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Spanish drivers and their aberrant driving behaviours
Transportation Research Part F 9 (2006) 129–137 www.elsevier.com/locate/trf
Spanish drivers and their aberrant driving behaviours M. Eugenia Gras a,*, Mark J.M. Sullman b, Monica Cunill a, Montserrat Planes a, Maria Aymerich a, Silvia Font-Mayolas a a
Quality of Life Research Institute, Department of Psychology, University of Girona, Emili Grahit, 77 17071, Spain b Department of Human Resource Management, Massey University, New Zealand Received 22 September 2004; received in revised form 5 September 2005; accepted 21 September 2005
Abstract One of the most commonly used frameworks for investigating the relationship between self-reported driving behaviour and crash involvement is the Manchester Driver Behaviour Questionnaire (DBQ). However, in spite of the fact that Spain has a relatively large road safety problem (annually more than 5000 people die and over 150,000 are injured in traffic accidents), only one study could be found using the DBQ to measure aberrant driving behaviour in this country. In addition that research solely reported the frequencies of the different driving behaviours they measured. The current research attempted to fill this gap by administering a translated Spanish version of the DBQ to a sample of drivers in Spain. Although factor analysis produced a four factor solution, there were a number of departures from the expected factor structure. The most unusual finding was that the first factor contained a mixture of lapses and errors. This may indicate that either some of the meaning was lost in the translation into Spanish, or that the distinction between these two factors may not apply to Spanish drivers. The second factor was a strong violations factor and was constructed of violations and aggressive violations to do with getting somewhere in a hurry. The third factor once again confirmed that interpersonal violations are a separate entity from the other aggressive violations, meaning that this research did not support the ‘‘aggressive violations’’ factor. The fourth factor had very low internal reliability and contained only three lapses. However, in agreement with previous research, it was the violations factor which was predictive of crash involvement after the contributions of the demographic and descriptive variables had been partialled out. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Errors; Lapses; Violations; Driver behaviour; Crashes
1. Introduction The Manchester Driver Behaviour Questionnaire—DBQ (Reason, Manstead, Stradling, Baxter, & Campbell, 1990) is arguably one of the most commonly used frameworks for investigating the relationship between
*
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1369-8478/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.trf.2005.09.004
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self-reported driving behaviour and crash involvement. Although initially developed to distinguish between two empirically different classes of behaviour, errors and violations, more recent versions of the questionnaire have four different subscales: errors, lapses, violations and aggressive violations (Lajunen, Parker, & Summala, 2004; Lawton, Parker, Manstead, & Stradling, 1997; Mesken, Lajunen, & Summala, 2002; Sullman, Meadows, & Pajo, 2002). Violations are deliberate departures from those behaviours thought necessary to safely drive a vehicle. Violations include behaviours such as exceeding the speed limit and following another vehicle too closely. Aggressive violations, on the other hand, are to do with expressing hostility towards other road users or driving in an aggressive manner and include behaviours such as making a rude gesture at another motorist. Whilst both types of violations are deliberate, lapses and errors are not. Lapses can be described as problems with attention and memory and include such things as forgetting where your car was parked. Errors are failures of observation and misjudgement, and include such things as braking too quickly on a slippery road. The separate subscales of the DBQ have been found to have satisfactory internal reliability. For example, Westerman and Haigney (2000) found CronbachÕs alpha coefficients of 0.76 for the errors scale, 0.74 for the violations scale and 0.74 for the lapses scale. Similar findings have been made elsewhere (Dobson, Brown, & Ball, 1999; Parker, Stradling, & Lajunen, 1998; Parker, West, Stradling, & Manstead, 1995). The internal reliability of the extended violations scale has also been found to be acceptable, with 0.76 for the ordinary violations subscale and 0.70 for the aggressive violations subscale (Lajunen, Parker, & Stradling, 1998). The DBQ scores have also been found to be reliable over time (Parker, Reason, Manstead, & Stradling, 1995). Parker, Reason, et al. (1995) examined the test-retest reliability of the DBQ by getting 80 respondents to complete the DBQ twice within a seven month period. They found relatively high test-retest correlations of; 0.69 for the errors subscale, 0.81 for the violation subscale and 0.75 for the lapse subscale. In addition, research has also found that the scale was only slightly affected by socially desirable responding (Lajunen & Summala, 2003). Not only does the DBQ have good psychometric properties, the findings using the scale have also been relatively consistent. Research using the DBQ on private car drivers has produced relatively stable findings in terms of the factor structure. This stability has been found both within and across different countries and cultures. For example, using the 28-item version of the DBQ, four factor solutions have been found by researchers in Britain (Lajunen et al., 2004), Finland (Lajunen et al., 2004; Mesken et al., 2002), The Netherlands (Lajunen et al., 2004) and in New Zealand (Sullman et al., 2002). Although small differences in the factor structure have been reported, the overall factor structure has been supported by these studies. As has been mostly the case with the DBQs factor structure, an impressive body of literature has built up showing that crash involvement can be predicted using subscales of the DBQ (e.g., Meadows, Stradling, & Lawson, 1998; Parker, Reason, et al., 1995; Mesken et al., 2002; Sullman et al., 2002). Unfortunately, only two of these studies have used the 28-item version of the DBQ and only one of these was on car drivers (Mesken et al., 2002). However, the majority of the research using earlier versions of the scale has consistently found that it is the violations subscale which is predictive of crash involvement (Kontogiannis, Kossiavelou, & Marmaras, 2002; Parker, Reason, et al., 1995; Parker, West, et al., 1995; Stradling, Parker, Lajunen, Meadows, & Xie, 1998). Unfortunately, the generalisability of any version of the DBQ to the Spanish population has yet to be established. Only one published study (published on the internet) has measured aberrant driving behaviour amongst Spanish drivers. The Royal Automobile Club of Catalunya (RACC, 2004) recently published the results of a 75-item self-report measure of aberrant driving behaviour amongst 309 Spanish drivers. They studied the frequency of these different types of driving behaviours and found that the most commonly reported behaviours were related to the violation of speed limits (RACC, 2004). Unfortunately they did not analyse the factor structure of the scale or the relationship the reported behaviours had with crash involvement. The current study had three main aims. Firstly, this research aimed to test the generalisability of the 28item version of the DBQ to a sample of Spanish drivers. In addition, the level of self-reported aberrant driving behaviour was investigated, along with the reliability of the four hypothetical factors. The final aim of the study was to further investigate the relationship between the resultant DBQ factors and crash involvement.
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2. Method 2.1. Materials The questionnaire used in this research included five sections. This paper reports the results of the second section, which consisted of a 28-item version of the Manchester Driver Behaviour Questionnaire (DBQ) translated into Spanish. Translation was carried out by two native Spanish speakers and these translations were then translated back into English by a native English speaker. The 28-item English version of the scale consists of eight errors and eight lapses, along with the extended violations scale (six violations and six aggressive violations) developed by Lawton et al. (1997). This version of the scale had been used previously amongst; company car drivers (Dimmer & Parker, 1999), truck drivers (Sullman et al., 2002) and private motorists (Mesken et al., 2002). The participants received the standard instructions, as per Parker, Reason, et al. (1995), but in Spanish. The scale anchors range from 0 (never) to 5 (all the time). In addition to the DBQ, the survey also assessed demographic variables, driver history (how long they have held a driving license and annual mileage) and how many crashes they have been involved in over the last five years. 2.2. Participants One third of the employees working full or partial time at the University of Girona (600 workers) were selected. Systematic random sampling within strata was used for selection. Strata criterion were; kind of work (academic or non-academic workers) and Faculty (Arts, Law, Educational, Business, Engineering, Science, Nursing, and Tourism). Random numbers between one and three were used to decide where to begin the selection process, and then every third element (person) was chosen in each stratified sample frame. Of the 600 selected, 19 had no driving license, 21 licensed drivers had not driven a vehicle during the last six months (participants must have driven a car at least once in the last six months), 189 did not respond to the questionnaire and 21 missed at least one of the DBQ items. The remaining 350 workers had a mean age of 37.4 (SD = 9.4), with 47% being male and 53% female. The mean annual mileage was 17,226 km/year (range, 10– 200,000) and the participants had an average of 17.8 years driving experience (range, 1–50 years). In total 46% of the drivers had not been involved in a crash during the previous five years, while 53% reported at least one crash (1% did not answer the question). 2.3. Procedure Participants were firstly contacted by email. This email explained the purpose of the study and asked them to participate. The questionnaires were then sent to them via university mail. The majority of the questionnaires were collected in person by the researchers, but a small number of participants returned the questionnaires by university mail. Anonymity and confidentiality of the data were guaranteed. SPSS, Version 12.0, was used for data analysis. 3. Results The characteristics of the sample and the Spanish car driving population are shown in Table 1. This shows that the gender distribution of the sample is very similar to the population, but the age range is slightly wider amongst the population of Spanish car drivers. Table 1 Characteristics of the sample and Spanish car driving population Gender
Sample Spanish car driving population
Age range
Male (%)
Female (%)
47.2 48.9
52.8 51.1
22 to 75 18 to 74+
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Table 2 shows the means and standard deviations of the DBQ items, by subscale. The most frequently reported aberrant behaviours were three types of violations: ‘‘disregard the speed limit on the highway’’ (violation), ‘‘sound horn to indicate annoyance at another road user’’ (aggressive violation) and ‘‘disregard the speed limit on a residential road’’ (violation). The most common error was ‘‘fail to notice that pedestrians are crossing when turning into a side street from a main road’’ and the most frequent lapse was ‘‘forget where you left the car in the car park’’. The least frequently reported driving behaviours were a lapse (misread the signs and exit from a roundabout on the wrong road) and an aggressive violation (become angered by another driver and give chase with the intention of giving him/her a piece of your mind). The 28 items were then subjected to principle component analysis (PCA) in order to determine the factor structure. Although seven factors had eigenvalues larger than one, parallel analysis suggested a four factor solution was more appropriate, and the analysis was rerun specifying four factors. As there were relatively low intercorrelations (<0.30) between oblique factors, varimax rotations were used. The factor structure and items loading over 0.40 are shown in Table 3. Only 27 items are shown because one of the lapses (misread the signs and exit from a roundabout on the wrong road) did not load strongly enough on any factor. This solution explained 40.95% of the variance. Although a four factor solution was confirmed, there were a number of departures from the expected factor structure. The first factor explained 21.21% of the variance and included a mixture of errors (seven) and lapses (four), and also one aggressive violation. This factor had good internal consistency, with an alpha coefficient of 0.82. The second factor consisted of all six violations, three
Table 2 Means by category Mean (SD) Violations Disregard the speed limit on the highway Disregard the speed limit on a residential road Cross an intersection knowing the traffic lights have already turned against you Drive close to the car in front, making it difficult to stop in an emergency Overtake a slow driver on the inside Drive when you suspect you may be over the legal alcohol limit
1.56 1.41 1.13 0.87 0.61 0.61
(±1.10) (±1.00) (±0.82) (±0.85) (±0.83) (±0.80)
Aggressive violations Sound your horn to indicate your annoyance at another road user Angered by a certain type of driver, show your hostility Race away from the traffic lights to beat another driver Stay in a lane about to close until the last minute, then dive in Pull out of an intersection so far you force your way into the traffic Angered by another driver, give chase
1.44 1.07 1.00 0.73 0.70 0.14
(±0.97) (±0.86) (±0.96) (±0.83) (±0.63) (±0.43)
Errors Fail to notice pedestrians crossing when turning into a side street Fail to check rear-view mirror before a manoeuvre Queuing to turn left, nearly hit the car in front Underestimate the speed of an oncoming vehicle when overtaking Miss ‘‘give way‘‘ sign and narrowly avoid a collision Brake too quickly, or steer the wrong way into a skid On turning right, nearly hit a cyclist coming up on your inside Attempt to overtake someone signalling a right turn
1.03 0.98 0.72 0.60 0.52 0.46 0.39 0.35
(±0.80) (±0.80) (±0.74) (±0.65) (±0.67) (±0.63) (±0.66) (±0.55)
Lapses Forget where you left your car in the car park ‘‘Wake up’’ to find yourself on a wrong, but more familiar destination Switch on one thing when you meant to switch on something else Hit something when reversing that you have not previously seen No clear recollection of the road along which you have just travelled Attempt to drive away from the traffic lights in third gear Get into the wrong lane approaching a roundabout or junction Misread the signs and exit from a roundabout on the wrong road
1.34 1.25 1.02 0.81 0.60 0.54 0.36 0.11
(±1.00) (±0.91) (±0.90) (±0.70) (±0.78) (±0.75) (±0.64) (±0.79)
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Table 3 Factor structure of the DBQ Original item type
Load
Factor 1: Errors—21.21% of the variance Miss ‘‘give way‘‘ sign and narrowly avoid a collision Fail to notice pedestrians crossing when turning into a side street Switch on one thing when you meant to switch on something else On turning right, nearly hit a cyclist coming up on your inside Pull out of an intersection so far your force your way into the traffic Fail to check rear-view mirror before a manoeuvre Queuing to turn left, nearly hit the car in front Break too quickly, or steer the wrong way into a skid Attempt to drive away from the traffic lights in third gear Hit something when reversing you had not previously seen Get into the wrong lane approaching a roundabout or junction Attempt to overtake someone signalling a right turn
E E L E A E E E L L L E
0.722 0.639 0.624 0.613 0.593 0.591 0.570 0.463 0.458 0.457 0.450 0.444
Factor 2: Violations—9.24% of the variance Disregard the speed limit on the highway Stay in a lane about to close until the last minute, then dive in Cross an intersection knowing the traffic lights have already turned against you Race away from the traffic lights to beat another driver Drive so close to the car in front making it difficult to stop in an emergency Disregard the speed limit on a residential road Overtake a slow driver on the inside. Underestimate the speed of an oncoming vehicle when overtaking Drive when you suspect you may be over the legal alcohol limit
V A V A V V V E V
0.738 0.666 0.641 0.637 0.629 0.590 0.506 0.477 0.464
Factor 3: Interpersonal violations—5.75% of the variance Angered by a certain type of driver, show hostility Sound your horn to indicate your annoyance at another road user Angered by another driver, give chase
A A A
0.799 0.754 0.497
Factor 4: Lapse—4.75% of the variance Forget where you left your car in the car park ‘‘Wake up’’ to find yourself on a wrong, but more familiar route No clear recollection of the road along which you have just travelled
L L L
0.697 0.537 0.493
aggressive violations and the remaining error. This factor accounted for 9.24% of the variance and had good internal validity, with a CronbachÕs alpha of 0.81. The third factor accounted for 5.75% of the variance and contained the three aggressive violations which involved expressing anger or hostility towards another road user. This factor had marginal internal reliability (0.59), which could be improved, by dropping the item ‘‘become angered by another driver and give chase with the intention of giving him/her a piece of your mind’’ the alpha value increased to 0.70. However, the item was not removed as this grouping has previously been reported a number of times and this factor contained only three items. The fourth factor was a very weak factor, contained only three lapses and accounted for 4.75% of the variance. In order to analyse the relationship between the four factors and crash involvement, factor scores from the PCA were saved as variables using the regression method. The mean scores of crash involved and non-crash involved participants were compared using t-tests. Participants who had not held a driving license for at least five years were not included in the analysis (n = 328). Age, annual mileage and the number of years holding a driving license were also tested. The means and standard deviations for both groups and the results of the t-tests are shown in Table 4. Crash involved participants had higher factor scores in factor 2 (violations), but there were no significant differences between the two groups in annual mileage, driving license tenure or the other three DBQ factors. In order to test whether any of the DBQ variables were able to predict crash involvement over the previous five year period, hierarchical logistic regression was performed. The demographic and descriptive variables (annual mileage, age and gender) were entered first, followed by the factor scores from the four factors. As shown in Table 5, the demographic and descriptive variables did not make a significant contribution to the
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Table 4 Comparison of crash involved with non-crash involved drivers Variable
Crash involved mean (SD)
Non-crash involved mean (SD)
T
d.f.
p
Age Annual mileage (km) Driving license life Errors factor Violations factor Aggressive viol. factor Lapse factor
37.2 (9.7) 17,394.9 (10,216.2) 17.9 (8.9) 0.07 (0.9) 0.19 (0.9) 0.01 (1.0) 0.09 (1.0)
38.5 (8.8) 17,085.3 (17,756.5) 19.2 (8.6) 0.07 (1.1) 0.20 (1.0) 0.01 (1.0) 0.09 (0.9)
1.3 0.19 1.14 1.26 3.60 0.18 1.66
320 304 324 324 324 324 324
n.s. n.s. n.s. n.s. p < 0.0005 n.s. n.s.
Wald
Exp (B)
0.007 1.640 1.838 1.667 7.623** 0.145 2.912
1.000 0.984 1.372 1.161 1.437 1.044 1.228
Table 5 Results of the stepwise logistic regression for crash involvement over the previous five years Block 1 1 1 2 2 2 2
Model chi-square improvement Mileage Age Sex Errors Violations Interpersonal Lapse
* **
% Correctly classified
3.202
54.5
12.934*
60.1
B .000 .016 .316 .149 .363 .044 .205
p < 0.05. p < 0.01.
prediction of crash involvement. Although entering the DBQ factor scores made a significant contribution, it was only the violations factor scores which made a significant contribution. It should be noted that the total model only accounted for 6.5% of the variability in crash involvement. However, the odds ratio for the violations factor shows that for every one unit increase in the violations score, the odds of being crash involved increased by 44%. 4. Discussion Consistent with previous research (e.g., Lawton et al., 1997; Meadows et al., 1998; Parker, Reason, et al., 1995; Sullman et al., 2002) crash involved participants reported engaging significantly more often in violations than those who did not report being crash involved. Furthermore, it was only the violations factor that predicted crash involvement, even after the contributions of the demographic and descriptive variables had been partialled out. The odds ratio for the violations factor was 1.44, which means that for each one unit increase in the violations factor score the odds of being crash involved increased by 44%. This finding is similar in magnitude to Mesken et al.Õs (2002) odds ratio of 1.43 for the prediction of passive crash involvement by interpersonal violations, but is considerably lower than the 1.70 they found for the prediction of active crashes by their errors factor. The odds ratio found in the current research is also very similar to the 1.50 for the prediction of crash involvement amongst truck drivers using the violations factor (Sullman et al., 2002). The current finding indicates that the predictive strength of the violations factor remains despite the differences in culture, language, and driving conditions. The cross cultural consistency of this relationship also provides further evidence of the need to reduce the frequency at which drivers engage in these types of behaviours. As expected, factor analysis produced support for a four factor solution. However, although the analysis supported a four factor solution, there were also a number of departures from the hypothetical four factor solution. Firstly, only three lapses loaded on the lapse factor, and these appeared to be about ‘‘disorientation’’ (where is my car? where am I? where am I going?). This factor was highly unreliable, with an alpha coefficient of 0.46. Furthermore, the first factor consisted mainly of a mixture of errors and lapses. Although it is not unusual to have the occasional error loading on the lapse factor, and vice versa (see Lajunen et al., 2004; Sullman et al., 2002), it is unusual to find such a thorough mix of errors and lapses in one factor. The vast majority
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˚ berg & of previous research has found a relatively clear distinction between these two subscales (e.g., A Rimmo¨, 1998; Lajunen et al., 2004; Mesken et al., 2002; Sullman et al., 2002). There are several possible explanations for this finding. Firstly it could due to some of the meaning being lost with their translation into Spanish. A similar explanation was used by Lajunen et al. (2004) to explain why an aggressive violation loaded on their error factor. A second possible explanation is that Spanish drivers simply do not perceive errors and lapses to be different. Future research should be undertaken to investigate which of these explanations is accurate. The aggressive violation item ‘‘pull out of an intersection so far that the driver with right-of-way has to stop and let you out’’ loaded on the errors factor. This has been previously found amongst New Zealand truck drivers (Sullman et al., 2002), New Zealand car drivers (Sullman, 2004), as well as Finnish car drivers (Lajunen et al., 2004; Mesken et al., 2002). Although Lajunen et al. (2004) attributed this to ambiguities in the Finnish translation, that does not explain the two New Zealand findings. A more inclusive explanation would be that drivers interpret this behaviour as an error, rather than a deliberate attempt to drive their vehicle in an unsafe manner. Therefore, either this item should be labelled an error, or omitted from future research. This study found a clear violations factor which contained all of the violations and two of the aggressive violations. These two apparently misplaced aggressive violations are more to do with driving in a hurry rather than directing hostility towards another road user. Both of these aggressive violations have previously been found to load on the ordinary violations factor (e.g., Lajunen et al., 2004; Mesken et al., 2002; Sullman et al., 2002) and perhaps should be labelled as ordinary violations in future research. As with previous research (e.g., Lajunen et al., 2004; Lawton et al., 1997; Sullman et al., 2002), the aggressive violations factor only included the three behaviours which involved expressing anger or hostility at another road user. The current research, and previous research, indicates that the hypothetical fourth factor ‘‘aggressive violations’’ is not actually a cohesive factor. Therefore, the fourth hypothetical factor should actually be labelled ‘‘expressing hostility’’ or ‘‘interpersonal violations’’. Although this research found the violations factor explained a relatively modest portion of the variance in crash involvement, this has been reported elsewhere. For example, Kontogiannis et al. (2002) found that after partialling out the contributions of gender, annual mileage and experience, highway code violations accounted for only 1.2% of the variance in crash involvement. Although this 1.2% was statistically significant, it appeared to show an extremely weak relationship. However, the use of ordinary statistics in the case of crash involvement can be misleading (e.g., Hansen, 1989; McKenna, 2004; West, 1995). Crashes are rare events, and as there is some degree of chance involved, it is difficult to obtain good relationships between the precursors and crash involvement. Therefore, a number of researchers have recommended using epidemiological techniques for reporting the strength of the relationship between independent variables and crash involvement (Elander, West, & French, 1993; McKenna, 2004; West, 1995). Therefore, although the violations factor explains a relatively low proportion of the variance in crash involvement, this should not be used to judge the strength of the relationship. Instead the odds ratio suggests that a one unit increase in the factor score would result in a 44% increase in the odds of being crash involved. In line with previous research in several different countries (Parker, West, et al., 1995; RACC, 2004; Reason et al., 1990; Sullman et al., 2002) the reported behaviours with the highest and third highest means involved disregarding the speed limit. The second most frequently reported aberrant driving behaviour was ‘‘sound the horn to indicate annoyance at another road user’’. These behaviours were also found to be reported frequently in the previous Spanish research (RACC, 2004). As in our study, the most frequently reported item was ‘‘disregard the speed limit on the highway’’. Furthermore, four of the ten most frequently reported behaviours were related to disregarding the speed limit. This is also supported by observational research, which found that 70% of the 9020 drivers observed were exceeding the speed limit (RACE, 2002). ‘‘Forget where you left your car’’ was the most frequently reported lapse by Spanish drivers in the RACC (2004) research, as well as in the current study (mean 1.36 and 1.34 respectively). This item was also the second most frequent lapse reported by British (mean = 1.16) and New Zealand (mean = 1.12) drivers (Lajunen et al., 2004 & Sullman, 2004), but was the least frequently reported lapse amongst Finland drivers (mean = 0.48) (Mesken et al., 2002). Unlike most previous research (e.g., Dimmer & Parker, 1999; Parker, West, et al., 1995; RACC, 2004; Reason et al., 1990) driving while under the influence of alcohol was not the least frequently reported item. Drink
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driving was rated the seventeenth most commonly reported form of aberrant driving behaviour. In terms of absolute value (mean = 0.61) it is considerably higher than that found in most recently reported research (e.g., Dimmer & Parker, 1999; Mesken et al., 2002; Sullman et al., 2002). For example, amongst company car drivers Dimmer and Parker reported a mean of 0.23, Sullman et al. reported a mean of 0.24 for their sample of New Zealand truck drivers, while Mesken et al. reported a mean of 0.27 for Finnish drivers. Therefore, it appears that drink driving amongst Spanish drivers is a relatively frequent behaviour, as a number of researchers have noted (Montoro, 1997; Oliveras, Planes, Cunill, & Gras, 2002), and needs to be greatly reduced. One of the main problems is that this behaviour is still widely socially accepted in Spain, and many people erroneously believe that their driving performance will not be affected after drinking (Oliveras et al., 2002; Pardo, 2003). Therefore, a great deal of work needs to be undertaken to reduce the social acceptability of drink driving in Spain, and also to educate Spanish drivers about the decrement in their driving performance caused by alcohol. The study reported here clearly had a number of methodological limitations. Firstly there is the possibility of sampling bias. As the participants all worked at the University of Girona, it is possible that they differ significantly from the general population in some way. However, confidence in this data can be drawn from the fact that the gender distribution and age range in the present study were very similar to that of the population of Spanish drivers (see Table 1). Further confidence can be drawn from the fact that the frequency of the different driving behaviours found here were very similar to those found in the only other Spanish research in this area. The present study also suffers from the usual perceived weakness of research using self-reported data. The most common criticism of self-reported data is the possibility that it suffers from social desirability bias. However, research using the DBQ found that socially desirable responding had a very minimal effect on participantsÕ responses (Lajunen & Summala, 2003). Another common criticism of self-report is that there is great variance between what drivers report and their actual behaviour. However, there are also a number of studies that have found self-reported driving behaviour to be significantly related to actual driving behaviour (e.g., Parker, 1997; Rolls, Hall, Ingham, & McDonald, 1991; Walton, 1999; West, 1995; West, French, Kemp, & Elander, 1993). There has also been research which has suggested a great deal of forgetting or underreporting of driving events, such as crashes and incidents (see Chapman & Underwood, 2000). The result of drivers not reporting crashes would simply be to reduce the variability in crash involvement, thereby decreasing the strength of any potential relationships. Therefore, the finding that the violations factor was a significant predictor of self-reported crash involvement was made in spite of the possible underreporting of crash involvement. Furthermore, the finding that a one unit increase in the violations factor results in a 44% increase in the risk of being crash involved may actually be an underestimate of the true strength of the relationship. References ˚ berg, L., & Rimmo¨, P. (1998). Dimensions of aberrant driver behaviour. Ergonomics, 41, 39–56. A Chapman, P., & Underwood, G. (2000). Forgetting near-accidents: the roles of severity, culpability and experience in the poor recall of dangerous driving situations. Applied Cognitive Psychology, 14, 31–44. Dimmer, A. R., & Parker, D. (1999). The accidents, attitudes and behaviour of company car drivers. In G. B. Grayson (Ed.), Behavioural research in road safety IX. Crowthorne: Transport Research Laboratory. Dobson, A., Brown, W., & Ball, J. (1999). Women driversÕ behaviour, socio-demographic characteristics and accidents. Accident Analysis and Prevention, 31, 525–535. Elander, J., West, R. J., & French, D. J. (1993). Behavioral correlates of individual differences in road-traffic crash. Psychological Bulletin, 113, 279–294. Hansen, C. P. (1989). A causal model of the relationship among accidents, biodata, personality, and cognitive factors. Journal of Applied Psychology, 74, 81–90. Kontogiannis, T., Kossiavelou, Z., & Marmaras, N. (2002). Self-reports of aberrant behaviour on the roads: errors and violations in a sample of Greek drivers. Accident Analysis and Prevention, 34, 381–399. Lajunen, T., Parker, D., & Stradling, S. G. (1998). Dimensions of driver anger, aggressive and highway code violations and their mediation by safety orientation in UK drivers. 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Lawton, R., Parker, D., Manstead, A. S. R., & Stradling, S. G. (1997). The role of affect in predicting social behaviours: the case of road traffic violations. Journal of Applied Social Psychology, 27, 1258–1276. McKenna, F. (2004). Personal Communication. Meadows, M. L., Stradling, S. G., & Lawson, S. (1998). The role of social deviance and violations in predicting road traffic accidents in a sample of young offenders. British Journal of Psychology, 89, 417–431. Mesken, J., Lajunen, T., & Summala, H. (2002). Interpersonal violations, speeding violations and their relation to accident involvement in Finland. Ergonomics, 45, 469–483. Montoro, L. (1997). Alcohol, drogas, seguridad vial y accidentes de tra´fico. Revista Espan˜ola de Drogodependencias, 22(3), 161–164. Oliveras, C., Planes, M., Cunill, M., & Gras, M. E. (2002). Efectos del alcohol y conduccio´n de vehı´culos: creencias y conductas de los jo´venes. Revista Espan˜ola de Drogodependencias, 27(1), 66–80. Pardo, V. (2003). Tra`nsit i alcohol, un binomi impossible. Butlletı´ del Servei Catala` de Tra`nsit, 14, 2–3. Parker, D. (1997). The relationship between speeding attitudes and speeding behaviour. In G. B. Grayson (Ed.), Behavioural research in road safety VII (pp. 176–184). Crowthorne, England: Transport Research Laboratory. Parker, D., Stradling, S. G., & Lajunen, T. (1998). Attitudinal determinants of interpersonally aggressive violations on the road. Transportation Research Part F, 1, 11–24. Parker, D., Reason, J. T., Manstead, A. R. S., & Stradling, S. G. (1995). Driving errors, driving violations and accident involvement. Ergonomics, 38, 1036–1048. Parker, D., West, R., Stradling, S., & Manstead, A. S. R. (1995). Behavioural characteristics and involvement in different types of traffic accident. Accident Analysis and Prevention, 27, 571–581. RACC (2004). Los conductores espan˜oles suspenden en respeto a los lı´mites de velocidad. Accessed 10.06.04. RACE (2002). Estudio RACE sobre velocidad 2002. Accessed 09.09.04. Reason, J., Manstead, A., Stradling, S., Baxter, J., & Campbell, K. (1990). Errors and violations on the roads: a real distinction? Ergonomics, 33, 1315–1332. Rolls, G. W. P., Hall, R. D., Ingham, R., & McDonald, M. (1991). Accident risk and behavioural patterns of young drivers. Hampshire, England: AA Foundation for Road Safety Research. Stradling, S. G., Parker, D., Lajunen, T., Meadows, M. L., & Xie, C. Q. (1998). Normal behaviour and traffic safety: Violations, errors, lapses and crashes. Available from http://millemiglia.ce.unipr.it/ARGO/theysay/urban/. Sullman, M. J. M. (2004). Dimensions of aberrant driving behaviour and their relation to crash involvement. Paper presented at the 3rd International Conference on Traffic & Transport Psychology. 5–9 September, Nottingham, UK. Sullman, M. J. M., Meadows, M. L., & Pajo, K. B. (2002). Aberrant driving behaviours amongst New Zealand truck drivers. Transportation Research Part F, 5, 217–232. Walton, D. (1999). Examining the self-enhancement bias: professional truck driversÕ perceptions of speed, safety, skill and consideration. Transport Research Part F Traffic Psychology and Behaviour, 2, 91–113. West, R. J. (1995). Individuals in accident risk: a review of findings and an examination of methods. In Proceedings of behavioural research in road safety VI. 4–5 September, London, England. West, R. J., French, D., Kemp, R., & Elander, J. (1993). Direct observation of driving, self reports of driving, and accident involvement. Ergonomics, 36, 557–567. Westerman, S. J., & Haigney, D. (2000). Individual differences in driver stress, error and violation. Personality & Individual Differences, 29, 981–998.
Spanish drivers and their aberrant driving behaviours M. Eugenia Gras a,*, Mark J.M. Sullman b, Monica Cunill a, Montserrat Planes a, Maria Aymerich a, Silvia Font-Mayolas a a
Quality of Life Research Institute, Department of Psychology, University of Girona, Emili Grahit, 77 17071, Spain b Department of Human Resource Management, Massey University, New Zealand Received 22 September 2004; received in revised form 5 September 2005; accepted 21 September 2005
Abstract One of the most commonly used frameworks for investigating the relationship between self-reported driving behaviour and crash involvement is the Manchester Driver Behaviour Questionnaire (DBQ). However, in spite of the fact that Spain has a relatively large road safety problem (annually more than 5000 people die and over 150,000 are injured in traffic accidents), only one study could be found using the DBQ to measure aberrant driving behaviour in this country. In addition that research solely reported the frequencies of the different driving behaviours they measured. The current research attempted to fill this gap by administering a translated Spanish version of the DBQ to a sample of drivers in Spain. Although factor analysis produced a four factor solution, there were a number of departures from the expected factor structure. The most unusual finding was that the first factor contained a mixture of lapses and errors. This may indicate that either some of the meaning was lost in the translation into Spanish, or that the distinction between these two factors may not apply to Spanish drivers. The second factor was a strong violations factor and was constructed of violations and aggressive violations to do with getting somewhere in a hurry. The third factor once again confirmed that interpersonal violations are a separate entity from the other aggressive violations, meaning that this research did not support the ‘‘aggressive violations’’ factor. The fourth factor had very low internal reliability and contained only three lapses. However, in agreement with previous research, it was the violations factor which was predictive of crash involvement after the contributions of the demographic and descriptive variables had been partialled out. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Errors; Lapses; Violations; Driver behaviour; Crashes
1. Introduction The Manchester Driver Behaviour Questionnaire—DBQ (Reason, Manstead, Stradling, Baxter, & Campbell, 1990) is arguably one of the most commonly used frameworks for investigating the relationship between
*
Corresponding author. Tel.: +34 97241 8018; fax: +34 97241 8301. E-mail address: [email protected] (M. Eugenia Gras).
1369-8478/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.trf.2005.09.004
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self-reported driving behaviour and crash involvement. Although initially developed to distinguish between two empirically different classes of behaviour, errors and violations, more recent versions of the questionnaire have four different subscales: errors, lapses, violations and aggressive violations (Lajunen, Parker, & Summala, 2004; Lawton, Parker, Manstead, & Stradling, 1997; Mesken, Lajunen, & Summala, 2002; Sullman, Meadows, & Pajo, 2002). Violations are deliberate departures from those behaviours thought necessary to safely drive a vehicle. Violations include behaviours such as exceeding the speed limit and following another vehicle too closely. Aggressive violations, on the other hand, are to do with expressing hostility towards other road users or driving in an aggressive manner and include behaviours such as making a rude gesture at another motorist. Whilst both types of violations are deliberate, lapses and errors are not. Lapses can be described as problems with attention and memory and include such things as forgetting where your car was parked. Errors are failures of observation and misjudgement, and include such things as braking too quickly on a slippery road. The separate subscales of the DBQ have been found to have satisfactory internal reliability. For example, Westerman and Haigney (2000) found CronbachÕs alpha coefficients of 0.76 for the errors scale, 0.74 for the violations scale and 0.74 for the lapses scale. Similar findings have been made elsewhere (Dobson, Brown, & Ball, 1999; Parker, Stradling, & Lajunen, 1998; Parker, West, Stradling, & Manstead, 1995). The internal reliability of the extended violations scale has also been found to be acceptable, with 0.76 for the ordinary violations subscale and 0.70 for the aggressive violations subscale (Lajunen, Parker, & Stradling, 1998). The DBQ scores have also been found to be reliable over time (Parker, Reason, Manstead, & Stradling, 1995). Parker, Reason, et al. (1995) examined the test-retest reliability of the DBQ by getting 80 respondents to complete the DBQ twice within a seven month period. They found relatively high test-retest correlations of; 0.69 for the errors subscale, 0.81 for the violation subscale and 0.75 for the lapse subscale. In addition, research has also found that the scale was only slightly affected by socially desirable responding (Lajunen & Summala, 2003). Not only does the DBQ have good psychometric properties, the findings using the scale have also been relatively consistent. Research using the DBQ on private car drivers has produced relatively stable findings in terms of the factor structure. This stability has been found both within and across different countries and cultures. For example, using the 28-item version of the DBQ, four factor solutions have been found by researchers in Britain (Lajunen et al., 2004), Finland (Lajunen et al., 2004; Mesken et al., 2002), The Netherlands (Lajunen et al., 2004) and in New Zealand (Sullman et al., 2002). Although small differences in the factor structure have been reported, the overall factor structure has been supported by these studies. As has been mostly the case with the DBQs factor structure, an impressive body of literature has built up showing that crash involvement can be predicted using subscales of the DBQ (e.g., Meadows, Stradling, & Lawson, 1998; Parker, Reason, et al., 1995; Mesken et al., 2002; Sullman et al., 2002). Unfortunately, only two of these studies have used the 28-item version of the DBQ and only one of these was on car drivers (Mesken et al., 2002). However, the majority of the research using earlier versions of the scale has consistently found that it is the violations subscale which is predictive of crash involvement (Kontogiannis, Kossiavelou, & Marmaras, 2002; Parker, Reason, et al., 1995; Parker, West, et al., 1995; Stradling, Parker, Lajunen, Meadows, & Xie, 1998). Unfortunately, the generalisability of any version of the DBQ to the Spanish population has yet to be established. Only one published study (published on the internet) has measured aberrant driving behaviour amongst Spanish drivers. The Royal Automobile Club of Catalunya (RACC, 2004) recently published the results of a 75-item self-report measure of aberrant driving behaviour amongst 309 Spanish drivers. They studied the frequency of these different types of driving behaviours and found that the most commonly reported behaviours were related to the violation of speed limits (RACC, 2004). Unfortunately they did not analyse the factor structure of the scale or the relationship the reported behaviours had with crash involvement. The current study had three main aims. Firstly, this research aimed to test the generalisability of the 28item version of the DBQ to a sample of Spanish drivers. In addition, the level of self-reported aberrant driving behaviour was investigated, along with the reliability of the four hypothetical factors. The final aim of the study was to further investigate the relationship between the resultant DBQ factors and crash involvement.
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2. Method 2.1. Materials The questionnaire used in this research included five sections. This paper reports the results of the second section, which consisted of a 28-item version of the Manchester Driver Behaviour Questionnaire (DBQ) translated into Spanish. Translation was carried out by two native Spanish speakers and these translations were then translated back into English by a native English speaker. The 28-item English version of the scale consists of eight errors and eight lapses, along with the extended violations scale (six violations and six aggressive violations) developed by Lawton et al. (1997). This version of the scale had been used previously amongst; company car drivers (Dimmer & Parker, 1999), truck drivers (Sullman et al., 2002) and private motorists (Mesken et al., 2002). The participants received the standard instructions, as per Parker, Reason, et al. (1995), but in Spanish. The scale anchors range from 0 (never) to 5 (all the time). In addition to the DBQ, the survey also assessed demographic variables, driver history (how long they have held a driving license and annual mileage) and how many crashes they have been involved in over the last five years. 2.2. Participants One third of the employees working full or partial time at the University of Girona (600 workers) were selected. Systematic random sampling within strata was used for selection. Strata criterion were; kind of work (academic or non-academic workers) and Faculty (Arts, Law, Educational, Business, Engineering, Science, Nursing, and Tourism). Random numbers between one and three were used to decide where to begin the selection process, and then every third element (person) was chosen in each stratified sample frame. Of the 600 selected, 19 had no driving license, 21 licensed drivers had not driven a vehicle during the last six months (participants must have driven a car at least once in the last six months), 189 did not respond to the questionnaire and 21 missed at least one of the DBQ items. The remaining 350 workers had a mean age of 37.4 (SD = 9.4), with 47% being male and 53% female. The mean annual mileage was 17,226 km/year (range, 10– 200,000) and the participants had an average of 17.8 years driving experience (range, 1–50 years). In total 46% of the drivers had not been involved in a crash during the previous five years, while 53% reported at least one crash (1% did not answer the question). 2.3. Procedure Participants were firstly contacted by email. This email explained the purpose of the study and asked them to participate. The questionnaires were then sent to them via university mail. The majority of the questionnaires were collected in person by the researchers, but a small number of participants returned the questionnaires by university mail. Anonymity and confidentiality of the data were guaranteed. SPSS, Version 12.0, was used for data analysis. 3. Results The characteristics of the sample and the Spanish car driving population are shown in Table 1. This shows that the gender distribution of the sample is very similar to the population, but the age range is slightly wider amongst the population of Spanish car drivers. Table 1 Characteristics of the sample and Spanish car driving population Gender
Sample Spanish car driving population
Age range
Male (%)
Female (%)
47.2 48.9
52.8 51.1
22 to 75 18 to 74+
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Table 2 shows the means and standard deviations of the DBQ items, by subscale. The most frequently reported aberrant behaviours were three types of violations: ‘‘disregard the speed limit on the highway’’ (violation), ‘‘sound horn to indicate annoyance at another road user’’ (aggressive violation) and ‘‘disregard the speed limit on a residential road’’ (violation). The most common error was ‘‘fail to notice that pedestrians are crossing when turning into a side street from a main road’’ and the most frequent lapse was ‘‘forget where you left the car in the car park’’. The least frequently reported driving behaviours were a lapse (misread the signs and exit from a roundabout on the wrong road) and an aggressive violation (become angered by another driver and give chase with the intention of giving him/her a piece of your mind). The 28 items were then subjected to principle component analysis (PCA) in order to determine the factor structure. Although seven factors had eigenvalues larger than one, parallel analysis suggested a four factor solution was more appropriate, and the analysis was rerun specifying four factors. As there were relatively low intercorrelations (<0.30) between oblique factors, varimax rotations were used. The factor structure and items loading over 0.40 are shown in Table 3. Only 27 items are shown because one of the lapses (misread the signs and exit from a roundabout on the wrong road) did not load strongly enough on any factor. This solution explained 40.95% of the variance. Although a four factor solution was confirmed, there were a number of departures from the expected factor structure. The first factor explained 21.21% of the variance and included a mixture of errors (seven) and lapses (four), and also one aggressive violation. This factor had good internal consistency, with an alpha coefficient of 0.82. The second factor consisted of all six violations, three
Table 2 Means by category Mean (SD) Violations Disregard the speed limit on the highway Disregard the speed limit on a residential road Cross an intersection knowing the traffic lights have already turned against you Drive close to the car in front, making it difficult to stop in an emergency Overtake a slow driver on the inside Drive when you suspect you may be over the legal alcohol limit
1.56 1.41 1.13 0.87 0.61 0.61
(±1.10) (±1.00) (±0.82) (±0.85) (±0.83) (±0.80)
Aggressive violations Sound your horn to indicate your annoyance at another road user Angered by a certain type of driver, show your hostility Race away from the traffic lights to beat another driver Stay in a lane about to close until the last minute, then dive in Pull out of an intersection so far you force your way into the traffic Angered by another driver, give chase
1.44 1.07 1.00 0.73 0.70 0.14
(±0.97) (±0.86) (±0.96) (±0.83) (±0.63) (±0.43)
Errors Fail to notice pedestrians crossing when turning into a side street Fail to check rear-view mirror before a manoeuvre Queuing to turn left, nearly hit the car in front Underestimate the speed of an oncoming vehicle when overtaking Miss ‘‘give way‘‘ sign and narrowly avoid a collision Brake too quickly, or steer the wrong way into a skid On turning right, nearly hit a cyclist coming up on your inside Attempt to overtake someone signalling a right turn
1.03 0.98 0.72 0.60 0.52 0.46 0.39 0.35
(±0.80) (±0.80) (±0.74) (±0.65) (±0.67) (±0.63) (±0.66) (±0.55)
Lapses Forget where you left your car in the car park ‘‘Wake up’’ to find yourself on a wrong, but more familiar destination Switch on one thing when you meant to switch on something else Hit something when reversing that you have not previously seen No clear recollection of the road along which you have just travelled Attempt to drive away from the traffic lights in third gear Get into the wrong lane approaching a roundabout or junction Misread the signs and exit from a roundabout on the wrong road
1.34 1.25 1.02 0.81 0.60 0.54 0.36 0.11
(±1.00) (±0.91) (±0.90) (±0.70) (±0.78) (±0.75) (±0.64) (±0.79)
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Table 3 Factor structure of the DBQ Original item type
Load
Factor 1: Errors—21.21% of the variance Miss ‘‘give way‘‘ sign and narrowly avoid a collision Fail to notice pedestrians crossing when turning into a side street Switch on one thing when you meant to switch on something else On turning right, nearly hit a cyclist coming up on your inside Pull out of an intersection so far your force your way into the traffic Fail to check rear-view mirror before a manoeuvre Queuing to turn left, nearly hit the car in front Break too quickly, or steer the wrong way into a skid Attempt to drive away from the traffic lights in third gear Hit something when reversing you had not previously seen Get into the wrong lane approaching a roundabout or junction Attempt to overtake someone signalling a right turn
E E L E A E E E L L L E
0.722 0.639 0.624 0.613 0.593 0.591 0.570 0.463 0.458 0.457 0.450 0.444
Factor 2: Violations—9.24% of the variance Disregard the speed limit on the highway Stay in a lane about to close until the last minute, then dive in Cross an intersection knowing the traffic lights have already turned against you Race away from the traffic lights to beat another driver Drive so close to the car in front making it difficult to stop in an emergency Disregard the speed limit on a residential road Overtake a slow driver on the inside. Underestimate the speed of an oncoming vehicle when overtaking Drive when you suspect you may be over the legal alcohol limit
V A V A V V V E V
0.738 0.666 0.641 0.637 0.629 0.590 0.506 0.477 0.464
Factor 3: Interpersonal violations—5.75% of the variance Angered by a certain type of driver, show hostility Sound your horn to indicate your annoyance at another road user Angered by another driver, give chase
A A A
0.799 0.754 0.497
Factor 4: Lapse—4.75% of the variance Forget where you left your car in the car park ‘‘Wake up’’ to find yourself on a wrong, but more familiar route No clear recollection of the road along which you have just travelled
L L L
0.697 0.537 0.493
aggressive violations and the remaining error. This factor accounted for 9.24% of the variance and had good internal validity, with a CronbachÕs alpha of 0.81. The third factor accounted for 5.75% of the variance and contained the three aggressive violations which involved expressing anger or hostility towards another road user. This factor had marginal internal reliability (0.59), which could be improved, by dropping the item ‘‘become angered by another driver and give chase with the intention of giving him/her a piece of your mind’’ the alpha value increased to 0.70. However, the item was not removed as this grouping has previously been reported a number of times and this factor contained only three items. The fourth factor was a very weak factor, contained only three lapses and accounted for 4.75% of the variance. In order to analyse the relationship between the four factors and crash involvement, factor scores from the PCA were saved as variables using the regression method. The mean scores of crash involved and non-crash involved participants were compared using t-tests. Participants who had not held a driving license for at least five years were not included in the analysis (n = 328). Age, annual mileage and the number of years holding a driving license were also tested. The means and standard deviations for both groups and the results of the t-tests are shown in Table 4. Crash involved participants had higher factor scores in factor 2 (violations), but there were no significant differences between the two groups in annual mileage, driving license tenure or the other three DBQ factors. In order to test whether any of the DBQ variables were able to predict crash involvement over the previous five year period, hierarchical logistic regression was performed. The demographic and descriptive variables (annual mileage, age and gender) were entered first, followed by the factor scores from the four factors. As shown in Table 5, the demographic and descriptive variables did not make a significant contribution to the
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Table 4 Comparison of crash involved with non-crash involved drivers Variable
Crash involved mean (SD)
Non-crash involved mean (SD)
T
d.f.
p
Age Annual mileage (km) Driving license life Errors factor Violations factor Aggressive viol. factor Lapse factor
37.2 (9.7) 17,394.9 (10,216.2) 17.9 (8.9) 0.07 (0.9) 0.19 (0.9) 0.01 (1.0) 0.09 (1.0)
38.5 (8.8) 17,085.3 (17,756.5) 19.2 (8.6) 0.07 (1.1) 0.20 (1.0) 0.01 (1.0) 0.09 (0.9)
1.3 0.19 1.14 1.26 3.60 0.18 1.66
320 304 324 324 324 324 324
n.s. n.s. n.s. n.s. p < 0.0005 n.s. n.s.
Wald
Exp (B)
0.007 1.640 1.838 1.667 7.623** 0.145 2.912
1.000 0.984 1.372 1.161 1.437 1.044 1.228
Table 5 Results of the stepwise logistic regression for crash involvement over the previous five years Block 1 1 1 2 2 2 2
Model chi-square improvement Mileage Age Sex Errors Violations Interpersonal Lapse
* **
% Correctly classified
3.202
54.5
12.934*
60.1
B .000 .016 .316 .149 .363 .044 .205
p < 0.05. p < 0.01.
prediction of crash involvement. Although entering the DBQ factor scores made a significant contribution, it was only the violations factor scores which made a significant contribution. It should be noted that the total model only accounted for 6.5% of the variability in crash involvement. However, the odds ratio for the violations factor shows that for every one unit increase in the violations score, the odds of being crash involved increased by 44%. 4. Discussion Consistent with previous research (e.g., Lawton et al., 1997; Meadows et al., 1998; Parker, Reason, et al., 1995; Sullman et al., 2002) crash involved participants reported engaging significantly more often in violations than those who did not report being crash involved. Furthermore, it was only the violations factor that predicted crash involvement, even after the contributions of the demographic and descriptive variables had been partialled out. The odds ratio for the violations factor was 1.44, which means that for each one unit increase in the violations factor score the odds of being crash involved increased by 44%. This finding is similar in magnitude to Mesken et al.Õs (2002) odds ratio of 1.43 for the prediction of passive crash involvement by interpersonal violations, but is considerably lower than the 1.70 they found for the prediction of active crashes by their errors factor. The odds ratio found in the current research is also very similar to the 1.50 for the prediction of crash involvement amongst truck drivers using the violations factor (Sullman et al., 2002). The current finding indicates that the predictive strength of the violations factor remains despite the differences in culture, language, and driving conditions. The cross cultural consistency of this relationship also provides further evidence of the need to reduce the frequency at which drivers engage in these types of behaviours. As expected, factor analysis produced support for a four factor solution. However, although the analysis supported a four factor solution, there were also a number of departures from the hypothetical four factor solution. Firstly, only three lapses loaded on the lapse factor, and these appeared to be about ‘‘disorientation’’ (where is my car? where am I? where am I going?). This factor was highly unreliable, with an alpha coefficient of 0.46. Furthermore, the first factor consisted mainly of a mixture of errors and lapses. Although it is not unusual to have the occasional error loading on the lapse factor, and vice versa (see Lajunen et al., 2004; Sullman et al., 2002), it is unusual to find such a thorough mix of errors and lapses in one factor. The vast majority
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˚ berg & of previous research has found a relatively clear distinction between these two subscales (e.g., A Rimmo¨, 1998; Lajunen et al., 2004; Mesken et al., 2002; Sullman et al., 2002). There are several possible explanations for this finding. Firstly it could due to some of the meaning being lost with their translation into Spanish. A similar explanation was used by Lajunen et al. (2004) to explain why an aggressive violation loaded on their error factor. A second possible explanation is that Spanish drivers simply do not perceive errors and lapses to be different. Future research should be undertaken to investigate which of these explanations is accurate. The aggressive violation item ‘‘pull out of an intersection so far that the driver with right-of-way has to stop and let you out’’ loaded on the errors factor. This has been previously found amongst New Zealand truck drivers (Sullman et al., 2002), New Zealand car drivers (Sullman, 2004), as well as Finnish car drivers (Lajunen et al., 2004; Mesken et al., 2002). Although Lajunen et al. (2004) attributed this to ambiguities in the Finnish translation, that does not explain the two New Zealand findings. A more inclusive explanation would be that drivers interpret this behaviour as an error, rather than a deliberate attempt to drive their vehicle in an unsafe manner. Therefore, either this item should be labelled an error, or omitted from future research. This study found a clear violations factor which contained all of the violations and two of the aggressive violations. These two apparently misplaced aggressive violations are more to do with driving in a hurry rather than directing hostility towards another road user. Both of these aggressive violations have previously been found to load on the ordinary violations factor (e.g., Lajunen et al., 2004; Mesken et al., 2002; Sullman et al., 2002) and perhaps should be labelled as ordinary violations in future research. As with previous research (e.g., Lajunen et al., 2004; Lawton et al., 1997; Sullman et al., 2002), the aggressive violations factor only included the three behaviours which involved expressing anger or hostility at another road user. The current research, and previous research, indicates that the hypothetical fourth factor ‘‘aggressive violations’’ is not actually a cohesive factor. Therefore, the fourth hypothetical factor should actually be labelled ‘‘expressing hostility’’ or ‘‘interpersonal violations’’. Although this research found the violations factor explained a relatively modest portion of the variance in crash involvement, this has been reported elsewhere. For example, Kontogiannis et al. (2002) found that after partialling out the contributions of gender, annual mileage and experience, highway code violations accounted for only 1.2% of the variance in crash involvement. Although this 1.2% was statistically significant, it appeared to show an extremely weak relationship. However, the use of ordinary statistics in the case of crash involvement can be misleading (e.g., Hansen, 1989; McKenna, 2004; West, 1995). Crashes are rare events, and as there is some degree of chance involved, it is difficult to obtain good relationships between the precursors and crash involvement. Therefore, a number of researchers have recommended using epidemiological techniques for reporting the strength of the relationship between independent variables and crash involvement (Elander, West, & French, 1993; McKenna, 2004; West, 1995). Therefore, although the violations factor explains a relatively low proportion of the variance in crash involvement, this should not be used to judge the strength of the relationship. Instead the odds ratio suggests that a one unit increase in the factor score would result in a 44% increase in the odds of being crash involved. In line with previous research in several different countries (Parker, West, et al., 1995; RACC, 2004; Reason et al., 1990; Sullman et al., 2002) the reported behaviours with the highest and third highest means involved disregarding the speed limit. The second most frequently reported aberrant driving behaviour was ‘‘sound the horn to indicate annoyance at another road user’’. These behaviours were also found to be reported frequently in the previous Spanish research (RACC, 2004). As in our study, the most frequently reported item was ‘‘disregard the speed limit on the highway’’. Furthermore, four of the ten most frequently reported behaviours were related to disregarding the speed limit. This is also supported by observational research, which found that 70% of the 9020 drivers observed were exceeding the speed limit (RACE, 2002). ‘‘Forget where you left your car’’ was the most frequently reported lapse by Spanish drivers in the RACC (2004) research, as well as in the current study (mean 1.36 and 1.34 respectively). This item was also the second most frequent lapse reported by British (mean = 1.16) and New Zealand (mean = 1.12) drivers (Lajunen et al., 2004 & Sullman, 2004), but was the least frequently reported lapse amongst Finland drivers (mean = 0.48) (Mesken et al., 2002). Unlike most previous research (e.g., Dimmer & Parker, 1999; Parker, West, et al., 1995; RACC, 2004; Reason et al., 1990) driving while under the influence of alcohol was not the least frequently reported item. Drink
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driving was rated the seventeenth most commonly reported form of aberrant driving behaviour. In terms of absolute value (mean = 0.61) it is considerably higher than that found in most recently reported research (e.g., Dimmer & Parker, 1999; Mesken et al., 2002; Sullman et al., 2002). For example, amongst company car drivers Dimmer and Parker reported a mean of 0.23, Sullman et al. reported a mean of 0.24 for their sample of New Zealand truck drivers, while Mesken et al. reported a mean of 0.27 for Finnish drivers. Therefore, it appears that drink driving amongst Spanish drivers is a relatively frequent behaviour, as a number of researchers have noted (Montoro, 1997; Oliveras, Planes, Cunill, & Gras, 2002), and needs to be greatly reduced. One of the main problems is that this behaviour is still widely socially accepted in Spain, and many people erroneously believe that their driving performance will not be affected after drinking (Oliveras et al., 2002; Pardo, 2003). Therefore, a great deal of work needs to be undertaken to reduce the social acceptability of drink driving in Spain, and also to educate Spanish drivers about the decrement in their driving performance caused by alcohol. The study reported here clearly had a number of methodological limitations. Firstly there is the possibility of sampling bias. As the participants all worked at the University of Girona, it is possible that they differ significantly from the general population in some way. However, confidence in this data can be drawn from the fact that the gender distribution and age range in the present study were very similar to that of the population of Spanish drivers (see Table 1). Further confidence can be drawn from the fact that the frequency of the different driving behaviours found here were very similar to those found in the only other Spanish research in this area. The present study also suffers from the usual perceived weakness of research using self-reported data. The most common criticism of self-reported data is the possibility that it suffers from social desirability bias. However, research using the DBQ found that socially desirable responding had a very minimal effect on participantsÕ responses (Lajunen & Summala, 2003). Another common criticism of self-report is that there is great variance between what drivers report and their actual behaviour. However, there are also a number of studies that have found self-reported driving behaviour to be significantly related to actual driving behaviour (e.g., Parker, 1997; Rolls, Hall, Ingham, & McDonald, 1991; Walton, 1999; West, 1995; West, French, Kemp, & Elander, 1993). There has also been research which has suggested a great deal of forgetting or underreporting of driving events, such as crashes and incidents (see Chapman & Underwood, 2000). 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