Young men driving dangerously: Development of the Motives for Dangerous Driving Scale (MDDS)

Abstract

The present study was conducted to identify the motives underlying dangerous driving among young males. Exploratory factor analysis (N = 200) yielded a three-factor structure representing three major motives for risky driving – driving fast/risk taking, confidence in one's driving skills, disrespect for traffic laws. Confirmatory factor analysis (N = 264) confirmed and further clarified this factor structure in representing the motives underlying young males' driving behavior. Correlation analysis between the three identified motives for risky driving and self-reports of experiences/frequencies of traffic accidents and traffic offences offered support for the newly developed Motives for Dangerous Driving Scale's criterion-related validity. The implications of the findings with regard to the development of effective intervention strategies for dangerous driving among young males are discussed.

The recently published World Report on Road Traffic Injury Prevention (Peden, 2004) identified road traffic injuries as a major cause of morbidity and mortality worldwide, with an estimated 1.2 million people killed in road traffic crashes each year and as many as 50 million injured or disabled. Projections indicate that these figures will increase by about 65% over the next 20 years unless there is new commitment to prevention. More specifically, without appropriate action, by 2020, road traffic injuries are predicted to be the third leading contributor to the global burden of disease and injury (Murray & Lopez, 1996). Such statistics are no less appalling in Australia, where deaths due to transportation-related crashes now rank eighth among the ten leading causes of death (Australian Bureau of Statistics, 2003).

While motor vehicle crashes result from a variety of factors, studies examining demographic factors relating to traffic fatalities show that age (Arnett, 1990; Evans, 1991; Lourens, 1992; Levy, 1990) and gender (Australian Transport Safety Bureau, 2004; Kreisfeld, Newson, & Harrison, 2004) are major predictors of involvement in accidents. In particular, their interactive effect clearly points to young male drivers as a high risk group in regard to accident involvement. While males made up 72% of motor vehicle traffic deaths in Australia in 2002, males in the 15 – 29 year age range accounted for 29% of such fatalities in the same period. Statistics show that within the young driver group, males have a higher risk of being involved in a road crash leading to injury or death due to risky driving (Groeger & Brown, 1989), aggressive driving (Simon & Corbett, 1996), and excessive speed (Federal Office of Road Safety, 1997). These causal factors suggest that the high crash rates among young male drivers may not be due simply to their relative inexperience as drivers or possible exposure to particularly hazardous driving conditions, but rather that there is something problematic about the judgments or decisions that they make when driving (Harré, 2000). In particular, the plethora of research evidence suggests that it is the propensity to take risks by young male drivers that explains their tendency to be involved in dangerous driving practices (DiBlasio, 1986; Evans, 1991; Hurrelmann, 1990).

A considerable body of literature has examined the underlying factors that may contribute to risky driving by young males. These factors include personality, attitude, risk acceptance, and overconfidence.

Personality

Of particular importance is the link between the personality trait of sensation seeking and risk taking behavior which is often cited as a characteristic of young male drivers that explains their higher crash risk. Sensation seeking is defined as the need for varied, novel and complex sensations and experiences and the willingness to take physical, social, legal, and financial risks for the sake of such experiences (Zuckerman, 1994). In his review and synthesis of the literature relating to sensation seeking and risky driving, Jonah (1997) reported that: (i) sensation seeking is higher in males than females and is highest in the 16 – 19 year age group, and (ii) risky driving behaviors such as speeding, impulsivity, driving under the influence of alcohol, and failure to wear set belts all correlated highly with thrill seeking and boredom susceptibility. Together, these findings suggest that young male drivers who rate highly on sensation seeking tend to drive dangerously.

Attitude

Driving safely or dangerously is a choice a driver makes. Young drivers have been shown in a number of studies to have different attitudes toward safe driving compared to older drivers. For example, while older drivers have been found to drive with the aim of getting from point A to point B, young male drivers tend to drive for recreational purposes as it provides them with a sense of freedom and control over their lives (Zimbardo, Keough, & Boyd, 1997). Their attitudes toward law enforcement and in particular, traffic laws also differ from other groups. Young male drivers expect less negative outcomes as a result of committing traffic offences, perceive such offences as socially acceptable, and experience less self-control when conducting such behavior (Parker, Manstead, Stradling, Reason, & Baxter, 1992). This display of non-compliance with law enforcement, lack of self-control, and perceived social acceptability, appears also to be related to their attitudes toward speed and a desire for danger.

Risk acceptance

Risk acceptance is the individual's level of perceived risk, or risk threshold in which they are willing to accept and consequently act upon. This level of risk acceptance provides a number of explanations as to why an individual engages in dangerous driving, such as continuing to speed when a pedestrian walks onto the road. First, such behavior may reflect their poor risk perception and/or their overconfidence, resulting in the driver misjudging the distance to the pedestrian and the time to brake effectively. Second, the behavior may reflect poor driving skills, which may not allow the driver enough time to respond and slow down. Finally, a high level of risk acceptance, combined with overconfidence in their driving ability may motivate the young driver to accept risks in order to minimize time delays (Deery, 1999).

Overconfidence/optimism bias

A number of studies have demonstrated higher levels of confidence in young drivers compared to older drivers (Dejoy, 1992; Guerin, 1994; Guppy, 1993). However, this bias appears to be particularly strong in young male drivers who have consistently been found to perceive themselves as at less risk of having an accident than their peers of the same age and sex (Bragg & Finn, 1982; Matthews & Moran, 1986). McKenna, Stanier, and Lewis (1991) also reported that individuals tend to exhibit either a positive self-judgment and/or negative other-judgment. They found that when drivers rated their driving skill on a 1 – 10 scale, ranging from very poor to very good, they tended to rate the skill of other drivers as average, while rating themselves as above average. These findings suggest that while drivers do not have a negative view of others, they do see themselves as better than the average driver.

The present study was designed to examine the motives underlying dangerous driving among young males. Although a number of factors have been identified that may predispose some young male drivers to engage in risky driving, a review of the literature showed that there are presently no assessment tools that tap into the motives for dangerous driving. Although there are a number of measures such as Zuckerman's (1994) Sensation Seeking Scale, which measures sensation-seeking and risk taking behaviors, and the Driver Anger Scale, which measures a driver's level of anger in a variety of driving situations (Deffenbacher, Oetting, & Lynch, 1994), these scales are essentially unidimensional and each identifies only one major facet of driving practices. As there is no one parsimonious motive for young males to drive dangerously, it would be advantageous to have available a multidimensional and valid measurement tool that can reliably tap into different motives simultaneously.

Study 1: Development of the Motives for Dangerous Driving Scale

The primary aim of this study was to develop a comprehensive, multidimensional instrument that can tap into the motives underlying dangerous driving by young males.

Step 1

A focus group consisting of 15 males between the ages of 18 – 28 years and who held a current driver's license were involved in this stage of the study. The participants, who volunteered for the study, were recruited from a university campus in the Brisbane metropolitan area in Queensland, Australia. At the initial stage of this study, the participants took part in a group discussion of the types of dangerous behaviors young males engage in while driving. After approximately 15 minutes of discussion, the participants were asked to write down as many reasons as they could think of as to why young males engage in these types of dangerous driving behavior. A total of 94 reasons for driving dangerously were generated and recorded.

The 94 responses were initially grouped together by two judges on the basis of similarity of phrasing. For example, “they get excited about driving fast” was grouped with “speed excites them”. A third independent judge resolved any disparities. This reduced the number of responses to 72. The responses were then content-analyzed, based on an arbitrary frequency criterion in which responses listed at least four times were grouped. For example, based on the frequency criterion, responses reflecting motives toward speed were grouped together. This further reduced the number of responses to 54.

A subsequent content analysis (on the basis of similarity of meaning) was carried out on these responses. From the analysis, four thematic categories of responses were identified as motives for dangerous driving. These categories were labeled “risk-taking”, “mood”, “attitude”, and “skill”. Finally, in order to reduce the number of responses to a more manageable unit, a number of statements were written by the authors to reflect the meaning-content of each of the four categories. A total of 40 statements were written (10 statements for each category/motive) and these were included in a questionnaire for final scale construction and item analysis.

Step 2: Exploratory factor analysis (EFA)

Participants and procedure

A total of 200 male participants from the Brisbane metropolitan area, Australia volunteered to fill in the study's questionnaire. The participants held a current driver's license for an average of two years and three months. The majority of the participants (47%) was employed at the time of the study, and had a mean income ranging from $10,001 – $20,000 per year. Their ages ranged from 18 – 24 years, with a mean age of 21 years. For this sample, participants aged between 18 and 19 years represented 30.9% of the sample (compared with 48.9% in the Australian population) while those aged 20 – 24 years represented 69.1% of the sample (compared with 51.1% in the Australian population) (Australian Bureau of Statistics, 2006). Thus, the sample is under-represented in the 18 – 19 years age bracket and over-represented in the 20 – 24 years age bracket. This non-representative sample suggests that care should be taken when extrapolating the study's findings to the general population of young males.

Materials

Participants responded to a questionnaire consisting of two sections. Section 1 consisted of items written to elicit demographic information relating to the participant's age, level of education, personal income, employment status, and how long they have held a driver's license.

Section 2 consisted of the 40 statements written to reflect the 4 categories/motives for dangerous driving identified in step 1 of the study. Each statement was to be rated on a 6-point Likert scale with high scores indicating strong endorsement of the driving motives: 1 = strongly disagree, 2 = moderately disagree, 3 = barely disagree, 4 = barely agree, 5 = moderately agree, 6 = strongly agree.

Results

Participants' responses to the 40-item questionnaire were subjected to a principal components analysis, followed by oblique rotation. Inspection of the results revealed that seven factors had eigen-values greater than 1.00. However, examination of the items that loaded on these seven factors indicated that only three factors were interpretable, as well as containing the fewest number of cross-loaded items. In conjunction with results obtained from the scree-plot, these findings suggested a three factor solution. These three factors accounted for 41.12, 7.53, and 5.01% of the total variance respectively, for a combined total of 53.67%. Since the factor correlation matrix showed that the factors were correlated (.29 to .35), oblique rotation, limited to three factors was then conducted.

From the obtained pattern matrix, a total of 29 items were retained, using the criteria of selecting items with factor structure coefficients greater than or equal to .33 and no significant cross-loadings. The use of the .33 value as a criterion for selecting items is based on the logic that squaring the correlation coefficient (.33²) yields approximately 10% of the variance explained. Of the 29 items, 15 loaded on Factor 1, eight loaded on Factor 2, and six loaded on Factor 3. Examination of the items that loaded with these three factors indicated that Factor 1 consisted of items that reflected a desire to drive fast and/or to take risks while driving (e.g., driving fast calms me down; I often overtake on the solid line on my side of the lane). Factor 2 consisted of items that reflected confidence in one's driving skills (e.g., I am a skillful driver and am always in control of my driving; my driving skills allow me to negotiate traffic hazards safely). Factor 3 comprised of items that reflected a negative attitude (disrespect) toward traffic laws (e.g., the present traffic laws are too harsh; it is okay to violate traffic laws).

In order to maximize the internal consistency of the derived factor solution, the items representing each of the three factors were item analyzed. Two criteria were used to eliminate items from these factors. First, an item was eliminated if the inclusion of that item resulted in a substantial lowering of Cronbach's alpha (Walsh & Betz, 1985). Second, an item was considered to have an acceptable level of internal consistency if its corrected item-total (IT) correlation was equal to or greater than .33 (Hair, Anderson, Tatham, & Black, 1997). Examination of the Cronbach's alphas for the three factors and their items' IT correlations showed that all items were acceptable based on the above two criteria. As such, all 29 items were retained to represent their respective factors. Table I presents the three-factor multidimensional Motives for Dangerous Driving Scale (MDDS), together with the factor loadings and corrected item-total correlations for the 29 items.

Table I. Factor loadings and corrected item-total (IT) correlations for the motives for dangerous driving scale

	Factor loadings	IT correlations
Factor 1: Driving fast/risk taking
Driving fast calms me down (m34)*	0.77	0.79
I like to drive close behind slower drivers (m8)	0.74	0.80
I often sound my horn or make obscene gestures at other drivers if they cut in front of me (m7)	0.73	0.62
I often overtake on the solid line on my side of the lane (m25)	0.72	0.63
When driving at night, it is okay to drive through red lights or stop signs as long as I am careful (m33)	0.71	0.72
I have no problems exceeding the speed limit because I know I am a good driver (m31)	0.67	0.82
Driving fast is one way of showing my friends that I am a skillful driver (m15)	0.66	0.80
I often pull out into oncoming traffic (m39)	0.64	0.68
I like to “race” other cars at the traffic lights (m32)	0.61	0.71
I try my best not to violate traffic laws (m38*^1)	0.56	0.50
Playing loud music in the car makes me drive faster (m9)	0.55	0.70
I often like to change lanes even in heavy traffic (m6)	0.52	0.73
I take out my frustrations by driving fast (m2)	0.49	0.73
I tend to drive fast so I can get to my destination sooner (m13)	0.45	0.79
I often drive through traffic lights when the light is amber (m26)	0.42	0.58
Cronbach coefficient = .94
Factor 2: Confidence in one's driving skills
I react quickly when faced with unexpected traffic hazards (m40)	0.81	0.66
I am fluent in changing lanes in heavy traffic (m37)	0.75	0.79
I am a skillful driver and am always in control of my driving (m4)	0.60	0.62
My driving skills allow me to negotiate traffic hazards safely (m21)	0.60	0.62
I am able to judge accurately the speed of an oncoming car (m36)	0.57	0.64
I am a more skillful driver than most other drivers on the road (m24)	0.57	0.67
I often pay attention to other road users (m27)	0.48	0.34
I can easily re-gain control of my car if it skids in wet weather (m29)	0.42	0.50
Cronbach coefficient = .86
Factor 3: Disrespect for traffic laws
It is okay to drink and drive as long as I know I am in control of my car (m18)	0.77	0.54
It is highly unlikely that my driving will ever cause an accident (m30)	0.69	0.40
The present traffic laws are too harsh (m28)	0.62	0.54
Exceeding the speed limit by 10 km per hour is no big deal (m11)	0.59	0.71
It is okay to violate traffic laws (m3)	0.55	0.67
I would rather drive a car that is powerful than one that is comfortable (m10)	0.53	0.67
Cronbach coefficient = .82
*Order of items in questionnaire: 1 = strongly disagree; 6 = strongly agree.
*^1Reverse-scored.

Study 2: Tests of validity

Construct validity (Confirmatory factor analysis)

Confirmatory factor analysis (CFA) was carried out to evaluate the adequacy of the factor structure identified in the exploratory factor analysis. CFA, unlike exploratory factor analysis, allows the researcher to explicitly posit an a priori model (e.g., on the basis of the factors identified through exploratory factor analysis) and to assess the fit of this model to the observed data. Based on the factor structure identified through exploratory factor analysis, a three-factor model representing the three motives for dangerous driving was posited. For this measurement model, the three latent constructs of “driving fast/risk taking”, “confidence in one's driving skills”, and “disrespect for traffic laws” were represented by 15, 8, and 6 indicator items respectively (generated from EFA in step 2). While it can be argued that a greater number of indicators per latent construct will represent that latent construct to a higher degree than fewer indicators, in practice however, too many indicators make it difficult if not impossible to fit a model to data (Bentler, 1980). Based on Hair et al.'s (1997) suggestion that three is the preferred minimum number of indicators to represent a construct, it was decided to limit the number of indicators to three for each of the model's latent construct. This was achieved by using item parcels to represent the original number of items for each latent construct.

Item parcels

This technique involves summing responses to individual items and then using scores on these summed parcels in the latent variable analysis. For example, on the basis of a reliability analysis of the 15 items representing the latent driving motive of “driving fast/risk taking”, the items were divided into three parcels, and the items in each parcel were then summed to form three measured variables to operationalize the latent construct. Adapting the procedure described by Russell, Kahn, Spoth, and Altmaier (1998), the development of these item parcels involved the following steps:

1.	A reliability analysis on the 15 items assessing “driving fast/risk taking” was conducted.
2.	The items were rank-ordered on the basis of their corrected item-total (I-T) correlation coefficients.
3.	Items were assigned to parcels in a way that equated the average I-T coefficient of each parcel of items with the factor.

Specifically, items ranked 1, 2, 7, 14 and 15 were assigned to parcel 1; items ranked 3, 4, 8, 12, and 13 were assigned to parcel 2; and items ranked 5, 6, 9, 10, and 11 were assigned to parcel 3. This procedure ensured that the resulting item parcels reflected the underlying latent driving motive of “driving fast/risk taking” to an equal degree.

Figure 1 presents the three-factor measurement model representing the three motives for driving dangerously (driving fast/risk taking; confidence in one's driving skills; disrespect for traffic laws). Each latent driving motive was represented by three computed indicator variables (item parcels). For this model, all factor loadings were freed, indicators were allowed to correlate with only one factor, and the three factors were allowed to correlate (equivalent to oblique rotation).

Figure 1. Confirmatory factor analysis model for dangerous driving motives

Participants and procedure

The sample consisted of 264 male participants who volunteered to fill in the study's questionnaire. The participants were recruited from the Rockhampton and Brisbane metropolitan areas, Australia by the researcher and fourth year psychology students from Central Queensland University. None of these participants were part of the exploratory factor analysis stage of the study. Their ages ranged from 18 to 28 years, with a mean age of 21 years. The participants held a current driver's license for an average of four years. The majority of the participants (48%) was employed at the time of the study, and had a mean income ranging from $10,001 – $30,000 per year.

Materials

Participants responded to a questionnaire consisting of five sections. Section 1 consisted of 5 items written to tap the participant's age, level of education, personal income, employment status, and how long they have held a driver's license.

Section 2 consisted of the 29-item Motives for Dangerous Driving Scale, representing the three identified motives of driving fast/risk taking, confidence in one's driving skills, and disrespect for traffic laws. The items were to be rated on 6-point Likert scales with high scores indicating strong endorsement of the driving motives.

Section 3 consisted of Zuckerman's (1994) Sensation Seeking Scale (SSS) (Form V). The 40 forced choice items on this scale require participants to choose between a statement which reflects a desire for sensation (“I like wild and uninhibited parties”) and one that reflects a more cautious predilection (I prefer quiet parties with good conversation”). The forced choice items were to be scored with “0” and “1” responses, with high scores indicating high sensation seeking. It is important to note that none of the items refer to driving behavior. The SSS yields four sub-scales: Thrill and Adventure Seeking (TAS), Experience Seeking ES), Disinhibition (DIS) and Boredom Susceptibility (BS). The four subscales, when summed together, provide and overall index of the sensation seeking trait.

Section 4 consisted of the 19-item Danger Assessment Questionnaire (Franken, Gibson, & Rowland, 1992). This measurement tool measures the extent to which a variety of activities are considered to be dangerous. Each item was to be rated on a 6-point Likert scale from 1 (not at all dangerous) to 6 (very dangerous), with high scores indicating strong endorsement of that activity as being dangerous.

Section 5 consisted of two sets of questions written to measure the participants' reported negative driving outcomes. The first set of questions asked the participants “have you been involved in any traffic accidents (regardless of whether or not you were responsible for the accidents) while driving a car in the past two year?” (coded 1 = yes, 2 = no), and if the answer was “yes”, then they were asked “approximately how many accidents you have been involved in as a driver?”. The second set of questions asked “have you been charged by the police with any traffic offences in the past two years?” (coded 1 = yes, 2 = no), and if the answer was “yes”, then they were asked “approximately how many traffic offences have you been charged with?”.

Results

The purpose of this phase of the study was to evaluate the posited a priori model of dangerous driving motives (Figure 1). A χ² goodness-of-fit test (via the statistical program AMOS 5.0; SPSS, Inc. 1997) was employed to test the null hypothesis that the sample covariance matrix was obtained from a population that has the proposed model structure. Table II presents the goodness-of-fit indices for this model.

Table II. χ² goodness-of-fit value, normed fit index (NFI), incremental fit index (IFI), Tucker-Lewis index (TLI), comparative fit index (CFI), and root mean square error of approximation (RMSEA)

Model	χ^2 (N = 264)	df	p	NFI	IFI	TLI	CFI	RMSEA
Null Model	1883.87	36	<.001	0.00	0.00	0.00	0.00	0.37
Three-factor Model	110.92	24	<.001	0.94	0.95	0.93	0.95	0.07

Although the overall chi-square value was significant, χ² (df = 24, N = 264) = 110.92, p < .001, the incremental fit indices (NFI, IFI, TLI, CFI) are all above 0.90 (range: 0.93 – 0.95). These fit indices indicated that the model provided a good fit relative to a null or independence model (i.e., the posited model represented over 90% improvement in fit over the null or independence model), and support the hypothesized structure of the posited three-factor model for dangerous driving. The RMSEA value of 0.07 is also within the range suggested by Browne and Cudeck (1993) and indicates that the model fits the population covariance matrix reasonably well. Table III presents the standardized regression weights, residuals, and explained variances for the three-factor model.

Table III. Standardized regression weights, residual variances, and explained variances for the dangerous driving motives indicator variables

Parameter	Standardised regression weights	Residual variances	Explained variances
Driving fast/risk taking → FA_RI1	0.83	0.32	0.68
Driving fast/risk taking → FA_RI2	0.84	0.29	0.71
Driving fast/risk taking → FA_RI3	0.95	0.11	0.89
Confidence → CONF1	0.77	0.41	0.59
Confidence → CONF2	0.75	0.43	0.57
Confidence → CONF3	0.80	0.37	0.63
Disrespect for traffic laws → DISR1	0.68	0.54	0.46
Disrespect for traffic laws → DISR2	0.65	0.58	0.42
Disrespect for traffic laws → DISR3	0.81	0.34	0.66

The standardized regression coefficients (factor loadings) for the measurement indicators were all positive and significant by the critical ratio test, p < .001. Standardized loadings ranged from 0.65 to 0.95 (M = 0.78). These values indicated that the indicator variables hypothesized to represent their respective latent driving motives did so in a reliable manner. The percentage of residual (unexplained) variances for the 9 indicator variables ranged from 11% (i.e., 89% of the variance explained) (FA_RI3) to 58% (i.e., 42% of the variance explained) (DISR2).

While CFA has confirmed the fit of the three-factor model, an evaluation of the factor correlations yielded by EFA, CFA, and the raw scale scores would be useful in identifying the extent of the overlap between the factors. Table IV presents these factor correlations.

Table IV. Factor correlations generated from exploratory factor analysis, confirmatory factor analysis, and raw scale scores

	Driving fast/risk taking	Confidence	Disrespect for traffic laws
Driving fast/risk taking
Confidence	0.21^a
	0.56^b
	0.49^c
Disrespect for Traffic laws	0.33^a	0.31^a
	0.78^b	0.58^b
	0.63^c	0.47^c
^aExploratory factor analysis; ^bConfirmatory factor analysis; ^cScale scores.

The results indicated that the three factors were moderately correlated (range: 0.21 – 0.78; M = 0.48) and suggest some overlapping between the three motives for dangerous driving. These correlations are not unexpected given that all three motives reflect reasons to engage in risky driving. Indeed, the correlations between these motives suggest that they are jointly implicated, either directly or indirectly, in the decision-making process of young males when they get behind the steering wheels of their cars.

Criterion-related validity

Criterion-related validity refers to the relationship between a test score and some desired outcome, and is conducted by collecting both the test scores that will be used and information on the criterion for the same participants. The test scores are then correlated with the criterion to determine how well they represent the criterion behaviour. Test of criterion-related validity for the developed Motives for Dangerous Driving Scale (MDDS) was demonstrated by correlating the summated scales for the three identified motives of driving fast/risk taking, confidence in one's driving skills, and disrespect for traffic laws with the participants' reported past experiences with traffic accidents and traffic offences (i.e., whether or not they have been: (i) involved in traffic accidents, and (ii) charged with traffic offences in the past two years, and their frequencies). It is hypothesized that the three identified motives of driving fast/risk taking, confidence in one's driving skills, and disrespect for traffic laws will be: (i) negatively correlated with experiences with traffic accidents and traffic offences, and (ii) positively correlated with their frequency of occurrences.

Results

The items representing the three motives for dangerous driving were summed across their respective factors and their means computed. Pearson's product-moment correlation analysis was then conducted to investigate the direction and strength of the relationships between the three driving motives and the participants' reported past experiences with traffic accidents and traffic offences. The results of this analysis are presented in Table V.

Table V. Correlations between the driving motives of driving fast/risk taking, confidence in one's driving skills, and disrespect for traffic laws with reported past experiences with traffic accidents and traffic offences

	Traffic accidents	Traffic offences
Driving fast/risk taking	−.25***^a	−.25***^a
	.31***^b	.24**^b
Confidence	−.07^a	−.18***^a
	.19*^b	.05^b
Disrespect traffic laws	−.11*^a	−.23***^a
	.12^b	.24**^b
***p < .001; **p < .01; *p < .05.
^aPast experiences with traffic accidents and traffic offences (coded 1 = yes, 2 = no).
^bFrequency of reported traffic accidents and traffic offences.

The results indicated that all three identified motives of driving fast/risk taking, confidence in one's driving skills, and disrespect for traffic laws were significantly and negatively correlated with the experience of being charged with traffic offences (p < .001). Thus, the stronger the participants' desire to drive fast/take risks, the stronger their confidence in their driving skills, and the greater their disrespect for traffic laws, the higher the likelihood of them reporting being charged with traffic offences in the past. The motives to drive fast/take risks and disrespect for traffic laws were also found to be significantly and positively related to the frequency of reported traffic offences charged with. Thus, the stronger the participants' desire to drive fast/take risks, and the greater their disrespect for traffic laws, the higher the number of traffic offences they reported they were charged with. The motive of “confidence in driving skills” was not found to be significantly related to the frequency of reported traffic offences.

The results also indicated that the motive of driving fast/risk taking was significantly correlated with both the experience of traffic accidents (p < .001) and the frequency of accidents reported (p < .05). Thus, the stronger the participants' desire to drive fast/take risks, (i) the higher the likelihood of their reporting of being involved in traffic accidents in the past, and (ii) the greater the frequency of accidents reported. The motive of “confidence in driving skills” was not found to be significantly related to the experience of traffic accidents, but was found to be significantly related to the frequency of accidents reported (p < .05). Thus, the stronger the participants' confidence in their driving skills, the greater the frequency of accidents reported. The motive of “disrespect for traffic laws” was found to be significantly related to the experience of traffic accidents (p < .05) but not to the frequency of accidents reported. Thus, the greater the participants' disrespect for traffic laws, the higher the likelihood of their reporting of being involved in traffic accidents in the past. These findings are generally in line with the study's hypotheses and offer support for the developed Motives for Dangerous Driving Scale's criterion-related validity.

Discussion

The substantive purpose of this study was to identify motives underlying the dangerous driving behaviors of young males. Initial exploratory factor analysis of responses derived from qualitative analysis identified a three-factor structure representing three major reasons for risky driving. Reliability analysis indicated good internal consistency for all three factors. Confirmatory factor analysis confirmed and further clarified the adequacy of this factor structure in representing the motives underlying young males' driving behavior. Finally, correlation analysis offered support for the developed Motives for Dangerous Driving Scale's criterion-related validity. While these findings suggest that the decision by young males to engage in risky driving is a joint function of their desire to drive fast and to take risks, an inflated sense of confidence in their driving ability, and a negative attitude (disrespect) toward traffic laws, the possibility of socially desirable responses to the MDDS represents an important confound. Controlling for socially desirable responses in such high-stakes situation is extremely difficult if not impossible. Nevertheless, it is hoped that the guarantee of anonymity and the confidentiality of the participants' responses would have gone some way in ameliorating this problem.

The development of the multidimensional Motives for Dangerous Driving Scale (MDDS) may represent an important contribution to the identification, measurement and ultimately, the understanding of the motives underlying the dynamics of risk-taking behaviors among young male drivers. Such an understanding may provide the basis for developing tools and strategies that can be employed to predict at-risk drivers as well as to evaluate and guide responses to them. For example, as young males over-represent crash statistics, it is imperative that driver-training and traffic-safety programs are effective at tapping into what motivates them to engage in high-risk driving practices. Through the development of both reliable and valid assessment tools, researchers and program planners may be able to focus on specific motives for dangerous driving practices. Given the ability of the MDDS to discriminate between motives for dangerous driving, the scale may be used as a screening tool for identifying possible at-risk individuals. By identifying sub-groups of high-risk drivers, interventions or training programs may be tailored specifically to that group.

The MDDS may also be useful in the evaluation of driver-safety programs, particularly where young male drivers may be required to enter court-ordered driver-safety programs as a result of traffic violations. The effectiveness of such programs can be evaluated by applying the MDDS prior to and at the completion of these programs and examining any changes in the “sub-scales” scores. Similarly, the MDDS may also be utilized in the evaluation of traffic safety campaigns such as those that focus on peer intervention programs. These programs are aimed at motivating the young driver's peers to intervene when high-risk behaviors are likely in a given situation, such as drink-driving after a party.

The overall findings fit well with the growing body of literature that characterizes those at greatest driver risk as: high risk takers, sensation seeking, overconfident in their driving ability, low in danger perception, disrespectful of traffic laws, and male. In particular, past research has shown young male drivers' overconfidence in their driving ability to be primarily responsible for the way they assess risk and danger when driving. The relationship is clearly demonstrated in the correlation analysis which showed that the young male drivers' confidence in their driving abilities increased their negative attitude (disrespect) toward traffic laws as well as their willingness to drive fast and take risks. The problem of overconfidence may, paradoxically, lie with the very driver training courses that have been and are still used to train novice drivers the skills to handle and to control their vehicle. While such skills are necessary to be able to even begin to drive, a by-product of such skill-based training is an overestimation of the young drivers' skills. A more effective training strategy may be one that moves the emphasis on training new drivers in basic driving skills to one that helps them to have some insight into their own limits as drivers. The rationale underlying “insight training” is that by making young drivers more aware of the limits to their ability to handle the driving situation, their overconfidence will be reduced (Gregersen, 1996).

A review of the dangerous driving literature will show that there has been more relevant research on risk seeking than on any other factors. Yet, it is probably the most difficult state to shift. This is largely because it is propped up with an entire social system of norms and media images that equate fast driving and “skillful” maneuvers with masculinity, adulthood, and peer group approval (Harré, 2000). At the individual level, intervention strategies are unlikely to succeed if they fail to acknowledge the youthful imperative to increase social status by courting danger to demonstrate courage. As pointed out by Nell (2002), driving represents the most common form of sensation seeking in young men because “it bypasses the genetic endowments of strength and speed and makes the demonstration of courage available to all young men, including the slow and the weak” (p. 78). At the social level, risk taking is a highly prized social virtue. One only has to look to certain groups of people – soldiers, police, paramedics, firefighters – to see that risk taking is not only highly valued but is also entrenched in our social establishment.

In conclusion, the development of the MDDS provides future researchers with an instrument that can act as a quick screening tool to evaluate driving behaviors in young males. Understanding how young male drivers think and why they think the way they do provides directions for the development of effective interventions, as well as the identification of high risk individuals and situations. While these findings contribute to the understanding of the decision-making process underlying risky driving choices, continued investigation of this area is crucial if effective intervention programs are to be developed that can effectively lower the high road injury rate of this group of drivers.