belt and being vigilant to roadway cues. On

the other hand, those who see themselves as playing little or

no part in the unfolding of evens will act in a less cautious

manner, believing that fate will achieve its goals no matter

what the individual does (p. 39).

A great many studies have investigated the effects of locus of control on

driving behaviour, but results have been inconsistent. Guastello and Guastello (1986)

used the Rotter (1966) locus of control scale and their own transitional instrument in a

study of American college students. Their results indicated that internals had been

involved in fewer crashes than externals on their transitional scale but there was no

such relation between crashes and scores on the Rotter scale. Serious methodological

problems may have plagued this study, however, as Cronbach’s alpha values for the

scales of their transitional instrument were below accepted criteria and scale content,

which focused heavily on situational scenarios, only partially represented the original

locus of control concept. In a subsequent study, however, Iversen and Rundmo

(2002) also failed to find an association with risky driving or crash involvement.

66

Although externals reported more risk taking this trend was not significant, leading

the authors to conclude that “if a relationship exists, it may not be of sufficient

magnitude to be of value. (p. 1260).

On the other hand, when Lajunen and Summala (1995) gave Finnish

university students a series of questionnaires that assessed driving abilities and

personality, driving skills were negatively correlated with externality scores. The

same driving skill scores were positively correlated with an internal locus of control.

Özkan and Lajunen (2005) found that internals reported a higher number of total

crashes, offences, aggressive and ordinary traffic violations and driving errors,

although scores on externality dimensions did not relate significantly to any of those

dependent variables. In a meta-analysis of information-processing, cognitive,

personality and demographic/biographical predictors of vehicular involvement, an

internal locus of control was found to be associated with lower levels of crash

involvement and with higher levels of cognitive ability. In a similar study

investigating personality attributes and driving behaviour, Verwey and Zaidel (2000)

observed that people scoring high on measures of external locus of control made more

road departure errors than those scoring high on measures of with an internal locus of

control. In a much earlier study, Hoyt (1973) reported that internals reported wearing

seat belts more often and experienced highway travel as more interesting and

involving.

Arthur et al. (1991) argued that these equivocal results were due to overly

simple research designs which tended to investigate direct effects of locus of control,

rather than examining its interaction with other predictors. In an important study,

Gidron, Gal and Desevilya (2003) investigated the interaction between road-hostility

and internal locus of control in predicting the occurrence of self-reported dangerous

driving behaviour (DDB). They found that, although internality was unrelated to

DDB, it strongly moderated the relationship between hostility and DDB. That is,

hostility was associated with worse DDB to a greater extent among drivers scoring

low on internality than among drivers scoring high on internality. This study

provided support for the view that the effects of personality traits on driving

67

behaviour can be better understood by adopting a more holistic approach in which

interactions, moderating and mediating relationships are investigated. This point had

also been argued earlier by Arthur et al. (1991), Noy (1997), Richardson and Downe

(2000) and others.

2.5.3.1.3 Locus of Control and Ethnicity

Dyal (1984) argued that post-War geopolitical expansion and a growing

interest in the role of attributions, reinforcement and sociocultural processes created

fertile ground for cross-cultural studies using the locus of control construct. In very

early research, Hsieh, Shybut and Lotsof (1969) sampled three groups of high school

students (Hong Kong Chinese, US-born Chinese and Caucasian Americans). Noting

that Chinese culture, with situation-centred Confucian foundations, is based on the

notion that

… luck, chance and fate are taken for granted in life, which

is considered to be full of ambiguity, complexity and

unpredictability. Life situations may be viewed as being

largely determined by circumstances outside personal

control (p. 122).

Their results, after correction for differences in socioeconomic status,

indicated that, as hypothesised, Hong Kong Chinese students were more externally

controlled on Rotter’s (1969) I-E scale, whereas Americans scored high on internal

control and Chinese-Americans were somewhere in-between.

More recent research has continued to find differences in locus of control

between cultures and between sub-groups within cultures. Parsons and Schneider

(1974) administered Rotter’s (1969) I-E scale to 120 male and female students in

Canada, France, Germany, Israel, Italy, India, Japan, and the USA. Japanese students

had significantly higher external scores than in all other countries, while externality

scores for Indian students were significantly lower than those in France, Canada and

Japan. Crittendon (1991) found that female university students in Taiwan were more

68

externally-controlled and more self-effacing than either American females or

Taiwanese males.

In very early research, Carment (1974) found Indian university students to be

significantly more internal than Canadian students on the full scale score for Rotter’s

(1969) I-E instrument, due largely to high scores on items measuring political

ideology and social system control. He attributed this to the belief that dealing with

widespread nepotism, ingratiation and bribery found in India requires effort, skill and

ability, all internal characteristics. At the same time, Indian students were more

external than Canadians on personal control factors, a finding Carment interpreted as

reflecting greater dependency on and conformity within the somewhat indulgent and

closely-connected Indian family structure. Much more recent research by Sinha and

Watson (2007) used Levenson’s (1984) multidimensional model to show that Indian

university students are significantly more externally controlled by fate and chance

than Canadian university students, although there were no differences in internality

nor externality involving powerful others.

To the author’s knowledge, only Cheung, Cheung, Howard and Lim (2006)

have offered research evidence related to cross-cultural differences in locus of control

within Malay, Chinese and Indian populations, and this was provided as part of a

larger study comparing Singaporean ethnic groups to a Chinese normative sample

from the People’s Republic of China. Using an English version of the Cross-Cultural

Chinese Personality Assessment Inventory (CPAI-2), they found that the three ethnic

groups in Singapore had greater commonalities in the measured personality constructs

than Singaporean-Chinese subjects had with the normative sample. This was very

true for the locus of control variable, where no significant differences were found

between those of Indian, Chinese of Malay extraction, but all three groups differed

from the sample drawn in China. No published accounts of research conducted in

Malaysia with regard to locus of control differences among members of Malay,

Chinese and Indian ethnic groups were found.

69

Justification of hypotheses about locus of control. In the present study, locus

of control was considered a distal variable that would have an effect on participants’

behaviour in traffic and, indirectly, on crash and injury occurrence. Given the strong

research evidence suggesting an association between internal locus of control and less

risky driving behaviour (Lajunen & Summala, 1995; Montag & Comrey, 1987; Özkan

& Lajunen, 2005), it was hypothesised that internality would have a negative

association with unsafe behaviour in traffic, while the two dimensions of externality

would have positive associations. Based on the findings reported by Gidron et al.

(2003), it was hypothesised that locus of control would moderate the relationship

between aggression and behaviour in traffic. Finally, given the large number of

studies indicating ethnic differences in locus of control (Crittendon, 1991; Sinha &

Watson, 2007), it was hypothesised that Chinese participants would tend toward

higher externality scores while Indian participants would tend toward higher

internality.

2.5.3.2 Hopelessness

Rothengatter (2002) and Groeger (1997) have both noted the paucity of

research on affect and driver behaviour. Personality traits closely aligned with given

mood states might well be expected to have an impact on the performance on driving

tasks. Hopelessness is one such trait in which the behaviour of individuals is derived

from specific cognitive distortions that systematically misconstrue experiences in a

negative way and, without objective basis, anticipate a negative outcome to any

attempts that may be made to attain the individual’s major objectives or goals (Beck,

Kovacs and Weissman, 1975).

Hopelessness has not been previously studied as a predictor either of driving

behaviour or of crash risk, but there are two conceptual arguments for doing so. First,

hopelessness has been consistently shown to be a predictor of suicidal intent (Beck, et

al, 1975; McMillan, Gilbody, Beresford & Neilly, 2007; Niméus, Träskman-Bendz &

Alsén, 1997; Weissman, Fox & Klerman, 1973). Ohberg, Pentilla and Lonnqvist

(1997) studied all fatal car crashes in Finland from 1987 to 1991 and found that 5.9%

could be classified as having an intentional suicidal component. Cases usually

70

involved head-on collisions between two vehicles with a large weight disparity and

victims had often suffered from life-event stress, mental disorders and alcohol misuse.

Hernetkoski and Keskinen (1998), in a more detailed study, found that the most

commonly reported mental state among Finnish drivers dying in crashes classified as

suicidal had been “depression” and “hopeless”. They also classified a group of

drivers whose highly negligent actions, whose crashes had resulted from extreme

risks, usually when impaired by alcohol or drug use.

Very early on, it was suggested that “many persons with self-destructive

inclinations may unconsciously attempt to destroy or injure themselves through

automobile accidents and that these accidents are rarely perceived as suicidal attempts

by either the driver or the public” (Selzer & Payne, 1962). Several authors, in fact,

have proposed that potentially self-destructive behaviours, including risky driving,

can be placed along a continuum between high hope for the future at the positive pole

and a sense of hopelessness at the negative pole (Aylott, 1998; Firestone & Seiden,

1990; Henderson, 1976; Mendel, 1974).

Second, hopelessness has been associated with personality and behavioural

factors that have been shown to be good predictors of driver behaviour and crash risk.

Prociuk, Breen and Lussier (1976), for instance, investigated the relationship between

hopelessness, locus of control and depression with university students in western

Canada, finding that persons who perceived reinforcements to be a function of

powerful others, luck, chance or fate not only expressed greater pessimism about the

future but were more likely to report depressive states. Chioqueta and Stiles (2005)

showed that hopelessness in Norwegian university students was positively predicted

by high scores in neuroticism and depression, and negatively predicted by

extraversion, assertiveness and positive emotion.

Justification of hopelessness hypotheses. In the present study, hopelessness

was considered a distal variable that would have an effect on participants’ behaviour

in traffic and, indirectly, on crash and injury occurrence. Based on earlier findings

about the relationship between depressive-suicidal states, in which hopelessness plays

a significant part, and crash risk (Ohberg et al,. 1997; Selzer & Payne, 1962), it was

71

hypothesised that participants scoring high on a measure of hopelessness would be

likely to engage in behaviour in traffic that was less cautious. In a largely unrelated

study, Binzer (1999) suggested that hopelessness may play a moderating role in the

effects of locus of control on some psychiatric symptoms associated with unconscious

motivations. It was hypothesised here that hopelessness would moderate the manner

in which locus of control affected behaviour in traffic.

2.5.3.3 Aggression

Since the 1980s, attention to the issue of aggressive driving has grown

exponentially, sparked by a number of highly publicised accounts of road rage and

improved techniques for measuring anger and aggression in drivers. While media

reports and some authors (James & Nahl, 2000; Mizell, 1999) have argued that there

has been a marked increase in the frequency of aggressive incidents and anger-related

crashes, it is difficult to accurately assess whether the problem is becoming more

common or whether greater visibility has been due to a growth in awareness and

reporting (Galovski, Malta & Blanchard, 2006).

Although uncertainty persists as to whether road aggression is actually

increasing, there is no shortage of evidence to suggest a consistent and reliable

association between aggressive driving and motor vehicle crashes (Blanchard, Barton

and Malta, 2000; Chliaoutaks, Demakakos, Tzamalouka, Bakou, Koumaki, & Darviri,

2002; Deffenbacher, Filetti, Richards, Lynch & Oetting, 2003; Underwood, Chapman,

Wright & Crundall, 1999; Wells-Parker et al., 2002). Most authors seem to agree

with the early contention by Näätänen and Summala (1976) that aggressive driving

could result from driver frustration at obstructions such as traffic congestion; this

concept became the basis for what is now known as the frustration-aggression

hypothesis of aggressive driving. Novaco (1991) proposed that driver aggression is

produced when environmental triggers interact with a variety of predisposing factors,

including subjective feelings of stress, physiological arousal, learned cognitive scripts,

learned disinhibitory cues. O’Connell (2002) has described the use of alcohol, which

acts to counter the influence of normative moral codes and to increase people’s

impulsive responses to stimuli as one such disinhibitory cue, and deindividuation,

72

which creates a sense of anonymity and diminished personal responsibility, as

another. Shinar (1998) argued that the frustration-aggression hypothesis provided an

appropriate model for aggressive driving, but needed to be expanded to account for

the influence of personality factors such as Type A personality behaviour (TAPB),

cultural driving norms and situational conditions. Groeger (2000), though, raised the

point that:

It seems to me possible that the peculiar cocktail of

personal challenge, threat to own safety and self-eesteem,

stress induced by time pressure, lack of control over events,

and frustration of goals that comprises the driving task in

the modern world, does indeed have all the ingredients that

might give rise to increased levels of anger and hostility.

However, it may equally be that the people involved would

be aggressive in situations beyond driving – with driving

being an opportunity for, rather than a cause of, the display

of aggression (p. 163).

Schwebel et al. (2006) extended Shinar’s (1998) efforts to broaden the focus

of frustration-based explanations by showing that sensation seeking interacted with

anger and hostility to influence driving violations. Bettencourt, Talley, Benjamin and

Valentine (2006) conducted a meta-analysis of studies assessing personality variables

and aggressive behaviour. They reported that trait aggressiveness and trait irritability

influenced aggressive behaviour under both provoking and neutral conditions but that

other personality variables, such as TAPB, angry thinking and trait anger influenced

aggressive behaviour only under conditions in which the individual was provoked.

Meichenbaum (1977) pioneered the use of cognitive restructuring, through the

use of self-statements, to better cope with stress and achieve behavioural change.

This led to an interest in the sorts of self-talk in which individuals engaged under

varying conditions and corresponded with the emergence of cognitive therapies

(Beck, 1976; Ellis, 1962). More recently, Snyder, Crowson, Houston, Kurylo and

Poirier (1997) created the Hostile Automatic Thoughts Scale to reflect the frequency

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with which individuals make cognitive statements reflecting aggressive sentiments.

Those authors used the instrument to assess hostility and negative affect within a

population of veterans diagnosed with combat-related posttraumatic stress disorder

(Crowson, Frueh & Snyder, 2001). Later still, Deffenbacher, Petrilli, Lynch, Oetting

and Swaim (2003) used the same approach to examine angry cognitions made by

drivers under varying conditions of provocation. They found that perjorative labelling

and vengeful or retaliatory thoughts correlated highly with self-reported aggressive

driving.

Justification of aggression-related hypotheses. In the present study,

aggression was considered a distal variable that would have an effect on participants’

behaviour in traffic and, indirectly, on crash and injury occurrence. Based on the

extensive research on the association between aggression and unsafe driving

behaviour (Galovski et. al., 2006; James & Nahl, 2000; Bettencourt et al, 2006), it

was hypothesised that aggression would have a negative effect on behaviour in traffic.

It was also hypothesised, consistent with earlier research by Deffenbacher et al.

(2003), that the total amount, and specific content, of hostile automatic thought would

moderate the relationship between aggression and behaviour in traffic.

2.6 Proximal Variables in the Present Research

2.6.1 Type A Behaviour Pattern and Motor Vehicle Crashes

The Type A Behaviour Pattern (TAPB) has been associated with a wide range

of behavioural outcomes and is perhaps the most widely publicised and popularly

discussed biotype (Rice, 1999). Originally identified by Friedman and Rosenman

(1974), TAPB is characterised by a sense of time urgency, impatience, insecurity

about status, competitiveness, aggression, hostility and difficulty achieving states of

relaxation (Ben-Zur, 2002; Blumenthal, McKee, Williams & Haney, 1981; Karlberg,

Undén, Elofsson & Krakau, 1998; Rice, 1999; Sato, Kamada, Miyake, Kumashiro &

Kume, 1999; Thurman, 1985).

Dewar (2002b) noted that TAPB has been one of the variables most

consistently linked to driving performance. Magnavita, Narda, Sani, Carbone,

74

DeLorenzo and Sacco (1997), for instance, studied police officers in Italy, where

Type A drivers were 4.2 times more likely to have an accident than others. Perry

(1986) fond significant simple correations between scores on a commonly used

measure of TAPB, the Jenkins Activity Survey (Jenkins, Zzanski & Rosenman, 1979)

and number of accidents, violations and self-reported driving impatience in a sample

of 54 American students. In a correlational study of British drivers, West, Elander

and French (1993) found that TAPB had a strong association with excessive driving

speed, but not with accident risk. In none of these studies, however, was driving

frequency, traffic exposure or driver gender controlled as variables.

Nabi, Consoli, Chastang, Chiron, Lafont and Lagarde (2005), however, did

control for the effects of a range of potential confounding variables – annual mileage,

driving style, alcohol consumption, age, gender, socio-professional category, category

of vehicle, and drivers’ attitudes toward traffic regulations – when examining the

association between motor vehicle crashes and Type A scores in a prospective study

of 20,000 employees of a French oil and gas company. They found a robust

association between scores on a measure of TAPB and later serious crashes.

Although their research design accounted for the influence of potential confounds, it

may have been flawed by methodological deficiencies discussed by af Wählberg

(2003) and by Elander et al. (2003) with respect to data collection time periods. Nabi

et al. tested drivers on a TABP questionnaire in 1993 and then tracked their driving

behaviour to record crash history from 1994 to 2001. Although there is some

evidence as to the long-term stability of TAPB (Keltikangas-Jarninen, 1989;

Raikkonen, 1990), it may be questionable as to whether subjects would have recorded

the same score on the measure of Type A behaviour on the day of their motor vehicle

crash as they did when tested in the laboratory up to eight years earlier.

Other authors have examined which of the behavioural dimensions of TAPB

has the strongest impact on driving outcomes. Perry and Baldwin (2000) argued that

it was the tendency of Type A drivers toward a heightened sense of urgency and

impatience that created crash risk, particularly in driving situations that require

prudence. Karlberg et al. (1998), similarly, focused on the time urgency component

75

of TAPB that had the most significant influence on driving risks. Miles and Johnson

(2003), on the other hand, stressed the relationship between Type A individuals and

the tendency to engage in more aggressive acts while driving as the key factor in the

relationship between TAPB and crashes.

2.6.2 A Conceptual Shift from TAPB to Behaviour in Traffic (BIT) as a

Variable

Synodinos and Papacostas (1985) also attempted to examine the TAPB

dimensions that related to driving behaviour in a sample of Hawaiian university

students. Using an instrument with items at extreme ends of the Type A/B

continuum, they examined the influence of TAPB on a number of driving outcomes,

specifically measuring the effects of drivers’ usurpation of right-of-way (lane

violations and reluctance to yield), freeway urgency (excessive speed choice),

externally-focused frustration (congestion irritation and hostility toward other drivers)

and destination-activity orientation (inattention to the driving task related to journey

motives or outcomes). The BIT scale was positively correlated with the student

version of the Jenkins Activity Survey (SJAS; Glass, 1977), with higher BIT scores

reflecting a stronger Type A orientation. Gender, ethnicity, driving exposure and the

place where they had learned to drive all had direct effects on Type A results.

In a subsequent study, Papacostas and Synodinos (1988) reported several

further analyses of their original data, emphasising the four individual dimensions of

behaviour in traffic rather than the composite BIT total score. Of the four BIT

factors, only externally-focused frustration was consistently correlated with Type A

behaviour, as measured by the student version of the SJAS. At the same time, all four

BIT factors significantly predicted participants’ self-reported driving characteristics.

Papacostas and Synodinos concluded that:

Type A/B behaviour is consistently related to only one of

the four driving factors obtained by the BIT, namely

“externally-focused frustration”. If all four BIT factors

contribute to accident proneness, then use of the Type A/B

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construct as the basis of further investigation into the

question of highway safety will provide only an incomplete

picture. Specifically, it will not be sensitive to “usurpation

of right-of-way” which is related to aggressive driving;

“freeway urgency’’ which manifests itself in speeding and

frequent lane changing; and “destination-activity

orientation” which is possibly a cause of inattentive driving

(p. 13).

They argued that it would be preferable, in studying the effects of Type A

behaviour on safe driving patterns to use the BIT scale instead of measures of TABP

because the latter tended to allow only for the direct measurement of Type A-related

hostility and were often insufficiently sensitive to the effects of other components of

the behaviour pattern on driving.

In neither of their studies, though, did Papacostas and Synodinos (1985, 1988)

attempt to relate scores on their BIT scale to crash frequency or injury. At the present

time, all that can be concluded about the BIT concept is that composite scores have

been indicative of high Type A scores on the student version of the SJAS and that the

SJAS was unable to predict three of the four component scores, thought to be critical

in the relationship between TAPB and motor vehicle crashes, that are measured by the

BIT scale. Similarly, although ethnicity, gender and other demographic factors were

shown to affect BIT subscales, the extent to which other personality factors influence

the four components comprising behaviour in traffic was not investigated. To the

author’s knowledge, no further use of BIT scale has been reported in studies of

driving safety.

Justification of BIT-related hypotheses. In the present study, participants’

behaviour in traffic was considered a proximal variable that, on one hand would have

an effect on crash and injury occurrence and, on the other hand, would be influenced

by drivers’ psycho-social characteristics, including gender, ethnicity, driving

experience, locus of control, hopelessness, aggression and the amount and content of

77

hostile automatic thought. Many studies have suggested that drivers’ TABP is a

factor in motor vehicle crashes (Perry, 1986; Miles & Johnson, 2003; Nabi et al.,

2005; West et al., 1993) and, since the composite BIT score has been shown to be an

accurate reflection of over-all Type A behaviour (Synodinos & Papacostas, 1985), it

was hypothesised here that BIT total scores would have a positive effect on both crash

and injury occurrence. Further, since Papacostas and Synodinos (1988) found that all

four component factors of BIT were related to driving characteristics, it was

hypothesised that drivers’ scores on measures of usurpation of right-of-way,

externally-focused frustration, freeway urgency and destination-activity orientation

would each have positive effects on both crash and injury occurrence.

78

CHAPTER 3

METHOD OF INVESTIGATION

3.1 Conceptualisation and the Research Framework

Based on the discussion in the previous chapter, the present research

attempted to support the notion that variables in the distal context (psychological

factors) contributed to crashes and injuries, through their action on proximal

variables (behaviour in traffic). The extent to which drivers’ self-reported behaviour

in traffic (BIT) predicted motor vehicle crash occurrence and injury occurrence was

assessed first by examining each of five successive samples of drivers. Then, each

study explored the extent to which demographic, driving and psychological variables

were linked to each other and to self-reported driving behaviour. The research model

was developed and tested over the course of three separate studies:

Study 1: Units of analysis consisted of only automobile drivers

Study 2: Units of analysis consisted of only motorcycle drivers

Study 3: Units of analysis consisted of only taxicab drivers

In Study 1, using automobile drivers as the units of analysis, the research

model was developed and tested in studies 1A, 1B and 1C, each of which sought to

replicate and expand the previous one. Study 1A investigated the effects of

demographic (driver age, gender and ethnicity) and psychological (locus of control

and hopelessness) variables in predicting self-reported BIT and then on self-reported

crash and injury occurrence (see Figure 3.1). In Study 1B, the effects of the same

demographic and psychological variables in predicting self-reported BIT and then in

predicting self-reported crash and injury occurrence were evaluated, with the

addition of a third psychological variable, aggression (see Figure 3.2). In Study 1C,

the effects of the same demographic and psychological variables as used in Study 1B

were evaluated as predictors of self-reported BIT and then of self-reported crash and

injury occurrence, with the addition of a fourth psychological variable, hostile

automatic thoughts (see Figure 3.3).

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In Study 2, using motorcycle drivers as units of analysis, the research model

was tested to investigate the effects of demographic (driver age, gender and

ethnicity) and psychological factors (locus of control and hopelessness) in predicting

self-reported BIT and then on self-reported crash and injury occurrence (see Figure

3.2).

In Study 3, using taxicab drivers as units of analysis, the research model was

tested to study the effects of demographic (driver age, gender and ethnicity) and

psychological factors (locus of control, hopelessness and aggression) in predicting

self-reported BIT and then on self-reported crash and injury occurrence (see Figure

3.4).

80

Behaviour in Traffic (BIT) Usurpation of right-of-way Freeway urgency Externally-focused frustration Destination-activity orientation

Locus of Control Internality Externality (chance) Externality (Powerful

Other)

Demographic Variables Gender Ethnicity Age

Hopelessness (BHS)

Injury Occurrence

Crash Occurrence

Driver Characteristics Driver experience Driving frequency

H1.1

Figure 3.1: Research Model (Study 1A and Study 2)

DISTAL CONTEXT PROXIMAL CONTEXT OUTCOME

H2

H3

H4 H5

H7 H6

H8

H9 BHS x Locus of Control

H1.2

81

Locus of Control Internality Externality (chance) Externality (Powerful

Other)

Demographic Variables Gender Ethnicity Age

Hopelessness (BHS)

Driver Characteristics Driver experience Driving frequency

Aggression (AQ) Physical aggression Verbal aggression Anger Hostility Indirect aggression

PROXIMAL CONTEXT DISTAL CONTEXT

Figure 3.2: Research Model (Study 1B)

Locus of Control x AQ

BHS x Locus of Control

Behaviour in Traffic (BIT)

Usurpation of right-of-way Freeway urgency Externally-focused frustration Destination-activity orientation

OUTCOME

Crash Occurrence

Injury Occurrence

H2

H3

H5

H4

H10

H11

H8

H6 H7

H9

H12

H1.1

H1.2

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Behaviour in Traffic (BIT) Usurpation of right-of-way Freeway urgency Externally-focused frustration Destination-activity orientation

Locus of Control

Demographic Variables Gender, Ethnicity & Age

Hopelessness

Injury Occurrence

H10

H5

H12

Aggression Questionnaire (AQ)

Hostile Automatic Thoughts (HAT)

Physical Aggression Derogation of Others Revenge

H13

Driver Characteristics Driver experience Driving frequency

PROXIMAL CONTEXT OUTCOME DISTAL CONTEXT

H2

H3

H14

H11

H8

H7

H4

BHS x Locus of Control

HAT x AQ

H9

H15

H6

Crash Occurrence

H1.2

H1.1

Figure 3.3: Research Model (Study 1C)

Locus of Control x AQ

83

Locus of Control Internality Externality (chance) Externality (Powerful

Other)

Aggression (AQ) Physical Aggression Verbal Aggression Anger Hostility Indirect Aggression

PROXIMAL CONTEXT DISTAL CONTEXT

Figure 3.4: Research Model (Study 3)

Locus of Control x AQ

Behaviour in Traffic (BIT)

Usurpation of right-of-way Freeway urgency Externally-focused frustration Destination-activity orientation

OUTCOME

Crash Occurrence

Injury Occurrence

H3

H4

H10

H11

H8

H12

Demographic Variables Ethnicity & Age

H1.2

H1.1

Driver Characteristics Driver experience Taxicab experience H2

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3.2 Definition of the Variables

This section identifies, classifies and provides an operational definition of each

of the variables used in the present research. Variables included (a) self-reported driving

characteristics; (b) demographic variables; (c) locus of control; (d) hopelessness; (e)

aggression; (f) hostile automatic thoughts; (g) self-reported behaviour in traffic; (h) self-

reported crash occurrence; and (i) self-reported injury occurrence. Variables (a) through

(f) were grouped within a super-ordinate class as distal variables. Variable (g) was

considered as a proximal variable. Variables (h) and (i) were grouped within a super-

ordinate class as outcome variables.

3.2.1 Driver Characteristics: Driver Experience and Driving Frequency

Driver experience was defined as the length of time, in months, that participants

reported they had held a valid driving licence, consistent with the approach used in

earlier research by Synodinos and Papacostas (1985). Frequency of travel was measured

by asking participants to respond to the question, “how often do you travel in a car” as a

driver, using a six-point Likert-type scale.

3.2.2 Demographic Variables: Age, Gender and Ethnicity

Participants reported their age in years, their gender and chose a descriptor of

their “ethnic background” from a list including “Malay”, “Chinese-Malaysian”, “Indian-

Malaysian” and “Other, please specify”. The use of ethnic self-identification has been

the most frequently used means of establishing cultural affiliation in previous studies of

driving behaviour (Ey, Klesges, Patterson, Patterson, Hadley, Barnard & Alpert, 2000;

Romano, Tippetts & Voas, 2005a, 2005b).

3.2.3 Locus of Control

Locus of control refers to the extent to which individuals believe that they are in

control of the events that affect them (Rotter, 1966). The present research, adopted

Levenson’s (1973) assumption that there are three independent dimensions to locus of

control: internality; chance and powerful others. Within this model, one can endorse

85

each of these dimensions independently and at the same time, such that an individual

might simultaneously believe that oneself and powerful others control an outcome, but

not chance. For each of the five studies undertaken, a separate score for internality (I),

externality related to chance (C) and externality related to the influence of powerful

others (P) was obtained.

3.2.4 Hopelessness

Hopelessness has been defined as a cognitive or motivational state characterised

by negative expectancies, a future directed information-processing bias or schema which

functions to distort individuals’ subjective experience of external reality (Velting, 1999).

According to Farran et al (1995):

Hopelessness constitutes an essential experience of the

human condition. It functions as a feeling of despair and

discouragement; a thought process that expects nothing;

and a behavioural process in which the person attempts

little or takes inappropriate action (p. 25).

In the present research, hopelessness was measured as a unidimensional

construct, consistent with the way the variable has been described by Beck (1987a) and

Beck, Weissman, Lester and Trexler (1974).

3.2.5 Aggression

Spielberger et al (1995), Galovski et al (2006) and others have noted that the

definitions of anger, hostility and aggression are often inconsistent, overlapping and

ambiguous. For the purposes of the present research, anger was defined as a negative

internal state of physiological arousal and cognition that involves interactions between

physiological, affective, cognitive, motoric and verbal components (Sharkin, 1988) and

tends to manifest itself under antagonistic conditions (Novaco, 1994). While Beck

(1999) and others have reserved the term anger for the feeling that accompanies such an

internal state, aggression is regarded here as referring to the hostile behaviour that

occurs as a result of it. It has been accepted for a long time that aggression finds its

86

expression in a range of behavioural manifestations (Buss & Durkee, 1957;

Deffenbacher, Oetting, Lynch & Morris, 1996). In the present research, the following

variables were examined:

(a) physical aggression – the tendency to use physical force when

expressing anger or aggression, through fighting, hitting or

interpersonal violence;

(b) verbal aggression – the tendency to be unduly argumentative

and to use quarrelsome and hostile speech in dealing with

others in antagonistic situations;

(c) overt anger – the tendency to experience high levels of

emotional arousal and a perceived loss of control, expressed

through the presence of irritability, frustration, emotional

lability and temperamental gesturing;

(d) hostility – including attitudes of bitterness, social alienation

and paranoia, generally to the point where the needs or

feelings of others cannot be taken into consideration; and,

(e) indirect aggression – the tendency to express aggressive

impulses in actions that avoid direct confrontation.

The effects of participants’ total aggression, taken as a sum of measures of each

of the foregoing, were also investigated.

3.2.6 Hostile Automatic Thoughts

While the role of cognitive self-talk in directing behavioural responses has been

accepted since the early work of Beck (1976), Ellis (1962) and Meichenbaum (1977),

but it has only relatively recently been considered in light of driving aggression

(Deffenbacher et al, 2003; Vallières, Bergeron & Vallerand, 2005). The present

research examined the effects of a set of cognitive statements – described as hostile

automatic thoughts (Snyder et al, 1997) – on the behaviour in traffic of drivers in Study

1C. Specifically, the three forms of hostile automatic thoughts were defined as:

87

(a) physical aggression – cognitive self-talk that contained

content indicating an intent or desire to violently attack, hit or

kill another individual;

(b) derogation of others – cognitive self-talk that contained

content which belittled, degraded or wished to be rid of

another individual; and,

(c) revenge – cognitive self-talk that contained content indicating

an intent or desire to take some action against another

individual to get even for a perceived wrong.

3.2.7 Behaviour in Traffic (BIT)

The present research attempted to determine the effects of variables related to the

self-reported patterns of driving behaviour among car drivers, motorcyclists and taxicab

drivers sampled in the five successive studies undertaken. A global measure of self-

reported driving tendencies, the BIT score, was defined as indicative of Type A

Behaviour Pattern (TABP), characterised by excessive impatience, competitiveness,

hostility and time pressure (Karlberg et al., 1998). Four separate dimensions of BIT

were examined:

(a) usurpation of right-of-way – representing self-reported

behaviours that took the form of evasive or uncooperative

manoeuvres such as speeding to get away from others, not

allowing others to merge or overtake, and an expressed

preference for operating powerful vehicles;

(b) freeway urgency – including an expressed preference for

freeway driving, frequent lane changing, driving consistently

in the fast lane and travelling above the speed limit;

(c) externally-focused frustration – consisting of emotional

reactions to the actions of other drivers on the road (e.g., being

irritated by slow drivers) and of directive behaviours toward

88

them (e.g., urging others to move faster or out of the way by

sounding the horn); and,

(d) destination-activity orientation – defined as a preoccupation

on the part of drivers with reaching their destinations on time

and with the tasks to be performed there, to the extent of

inattention to contemporaneous roadway and traffic

conditions.

3.2.8 Crash Occurrence

Participants reported whether or not they had experienced a motor vehicle crash,

while driving, within the preceding twelve months and provided details about the nature

of the crash. In the resulting measure of this variable, a “1” was scored if the participant

reported a crash and a “0” was scored if the participant did not report a crash.

3.2.9 Injury Occurrence

Participants also indicated whether they had been forced to seek medical

treatment for an injury incurred during the reported crash. In the resulting measure of

this variable, a “1” was scored if the participant reported that they had sought treatment

at a medical clinic or had required hospitalisation as a result of physical injury sustained

during the crash and a “0” was scored if the participant reported that the crash had

resulted in no damage or only damage to the vehicle.

3.3 Research Design of the Study

3.3.1 Study 1A

Specifically, in Study 1A, the extent to which self-reported BIT of automobile

drivers predicted crash and injury occurrence was assessed while controlling the effects

of driving experience and the drivers’ self-reported travel frequency. Then, the influence

of driving experience, travel frequency, three demographic variables (driver age, gender

and ethnicity) and two psychological variables (locus of control and hopelessness) on

BIT was tested. Then, the interrelationships between the demographic variables,

89

psychological variables (locus of control and hopelessness) and BIT were examined. In

this study, the moderating effect of hopelessness on the relationship between locus of

control and BIT was tested. Finally, the mediating effect of BIT on the relationship

between psychological factors and crash occurrence and injury occurrence was tested.

Figure 3.1 illustrates the research design for Study 1A.

3.3.2 Study 1B

While again controlling the effects of driving experience and drivers’ self-

reported travel frequency, the extent to which self-reported BIT predicted crash and

injury occurrence in automobile drivers was assessed. Then, the influence of driving

characteristics, travel frequency, three demographic variables (driver age, gender and

ethnicity) and three psychological variables (locus of control, hopelessness and

aggression) on BIT was tested. Then, the interrelationships between the demographic

variables, the psychological variables and BIT were examined. In Study 1B, two

moderating effects were tested: (a) the moderating effects of hopelessness on the

relationship between locus of control and BIT, and (b) the moderating effect of locus of

control on the relation between aggression and BIT. Finally, the mediating effect of the

BIT on the relations between psychological variables and crash occurrence and injury

occurrence was tested. Figure 3.2 illustrates the research design for Study 1B.

3.3.3 Study 1C

In Study 1C, the effects of driving experience and drivers’ self-reported travel

frequency were controlled and the extent to which self-reported BIT predicted crash and

injury occurrence in automobile drivers was assessed. Then, the influence of driving

characteristics, travel frequency, three demographic variables (driver age, gender and

ethnicity) and four psychological variables (locus of control, hopelessness, aggression

and hostile automatic thoughts) on BIT was tested. Then, the interrelationships between

the demographic variables, the psychological variables and BIT were examined. In this

study, three moderating effects were measured: (a) the moderating effect of hopelessness

on the relationship between locus of control and BIT, (b) the moderating effect of locus

90

of control on the relationship between aggression and BIT, and (c) the moderating effect

of hostile automatic thoughts on the relationship between aggression and BIT. Finally,

the mediating effect of the BIT on the relations between psychological variables and

crash occurrence and injury occurrence was tested. Figure 3.3 illustrates the research

design for Study 1C.

3.3.4 Study 2

The research design for Study 1A was replicated, using a sample that indicated

motorcycles as their primary mode of transportation. Variables and analyses in Study 2

were the same as those carried out in Study 1A. Figure 3.1 illustrates the research

design for Study 2.

3.3.5 Study 3

The final study used a sample of on-duty taxicab drivers. Two measures of

experience were included: (a) driving experience, or the length of time they had held a

valid automobile operator’s licence; and (b) taxi experience, or the length of time they

had been licensed to operate a taxicab. In Study 3, the effects of both measures of

experience were controlled and the extent to which self-reported BIT predicted crash

and injury occurrence in automobile drivers was assessed. Then, the influence of

experience, three demographic variables (driver age and ethnicity) and two

psychological variables (locus of control and aggression) on BIT was tested. Then, the

interrelationships between the demographic variables, the psychological variables and

BIT were examined. In Study 3, the moderating effect of locus of control on the

relationship between aggression and BIT was assessed. Finally, the mediating effect of

the BIT on the relations between psychological variables and crash occurrence and

injury occurrence was tested. Figure 3.4 illustrates the research design for Study 3.

It should be noted that certain of the variables examined with the automobile

drivers in Study 1 and with the motorcycle drivers in Study 2 were not included when

taxicab drivers were sampled in Study 3. This was justified for three reasons. First,

91

given that data were collected during in situ interview and testing sessions with drivers

during a rolling trip from point to point through Kuala Lumpur streets, limitations were

imposed on administration time by driver willingness to participate and by research cost

considerations. Second, instruments employed to measure hopelessness and hostile

automatic thoughts used language and response formats not conducive to verbal

administration procedures, potentially raising questions of reliability and validity.

Third, the measurement of hopelessness and hostile automatic thoughts would have

required a level of attention and concentration on the part of the drivers that could have

distracted them from the safe operation of their taxicabs, a risk that would have been

unethical to impose both on the drivers and on the research assistants who were

collecting the data. Gender was not included as a demographic variable in Study 3

because all taxicab drivers in the sample were male.

3.4 Formulation of Hypotheses

Based on the conceptualisation and research framework, the following fifteen

hypotheses and sixty sub-hypotheses were formulated:

Table 3.1: Research Hypotheses

STUDY

1A 1B 1C 2 3

H1: Behaviour in traffic will have a positive influence on motor vehicle crash outcomes H1.1: Total BIT score will have a positive influence on crash occurrence Y Y Y Y Y

H1.1.1: Usurpation of right-of way will have a positive influence on crash occurrence Y Y Y Y Y H1.1.2 :Freeway urgency will have a positive influence on crash occurrence Y Y Y Y Y H1.1.3:Externally-focused frustration will have a positive influence on crash occurrence Y Y Y Y Y H1.1.4:Destination-Activity orientation will have a positive influence on crash occurrence Y Y Y Y Y

H1.2: Total BIT score will have a positive influence on injury occurrence Y Y Y Y Y H1.2.1: Usurpation of right-of way will have a positive influence on injury occurrence Y Y Y Y Y H1.2.2: Freeway urgency will have a positive influence on injury occurrence Y Y Y Y Y H1.2.3: Externally-focused frustration will have a positive influence on injury occurrence Y Y Y Y Y H1.2.4: Destination-Activity orientation will have a positive influence on injury occurrence Y Y Y Y Y

H2: Driver characteristics will influence behaviour in traffic H2.1: Driver experience will have a negative influence on total BIT score Y Y Y Y Y H2.2: Travel frequency will have a negative influence on total BIT score Y Y Y Y H2.3: Taxicab experience will have a negative influence on total BIT score Y

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Table 3.1 (continued)

STUDY

1A 1B 1C 2 3 H3: Demographic variables will influence behaviour in traffic

H3.1: Gender will influence total BIT score Y Y Y Y H3.2: Ethnicity will influence total BIT score Y Y Y Y Y H3.3: Age will have a negative influence on total BIT score Y Y Y Y Y

H4: Demographic variables will influence locus of control H4.1.1: Gender will influence Locus of Control: Internality Y Y Y Y H4.1.2: Gender will influences Locus of Control: Externality-Chance Y Y Y Y H4.1.3: Gender will influence Locus of Control: Externality-Powerful-Others Y Y Y Y H4.2.1: Ethnicity will influence Locus of Control: Internality Y Y Y Y Y H4.2.2: Ethnicity will influence Locus of Control: Externality-Chance Y Y Y Y Y H4.2.3: Ethnicity will influence Locus of Control: Externality-Powerful-Others Y Y Y Y Y H4.3.1: Age will influence Locus of Control: Internality Y Y Y Y Y H4.3.2: Age will influence Locus of Control: Externality-Chance Y Y Y Y Y H4.3.3: Age will influence Locus of Control: Externality-Powerful-Others Y Y Y Y Y

H5: Demographic variables will influence hopelessness H5.1: Gender will influence Hopelessness Y Y Y Y H5.2: Ethnicity will influence Hopelessness Y Y Y Y H5.3: Age will have a negative influence on Hopelessness Y Y Y Y

H6.1: Internality will have a negative influence on Hopelessness Y Y Y Y H6.2: Externality-Chance will have a positive influence on Hopelessness Y Y Y Y H6.3: Externality-Powerful-Others will have a positive influence on Hopelessness Y Y Y Y

H7: Hopelessness will have a positive influence on behaviour in traffic H7.1: Hopelessness will have a positive influence on Usurpation of Right-of Way Y Y Y Y H7.2: Hopelessness will have a positive influence on Freeway Urgency Y Y Y Y H7.3: Hopelessness will have a positive influence on Externally-focused Frustration Y Y Y Y H7.4: Hopelessness will have positive influence on Destination-Activity Orientation Y Y Y Y

H8: Locus of Control will influence behaviour in traffic H8.1: Internality will have a negative influence on total BIT score Y Y Y Y Y H8.2: Externality-Chance will have a positive influence on total BIT score Y Y Y Y Y H8.3: Externality-Powerful-Others will have a positive influence on total BIT score Y Y Y Y Y

H9: Hopelessness will moderate the locus of control-BIT Relationship H9.1: Hopelessness will moderate the Internality-BIT relationship Y Y Y Y H9.2: Hopelessness will moderate the Externality(Chance)-BIT relationship Y Y Y Y H9.3: Hopelessness will moderate the Externality(Powerful-Other)-BIT relationship Y Y Y Y

H10: Demographic variables will influence aggression H10.1: Gender will influence Aggression Y Y H10.2: Ethnicity will influence Aggression Y Y Y H10.3: Age will have a negative influence Aggression Y Y Y

H11: Aggression will have a positive influence on behaviour in traffic H11.1: Aggression will have a positive influence on Usurpation of Right-of Way Y Y Y H11.2: Aggression will have a positive influence on Freeway Urgency Y Y Y H11.3: Aggression will have a positive influence on Externally-focused Frustration Y Y Y H11.4: Aggression will have a positive influence on Destination-Activity Orientation Y Y Y

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Table 3.1 (continued)

STUDY

1A 1B 1C 2 3 H12: Locus of control will moderate the aggression-BIT relationship

H12.1: Internality will moderate the Aggression-BIT relationship Y Y Y H12.2: Externality(Chance) will moderate the Aggression-BIT relationship Y Y Y H12.3: Externality (Powerful-Other) will moderate the Aggression-BIT relationship Y Y Y

H13: Demographic factors will influence hostile automatic thoughts H13.1: Gender will have a positive influence on hostile automatic thoughts Y H13.2: Ethnicity will influence hostile automatic thoughts Y H13.3: Age will have a negative influence on hostile automatic thoughts Y

H14: Hostile automatic thoughts will have a positive influence on Behaviour in Traffic H14.1: Thoughts of Physical Aggression will have a positive influence on total BIT Y H14.2: Thoughts of the Derogation of Others will have a positive influence on BIT Y H14.3: Thoughts of Revenge will have a positive influence on total BIT score Y

H15: Hostile automatic thoughts will moderate the Aggression-BIT relationship H15.1: Thoughts of Physical aggression will moderate the Aggression-BIT relation Y H15.2: Thoughts of Derogation-of-Others will moderate the Aggression-BIT relation Y H15.3: Thoughts of Revenge will moderate the Aggression-BIT relation Y

Note: Y=YES

3.5 Methods of Data Collection and Analysis

3.5.1 The Sample

Participants in Study 1 were undergraduate students at a private university in

peninsular Malaysia, registered in a freshman course offered within the Faculty of

Management. Only participants with a valid driving licence who had indicated that a

car was the mode of transportation they used most of the time when they travelled were

included. Data were collected during three consecutive trimesters, within a 14-month

period, with psychological tests and inventories administered to groups of students

during lecture sessions. Participants from the first round of data collection were

included in Study 1A, those from the second round of data collection were included in

Study 1B, and those from the third round of data collection were included in Study 1C.

Participants in Study 2 were undergraduate students at the same private

university, using the same procedures as in Study 1. All participants had a valid driving

licence but had indicated that a motorcycle was the mode of transportation most of the

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time when they travelled. This sample was combined from all three rounds of classroom

data collection.

Participants in Study 3 were taxicab drivers in the Kuala Lumpur area, all of

whom possessed a valid driving licence and a commercial permit for the operation of a

taxicab. For inclusion in the study, participants had to have a minimum of six months’

experience as a taxicab drivers and no gaps in their taxi operator’s license longer than

three months. Participants were recruited during a curb-side introduction of the study by

one of a group of four research assistants. Data collection took place within the taxicab,

during a point to point trip, while participants were driving.

Participants in Studies 1 and 2 were not remunerated, although results were used

to demonstrate teaching points related to the syllabus at a point later in the trimester.

Participants in Study 3 received the meter or negotiated fare for the trip. In all cases,

participation was voluntary and confidentiality was assured. Participants were provided

with an opportunity to receive a debriefing report about the results of the study by e-mail

and/or, in the case of Study 3 participants, by postal mail.

3.5.2 Research Instruments

3.5.2.1 Behaviour in Traffic (BIT) Scale

This 52-item measures time-urgent behaviour, consistent with of a Type A

Behaviour Pattern (TAPB) when driving (see Appendix A). High total scores on the

scale are considered to be indicative of Type A behaviour while low total scores were

considered to be indicative of a type B approach to driving.

Drawing from the earlier Driving Habits Questionnaire (DHQ; Stokols, Novaco,

Stokals & Campbell, 1978), Synodinos and Papacostas (1985) developed 26 pairs of

two-alternative items, with one of the items in each pair written to measure a TABP

response and the other a contradictory statement (e.g., “ When a traffic light turns green

and the car in front of me doesn’t get going immediately, I try to urge its driver to move

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on” versus “When a traffic light turns green and the car in front of me doesn’t get going

immediately, I just wait for a while until it moves”) for a total of 52 item stems. Items

were presented in two alternate forms (Form A and Form B), such that if the TABP

alternative of an item pair was placed in Form A, then the item of the pair representing

the opposite end of the continuum was placed in Form B and vice versa. Items in Form

A and Form B are presented in random order. On each form, 20 items are answered on a

5-point Likert-type scale ranging from “most like me” to “least like me”. Six of the

items are answered on a 5-point Likert-type scale ranging from “strongly agree” to

“strongly disagree”. Synodinos and Papacostas reported that Form A and Form B

(which correlated .91) were found to be internally consistent, with a coefficient alpha of

.80.

In a later study, Papacostas and Synodinos (1988) provided additional

psychometric parameters of the BIT scale, based on a principal components analysis of

earlier-reported data. Their analysis revealed four dimensions, as indicated in table 3.2.

Table 3.2: Dimensions of the BIT scale

Factor No. of items Sample items I. Usurpation of right-of-

way 24 “When I am in a traffic jam and the lane next to mine

starts to move, I try to move that lane as soon as possible.”

“When a motor vehicle cuts in front of me, I usually feel like pushing them off the road.”

II. Freeway urgency 14 “On a clear highway, I usually drive a few kilometres above the speed limit.”

“I get extremely irritated when I am travelling behind a slow moving vehicle.”

III. Externally-focused frustration

6 “I usually get upset at drivers who do not signal their driving intentions.”

“I often blow my horn at someone as a way of expressing my frustration.”

IV. Destination-activity orientation

8 “I often find myself checking the time while driving to work, to school or to an appointment with someone.”

“While travelling to work (or to school), I usually think about what I have to do when I get there.”

Total 52

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Certain items in the original American version were re-worded to make them

relevant to the Malaysian context and driving jargon. References to “miles per hour”

were changed to “kilometres per hour”. The phrase “cross-junction” was added to items

pertaining to behaviour at intersections. References to the “gas pedal” were replaced by

“accelerator”. References to the faster, passing lane were changed from “left lane” to

“right lane” and the word “pass” was replaced with “overtake”.

3.5.2.2 Levenson Locus of Control Scale

This widely-used questionnaire is based on Levenson’s (1981) multidimensional

view of locus of control. It contains three 8-item sub-scales that measure perceptions

about the level of control exercised over the events and circumstances in their lives.

Participants scored all 24 items on a 6-point scale, ranging from +3 (“agree strongly”) to

–3 (“disagree strongly”).

High scores on the internality (I) scale indicate that respondents expect to have a

high degree of control over their own lives. A sample item is “When I get what I want,

it’s usually because I worked hard for it”. High scores on the externality-chance (C)

scale indicate that respondents expect expect chance forces or luck to have control over

their lives. A sample item is “I have often found that what is going to happen will

happen”. High scores on the externality-powerful-others (P) scale indicate that

respondents expect that powerful others exert a high degree of control over their lives.

A sample item is “Although I might have good ability, I will not be given leadership

responsibility without appealing to those in positions of power”.

Luckner (1989) noted that this instrument has among the highest reliability and

validity of all locus of control tests and is particularly applicable when gearing

instruments to broad linguistic structures and varying academic levels.

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3.5.2.3 Beck Hopelessness Scale (BHS)

This 20-item scale contains true-false statements that assess the extent of

negative expectancies about the immediate and long-range future (Beck & Steer, 1993;

Beck et al, 1974). Each of the 20 statements is scored 1, if endorsed, or 0, if not. Of the

20 true-false statements, 9 are keyed false to indicate optimism about the future, a

sample item of which is “I look forward to the future with hope and enthusiasm”. Eleven

items are keyed true to indicate pessimism about the future, a sample item of which is “I

might as well give up because there is nothing I can do about making things better for

myself”. Item scores are summed to yield a total score ranging from 0 to 20. High

scores are taken to indicate a generally pessimistic view of the future and a high degree

of hopelessness. High internal consistency has been reported across a range of samples

(Benzein & Berg, 2005; Durham, 1982; Tanaka et al, 1996).

3.5.2.4 Aggression Questionnaire (AQ)

This questionnaire is a 34-item scale measuring constructs related to the

expression of aggression. A total aggression score can be calculated from summed item

responses, and five subscales measure physical aggression, verbal aggression, anger,

hostility and indirect aggression (see Table 3.3). Participants indicate a response on a

five-point Likert-type scale (1 = “not at all like me”; 5 = “completely like me”) that best

represent how well the item describe them.

Table 3.3: The Five Subscales of the Aggression Questionnaire

Subscale No. of items Sample Items:

Physical Aggression (PHY) 8 “At times I can’t control the urge to hit someone.”

“I get into fights more than most people.” Verbal Aggression

(VER) 5 “I often find myself disagreeing with people.” “When people annoy me, I may tell them what I think of them.”

Anger (ANG) 7 “I let my anger show when I do not get what I want.” “At times I feel like a bomb ready to explode.”

Hostility (HOS) 8 “At times I feel I have gotten a raw deal out of life.” “I sometimes feel that people are laughing at me behind my back.”

Indirect Aggression (IND) 6

“If I’m angry enough, I may mess up someone’s work.” “When someone really irritates me, I might give him or her the silent treatment.”

Total 34

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High internal consistency has been reported with a coefficient alpha of .94 for

the total aggression scores and ranging from .71 to .88 for the five subscales (Buss &

Warren, 2000). Previous studies have established high levels of concurrent validity

(Harris, 1997; Shapiro, 2000) and discriminant validity (Archer & Haigh, 1997;

Williams, Boyd, Cascardi & Pythress, 1996).

3.5.2.5 Hostile Automatic Thoughts (HAT)

This 30-item self-report index measures the frequency of recurring hostile

thoughts. Each item includes a statement expressing some hostile thought and

respondents are asked to indicate “whether that thought (or one like it) has occurred to

you about another driver when you have been driving.” Participants responded on a 5-

point Likert-type scale (1 = “not at all”; 5 = “all the time”). Three factors – physical

aggression, derogation of others and revenge – were identified and are included as sub-

scales (see table 3.4). High scores on a sub-scale indicated that type of hostile thinking

had occurred to the participant frequently. Snyder et al. (1997) reported high internal

consistency for all three sub-scales, with coefficient alpha values of .92, .88 and .91 for

physical aggression, derogation of others and revenge respectively.

Table 3.4: The Three Subscales of the Hostile Automatic Thoughts (HAT) Scale

Factor No. of Items Sample Items Physical aggression 11 “If I could get away with it, I’d kill this person!”

“I’d like to knock his/her teeth out” Derogation of others 10 “What an idiot!”

“This person is a loser.” Revenge 9 “I want to get back at this person.”

“I just want to hurt this person as bad as s/he hurt me.” Total 30

3.5.2.6 Personal Information Form (PIF)

Participants also completed a 4-page questionnaire recording personal

information. Questions included details about the participant’s licensing and driving

background, age, gender, ethnicity and history of motor vehicle crashes and injuries.

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3.6 Procedure

3.6.1 Studies 1 and 2

Data collection took place within a classroom or lecture hall during a regularly-

scheduled class periods. Participants were informed about the study and invited to

participate on a voluntary basis. A brief written description of the purpose of the

research and general instructions (see Appendix B) was distributed. Instructions advised

the participants to (a) answer all questions on each questionnaire, (b) give honest

answers that described themselves rather than putting the “best things to say”, (c) not

discuss answers with others as they were completing the questionnaires, (d) read

instructions for each questionnaire very carefully and complete them in the order they

were distributed, (e) put down the first response that came into their mind, and (f) not

spend too much time on any one answer.

After the briefing period, packages of research instruments were distributed as

follows:

Study 1A: PIF, BHS, Levenson and BIT scale;

Study 1B: PIF, BHS, Levenson, BIT scale and AQ;

Study 1C: PIF, BHS, Levenson, BIT scale, AQ and HAT.

In studies 1 and 2, the instruments were presented in the following order: (a) the

PIF was the first one in the package; (b) the second instrument was either Form A or

Form B of the BIT scale; (c) the last instrument in the package was the opposite for of

the BIT from the one presented second; (d) the remaining instruments used in that

particular study were presented, in random order, between the two forms of the BIT.

Participants were provided with up to 60 minutes to complete the scales but in no cases

did the average administration time exceed 45 minutes. Participants were assured of

confidentiality and were de-briefed, upon request, with an e-mail summary of results.

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3.6.2 Study 3

For study 3, four female final-year undergraduate students, aged 22 to 24 years,

with prior research experience were retained to assist in the study. All four research

assistants spoke fluent English and Bahasa Malaysia, as well as at least two additional

Malaysian languages. Data collection took place in taxicabs. Two to four times daily,

each research assistant hired a taxicab to drive to some location elsewhere in the Kuala

Lumpur area. Taxis were flagged down at roadside, approached at a taxi stand or

booked over the telephone. For safety reasons, data collection was confined to times

between 8:00 am and 9:00 pm. At initial contact, the research assistant informed the

taxicab driver about the study, provided assurance of confidentiality and secured

participation. Over the course of the trip, research assistants verbally administered the

PIF, BIT, AQ and Levenson scales. The PIF was always administered first, with the

remaining instruments administered in random order. Single-word substitutions in items

were made in English or the driver’s first language if comprehension difficulties arose,

with the team of research assistants meeting regularly three times each week to calibrate

administration procedures.

3.7 Analysis of the Data

Data collected were entered and processed using Statistical Package for the

Social Science (SPSS for Windows, rel. 13.0, 2004). Independent-sample t-tests,

analyses of variance (ANOVA), linear and multiple regression analyses and logistic

regression analyses were used to test the hypotheses. Reliability coefficients of all

instruments were calculated using SPSS. Confirmatory Factor Analyses (CFA) were

performed on the BIT, Levenson Locus of Control scale, AQ and HAT to determine

validity using LInear Structural RElations software (LISREL, rel. 8.5, 2002). Structural

equation models and path analyses were estimated using the same version of LISREL, as

well.

Specific statistical tests have been summarised in Table 3.5. This section

provides a brief example of each one and details its use in the present research.

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Table 3.5: Statistical Methods for Hypothesis Testing

Data Analysis Methods

The Direct Effect of BIT on Accident Involvement

H1.1: The level of BIT influence the crash occurrence H1.2: The level of BIT influence the crash injury

Logistic Regression Logistic Regression

The Direct Effect of Driving characteristics on BIT

H2.1: “Length of having driving licence” influence the level of BIT H2.2: “Frequency of traveling” influence the level of BIT H2.3: “Access to a motor vehicle” influence the level of BIT

Analysis of Variance Analysis of Variance Analysis of Variance

The Direct Effect of Demographic Factors on BIT

H3.1: Gender influence the level of BIT H3.2: Ethnicity background influence the level of BIT H3.3: Age influence the level of BIT

Independent Sample t-Test Analysis of Variance Analysis of Variance

The Direct Effect of Demographic Factors on Locus of Control

H4.1: Gender influence the Locus of Control H4.2: Ethnicity influence the Locus of Control H4.3: Age influence the Locus of Control

Independent Sample t-Test

Analysis of Variance Analysis of Variance

The Direct Effect of Demographic Factors on Hopelessness

H5.1: Gender influence the level of Hopelessness H5.2: Ethnicity background influence the level of Hopelessness H5.3: Age influence the level of Hopelessness

Independent Sample t-Test Analysis of Variance Analysis of Variance

The Direct Effect of Locus of Control on Hopelessness

H6.1: Internality is negatively related to hopelessness H6.2: Externality (Chance) is positively related to Hopelessness H6.3: Externality (Powerful-Other) is positively related to Hopelessness

Linear Regression Linear Regression Linear Regression

The Direct Effect of Hopelessness on BIT

H7.1: Hopelessness influence the level of Usurpation of Right-of Way H7.2: Hopelessness influence the level of Freeway Urgency H7.3: Hopelessness influence the level of Externally-focused Frustration H7.4: Hopelessness influence the level of Destination-activity Orientation

Linear Regression Linear Regression Linear Regression Linear Regression

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Table 3.5 (continued)

Data Analysis Methods

The Direct Effect of Locus of Control on BIT

H8.1: The higher the Internality, the lower the BIT level H8.2: The higher Externality (Chance), the higher the BIT level H8.3: The higher Externality (Powerful-Other), the higher the BIT level

Additional Analysis: The Interaction Effect of Ethnicity and Locus of Control on BIT

Linear Regression Linear Regression Linear Regression

GLM Univariate

Analysis of Variance

The Moderating Effect of Hopelessness on Locus of Control-BIT Relation

H9.1: Hopelessness moderates the Internality-BIT relation H9.2: Hopelessness moderates the Externality(Chance)-BIT relation H9.3: Hopelessness moderates the Externality (Powerful-Other)-BIT relation

Multiple Linear Regression Multiple Linear Regression Multiple Linear Regression

The Direct Effect of Demographic Factors on Aggression

H10.1: Gender influences the level of Aggression H10.2: Ethnicity background influences the level of Aggression H10.3: Age influences the level of Aggression

Independent Sample t-Test Analysis of Variance Analysis of Variance

The Direct Effect of Aggression on BIT

H11.1: Aggression influence the level of Usurpation of Right-of Way H11.2: Aggression influence the level of Freeway Urgency H11.3: Aggression influence the level of Externally-focused Frustration H11.4: Aggression influence the level of Destination-activity Orientation

Additional Analysis: The Interaction Effect of Ethnicity and Aggression on BIT

Linear Regression Linear Regression Linear Regression Linear Regression Linear Regression

GLM Univariate Analysis of Variance

The Moderating Effect of Locus of Control on Aggression-BIT Relation

H12.1: Internality moderates the Aggression-BIT relation H12.2: Externality(Chance) moderates the Aggression-BIT relation H12.3: Externality (Powerful-Other) moderates the Aggression-BIT relation

Multiple Linear Regression Multiple Linear Regression Multiple Linear Regression

The Direct Effect of Demographic Factors on HAT

H13.1: Gender has a positive influence on hostile automatic thoughts H13.2: Ethnicity influences hostile automatic thoughts H13.3: Age has a negative influence on hostile automatic thoughts

Independent Sample t-Test Analysis of Variance Analysis of Variance

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Table 3.5 (continued)

Data Analysis Methods

The Direct Effect of HAT on BIT

H14.1: Thoughts of Physical Aggression have a positive influence on BIT H14.2: Thoughts of Derogation-of-Others have a positive influence on BIT H14.3: Thoughts of Revenge have a positive influence on BIT

Linear Regression Linear Regression Linear Regression

The Moderating Effect of HAT on the Aggression-BIT Relation

H15.1: Thoughts of Physical Aggression will moderate the Aggression-BIT relation H15.2: Thoughts of Derogation-of-Others will moderate the Aggression- BIT relation H1353: Thoughts of Revenge will moderate the Aggression-BIT relation

Multiple Linear Regression Multiple Linear Regression Multiple Linear Regression

3.7.1 Independent-sample t-tests

Generally, t-tests are used to compare the means of two groups. In the present

research, t-tests were used to determine whether participants’ scores on psychological

variables (BIT, locus of control, hopelessness, aggression and hostile automatic

thoughts) differed for male and female drivers.

3.7.2 One-way analysis of variance (ANOVA)

ANOVA is used to compare means for more than two groups. In the present

study, ANOVA was used to determine whether participants’ scores on psychological

variables (BIT, locus of control, hopelessness, aggression and hostile automatic

thoughts) differed for drivers with different ethnic backgrounds.

When significant differences were observed, post hoc analyses were carried out

using the Scheffé method (Klockars & Hancock, 2000).

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3.7.3 The General Linear Model (GLM) Univariate Analysis

This procedure allows a factorial analysis of variance by comparing means of a

dependent variable for groups defined by factor variables. It is useful for analysis of

variance models with one or more factor variables or covariates and a single dependent

variable. In the present research, GLM univariate analysis of variance was used to

determine whether there was an interaction effect between ethnic background and

psychological factors (locus of control, hopelessness, aggression and hostile automatic

thoughts) on behaviour in traffic (BIT).

3.7.4 Linear Regression Analysis

This analysis is used to determine if a relationship exists between a dependent

variable and an independent variable and, if so, the direction of the relationship (positive

or negative). In the present research, linear regression was applied to examine the

relationship between psychological variables (locus of control, hopelessness, aggression

and hostile automatic thoughts) and behaviour in traffic (BIT).

3.7.5 Multiple Regression Analysis

This analysis aims to examine if there is a relationship between the dependent

variable and independent variables. Application of multiple regression analysis involves

more than one single independent variable. In the present research, multiple regression

analysis was used to test whether internality (I), externality-chance (C) and externality-

powerful-others (P) have an effect on hopelessness. Also, the moderating effects of the

variables were tested using hierarchical regression methods. For instance, to test

whether hopelessness moderated the P-BIT relationship, first P scores were entered into

the regression equation; second, the products of P x hopelessness scores were added into

the regression equation. R-square and coefficient values were then estimated to

determine the significance of the moderating effect of hopelessness.

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3.7.6 Logistic Regression Analysis

Logistic regression is similar to linear regression but differs with respect to the

nature of data that can be treated and in the manner in which coefficients are interpreted.

Linear or multiple regression seek to measure the degree of influence that variables will

have on a dependent variable; logistic regression, on the other hand, seeks to determine

the odds that an event will or will not occur. In the present research, each of the two

outcome variables (crash occurrence and injury occurrence) was framed as a binary

variable. That is, “1” was scored if a crash had occurred and “0” if no crash had

occurred; “1” was scored if a crash injury had occurred and “0” if no crash injury had

occurred. Since driver experience and travel frequency were expected to have an

influence on the outcome variables, these variables were controlled as covariates in the

logistic regression equation. Covariates (driver experience and travel frequency) and the

independent variable (BIT) were entered into the logistic regression equation to predict

the probability of participants’ crash and injury occurrence.

3.7.7 Structural Equation Modelling.

Hair et al (2006) has defined structural equation modelling (SEM) as a

“multivariate technique combining aspects of factor analysis and multiple regression that

enables the research to simultaneously examine a series of interrelated dependent

relationships among the measured variables and latent constructs (variates), as well as

between several latent constructs” (p. 710). In the present research, SEM was carried

out, using LISREL, to (a) assess the validity of the instruments; and (b) examine the

interrelationships among variables included in the research design. The result was a

measurement model described as a contextual-mediated model, the purpose of which

was to distinguish the distal and proximal contextual factors related to crash outcomes.

Path coefficients were calculated to demonstrate correlates of unsafe driving according

to their contextual proximity to crash and injury occurrence.

The validity of this measurement model was dependent on its goodness-of-fit

and on the construct validity of its component variables. Goodness-of-fit indicates how

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well the measurement model reproduces the covariance matrix among indicator items

That is,

Once a researcher’s theory is used to specify a model from

which the parameters are estimated, the model fit compares

the theory to reality as represented by the data. If a

researcher’s theory were perfect, the estimated covariance

matrix (∑k) and the actual covariance matrix (S) would be the

same. Thus, the estimated covariance matrix is compared

mathematically to the actual observed covariance matrix to

provide an estimate of model fit. The closer the values of

these two matrices are to each other, the better the model is

said to fit. (Hair et al., 2006; p. 745).

The fundamental measure of fit is the chi-square (χ2) statistic (Byrne, 1998), but

a wide array of tests of the overall fit of SEM models – “more, in fact, than anyone

would want to report” (Maryuma, 1998) – presently exists. According to Marsh et al.

(1988), these can be classified as absolute fit indexes and relative or incremental fit

indexes. Absolute fit measures are a direct measure of how well the model specified by

the researcher reproduces the researcher’s data. Incremental fit measures assess how

well a specified model fits relative to some alternative baseline model.

In the present research, the absolute fit measures included the χ2 statistic, the

χ2/df ratio the goodness-of-fit index (GFI), the root mean square error of approximation

(RMSEA) and the root mean square residual (RMR). Incremental fit measures

included the comparative fit index (CFI). For Study 1C, additional measures were used

to compare the relative fit of two models under consideration, including: (1) two

absolute indexes, the adjusted goodness-of-fit index (AGFI) and the expected cross-

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validation index (ECVI); one incremental index, the normed fit index (NFI); and a

measure of parsimony fit, the parsimony goodness-of-fit index (PGFI).

3.7.7.1 Chi-Square (χ2), p-Value and χ2/df Ratio

χ2 is the fundamental measure used in SEM to quantify the differences between

the observed and estimated covariance matrices (Hair et al, 2006). The probability value

associated with χ2 indicates the likelihood of obtaining a χ2 value that exceeds the χ2

value when null hypothesis (specific matrices for the model under study is valid) is true

(Byrne, 1998). Thus, the higher the probability associated with χ2, the closer the fit

between the hypothesized model (established under the null hypothesis). However, Hair

et al. (2006) have highlighted that for sample size greater than 250 (with a number of

observed variables less than 12), an insignificant p-value can result in good fit. For a

sample size less than 250 (and with number of observed variables that is less than 12),

an insignificant p-value is expected. Carmines and McIver (1981) have noted that,

when the ratio of χ2 to df yields a value of less than 3.0, the ratio indicates a good fit.

3.7.7.2 Degrees of freedom (df)

The df measure the amount of mathematical information available to estimate the

model parameters and are calculated based on the number of unique covariances and

variances in the observed covariance matrix (Hair et al., 2006).

3.7.7.3 Root Mean Square Error of Approximation (RMSEA) and Root Mean

Square Residual (RMR)

This index measures the error of approximation in the population and to question

“how well would the model, with unknown but optimally chosen parameter values, fit

the population covariance matrix if it were available” (Byrne, 1998, pp. 112). RMSEA

values can range from zero to 1.00 in which values greater than .10 indicate poor fit.

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Root mean square residual (RMR) is another badness-of-fit measure. Thus, an RMR

greater than .10 usually suggests a poor fit of the data for the model.

3.7.7.4 Normed Fit Index (NFI)

One of the original incremental measures of fit, the normed fit index (NFI;

Bentler & Bonnet, 1980) represents a ratio of the difference in the χ2 value for the fitted

model and a null model divided by the χ2 value for the null model. The index ranges

between zero and 1.00, and a model with perfect fit would produce an NFI of 1.00.

3.7.7.5 Goodness-of-Fit Index (GFI) and Comparative Fit Index (CFI)

The GFI can range from zero to 1.00 with value closes to 1.00 being indicative

of good fit. CFI is an improved version of the normed fit index. Since the CFI is

insensitive to model complexity, it is known as one of the most widely used indices

(Hair et al., 2006). The index can range from zero to 1.00 with value more than .90 is

usually associated with a model that fits well.

3.7.7.6 Adjusted Goodness-of-Fit Index (AGFI)

An adjusted goodness-of-fit index (AGFI; Tanaka & Huba, 1985) accounts for

differing degrees of model complexity by adjusting the GFI by a ratio of the degrees of

freedom used in a model to the total degrees of freedom available. The AGFI penalises

more complex models and favours those with a minimum number of free paths. Values

range from zero to 1.00, with higher values indicating better fit, but AGFI values are

typically lower than GFI values in proportion to model complexity.

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3.7.7.7 Expected Cross-Validation Index (ECVI)

The expected cross-validation index (ECVI; Browne & Cudeck, 1989) is an

approximation of goodness-of-fit that the estimated model would achieve in another

sample of the same size Based on the sample covariance matrix, it takes into account

the actual sample size and the difference that could be expected in another sample. The

ECVI also takes into account the number of estimated parameters for a given model.

Although values range from zero to 1.00, it is most commonly used when comparing the

performance of one model to another. In such cases, the model with the higher ECVI

value is generally regarded as presenting a better fit.

3.7.7.8 Parsimony Goodness-of-Fit Index (PGFI)

A third class of measures is sometimes recognised as the parsimony indices,

designed specifically to provide information about which model among a competing set

of models is best, considering its fit relative to its complexity. A parsimony fit measure

is improved by a better fit and/or a simpler model which, in this case, means a model

with fewer estimated parameter paths (Hair et al., 2006).

The parsimony ratio is calculated as the ratio of degrees of freedom used by a

model to the total degrees of freedom available (Marsh & Balla, 1994). The parsimony

goodness-of-fit index (PGFI; James, Mulaik & Brett, 1982) uses the parsimony ratio to

adjust the GFI in order to compare two models. Values range between zero and 1.00,

and the model with the higher PGFI is considered preferable, based on the combination

of fit and parsimony represented by the index. It should be noted that, “a PGFI taken

alone is not a useful indicator of a single model’s fit. Like other parsimony fit indices, a

PGFI value is meant only to be used in comparing it to another model’s PGFI value”

(Hair et al., 2006; p. 750).

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3.7.8 Kolmogorov-Smirnov One-Sample Test

The Kolmogorov-Smirnov test is concerned with the degree of agreement

between the distribution of a set of sample values (or observed scores) and some

specified theoretical distribution. It determines whether the scores in the sample can

reasonably thought to have come from a population having the theoretical distribution

(Siegel, 1956). In this case, the Kolmogorov-Smirnov test was used to assess whether

there was a significant departure from normality in the distribution of variable scores.

When p-values for the Kolmogorov-Smirnov statistic are greater than our α=.05, then it

is possible to conclude the the data do not violate the normality assumption (Carver &

Nash, 2000).

3.7.9 Skewness and Kurtosis

Skewness refers to the symmetry or asymmetry of a frequency distribution. If a

distribution is assymetrical and the larger frequencies tend to be concentrated toward the

low end of the variable and the smaller frequencies toward the high end, it is said to be

positively skewed. If the opposite holds, the larger frequencies being concentrated

toward the high end of the variable and the smaller frequencies toward the low end, the

distribution is said to be negatively skewed (Ferguson, 1976).

Kurtosis refers to the flatness or peakedness of one distribution in relation to

another, in this case, the distribution of test scores to the normal distribution. “It is

conventional to speak of a distribution as leptokurtic if is more peaked than … the

normal distribution, and platykurtic if it is less peaked. The normal distribution is

spoken of as mesokurtic, which means that it falls between leptokurtic and platykurtic

distributions” (Ferguson, 1976; p. 37).

Many parametric statistics assume that variables are distributed approximately

normally and SPSS calculates values for skewness and kurtosis to assist in determing

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normality of variable distributions. A commonly used guideline is that, if skewness and

kurtosis less than ±1, the variable is at least approximately normal (Leech, Barrett &

Morgan, 2005; Marcoulides & Hershberger, 1997).

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CHAPTER 4

ANALYSIS OF THE DATA

This chapter presents the results of the research. It begins with a discussion of reliability

and validity tests of the instruments. Then, descriptive statistics are presented and the

results of hypothesis testing are reported. A contextual mediated model showing

interrelationships between variables is introduced. The contextual mediated model was

tested using (1) regression analysis (SPPS) and (2) structural equation modelling

(LISREL), with results of these tests reported in this chapter.

4.1 Description of the Samples

4.1.1 Age, Gender and Ethnicity

Participants were 992 undergraduate students at an English-language Malaysian

university. Thirteen of the participants did not complete all the questionnaires and two

participants completing questionnaires reported that they did not have driving licences.

Thus 977 participants were included in the analysis. Ages of participants ranged from

18 to 29 years, with a mean age of 20.13 years (SD = 1.55). There were 855 participants

for whom the primary mode of transportation was the automobile and 133 for whom the

motorcycle was the primary mode of transportation (see Table 4.1).

Table 4.1: Gender and Ethnicity of the Sample for Studies 1 and 2

Ethnicity

Total Malay Malaysian-Chinese

Malaysian- Indian

Gender Male Count 148 229 64 441 % within Gender 33.6% 51.9% 14.5% 100% % of Total 15.1% 23.4% 6.6% 45.1% Female Count 121 333 82 536 % within Gender 22.6% 62.1% 15.3% 100% % of Total 12.4% 34.1% 8.4% 54.9% Total Count 269 562 146 977 % within Gender 27.5% 57.5% 14.9% 100% % of Total 27.5% 57.5% 14.9% 100%

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Female participants (approximately 55 per cent) slightly out-numbered males.

Malaysian-Chinese represented the highest number of participants (57.5 per cent),

followed by Malay (27.5 per cent) and Malaysian-Indian (14.9 per cent). A cross-

tabulation between gender and ethnicity (see Table 4.1) showed that most of the drivers

were female Malaysian-Chinese.

In Study 1A, 301 undergraduate students who had indicated their primary mode

of transportation to be the automobile comprised the sample, with a mean age of 20.43

years (SD = 1.68, range of 18 to 26).

In Study 1B, 302 undergraduate students who had indicated their primary mode

of transportation to be the automobile comprised the sample, with a mean age of 19.89

years (SD = 1.35, range from 18 to 25).

In Study 1C, 252 undergraduate students who had indicated their primary mode

of transportation to be the automobile comprised the sample, with a mean age of 20.01

years (SD = 1.53, range from 18 to 27).

In Study 2, 122 undergraduate students who had indicated their primary mode of

transportation to be the motorcycle comprised the sample, with a mean age of 20.25

years (SD = 1.63, range from 18 to 29).

In Study 3, 149 taxicab drivers participated, but 16 were excluded from the

sample due to language and comprehension difficulties or because they chose to

withdraw from data collection before all instruments had been administered. Thus,

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responses from 133 taxicab drivers were included in data analysis. The mean age was

43.19 years (SD = 11.65, range from 23 to 73).

Descriptive data for each sample are provided in Table 4.2.

Table 4.2: Age, Gender and Ethnicity of Participants in Studies 1, 2 and 3

STUDY

N Mean Age S.D. Gender Ethnicity

Male Female Malay Malaysian-Chinese

Malaysian-Indian

1A

301

20.43 1.68 105 196 68 202 31

1B

302

19.89 1.35 175 127 87 166 49

1C

252

20.01 1.53 88 164 81 128 43

2

122

20.25 1.63 73 49 33 66 23

3

133

43.19 11.65 133 0 55 52 26

Note: N=sample size ; SD = standard deviation

4.1.2 Geographic Distribution of Samples in Study 1

Although participants in Studies 1A, 1B and 1C were all students at a single

Malaysian university, they hailed from across the country (see table 4.3). Participants

who had received their driving licenses in Selangor, Kuala Lumpur, Johor or Perak

made up 53.3% of the sample. Participants from East Malaysia comprised 5.4% of the

sample.

Table 4.3: States from Which Study 1 Participants Had Acquired Their Original Drivers’ Licenses

N % Johor 109 12.7 Kedah 42 4.9 Kelantan 20 2.3 Kuala Lumpur 98 11.5 Melaka 70 8.2 Negeri Sembilan 61 7.1 Pahang 56 6.5

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Penang 67 7.8 Perak 102 11.9 Perlis 6 0.7 Sabah 27 3.2 Sarawak 19 2.2 Selangor 147 17.2 Terengganu 31 3.6

855 100

4.1.3 Geographic Distribution of the Sample in Study 2

Participants in Study 2 were all students at a single Malaysian university, but

again they held licenses from various states (see table 4.4). Participants who had

received their driving licenses in Selangor, Perak or Penang made up 50.9% of the

sample. Participants from East Malaysia comprised 4.1% of the sample.

Table 4.4: States from Which Study 2 Participants Had Acquired Their Original Motorcyclists’ Licenses

N % Johor 17 13.9 Kedah 9 7.4 Kelantan 1 0.8 Kuala Lumpur 11 9.0 Melaka 9 7.4 Negeri Sembilan 5 4.1 Pahang 11 9.0 Penang 13 10.7 Perak 14 11.5 Perlis 2 1.6 Sabah 2 1.6 Sarawak 3 2.5 Selangor 18 14.8 Terengganu 7 5.7 122 100

4.1.4 Geographic Distribution of the Sample in Study 3

Participants in Study 3 were all professional taxicab operators who had been

licensed to drive their vehicles commercially within Kuala Lumpur. As the sample was

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intended to be representative of Kuala Lumpur taxicab drivers, no attempt was made to

determine the geographic location in which drivers had originally received non-

commercial drivers’ licenses.

4.2 Reliability and Validity

4.2.1 Reliability Test Results: Cronbach’s Alpha

Cooper and Schindler (2000) claimed that reliability relates to the accuracy and

precision of a measurement procedure. Neuman (2003) defined reliability is defined as

“dependability or consistency” and further explained that reliability suggests that the

same event is repeated or recurs under identical or very similar conditions. Sekaran

(2000) offered a similar definition in which the reliability of a measure indicates the

stability and consistency with which the instrument measures a concept and helps to

assess the “goodness” of the measure.

In the present research, reliability was measured using Cronbach’s coefficient

alpha. This statistic reflects the consistency of respondents’ answers compared to all the

items in a measure (Sekaran, 2000). The closer Cronbach’s Alpha is to 1, the higher is

the internal consistency of the measure. A Cronbach’s Alpha of .70 or greater is

generally considered acceptable (Nunnally, 1978). The reliability of the measures used

in this research was calculated for each of the three studies and values for Cronbach’s

Alpha in all cases were found to be satisfactory (see Table 4.5).

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Table 4.5: Summary of Internal Reliability Coefficient Results

Variables No. of Item

Study 1A (N=301)

Study 1B (N=302)

Study 1C (N=252)

Study 2 (N=122)

Study 3 (N=133)

Automobile Drivers (student sample)

Motorcycle Drivers

(student sample)

Taxicab Drivers

α α α α α Behaviour In Traffic (BIT) Usurpation of right-of-way Freeway Urgency Externally-Focused Frustration Destination-Activity Orientation

26 11 8 3 4

.830 .740 .703 .711

.890 .786 .714 .739

.742 .737 .718 .735

.824 .811 .702 .720

.756 .754 .727 .734

Locus of Control Internality Externality (Chance) Externality (Powerful Other

8 8 8

.741 .701 .768

.740 .782 .784

.782 .774 .810

.707 .720 .738

.827 .747 .788

Aggression (AQ) Physical Aggression Verbal Aggression Anger Hostility Indirect Aggression

8 5 7 8 6

Not Applicable

.817 .727 .808 .798 .783

.808 .715 .738 .733 .781

Not Applicable

.910 .715 .906 .783 .726

Hopelessness (BHS) 20 .749 .772 .730 .701 Not Applicable

Hostile Automatic Thought (HAT) Physical Aggression Derogation of Others Revenge

11 10 9

Not Applicable

Not Applicable

.904 .887 .881

Not Applicable

Not Applicable

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4.2.2 Parallel-Form Reliability

In the case of the BIT scale, it was also possible to measure reliability as a

coefficient of correlation between Form A and Form B (Synodinos &Papacostas, 1985).

Reliability coefficients in all studies where both forms were used are acceptable since

Form A and Form B were highly correlated, more than .80. Sekaran (2003) notes that “if

two comparable forms are highly correlated (.80 or above), we may be fairly certain that

the measures are reasonably reliable, with minimal error variance caused by wording,

ordering or other test construction factors” (p. 205). The results of parallel-form

reliability for the BIT instrument in different studies are shown in Table 4.6. In Study 3,

only Form A was used.

Table 4.6: Parallel-Form Reliability for Form A and Form B (BIT)

Form A & Form B Study 1A

Study 1B

Study 1C

Study 2

BIT .958 .953 .804 .929 Usurpation of right-of way .916 .903 .801 .857 Freeway Urgency .804 .876 .805 .807 Externally-Focused Frustration .803 .800 .811 .806 Destination-Activity Orientation .807 .804 .808 .802

4.2.3 Validity Test Results

In the present research, confirmatory factor analyses using LISREL (Jöreskog &

Sörbom, 2002) was used to establish evidence of construct validity for various measures.

The root mean square error of approximation (RMSEA) index measures the error of

approximation in the population and determines whether the model, with unknown but

optimally chosen parameter values, fits the population correlation matrix or covariance

matrix, depending on which is used (Byrne, 1998). RMSEA values less than .05 indicate

good fit; values ranging from .08 to .10 indicate a mediocre fit; and those greater than

.10 indicate poor fit (MacCallum et al, 1998; Byrne, 1998).

The Goodness-of-Fit Index (GFI) is a measure of the relative amount of variance

and covariance in sample data that is jointly explained by sample data (Byrne, 1998).

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Jöreskog and Sörbom (1993) reported that, although the GFI index ranges from zero to

1.00 (the closer to 1.00, the higher the goodness-of-fit), it is possible to have negative

GFI. This reflects that the model fits worse than no model at all. A third statistic, the

Comparative Fit Index (CFI) is estimated to indicate whether complete covariation in the

data is achieved. If the value of CFI exceeds .90, it is generally considered an acceptable

fit to the data (Bentler, 1992).

4.2.3.1 Confirmatory Factor Analysis of the BIT Scale

In the present research, drivers’ behaviour in traffic was measured by the four

component factors of the BIT scale: usurpation of right-of-way; freeway urgency;

externally-focused frustration, and destination-activity orientation. As shown in Table

4.7, parameter values for all four of these factors were within acceptable ranges.

RMSEA values in each case were less than .100; and both GFI and CFI were more than

.90, indicating good fits.

Table 4.7: Validity of BIT scales – Summary of Confirmatory Factor Analyses

Study 1A

Study 1B Study 1C

RMSEA

GFI CFI RMSEA GFI CFI RMSEA GFI CFI

Behaviour In Traffic (BIT) Usurpation of right-of-way Freeway Urgency Externally-Focused Frustration Destination-Activity Orientation

.098

.070

.000

.000

.91 .97 1.00 1.00

.92 .96 1.00 1.00

.074 .077 .000 .089

.93 .96 1.00 .99

.98 .97 1.00 .99

.048 .047 .000 .024

.96 .98 1.00 1.00

.96 .98 1.00 1.00

Study 2

Study 3

RMSEA

GFI CFI RMSEA GFI CFI

Behaviour In Traffic (BIT) Usurpation of right-of-way Freeway Urgency Externally-Focused Frustration Destination-Activity Orientation

.097

.000

.000

.054

.91 .98 1.00 .99

.92 1.00 1.00 .99

.061

.000

.000

.097

.92 .97 1.00 .99

.95 1.00 1.00 .98

Note: RMSEA=Root mean square error of approximation; GFI= Goodness-of-Fit Index; CFI= Comparative Fit Index

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4.2.3.2 Confirmatory Factor Analysis of the Levenson Locus of Control Scale

Locus of control was measured across three dimensions: internality (I),

externality-chance (C) and externality-powerful-others (P). Each component of the locus

of control was measured separately, under the assumption that locus of control is a

multidimensional phenomenon. CFA revealed that parameter values for I, C and P

scales were all within acceptable ranges. RMSEA values were less than .100; and both

GFI and CFI were more than .90, indicating good fits (See Table 4.8.

Table 4.8: Validity of the Levenson Locus of Control Scale – Summary of Confirmatory Factor Analysis

Study 1A

Study 1B Study 1C

RMSEA

GFI CFI RMSEA GFI CFI RMSEA GFI CFI

Locus of Control Internality Externality (Chance) Externality (Powerful-Other)

.085

.081

.091

.95

.91

.93

.93

.93

.93

.085

.058

.073

.95

.97

.96

.92

.98

.97

.030

.059

.063

.98

.96

.96

.99

.98

.98

Study 2

Study 3

RMSEA

GFI CFI RMSEA GFI CFI

Locus of Control Internality Externality (Chance) Externality (Powerful-Other)

.083

.071

.096

.93

.92

.92

.91

.93

.91

.000

.081

.052

.99

.93

.95

1.00 .96 .98

Note: RMSEA=Root mean square error of approximation; GFI= Goodness-of-Fit Index; CFI= Comparative Fit Index

4.2.3.3 Confirmatory Factor Analysis of the AQ Scale

The AQ was used to measure driver aggression in Study 1B and 1C (with

automobile drivers sampled from a student population) and in Study 3 (with taxicab

drivers). Five component factors of aggression were measured: physical aggression

(PHY), verbal aggression (VER), anger (ANG), hostility (HOS) and indirect aggression

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(IND). A total aggression score was arrived at by summing the five subscale scores.

CFA revealed that parameter values for all five aggression subscales were within

acceptable ranges. RMSEA values were less than .100; and both GFI and CFI were

more than .90, indicating good fits (See Table 4.9).

Table 4.9: Validity of the AQ scales – Summary of Confirmatory Factor Analysis

Study 1B

Study 1C Study 3

RMSEA

GFI CFI

RMSEA

GFI CFI RMSEA GFI CFI

Aggression (AQ) Physical Aggression Verbal Aggression Anger Hostility Indirect Aggression

.098

.090

.070

.047

.088

.94

.98

.97

.97

.97

.96

.97

.98

.98

.98

.081

.081

.055

.025

.073

.95

.98

.97

.98

.97

.96

.97

.98

.99

.98

.096

.070

.098

.058

.083

.92

.98

.94

.95

.96

.98

.97

.98

.98

.96 Note: RMSEA=Root mean square error of approximation; GFI= Goodness-of-Fit Index; CFI= Comparative Fit Index

4.2.3.4 Confirmatory Factor Analysis of the HAT Scale

The HAT was only used in Study 1C (with automobile drivers sampled from a

student population). Three classes of hostile automatic thoughts were measured:

physical aggression; derogation of others and revenge. CFA revealed that parameter

values for all three measurement scales were within acceptable ranges. RMSEA values

were less than .100; and both GFI and CFI were more than .90, indicating good fit (see

Table 4.10).

Table 4.10: Summary of LISREL Results on Validity for HAT (Study 1C)

RMSEA

GFI CFI

Hostile Automatic Thought (HAT) Physical Aggression Derogation of Others Revenge

.095

.089

.088

.92

.92

.93

.97

.97

.97 Note: RMSEA=Root mean square error of approximation; GFI= Goodness-of-Fit Index; CFI= Comparative Fit Index

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4.3 Normality, Skewness and Kurtosis

Data were studied to determine whether they were normally distributed and

therefore capable of satisfying parametric assumptions. In all cases, values for the

Kolmogorov-Smirnov statistic were non-significant (p>.05), indicating that the

distribution of scores did not depart from normality.

Normality can also be assessed by examining sknewness and kurtosis values

(Hair et al., 2006). Skewness and kurtosis values of ± 1 are acceptable (Leech et al.,

2005; Marcoulides & Hershberger, 1997). Table 4.11 indicates that variable distribution

fell within these limits, but with a non-signficant platykurtic tendency for the locus of

control data.

Table 4.11: Normality Tests, Kurtosis and Skewness Statistics

Kolmogorov-

Smirnov Z

(Significance Level)

Kurtosis Statistic (Standard Error)

Skewness Statistic (Standard

Error)

Study 1A Internality 1.297 (.091) .409(.280) -.875(.140) Externality (Chance) 1.082 (.192) -.179(.280) -.241(.140) Externality (Powerful Other) 1.085 (.190) .064(.280) .582(.140) Hopelessness 1.323 (.064) -.091(.280) -.126(.140) BIT Usurpation right-of-way Freeway Urgency Externally Focused Frustration Destination-activity Orientation

1.022 (.183) 1.107 (.099) 1.020 (.186) 1.351 (.057)

.560(.280)

.099(.280) .297(.280) .278(.280)

-.126(.140)

.080(.140) .246(.140) -.410(.140)

Study 1B Internality 1.195 (.120) .409(.280) -.805(.140) Externality (Chance) 1.226 (.099) -.179(.280) -.331(.140) Externality (Powerful Other) 1.239 (.085) .064(.280) -.192(.140) Hopelessness 1.332 (.106) -.091(.280) .920(.140) BIT Usurpation right-of-way Freeway Urgency Externally Focused Frustration Destination-activity Orientation

1.219 (.102) 1.094 (.183) 1.085 (.107) 1.256 (.085)

.560(.280) .099(.280) .297(.280) .278(.280)

-.037(.140) -.403(.140) -.154(.140) -.353(.140)

AQ Physical Aggression Verbal Aggression Anger Hostility Indirect Aggression

1.356 (.052) 1.034 (.191) 1.105 (.069) 1.010 (.260) .962 (.428)

-.719(.280) .188(.280)

-.656(.280) -.379(.280) -.204(.280)

.408(.140) .511(.140) .203(.140) .146(.140) .453(.140)

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Table 4.11 (continued) Kolmogorov-

Smirnov Z

(Significance Level)

Kurtosis Statistic (Standard Error)

Skewness Statistic

(Standard Error)

Study 1C Internality 1.297 (.084) .913(.306) -.972(.153) Externality (Chance) 1.160 (.135) -.324(.306) -.300(.153) Externality (Powerful Other) 1.223 (.101) -.940(.306) -.048(.153) Hopelessness 1.293 (.098) .973(306) .915(.153) BIT Usurpation right-of-way Freeway Urgency Externally Focused Frustration Destination-activity Orientation

1.011 (.259) .919 (.366)

1.195 (.128) .986 (.321)

.277(.360)

-.138(.360) .053(.360) .994(.360)

.147(.153) .276(.153) .503(.153) .451(.153)

AQ Physical Aggression Verbal Aggression Anger Hostility Indirect Aggression

1.279 (.052) 1.338 (.057) 1.354 (.051) .807 (.533) .962 (.426)

.106(.306)

.478(.306) -.062(.306) -.120(.306) .130(.306)

.852(.153)

.540(.153)

.295(.153)

.131(.153)

.799(.153) HAT Physical Aggression Degrerotion Revenge

.805 (.270)

1.276 (.098) 1.001 (.264)

.713(.306)

-.366(.306) .443(.306)

983(.153) .497(.153) .884(.154)

Study 2 Internality .847 (.469) -.501(.435) -.156(.219) Externality (Chance) 1.003 (.267) -.106(.435) -.256(.219) Externality (Powerful Other) .822 (.510) -.159(.435) -.007(.219) Hopelessness 1.359 (.051) -.392(.435) .423(.219) BIT Usurpation right-of-way Freeway Urgency Externally Focused Frustration Destination-activity Orientation

1.022 (.247) .913 (.375)

1.106 (.138) 1.022 (.247)

-.209(.435) -.362(.435) -.841(.435) -.147(.435)

.567(.219) .271(.219) .186(.219) .370(.219)

Study 3 Internality 1.266 (.099) .978(.417) -.979(.210) Externality (Chance) 1.024 (.100) -.467(.417) -.681(.210) Externality (Powerful Other) .911 (305) -.852(.417) -.198(.210) BIT Usurpation right-of-way Freeway Urgency Externally Focused Frustration Destination-activity Orientation

1.128 (.157) .959 (.317)

1.327 (.064) 1.088 (.187)

-.812(.417) -.142(.417) -.629(.417) -.463(.417)

-.244(.210) .030(.210) .236(.210) .006(.210)

AQ Physical Aggression Verbal Aggression Anger Hostility Indirect Aggression

1.110 (.104) 1.024 (.265) 1.359 (.052) 1.070 (.214) 1.113 (.102)

.962(.417) -.414(.417) .567(.417) .053(.417) .715(.417)

.948(.210)

.640(.210)

.537(.210)

.719(.210)

.952(.210)

124

4.4 Crash and Injury Occurrence Data

Participants in all studies indicated whether they had experienced an accident

within the preceding twelve months in which their vehicle had sustained more than

RM100 damage and, if so, whether the accident had resulted in (1) no injuries or injuries

insufficiently severe to seek medical attention (see Table 4.12; column a), (2) injuries

severe enough to require out-patient treatment at a medical clinic (see Table 4.12;

column b), (3) injuries requiring hospitalisation (see Table 4.12; column c). Between 10

and 13 per cent of all automobile drivers in Study 1 sought medical treatment at a

hospital in the preceding year as a result of motor vehicle crashes. For motorcycle

drivers, injury occurrence was much higher, with 44.3 per cent being hospitalised.

Table 4.12: Crash and Injury Occurrence OUTCOME VARIABLES

STUDY N Number of

Participants’ Reported Crash

Vehicle Damage

a

Out-patient Treatment

b

Hospital Admission

c 1A 301 181 84 68 29 1B 302 255 142 75 38 1C 252 174 102 47 25 2 122 157 45 58 54 3 133 22 17 3 2

More than half of the automobile drivers sampled in Studies 1A, 1B and 1C self-

reported that they had been involved in at least one motor vehicle crash over the

preceding year (see Table 4.13). Male and female automobile drivers reported

involvement in one crash with almost the same frequency. However, males were more

than twice as likely to report involvement in two or three automobile crashes.

125

Table 4.13: Crash Occurrence Frequency, Gender and Ethnicity in Study 1 (N=855) Automobile Drivers

Gender Total Male Female

No. involved in one crash

Ethnicity Malay Malaysian-Chinese Malaysian-Indian

80 57 137 119 103 222 33 44 77

Total 232 204 436

No. involved in two crashes

Ethnicity Malay Malaysian-Chinese Malaysian-Indian

24 19

7

9 7 2

33 26

9 Total 50 18 68

No. involved in three crashes

Ethnicity Malay Malaysian-Chinese Malaysian-Indian

12 9 1

5 3 2

17 12

3 Total 22 10 32

More than half of the motorcycle drivers sampled in Study 2 reported that they

had been involved in at least one motor vehicle crash over the preceding year (see Table

4.14) Regardless of ethnic background, male motorcycle drivers reported higher crash

occurrence than female motorcycle drivers.

Table 4.14: Crash Occurrence Frequency, Gender and Ethnicity in Study 2 (N=122) Motorcycle drivers

Gender Total Male Female

No. involved in one crash

Ethnicity Malay Malaysian-Chinese Malaysian-Indian

20 8 28 25 13 38 16 1 17

Total 61 22 83

No. involved in two crash

Ethnicity Malay Malaysian-Chinese Malaysian-Indian

17 15 11

4 4 0

21 19 11

Total 43 8 51

No. involved in three crashes

Ethnicity Malay Malaysian-Chinese Malaysian-Indian

10 6 3

1 1 0

11 7 3

Total 19 2 21

126

4.5 Distal and Proximal Variable Data 4.5.1 Results of Study 1

Study 1A. Table 4.15 shows means, standard deviations and relationships

between distal, proximal and outcome variables within the sample of automobile drivers.

All distal and proximal variables were positively correlated except internality (I) which

was negatively corrected with other variables. All these correlations were significant

(p<.05). Hopelessness (BHS) was the only independent variable that was not

significantly correlated with crash occurrence and injury occurrence.

Study 1B. Table 4.16 shows means, standard deviations and relationships

between distal, proximal and outcome variables within the sample of automobile drivers.

All distal and proximal variables were positively correlated except I which was

negatively corrected with other variables. Most of these correlations were significant

(p<.05). However, in Study 1B, BHS was not significantly correlated with verbal

aggression (VER), crash occurrence and crash injury. Also, VER was not correlated

with the total score for behaviour in traffic (BIT) nor with any of the BIT subscales:

usurpation of right-of-way; freeway urgency; externally-focused frustration; and

destination-activity orientation. Although VER was significantly correlated with crash

occurrence, it was not correlated with injury occurrence.

Study 1C. Table 4.17 shows means, standard deviations and relationships

between distal, proximal and outcome variables within the sample of automobile drivers.

All distal and proximal variables were positively correlated except I which was

negatively corrected with other variables. Most of these correlations were significant

(p<.05). I was significantly correlated with all variables except with VER and hostility.

Both externality-chance (C) and externality-powerful-others (P) were not significantly

correlated with the HAT subscale measuring hostile automatic thoughts related to the

derogation-of-others.

127

Table 4.15: Means, Standard Deviations and Bivariate Correlations for Variables in Study 1A (n=301) Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 Distal Variables1 1 Internality (I) 9.22 6.45 1 2 Externality-Chance (P) 3.08 6.88 -.345** 1 3 Externality-Powerful-Other (O) 2.44 7.23 -.471** .516** 1 4 Hopelessness (BHS) 4.00 2.69 -.306** .239** .342** 1 Proximal Variables1 5 Behaviour in Traffic (BIT) 165.52 24.5 -.562** .435** .513** .278** 1 6 Usurpation of right-of-way 34.97 5.64 -.544** .405** .482** .246** .942** 1 7 Freeway Urgency 43.96 7.76 -.553** .381** .434** .191** .901** .818** 1 8 Externally-Focused Frustration 19.04 3.57 -.391** .340** .388** .280** .804** .716** .662** 1 9 Destination-activity Orientation 26.78 4.58 -.396** .339** .416** .247** .749** .625** .533** .566** 1 Outcome Variables2 10 Crash Occurrence .3455 .476 -.231** .218** .147* .036 .371** .316** .376** .376** .209** 1 11 Injury Occurrence .2691 .442 -.147* .155** .129* .027 .211** .186** .202** .201** .152** .331** 1 * Correlation is significant at .05 level (2-tailed) ** Correlation is significant at .01 level (2-tailed)

128

Table 4.16: Means, Standard Deviations and Bivariate Correlations for Variables in Study 1B (n=302)

Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Distal Variables1 1 9.86 6.56 1 2 4.69 8.06 -.407** 1 3 2.84 7.97 -.335** .587** 1 4 4.66 3.45 -.341** .254** .312** 1 5 87.53 19.5 -.337** .363** .376** .278** 1 6 17.48 5.82 -.236** .254** .271** .140* .779** 1 7 13.48 3.25 -.039 .028 .051 .003 .586** .347** 1 8 18.50 5.55 -.353** .342** .393** .355** .847** .555** .382** 1 9 21.84 5.60 -.279** .343** .343** .310** .855** .518** .444** .669** 1 10 16.22 4.65 -.334** .358** .319** .195** .763** .481** .331** .550** .584** 1 Proximal Variables1 11 170.9 28.9 -.515** .509** .514** .200** .520** .380** .103 .491** .505** .438** 1 12 71.43 12.9 -.542** .531** .523** .254** .540** .400** .103 .521** .516** .445** .964** 1 13 46.85 9.00 -.462** .430** .411** .099 .461** .355** .089 .434** .418** .403** .921** .842** 1 14 19.41 3.91 -.369** .372** .414** .157** .491** .338** .159 .452** .496** .386** .816** .762** .697** 1 15 27.14 4.97 -.366** .401** .443** .153** .324** .213** .028 .286* .378** .272** .816** .731** .688**.602** 1 Outcome Variables2 16 .5695 .4960 -.162** .148* .178** .067 .276** .172** .167** .268** .275** .172** .463** .448** .489**.408** .294** 1 17 .3079 .4624 -.176* .150** .173* .013 .225** .213** .071 .240** .147** .331** .380** .355** .440**.298** .213** .580** 1

Note: (1) Internality (2) Externality-Chance (3) Externality-Powerful-Other (4) Hopelessness (5) Total Aggression (6) Physical Aggression (7) Verbal Aggression (8) Anger (9) Hostility (10) Indirect Aggression (11) Behaviour in Traffic (12) Usurpation of right-of-way (13) Freeway Urgency (14) Externally-Focused Frustration (15) Destination-activity Orientation (16) Crash Occurrence (17) Injury Occurrence

* Correlation is significant at .05 level (2-tailed) ** Correlation is significant at .01 level (2-tailed)

129

Table 4.17: Means, Standard Deviations and Bivariate Correlations for Variables in Study 1C (n=252)

Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Distal Variables1 1 10.37 6.70 1 2 4.52 7.81 -.191** 1 3 .78 8.36 -.235** .641** 1 4 4.42 3.18 -.186** .254** .354** 1 5 88.80 17.8 -.235** .356** .392** .348** 1 6 16.38 5.64 -.183** .296** .278** .270** .745** 1 7 13.70 3.31 -.075 .148** .120 .110 .592** .277** 1 8 19.03 5.03 -.183** .320** .323** .296** .804** .481** .422** 1 9 22.81 5.05 -.081 .202** .298** .357** .747** .423** .364** .531** 1 10 16.89 5.17 -.306** .302** .366** .202** .749** .454** .373** .476** .395** 1 11 65.85 19.7 -.183** .203** .150* .051 .518** .465** .310** .345** .338** .424** 1 12 18.67 7.86 -.230** .227** .218** .082 .508** .530** .261** .324** .292** .412** .895** 1 13 26.70 8.17 -.103** .057 .038 .033 .383** .241** .306** .278** .291** .313** .838** .588** 1 14 20.49 6.69 -.139** .259** .230** .095 .456** .448** .228** .292** .293** .378** .862** .735** .549** 1 Proximal Variables1 15 161.7 28.9 -.446** .434** .502** .288** .545** .402** .219** .370** .385** .565** .379** .413** .192**.349** 1 16 67.11 12.9 -.402** .390** .451** .275** .483** .377** .181** .343** .304** .506** .364**.401** .210**.340** .856** 1 17 43.58 9.00 -.367** .311** .308** .151* .387** .304** .221** .259** .210** .404** .277**.305** .162**.258** .725** .534** 1 18 19.31 3.91 -.245** .281** .368** .141* .428** .263** .150* .281** .296** .526** .307**.294** .199**.314** .615** .484** .265** 1 19 25.98 4.97 -.166** .131* .228** .151* .216** .069 .109 .185** .229** .199** .224**.224** .101**.271** .342** .250** .137* .079 1 Outcome Variables2 20 .516 .501 .-181** .191** .166** .003 .196** .106 .192** .130** .109 .209** .268**.226** .246** .221** .343** .355** .254** .264** .119* 1 21 .230 .422 -.212** .241** .270** .016 .252** .178** .189** .222** .095 .251** .193**.158** .174** .167** .286** .277** .275** .189** .076 .530** 1 Note: (1) Internality (2) Externality-Chance (3) Externality-Powerful-Other (4) Hopelessness (5) Total Aggression (6) Physical Aggression (7) Verbal Aggression (8) Anger (9) Hostility (10) Indirect Aggression (11) Hostile Automatic Thoughts (12) HAT-Physical Aggression (13) HAT-Derogation of others (14) HAT-Revenge (15) Behaviour in Traffic (16) Usurpation of right-of-way (17) Freeway Urgency (18) Externally-Focused Frustration (19) Destination-activity Orientation (20) Crash Occurrence (21) Injury Occurrence * Correlation is significant at .05 level (2-tailed) ** Correlation is significant at .01 level (2-tailed)

S

130

BHS was significantly correlated to the total BIT score and to BIT subscales:

usurpation of right-of-way; freeway urgency; externally-focused frustration; and

destination-activity orientation. However, it was not correlated with crash occurrence or

with crash injury. Hostility (HOS) was significantly correlated with all other variables

except with crash occurrence and crash injury. The BIT subscale measuring destination-

activity orientation was significantly correlated to crash occurrence but not to injury

occurrence.

4.5.2 Results of Study 2

Table 4.18 shows means, standard deviations and relationships between distal,

proximal and outcome variables within the sample of motorcycle drivers in Study 2. All

distal and proximal variables were positively correlated except internality (I) which was

negatively corrected with the BIT total score, all BIT subscales, crash occurrence and

injury occurrence. Of these negative the BIT subscales measuring freeway urgency and

externally-focused frustration were significant.

Similar to observed results in study 1A, 1B and 1C, BHS was significantly

correlated with total BIT score and with all of the BIT subscales, but it was not correlated

with crash occurrence or injury occurrence.

131

Table 4.18: Means, Standard Deviations and Bivariate Correlations for Variables in Study 2 (n=122)

Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 Distal Variables 1 Internality (I) 3.48 8.179 1 2 Externality-Chance (C) 5.66 7.122 .580** 1 3 Externality-Powerful-Others (P) 1.66 7.917 .139 .413** 1 4 Hopelessness (BHS) 5.55 3.323 .072 .290** .371** 1 Proximal Variables 5 Behaviour in Traffic (BIT) 175.50 23.485 -.182* .409** .269** .418** 1 6 Usurpation of right-of-way 73.76 11.081 -.111 .428** .226** .415** .941** 1 7 Freeway Urgency 48.06 8.035 -.200* .334** .264** .349** .876** .758** 1 8 Externally-Focused Frustration 20.14 3.621 -.192* .251** .201* .317** .750** .614** .562** 1 9 Destination-activity Orientation 27.30 3.880 -.150 .219** .183* .232** .630** .500** .376** .535** 1 Outcome Variables 10 Crash Occurrence .6803 .4683 -.025 .233** .212* .167 .413** .356** .374** .314** .313** 1 11 Injury Occurrence .5738 .4966 -.043 .240** .165 .028 .383** .325** .367** .259** .291** .795** 1 * Correlation is significant at .05 level (2-tailed) ** Correlation is significant at .01 level (2-tailed)

132

4.5.3 Results of Study 3

Table 4.19 shows means, standard deviations and relationships between distal,

proximal and outcome variables within the sample of taxicab drivers in Study 3. In this

study, significant negative correlations were observed between the internality (I)

variable and with physical aggression (PHY) subscale scores on the AQ and with BIT

total scores, but remaining I correlations with BIT and AQ subscales did not achieve

significance. Externality-chance (C) and externality-powerful-others (P) scores were

significantly and positively correlated with crash occurrence and injury occurrence.

However, neither an observed weak positive correlation between I and C, nor a weak

negative correlation between I and P achieved significance. In general, correlations

between I and distal, proximal or outcome variables in Study 3 were weaker than those

observed in Studies 1 and 2.

As indicated in Table 4.19, AQ subscales were significantly and positively

correlated with each other. While physical aggression (PHY) and hostility (HOS) were

significantly correlated with crash occurrence and injury occurrence, verbal aggression

(VER) and anger (ANG) were significantly correlated only with crash occurrence.

Differing from Studies 1A, 1B, 1C and 2, BIT total scores had a significant positive

correlation with crash occurrence but not with injury occurrence.

133

Table 4.19: Means, Standard Deviations and Bivariate Correlations for Variables in Study 3 (n=133) Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Distal Variable 1 Internality (I) 12.05 7.31 1 2 Externality-Chance (C) 3.10 8.43 .072 1 3 Externality –Powerful-Others (P) 1.42 8.45 -.070 .371** 1 4 Total Aggression (AQ) 66.74 19.3 -.151 .236** .218* 1 5 Physical Aggression (PHY) 15.54 6.32 -.193* .197* .117 .872** 1 6 Verbal Aggression (VER) 11.08 3.82 -.161 .240** .114 .749** .561** 1 7 Anger (ANG) 15.11 5.12 -.109 .128 .222* .853** .658** .618** 1 8 Hostility (HOS) 15.35 4.51 -.182* .246** .180** .816** .646** .576** .588** 1 9 Indirect Aggression (IND) 11.65 3.99 -.032 .194* .271** .807** .643** .454** .636** .604** 1 Proximal Variables 10 Behaviour in Traffic (BIT) 75.15 10.4 -.234** .018 .095 .204* .263** .165 .121 .229** .117 1 11 Usurpation of right-of-way 32.17 5.13 -.141 .091 .106 .121 .152 .116 .072 .194* .060 .864** 1 12 Freeway Urgency 20.07 3.84 -.156 .025 .120 .048 .156 .030 .060 .039 -.020 .721** .528** 1 13 Externally-Focused Frustration 8.82 2.06 -.166 .023 .117 .235** .235** .149 .149 .257** .200* .622** .418** .338** 1 14 Destination-activity Orientation 11.32 2.88 -.091 .401** .443** .153** .324** .213** .028 .286* .378** .521** .292** .061 .254** 1 Outcome Variables 15 Crash Occurrence .2000 .404 -.112 .148* .178** .067 .276** .172** .167** .268** .275** .373** .261** .255** .245** .289** 1 16 Injury Occurrence .0301 .171 -.177 .150** .173* .013 .225** .213** .071 .240** .147** .040 .023 .054 .092** .103 .021 1 * Correlation is significant at .05 level (2-tailed) ** Correlation is significant at .01 level (2-tailed)

134

4.6 Hypothesis Testing

This section reports the results of analyses to test the hypotheses formulated in

chapter 3 (see Table 3.1).

4.6.1 Hypothesis 1: Behaviour in Traffic Influences Motor Vehicle Crash

Outcomes

First, analyses were conducted to test whether BIT scores influenced crash

occurrence. While controlling driver experience and driving frequency, results from the

logistic regression analyses in all studies indicated strong relationships between total BIT

scores and the likelihood of a motor vehicle crash occurrence (Study 1A: B=.034, p<.01;

Study 1B: B=.04, p<.01; Study 1C: B=.041, p<.01; Study 2: B=.048, p<.01 and Study 3:

B=.125, p<.01). These results supported H1.1, that behaviour in traffic influences crash

occurrence.

When the relationships between the four component factors of the BIT scale and

the likelihood of a crash outcome were tested, results of logistic regression analyses

indicated that usurpation of right-of way, freeway urgency, and externally-focused

frustration, but not destination-activity orientation, were significantly related to crash

occurrence (see Table 4.20). These results supported H1.1.1 through H1.1.3 inclusive. For

the destination-activity factor, H1.1.4 was not supported.

Table 4.20: Results of Logistic Regression Analyses Showing the Effects of BIT

Component Factors on Crash Occurrence

Study 1A

Study 1B Study 1C Study 2 Study 3

Usurpation of Right-of Way B=.063, p<.01 B=.090, p<.01 B=.063, p<.01 B=.080, p<.01 B=.146, p<.01

Freeway Urgency B=.102, p<.01 B=.135, p<.01 B=.088 p<.01 B=.120, p<.01 B=.172, p<.01

Externally-focused Frustration B=.238, p<.01 B=.229, p<.01 B=.095, p<.01 B=.180, p<.01 B=.315, p<.01

Destination-activity Orientation B=.095, p<.01 B=.117, p<.01 Not Significant B=.202, p<.01 B=.278, p<.01

135

Behaviour in traffic also influenced injury occurrence in all studies except Study

3. When driver experience and travel frequency were controlled, the results of logistic

regression showed that total BIT scores were strongly regressed with the likelihood of

experiencing an injury related to a motor vehicle crash (Study 1A: B=.019, p<.01; Study

1B: B=.033 p<.01; Study 1C: B=.038, p<.01 and Study 2: B=.035, p<.01). These results

supported H1.2, that behaviour in traffic would influence injury occurrence.

When the relationships between the likelihood of injury occurrence and the four

component factors of the BIT scale were tested, logistic regression analyses indicated

that usurpation of right-of way, freeway urgency, externally-focused frustration and

destination-activity orientation were significantly related to traffic crash injury in all

studies except Study 3 (See Table 4.21).

Table 4.21: Results of Logistic Regression Analyses Showing the Effects of BIT

Component Factors on Injury Occurrence

Study 1A

Study 1B Study 1C Study 2 Study 3

Usurpation of Right-of Way B=.035, p<.01 B=.064, p<.01 B=.069, p<.01 B=.059, p<.01 Not Significant

Freeway Urgency B=.054, p<.01 B=.140, p<.01 B=.075 p<.01 B=.091, p<.01 Not Significant

Externally-focused Frustration B=.118, p<.01 B=.165, p<.01 B=.095, p<.01 B=.120, p<.01 Not Significant

Destination-activity Orientation B=.074, p<.05 B=.087, p<.01 Not Significant B=.158, p<.01 Not Significant

4.6.2 Hypothesis 2: Driver Characteristics Influence Behaviour in Traffic

ANOVA indicated that driver experience and travel frequency had a statistically

significant effect on total BIT scores of automobile drivers sampled in Studies 1A, 1B

and 1C (see Table 4.22, Table 4.23 and Table 4.24, respectively).

136

Table 4.22: The Influence of Driver Characteristics on Total BIT Scores in Study 1A (N=301)

Variable N M SD F Driver experience

3 years or less 186 161.73 21.64 4.320** 3 to 5 years 88 170.98 26.98 5 to 7 years 18 171.44 33.30 More than 7 years 9 178.89 22.35 Travel frequency Everyday 64 173.77 27.25 5.600** Several times a week 110 165.48 25.06 About once or twice a week 41 171.15 19.43 About once every two weeks 17 161.35 20.29 Almost never 69 155.64 21.25

Note: ** p<.01

Table 4.23: The Influence of Driver Characteristics on Total BIT Scores in Study 1B (N=302)

Variable N M SD F Driver Experience 3 years or less 221 168.56 28.32 3.074* 3 to 5 years 60 175.60 28.35 5 to 7 years 19 185.32 33.68 More than 7 years 2 147.50 26.16 Travel Frequency Everyday 110 181.82 25.88 8.184** Several times a week 81 168.41 28.35 About once or twice a week 37 167.92 24.82 About once every two weeks 45 157.31 33.52 Almost never 29 161.03 25.77

Note: ** p<.01, * p<.05.

137

Table 4.24: The Influence of Driver Characteristics on Total BIT Scores in Study 1C (N=252)

Variable N M SD F Driver Experience 3 years or less 187 159.81 16.14 3.345* 3 to 5 years 46 167.61 15.53 5 to 7 years 16 165.88 17.73 More than 7 years 3 167.00 24.52 Driving Frequency Everyday 67 170.12 15.00 8.060** Several times a week 69 161.06 14.39 About once or twice a week 33 160.73 19.77 About once every two weeks 45 157.12 16.01 Almost never 38 154.29 14.06

Note: ** p<.01, * p<.05.

In Study 1A, post hoc analyses indicated that drivers with 3 years or less of

licensed driving experience had significantly lower total BIT scores when compared with

drivers that had 3 years experience but less than 5 years of licensed driving experience

(p<.01). Drivers who travelled everyday had significantly higher total BIT scores when

compared to those who almost never travelled (p<.01). Drivers who travelled about

once or twice a week had significantly higher total BIT when compared to those who

almost never travelled (p<.05). In Study 1B, drivers who travelled everyday had

significantly higher total BIT scores when compared to those who travelled several times

a week (p<.05) and about once every two weeks (p<.01). In Study 1C, drivers with 3

years of licensed driving experience had significantly lower total BIT scores when

compared with drivers that had 3 years experience but less than 5 years of licensed

driving experience (p<.05). Drivers who travelled every day had significantly higher

total BIT scores when compared to those who travelled several times a week (p<.05),

about once every two weeks (p<.01), and those who almost never travelled (p<.01).

In Study 2, motorcycle drivers’ experience was not significantly related to total

BIT scores (see Table 4.25). On the other hand, the effect of travel frequency on total

BIT score was significant.

138

Table 4.25: The Influence of Driver Characteristics on Total BIT Scores in Study 2 (N=122)

Variable N M SD F Driver Experience 3 years or less 77 174.52 24.68 1.437 (N.S) > 3 years but < 5 years 31 172.58 20.80 5 to 7 years 10 188.50 22.09 More than 7 years 4 184.50 15.63 Driving Frequency Everyday 52 182.81 24.89 3.528** Several times a week 32 175.81 20.27 About once or twice a week 7 161.71 14.81 About once every two weeks 17 168.82 22.94 Almost never 14 162.64 20.56

Note: ** p<.01, * p<.05, N.S. Not significant

In Study 3, both driver experience and taxicab experience were tested. It was

found that the driver experience was statistically related to total BIT score (see Table

4.26). However, taxicab driver experience was not statistically related to total BIT score.

In other words, the difference in means of total BIT scores among the drivers in Study 3

was not statistically significant regardless of their years of experience as taxicab drivers.

Table 4.26: The Influence of Driver Characteristics on Total BIT Scores in Study 3 (N=133)

Variable N M SD F Driver Experience 5 years or less 3 82.33 5.859 2.753* >5 years but < 10 years 16 78.31 11.97 10 to 15 years 23 78.65 8.381 More than 15 years 91 73.47 10.31 Taxicab Driving Experience 5 years or less 38 77.55 10.62 1.920 (N.S) >5 years but < 10 years 48 73.60 10.26 10 to 15 years 27 72.74 10.37 More than 15 years 20 77.55 9.316

Note: ** p<.01, * p<.05, N.S. Not significant

Therefore, it is concluded that Hypothesis 2, that drivers’ demographic

characteristics would significantly influence total BIT scores was supported in studies of

automobile drivers. However, the direction of the difference was opposite to what had

139

been predicted by H2.1 and H2.2. Contrary to the two sub-hypotheses, driver experience

and travel frequency actually increased total BIT scores.

Hypothesis 2 was partially supported in the study of motorcycle drivers. In Study

2, only H2.2 was supported in that travel frequency significantly influenced total BIT

score but driver experience had no statistically significant effect. Again, though, the

observed effect was opposite to what had been predicted by H2.2. Contrary to the sub-

hypothesis, travel frequency actually increased total BIT scores.

Hypothesis 2 was partially supported in the study of taxicab drivers. In Study 3,

only H2.1 was confirmed, in that driver experience significantly influenced total BIT

score but taxicab experience had no statistically significant effect. In this case, the

direction of the effect was consistent with the hypothesised relationship between driver

experience and total BIT score; the longer the taxicab operator had been driving, the

lower was the total BIT score.

4.6.3 Hypothesis 3: Demographic Variables Influence Behaviour in Traffic

The direct effects on total BIT scores of three demographic variables – gender,

ethnicity and age – were investigated. In Studies 1A, 1B, 1C and 2, t-tests indicated that

mean total BIT scores differed significantly between male and female participants,

where male automobile and motorcycle drivers scored significantly higher than female

drivers. ANOVA results for age, however, indicated no significant differences in mean

total BIT scores. For ethnicity, ANOVA indicated that mean total BIT scores in Studies

1A, 1B, 1C and 2 differed between different ethnic groups (see Table 4.27).

140

Table 4.27: Effects of Demographic Factors on total BIT Scores

Study 1A

Study 1B Study 1C Study 2 Study 3

Gender

t=2.53, p<.05 t=2.68, p<.01 t=3.44, p<.01 t=3.62, p<.01 Not Applicable

Ethnicity

F=11.66, p<.01 F=8.99, p<.01 F=19.9, p<.01 F=9.12, p<.01 F=3.74, p<.05

Age

F=1.98, N.S. F=2.81, N.S. F=.05, N.S. F=1.00, N.S F=4.56, N.S.

Note: Not significant

In Study 1A, 1C and Study 2, post hoc analyses indicated that Malaysian-

Chinese automobile drivers and motorcyclists scored significantly lower total BIT scores

than either Malaysian-Indian or Malay drivers (p<.05). In Study 1B, Malaysian-Indian

automobile drivers scored significantly higher total BIT scores than Malaysian-Chinese

automobile drivers (p<.01). In Study 3, Malaysian-Indian taxicab drivers had

significantly lower total BIT scores than either Malaysian-Chinese taxicab drivers

(p<.05).

Therefore, it is concluded that Hypothesis 3 was partially supported in studies of

both automobile and motorcycle drivers. H3.1 and H3.2 were confirmed, in that gender

and ethnicity significantly influenced total BIT scores. For taxicab drivers studied in

Study 3, H3.2 was confirmed, in that ethnicity significantly influenced total BIT scores.

In all studies, age had no statistically significant direct effect on total BIT scores, so the

null hypothesis could not be rejected and H3.3 was not supported.

4.6.4 Hypothesis 4: Demographic Variables Influence Locus of Control

The direct effects of the same three demographic variables on locus of control

were also investigated. In Study 1B, results showed that gender had no influence on the

three dimensions of locus of control: Internality (I), Externality-Chance (C), and

Externality-Powerful-Others (P). In Study 1C, however, it was found that female

automobile drivers scored significantly higher levels on the I dimension when compared

to male automobile drivers, t(250) = 2.562, p<.05. In Study 1A and Study 2, male

141

automobile and motorcycle drivers scored significantly higher on the P dimension than

did female automobile and motorcycle drivers, t(120) = 2.490, p<.05; t(299) = 2.503,

p<.05 respectively.

In Study 1A, all ethnic groups had significantly different mean I, E and P scores,

F(2, 298) = 6.370, p<.01; F(2, 298) = 3.941, p<.05 and F(2, 298) = 3.476, p<.05

respectively. Post hoc analyses indicated that Malaysian-Indian automobile drivers had

significantly lower I scores than did either Malaysian-Chinese or Malay drivers (p<.01).

Malaysian-Indian drivers had significantly higher scores on the C dimension than did

Malay drivers (p<.05) and Malaysian-Indian drivers scored significantly higher on the P

dimension than did drivers in all other ethnic groups (p<.01).

In Study 1B, ethnic group differences were significant only with respect to mean

C and P subscale scores, F(2, 299) = 3.462, p<.05 and F(2, 299) = 5.527, p<.01

respectively. Consistent with findings in Study 1A, post hoc analyses showed that

Malaysian-Indian drivers scored significantly higher on the C and P dimensions than did

Malaysian-Chinese drivers (p<.05 and p<.01 respectively).

In Study 1C, ethnic group differences were significant only with respect to P

subscale scores, F(2, 249) = 3.566, p<.05. Post hoc analysis showed that Malaysian-

Indian automobile drivers scored significantly higher than did Malaysian-Chinese

drivers (p<.05).

In Study 2, ethnic group differences were significant only with respect to I

subscale scores, F(2, 119) = 5.041. Post hoc analyses showed that Malaysian-Indian

motorcycle drivers scored significantly lower than all other ethnicity groups on the I

dimension (p<.05).

For Studies 1A, 1B, 1C, 2 and 3 the age variable had no significant direct effects

on any of the three dimensions of locus of control.

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Therefore, based on the results of t-tests and ANOVA, Hypothesis 4 was

partially supported in studies of both automobile and motorcycle drivers. H4.1.3 was

supported, in that gender was observed to significantly influence the externality-

powerful-others scores. In Study 1, H4.2.1, H4.2.2 and H4.2.3 were supported, that ethnicity

significantly influenced internality, externality-chance and externality-powerful-others.

In Studies of both automobile and motorcycle drivers, it was observed that age had no

significant effect on any of internality, externality-chance and externality-powerful-

others, so H4.3.1, H4.3.2 and H4.3.3 were not supported.

4.6.5 Hypothesis 5: Demographic Variables Influence Hopelessness

The direct effects of gender, ethnicity and age on hopelessness were investigated.

Independent sample t-tests on data from Studies 1A, 1B and 1C found no significant

differences in mean scores of hopelessness (BHS) between male and female automobile

drivers. ANOVA results found no significant differences in mean BHS scores between

ethnic groups or different age groups among automobile drivers in Studies 1A, 1B or

1C.

However, in Study 2, it was found that the gender and ethnicity of motorcycle

drivers did have a significant direct effect on hopelessness. Female motorcycle drivers

scored significantly lower than male motorcycle drivers, t(120) = 2.079, p<.05. In

addition, Malay motorcycle drivers had a significantly higher BHS score when compared

to Malaysian-Chinese motorcycle drivers (p<.01). Age was found to have no influence

on BHS scores with the sample of motorcycle drivers in Study 2.

Therefore, Hypothesis 5 was not supported with respect to automobile drivers in

Study 1. Hypothesis 5 was partially supported in Study 2 with the sample of motorcycle

drivers. H5.1 and H5.2, that gender and ethnicity influence hopelessness, were supported.

H5.3, that age influences hopelessness, was not supported in either Study 1 or Study 2.

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4.6.6 Hypothesis 6: Locus of Control Influences Hopelessness

In Study 1A, internality (I) had a significant negative effect on hopelessness

(BHS) (B = -.306, p<.01) but externality-chance (C) and externality-powerful-others (P)

had significant positive effects on BHS scores (B = .239, p<.01 and B = .342, p<.01,

respectively). In Study 1B, I was found to have a significant negative effect on BHS

scores (B = -.341, p<.01) but C and P had significant positive effects on BHS scores (B =

.254, p<.01 and B = .312, p<.01 respectively). In Study 1C, I had a significant negative

effect on BHS scores (B = -.186, p<.01) but C and P had significant positive effects on

BHS scores (B = .254, p<.01 and B = .354, p<.01, respectively).

In Study 2, no significant effects were observed between I and BHS scores in the

sample of motorcycle drivers, but C and P had significant positive effects on BHS scores

(B = .290, p<.01 and (B = .371, p<.01, respectively).

Therefore, it is concluded that Hypothesis 6 was supported in studies of

automobile drivers. H6.1, H6.2 and H6.3, that the three locus of control dimensions

influence hopelessness, were supported, with higher levels of internality related to lower

levels of hopelessness and higher levels of both externality dimensions associated with

higher hopelessness.

Hypothesis 6 was partially supported in Study 2, with the sample of motorcycle

drivers. H6.2 and H6.3, that externality-chance and externality-powerful-others would

influence hopelessness, were supported. H6.1, that internality would influence

hopelessness, was not supported.

4.6.7 Hypothesis 7: Hopelessness Influences Behaviour in Traffic

In studies of both automobile and motorcycle drivers, results of linear regression

analyses indicated that hopelessness had a significant positive effect on total BIT scores

and on scores for each of the four BIT component factors (see table 4.28).

144

Table 4.28: Direct effects of hopelessness on BIT scores

Study 1A Study 1B Study 1C Study 2 Total BIT score B=.278, p<.01 B=.200, p<.01 B=.288, p<.01 B=.418, p<.01 Usurpation of Right-of-way B=.287, p<.01 B=.254, p<.01 B=.275, p<.01 B=.415, p<.01 Freeway Urgency B=.191, p<.01 B=.099, N.S. B=.151, p<.05 B=.349, p<.01 Externally-Focused Frustration B=.280, p<.01 B=.157, p<.01 B=.141, p<.05 B=.317, p<.01 Destination-Activity Orientation B=.247, p<.01 B=.153, p<.05 B=.151, p<.05 B=.232, p<.05

In Study 1A, it was observed that the higher the hopelessness (BHS) scores, the

higher were BIT subscale scores for usurpation of right-of-way (B = .287, p<.01),

freeway urgency (B =.191, p<.01), externally-focused frustration (B = .280, p<.01) and

destination-activity orientation (B = .247, p<.01). In Study 1B, the higher the

hopelessness scores, the higher were BIT subscale scores for usurpation of right-of-way

(B = .254, p<.01), externally-focused frustration (B = .157, p<.05) and destination-

activity orientation (B = .153, p<.05) but not for freeway urgency. In Study 1C, the

higher the hopelessness scores, the higher were BIT subscale scores for usurpation of

right-of-way (B = .275, p<.01), freeway urgency (B = .151, p<.05), externally-focused

frustration (B = .141, p<.05) and destination-activity orientation (B = .151, p<.05). In

Study 2, it was observed that the higher the hopelessness scores, the higher were BIT

subscale scores for usurpation of right-of-way (B = .415, p<.01), freeway urgency (B =

.349, p<.01), externally-focused frustration (B = .317, p<.01) and destination-activity

orientation (B = .232, p<.05).

Therefore, it is concluded that Hypothesis 7, that hopelessness would have a

significant positive influence on total BIT scores, was supported in Studies 1A, 1C and

2, with both automobile and motorcycle drivers. H7.1, H7.3 and H7.4, that hopelessness

would have a significant positive direct effect on usurpation of right-of-way, externally-

focused frustration and destination-activity were supported in both Studies 1 and 2. H7.2,

that hopelessness would have a significant positive effect on freeway urgency was not

supported in Study 1B, meaning that H7 was only partially supported for that study.

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4.6.8 Hypothesis 8: Locus of Control Influences Behaviour in Traffic

It was hypothesised that internality (I) would have a negative influence on total

BIT scores while both externality-chance (C) and externality-powerful-others (P) would

have a positive influence on total BIT scores. Results of multiple regression analyses (in

studies of car, motorcycle and taxicab drivers), provided support for hypothesis H8.1,

that the higher the subscale score for I, the lower were mean total BIT scores. With

regard to H8.2, results indicated that the higher were subscale scores for C, the higher

were mean total BIT scores of automobile and motorcycle drivers in Studies 1 and 2, but

not of the sample of taxicab drivers in Study 3. With regard to H8.3, results indicated that

the higher were subscale scores for P, the higher were mean total BIT scores automobile

drivers in Study 1, but not of the motorcycle and taxicab drivers in Studies 2 and 3 (See

Table 4.29).

Therefore, it is concluded that Hypothesis 8 was supported for automobile

drivers in Study 1. H8.1, H8.2 and H8.3, that locus of control would influence total BIT

scores were supported in Study 1, with internality observed to exert a positive effect on

BIT and the two externality dimensions to exert negative effects. Hypothesis 8 was

partially supported for motorcycle drivers in Study 2. H8.1 and H8.2, that internality and

externality-chance would influence total BIT scores were supported, but not H8.3 that

externality-powerful-others would influence total BIT scores. Hypothesis 8 was partially

supported for taxicab drivers in Study 3, where only H8.1, that internality would

negatively influence total BIT scores was supported.

Table 4.29: Direct Effects of Locus of Control on Total BIT Scores

Study 1A

Study 1B Study 1C Study 2 Study 3

I B=-.388, p<.01 B=-.336, p<.01 B=-.339, p<.01 B=-.625, p<.01 B=-.229, p<.01 C B=.178, p<.01 B=.208, p<.01 B=.168, p<.05 B=.753, p<.01 B=.006, N.S. P B=.239, p<.01 B=.297, p<.01 B=.315, p<.01 B=.044, N.S. B=.077, N.S.

146

Additional analysis: Interaction of ethnicity and locus of control on BIT.

Scores for the three locus of control dimensions – internality, externality -chance and

externality-powerful-others – were split at the median to form high and low groups so

that the interaction effect of ethnicity and locus of control could be tested on total BIT

scores and subscale scores for the four component factors. In Study 1C, it was found that

Malay automobile drivers with high internal locus of control scored significantly lower

in total BIT than did Malaysian-Indian automobile drivers with high internal control

Malaysian-Indian student car drivers, F=7.710, p<.01 (see Figure 4.1). Further, results

revealed that Malay automobile drivers with high internal control had significantly lower

scores on BIT subscales measuring usurpation of right-of-way, freeway urgency and

externally-focused frustration than did Malaysian-Indian automobile drivers with high

internal control, F=4.272, p<.05; F=4.909, p<.01 and F=8.581, p<.01 respectively (see

Figure 4.1).

highlow

Internality

175

170

165

160

155

150

Mea

n Sc

ore

on B

ehav

iour

in T

raffi

c(B

IT)

Malaysian-Indian

Malaysian-Chinese

MalayEthnicty

Figure 4.1: Interaction Effects between Ethnicity and Internality on BIT

In Study 1C, it was found that Malay automobile drivers with high externality-

chance scores had significantly higher BIT subscale scores for usurpation of right-of way

than did Malaysian-Chinese automobile drivers with low externality-chance scores,

=8.704, p<.01 (see Figure 4.2).

147

highlow

Externality (Chance)

74.00

72.00

70.00

68.00

66.00

64.00

62.00Mea

n Sc

ore

on U

surp

atio

n of

Rig

ht-o

f Way

Malaysian-Indian

Malaysian-Chinese

MalayEthnicty

Figure 4.2: Interaction Effect between Ethnicity and Externality-Chance on Usurpation of

Right-of Way

4.6.9 Hypothesis 9: Hopelessness Moderates the Relationship between Locus of

Control and Behaviour in Traffic

For Studies 1A, 1B and 1C, hopelessness did not moderate the relationship between

locus of control and BIT. However, in Study 2, multiple regression showed mixed

results. Hopelessness moderated the relationship between internality and the total BIT

score and between externality-chance and to total BIT score. First, the results of

hierarchical regression indicated that the R2 value changed after the internality x

hopelessness interaction was added in the regression model (R2=.033; R2=.034;

F=4.282, p<.05; Residuals Normality: Skewness=.444; Kurtosis=-.537) and the

moderator (hopelessness) showed a significant result, B = .327, p<.05. This means that

motorcycle drivers with high internality scores and high hopelessness scores tended to

have higher total BIT scores when compared to motorcycle drivers with high internality

scores but low hopelessness scores (see Figure 4.3).

148

The R2 value also changed after the externality-chance x hopelessness interaction was

added in the regression model (R2=.167; R2=.070; F=18.463, p<.01; Residuals

Normality: Skewness=.608; Kurtosis=-.371), and the moderator (hopelessness) showed

a significant result, B = .459, p<.01. This means that motorcycle drivers with high

externality-chance scores and high hopelessness scores tended to have higher total BIT

scores when compared to motorcycle drivers with high externality-chance scores but low

hopelessness scores (see figure 4.4).

Externality (Chance)

BIT

Lev

el

Effect for drivers with low hopelessness score

Effect for drivers with high hopelessness score

Figure 4.4: Moderating Effect of BHS on the Externality (Chance) -BIT Relationship

Internality

BIT

Lev

el

Effect for drivers with low hopelessness score

Effect for drivers with high hopelessness score

Figure 4.3: Moderating Effect of BHS on the Internality-BIT Relationship

149

Therefore, it is concluded that Hypothesis 9 was not supported in Study 1.

Hopelessness did not moderate the locus of control-BIT relation for automobile drivers.

However, Hypothesis 9 was partially supported in Study 2. With motorcycle drivers, the

H9.1, that hopelessness would moderate the relationship between internality and total BIT

scores, and H9.2, that hopelessness would moderate the relationship between externality-

chance and total BIT scores, were supported.

4.6.10 Hypothesis 10: Demographic Factors Influence Aggression

Analyses tested whether gender, ethnicity and age exerted direct effects on

drivers’ Aggression Questionnaire (AQ) scores. Mean total AQ scores differed

significantly between male and female participants in Studies 1B and 1C, t(300) = 2.690,

p<.01; and t(250) = 2.603, p<.05 respectively. In both studies, male automobile drivers

had significantly higher total AQ scores than did female automobile drivers. When mean

subscale scores for the five AQ component factors were tested, results indicated that

male automobile drivers scored significantly higher than female drivers on measures of

physical aggression, verbal aggression and indirect aggression (see Table 4.30).

Table 4.30: Direct Effects of Gender on AQ Total and Subscale Scores

The relationship between ethnic background and aggression was tested for

automobile drivers in Studies 1B, 1C and 3. In Study 1B and Study 3, ANOVA revealed

no significant differences between ethnic groups in mean total AQ scores. In Study 1C,

however, mean total AQ scores differed significantly between ethnic groups, F(2, 249) =

5.521, p<.01 (see table 4.31). Post hoc analysis showed that Malaysian-Chinese

Study 1B Study 1C Total Aggression (AQ) score t=2.690, p<.01 t=2.603, p<.05 Physical Aggression (PHY) t=4.298, p<.01 t=4.210, p<.01 Verbal Aggression (VER) t=2.032, p<.05 t=2.677, p<.01 Anger (ANG) t=.187, N.S t=-.467, N.S. Hostility (HOS) t=1.780, N.S t= .480, N.S Indirect Aggression (IND) t=2.164, p<.05 t=2.820, p<.01

150

automobile drivers in Study 1C had significantly lower total AQ scores than did Malay

or Malaysian-Indian automobile drivers (p<.01).

Table 4.31: Direct Effects of Ethnicity on AQ Total and Subscale Factors

When AQ subscale scores were tested for Study 1B and Study 1C, mixed results

were found. In Study 1B, the mean verbal aggression (VER) scores of Malay,

Malaysian-Chinese and Malaysian-Indian automobile drivers were significantly

different, F(2, 299) = 5.432, p<.01. Malay automobile drivers scored significantly

higher VER scores than drivers from other ethnic groups (p<.01). The mean indirect

aggression (IND) scores of Malay, Malaysian-Chinese and Malaysian-Indian automobile

drivers were also significantly different, F(2, 299) = 4.041, p<.05. Malaysian-Chinese

automobile drivers had significantly lower IND scores than drivers from other ethnic

groups (p<.01). In Study 1C, mean IND scores of Malay, Malaysian-Chinese and

Malaysian-Indian automobile drivers were significantly different, F(2, 249) = 10.567,

p<.01. Similar to the findings in Study 1B, Malaysian-Chinese automobile drivers had

significantly lower IND scores than drivers from other ethnic groups (p<.01). In Study

3, ANOVA revealed no significant differences between ethnic groups in mean scores in

any of the AQ subscale scores.

Mean total AQ scores and mean scores on the five AQ subscales did not differ

significantly between age groups either for automobile drivers in Studies 1B and 1C or

for taxicab drivers in Study 3.

Study 1B Study 1C Study 3 Total Aggression score F=2.904, N.S. F=5.521, p<.01 F=1.422, N.S. Physical Aggression (PHY) F=2.763, N.S. F=2.182, N.S. F=.632, N.S. Verbal Aggression (VER) F=5.432, p<.01 F=2.804, N.S. F=1.398, N.S. Anger (ANG) F=.526, N.S. F=1.561, N.S. F=2.155, N.S. Hostility (HOS) F=1.629, N.S. F=2.021, N.S F=1.564, N.S. Indirect Aggression (IND) F=4.041, p<.05 F=10.57, p<.01 F=1.077, N.S.

151

Therefore, it is concluded that Hypothesis 10 was partially supported. H10.1 (that

gender would influence aggression) was supported with respect to measures of total

aggression and to the same three (PHY, VER and IND) of five component factors

among automobile drivers sampled in both Studies 1B and 1C. H10.2 (that ethnicity

would influence aggression level) was supported only in Study 1C and only with respect

to total AQ, VER and IND subscale scores. H10.3 (that age would negatively influence

aggression) was not supported.

4.6.11 Hypothesis 11: Aggression Influences Behaviour in Traffic

In Study 1B and Study 1C, linear regression analyses indicated that total AQ

scores predicted total BIT scores and also scores measuring the four BIT component

factors: usurpation of right-of-way, freeway urgency, externally-focused frustration, and

destination-activity orientation (see Table 4.29). The higher the total aggression scores,

the higher were automobile drivers’ total BIT scores and scores on the four components.

In Study 3, however, linear regression analyses indicated that total AQ scores

predicted the total BIT score and the scores measuring only two of the four BIT

component factors: externally-focused frustration and destination-activity orientation

(See Table 4.32). This means that when taxicab drivers’ aggression scores were higher,

total BIT scores and scores on usurpation of right-of way and freeway urgency subscales

were higher.

Therefore, in studies of both automobile drivers and taxicab drivers, it is

concluded that Hypothesis 11, that aggression would have a positive influence on total

BIT scores, was supported. In Studies 1B and 1C, H11.1, H11.2, H11.3 and H11.4, that

aggression would have a direct positive effect on the usurpation of right-of way,

freeway urgency, externally-focused frustration and destination-activity orientation,

were all supported. However, with regard to the taxicab drivers sampled in Study 3, only

H11.3 and H11.4, that aggression would have a direct positive effect on externally-focused

frustration and on destination-activity orientation, respectively, were supported.

152

Table 4.32: Effect of Aggression on Total BIT Scores and on BIT Component Factors

Study 1B Study 1C Study 3 Total BIT Score B=.520, p<.01 B=.545, p<.01 B=.204, p<.05 Usurpation of Right-of-way B=.540, p<.01 B=.483, p<.01 B=.235, p<.01 Freeway Urgency B=.461, p<.01 B=.387, p<.01 B=.183, p<.05 Externally-Focused Frustration B=.491, p<.01 B=.428, p<.01 B=.121, N.S. Destination-Activity Orientation B=.324, p<.01 B=.216, p<.01 B=.048, N.S.

The effects of AQ subscale factors on the total BIT score were also tested. Linear

regression analyses indicated that physical aggression (PHY) had a significant positive

influence on total BIT scores in Study 1B, Study 1C and Study 3, B = .380, p<.01; B =

.370, p<.01; and B = .263, p<.01, respectively. Also, hostility (HOS) was found to have

a significant positive influence on BIT in Study 1B, Study 1C and Study 3, B = .505,

p<.01; B = .385, p<.01; and B = .229, p<.01, respectively. With both automobile and

taxicab drivers, the higher the levels of PHY and HOS, the higher were total BIT scores.

Results of regression analyses also showed that anger (ANG) had a significant

positive influence on total BIT scores in Study 1B and Study 1C, B = .370, p<.01 and B

= .263, p<.01 respectively, but not in Study 3. Similarly, indirect aggression (IND) was

also found to have significant positive influence on total BIT scores in Study 1B and

Study 1C, B = .438, p<.01 and B = .565, p<.01 respectively, but not in Study 3. This

implies that when automobile drivers have higher levels of ANG and IND, their total

BIT scores tend to be higher, but that this does not apply to taxi drivers. Verbal

aggression (VER) was found to have no significant influence on total BIT scores.

Additional analysis: Interaction effects of ethnicity and aggression on BIT.

When the interaction effect of ethnicity and hopelessness was tested on the BIT and its

four component factors, no interaction effects were found in all studies – Study 1A, 1B,

1C, Study 2 and Study 3. However, it was found that there was an interaction effect

between ethnicity and verbal aggression (VER) on freeway urgency, F=3.881, p<.05

(see Figure 4.5). Malay automobile drivers with high VER scores tended to score

153

significantly higher on freeway urgency than did Malaysian-Chinese automobile drivers

with high VER scores.

HighLow

Verbal Aggression

52.00

50.00

48.00

46.00

44.00

42.00

Mea

n S

core

on

Free

way

Urg

ency

Indian-Malaysian

Chinese-Malaysian

MalayEthnicty

Figure 4.5: Interaction of Ethnicity and Verbal Aggression on Freeway Urgency

4.6.12 Hypothesis 12: Locus of Control Moderates the Relationship between

Aggression and Behaviour in Traffic

4.6.12.1 Internality as a Moderator

Hierarchical regression analyses revealed that internality (I) moderated the

relationship between aggression and total BIT score. The moderating effect of I was

significant, B=-.362, p<.01; B=-.316, p<.01; and B=-.172, p<.05, for Study 1B, Study 1C

and Study 3, respectively, and R2 values changed after the I x AQ interaction was added

in the regression models (R2=.271; R2=.131; F=100.516, p<.01; Residuals Normality:

Skewness=-.645; Kurtosis=-.076; R2=.297; R2=.100; F=81.929, p<.01; Residuals

Normality: Skewness=.961; Kurtosis=-.003, respectively) This means that the

relationship between aggression and BIT would be stronger among drivers with low

scores on the I subscale than it would be among drivers with high scores on the I

subscale. In other words, aggressive drivers with low internal locus of control would

154

have higher BIT scores compared to drivers with high internal locus of control (see

figure 4.6). This applied to both automobile drivers and taxicab drivers.

4.6.12.2 Externality-chance and Externality-powerful-others as Moderators

In Study 1B and Study 1C, the hierarchical regression revealed that externality-

chance (C) and externality-powerful-others (P) moderated the relationship between

aggression and total BIT score. In Study 1B, R2 values changed after both the C x AQ

and P x AQ interactions were added in the respective regression models (R2=.271;

R2=.117; F=94.757, p<.01; Residuals Normality: Skewness=-463; Kurtosis=-.507;

R2=.271; R2=.109; F=91.694, p<.01; Residuals Normality: Skewness=-.360; Kurtosis=-

.431, respectively), and the moderating effects of C and P were significant, B = .387,

p<.01 and B = .369, p<.01 respectively.

Consistent with the findings from Study 1B, R2 values in Study 1C changed after

both the C x AQ and P x AQ interactions were added in the respective regression models

(R2=.297; R2=.069; F=71.897, p<.01; Residuals Normality: Skewness=-.794;

Kurtosis=-.606; R2=.297; R2=.088; F=78.015, p<.01; Residuals Normality: Skewness=

-.704; Kurtosis=.015, respectively), and the moderating effects of C and P were

Aggression Level

BIT

Lev

el

Effect for aggressive drivers with high internality score

Effect for aggressive drivers with low internality score

Figure 4.6: Moderating Effect of Internality on the Aggression-BIT Relationship

155

significant, B = .302, p<.01 and B = .332, p<.01 respectively. This means that aggressive

automobile drivers scoring high on either the C or P locus of control dimensions had

higher total BIT scores than automobile drivers scoring low on either the C or P

dimensions (see Figure 4.7).

However, hierarchical regression results showed that neither C nor P moderated

the relationship between aggression and total BIT scores for taxicab drivers in Study 3.

R2 values did not change after either the C x AQ or P x AQ interactions were added in

the regression models, and the moderation effect was not significant. This means that

aggressive taxicab drivers with high scores on either the C or P locus of control

dimensions did not differ greatly in total BIT scores from taxicab drivers with low scores

on the C or P dimensions.

Therefore, it is concluded that the Hypothesis 12 was supported in Studies 1B

and 1C, with the samples of automobile drivers study for student car drivers. H12.1,

H12.2, and H12.3, that the internality, externality-chance and externality-powerful-others

Aggression Level

BIT

Lev

el

Effect for aggressive drivers with low externality scores

Effect for aggressive drivers with high externality scores

Figure 4.7: Moderating Effects of Externality on the Aggression-BIT Relationship

156

dimensions of locus of control moderate the relationship between aggression and total

BIT scores were supported. However, Hypothesis 12 was only partially supported in

Study 3, with the sample of taxicab drivers. Only H12.1, that internality moderates the

relationship between aggression and total BIT scores was supported. H122 and H12.3, that

externality-chance and externality-powerful-others moderates the relationship between

aggression and BIT scores were not supported.

4.6.13 Hypothesis 13: Demographic Factors Influence Hostile Automatic Thoughts

Male automobile drivers in Study 1C scored significantly higher total HAT

scores than did female automobile drivers, t(249)=2.885, p<.01. Also, male automobile

drivers scored significantly higher on HAT subscales measuring statements about

physical aggression, t(250) = 3.263, p<.01 and revenge: t(249) = 3.314, p<.01 but not

on about the derogation of others.

ANOVA results showed that ethnic groups differed significantly with respect to

mean total HAT scores, F(2, 249) = 4.343, p<.05. Post hoc analysis indicated that

Chinese-Malaysian automobile drivers had significantly lower total HAT scores than

either Malay or Indian-Malaysian automobile drivers (p<.05). There were also

significant differences between ethnic groups on subscale scores measuring statements

about physical aggression F(2, 249) = 5.279, p<.01, and about revenge F(2, 248) =

3.737, p<.05. Post hoc analysis indicated that Chinese-Malaysian automobile drivers

had significantly lower scores on the subscale measuring statements about physical

aggression than did either Malay automobile drivers (p<.05) or Indian-Malaysian

automobile drivers (p<.01). On the subscale measuring statements about revenge,

Chinese-Malaysian automobile drivers had significantly lower scores than Indian-

Malaysian automobile drivers (p<.05). There were no significant differences between

ethnic groups with respect to hostile statements about the derogation of others.

No significant differences were observed between age groups with respect to

total HAT scores or to scores on any of the three HAT subscales.

157

Therefore, it is concluded that the Hypothesis 13, that demographic variables

would influence hostile automatic thoughts, was partially supported. H13.1 and H13.2, that

gender and ethnicity respectively would have significant direct effects on hostile

automatic thoughts, were supported. H13.3, that age would influence hostile automatic

thoughts, was not supported.

4.6.14 Hypothesis 14: Hostile Automatic Thoughts Influence Behaviour in Traffic

In Study 1C, linear regression analyses indicated that total HAT scores predicted

total BIT scores, B = .379, p<.01, and also scores measuring the four BIT component

factors: usurpation of right-of-way, B = .364, p<.01, freeway urgency, B = .277, p<.01,

externally-focused frustration, B = .307, p<.01 and destination-activity orientation, B =

.224, p<.01. This means that, the higher the total HAT scores, the higher were

automobile drivers’ total BIT scores and scores on the four components.

The effects of HAT subscales measuring the three classes of hostile automatic

thoughts, on total BIT score were also tested. Linear regression analyses indicated that

subscales measuring thoughts about physical aggression, derogation of others and

revenge had a significant positive influence on total BIT scores in Study 1C B =.413,

p<.01, B = .192, p<.01 and B = .394, p<.01, respectively. This means that, with the

sample of automobile drivers studied, the higher the scores on the three classes of hostile

automatic thought, the higher were total BIT scores.

Therefore, it is concluded that the Hypothesis 14, that hostile automatic thoughts

would influence behaviour in traffic, was supported. H14.1, H14.2 and H14.3, (that thoughts

about physical aggression, derogation of others and revenge) positively influence total

BIT scores, were supported.

158

4.6.15 Hypothesis 15: Hostile Automatic Thoughts Moderate the Aggression-BIT

Relationship

Hierarchical regression analysis indicated that HAT scores moderated the

relationship between aggression and BIT. R2 values changed after the HAT x AQ

interaction was added in the regression model (R2=.297; R2=.013; F=55.809, p<.01;

Residuals Normality: Skewness=-.565; Kurtosis=.085), and the moderating effect of

HAT was significant, B = .188, p<.05. This means that the relationship between

aggression and BIT would be stronger among automobile drivers with high total HAT

scores than it would be among drivers with low total HAT scores. In other words,

aggressive drivers who frequently entertained hostile automatic thoughts about others

would have higher total BIT scores compared to drivers who seldom entertained hostile

automatic thoughts about others (see Figure 4.8).

It was observed that two of the HAT subscales, Physical Aggression and

Revenge, also moderated the relationship between aggression and BIT. The R2 value

changed after the HAT-Physical Aggression x AQ interaction was added in the

regression model (R2=.297; R2=.002; F=57.911, p<.01; Normality Residuals:

Skewness=.-554; Kurtosis=.072), and the moderating effect of HAT-Physical

Aggression Level

BIT

Lev

el

Effect for aggressive drivers with low HAT score

Effect for aggressive drivers with high HAT score

Figure 4.8: Moderating Effect of Externality on the Aggression-BIT Relationship

159

Aggression was significant, B = .207, p<.01. The R2 value also changed after the HAT-

Revenge x AQ interaction was added in the regression model (R2=.297; R2=.026;

F=59.294, p<.01; Normality Residuals: Skewness=.475; Kurtosis=.092), and the

moderating effect of HAT-Revenge was significant, B = .246, p<.01. The HAT subscale

measuring thoughts about the derogation of others did not moderate the relationship

between aggression and behaviour in traffic.

Therefore, it is concluded that Hypothesis 15, that total HAT score would

moderate the relationship between aggression and behaviour in traffic, was supported.

H15.1 and H15.3, that hostile statements about physical aggression and revenge respectively

moderate the relationship between aggression and behaviour in traffic, were supported.

However, H15.2, that hostile statements about the derogation of others moderate the

relationship between aggression and behaviour in traffic, was not supported.

4.6.16 Summary of Hypothesis Testing

The following table provides summarised results for the hypotheses and sub-

hypotheses in this study (see Table 4.33).

160

Table 4.33: Summarised Results of the Hypotheses and Sub-hypotheses

STUDY

1A

1B 1C 2 3

H1: BIT will have a positive influence on motor vehicle crash outcomes S S P.S S P.S H1.1: Total BIT score will have a positive influence on crash occurrence S S S S S H1.1.1: Usurpation of right-of way will have a positive influence on crash occurrence S S S S S H1.1.2 :Freeway urgency will have a positive influence on crash occurrence S S S S S H1.1.3:Externally-focused frustration will have a positive influence on crash occurrence S S S S S H1.1.4:Destination-activity orientation will have a positive influence on crash occurrence S S N.S S S H1.2: Total BIT score will have a positive influence on injury occurrence S S S S N.S H1.2.1: Usurpation of right-of way will have a positive influence on injury occurrence S S S S N.S H1.2.2: Freeway urgency will have a positive influence on injury occurrence S S S S N.S H1.2.3: Externally-focused frustration will have a positive influence on injury occurrence S S S S N.S H1.2.4: Destination-activity orientation will have a positive influence on crash occurrence S S N.S S N.S

H2: Driver characteristics will influence behaviour in traffic S S S P.S P.S H2.1: Driver experience will have a negative influence on total BIT score S S S N.S S H2.2: Traveling frequency will have a negative influence on total BIT score S S S S H2.2: Taxicab experience will have a negative influence on total BIT score N.S

H3: Demographic variables will influence behaviour in traffic P.S P.S P.S P.S P.S. H3.1: Gender will influence total BIT score S S S S H3.2: Ethnicity will influence total BIT score S S S S S H3.3: Age will have a negative influence on total BIT score N.S N.S N.S N.S N.S

H4: Demographic variables will influence the Locus of Control P.S P.S P.S P.S N.S H4.1.1: Gender will influence Locus of Control: Internality N.S N.S S S

H4.1.2: Gender will influence Locus of Control: Externality-Chance N.S N.S N.S N.S H4.1.3: Gender will influence the Locus of Control: Externality-Powerful-Others S N.S N.S S H4.2.1: Ethnicity will influence the Locus of Control: Internality S N.S N.S S N.S H4.2.2: Ethnicity will influence the Locus of Control: Externality-Chance S S N.S N.S N.S H4.2.3: Ethnicity influence the Locus of Control: Externality-Powerful-Others S S S N.S N.S H4.3.1: Age will influence the Locus of Control: Internality N.S N.S N.S N.S N.S

161

Table 4.33 (Continued)

STUDY

1A

1B 1C 2 3

H4.3.2: Age will influence the Locus of Control: Externality-Chance N.S N.S N.S N.S N.S H4.3.3: Age will influence the Locus of Control: Externality-Powerful-Others N.S N.S N.S N.S N.S H5: Demographic variables will influence Hopelessness N.S N.S N.S P.S

H5.1: Gender will influence Hopelessness N.S N.S N.S S H5.2: Ethnic background will influence of Hopelessness N.S N.S N.S S H5.3: Age will influence Hopelessness N.S N.S N.S N.S

H6: Locus of Control will influence Hopelessness S S S P.S H6.1: Internality will have a negative influence on Hopelessness S S S N.S

H6.2: Externality-Chance will have a positive influence on Hopelessness S S S S H6.3: Externality-Powerful-Others will have a positive influence on Hopelessness S S S S

H7: Hopelessness will have a positive influence on behaviour in traffic S P.S S S

H7.1: Hopelessness will have a positive influence on Usurpation of right-of way S S S S H7.2: Hopelessness will have a positive influence on Freeway urgency S N.S S S H7.3: Hopelessness will have a positive influence on Externally-focused frustration S S S S H7.4: Hopelessness will have a positive influence on Destination-activity Orientation S S S S H8: Locus of Control will influence behaviour in traffic S S S P.S P.S H8.1: Internality will have a negative influence on total BIT score S S S S S H8.2: Externality-Chance will have a positive influence on total BIT S S S S N.S H8.3: Externality-Powerful-Others will have a positive influence on total BIT score S S S N.S N.S

H9: Hopelessness will moderate the Locus of Control-BIT relationship N.S N.S N.S P.S H9.1: Hopelessness will moderate the Internality-BIT relation N.S N.S N.S S

H9.2: Hopelessness will moderate the Externality-Chance--BIT relationship N.S N.S N.S S H9.3: Hopelessness will moderate the Externality-Powerful-Others--BIT relationship N.S N.S N.S N.S H10: Demographic variables will influence aggression

P.S P.S

N.S H10.1: Gender will influence Aggression S S H10.2: Ethnic background will influence Aggression N.S S N.S H10.3: Age will have a negative influence on Aggression N.S N.S N.S

S=Supported, P.S= Partially Supported, N.S= Not Supported, blank=Not Applicable

162

Table 4.33 (Continued)

STUDY

1A

1B 1C 2 3

H11: Aggression will have a positive influence on behaviour in traffic S S P.S H11.1: Aggression will have a positive influence on usurpation of right-of way S S S H11.2: Aggression will have a positive influence on Freeway urgency S S S H11.3: Aggression will have a positive influence on Externally-focused frustration S S N.S H11.4: Aggression will have a positive influence on Destination-activity orientation S S N.S

H12: Locus of Control will moderate the Aggression-BIT relationship

S S

P.S H12.1: Internality will moderate the Aggression-BIT relationship S S S H12.2: Externality-Chance will moderate the Aggression-BIT relationship S S N.S H12.3: Externality-Powerful-Others will moderate the Aggression-BIT relationship S S N.S

H13: Demographic variables will influence Hostile Automatic Thought s

P.S H13.1: Gender will have a positive influence on hostile automatic thoughts S

H13.2: Ethnic background will influence hostile automatic thoughts S H13.3: Age will have a negative influence on hostile automatic thoughts N.S

H14: Hostile Automatic Thoughts will have a positive influence on behaviour in traffic

S H14.1: Thoughts about Physical Aggression will have a positive influence on total BIT score S

H14.2: Thoughts about the Derogation of Others will have a positive influence on total BIT score S H14.3: Thoughts about Revenge will have a positive influence on total BIT score S

H15: Hostile Automatic Thoughts will moderate the Aggression-BIT relationship

P.S H15.1: Thoughts about Physical Aggression will moderate the Aggression-BIT relationship S

H15.2: Thoughts about the Derogation-of-Others will moderate the Aggression-BIT relationship N.S H15.3: Thoughts about Revenge will moderate the Aggression-BIT relationship S

S=Supported, P.S= Partially Supported, N.S= Not Supported, blank=Not Applicable

163

4.7 Testing the Contextual Mediated Model Using Structural Equation

Modelling (LISREL Analysis)

The contextual mediated model was tested using Structural Equation

Modelling (SEM) – path analysis through LISREL 8.52 (Jöreskog and Sörbom,

2002). Three studies (Study 1C: automobile driver; Study 2: motorcycle driver; and

Study 3: professional taxicab driver) with different sample data were used to

determine whether proximal context factors mediated the relationship between distal

context factors and the outcome. All proposed models measured: (1) internality,

externality (Chance) and externality (Powerful-Other) as distal context factors; (2)

usurpation of right-of-way, freeway urgency, externally-focused frustration and

destination-activity orientation as proximal context factors; and (3) crash occurrence

and injury occurrence as outcome. These models were re-specified by adding

different proximal context factors, e.g. hopelessness or subtracting the latent

variables in order to obtain the optimal goodness-of-fit index.

4.7.1 Study 1C

The contextual mediated model in this study was tested with four distal

factors – Locus of Control, Hopelessness, AQ and Hostile Automatic Thought

(HAT). This contextual mediated model was tested six times and the goodness-of-fit

indices for these models are indicated in Table 4.34.

Table 4.34: SEM Comparison (Study 1C)

Distal Factors Proximal

Factors

χ2

d.f. p-value GFI RMSEA

1C1 I, C, P F1, F2, F3, F4 49.38 23 .00111 .96 .068 1C2 I, C, P, BHS F1, F2, F3, F4 100.80 28 .00000 .93 .102 1C3 I, C, P, BHS, AQ F1, F2, F3, F4 104.90 33 .00000 .93 .093 1C4 I, C, P, BHS, AQ, HAT F1, F2, F3, F4 110.58 38 .00000 .93 .087 1C5 I, C, P, AQ, HAT F1, F2, F3 35.97 24 .05522 .97 .045 1C6 I, C, P, AQ, HAT F1, F2, F3, F4 63.02 33 .00126 .96 .060

Note: Internality (I), Externality Chance (C), Externality Powerful-Other (P), Hopelessness (BHS), Aggression (AQ), Hostile Automatic Thought (HAT), Usurpation of right-of-way (F1), freeway urgency (F2), externally-focused frustration (F3) and destination-activity orientation (F4)

Of the six models tested, two were worthy of further examination. Model

1C5 had better fit but necessitated dropping one of the component factors,

164

destination-activity orientation (F4), of the BIT score. An alternate model, C6,

retained all four of the BIT component factors and fit indices were acceptable, but

not as good as for C5.

For Model C5, goodness-of-fit was characterised as excellent (χ2=35.97,

d.f.=24, RMSEA=.045, RMR=.043, GFI=.97, CFI=.99) and constituted the best fit of

all six of the tested models. The investigation of structural path parameters indicated

that all possible paths from the distal context to the proximal context were

significant: Internality, Externality (Chance), Externality (Powerful-Other),

Aggression and Hostile Automatic Thought had effects on BIT, with path

coefficients = -.35, .14, .26, .29 and .22 respectively (see Figure 4.10). The BIT

displayed a significant effect (path coefficient=.92) on accident involvement. The

five distal variables accounted for 67% of the variance in BIT scores.

For Model C6, goodness-of-fit was characterised as very good (χ2=63.02,

d.f.=33, RMSEA=.060, RMR=.043, GFI=.96, CFI=.98). The investigation of

structural path parameters indicated that all possible paths from the distal context to

the proximal context were significant: Internality, Externality (Chance), Externality

(Powerful-Other), Aggression and Hostile Automatic Thought had effects on BIT,

with path coefficients = -.32, .13, .26, .28 and .23 respectively (see Figure 4.10). The

BIT displayed a significant effect (path coefficient=.92) on accident involvement.

The five distal variables accounted for 70% of the variance in BIT scores.

Making a decision to select one of these models over the other raised a

number of interesting points, which are detailed in sect. 5.5.3. To aid this discussion,

subsequent additional analysis was carried out to calculate a range of comparative fit

indices. For Model C5, values for these additional indices were: NFI=.97;

AGFI=.94; ECVI=.42; and PGFI=.42. For Model C6, values were: NFI=.96;

AGFI=.91; ECVI=.51 and PGFI=.48.

165

Distal Context Proximal Context Outcome

Behaviour in Traffic (BIT)

Accident Involvement .92*

χ2=35.97 GFI=.97 d.f =24 CFI=.99 P-value = .005522 N=252 RMSEA=.045 RMR=.043 Note: Values showed are path coefficients. *p<.05

Model Statistics

Internality

Externality (Chance)

Externality (Powerful Other) .26*

.13*

Figure 4.9: Contextual Mediated Model 1C5 (Three BIT Factors)

-.32*

BITF1=Usurpation of Right-of way, BITF2=Freeway Urgency, BITF3=Externally-Focused Frustration, BITF4=Destination-Activity Orientation

Crash Occurrence .51*

.57*

.29*

Aggression (AQ)

Hostile Automatic Thought

.22*

Injury Occurrence

BIT1 BIT2

BIT3

.79* .58* .63*

166

Distal Context Proximal Context Outcome

Behaviour in Traffic (BIT)

Accident Involvement .92*

χ2=63.02 GFI=.96 d.f =33 CFI=.98 P-value = .00126 N=252 RMSEA=.060 RMR=.043 Note: Values showed are path coefficients. *p<.05

Model Statistics

Internality

Externality (Chance)

Externality (Powerful Other) .26*

.13*

Figure 4.10: Contextual Mediated Model 1C6 (Four BIT Factors)

-.31*

BITF1=Usurpation of Right-of way, BITF2=Freeway Urgency, BITF3=Externally-Focused Frustration, BITF4=Destination-Activity Orientation

Crash Occurrence .50*

.58*

.29*

Aggression (AQ)

Hostile Automatic Thought

.22*

Injury Occurrence

BIT1 BIT2

BIT3

BIT4

.77* .56* .63* .39*

167

In addition, using automobile drivers sampled in Study 1C, the contextual

mediated model was tested using Aggression and Hostile Automatic Thoughts and their

latent variables (component factors) as distal context factors. The proposed contextual

mediated model was tested five times (see Table 4.35). The results for the goodness-of-

fit indexes are shown as follows:

Table 4.35: Different Contextual Models (Study 1C)

Distal Factors Proximal Factors

χ2

d.f. p-value GFI RMSEA

PHY, VER, ANG, HOS, IND F1, F2, F3, F4 108.73 42 .00111 .93 .080 PHY, VER, ANG, HOS, IND, HAT-P, HAT-D, HAT-R F1, F2, F3, F4 169.66 61 .00000 .91 .084

PHY, ANG, HOS, IND, HAT-P, HAT-D, HAT-R F1, F2, F3 131.94 50 .00000 .92 .081

PHY, VER, ANG, HOS, IND, HAT-P, HAT-D, HAT-R F1, F2, F3 169.66 61 .00000 .91 .084

PHY, ANG, HOS, IND, HAT-P, HAT-D, HAT-R F1, F2, F3, F4 153.41 61 .00000 .91 .078

Note: Physical aggression (PHY), Verbal aggression (VER), Angry (ANG), Hostility (HOS), Indirect aggression (IND), Aggression (AQ), Hostile Automatic Thought-Physical aggression (HAT-P), Hostile Automatic Thought-Derogation of others (HAT-D), Hostile Automatic Thought-Revenge(HAT-R), Usurpation of right-of-way (F1), freeway urgency (F2), externally-focused frustration (F3) and destination-activity orientation (F4)

As depicted in Figure 4.10, the final model has provided a reasonable fit to the

data (χ2=153.41, d.f.=61, RMSEA=.078, GFI=.91, CFI=.95). It was found that both

structural paths from the distal context to the proximal context were significant:

Aggression and Hostile Automatic Thought have effects on the Behaviour of Traffic

(BIT), path coefficients = .65 and .13 respectively. Hostile Automatic Thought was

found to have a direct effect on the AQ (path coefficient=.66). The BIT displayed a

significant effect (path coefficient=.80) on the accident involvement.

168

Distal Context Proximal Context Outcome

Behaviour in Traffic (BIT)

Accident Involvement

χ2=153.41 GFI=.91 d.f =61 CFI=.95 P-value = .000 N=252 RMSEA=.078 RMR=.058 Note: Values showed are path coefficients. *p<.05

Model Statistics

Physical Aggression

.68*

BIT1=Usurpation of Right-of way, BIT2=Freeway Urgency, BIT3=Externally-Focused Frustration, BIT4=Destination-Activity Orientation

Aggression (AQ)

Hostile Automatic Thought

Anger

Hostility

Indirect Aggression

Physical Aggression

Derogation of Other

Revenge

BIT1 BIT2

BIT3

BIT4

.61*

.69*

.72*

.90*

.66*

.82*

.83* .62* .58* .29*

.65*

.60*

.80*

.63*

.13*

.65*

Crash Occurrence

Injury Occurrence

Figure 4.11: Contextual Mediated Model Study 1C (Aggression and Hostile Automatic Thoughts)

169

4.7.2 Study 2

In Study 2, the participants were motorcycle drivers. The contextual mediated

model was tested using locus of control and hopelessness as distal context factors. The

proposed contextual mediated model was tested three times (see Table 4.36). The

goodness-of-fit indexes for these models are shown as follow:

Table 4.36: Different Contextual Models (Study 2)

Distal Factors Proximal Factors

χ2

d.f. p-value GFI RMSEA

I, C, P F1, F2, F3, F4 29.12 23 .17631 .94 .047 I, C, P, BHS F1, F2, F3 39.33 28 .07580 .95 .058 I, C, P, BHS F1, F2, F3, F4 33.86 23 .06722 .94 .062

Note: Internality (I), Externality Chance (C), Externality Powerful-Other (P), Hopelessness (BHS), Usurpation of right-of-way (F1), freeway urgency (F2), externally-focused frustration (F3) and destination-activity orientation (F4)

The model including Locus of Control has provided the best goodness-of-fit to

the data (χ2=29.12, d.f.=28, RMSEA=.047, GFI=.94, CFI=.98). The investigation of

structural path parameters indicated that three paths from the distal context to the

proximal context were significant. Compared to the Study 1 for student car drivers, the

final model for the student motorcycle drivers did not include hopelessness. Internality

and Externality (Chance) the Behaviour in Traffic (BIT) but not Externality (Powerful-

Other), path coefficients = -.65 and .80 respectively (see Figure 4.12). The BIT

displayed a significant effect (path coefficient=.66) on the accident involvement. The

four distal variables accounted for 49% of the variance in BIT.

170

Distal Context Proximal Context Outcome

Behaviour in Traffic (BIT)

Accident Involvement .66*

χ2=29.12 GFI=.95 d.f =23 CFI=.99 P-value = .17631 N=122 RMSEA=.047 RMR=.046 Note: Values showed are path coefficients. *p<.05

Model Statistics

Internality

Externality (Chance)

Externality (Powerful Other)

.05

.80*

Figure 4.12: Contextual Mediated Model Study 2

-.65*

BIT1=Usurpation of Right-of way, BIT2=Freeway Urgency, BIT3=Externally-Focused Frustration, BIT4=Destination-Activity Orientation

BIT1 BIT2 BIT3 BIT4

.57* .70* .83* .88*

.89*

.78*

Crash Occurrence

Injury Occurrence

171

4.7.3 Study 3

In Study 3, the participants were taxi drivers. The contextual mediated model

was tested using locus of control and hopelessness as distal context factors. This

contextual mediated model was tested four times (see Table 4.37). The goodness-of-fit

indexes for these models are shown as follow:

Table 4.37: Different Contextual Models (Study 3)

Distal Factors Proximal Factors Outcomes

χ2

d.f. p-value GFI RMSEA

I, C, P F1, F2, F3, F4 Crash Occurrence, Injury Occurrence 37.22 23 .03084 .94 .068

I, C, P, AQ F1, F2, F3, F4 Crash Occurrence, Injury Occurrence 50.82 28 .00524 .93 .079

I, C, AQ F1, F2, F3, F4 Crash Occurrence 18.59 17 .35265 .97 .027 I, C, P, AQ F1, F2, F3, F4 Crash Occurrence 31.39 21 .06743 .95 .061

Note: Internality (I), Externality Chance (ExC), Externality Powerful-Other (ExPo), Hopelessness (H), Usurpation of right-of-way (F1), freeway urgency (F2), externally-focused frustration (F3) and destination-activity orientation (F4)

Model included locus of control, AQ and only crash occurrence as outcome has

provided the best goodness-of-fit to the data (χ2=31.39, d.f.=21, RMSEA=.061, GFI=.95,

CFI=.95). The investigation of structural path parameters indicated that two out of four

possible paths from the distal context to the proximal context were significant.

Internality and AQ, but not Externality, have effects on the Behaviour in Traffic (BIT),

path coefficients = -.20 and .20 respectively (see Figure 4.13). The BIT displayed a

significant effect (path coefficient=.40) on the accident involvement. The four distal

variables accounted for 12% of the variance in BIT.

172

Distal Context Proximal Context Outcome

Behaviour in Traffic (BIT)

Crash Occurrence .40*

χ2=31.39 GFI=.95 d.f =21 CFI=.95 P-value = .06743 N=133 RMSEA=.061 RMR=.053 Note: Values showed are path coefficients. *p<.05

Model Statistics

Internality

Externality (Chance)

Externality (Powerful Other)

Aggression (AQ)

.13

.20*

-.03

Figure 4.13: Contextual Mediated Model Study 3

-.20*

BIT1=Usurpation of Right-of way, BIT2=Freeway Urgency, BIT3=Externally-Focused Frustration, BIT4=Destination-Activity Orientation

BIT1 BIT2 BIT3 BIT4

.39* .61* .63* .74*

173

4.8 Testing Mediational Relationships Using SPSS

The mediating effects of BIT on: (1) the relationship between hopelessness and

accident involvement; (2) the relationship between locus of control and accident

involvement; (3) the relationship between aggression and accident involvement; and, (4)

the relationship between hostile automatic thoughts and accident involvement were

tested using the four-step procedure proposed by Baron and Kenny (1986).

4.8.1 BIT Mediates the Relationship between Hopelessness and Crash Outcomes

In all studies, consistent with path analysis results, hopelessness did not

significantly influence the crash outcomes (see Table 4.38). Therefore, the mediating

effect of BIT on hopelessness and crash outcomes relationship could not be estimated.

Table 4.38: BIT Mediates the Relationship between Hopelessness and Crash Outcomes

BIT mediates Hopelessness-Crash Occurrence Step 1 Step 2 Step 3 Step 4

Study 1A Significant Not Significant Significant Not Applicable Study 1B Significant Not Significant Significant Not Applicable Study 1C Significant Not Significant Significant Not Applicable Study 2 Significant Not Significant Significant Not Applicable BIT mediates the Hopelessness-Injury Occurrence Relation Step 1 Step 2 Step 3 Step 4

Study 1A Significant Not Significant Significant Not Applicable Study 1B Significant Not Significant Significant Not Applicable Study 1C Significant Not Significant Significant Not Applicable Study 2 Significant Not Significant Significant Not Applicable

Notes: Step 1=independent variable has a significant effect on the mediator; Step2=independent variable has a significant effect on the dependent variable; Step 3=mediator has a significant effect on the dependent variable; Step4=Significance level of the relationship between independent variable and dependent variable is reduced indicating a partial mediating effect – or – independent variable does have a significant effect on the dependent variable indicating a complete mediating effect. Not applicable = mediating effect could only be tested when conditions in Step1, 2 and 3 are satisfied.

4.8.2 BIT Mediates the Relationship between Aggression and Crash Outcomes

The four-step regression analysis showed that BIT strongly mediated the

relationship between aggression and crash outcomes (see table 4.39). BIT was a

complete mediator for the relationship between AQ total score and crash occurrence.

174

BIT was a partial mediator for the relationship between aggression and injury

occurrence.

Table 4.39: BIT Mediates the Relationship between Aggression and Crash Outcomes BIT mediates Aggression-Crash Occurrence Relation Step 1 Step 2 Step 3 Step 4

Study 1B Significant Significant Significant Complete Mediator Study 1C Significant Significant Significant Complete Mediator BIT mediates Aggression-Injury Occurrence Relation Step 1 Step 2 Step 3 Step 4

Study 1B Significant Significant Significant Partial Mediator Study 1C Significant Significant Significant Complete Mediator

4.8.3 BIT Mediates the Relationship between Hostile Automatic Thought and

Crash Outcome

The regression results showed that the BIT partially mediated the relationship between

hostile automatic thought and crash outcomes (see Table 4.40).

Table 4.40: BIT Mediates the Relationship between Hostile Automatic Thought and Crash

Outcomes BIT mediates Hostile Automatic Thought-Crash Occurrence Relation Step 1 Step 2 Step 3 Step 4

Study 1C Significant Significant Significant Partial Mediator BIT mediates Hostile Automatic Thought-Injury Occurrence Relation Step 1 Step 2 Step 3 Step 4

Study 1C Significant Significant Significant Partial Mediator

4.8.4 BIT Mediates the Relationship between Locus of Control and Crash

Outcomes

For automobile drivers, in Studies 1A, 1B and 1C, behaviour in traffic (BIT) had

complete or partial mediating effects on the relationship between the three locus of

control dimensions – Internality (I), Externality-Chance (C) and Externality-Powerful-

Others – and crash outcomes (See Table 4.41). Exceptions to this were found only with

respect to the relationship between P and both crash outcomes in Study 1A, where the

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mediating effect of BIT total scores could not be estimated because requisite conditions

in the second step of the analysis were not satisfied.

For motorcycle drivers in Study 2, BIT had a complete mediating effect on the

relationship between C and both crash outcomes. With respect to the relationship

between I and the crash outcomes and the relationship between P and the crash

outcomes, no mediating effect of BIT could be estimated since requisite conditions in

the second step of the analysis were not satisfied. For taxicab drivers in Study 3, BIT

had no mediating effects on the relationship between I, C or P and the two crash

outcomes.

Table 4.41: BIT Mediates the Relationship between Locus of Control and Crash Outcomes

BIT mediates I-Crash Occurrence Relation Step 1 Step 2 Step 3 Step 4

Study 1A Significant Significant Significant Complete Mediator Study 1B Significant Significant Significant Partial Mediator Study 1C Significant Significant Significant Partial Mediator Study 2 Significant Not Significant Significant Not Applicable Study 3 Significant Not Significant Significant Not Applicable BIT mediates I-Injury Occurrence Relation Step 1 Step 2 Step 3 Step 4

Study 1A Significant Significant Significant Complete Mediator Study 1B Significant Significant Significant Partial Mediator Study 1C Significant Significant Significant Partial Mediator Study 2 Significant Not Significant Significant Not Applicable Study 3 Significant Not Significant Significant Not Applicable BIT mediates C-Crash Occurrence Relation Step 1 Step 2 Step 3 Step 4

Study 1A Significant Significant Significant Partial Mediator Study 1B Significant Significant Significant Complete Mediator Study 1C Significant Significant Significant Partial Mediator Study 2 Significant Significant Significant Complete Mediator Study 3 Not Significant Significant Significant Not Applicable BIT mediates C-Injury Occurrence Relation

Step 1 Step 2 Step 3 Step 4

Study 1A Significant Significant Significant Partial Mediator Study 1B Significant Significant Significant Complete Mediator Study 1C Significant Significant Significant Partial Mediator Study 2 Significant Significant Significant Complete Mediator Study 3 Not Significant Significant Not Significant Not Applicable

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Table 4.41: BIT Mediates the Relationship between Locus of Control and Crash Outcomes

(Continued)

BIT mediates P-Crash Occurrence Relation Step 1 Step 2 Step 3 Step 4

Study 1A Significant Not Significant Significant Not Applicable Study 1B Significant Significant Significant Partial Mediator Study 1C Significant Significant Significant Partial Mediator Study 2 Significant Not Significant Significant Not Applicable Study 3 Not Significant Significant Significant Not Applicable BIT mediates P-Injury Occurrence Relation Step 1 Step 2 Step 3 Step 4

Study 1A Significant Not Significant Significant Not Applicable Study 1B Significant Significant Significant Partial Mediator Study 1C Significant Significant Significant Partial Mediator Study 2 Significant Not Significant Significant Not Applicable Study 3 Not Significant Significant Not Significant Not Applicable 4.9 Comparison of Automobile Drivers, Motorcycle Drivers and Taxicab

Drivers

4.9.1 Differences between Automobile Drivers and Motorcycle Drivers

In a subsequent analysis, scores for distal variables (locus of control and

hopelessness), proximal variables (behaviour in traffic) and crash outcomes (crash

occurrence and injury occurrence) were compared between automobile drivers from

Study 1 and motorcycle drivers from Study 2. It was found that there were significant

differences in scores for hopelessness, Study 1A vs. Study 2: t(421)= -4.993, p <.01;

Study 1B vs. Study 2: t(422)= -2.442, p <.05; Study 1C vs. Study 2: t(372)= -3.162, p

<.01. Automobile drivers in Studies 1A, 1B and 1C scored significantly lower on

hopelessness than did motorcycle drivers.

With respect to the three dimensions of locus of control, automobile drivers

scored higher than motorcycle drivers on I, Study 1A vs. Study 2: t(421)= 7.663, p <.01;

Study 1B vs. Study 2: t(422)= 8.426, p <.01; Study 1C vs. Study 2: t(372)= 8.665, p

<.01. Automobile drivers also scored significantly lower than motorcycle drivers on C,

Study 1A vs. Study 2: t(421)= -3.837, p <.01. There was no significant difference in

scores on the P dimension between automobile drivers and motorcycle drivers.

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Automobile drivers scored significantly lower than motorcycle drivers with

respect to the total BIT score, Study 1A vs. Study 2: t(421)= -3.837, p <.01; Study 1C vs.

Study 2: t(372)= -6.577, p <.01.

Automobile drivers scored significantly lower than motorcycle drivers in crash

occurrence, Study 1A vs. Study 2: t(421)= -8.261, p <.01; Study 1B vs. Study 2: t(422)=

-4.186, p <.01; Study 1C vs. Study 2: t(372)= -5.861, p <.01. Automobile drivers scored

significantly lower than motorcycle drivers in injury occurrence, Study 1A vs. Study 2:

t(421)= -7.402, p <.01; Study 1B vs. Study 2: t(422)= -6.200, p <.01; Study 1C vs. Study

2: t(372)= -7.687, p <.01.

4.9.2 Differences between Automobile Drivers and Taxicab Drivers

With respect to locus of control, taxicab drivers scored higher than automobile

drivers on the I dimension, t(986)= 3.801, p <.01. There were no significant differences

scores on either C or P between the automobile drivers and taxicab drivers.

Automobile drivers scored higher that taxicab drivers on total BIT scores,

t(986)= 7.747, p <.01, and on all four BIT subscales: “usurpation of right-of way”,

“freeway urgency”, “externally-focused frustration” and “destination-activity

orientation”, t(986)= 37.704, p <.01; t(986)= 34.211, p <.01; t(986)= 30.433, p <.01; and

t(986)= 35.775, p <.01, respectively. Also, automobile drivers scored higher than taxicab

drivers with respect to crash occurrence, t(986)= 5.977, p <.01, and to injury occurrence,

t(986)= 6.484, p <.01.

4.9.3 Differences between Motorcycle Drivers and Taxicab Drivers

Taxicab drivers scored higher than motorcycle drivers on the I locus of control

dimension, t(253)= 8.926, p <.01. Motorcycle drivers scored higher than taxicab drivers

on C, t(253) = 2.614, p <.01. There were no differences between motorcycle drivers and

taxicab drivers on the P dimension.

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Motorcycle drivers had higher total BIT scores than taxicab drivers, t(253)=

8.982, p <.01, and all four BIT subscales: “usurpation of right-of way”, “freeway

urgency”, “externally-focused frustration” and “destination-activity orientation”, t(253)=

39.977, p <.01; t(253)= 35.946, p <.01; t(253)= 31.016, p <.01; and t(253)= 37.567, p

<.01, respectively. Also, drivers scored higher with respect to crash occurrence, t(253)=

8.737, p <.01and to injury occurrence, t(253)= 11.881, p <.01.

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CHAPTER 5

DISCUSSION 5.1 A Contextual Mediated Model for Understanding Factors Influencing

Unsafe Driving

Traffic psychologists, road engineers and ergonomists interested in motor vehicle

safety have tried for a long time to understand the role played by human factors in

determining traffic safety outcomes. While it has been generally assumed and

frequently stated that driver characteristics, including gender, age and personality may

be the most important factors in crash causation (Bridger, 1995; Elander et al., 1993;

Evans, 1991), researchers have been frequently frustrated when attempting to quantify

the effects of psycho-social variables on either driving behaviour or crash outcomes.

Often, human factors that conceptually might be expected to have a strong influence

over driving behaviour and crash occurrence end up, upon examination, exerting weaker

influence or more equivocal results than anticipated (Dewar, 2002b).

Elander et. al. (1993), Parker (2004) and others have stressed the importance of

examining crash causation from a broader, multi-factorial perspective, in which the roles

played by variables in mediating and moderating effects of personality factors are more

closely examined than in the past. The present research applied Sümer’s concept of a

contextual mediated model, in which a set of personality and demographic factors are

thought to exert effects, not directly on driving behaviour and crash outcomes but rather

on some intervening variable located more proximally to the event (see sect. 2.4.2.1).

In an earlier study, Papacostas and Synodinos (1988) investigated four

dimensions of driving behaviour conceptually related to the Type A behaviour pattern

(TABP). Composite BIT scores were comprised of measures of usurpation of right-of-

way, freeway urgency, externally-focused frustration and destination-activity

orientation. They found gender, ethnic and driver experience differences with respect to

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total BIT score and component scores, but did not examine the effects of BIT scores on

crash outcomes.

In the present research, the proximal variable, BIT, significantly predicted self-

reported crash occurrence in all replications and with all classes of drivers studied.

Further, it predicted self-reported injury occurrence in all cases, except with taxicab

drivers. This was true with respect to both the composite BIT score and individual

scores of each of the four component factors. Since high BIT scores indicated driving

behaviour consistent with TABP, Type A individuals were significantly more likely to

have found themselves involved in traffic crashes and, for automobile drivers and

motorcyclists, were significantly more likely to have been injured while driving.

But findings were more complex than that. In the contextual mediated model,

BIT scores are considered proximal to the crash event. In other words, BIT composite

scores are also expected to mediate the effects of locus of control, hopelessness,

aggression and hostile automatic thoughts on crash and injury occurrence, and did so in

all cases but hopelessness.

Results reported here suggest an elaborate relationship, particularly between

psychological variables and crash outcomes, which somewhat complicates our attempt

to stay true to our title: “Cause and Prevention of Roadway Crashes”. All too often, the

term “cause” conveys the notion of a single causative element, in the deterministic sense

in which it is used in the physical sciences or engineering. A rich variety of individual

factors exists which, if different, alter the outcome or probability of occurrence of

crashes and these have been classified into broad categories using different schemes

(Evans, 1991). The matrix proposed by Haddon (1972) is one example. One recurrent

complexity that arises when trying to understand traffic safety, though, is that factors

interact with each other. Every aspect of the traffic system is in some way connected to

every other aspect. As a result,

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… the word cause has largely disappeared from the technical

literature on safety, and for good reasons. Suppose that on a

dark rainy morning a young man argues with his wife about

the purchase of a sofa, leaves the house late for work in a rage,

drives his poorly-maintained car too fast on a badly-designed,

poorly-lit curve. Suppose further that he skids, and is killed in

a crash with a truck driven by an older driver. It is of little

value to say that the death was caused by the car driver’s

youth or maleness, the truck driver’s old age, the car’s bald

tires, the high cost of sofas, emotional stress, the non-use of a

safety belt, inadequate police enforcement, rain or any other of

the many factors which, if different on this particular

occasion, would have prevented the death (Evans, 1991; p. 60)

Causative factors, then, are difficult to partial out and, it might be argued, cannot

really be studied in isolation. For this reason, the use of a model based on interactive

relationships between personality or demographic characteristics of drivers and the

components making up a particular pattern of driving behaviour, in this case a Type A

behaviour pattern, makes good sense. The model proved useful in describing and

explaining the relationship between distal and proximal variables involved in crash and

injury occurrence. Personality and demographic variables had significant effects on a

measure of behaviour in traffic which, in turn, had a robust association with self-

reported crash and injury occurrence. What may have resulted here is less an

identification of causes and their prevention and more a framework in which to consider

the complex interactions of several factors that contribute to motor vehicle crashes.

Within the contextual mediated model used here, a significant relationship was

observed between the BIT construct and outcome measures, suggesting that the

contention of Papacostas and Synodinos (1988), that it may be preferable to existing

measures of TABP, is supported. As has been already noted, a range of personality and

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demographic variables were observed to interact with BIT scores and the remainder of

this discussion is devoted to a consideration of these findings.

5.2 Hopelessness

It has been noted by several authors that little attention has been paid to affective

characteristics of drivers (Rothengatter, 1998; 2002; Elander et al., 1993). It is widely

accepted that emotions alter attention, thought patterns, decision making and memory

(Groeger, 1997). Hopelessness is a personality trait with strong affective and cognitive

components, characterised by a sense of despair, pessimism about the future, chronic

exhaustion and a deep personal orientation that nothing one can do to bring meaning,

zest or enthusiasm to life (Farran et al., 1995). Hopeless individuals tend to believe that

nothing will turn out right for them, that they will never succeed at what they attempt to

do, that their important goals can never be attained, and that their worst problems will

never be solved (Beck & Steer, 1993). They score high on scales measuring

neuroticism and low on extraversion, feel as though they lack physical fitness and self-

confidence and are often dissatisfied with their accommodation, marital state and

workplace (Tanaka, Sakamoto, Ono, Fujihara & Kitamura, 1996; 1998). Often people

with a high degree of hopelessness feel compelled to do more and more as a way of

compensating, feeling as though “they must climb a mountain that has no top and that

there is no way to end the necessity of climbing” (LeShan, 1989; p.108). Often these

efforts are seen as impulsive, irrational and generally without an apparent goal, and are

just as frequently prone to premature termination.

Certainly, it is not difficult to see that internal states arising from this interplay of

despondency, perceived fatigue and sense of slowing down while feeling compelled to

do more, have a strong potential to influence driving behaviour negatively. In the

present research, hopelessness was associated with less cautious self-reported behaviour

in traffic. The higher were participants’ scores on a measure of hopelessness, the more

they indicated they would be likely to engage in BIT that was unsafe. Specifically,

persons reporting a high degree of hopelessness had a tendency to disrespect others’

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right-of-way, to engage in risky lane deviations and to commit lapses or violations at

intersections or stoplights. Generally, very little is known about demographic and

personality characteristics of drivers who fail to halt at stop signs or pedestrian

crossings, commit red light violations or make risky lane deviation manoeuvres

(Brodsky, 2001; Romano et al., 2005a; 2005b). The finding that hopelessness is

associated with risky right-of-way behaviour may be consistent with the attention

deficiencies, impulsivity and lack of caring thought to characterise persons with this trait

(Farran et al., 1995).

When it comes to the relationship between hopelessness and driving behaviour,

motorcyclists present some unique differences. Motorcycle drivers had higher

hopelessness scores than either automobile or taxicab drivers. Among motorcyclists,

males were significantly higher than females in hopelessness and Malay motorcycle

drivers had higher hopelessness levels than their Chinese-Malaysian or Indian-

Malaysian counterparts. Motorcyclists generally have a high frequency of right-of-way

crashes at three-legged junctions, crossroads and roundabouts where the driving

manoeuvres tend to be fairly complex, requiring vehicle control skills and focused

attention to avoid conflicting movements with other road users (Pai & Saleh, 2008).

Clarke, Ward, Bartle and Truman (2007) have also noted that, because of their

configuration, motorcycles are particularly prone to ‘right of way crashes’ and those

involving loss of control on curves or bends. With the likelihood of this type of crash

already fairly high for motorcyclists, the finding in the present study that hopelessness

was strongly associated with driver behaviour involving the usurpation of right of way

may signal an exacerbated level of danger that needs to be explored in future research.

For motorcyclists, also, hopelessness moderates the relationship between both I

and C locus of control dimensions, such that motorcyclists with a strong internal locus of

control who score high on the hopelessness trait tend to report that they engage in less

safe behaviour in traffic than do motorcycling internals who score low on the

hopelessness trait. On the other hand, motorcyclists with a strong belief that their life is

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determined by chance or fate and that their future is coloured by feelings of hopelessness

tend to report more dangerous driving than do fatalistically-directed externals who are

not feeling hopeless.

The relationship between hopelessness and locus of control is complex, in that

logically either a high or a low sense of internal control can be a component of

hopelessness. Often persons who feel hopeless have a low sense of personal control.

They have lost faith in their own ability to achieve some goal and therefore have an

image of themselves that they feel has been devalued both by themselves and by other

people (Engel, 1968; Isani, 1963; Prociuk et al., 1976). In other cases, however, persons

have a high but unrealistic sense of internality. They may feel very responsible for their

own fate and may feel that no other help is available to them (Engel, 1971). “In this

situation, even though individuals are making some attempt at maintaining control, their

goals may be inappropriate or their resources may not be adequate to meet the desired

outcome” (Farran et al, 1995; p. 33).

Similarly, either a high or a low sense of external control can also be a

component of hopelessness. A person with a high external locus of control may

unrealistically anticipate that help from others or from the external environment will

resolve the dilemma (Engel, 1968), thus assuming little or no personal control.

However, persons who are feeling hopeless may also have a low sense of external

control simply because they believe that others have so frequently failed or frustrated

them (Engel, 1971). Farran et al. (1995) called for further studies to explore the nature

of hopelessness and other moderating variables like self-esteem, locus of control and

self-efficacy. Future research investigating the process through which locus of control

interacts with hopelessness in influencing driver behaviour are needed in particular.

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5.3 Locus of Control

5.3.1 Internal and External Locus of Control as Determinants of Driving

Behaviour

Previous studies investigating the effects of locus of control on driving behaviour

arrived at inconsistent results. For example, Guastello and Guastello (1986) found that

internals had been involved in fewer crashes than externals on their transitional scale but

that there was no such relation between crashes and scores on the Rotter (1996) I-E

scale. Özkan and Lajunen (2005) found that internals reported a higher number of total

crashes, ordinary traffic violations and driving errors, although scores on externality

dimensions had no effect. In the present study, locus of control was found to play a

significant role in influencing driving behaviour. Drivers who had a strong internal locus

of control regardless of automobile, motorcycle or taxicab drivers, reported engaging in

behaviour in traffic that was relatively safe. This observation was true for all three

groups studied: automobile drivers, motorcyclists and taxicab drivers. On the other

hand, those who believed life events to be determined by chance or fate reported

engaging in behaviour that was far more consistent with a less safe TAPB pattern. This

was true for the samples of automobile and motorcycle drivers, all of whom were

university students, but not for professional taxicab drivers.

In short, all participants in this study who were internals reported driving more

safely than those who were not; and university students who were strongly externally

controlled reported driving less safely than those who were not. This finding is exactly

what the general body of thought about locus of control and driving would predict it to

be. It has been generally assumed that, because externals believe that they have little

personal control over what happens to them, they tend to consciously focus less on the

driving task (Elander et al., 1993). In cognitive ergonomic terms, it is as if they are

willing to cede responsibility for shouldering the mental workload (de Waard, 2002)

associated with the driving task because they consider it to be under the control of

external forces.

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It might be expected, though, that this effect would be become less pronounced

as drivers become more experienced. Groeger (2002) has pointed out that, with more

experienced drivers, more of what they do becomes routine and, as a result, not under

direct conscious control. With increasing automatisation of the driving process, the

influence of externality over specific decisions and actions that the driver must make

would be diminished. Shiffrin and Schneider (1977) described how automaticity

develops as a function of consistent reactions to a particular stimulus, even to the point

where there is little recollection of specific elements of the task (Underwood & Everatt,

1996). Of course, there will always be “black events”, or risk-predisposing

circumstances, in which the driver switches from automatic to personally controlled

processes but, even at those times, drivers with more experience will have better

knowledge and quicker reactions due to their broader exposure to prior stimuli (Brown,

1982).

Because more of the driving task is performed automatically by experienced

drivers, cognitive attributions about an internal or external locus of control become less

important, unless one makes a basically untestable assumption about sub-conscious

factors operating on what is generally described as an open-loop control system

(Groeger & Clegg, 1997). Given that this sort of system involves sequences of actions

which do not rely on feedback from the results of preceding actions before subsequent

actions are performed (Bridger, 1995), even if attributional cognitions could

theoretically operate at the sub-conscious level, it is hard to envision the sort of

mechanism through which they could influence automaticized driving behaviour.

Further, Laapotti et al. (2001) have argued that novice drivers make errors in

applying knowledge models at both the lower (vehicle manoeuvring) and upper

dimensions (incorporating lifestyle goals and skills) within the driving process (see

Figure 2.8; sect. 2.5.2.1). While it might be assumed that errors at the lower level are

caused by skill deficits, abrogation of control over the vehicle due to a belief in

externality would be a mental process that occurs at the upper end of the cognitive

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hierarchy. This would be an error more likely to be made by younger novice or less

experienced drivers.

In the present study, taxicab drivers were considerably older (43.2 years,

SD=11.66) than the automobile drivers and motorcyclists (20.01years, SD=1.53; 20.25

years, SD=1.63, respectively). They were also more experienced (266.6 months as

licensed drivers, SD=131.10) than the automobile drivers and motorcyclists (28.7

months, SD=.16.1; and 36.1 months, SD=22.5, respectively). Because of occupational

demands, it might also be assumed that their traffic exposure was greater than that of the

students, although driving frequency was not measured for taxicab drivers.

For taxicab drivers, internals reported that they engaged in safer behaviour in

traffic, but the externality-chance dimension had no significant effect, as it did with the

less experienced university students in the other groups. It appears that belief in chance

or fate outcomes may be a more important factor in shaping the driving behaviour of

novice or inexperienced drivers than it is for more experienced ones. Of course, there

are other possible influences, as well. For taxicab drivers, the continued operation of

their vehicle is fundamental to their livelihood so motivational factors may take

precedence over attributions of external control. By virtue of their age and occupation,

it might be assumed that taxicab drivers could well have a broader social network.

Inclán, Hijar and Tovar (2005) have noted that social capital, the extent to which an

individual is connected to others through interpersonal networks, social trust and norms

that promote coordination and cooperation, affected driving behaviour and decisions

about the use of public roads. Further research is needed to examine the influence of

driving experience, traffic exposure and other variables on the effects that internality and

externality exert on driving behaviour.

5.3.2 Locus of Control and Ethnicity: Indian-Malaysian Drivers

The present research also compared differences in locus of control among three

ethnic groups in the culturally diverse Malaysian society. Malaysian-Indian automobile

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drivers were significantly less internally controlled than Malay and Malaysian-Chinese

drivers but scored higher than the other groups on both externality dimensions (chance

and powerful others). There were no significant differences between Malay and

Malaysian-Chinese participants on any of the three locus of control dimensions.

Research completed some thirty years earlier by Carment (1974) found that

university students in India were strongly internally-controlled, when compared to

Canadian students, in terms of political ideology and interaction with the social system.

He attributed this to the socio-political environment of the time, rife with bureaucracy,

corrupt practices, influence peddling and status-related privileges, which would have led

young Indians to perceive that skills in overcoming systemic barriers, along with self-

promotion skills, were necessary to succeed. Since perceived success under such

circumstances might be expected to be largely due to personal proficiency in such areas,

individuals would be more likely to develop attributions of internal control.

Carment (1974) also found, however, that the Indian students were strongly

external with respect to matters in their personal life. He explained this by pointing to

the close and interdependent Indian family structure, in which members look to each

other and especially to maternal figures within the home, for support in effecting the life

outcomes that are important to them. In an environment where career choice, spousal

selection, financial matters and social affiliations are made, or at least strongly

influenced by outside forces, it is easy to see how expectancies for the external control

of life outcomes can develop.

With the Indian-Malaysian sample studied in the present research, findings with

regard to locus of control differed from Carment’s (1974) earlier results. Participants

scored high in terms of attributions about the controlling nature of fate and powerful

others, perhaps due as argued earlier, to cultural values of intra-family dependency and

parental influence that have persisted within the Tamil and other Indian communities in

Malaysia (Abdullah & Peterson, 2003; Devashayam, 2005). The finding that Indian-

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Malaysian participants scored lower in internality was contrary to Carment’s earlier

conclusions with individuals from India, although they were consistent with more recent

research by Sinha & Watson (2007). In reference to Carment’s explanation of the earlier

results, there is considerable evidence that the socio-political environment in current-day

Malaysia is different from that of India in the mid-1970s (Corbridge & Kumar, 2002;

Gomez, 1999; Nandy, 1999) where one’s skills at manipulating the system may have

fostered a need for greater internal control. Indeed, Willford (2003) concluded that

Indian-Malaysians, as a group, have been largely limited from participation in social and

political processes that would necessitate that sort of social skill development and, by

extension, an internal locus of control.

5.3.3 Locus of Control and Ethnicity: Malay and Chinese-Malaysian Drivers

The present research found no significant difference between Malay and Chinese

participants in any of the three dimensions of locus of control. This is consistent with

recent findings among Malay and Chinese ethnic groups in Singapore, where Cheung et

al. (2006) found greater commonalities in personality traits, including locus of control,

than between Singaporean-Chinese and Chinese participants from China. It is also

consistent with the results of a study by Fontaine and Richardson (2003) which found

differences in cultural values within the workplace were not significant among the three

Malaysian ethnic groups.

Again, the reasons for this commonality in outlook are probably multi-factorial,

but two possible influences stand out. The first of these is the steady rate of urbanisation

which began in the early-1960s and has continued through the last three decades

(Gomez, 1999; Sendut, 1966; Salih &Young, 1981). The size of the urban population in

Malaysia increased by 4.5% annually from 9.5 million in 1991 to 11.8 million in 1996,

and, as a result, the proportion of the population residing in urban areas increased from

51% in 1991 to 55.7 in 1996. Closer proximity of cultural groups can be assumed to

increase contact and transmission of knowledge and awareness of value systems

(Hofstede, 1998; 1999). Armstrong (1987) argued that urbanisation has affected

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women’s friendship patterns, bringing them closer together in outlook. Hewstone and

Ward (1985) showed that participants of Chinese descent in Singapore and Malaysia

made about the same attributions about behaviour of Malay subjects as they did with

regard to same-ethnicity subjects.

The second factor toward reducing differences between ethnic groups may be

related to government efforts promoting a multi-cultural scripting of the national identity

(Bunnell, 2002). Government initiatives have aimed at re-positioning Malaysia as a

highly networked information economy and society, in which members of ethnic groups

are encouraged to adopt a ‘Malaysian outlook’ on life, including perhaps attributions

about the control of events. Brown (2007) has examined educational practices in

Malaysia within the context of ethnicity and nation-building, with the resulting

“emergence of ‘ethnic citizens’ who have been encouraged to participate in the

Malaysian nation uncritically through the virtual worship of development symbols and

unquestioning deference to political leadership” and national value systems (p. 318).

5.4 Aggression

Haight (2004) has suggested that the concept of the accident-prone driver may

have been replaced in the 1990s by that of the alcohol-impaired driver and, more

recently, by the enraged driver. Nonetheless, there is a large body of evidence that

aggression plays a significant role in unsafe driving behaviour and in crash outcomes

(Deffenbacher, Huff, Lynch, Oetting & Salvatore, 2000; Dukes, Clayton, Jenkins, Miller

& Rodgers, 2001; King & Parker, 2008; Lawton & Nutter, 2002; Miles & Johnson,

2003; Parkinson, 2001)

In the present research, aggression had a strong influence on behaviour in traffic.

Consistently, among automobile drivers and motorcyclists, participants scoring higher

on a measure of total trait aggression reported driving patterns that involved right-of-

way violations, feeling more frustrated at external sources, driving in a more urgent

fashion and concentrating more on destination activities than on road and traffic

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conditions. With taxicab drivers, higher aggression levels related to a strong tendency to

commit right-of-way violations and to drive more urgently, but had no significant effect

on externally-focused frustration or destination oriented activity. Male drivers tended to

score higher than female drivers with respect to total aggression, physical aggression,

verbal aggression and indirect aggression, but there were no gender differences with

respect to anger or hostility.

While there are plenty of studies establishing the link between aggression and

driving behaviour, there are only a few that have attempted to explore the mechanism

through which external or cognitive contexts trigger the effect. Underwood et al. (1999)

found that near accidents provoked feelings of anger, particularly where drivers felt that

they were not at fault in the incident. Further, on a journey by journey basis,

Underwood et al. found that drivers were more likely to report anger when congestion

was present, but that there was no evidence that the drivers who generally experienced

higher level of congestion also experienced more anger. Parker, Lajunen & Summala

(2002) similarly found that traffic density may play a role in triggering anger and

aggression among drivers sampled in three countries: Great Britain; Finland and the

Netherlands.

Angry and aggressive driving episodes have been related to hostile cognitive

statements that drivers make, either verbally or as unspoken thoughts (“self-talk”),

during such incidents. Deffenbacher, Oetting et al. (1996) and Deffenbacher, Petrilli et

al. (2003) found that drivers engaging in hostile automatic thoughts tended to report

more aggressive and riskier driving. Their findings were replicated in the present

research where it was shown that the higher the total frequency of hostile automatic

thoughts reported by drivers, the more dangerously they behaved in traffic. These

effects were observed most strongly when drivers had cognitions involving physical

violence toward other drivers (“I’d like to knock his/her teeth out”), a little less strongly

when they thought about taking revenge (“I want to get back at this person”) and least

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strongly, although still significantly, when entertaining cognitive statements that made

derogatory comments (“What an idiot!”) about other drivers.

Not only did drivers’ angry cognitions have a direct effect on their behaviour in

traffic, but they were found to moderate the effects of aggression on behaviour in traffic,

as well. Drivers who scored high in aggression and who entertained more frequent

hostile automatic thoughts about others tended to report driving patterns in traffic that

were less safe than drivers who scored high in aggression but who entertained less

frequent hostile automatic thoughts. These moderating effects were significant when the

content of the cognitions involved physical aggression or revenge motives, but not when

they involved the derogation of others.

Beck (1987b) hypothesised that three cognitive factors play an integral role in

the way emotion affects behaviour: (a) the native triad (a negative view of self, the

world and others); (b) schemas (underlying general assumptions about life), and (c)

cognitive distortions (ways in which people misinterpret their environment). In essence,

one’s interpretations of the environment lead one to react emotionally toward features

within the environment (Galovski et al., 2006). Each class of hostile automatic thought

can be thought to represent a reaction to these three factors (Snyder et al., 1997). That

is, perceiving another person’s slow driving as purposeful may lead to the emotional

experience of anger, a cognitive distortion which stimulates a cognitive response related

to physical aggression or revenge, and that cognitive event in turn triggers an aggressive

or punitive type of traffic behaviour. Such responses, in the samples studied here, would

be most likely to involve the usurpation of right-of-way or more urgent speeding

behaviour.

The effects of aggression on behaviour, however, were also modified by the

drivers’ locus of control. Aggressive automobile drivers with a tendency to believe that

outcomes are determined by chance or fate were significantly more likely to report

riskier driving patterns than were aggressive drivers who did not believe that outcomes

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are determined by chance or fate. Similarly, aggressive automobile drivers who

believed that outcomes are determined by powerful others were also significantly more

likely to report riskier driving patters than were aggressive drivers who did not believe

that outcomes are determined by powerful others. Finally, aggressive drivers of both

automobiles and of taxicabs who had low levels of internality (i.e., were unlikely to

believe that outcomes are determined by one’s own actions) were significantly more

likely to report riskier driving patterns in traffic than were aggressive drivers with high

internality scores. This last finding replicated and extended earlier research by Gidron et

al. (2003), who found that the association between road-hostility and drivers’ speed

choices and deviant behaviour (passing through a red light and overtaking from the

inside) was larger among participants with low rather than with high internality scores.

The relationship between aggression and driving behaviour is both important and

complex. It is moderated by cognitive processes, in the form of hostile automatic

thoughts, or self-talk, and also by attributions regarding locus of control. Generally, the

original cognitive behaviourists tended to regard cognitive statements as internal mental

stimuli that, true to operant learning principles, evoked specific behavioural responses

that then became subject to contingent reinforcement (Kanfer & Goldstein, 1990;

Meichenbaum, 1977). Certainly, this process may be instrumental in the relationship

between aggression and behaviour in traffic (“I’d like to knock his/her teeth out, so I’ll

overtake him/her at high speed on the inside and feel great – i.e., receive positive

reinforcement – when I see the shocked look on his/her face!”), but there may be more

to it than that.

A driver’s mental workload is increased by the number of task demands with

which one must contend in performing a function and, “in ergonomics, language

comprehension can be regarded as a mental task which, like any other mental task, has a

workload associated with it” (Bridger, 1995; p. 401). Language loaded with emotional

content, and particularly with negative emotion, has been shown to be difficult to

process (Dodge & Coie, 1987; Downe & Loke, 2004; Hochschild, 1979; Novaco, 1994;

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Robbins, 2000; Stein, Trabasso & Liwag, 1993), subject to ambiguous interpretation

(Chan, 1996; Martin, Watson & Wan, 2000; Tomkins, 1979) and disruptive to

attentional processing of external stimuli (Adolphs, 2002; Carretie, Hinojosa, Martin-

Loeches, Mercado & Tapia, 2004; Dien, 1999; Lambie & Marcel, 2002; Taylor &

Fragopanagos, 2005). In fact, Tavris (1989) referred to anger as the “misunderstood

emotion”. As the costs of achieving or maintaining a certain target level of performance

increase, so too does the mental effort required (de Waard & Brookhuis, 1997). As

drivers contend with heightened feelings of aggression and increased internal chatter, as

well as other task demands of driving in traffic, they may well reach a “red line” at

which more mental effort is required than is available. Making sense of, and attempting

to exercise control over, hostile automatic thoughts, and at the same time processing

feelings of responsibility arising from one’s internal locus of control requires the

investment of mental effort. As de Waard (2002) has argued:

There are limits to the investment of effort. Performance

(e.g., lane control in driving) will drop if effort investment is

insufficient or ceases. This can happen both in conditions of

very high task demand and in conditions where the driver’s

state is affected (p. 162).

The present research has demonstrated linkages between hostile automatic

thoughts, aggressive emotionality, internal locus of control and tendencies toward more

dangerous behaviour in traffic. Additional studies are needed to investigate whether this

relationship is best explained through an internal stimulus-response process, an over-

burdening of cognitive workload capacity or both.

5.5 Testing the Contextual Mediated Model Using Structural Equation

Modelling (SEM)

5.5.1 Advantages of Using SEM

Harlow (2005, p.1) defined multivariate thinking as “a body of thought processes

that illuminate interrelatedness between and within sets of variables” and proposed that

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multivariate methods provide a richer and more comprehensive examination on the

variables. Structural equation modelling (SEM), a multivariate technique, allows the

simultaneous estimation of multiple equations (Hair et al., 2006). The earliest use of

SEM has been attributed to Swedish Statistician, Karl Jöreskog, who in 1970, advanced

the idea of combining features of econometrics and psychometrics into a single model

(Klem, 2000).

According to Williams, Gavin and Hartman (2004), the growth in application of

SEM techniques has paralleled researchers’ access to computer based data analysis

software programs such as LISREL, EQS and AMOS. In addition, researchers are

attracted to the benefits that SEM can offer. First, SEM is deemed to be a unique

combination of factor analysis and multiple regression analysis (Hair et al., 2006). When

composing a model, the SEM depicts all of the relationships among constructs,

including dependent and independent variables, involved in the analysis. The constructs

may be comprised of unobservable, or latent, factors represented by multiple variables,

similar to the variables representing factors in a factor analysis. Having the

characteristics of multiple regression analysis, SEM can not only tell how well the

predictors, or independent variables, explain criterion, or dependent, variables but also

determine which specific predictors are most important in predicting dependent variable

outcomes (Maruyama, 1998). Second, using SEM to examine relationships among

factors allows the relationships to be free from measurement errors “because the error

has been estimated and removed, leaving only common variance” (Hardy and Bryman,

2004, p.434). By estimating and removing measurement error, the reliability of

measurement can thus be accounted for explicitly within the analysis. Finally, and

perhaps most important, SEM appears to be the only technique capable of examining a

set of relationships simultaneously when the phenomena of interest are complex and

multidimensional (Hardy and Bryman, 2004; Hair et al., 2006).

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5.5.2 Goodness of Fit

SEM is considered a confirmatory analysis for testing and potentially confirming

theory. Although many researchers have used SEM to examine a theoretically proposed

model, Williams et al. (2004) has been critical of most studies, in that they have failed to

compare or re-specify the proposed model with an alternative model to test a variety of

different theoretical propositions. Therefore, model re-specification by citing theoretical

support for the changes made is desired. In the present research, several alternative

models were tested against different propositions in order to arrive at a model with the

best possible fit.

Shook, Ketchen, Hult & Kacmar (2004) and Williams et al. (2004) commented

on inconsistencies in the way SEM results have been reported in the literature. Sümer

(2003) added that, despite the prominence of SEM as a statistical tool, there is a lack of

consensus on how best to evaluate the extent to which a proposed model fits the data.

Shook et al. (2004) noted that, when assessing the fits of measurement models, fit

indices such as chi-square statistics, the goodness of fit index (GFI), the comparative fit

index (CFI), and the root mean square residual were included, but that very few studies

have used multiple fit indices, as suggested by Hair et al. (2006).

Hair et al. (2006) have argued that no single ‘magic value’ for the fit indices has

been found to differentiate good from poor models. It is therefore not practical to apply a

single set of cutoff rules to the measurement models. Hair et al added that the

assessment of a model’s goodness-of-fit should include the following:

The χ2 value and the associated df

One absolute fit index (i.e. GFI, RMSEA or SMRM)

One incremental fit index (i.e. CFI or TLI)

One goodness-of-fit index (GFI, CFI, TLI, etc)

One badness-of-fit index (RMSEA, SRMR, etc)

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In the present research, the fit indices for assessing the model included χ2 value

and the associated df, GFI, CFI, RMSEA and the χ2 /degrees of freedom ratio. Although

chi-square is the most fundamental absolute fit index, it should not be used as the sole

indicator of SEM fit because it is affected by sample size (Hair et al., 2006) such that for

analyses of sample sizes more than 250, significant p-values can be expected. Fit index

values (e.g., CFI and CFI) greater than .90, RMSEA lower than .08 and a χ2 /degrees of

freedom ratio less than 3.00 have been recommended to indicate good model fit (Hair et

al., 2006). Sümer (2003) reported that some researchers have used critical χ2 /degrees of

freedom ratios from 2 to 5 to indicate an acceptable fit.

5.5.3 Best Fit or Best Model

It is important to test multiple plausible rival models, so that stronger evidence

supporting the correct specification of a model can be adduced (Thompson, 2000). As a

general rule, the model with the best goodness-of-fit indices is selected over alternate

models. This has become such a widely accepted principle that decisions over model

selection have become almost automatic (Klem, 2000) and there are many examples of

research studies, both dealing with traffic psychology (Sümer, 2003) and other

disciplines (Elangovan, 2001; Md-Sidin, Sambasivan & Ismail, 2009) that have tended

to select the model that has the best fit indices.

At the same time, it has been stressed repeatedly by several authors no definitive

set of rules has been established for model selection (Byrne, 1998, 2001; Hair et al,

2006; Maruyama, 1998). It is widely agreed that selection of the model should be a very

good or an excellent fit, but there is little guidance in the literature as to what course of

action should be taken when two models each meet standards for goodness-of-fit.

It is argued here that, provided competing models both present a pre-determined

standard for goodness-of-fit, it may be advisable to select one that offers the most useful

information even if indices are slightly lower than its alternative. Structural equation

modelling should, we would argue, be a process that balances utility with statistical

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soundness. In some cases, it makes sense to choose a model with a slightly poorer fit

but more useful information over the best-fitting model, provided the chosen one meets

pre-determined standards for goodness-of-fit.

There is some support for this position in the literature. Sobel and Bohrnstedt

(1985) pointed out that exclusive reliance on goodness-of-fit indices is unacceptable,

stating that, “Scientific progress could be impeded if fit coefficients (even appropriate

ones) are used as the primary criterion for judging the adequacy of the model” (p. 158).

Byrne (2001) argued that:

Fit indexes yield information bearing only on the model’s lack of

fit. More importantly, they can in no way reflect the extent to

which the model is plausible; this judgment rests squarely on the

shoulders of the research. Thus, assessment of model adequacy

must be based on multiple criteria that take into account

theoretical, statistical, and practical considerations (p. 88).

In the case at hand, two structural equation models, 1C5 and 1C6, of inter-

variable relationships for high-risk undergraduate automobile drivers were compared

using an initial set of goodness-of-fit indices (see sect. 4.7.3). Model 1C5 (see Figure

4.9) included all four components of the BIT scale, while Model 1C6 (see Figure 4.10)

excluded the fourth factor, destination-activity orientation. Both models were judged to

have an excellent or very good fit, a finding that was further supported by additional

subsequent analyses using a further series of goodness-of-fit indices. Index coefficients

from the original and subsequent analysis are shown in Table 5.1.

If selection criteria were to be based solely on goodness-of-fit parameters, the

choice between the two would be Model 1C6 given its superior index coefficients.

However, when taking into consideration “practical considerations”, as suggested by

Byrne (2001), it is concluded that the selection of Model 1C5 would be preferable

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Table 5.1: Goodness of Fit Statistics for Model 1C5 and 1C6 (Initial and Subsequent Analyses)

Fit Statistics

(Threshold values)

Model 1C5 Distal Context: I, C, P, AQ, HAT Proximal Context: BITF1, F2, F3 & F4 BITF1=Usurpation of Right-of way, BITF2=Freeway Urgency, BITF3=Externally-Focused Frustration, BITF4=Destination-Activity Orientation Outcomes: Crash Occurrence, Injury Occurrence

Model 1C6 Distal Context: I, C, P, AQ, HAT Proximal Context: BITF1, F2, F3 BITF1=Usurpation of Right-of way, BITF2=Freeway Urgency, BITF3=Externally-Focused Frustration, Outcomes: Crash Occurrence, Injury Occurrence

Degrees of Freedom 63.02 35.97

RMSEA 0.060 0.045

GFI 0.96 0.97

Chi-sq/Df 1.909 1.499

RMR 0.043 0.034

AIC 129.02 97.97

NFI 0.96 0.97

CFI 0.98 0.99

AGFI 0.91 0.94

PGFI 0.48 0.42

NCP 30.02 11.97

ECVI 0.51 0.39

Overall model fit Very Good Best

because it includes important information about destination-activity orientation

behaviour that would be lost if Model 1C6 were chosen. Given that multivariate

analysis revealed a significant association between this BIT component and crash

outcomes, it is apparent that this factor may be important to consider in future research

and crash prevention programmes and should not be excluded from models on which

they are based.

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It can be argued here that safety research demands a somewhat different standard

in terms of model construction and selection, one that favours the maximum use of

information in the cause of saving lives. When dealing with systems that are safety-

critical, in particular, the standard should be to include as much relevant information as

possible, based on the notion that each variable included may, farther along, provide the

key to reducing injuries and saving lives (Reason, 1990; Storey, 1996). By selecting

Model 1C5, a central psychological feature of the TABP in driving is not overlooked and

the BIT framework is kept intact. Results of this and other research have demonstrated

that, when drivers do let their attention wander from current road and traffic conditions

they are at increased risk of experiencing a crash outome (Moller, Kayumov, Nahn &

Shapiro, 2006; Parker, Reason, Manstead & Stradling, 1995; Schwebel, et al., 2006).

For practical reasons, this is an important component to retain in a contextual mediated

model of behaviour in traffic even if it does render a lower, but still acceptable,

goodness-of-fit.

Some justification for the selection of Model 1C5 over 1C6 is also found in the

analysis of the parsimony fit (PGFI) statistic (see Table 5.1). Sambasivan (2008) stated

that, when variables do not improve the AGFI and PGFI, they should be dropped.

However, in this analysis, the PGFI coefficient for Model 1C5 is 0.48, while for Model

1C6, it is 0.42. Hair et al. (2006) have noted that models with a higher PGFI are

preferable over competing models because they have a better fit relative to their

complexity.

Based on the practical advantages of including the destination-activity

orientation variable within the contextual mediated model and findings of the subsequent

comparative results of PGFI coefficients, the decision was made to select Model 1C5

over Model 1C6. Further discussion in this section refers only to Model 1C5.

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5.5.4 Testing the Contextual Mediated Models Using SEM

5.5.4.1 Study 1C: Automobile Drivers

Sümer (2003) pointed out that comparing the goodness of fit indices of several

alternative models can help to clearly identify where lack of fit arises within a model. In

Study 1C, for automobile drivers sampled, the base model (with only locus of control

variables) was compared against five alternative models (see section 4.6.1). The results

suggested that the alternative model, with five distal factors (internality, externality-

chance, externality-powerful other, aggression, and hostile automatic thoughts), four

latent constructs (usurpation of right-of-way, freeway urgency, externally-focused

frustration, and destination-activity orientation) for the proximal factor-BIT and two

latent construct (crash and injury occurrence) for outcomes ws preferable to alternative

models that included the hopelessness variable.

Examination of the predictive relationship between distal and proximal variables

yielded support for the contextual model and were consistent with previous findings

(e.g. Evans, 1991; Rothengatter, 2001; Sümer, 2003), indicating that driving behaviour

is closely related to involvement in motor vehicle crashes. Findings in this study

underscored the strong role of BIT in predicting accidents. Distal factors (locus of

control: internality, externality-chance, externality-powerful other, aggression and

hostile automatic thoughts) had significant effects on BIT scores (path coefficients = -

.35, .14, .26, .28 and .23 respectively) and the BIT displayed a significant effect on crash

outcomes (path coefficient = .66). Total scores of BIT were significantly correlated with

crash occurrence (r = .34) and injury occurrence (r = .29), indicating the importance of

this factor in crash outcomes. As observed from the investigation of structural paths,

internality and aggression had direct effects on BIT and indirect effects, via BIT, on

crash outcomes. They appeared to be the strongest predictors among the five distal

factors (path coefficients = -.35 and .28 respectively). Internality was significantly but

negatively correlated with BIT (r = -.45), crash occurrence (r = -.18) and injury

occurrence (r = -.21). This suggested that automobile drivers with high levels of

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internality were more likely to have low total BIT scores, as well as low probability of

crash and injury occurrence. Aggression, on the other hand, was significantly and

positively correlated with BIT (r = .55), crash occurrence (r = .20) and injury occurrence

(r = .25). Aggressive automobile drivers tended to have high level of BIT scores, and

high probabilities of crash and injury occurrence.

5.5.4.2 Study 2: Motorcyclists

In Study 2, which sampled motorcyclists, the base model (with only the locus of

control variable) was compared against two alternative models. The first alternative

model had four distal factors (locus of control: internality, externality-chance,

externality-powerful other and hopelessness), and four latent constructs (usurpation of

right-of-way, freeway urgency, externally-focused frustration, and destination-activity

orientation) as proximal factors. The second alternative model also had four distal

factors but only three latent constructs (usurpation of right-of-way, freeway urgency and

externally-focused frustration) for the proximal factor. Results indicated that the first

alternative model, with hopelessness removed as a distal factor but four latent constructs

(usurpation of right-of-way, freeway urgency, externally-focused frustration, and

destination-activity orientation) comprising the proximal factor and two latent constructs

(crash and injury occurrence) comprising the crash outcome variable, had a better fit

than other alternative models. Investigation of the path parameters revealed that only

two of the distal factors, internality and externality-chance (path coefficients = -.65 and

.80) indirectly and the proximal factor-BIT (path coefficient = .66) directly predicted

crash outcomes. One of the most compelling findings in Study 2 was that externality-

chance scores had the highest correlations with BIT (r = .41), crash occurrence (r = .23)

and injury occurrence (r = .24). This suggests that motorcyclists who believed that

outcomes were determined by fate or chance tended to report engaging in riskier

behaviour in traffic and had greater crash accident involvement.

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5.5.4.3 Study 3: Taxicab Drivers

In Study 3, with the sample of taxicab drivers, the base model (with only the

locus of control variable) was compared against three alternative models (see sect.

4.6.3). All models included four latent constructs (usurpation of right-of-way, freeway

urgency, externally-focused frustration, and destination-activity orientation) as proximal

factors. The first alternative included four distal factors (internality, externality-chance,

externality-powerful other, aggression). The second and third alternative models only

had one latent construct, crash occurrence, for crash outcomes. Results indicated that the

third alternative model, with four distal factors (internality, externality-chance,

externality-powerful other and aggression), four latent constructs (usurpation of right-of-

way, freeway urgency, externally-focused frustration, and destination-activity

orientation) comprising the proximal factor and one latent construct (crash occurrence)

comprising the outcome variable, had a better fit than alternative models. Investigation

of the path parameters revealed that there were only two of the four distal factors,

internality and aggression (path coefficients = -.20 and .24 respectively) that had direct

effects on the proximal factor and a simultaneous indirect effect on crash occurrence, via

BIT. This suggested that internality and aggression play important roles in affecting the

behaviour in traffic of taxicab drivers and, as a result, their crash occurrence. Both

dimensions of external locus of control had insignificant results.

5.5.5 What Can be Learned from Testing Contextual Models with SEM?

The use of SEM provided support for the contextual mediated model. Distal

factors, such as internality, aggression and hostile automatic thoughts, had significant

direct effects on the four latent constructs that comprised the proximal variable (BIT)

and, in turn and indirectly, on the two latent variables comprising crash outcomes.

However, the results of measurement model analysis showed that one of the distal

factors, hopelessness, had no significant effect on BIT scores. For motorcyclists, the

result was slightly different as a belief in chance as an outcome determinant had a

significant effect on BIT scores. Finally, for the sample of taxicab drivers, the best-

fitting model used only a single latent construct, crash occurrence, to measure outcome.

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5.6 Limitations of the Study and Methodological Considerations

5.6.1 Generalisabilty of Findings

A key feature of all research is the capacity for results obtained from a sample to

be applied to a larger population with proportionately the same degree of diversity

(Langdridge, 2004). Some authors have commented on the lack of generalisability of

findings in traffic psychology research (Dunbar, 2005; Huguenin, 2005).

In the present research, a total of five samples were taken, four of which were

comprised of students from a single university. The fifth sample was comprised of

professional taxicab drivers, chosen at random from taxi stands. To a large extent, both

and particularly the student samples constituted a convenience sample that may have

curtailed the generalisability of findings.

Sekaran (2003) points out, however, that convenience sampling is indeed the

least reliable of all sampling designs in terms of generalisability, “but sometimes it may

be the only viable alternative when quick and timely information is needed” (pp. 278-

279). Further, the fact remains that participants constituting the four student samples

were, by virtue of their age and driving experience within the highest risk group, an

argument used by Montag & Comrey (1978) and others who have studied young drivers.

With very few studies having been completed on Malaysian drivers to date, the present

research was intended as an initial attempt to examine the influence of psycho-social

variables on driving behaviour and crash outcomes among a given high risk sample.

An important question then becomes: Were the participants in Studies 1 and 2

representative of a high-risk population of young Malaysian drivers? This can be

answered in terms of age and the geographic location from where participants received

their driving licenses.

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In Malaysia, as elsewhere, young drivers are among the most likely to experience

a motor vehicle crash. Approximately one-third of all automobile crashes, during the

interval from 2000 to 2003, involved drivers aged 16 to 25 years, making it the single

highest risk age group (see Table 2.2). Ages of participants in this research ranged from

18 to 29 years, with a mean age of 20.13 years (SD = 1.55). The proportion of the total

sample for Studies 1 and 2 falling within the 16- to 25-year old high-risk group was

99.6% (Study 1A: 99.6%; Study 1B: 100%; Study 1C: 99.2% and Study 2: 99.2%).

With regard to whether the sample was representative of peoples of the various

states and regions of Malaysia, it is helpful to examine the distribution by state in which

research participants obtained their driving licenses. Since, in Malaysia, individuals

usually obtain their license in the state in which they are registered as resident, these

data may provide an indication of the extent to which the samples studied here are

representative of the fourteen states and districts of Malaysia.

Table 5.2 compares the percentage of the national population located in each

state and the percentage of the participants in the total sample coming from each state.

The Spearman rank correlation coefficient for these two sets of scores is rs=.31. Based

alone on the number of residents living in each state, the sample does not appear to be

very repreresentative of the Malaysian population. Sabah, Sarawak and Kelantan were

under-represented in the sample, while Malacca and Negeri Sembilan were over-

represented. The most populous state, Selangor, contributed the largest proportion of the

sample.

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Table 5.2: Distribution of National Population and Sampled Participants by State

But, in this case, attempting to determine sample representativeness based on

only state population would be flawed. It is important to remember that the purpose of

this research was to study the population of young, high-risk drivers in Malaysia. Not

all states have the same number of drivers, and there are different crash frequencies in

each one. For that reason, a better assessment of sample representativeness by state

would be to compare the proporation of participants with numbers of registered private

vehicles and with the numbers of crashes in each state of origin.

Table 5.3 compares the state of origin of participants in Study 1 with the more

relevant measures of vehicle registrations and crash occurrence. Table 5.4 provides

similar comparisons for the state of origin of the sample of motorcyclists. In both cases,

the state of origin is defined as the state in which the participants’ driving licenses were

issued.

State

State Population (approx)

Per cent of national population

Per cent (rank) of participants

sampled 1 Selangor 7,200,000 26.0 17.2 (1)

2 Sabah 3,387,880 12.2 3.2 (11)

3 Johor 3,300,000 11.9 12.7 (2)

4 Sarawak 2,500,000 9.0 2.2 (13)

5 Perak 2,260,576 8.2 11.9 (3)

6 Kelantan 2,100,000 7.6 2.3 (12)

7 Kuala Lumpur 1,887,674 6.8 11.5 (4)

8 Kedah 1,818,188 6.6 4.9 (9)

9 Penang 1,503,000 5.4 7.8 (6)

10 Pahang 1,396,500 5.0 6.5 (8)

11 Terengganu 1,150,286 4.2 3.6 (10)

12 Negeri Sembilan 1,004,807 3.6 7.1 (7)

13 Malacca 733,000 2.6 8.2 (5)

14 Perlis 215,000 0.19 0.7 (14)

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Table 5.3: State of Origin Compared with Crash Frequency and Vehicle Registrations (Study 1)

Average Motor

Vehicle Crash Frequency

(2000-2003)

%

Private Automobile Registrations (until 2003)

%

Participants’ State of Origin

(by license

%

Johor 34,144 12.4 703,735 12.96 109 12.75 Kedah 12,104 4.43 165,600 3.05 42 4.91 Kelantan 6,212 2.27 135,635 2.50 20 2.34 Kuala Lumpur 39,064 14.28 1,588,198 29.24 98 11.46 Melaka 9,170 3.35 156,920 2.89 70 8.19 Negeri Sembilan 13,026 4.76 181,496 3.34 61 7.13 Pahang 10,467 3.93 187,490 3.45 56 6.55 Penang 25,606 9.36 525,785 9.68 67 7.84 Perak 24,561 8.98 393,163 7.24 102 11.93 Perlis 1,003 0.37 10,230 0.19 6 0.70 Sabah 10,617 3.88 266,251 4.90 27 3.16 Sarawak 10,725 3.92 324,137 5.97 19 2.22 Selangor 70,768 25.88 698,041 12.85 147 17.19 Terengganu 6,029 2.20 92,093 1.70 31 3.63

273,496 5,428,774 855

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Table 5.4: State of Origin Compared with Crash Frequency and Vehicle Registrations (Study 2)

Average Motor

Vehicle Crash Frequency

(2000-2003)

%

Private Vehicle

Registrations (until 2003)

%

Participants’ State of Origin

(by license

%

Johor 34,144 12.4 933,288 15.14 17 13.93 Kedah 12,104 4.43 444,995 7.22 9 7.38 Kelantan 6,212 2.27 233,656 3.79 1 0.82 Kuala Lumpur 39,064 14.28 821,722 13.33 11 9.02 Melaka 9,170 3.35 255,856 4.15 9 7.38 Negeri Sembilan 13,026 4.76 310,305 5.03 5 4.10 Pahang 10,467 3.93 276,992 4.49 11 9.02 Penang 25,606 9.36 776,283 12.59 13 10.66 Perak 24,561 8.98 770,221 12.49 14 11.48 Perlis 1,003 0.37 36,679 0.59 2 1.64 Sabah 10,617 3.88 90,112 1.46 2 1.64 Sarawak 10,725 3.92 347,133 5.63 3 2.46 Selangor 70,768 25.88 705,727 11.45 18 14.75 Terengganu 6,029 2.20 161,989 2.63 7 5.74

273,496 6,615,958 122

209

Table 5.5 shows the The Spearman rank correlation coefficient (rs) for the

variables in Tables 5.3 and 5.4. There is a high correlation between ranks of the states

from which participants in Studies 1 and 2 received their licenses and the ranks of states

with regard to crash occurrence and to private vehicle registrations.

Table 5.5: Spearman rank correlations for States of Origin for Participants in Study 1 and Study 2

Were the participants studied in this research representative of high-risk

Malaysian drivers? In terms of their age and their regional origin, it can be argued that

they were. This sample was comprised of individuals within the age group that has the

most motor vehicle crashes. Even though data collection was carried out at a single

university location, participants came from – or, at least, were licensed as drivers in –

the states with the most registered vehicles and the highest numbers of crashes. At least

on these dimensions, it is possible to say that sampling, both for the studies of

automobile drivers and for the study of motorcyclists, was representative of a high risk

driver population.

Of course, there are many other dimensions on which members of a sample can

or cannot be representative of the population from which they have been drawn. Future

studies of Malaysian driving behaviour will need to expand the range participant

1 2 3 Study 1: Automobile Drivers

1 Automobile crash frequency (by state) 1 2 Vehicle registrations (by state) .908** 1 3 Participants’ state of origin .824** .701** 1

Study 2: Motorcyclists

1 Motorcycle crash frequency (by state) 1 2 Vehicle registrations (by state) .903** 1 3 Participants’ state of origin .814** .796** 1

210

characteristics used as a basis for sample-to-population comparisons. Additional studies

should be carried out in order to validate findings within the broader population.

5.6.2 Use of self-report methods

The use of self-report methods in traffic psychology has been strongly criticised

by af Wählberg (2002). Self-report data are prone to inaccuracy due to participant

memory lapses, social desirability response sets and fakeability (Aiken, 1979). However,

Hatakka, Keskinen, Katila and Laapotti (1997) have argued that, in studying driving

behaviour, as in other psychological research, the use of questionnaire data provides the

only practical possibility for gathering data at a low cost.

Much important data is available in official statistics, e.g.,

accident distributions by age. We can also get rough data of

exposure by age. The problem, however, is that this kind of

data is usually aggregated … From aggregated data we cannot

study the connections between accidents and age and

exposure. It would be impossible to find an answer to the

question “is the elderly group with low mileage at a higher

risk than younger drivers with high mileage?” In order to

explain the differences between different road-user roups in

accient risk, the data has to be disaggregated. Exposure,

accidents, demographic factors, attitudinal factors, violations

and accidents should be linked together. None of these

variables can be substituted by group means, unless the

variation within the group is very small. Again, the easiest

way to get data on several factors from the same subjects is by

simply asking the subjects (p. 296).

The issue becomes even harder to resolve when dealing with cognitive variables

that cannot be observed directly (Groeger & Rothengatter, 1998; Rothengatter, 1998;

2001). Elander et al. (1993) have similarly argued that other methods for studying the

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effects of personality on driving have drawbacks that cannot be overlooked, as well.

Miles and Johnson (2003) have noted that, in studies of driving behaviour, subjects

would tend to under-report dangerous or illegal activities and that this tendency might

usually be expected to be consistent across compared groups.

In the present research, questionnaires were administered to measure all variables

and, therefore, all data may be subject to the shortcomings of self-report methods.

Particularly, though, self-reported crash and injury histories and self-reported driving

patterns measured by the Behaviour in Traffic scale could be prone to inaccuracy. In

future studies, subjective accounts of crash or injury history should be validated against

objective measures, perhaps drawing from drivers’ insurance records or, as in a study

reported by Chalmé, Visser and Denis (2004), combined interview and observational

methods. Papacostas and Synodinos (1988) also stressed the need to undertake studies

investigating the correlation of BIT factors to “the usually measured physiological

responses of drivers (e.g., muscle tension, blood pressure, heart-rate acceleration and

electrodermal activity) and to overt driving behaviours (e.g., steering wheel reversals

and speed change frequencies)” (p. 13).

5.6.3 Timeframe for Data Collection

Elander et al. (1993) and af Wählberg (2003) have commented on the problem of

data collection timeframes in studies of motor vehicle crashes. Since generally motor

vehicle crashes are fairly rare events, inadequate data are collected when the research is

conducted over short periods of time. Yet, the longer the time period for data collection,

the more information is lost through memory lapses, errors of recall or contamination by

post-event information (Belli & Loftus, 1996). A further methodological problem

occurs when one tries to measure states or traits with psychological tests and associate

them retrospectively with crash situations that occurred some time ago. The assumption,

for instance, that the score that one receives on a measure of personality or behavioural

orientation today was the same some time ago when a crash occurred is often tenuous.

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In the present research, participants were asked to recall if a crash or resulting

injury had occurred within the past twelve months, a timeframe that is consistent with

reasonably accurate recall (Haber & Haber, 2002). Traits included in the contextual

mediated model as distal variables have been found to be relatively consistent and

resistant to change over this interval, as well.

5.6.4 Measurement of Driving Frequency

One of the self-report measures used in this research requires particular

discussion. Participants indicated on a 6-point Likert type scale how often they travelled

as a driver and as a passenger both in automobiles and on motorcycles. This method has

been used in previous studies by other authors (Pelz & Schuman, 1971). Results were

used as a measure of driving frequency for Studies 1 and 2, and the hypothesis (H2.2) that

higher levels would result in less risky behaviour in traffic was supported. The driving

frequency measure was alaso used a co-variate in analyses of relationships between

other distal variables and proximal variables.

It must be noted here that there are certain problems with measuring driving

frequency in this manner and that there were other alternative methods that could have

been built into the research design instead. First, it has to be acknowledged that the

measure is subjective, and that one participant’s perception of frequent automobile or

motorcycle use may seem infrequent to another’s. Second, there is a certain imprecision

to the measure, in that the measure tells us little about the circumstances under which

participants drove other than their perceived use against some unstated, individual

standard.

Unfortunately, other measures of driving frequency present as many or more

problems. Some authors have asked participants to estimate the distrance travelled

during a particular period (Lajunen & Summala, 1997; Mercer, 1999). The problem

with this approach is that it is every bit as subjective as the categorical judgements of

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frequency that were used in this research, and that people are consistently poor at

making these sorts of estimates accurately (Saad, 2002).

Some of the inaccuracy in the use of distance travelled as a driving frequency

measure can be simply attributed to random or systematic errors in prediction (Elander

et al., 1993). But, it is argued here that such inaccuracy is also likely due to a cognitive

phenomenon known as the availability heuristic, in which the perceived probability of an

event corresponds to the ease with thich the event comes to mind or, in other words, on

how available it is in our memories (Kahneman, 2003; Kahneman, Slovic & Tversky,

1982). Often, experiences that are more common than others tend to be the ones that are

most available, but not always. “Some events are more available than others not

because they tend to occur frewquently or with high probability, but because they are

inherently easier to think about, because they have taken place recently, because they are

highly emotional and so forth” (Plous, 1993; p. 121). This is why individuals tend to

irrationally overestimate the number of murders per year (Jaffe, 2004), their chances of

winning a lottery (Griffiths, 2003), and the likelihood of encountering a traffic jam

(Wood, Wood & Boyd, 2008). There is some evidence that the availability heuristic

exerts a greater impact when specific quantitative amounts or percentages have to be

estimated, as opposed to reporting perceptions in categorical form (Tversky &

Kahneman, 1973; 1974), although this has not been firmly established.

In much the same way, the accuracy of individuals’ self-report of the average

kilometres travelled would be influenced by their recollection of the length of drives that

were particularly arduous, eventful or recent. Levy (1997) argues that:

Unfortuantely, the problem in relying on the ease with which

event can be retrieved from memory for determining their

likelihood is that our perceptions cannot necessarily be counted

on as an accurate reflection of reality. Specifically, this

strategy leads us to overestimate their actual occurrence,

frequency or distribution in the world (p. 181).

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In the Malaysian environment, for example, where driving histories generally include

lengthy, emotionally-laden seasonal and holiday travel (Richardson & Downe, 2000), it

might be expected that participants’ driving estimates would be particularly prone to

influence by the availability heuristic, in the form of more vividly remembered trips to

family reunions during festive seasons.

Finally, the use of distance travelled doesn’t really solve the main problem in

travel frequency measurement, which is the lack of information about the circumstances

under which road use occurred (Odero et al., 1991). Driving 30 kilometres daily on quiet

city thoroughfares, during periods of low traffic volume, with adequate street lighting and

hevily-enforced speed limits is not the same as driving the same 30 kilometres at night on

a remote expressway at higher speeds, poorer pavement and more surrounding vehicles

(Åkerstedt & Kecklund, 2001) .

Of course, many of the inaccuracies involved with the use of self-reported

frequency data could have been solved by taking direct odometer readings. Similarly,

Deffenbacher et al. (2003), in their studies of roadway aggression, asked participants to

record the time of day, road conditions, traffic volume and so on in logbooks as a means

of controlling for differing driving circumstances.

A logbook approach was considered during the design of the present research, but

training participants in standardised record-keeping, auditing the accuracy of driver-

maintained records and scoring reams of data were all tasks that were judged to be

beyond the capability and scope of the present research. Sansone, Morf and Panter

(2004) argue that psycho-social research generally involves a balancing act between

idealised research questions, on one hand, and the availability of the resources necessary

to operationalise them. Given that sample sizes for this research had to be large enough

to apply structural equation modelling procedures, it was felt that the collection of

logbook data would have overextended time and financial resources at hand for the five

215

studies undertaken. In addition, collected logbook data would have been largely

qualitative in nature, creating new difficulties in their quantification (King, 2004).

To summarise, there is little disagreement on the importance of travelling

frequency as a variable in driving safety but little consensus on the best way to deal with

methodological problems associated with its measurement (Evans, 1991). Rothengatter

(2001) has argued that better measures of risk exposure are needed in traffic psychology.

In the present research, the decision was made to use participants’ subjective, categorical

perceptions of driving frequency, but this was done with an awareness of the

shortcomings of this measure. It was felt, during the study design process, that associated

methodological disadvantages were fewer than those of the competing approaches.

Further research is required, using other procedures for measuring driving

frequency – particularly in the form of estimated distance travelled and verified logbook

recordings of trip distances and conditions – in order to validate the categorical, self-

reported measure used here.

5.7 Implications and Areas for Further Study

5.7.1 Theory vs. Models in Traffic Psychology

It has been noted earlier that the emerging field of traffic psychology has yet to

arrive at a unified, over-arching theory (Rothengatter, 2002; Summala, 2005), but that

considerable effort has gone into the development of descriptive models, drawn from

empirical studies and demonstrating inter-variable relationships (Chaloupka-Risser,

2005). The function of useful scientific theory is to provide an explanatory summary of

facts pertaining to related phenomena and to predict events that are associated with them

(Huguenin, 1997). Good theories are simple, have high information content, are testable

and contain no contradictions, are of nomological character and can be applied

irrespective of time and space (Langdridge, 2004). While some authors have considered

the terms “theory” and “model” as synonymous (e.g., Michon, 1985; Ranney, 1994), the

difference is that models are generally seen as “more modest affairs that aim to illustrate

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patterns of relationships, or represent processes, often in graphical form (Grayson, 1997;

p. 94).

Throughout the development of traffic psychology, there has beeen an ongoing

discussion and, at times, debate as to whether the greater need exists for more theory or

for more data. Wilde (1982) has expressed concern that the study of traffic safety “is

characterised by a sparsity of comprehensive and articulate conceptions” (p. 294).

Huguenin (1997) has similarly argued that theories are necessary to treat a subject

scientifically, in particular to structure data, check facts, create links to other fields of

knowledge or to explain or predict circumstances.

Hauer (1987), on the other hand, took the position that it is the scarcity of

quantitative knowledge about safety that has brought about a “reign of ignorance” in

studies of driving behaviour and motor vehicle safety (p. 32). Grayson (1997) agreed,

stating that,

The first question is whether we need traffic psychology

theories. The answer is probably not. Although one might

agree with the statement (ascribed at different times to

Helmholtz and to Lewin) that “there is nothing so practical as

a good theory”, and while any data collection procedure must

have some element of theory if it is to have real purpose, the

fact remains that we have enough guidance already from

mainstream psychology. Attempts to develop ‘traffic-

specific’ theories have proved far less fruitful than has the

importation of established theories from other areas of

psychology. The second question is whether we need traffic

psychology models. The answer to this question is possibly

yes, if they are modest in ambition, if they aim to illuminate

and encourage research on specific topics rather than the

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entire spectrum of traffic behaviour, and if they are results-

centred (pp. 95-96).

This latter point was also stressed by Evans (1991), who argued that,

While many models offer insight into specific aspects of

driver behaviour, it seems unlikely that general theories

offering much more can be formulated. The problem arises

from an intrinsic dilemma. For a model to be elegant and have

derivable quantitative values of parameters it must be simple

… The quest for simplicity leads to monist models which

focus on one aspect of driving, while ignoring other factors

which are much too important to be ignored (p. 304).

This dichotomy of perspectives is not unique to traffic studies and driving

behaviour but seems to permeate all areas of applied psychology. The debate often

seems to revolve around the comparative merits of result-centred versus theory-centred

methods in research. Greenwald and Pratkanis (1988), for instance, argued that with

theory-centred methods there was an inherent danger of confirmation bias – a tendency

to evaluate ideas in a manner the meets existing expectancies (Chaplin, 1985) – that

could hinder scientific progress.

The present research probably more represents the sort of model building

favoured by Evans (1991) and Grayson (1997) than it does an attempt to test a broad

theory such as those described earlier (see sect. 2.3). Yet, the contextual mediated

model developed here may also go some distance toward breaking out of the narrow

monist frame of reference eschewed by Evans. In the present research, it has proved

capable of linking psychological and demographic variables with a pattern of driving

behaviour to illustrate influences on crash outcomes and injuries. In this case, those

variables included a diverse set of human traits including locus of control, hopelessness,

aggression and hostile automatic thoughts, but the framework constructed here can

easily accommodate a potentially endless range of both distal and proximal variables. In

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other studies, for instance, depression, anxiety, psychoticism, sensation seeking (Sümer,

2003), openness, conscientiousness, extraversion, agreeableness and neuroticism (Sümer

et al., 2005) were included as distal variables. The contextual mediated framework,

while still very much a model and not a theory, as defined by Grayson (1997), Kerlinger

(2000) and others, provides breadth of focus and a more holistic perspective than many

other attempts at modelling driving behaviour.

Future research should attempt to expand the contextual mediated approach

beyond studies of crash histories. Rather than describing and predicting the interaction

of factors involved in causing crashes, it may be even more fruitful to focus the model

on the interaction of variables that contribute to safe, crash-free driving. According to

Ranney (1994), much current research,

… has used performance-based measures to predict individual

accident histories. With several exceptions, it has been

conducted without the benefit of a process model of driving;

has focused primarily on accident-causing behaviour, not on

everyday driving; and has relied heavily on post-hoc

explanations. The general lack of success in identifying

predictors of safe driving, together with methodological

difficulties associated with the use of accident measures, lead

to the conclusion that we should abandon the differential

accident paradigm and define alternative measures of safe

driving.

5.7.2 Factors in Behavioural Adaptation (BA)

The major theories of driving behaviour and accident causation which do exist

are largely premised on the concept of behavioural adaptation (BA), the process through

which drivers make adjustments based on their perceptions of risk, competence or

hierarchical level of information processing (see sect. 2.3.4). While the present research

219

did not test any of those theories specifically, some of the variables considered are

conceptually tied to them.

Brown and Noy (2004) stressed that theories of BA and driving behaviour in

general, while intuitively appealing and providing some useful insight, fail by not

considering individual driver characteristics or the range of motivations that determine

driving behaviour. They argued that locus of control, along with trust in automation and

sensation seeking, is a concept that should be incorporated as an element in theories

seeking to explain BA and its role in driving.

Within their proposed conceptual framework, individuals viewing themselves as

being responsible for both positive and negative driving outcomes will be more likely to

take precautionary measures such as wearing seat belts and being vigilant to roadway

cures. On the other hand, those who see themselves playing little or no part in the

unfolding of events will act in a less cautious manner, believing that fate will achieve its

predetermined goals no matter what the individual does.

Following this reasoning, BA to in-vehicle safety measures may also be under

the influence of drivers’ locus of control. It is possible that drivers with an internal locus

of control will rely more on their own skills and abilities while they are driving and, no

matter how reliable a safety device, will always maintain more direct involvement with

the driving task than those scoring high on externality dimensions. Conversely, those

with an external locus of control may be more likely to give up control to an external

device, relying on it to competently perform the task it was designed for. Such

individuals would be more likely than internals to over-rely on a device to keep them

oriented and alert. As a result, they will become less involved with the driving task and

be less likely to react, or at least to react more slowly, should the device fail to perform

the task for which it was designed.

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In the present research, locus of control was found to exert effects on Type A

driving patterns and, consistent with the earlier findings of Gidron et al. (2003) to

moderate the effects of aggression on driving outcomes. Further research should focus

on the role played by locus of control in influencing patterns of BA, whether that

adaptation is the result of perceived risk (Wilde, 1982), task capability (Fuller, 2005;

Summala, 2006) or cognitive processing (Keskinen, 1996).

5.7.3 Driver Selection, Training and Rehabilitation

The results of the present research have important implications for the

improvement of driving behaviour. Specifically, scarce resources for screening drivers,

an area of increasing importance in fleet management (Barjonet & Tortosa, 1997; Christ

et al., 2004), can be focused specifically on combinations of risk factors at both the

distal and proximal levels. Drivers with combinations of TABP and aggression, external

locus of control and hostile attributions, once identified, could be screened out.

Programmes with content focused on building internal attributions and a sense of

personal responsibility would enhance training outcomes. Luckner (1989) and others

have successfully developed training curricula that encourage internality. Further

research is required to investigate implications for improving driving performance.

The treatment or rehabilitation of dangerous drivers has relied heavily on

cognitive behaviour modification applications (Deffenbacher et. al, 2002; Gidron &

Davidson, 1996). Typically, though, these approaches require special therapeutic

training and large efforts on behalf of drivers. Findings from the present research can

guide planners of remedial courses and counsellors to teach methods for increasing

internal attributions, which may mitigate the negative effects of roadway hostility.

Drivers may need to undergo cognitive restructuring about their beliefs about their own

responsibility over road safety in order to increase levels of internality. Coupled with

simulated or actual driving experience and methods to modify patterns of hostile self-

talk, changes in driver behaviour might be better targeted.

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5.7.4 Preventive Measures: “The Three E’s”

5.7.4.1 Generating and classifying crash prevention interventions

Ergonomists and safety scientists have, for the last fifty years, recognised that the

cardinal bases of accident prevention fall into three categories: engineering; education;

and the effective enforcement of regulatory legislation (Wheatley, 1957, 1961; World

Health Organisation, 1957). These have been euphemistically termed the “three E’s”.

Specific measures aimed at reducing accident occurrence or injury can be classed

according to whether they are predominantly based on engineering principles,

educational programming (including public awareness and driver training), or legal

intervention.

At the same time, the Haddon Matrix (Haddon, 1970) provides another system

for classifying highway events for the purposes of research or accident prevention (see

Figure 2.4). This framework can be integrated with the “Three E’s” to identify specific

crash prevention measures arising from the findings of the present research.

5.7.4.2 Engineering Interventions

Engineering applications in transportation have become increasingly cogniscent

of human thinking processes. Cooke and Durso (2008) have noted that:

Most industrial tasks require human operators to interact with

various technologies. Unlike 100 years ago, the tasks we ask

operators to perform today are highly cognitie, the technologies

sophisticated and the interactions among humans, teams of

humans, and machines are highly intricate (p. 1).

Slinn, Matthews and Guest (1999) have argued that traffic engineering has also

undergone a transition in emphasis over the last decade, in the the traffic engineer’s role

has increasingly become one of improving the efficiency of an existing roadway system

rather than building new higher capacity roads. From this has emerged the growing

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application of computer and information technology to transportation infrastructure and

vehicles, with the resulting transport systems generally referred to as Intelligent

Transportation Systems (ITS; Stough, Maggio & Jin, 2001). These have been applied

to in-car, roadway and environmental settings (see Table 5.6).

Several intelligent in-car systems have been developed to assist lane

manoeuvring and to control lateral deviation in the forward motion track. Lane-keeping

Assist Systems (LKA), for instance, reduce risks of extreme lane deviations by using a

motor to increase steering torque in a manner that creates a “driving in a bathtub”

sensation for the driver who nears a lane edge. (Bishop, 2005). The amount of toque

needed to adjust vehicle direction at highway speeds is quite small, so the systems are

easily overridden by even the weakest drivers when needed (Kawazoe, Murazami,

Sadano, Suda & Ono, 2001). Such systems are based on a shared control paradigm, or

the adaptive automation concept, in which in which the control of functions shifts

between machines and human beings dynamically, depending on environmental factors,

operator workload and performance (Inagaki, 2003). In the case of LKA, there is an

adaptive and cooperative relationship between the driver and the vehicle in ensuring that

lane deviation and roadway departure are controlled. Holzmann (2008) argues that there

is considerable potential for crash reduction in this sort of technology.

The findings of the present research that usurpation of right-of-way, is strongly

associated with crash outcomes would support the importance of further development of

LKA systems, as well as other in-vehicle technologies now being tested for future

implementation in automobiles and other vehicles (see Table 5.6). At the same time,

there may be limits to the number and level of sophistication of devices installed in

motor vehicles. Bishop (2005) has noted that there is still a lack of knowledge about the

threshold levels at which to set in-vehicle modifications. The aim should be to assist

drivers by making routine actions simpler, not to overwhelm them with complicated or

difficult override processes. Other authors have cautioned against engineering so many

devices and signals that driver attention becomes diverted from vehicle control tasks,

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with a resulting increase in crash risk (Noy, 1999; Brown & Noy, 2004; Richardson &

Downe, 2000).

Engineering solutions have also been suggested with respect to the design of

roadways and the general driving environment. A number of studies have reported that

roadside vegetation and predominantly natural environments elicit lower levels of driver

frustration and stress (Cackowsky & Nasar, 2003; Parsons, Tassinary, Ulrich, Hebl &

Grossman-Alexander, 1998). Recovery from lapses in attention has been faster in

“restorative environments” enhanced with horticultural and aesthetic features

(Heerwagen & Oriens, 1993; Herzog, Black, Fountaine and Knotts, 1997). Given that

the present research found that driver frustration and attentional lapses in the form of

destination-activity orientation were associated with risk of crash outcome, initiatives

aimed at improving environmental aesthetics may have a positive impact on roadway

safety.

The present research also found that freeway urgency, in the form of driving

above the speed limit and driving consistently in the fast lane, was associated crash

outcomes. This finding would lend support to the myriad of speed control devices now

under development, such as Automated Speed Enforcement (ASE), Intelligent Speed

Adaptation (ISA) and Adaptive Cruise Control (ACC) systems (see Table 5.6), but also

encourages at a more basic level the concept of traffic management as a policy

prescription:

Traffic management refers to the adaptation of the use of the

existing road network. Safety benefits from traffic

management can result from changes in the patterns of trffic

flow, changes in traffic speed, and management of parking and

loading arrangements that influence the speed of traffic.

Traffic management may also be carried out for easons other

than safety, in particular to pursue environmental, traffic

224

efficiency (capacity) or access objectives (Ogden, 1996; p.

309).

Current discussions of speed management go even beyond traditional traffic

management approaches, however, and now encompass the principle of “traffic calming”

(Brindle, 1992). This view embraces a philosophy and a set of goals that go far beyone

mere physical control and management of traffic, but extends into city-wide suppression

of traffic, questions of alternative urban structure, and substantial lifestyle changes aimed

at achieving an environmentally sustainable future (Ogden, 1996; Proctor, 1991).

Probably, engineering solutions have the least to offer in terms of behaviour in

traffic that involves risky levels of destination activity orientation. This refers to driving

while thinking about things unrelated to the driving task. Dietze, Lippold and Mayser

(2003) have noted that new driver assistance systems offer some promise for safety

improvements by providing additional information to drivers, ostensibly satisfying

wandering attentional needs and allowing vehicle operators to concentrate on tasks at

hand. Gregersen and Falkmer (2003), however, have pointed out that many problems

still exist in the implementation of such technological solutions and that some of these –

including the creation of higher mental workloads and overestimation tendencies in the

use of information – may be particularly salient for young, inexperienced drivers. Maakip

(2003) has also added that there is little understanding of exactly what specific pieces of

information drivers require or prefer to have, and whether this information varies

according to the situation, journey purpose or other human factors. Engineering

interventions capable of assisting in the focusing of attention to the driving task have

been largely understudied and considerable research will need to be carried out before

practical applications can be implemented effectively and dependably.

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Table 5.6: Engineering Applications for Crash Prevention

Finding Hi Vehicle Road Environment Drivers who usurp the right-of-way and commit lane violations are more likely to experience crash outcomes.

H 1.1.1

lane departure warning systems (LDWS) – have the ability to detect lane departures and to alert drivers of impending hazards;

road departure warning systems (RDWS) – curve speed warning systems advise drivers when their speed is too high for an upcoming curve;

lane keeping assist systems (LKA) – these reduce the driver’s need to make corrections through a motor-actuated increase in steering as the vehicle nears a lane;

blind spot monitoring systems – Doppler radar based systems operating at 24 GHz to detect vehicles within 2 to 10 feet of the right-side blind spot;

comprehensive lateral control assistance (LCA) – a combination of radar and vision sensing technology to combine lane and road departure warning, lane keeping, blind spot sensing and lange change assist.

lane marker improvements – using plastic, thermoplastic and epoxy materials to designate lane configurations;

integrated lane marker applications – these combine different materials to increase lane conspicuity and definition. Examples are the use of “Bot’s dots” or “rumble strips” in conjunction with high-intensity reflective devices

wider right-of-way – wider lanes, to allow easier overtaking and lane transitions

integrated traffic management systems – Many metropolitan areas have created intelligent traffic management centers (TMCs) with closed-circuit television (CCTV) camersas, traffic and weather sesors, variable message signs (VMS), traffic signals and ramp meters to monitor traffic on streets and expressways. Reducing congestion and increasing smooth traffic flow through driver information, reversible lanes and synchronised signals decreases the need for, and likelihood of, unsafe lane deviation.

cooperative vehicle highway systems (CVHS) – wireless communication systems embedded in the roadway infrastructure, generally controlled from a central point. Integrated with roadside sensors, the systems transmit information to drivers about traffic flow, road conditions, etc.

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(continued) H 1.1.1 lane deviation feedback systems – systems calculate the number of lane changes as a function of speed and cue the driver with performance data;

Radar- and millimetre-wave (MMW)-based inter-vehicle communications – systems that send data about the proximity of approaching or following vehicles, including those in adjoining lanes, to in-vehicle display terminals.

Drivers scoring high on a measure of freeway urgency are more likely to experience crash outcomes.

H 1.1.2

intelligent speed adaptation (ISA) – automated systems enabling vehicles to be “aware” of the preailing speed limit on rads and (at minimum) to provide feedback to the driver when that speed is being exceeded or (at maximum) to limit the vehicle’s speed to comply with the speed limit.

adaptive cruise control (ACC) – acting as a “longitudinal control co-pilot”, ACC systems provide cruise control but also track vehicles in the lane ahead of the host vehicle, adjusting speed as needed to maintaina safe, deriver-selectable inter-vehicle gap.

infrastructure-based Intersection Collision Avoidance (I-ICA) systems – involving the installation of sensors at “intelligent intersections”, t-junctions and pedestrian crossings that will trigger high-illumination warning signs for vehicles travelling at speeds higher than the safety standard.

road network modifications. Driving speed can be reduced through infrastructure modifications, including street and link closures;

intersection modification. Intersection devices (yield and stop signs; traffic lights) provide speed modification at a particular high-risk part of the thoroughfare.;

Cooperative Intersection Collision Avoidance (C-ICA) systems – involving similar sensor arrays as in I-ICA communicating with in-vehicle displays to warn the speeding driver of impending intersections, t-junctions or pedestrian crossings.

cooperative vehicle highway systems (CVHS) – wireless communication systems embedded in the roadway infrastructure, generally controlled from a central point. Integrated with roadside sensors, the systems transmit information to drivers about the speed at which surrounding vehicles are travelling.

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(continued) vertical displacement. The use of properly designed humps are effective in causing vehicles to reduce speed in their vicinity. “Speed tables”, at which the whole road space at an intersection is raised, has an advantage over intersection redesign by saving space;

horizontal displacement – these design features cause the driver to change direction quite sharply and change the visual cues presented by the roadway. Such devices include chicanes, pinch-points and gateways or arches.

automated speed enforcement – the use of high-volume speed cameras to regulate the speed of motor vehicles on roadways.

Externally-frustrated drivers are more likely to experience crash outcomes.

H 1.1.3

in-vehicle biofeedback devices – systems to measure and feedback levels of driver arousal, coupled with stress management training to better cope with frustration caused by driver interactions with road, environment and other vehicles.

aesthetic applications – beautification of median and roadside areas with vegetation to reduce frustration and effect a calming influence on drivers.

contrary messages – road-signs with calming or humorous content to evoke a contradictory affective response to frustration.

integrated traffic control centres – Systems that allow for synchronised timing of traffic signals, traffic flow moderation and other measures that can reduce congestion and other frustrating stimuli.

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(continued)

electronic variable message signs (VMSs) – roadside signboards which change messages to provide updated information about traffic congestion, weather-related road conditions, notification of construction ahead, safety messages, notice of future road construction and notice of public events. This information allows drivers to avoid or, at least, prepare for stress-provoking conditions and external frustration.

Destination-activity orientation is associated with a higher risk of crash outcomes.

H 1.1.4

in-vehicle biofeedback devices – systems that measure driver arousal and eye focal points and cue the driver when measures do not calibrate with attention to road conditions;

driver assistance systems – capable of providing information about destination conditions or journey progress, to reduce wandering thoughts and focus attention on driving tasks at hand.

dedicated broadcast of safety messages – radio or broadband messages with reminder to focus attention on driving tasks at hand.

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5.7.4.3 Education

According to Jacobs and Baguley (2004), the probems of poor driver behaviour

and knowledge in developing and emerging countries “are likely to be due, to some

extent, to inadequacies in driver training and testing. Professional driving instruction

tends to be inadequate because (a) driving instructors are not properly tested or

monitored, (b) there are no driving or instruction manuals, and (c) driving test standards

and requirements are inadequate” (p. 73).

The present research provides some useful additions to the knowledge base to be

imparted to professional driving instructors in Malaysia. It suggests that, in addition to

teaching the practical manoeuvres and driving techniques associated with managing a

vehicle, it is also important for them to devote some time to the affective and cognitive

components of the driving task. Training skills for anger management and frustration

tolerance, imparting a sense of personal responsibility consistent with a higher internal

locus of control and reducing freeway urgency through effective time management skills

and greater risk awareness would be important cognitive components to the training

syllabus.

Effective road safety education goes beyond driver training programmes,

however, and must include broader awareness about the dangers of roads and traffic. In

a study of traffic awareness in developing countries, Downing and Sager (1982) reported

that children were significantly less likely to recive advice than in the United Kingdom

from members of thir family, teachers or the police. They concluded that there is

clarealy a need to improve road safety education. The present research suggests that,

given ethnic differences observed with respect to drivers’ behaviour in traffic, it may be

effective to conduct such awareness bulding within cultural centres, like community

centres or places of worship. This is consistent with previous calls in Malaysia for

safety awareness education, publicity campaigns and incentive schemes to be offered as

part of the activities within mosques (Che Ali Bin Che Hitam, 2001).

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5.7.4.4 Enforcement

Howarth and Gunn (1982) noted that attempts to improve road safety are

generally of three types: (1) ergonomic and engineering measures to improve the

physical environment; (2) exhortatory and educational measures to encourage the

development of appropriate skills and attitudes; and (3) legal measures providing rules

governing the interaction of pedestrians and traffic, and penalties for infringement to

ensure that the rules are obeyed. They also stated, however, that “Of these three

approaches, legal measures change least often, evoke the least expectation and are least

often evaluated in terms of their effect on accidents” (p. 265).

Siegrist and Roskova (2001) have called for an integration of social science

views arising from traffic psychology with legislation and enforcement pertaining to

traffic. This twinning of psychological and legal perspectives derives two implications,

one practical and the other touching on the development of theory, from the findings of

the present research.

First, Jacobs and Baguley (2004) have stressed that changes in road laws and

police operations need to be well advertised in order to be effective. Success of the

yearly Ops Sitak safety campaign by the Royal Malaysian Police and other regulatory

bodies has been attributed to several “cumulative factors, such as visibility of

enforcement, road safety campaigns and media coverage” (Cheah, 2007; p. N6). The

results of the present research would suggest that messages about the link between

personal responsibility for one’s action, or an internal locus of control, and driving

within safe legal limits could be included within future public information campaigns.

Second, Yergil (2005) has discussed a number of cognitive biases that tend to

enhance a false sense of safety among drivers. The Belief in a Just World bias is the

tendency “to belive that they live in a world where people generally get what they

deserve” (Lerner and Miller, 1978; p. 1030). The bias of false consensus, or the

tendency to attribute one’s own attitudes and behaviour to others, was studied in a

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sample of drivers by Manstead, Parker, Stradling, Reason & Baxter, 1992). They

showed that the frequency of violations was related to the evaluated percentage of other

drivers who commit the same violations.

Both biases seem rooted in an external locus of control, on one hand attributing

outcomes to a fatalistic notion of Just World and, on the other, to consensual beliefs of

powerful others. Yergil (2005) notes that:

In order to maintain the self-concept of a law-abiding citizen,

drivers need to resolve the contradiction between cognitions:

“traffic laws are laws” and “I violate traffic laws.” One possible

way to minimize this contradiction and the resulting dissonance

is to attribute the same behaviour regarding violating the law to

other drivers (the bias of the false consensus). By doing so,

drivers create a sense of belionging to a majority group and

therefore negate the possibility that they are behaving in a

socially deviant manner (p. 498).

Another possible way to minimise the same contradiction is to diminish the significance

of the violation by labelling it as an action that, after all, is allowed to occur in a Just

World.

The theory of planned behaviour (TPB; Ajzen, 1991, 2001; Azjen & Fishbein,

2001) provides an interesting theoretical framework for considering the influences of

these cognitions and their interplay with regulatory controls. The TPB posits that a given

behaviour is determined by individuals’ intentions which in turn are influenced by the

individuals’ positive or negative evaluation of the behaviour (attitudes), opinions about

what significant others would think of the behaviour (subjective norms) and perceived

behaviour control (PCB).

Future research is needed to determine the extent to which beliefs in a Just World

and the false consensus bias influence both subjective norms and attitudes underlying

232

drivers’ decisions to adhere, or not adhere, to traffic regulations. Similarly, an orientation

toward an external locus of control may influence PCB factors underlying the decision to

comply with legal requirements or not. By examining drivers’ response to traffic laws in

the context of the theory of planned behaviour, it may be possible to come closer to the

integration of legal and psychological perspectives advocated by Siegrist and Roskova

(2001).

233

CHAPTER 6

CONCLUSION

The present research was an attempt to investigate interaction effects of

experiential, demographic and psychological characteristics of drivers on the occurrence

of self-reported motor vehicle crashes and crash-related injuries. Results have indicated

that, as expected, such human factors are important contributors to crash outcomes. In

the present research, it was concluded that driver experience, age, gender, ethnicity,

locus of control, hopelessness, aggression and tendency to entertain hostile automatic

thoughts all act upon driving behaviour patterns which, when risky, contribute to the

occurrence of crashes and injuries.

A contextual mediated model, derived from the earlier work of Sümer (2003),

was used to frame the relationship between these human factors, with demographic and

personality variables posited as distal and patterns of behaviour in traffic consistent with

the Type A behaviour pattern, as proximal to the crash outcomes. It is concluded here

that the contextual mediated model is useful in conceptualising and testing driving

behaviour and its outcomes and that further research incorporating other variables at

both the distal and proximal level should be carried out.

In doing so, structural equation modelling (SEM) was found to be a valuable

technique for articulating interactive relationships in a holistic manner. Studies using

structural equation modelling to interpret complex inter-variable relationships have

become increasingly prevalent within the traffic psychology literature (e.g., Iverson &

Rundmo, 2002; Sümer, 2003; Sümer et al., 2005; Wállen Warner & Åberg, 2006) and it

is anticipated that there will be many more.

234

In the current literature, the selection of one SEM over competing models has

been generally based on which has the better goodness-of-fit, although it is widely

acknowledged that this is not a hard and fast rule. In most cases, the best fit usually

implies the best model. However, it is argued here, that when faced with competing

models in safety studies, traffic psychologists and other researchers are advised to make

the choice based on theoretical and practical, as well as statistical grounds. This is

consistent with the position taken by Byrne (2001) and other authors.

Of the variables studied, one conclusion is that the locus of control construct

plays an important role in safety behaviour. Some previous studies have shown a link

between ‘fatalism’, or external locus of control, and accident risk (e.g., Harrell, 1995;

Montag & Comrey, 1987), while internal locus of control has been frequently associated

with safer work and lifestyle practices (Guastello & Guastello, 1986; Hoyt, 1973).

Further, the locus of control variable has been long recognised as a moderator variable in

a range of psychological processes involving stress (Lefcourt, 1983) and was earlier

found to moderate the effects of aggression on driving behaviour (Gidron et al., 2003).

The present research replicated earlier findings about the important influences of internal

and external locus of control over behaviour leading to safety problems and, like Brown

and Noy (2004), it has been argued here that it may be an important factor in

behavioural adaptation processes underlying risk homeostasis (Wilde, 1982), task

capability (Fuller, 2000) and hierarchical motivation theories (Näätänen and Summala,

1974).

It is further concluded that aggression also plays a significant role in behaviour

leading to crash outcomes. In the present research, measures of aggression had direct

effects on all components of self-reported behaviour in traffic, leading to the tentative

conclusion that it is the aggressive aspects of Type A behaviour that are instrumental in

relationships between TABP and safety outcomes. Of particular interest was the fact

that the effects of aggression on TABP were moderated by cognitive self-talk containing

two content types: physical aggression and revenge. Some inter-ethnic differences in

235

aggression were observed, as well. Additional studies of the role played by hostile

automatic thoughts and cultural influences in moderating the effects of aggression on

safety-related behaviour will provide a better understanding of psychological processes

and may offer new insights into the treatment of dysfunctional driving behaviour.

In examining inter-relationships among and between these variables, it became

apparent that motor vehicle crashes are indeed multi-factorial phenomena and that prior

assumptions of causality should always be subject to review. One of the benefits of

using structural equation modelling in such research is that it allows for a holistic, bird’s

eye view of the factors contributing a given outcome. It is argued that this is a

promising approach to future studies of crasch occurrence.

In interpreting these effects, a multi-disciplinary approach was used. Several

authors (e.g., Groeger & Rothengatter, 1998; Huguenin, 2005; Rothengatter, 2002) have

noted that this is a hallmark of the traffic psychology field and it is concluded here that

studies of the manner in which human factors influence safety behaviour require a range

of constructs pulled from various disciplines, including psychology (especially cognitive

and information processing), cultural anthropology, road engineering and ergonomics.

As Rothe (2002) has pointed out:

Traffic-safety systems are composed of complex behaviours

that imply complex causes and entangled factors. Each system

has its own experts whose lenses are focused almost

exclusively on their own subject matter. For example, a civil

engineer who uses laboratories to measure asphalt wearing

under different conditions, an economist who researches

economic factors of transportation without much concern for

geography, and a psychologist who studies cognition and

driving while turning away from laws and government; all are

professionals who know their area of expertise has collegially

or self-imposed limitations. However, in combination, they

236

form a complex traffic-safety reality in which systems form a

web of interdependent fields (p. 313).

Indeed, a multi-disciplinary approach leads not only to the greater level of

understanding described by Rothe (2002), but it also opens the door for a wider range

of preventive measures. In the present research, findings with regard to four

components of behaviour in traffic gave rise to a number of interventions in the

engineering, educational and enforcement spheres. A uni-disciplinary approach is not

sufficient to generate or integrate the range of actions that must be undertaken to

effectively bring about improvements to the roadway safety problem in Malaysia and

elsewhere.

It is to be hoped that future preventive measures and research will continue

along the multi-disciplinary path that has characterised both traffic psychology and road

engineering as emerging areas of specialisation. Additional studies should aim toward

a conceptual common ground and further examination of models and theories from

which broader understanding can be derived. Continued sharing between professional

associations and between design, management, regulatory and social science specialists

should be encouraged. Through a multi-disciplinary approach, significant impacts can

be made in reducing motor vehicle crashes, injuries and death.

237

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GLOSSARY

Acronyms and Symbols: ABS Anti-lock braking system AQ Aggression Questionnaire BA Behavioural adaptation BHS Beck Hopelessness Scale

BIT Behaviour-in-Traffic

C Chance DDB Dangerous driving behaviour FARS Fatality Analysis Reporting System HAT Hostile automatic thoughts

I Internality ITS Intelligent transportation system IFRB Industrial Fatigue Research Board

LoC Locus of control MVA Motor vehicle accident P Powerful others PBC Perceived behavioural control

RHT Risk Homeostasis Theory SEM Structural equation modelling SRS Supplementary restraint system TAPB Type A Behaviour Pattern TCI Task capability theory TPB Theory of Planned Behaviour TRA Theory of Reasoned Action

Definitions: Accident-proneness: a tendency toward accidents, presumably because of personality factors, a concept

generally attributed to Farmer and Chambers (1926; 1939) and arising from the individual differences

approach in psychology. (see also, differential accident involvement).

Anti-lock braking systems (ABS): in-vehicle technology that modulates the pressure in each wheel’s brake

line so that when a whell lock-up is anticipated or occurs, the brake line pressure is relates, allowing the

wheel to turn. Immediately after releasing the pressure, the ABS reapplies it until the wheels begin to

lock-up again. Because the wheels continue to turn during the braking manoeuvre, traction is maintained

steering and braking actions continue to be effective. As a result, ABS ensures that, on most surface

types, drivers stop sooner and in a more controlled manner than with traditional rack-and-pinion braking

systems.

Behavioural adaptation (BA): “the ability to adapt to novel conditions based on one’s experiences, the

outcome of which is typically reflected by perceived advantages, or benefits, to the individual” (Brown &

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Noy, 2004; p. 25). Also referred to as risk compensation, BA is a core concept in all risk theories of

driver behaviour. The central idea is that, when confronted by differing levels of perceived or objective

risk in the environment, drivers will alter their behaviour (see also, risk homeostasis theory, hierarchical

driver adaptation theory, task capability theory) .

Black event: a post-hoc extension of the black spot concept, it refers to a combination of circumstances,

including driver behaviour, that corresponds to an accumulation of crashes in the statistical mass. The

concept has been applied both to Summala’s (1996) hierarchical adaptation theory and to Fuller’s (2000)

task capability theory. (see also, black spot)

Black spot: a term attributed to Heikki Summala of the Traffic Research Unit at the University of

Helsinki. It refers to accumulations of motor vehicle crashes in amassed statistics, where effort to save

lives may be concentrated. Usually based on geographical location of the crash, it is essential when

searching for black spots to disaggregate the accident mass by splitting it into progressively smaller units

by type, road and traffic conditions, time of week and, where possible, characteristics of road users. (see

also, black event)

Contextual mediated model: an empirically-based path analysis, first offered by Nebi Sümer of the Middle

East Technical University in Ankara, Turke showing the manner in which the certain demographic,

driving and psychological variables influence self-reported behaviour in traffic and self-reported crash

outcomes. In the present research, the statistical model was based on the results of automobile users’

responses to psychological testing and questionnaires. The model posits certain variables as distal to the

crash event and predicts that their influence will moderate drivers’ self-reported tendencies to behave in

certain ways when in traffic situations. The contextual mediated model forms the basis of ordering

variables within the research design for studies reported in this thesis. (see also, proximal variable; distal

variable; crash outcome)

Differential accident involvement: a concept proposed by Frank P. McKenna of the University of Reading,

as an alternative to the largely discounted notion of accident proneness. It differs from accident proneness

in that it (a) denotes an area of study, rather than a theory; (b) does not prejudge the causation of

personality variables, therefore allowing for the influence of external factors; and (c) assumes that

individuals may vary along a continuum with regard to factors that affect their risk of crash. (see also,

accident proneness)

Differential psychology: see individual differences approach.

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Fatality Analysis Reporting System (FARS): is a database in the public domain maintained by the U.S.

Department of Transportation. It contains detailed information about fatalities resulting from motor

vehicle crashes on public roadways in the United States since 1975.

Haddon matrix: a model developed by the American traffic analyst, William Haddon Jr., which combines

the three components of the road traffic system – the human, the vehicle and the road – with the three

phases in a motor vehicle crash – pre-crash, in-crash, and after crash – to form a matrix with nine cells.

Each of the nine elements of the matrix represents a possible focus for road safety.

Headway: the distance between two vehicles travelling one in front of the other.

Individual differences approach: also referred to as differential psychology, this is an orientation in

psychology concerned with the study of traits or quantitative differences in traits by which any individual

may be distinguished from other individuals. Individual differences research typically focuses on the

domains of personality, motivation, intelligence, ability, aptitudes, interests, values, self-concept, self-

efficacy and self-esteem. In traffic psychology, a body of theoretical and empirical knowledge has been

generated from attempts to consider how individual differences – primarily in personality, demographic

and motivational variables – contribute to diving outcomes. (see also, personality)

Industrial Fatigue Research Board (IFRB): a body set up in Great Britain during World War One to

investigate the cause and prevention of industrial accidents. The name was changed to the British

Industrial Health Research Board in 1931. It was at the IFRB that statisticians and analysts first examined

the distribution of accident frequency, leading to the now largely discredited concept of accident

proneness. (see also, accident proneness)

Inner speech: see self-talk.

Locus of control (LoC): a concept generally credited to Julian B. Rotter of the University of Connecticut,

it refers to the degree to which individuals attribute the cause of their behaviour to environmental factors

(external LoC) or to their own decisions and actions (internal LoC). Later conceptualisations have viewed

LoC, not as a unidimensional, bipolar construct ranging from I (internal) to E (external) maximums, but

rather as a multidimensional variable made up of three or more dimensions. One such multidimensional

model of LoC was advanced by Hanna Levenson, then of the Veteran’s Administration Medical Centre in

San Francisco, who posited three dimensions: Internality (I); Externality Chance (C) and Externality

Powerful Others (P).

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Motor vehicle: a machine which incorporates an engine and wheels and is used for transportation on land,

most usually on roads. For the purposes of the present research, motor vehicles included automobiles,

motorcycles, motorised bicycles, trucks (lorries), mobile construction equipment or platforms, and buses,

but excluded those vehicles which operate on rails.

Motor vehicle crash: a collision or incident that may or may not lead to injury, occurring on a public road

and involving at least one moving motor vehicle. For the purposes of the present research, motor vehicle

crash was considered largely synonymous with the concept of motor vehicle accident.

Non-motorised transport: any transport that does not require a motor to generate energy. Included in this

term are walking, bicycling, and using animal-drawn or human-drawn carts or other devices.

Personality: is the integration of traits that can be investigated and described in order to render an account

of the unique quality of the individual. Different schools of psychology vary in their conceptualisation of

personality: the individual psychology of Gordon Allport views personality as “the dynamic integration

within individuals that determine their characteristic behaviour and thought”; the individual differences

approach, as expressed by Raymond Cattell, regards it as “that which permits a prediction of what a

person will do in a given situation; Freudian psychology considers it to be a manifestation of the

integration of the id, the ego and the superego; Adlerian psychology views it as “the individual’s style of

life, or characteristic manner of responding to life’s problems, including life goals” (Chaplin, 1985; p.

333-334).

Perceived behavioural control (PBC): a key concept in Azjen’s Theory of Planned Behaviour, PBC refers

to the degree of control that individuals believe they have over a given behaviour.

Private speech: see self-talk.

Risk compensation: see behavioural adaptation.

Risk homeostasis theory (RHT): Originally postulated by Gerald J.S. Wilde, Professor Emeritus at

Canada’s Queen’s University, the RHT posits that an underlying feedback system, somewhat analogous to

a thermostat, operates to keep user risk at an essentially constant level. When there is a discrepancy

between the level of perceived risk in the environment and an internalised “target risk” level, individuals

will engage in behaviour intended to eliminate the discrepancy. That is, if perceived risk exceeds target

risk, individuals will engage in more cautious behaviour; conversely, if perceived risk falls below the

target risk, individuals are likely to perform riskier behaviour. Wilde’s theory has generated equivocal

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research results and some heated controversy within the field of traffic psychology. (see also, behavioural

adaptation; target risk; zero risk theory)

Road infrastructure: road facilities and equipment, including the network, signage, parking spaces,

stopping places, draining system, bridges, overpasses, tunnels, archways and footpaths.

Road safety engineering: “a process, based on analysis of road and traffic related accident information,

which applies engineering principles in order to identify road design or traffic management improvements

that will cost-effectively reduce the cost of road accidents. Opportunities for road safety engineering in

general apply at four levels: (a) safety conscious planning of new road networks; (b) incorporation of

safety features in the design of new roads; (c) improvement of safety aspects of existing roads to avoid

future problems; and (d) improvement of known hazardous locations on the road network.” (Ogden, 1996;

p. 35).

Road traffic fatality: a death occurring within 30 days of a motor vehicle crash, as the result of injury

sustained in the crash.

Road traffic injuries: fatal or non-fatal injuries incurred as a result of a motor vehicle crash.

Road traffic system: Road traffic may be considered as a system in which three components (the human,

the vehicle and the road) interact with each other. A motor vehicle crash may be considered as a failure in

the system.

Road user: a person using any part of the road system as a non-motorised transport user or as a user of a

motor vehicle.

Self talk: a fundamental concept in most theories of cognitive behaviourism, most frequently attributed to

Donald Meichenbaum at Canada’s University of Waterloo. It refers to the constant stream of chatter that

goes on in one’s mind, at both conscious and unconscious levels. Cognitive self-statements may be either

negative or positive. Within the context of this research, self talk related to drivers’ cognitive statements

about other drivers was studied in terms of its direct effect on driving behaviour and as a mediator of trait

aggression on driving behaviour. Also referred to as private speech or inner speech.

Studded tyres: used primarily in countries that experience ice- and snow-covered roads during the winter

months, these tyres are manufactured with small metal studs – much like football cleats – inserted in the

treads. They enable better traction and shorter braking distances than non-studded counterparts, but only

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when manoeuvring on icy or hard-packed snowy surfaces. On dry roads, studded tyres actually increase

braking distances and decrease lateral control of the vehicle.

Supplementary restraint system (SRS): Also referred to as “airbags”. These are energy absorbing buffers

designed to protect drivers from injury during collision by preventing the head and upper body from

striking the steering wheel, instrument panel and windshield of a motorcar.

Target risk: a core concept in Wilde’s risk homeostasis theory. According to RHT proponents, it is a level

of risk that each individual is willing to accept. Individuals will undertake behaviour to ensure that the

level of risk in which they are engaged, remains constant at the target level. According to Wilde (1994),

target risk is determined by four “classes of utility factors”: (1) the benefits expected from risky behaviour

alternatives; (2) the costs expected from cautious behaviour alternatives; (3) the benefits expected from

cautious behaviour alternatives; and (4) the costs expected from risky behaviour alternatives. (see also,

risk homeostasis theory)

Task cube. A complex 3 X 5 X i matrix, where i represents the number of factors defining the functional

taxonomy dimension, derived from Summala’s (1996) hierarchical adaptation theory of driver behaviour.

Each cell of the matrix represents a combination of the three dimensions which form the basis of the

theory. (see also, hierarchical adaptation theory)

Theory of Planned Behaviour (TPB): as proposed by Icek Ajzen of the University of Massachusetts-

Amherst, the TPB posits that a given behaviour is determined by individuals’ intentions, which are the

best predictors of behaviour. Intentions are influenced by: (a) attitudes (the positive or negative evaluation

of the behaviour); (b) subjective norms (opinions about what significant others would think of the

behaviour); and (c) perceived behavioural control (PBC) over the performance of the behaviour (which

jointly influences both the intention and behavioural performance). The TPB has been applied to a wide

range of research problems in traffic psychology. (see also, theory of reasoned action; perceived

behaviour control)

Theory of Reasoned Action (TRA): was proposed by Martin Fishbein at the time with the University of

Illinois at Urbana and presently of the Annenberg School for Communication at University of

Pennsylvania. and Icek Ajzen at the University of Massachusetts-Amherst. The theory of reasoned action

(TRA) proposes that behaviour is a function of intentions, which in turn are a function of attitudes and

subjective norms. The TRA has been used as the basis for some driving safety research but is perhaps

more significant as a conceptual stepping-stone to the now widely used theory of planned behaviour. (see

also, theory of planned behavriour)

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Traffic management: planning, coordinating, controlling and organising traffic to achieve efficiency and

effectiveness of the existing road infrastructure capacity.

Traffic mix: the form and structure of different modes of transport, motorised and non-motorised, that

share the same road infrastructure.

Traffic psychology: a relatively new and developing field of applied psychology that, from its outset, has

embraced a multidisciplinary approach and a shared focus with human physiology, ergonomics, road

engineering, community planning, management science and economics. It is primarily concerned with the

study of the behaviour of road users and the psychological processes underlying that behaviour. In the

present research, the term is considered synonymous with transport psychology and with mobility

psychology. The emergence and impact of the field of traffic psychology is discussed in chapter 2 of this

thesis.

Transportation factors: a set of five domains operating on the process of moving goods or persons from

one place to another. The five basic transportation factors include: safety; comfort, time, convenience and

economy.

Zero risk theory: as proposed by Heikki Summala of the Traffic Research Unit at the University of

Helsinki, it posits that drivers attempt to maintain a stable balance between subjective and objective risk,

adapting behaviourally to driving conditions, only when a subjective threshold is exceeded and “feelings

of fear” are experienced. It is often proposed as an alternative to Wilde’s risk homeostasis theory. (see

also, behavioural adaptation; risk homeostasis theory)

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Appendix A:

List of Published and Research Scales

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A number of variables studied in the present research were measured using scales copyrighted by corporate publishers or by universities where they were developed. Published scales (AQ and BHS) are marketed only to professional psychologists capable of meeting criteria for psychometric knowledge and expertise. Research scales (BIT and HAT) were obtained and used with the permission of the authors, with the understanding that they would not be re-published. Information for obtaining copies of these instruments is provided below: Aggression Questionnaire (AQ; Buss & Warren, 2000). Available from: Western Psychological Services 12031 Wilshire Boulevard Los Angeles, CA 90025 USA

http://portal.wpspublish.com/portal/page?_pageid=53,70400&_dad=portal&_schema=PORTAL Beck Hopelessness Scale (BHS; Beck & Steer, 1993).

Available from: The Psychological Corporation (Harcourt, Brace & Company), 19500 Bulverde Road. San Antonio, TX 78259 USA http://pearsonassess.com/cgi-bin/MsmGo.exe?grab_id=0&page_id=1549&query=Beck%20Hopelessness%20Scale&hiword=BECKER%20Beck%20Hopelessness%20SCALED%20SCALES%20SCALING%20Scale%20

Behaviour in Traffic Scale (BIT; Papacostas & Synodinos, 1988) Obtained with permission from the authors: c/o Dr. C.S. Papacostas Department of Civil Engineering

2540 Dole Street University of Hawaii at Manoa Honolulu, Hawaii 96822 USA http://www.eng.hawaii.edu/~csp/csp.html

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Hostile Automatic Thoughts Scale (HAT; Snyder, Crowson, Houston, Kurylo & Poirier)

Obtained with permission from the late Dr. C.R. Snyder. Correspondence regarding this scale or the associated hope theory should be directed to: Graduate Training Program in Clinical Psychology The Department of Psychology 340 Fraser Hall University of Kansas 1415 Jayhawk Boulevard Lawrence, Kansas 66045 USA www.psych.ukans.edu/hope.

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Appendix B:

Personal Information Form (PIF)

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CONFIDENTIAL

Personal Information Form Please answer the following questions about Please answer the following questions about YOURSELF. We are not asking for your name, so please answer all questions as truthfully as you can. 1. Do you presently have a driver’s licence? (circle one) yes no If yes, please answer the following questions: 2. For how long have you had your driver’s licence?

__________ years and ___________months (number) (number)

3. In what city/town did you learn to drive? _______________, _________, __________

(city/town) (state) (country) 4. In what city/town have you lived most of your life?

_______________, _________, __________ (city/town) (state) (country)

5. Most of the time when you travel, what kind of transportation do you use? (please check

only one) ___ bus ___ motorcycle (driver) ___ car (driver) ___motorcycle (pillion, sitting behind driver) ___ car (passenger) ___ bicycle (non-motorized)

___ walk ___ other (please specify: _______________) 6. What type of motor vehicle do you most often drive? (please check only one) ___ car -- what manufacturer & model (e.g., Proton Wira) _______________ ___ motorcycle – what engine size (e.g., 250 cc) ______________ ___ other (please specify: _________________) 7. How often do you travel in a car: as a driver (please check only one): as a passenger (please check only one) ___ every day ___ every day ___ several times a week ___ several times a week ___ about once or twice a week ___ about once or twice a week ___ about once every two weeks ___ about once every two weeks ___ almost never ___ almost never ___ never ___ never

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8. How often do you travel on a motorcycle: as a driver (please check only one): as a passenger (please check only one) ___ every day ___ every day ___ several times a week ___ several times a week ___ about once or twice a week ___ about once or twice a week ___ about once every two weeks ___ about once every two weeks ___ almost never ___ almost never ___ never ___ never 9. When you want to use a car, do you have ready ACCESS to a car? (please check only one) ___ yes, all the time ___ yes, some of the time ___ yes, most of the time ___ no 10. When you want to use a motorcycle, do you have ready ACCESS to a motorcycle? (please

check only one) ___ yes, all the time ___ yes, some of the time ___ yes, most of the time ___ no 11. Within the last twelve (12) months, have you been in a motor vehicle accident that required

you to be hospitalised for injuries? yes no If yes, in what kind of vehicle were you travelling when the accident occurred? ___ bus ___ motorcycle (driver) ___ car (driver) ___motorcycle (pillion, sitting behind driver) ___ car (passenger) ___ bicycle (non-motorized)

___ I was walking ___ other (please specify: _______________) If yes, what phrase best describes what happened? (please check only one): ___ my vehicle was changing lanes and hit (or was hit by) another vehicle ___ my vehicle hit (or was hit by) another vehicle that was changing lanes ___ my vehicle went out of control and went off the side of the road ___ my vehicle went through a red light (or sign) at an intersection (junction) ___ the other vehicle went through a red light (or sign) at an intersection

___ my vehicle hit another vehicle from behind ___ my vehicle was hit from behind by another vehicle ___ other (please specify:__________________________________________ )

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12. Within the last twelve months, have you been in a motor vehicle accident that required you to go to a medical clinic for minor injuries? yes no

If yes, in what kind of vehicle were you travelling when the accident occurred? ___ bus ___ motorcycle (driver) ___ car (driver) ___motorcycle (pillion, sitting behind driver) ___ car (passenger) ___ bicycle (non-motorized)

___ I was walking ___ other (please specify: _______________) If yes, what phrase best describes what happened? (please check only one): ___ my vehicle was changing lanes and hit (or was hit by) another vehicle ___ my vehicle hit (or was hit by) another vehicle that was changing lanes ___ my vehicle went out of control and went off the side of the road ___ my vehicle went through a red light (or sign) at an intersection (junction) ___ the other vehicle went through a red light (or sign) at an intersection

___ my vehicle hit another vehicle from behind ___ my vehicle was hit from behind by another vehicle ___ other (please specify:__________________________________________ ) 13. Within the last twelve months, have you been in a motor vehicle accident that resulted in

damage over RM100 to your vehicle, but no injuries?

If yes, in what kind of vehicle were you travelling when the accident occurred? ___ bus ___ motorcycle (driver) ___ car (driver) ___motorcycle (pillion, sitting behind driver) ___ car (passenger) ___ bicycle (non-motorized)

___ I was walking ___ other (please specify: _______________) If yes, what phrase best describes what happened? (please check only one): ___ my vehicle was changing lanes and hit (or was hit by) another vehicle ___ my vehicle hit (or was hit by) another vehicle that was changing lanes ___ my vehicle went out of control and went off the side of the road ___ my vehicle went through a red light (or sign) at an intersection (junction) ___ the other vehicle went through a red light (or sign) at an intersection

___ my vehicle hit another vehicle from behind ___ my vehicle was hit from behind by another vehicle ___ other (please specify:__________________________________________ ) 15. What is your gender? ___ male ___ female 16. What is your age? _____ years 17. What is your ethnic background? (please specify only one:) ___ Malay ___ Chinese-Malaysian ___ Indian-Malaysian ___ other (please specify: _____________)

THANK YOU VERY MUCH FOR YOUR COOPERATION