TYPE Original Research PUBLISHED 18 May 2023 DOI 10.3389/fpsyg.2023.1058927 Psychological predictors of risky OPEN ACCESS driving: the role of age, gender, personality traits (Zuckerman’s EDITED BY Lambros Lazuras, Sheffield Hallam University, United Kingdom REVIEWED BY Luca Mallia, and Gray’s models), and Foro Italico University of Rome, Italy James D. Kean, The University of Melbourne, Australia decision-making styles Cynthia Thomson, University of the Fraser Valley, Canada Anton Aluja 1,2*, Ferran Balada 2,3, Oscar García 2,4 and *CORRESPONDENCE Luis F. García 2,5 Anton Aluja anton.aluja@udl.cat 1 Deparment of Psychology, University of Lleida, Lleida, Spain, 2 Lleida Institute for Biomedical Research, RECEIVED 30 September 2022 Dr. Pifarré Foundation, Lleida, Spain, 3 Department of Psychobiology and Methodology of Health ACCEPTED 19 April 2023 Sciences, Autonomous University of Barcelona, Catalonia, Spain, 4 Deparment of Psychology, European PUBLISHED 18 May 2023 University of Madrid, Madrid, Spain, 5 Deparment of Biological Psychology and Health, Autonomous University of Madrid, Madrid, Spain CITATION Aluja A, Balada F, García O and García LF (2023) Psychological predictors of risky driving: the The present study was planned to study the relationships between age, personality role of age, gender, personality traits (Zuckerman’s and Gray’s models), and (according to Zuckerman’s and Gray’s psychobiological models) and decision- decision-making styles. making styles in relation to risky driving behaviors. The participants were habitual Front. Psychol. 14:1058927. drivers, 538 (54.3%) men and 453 (45.7%) women, with a mean age around doi: 10.3389/fpsyg.2023.1058927 45 years and mainly of middle socioeconomic status. The results indicate that COPYRIGHT the youngest men and women reported more Lapses, Ordinary violations, and © 2023 Aluja, Balada, García and García. This is an open-access article distributed under the Aggressive violations than the oldest men and women. Women reported more terms of the Creative Commons Attribution Lapses (d =−0.40), and men more Ordinary (d =0.33) and Aggressive violations License (CC BY). The use, distribution or (d =0.28) when driving. Linear and non-linear analysis clearly support the role reproduction in other forums is permitted, provided the original author(s) and the of both personality traits and decision-making styles in risky driving behaviors. copyright owner(s) are credited and that the Aggressiveness, Sensitivity to Reward, Sensation Seeking played the main role original publication in this journal is cited, in from personality traits, and Spontaneous and Rational decision-making style also accordance with accepted academic practice. No use, distribution or reproduction is accounted for some variance regarding risky driving behaviors. This pattern was permitted which does not comply with these broadly replicated in both genders. The discussion section analyses congruencies terms. with previous literature and makes recommendations on the grounds of observed results. KEYWORDS personality traits, decision-making styles, Zuckerman’s alternative five factor personality model, Gray’s personality model, risky driving 1. Introduction Victims and injuries from traffic accidents are considered a serious social and health problem. A global status report on road safety by World Health Organization (2018) showed that road crashes are the leading cause of death for young adults under 29 years of age (Zeyin et al., 2022). In 2021, there were 1,004 deaths and 3,728 serious injuries in traffic accidents in Spain.1 Among the most common human causes of traffic accidents are distractions such as the use of smartphones, fatigue, high speed, alcohol or other drugs, lack of experience, or drowsiness 1 https://revista.dgt.es/es/noticias/nacional/2022/01ENERO/0107-Balance-prov-accidentalidad-2021.shtml Frontiers in Psychology 01 frontiersin.org Aluja et al. 10.3389/fpsyg.2023.1058927 (Bucsuházy et al., 2020). Dangerous driving is a serious traffic offense Some gender differences in driving style could be related to gender in which the driver of a vehicle does not respect traffic regulations, and differences in personality, including Sensation Seeking, Aggressiveness it is responsible for many traffic violations and accidents causing and impulsivity traits (Cross et al., 2011, 2013). deaths and other damage. An aggressive/risky driving behavior refers to habitual driving behavior dominated by excitatory motives (Sagberg et al., 2015). 1.2. Personality traits and driving There are many questionnaires to assess risky or aberrant driving behavior. The Manchester Driver Behavior Questionnaire (DBQ; Since the 1970s certain disinhibited personality traits, such as Reason et al., 1990) is well known, and it has a robust factorial Sensation Seeking, have been studied in relation to risky driving and structure of four factors: Lapses, Errors, Ordinary violations, and speeding (Zuckerman and Neeb, 1980). The Sensation Seeking trait Aggressive violations (Lajunen et al., 2004). Lapses are considered proposed by Zuckerman is defined as “The seeking of varied, novel, attention and memory problems, such as forgetting where your car complex, and intense sensations and experiences and the willingness to was parked. Errors are failures in observation or misjudgments, such take physical, social, legal, and financial risks for the sake of such as braking too quickly. Ordinary violations are behaviors such as experiences” (Zuckerman, 1994, p. 27). It is a trait associated with speeding or driving too close to another vehicle. Aggressive violations social and antisocial risk behaviors of different types, including risky are related to the expression of hostility toward other drivers or driving (Jonah, 1997; Jonah et al., 2001; Lucidi et al., 2010, 2019). The making rude gestures (Gras et al., 2006). It is important to remark that most recent meta-analysis is that of Zhang et al. (2019), which showed lapses and errors are mainly unintentional, whereas violations, significant correlations of Sensation Seeking with risky driving, especially aggressive violations, are intentional and sufficiently strong aggressive driving, errors, and accident involvement. Most studies to override the perceived risks related to committing the violation. In used the Sensation Seeking Scale, Form V (SSS-V; Zuckerman et al., a meta-analysis, de Winter and Dodou (2010) reported that violations 1964), but there is a new version within Zuckerman’s questionnaire of were predictors of traffic accidents. five factors, the Zuckerman-Kuhlman-Aluja Personality Questionnaire Risky driving has been linked to several factors such as age, (ZKA-PQ; Aluja et al., 2010), which includes a revised Sensation gender, and individual differences dimensions (Hassan and Abdel-Aty, Seeking dimension with four facets (see the review of Zuckerman and 2013), certain disinhibited personality traits [for example, adventure- Aluja, 2015): Thrill and adventure seeking, Experience-seeking, seeking traits that lead to an underestimation of risk (Jonah, 1997; Disinhibition and Boredom susceptibility/impulsivity. Zhang et al., 2019)]. Moreover, driving is a multifaceted decision- Aggressiveness, as a personality trait, has also been linked to making process. Errors at different stages of these processes may lead risky driving style. Iancu et al. (2016) carried out a meta-analysis to situations of risk and contribute to accidents (Gugliotta et al., 2017). about the association between personality and aggressive driving. Therefore, decision-making style could be an individual variable to They compared two personality models: The Five Factor Model consider in motor driving risk research. (FFM) and Zuckerman’s Alternative Five model (AFFM). Regarding FFM, the results indicated a significant association between Neuroticism and Agreeableness with Aggressive driving, and a 1.1. Age, gender, and driving marginal one with Extraversion. Regarding the AFFM (using the old ZKPQ; Zuckerman et al., 1993), a significant effect was also found Young drivers are riskier and have higher mortality (Tefft, 2017; for Anxiety-Neuroticism, a marginal effect for Sociability, and a Alderman et al., 2018; Das et al., 2018). This association between age moderate effect for Aggression-hostility. It should be noted that the and risky driving has been reported in both Western and Eastern AFFM was revealed to be more related to aggressive driving than countries (McCartt et al., 2009; Zeyin et al., 2022). Furthermore, FFM (Iancu et al., 2016). A driving study used the ZKA-PQ (Martí- young men perceive themselves as more self-confident, although Belda et al., 2019) in which the authors compare the scores on the they are more distractible and engage in more risky behavior ZKA-PQ in three groups of drivers: Group A Non-offenders, Group compared to women (Barr et al., 2015). In this sense, age has also B Court Offenders and C Penalty Point Offenders. Groups B and C been negatively related to Lapses, Errors, Ordinary violations and showed significantly higher scores than Group A in the Sensation Aggressive violations using the DBQ (Berdoulat et al., 2013; Lucidi Seeking dimension. Furthermore, these two groups also scored et al., 2019). The effect of age on driving also differs according to significantly higher in Aggressiveness, Activity, and Neuroticism gender. For instance, comparing male and female teenagers, (Martí-Belda et al., 2019). It is worth noting that Sensation Seeking adolescent male had more excess speeding crashes than female has been related to Hostility and Aggressiveness (Aluja and (Swedler et al., 2012). Torrubia, 2004). Gender differences have also been noted, with women scoring Anger at the wheel is also related to aggressive driving and this more on Lapses, and men presenting more Ordinary violations and would be associated with impatience, annoyance, hostility and/or an Aggressive violations (Lajunen et al., 2022). A recent study examines attempt to save time (Tasca, 2000). Demir et al. (2016) performed a the relationship between road rage and masculinity-femininity gender meta-analysis on driving anger and found that anger (physical and roles in young drivers (Deniz et al., 2021). The results indicated that verbal aggression expressions) was significantly associated with the masculinity and anger were positively related to impolite behaviors in behavior of violations and errors. Drivers high on the anger rating men, but femininity was negatively related to verbal aggression while tend to perceive many other drivers’ behavior as intentionally driving. According to the authors, these results suggest that gender aggressive (Kerwin and Bushman, 2020). Verbal aggressiveness has moderates the relationship between road rage among young drivers. been linked to self-reported driver aggression, while physical Frontiers in Psychology 02 frontiersin.org Aluja et al. 10.3389/fpsyg.2023.1058927 aggressiveness has been linked to aggressive behavior (Lajunen and in the Rational style, the greater the possibility of taking risks while Parker, 2001). In another study, aggressiveness was a robust predictor driving. There were also positive associations between risky driving for risky driving for young men, but not for women behaviors and Spontaneous and Avoidant decision-making styles. In (Šeibokaitė et al., 2014). Related with anger, the more general general, decision-making style is related to driving and certain styles Neuroticism trait has been linked to errors and accidents when can increment the likelihood of having a traffic accident (French et al., driving (Alavi et al., 2017). In this way, Wang et al. (2018) found that 1993; Barati et al., 2020). Neuroticism was the best personality predictor of errors and lapses. People with high neuroticism trait are at greater risk of errors and lapses when driving because they are more easily distracted while 1.4. Aims of the present study driving (see also Hansen, 1989). From Gray’s Reinforcement Sensitivity Theory (RST), Neuroticism is represented by the Sensitivity As far as we know, there are many studies analyzing the role of age, to Punishment trait (Corr, 2004; Aluja and Blanch, 2011). gender, personality traits and decision-making styles in risky behavior, On the other hand, the inhibition deficits related to impulsivity but none have considered all these variables altogether. Hence, the can lead to aggressive behavior behind the wheel (Čabarkapa et al., general aim of this study is to examine at the same time and in a large 2018). In addition, Impulsiveness and Sensation Seeking predict sample of drivers the role of age, gender, personality -according to behaviors related to crashes, aggressive driving, risky driving, and Zuckerman’s and Gray’s models- and decision-making style in risky expression of anger while driving (Dahlen et al., 2005). Gray’s driving behavior to obtain a description of the risky driver. According Reinforcement Sensitivity Theory (RST) has been used extensively in to the studies reviewed, the following things are expected: (a) a research on impulsivity and driving (Castellà and Pérez, 2004; Scott- negative association between age and risky driving, (b) significant Parker et al., 2012, 2013; Mohammadzadeh-Ebrahimi and Alavi, higher scores of women in driving lapses, while men would have 2021). Scott-Parker and Weston (2017), in a revision of literature higher scores in ordinary/aggressive violations, (c) Aggressiveness, about Sensitivity to Reward and risky driving, concluded that young Sensation Seeking and Reward to Sensitivity would be related to some drivers with higher Sensitivity to Reward drive in a riskier way, drive aspects of risky driving, and (d) people with Rational decision-making faster, have more crashes, and have more violations. Sensitivity to styles are expected to have fewer Lapses, Errors, and Ordinary/ Reward is also associated with Sensation Seeking (Aluja et al., 2013), aggressive violations, while avoiders and, especially, spontaneous and both traits are related to risky driving, mostly in young men people would have more Lapses, Errors, and Ordinary/aggressive (Scott-Parker et al., 2013). The literature review reports that men are violations. We also compare the predictive power of personality traits more impulsive than women (Cross et al., 2011), and take part in and decision-making styles regarding risky driving. riskier behaviors when driving. 2. Method 1.3. Decision making style and driving 2.1. Participants and procedure Decision-making style is a cognitive variable since it depends on how people process information (Hunt et al., 1989; Urieta et al., 2022). The sample consisted of 991 participants, who were habitual One of the most used decision-making instruments in research is the drivers of motor vehicles with 2 or 4 wheels. 538 (54.3%) were men General Decision-Making Styles (GDMS) proposed by Scott and and 453 (45.7) were women. The mean ages for men and women Bruce (1995). The GDMS includes five decision-making styles: were 45.67 (SD = 16.07) and 44.04 (SD = 15.25), respectively. The age Rational, Intuitive, Dependent, Avoidant, and Spontaneous. Rational range was 18–90 years with a normal distribution (kurtosis = −0.64 decision-making style involves the use of reasoning, logical and and skewness = −0.14). The sample was obtained from the general structured approaches to decision-making. Intuitive decision-making population with the help of students, who administered the style is defined by reliance upon hunches, feelings, impressions, questionnaire protocol anonymously and voluntarily to healthy instinct, and good feelings. Dependent style is defined by a search for members of the community. Each student was required to recruit advice and guidance from others before making important decisions. men and women from the following age ranges: 18–35, 36–45, Avoidant decision- making style is defined by withdrawing, 46–60, and more than 60 years. The Hollingshead Social Position postponing, moving back and negating the decision scenarios. A Index (Hollingshead, 1957; Hollingshead and Redlich, 1958) was Spontaneous style is characterized by a feeling of immediacy and a obtained using the following formula: (Occupation score * desire to get through the decision-making process as quickly as 7) + (Education score * 4). The range of scores 11–17, 18–31, 32–47, possible (Scott and Bruce, 1995). Women have higher average scores 48–63, and 64–77 correspond to upper, upper-middle, middle, in the Dependent and Intuitive styles, and men in the Rational one lower-middle, and lower social position, respectively. The (Chen, 2013; Delaney et al., 2015; Bayram and Aydemir, 2017). Men participants were asked if they had a driving license and if they drove also have higher average scores in the Spontaneous one (Alacreu- regularly. Those who answered no to either of the two questions were Crespo et al., 2019). excluded from the study. No information about the type of vehicle Previous studies report that drivers with a Rational decision- used was collected. Participants completed Spanish versions of all making style seek relevant information, assess the consequences and questionnaires. The protocol was part of a wider study that included act logically, while Intuitive drivers do not anticipate the consequences different electrophysiological tests in our Human Behavior of their actions when making their decisions. So, the lower the score Laboratory within the framework of a National Research Project. All Frontiers in Psychology 03 frontiersin.org Aluja et al. 10.3389/fpsyg.2023.1058927 participants who performed the laboratory tests signed an informed Ordinary violations (OVI) and Aggressive violations (AVI). consent. The study was conducted in accordance with the Violations require explanation in terms of social and motivational Declaration of Helsinki and was approved by the ethical commission factors, whereas errors (slips, lapses, and mistakes) may be accounted of the University. for by the information-processing characteristics of the individual (Reason et al., 1990). 2.2. Instruments 2.3. Analysis of data 2.2.1. Zuckerman-Kuhlman-Aluja personality questionnaire shortened form Descriptive analysis, and comparisons between genders for age, The ZKA-PQ/SF (Aluja et al., 2018) is a shortened version of 80 social position and psychometric variables was performed. Cronbach’s items from the original 200-item ZKA-PQ. The response format is a alpha internal consistency value of the scales was also calculated. 4-point Likert-type scale ranging from 1 (strongly disagree) to 4 Graphical comparisons of the DBQ by age and gender ranges were (strongly agree). The questionnaire has five personality domains: also presented using ANOVA and Scheffe post-test. Additionally, an Aggressiveness (AG), Activity (AC), Extraversion (EX), Neuroticism ANOVA 2×5 (gender x age ranges) was computed for the four scales (NE), and Sensation Seeking (SS) and 20 facets (for details see, Aluja of the DBQ. Zero-order correlations, independently for men and et al., 2018). Note that no analysis was conducted at the facet-level in women, were also considered for all variables. In addition, empirical the present study. The ZKA-PQ and ZKAPQ/SF showed good validity network analysis was used to analyze the unique relationships between and reliability in the original studies (Aluja et al., 2010, 2018). Both age, ZKA-PQ, SPSRQ-20, GDMS and DBQ scales (GLASSO questionnaires have also been validated in numerous cultures and algorithm, EBIC, and mgm, the last for estimating explained variance; languages (Rossier et al., 2016; Aluja et al., 2022). The ZKA-PQ/SF Chen and Chen, 2008; Friedman et al., 2014). Furthermore, both sets items are included in the appendix of Aluja et al. (2018). of domains were also analyzed together to test the connections between them. The use of this technique makes it possible to estimate 2.2.2. The sensitivity to punishment and the partial correlations between each pair of scales while controlling sensitivity to reward questionnaire the inflation of Type I error thanks to the GLASSO regularization This is a 20-item shortened version (Aluja and Blanch, 2011) of technique (Tibshirani, 1996; Epskamp, 2016). the Sensitivity to Punishment and Sensitivity to Reward To compute the association and the predictive power of Questionnaire developed by Torrubia et al. (2001). The SPSRQ-20 personality and decision-making styles variables with risky driving, has 20 items, 10 items for each scale: Sensitivity to Punishment (SP) two analyses were performed. First, the factorial convergence of all the and Sensitivity to Reward (SR). SP is considered a measure of psychometric scales used was analyzed. In this way, a principal anxiety, and SR a measure of impulsivity within Gray’s theory. This component analysis with Varimax rotation was computed. This instrument has a 4-option Likert type answer format. The SPSRQ analysis allows for testing all associated variables together, and which 20-item version shows a robust factor structure and a satisfactory of them load on the same factor suggesting common variance. Later, adjustment to observed data in a covariance structure context, a multiple linear regression analysis was performed for men and supporting the use of the shorter version to assess Gray’s women separately, using the total DBQ score as dependent variable Reinforcement Sensitivity Theory (Torrubia et al., 2001). Original and age, ZKA-PQ/SF, SPSRQ-20 and GDMS scales as independent alpha internal consistency values for SP were 0.77 (men) and 0.80 variables. Stepwise method was used with a probability-to-enter (PIN) (women), and for SR 0.73 (men) and 0.70 (women). of p < 0.05. Every DBQ scale was then predicted after age, personality variables and decision-making styles. The regression was also 2.2.3. General decision-making scale computed separately for men and women but in this case, the The GDMS (Scott and Bruce, 1995) is a self-administered 22-item introduction method was used in the regression to allow for questionnaire adapted to Spanish by Alacreu-Crespo et al. (2019). The comparing all variables across the four DBQ scales. response format is a 5-point Likert-type scale ranging from 1 (strongly Nonparametric local LOESS graphical analysis (Fox, 2000) was disagree) to 5 (strongly agree). The confirmatory factor analysis also performed to detect any nonlinear patterns. The local area supported the five-factor structure of GDMS as well as measurement nonparametric LOESS polynomial regression procedure was used to invariance across gender. Alpha internal consistency values ranged produce data points for the fully running DBQ (T-scores) and from 0.72 to 0.91. GDMS and ZKA-PQ/SF-SPSRQ-20 domains (z-scores). The method The GDMS has five different scales, each representing a decision- involves a series of local regression analyses that allows the shape of making style: Rational (RA), Intuitive (IN), Dependent (DE), Avoidant a curve to vary through the continuous variable. The procedure is a (AV), and Spontaneous (SPO). robust adjustment method that is flexible and ideal for potentially revealing complex and unforeseen association patterns between 2.2.4. The manchester driver behavior variables (O’Connor, 2005). To conduct this analysis, a global score questionnaire on DBQ was computed by summing the four scales. This total score The DBQ (Reason et al., 1990) has been used in many studies can be computed in this way since all DBQ scales present high about risky driving. It has 34 items and was adapted and validated intercorrelations among them (Table 1). Statistical analyzes have in Spain by López de Cózar et al. (2004, 2005). It has a response been carried out using the SPSS 26.0 (IBM Corp, 2019) the corrplot format from 0 (never) to 10 (always). The Spanish validation (Wei and Simko, 2017) and qgraph (Epskamp et al., 2012) replicated the four-factor structure: Lapses (LA), Errors (ER), R packages. Frontiers in Psychology 04 frontiersin.org Frontiers in Psychology Aluja et al. TABLE 1 Inter-correlation matrix for age DBQ, ZKA-PQ/SF, SPSRQ-20, and GDMS. Aggressiveness Activity factor Spontaneous Sensitivity to Sensitivity to Extraversion Neuroticism punishment Dependent Aggressive Sensation violations violations Avoidant Ordinary Rational Intuitive seeking reward Lapses Errors Age Age −0.16 −0.03 −0.36 −0.11 0.02 −0.10 −0.01 0.11 −0.10 −0.11 −0.03 −0.08 −0.11 −0.10 −0.26 −0.17 Extraversion −0.10 −0.34 0.24 −0.22 0.18 0.11 −0.42 0.03 0.24 −0.06 −0.26 −0.01 −0.01 −0.04 0.04 0.03 Neuroticism −0.11 −0.38 −0.01 0.41 −0.02 0.18 0.67 −0.06 −0.17 0.37 0.41 0.14 0.22 0.16 0.05 0.09 Sensation 0.37 −0.37 0.27 0.00 0.20 0.24 0.35 −0.19 −0.04 0.17 −0.02 0.01 0.15 0.19 0.30 0.20 seeking Aggressiveness −0.17 −0.16 0.47 0.11 0.08 0.30 0.17 −0.19 −0.02 0.07 0.19 0.35 0.27 0.29 0.26 0.38 Activity factor 0.04 0.28 −0.07 0.19 0.07 0.15 −0.09 0.11 0.11 −0.07 −0.04 10 −0.01 −0.01 0.03 0.07 Sensitivity to 0.28 −0.25 0.15 0.24 0.33 0.38 0.26 0.09 −0.01 0.07 0.12 0.19 0.19 0.21 0.21 0.28 05 reward Sensitivity to −0.04 0.02 −0.44 0.62 −0.17 0.18 −0.12 0.10 0.04 −0.22 0.35 0.39 0.11 0.03 −0.07 −0.06 punishment Rational 0.12 0.06 −0.12 −0.11 −0.23 0.16 −0.10 −0.14 0.07 0.12 −0.09 −0.48 −0.16 −0.12 −0.14 −0.22 Intuitive 0.04 0.19 0.04 0.21 0.13 0.17 0.19 −0.04 0.11 −0.11 −0.20 0.15 0.01 −0.07 −0.03 0.02 Dependent 0.04 −0.08 0.34 −0.11 0.10 0.06 0.11 0.32 0.15 0.13 0.34 −0.10 0.11 0.06 −0.07 −0.08 Avoidant −0.10 −0.33 0.50 −0.07 0.25 −0.17 0.17 0.49 −0.24 −0.01 0.31 0.16 0.23 0.20 0.09 0.11 Spontaneous −0.23 0.04 0.24 0.32 0.36 0.04 0.34 0.10 −0.40 0.20 0.00 0.31 0.14 0.09 0.20 0.25 Lapses −0.22 −0.11 0.27 0.06 0.26 −0.07 0.19 0.20 −0.16 −0.04 0.07 0.11 18 0.74 0.53 0.51 Errors −0.18 −0.10 0.24 0.07 0.23 0.01 0.26 0.14 −0.15 −0.03 0.06 0.14 22 0.67 0.68 0.62 Ordinary −0.40 0.00 0.13 0.28 0.24 −0.02 0.28 −0.02 −0.11 0.01 0.07 0.09 20 0.47 0.61 0.69 violations 10.3389/fpsyg.2023.1058927 Aggressive −0.34 0.02 0.13 0.19 0.43 0.08 0.36 −0.02 −0.10 0.01 −0.02 0.04 0.23 0.44 0.58 0.70 violations Lower left represents results for men. Upper right for women. r: 0.09 or higher p < 0.05; r: 0.12 or higher p < 0.01; r: 0.15 or higher p < 0.001. Correlations equal to or greater than 0.30 are in boldface. frontiersin.org Aluja et al. 10.3389/fpsyg.2023.1058927 3. Results 3.2. Age ranges and gender comparisons by driving behaviors scales 3.1. Descriptive statistics of age, social position index, ZKA-PQ/SF, SCSRQ-20, The differences between age in both genders in the four DBQ GDMS, DBQ, and gender comparisons scales are presented in Figure 1. The youngest people, in both men and women, show higher scores in Lapses than older ones (1 > 4, 5). Table 2 shows the descriptive statistics of the two Women score significantly more than men in all age ranges. In errors, sociodemographic variables (age and social position), and the there were no differences between age ranges or genders. On the other psychometric scales for each gender. It also reports alpha hand, the youngest group clearly score higher in Ordinary violations coefficients as well as t-test of gender comparisons. There were no than the other age groups, and men tend to score higher than women differences in age and social position between genders. Both men in 3 out of 5 groups. In aggressive violations, the youngest women and women reported a social position index around 31, which (groups 1 and 2) scored higher than age groups 3, 4 and 5, and men corresponds to a “middle” level according to Hollingshead (1957). also scored significantly higher than women in age groups 1, 3 and 4. The higher means in men were in Ordinary violations (p < 0.001; Complementary to this analysis, an ANOVA 2×5 (Gender x Age d = 0.33), Aggressive violations (p < 0.001; d = 0.28), Sensation range) was performed to test the possible interaction effects between Seeking (p < 0.001; d = 0.32) and Sensitivity to Reward (p < 0.001; the gender and age variables. This analysis also allows for testing the d = 0.23). In contrast, women obtained higher means in Lapses effect size of the gender, as well as age variables and the interaction. (p < 001; d = −0.40), Intuitive (p < 0.001; d = −0.22) and Dependent For the Lapses scale, F was significant for gender (p < 0.001) and age (p < 0.001; d = −0.22) decision-making style, and personality traits (p < 0.001), but not for the interaction. For the error scale, it was only such as Neuroticism (p < 0.001, d = −0.44) and Sensitivity to significant for age (p < 0.003). Regarding ordinary violations, there was Punishment (p < 0.001, d = −0.43). Alpha coefficients showed a significant effect of gender (p < 0.001), age (p < 0.001) and the adequate values in all scales. interaction (p < 0.04). This interaction effect is due to the higher mean TABLE 2 Descriptive statistics, gender comparisons and Cronbach’s alpha internal consistency. Men (n=539) Women (n=453) All M SD M SD t-test p< d Alpha Age 45.62 16.09 44.04 15.24 1.58 0.115 0.10 – Social position index 31.95 17.81 31.36 17.69 0.47 0.638 0.03 – ZKA-PQ/SF Aggressiveness 32.62 8.99 32.54 8.61 0.14 0.888 0.00 0.88 Activity 41.93 7.13 42.11 7.37 −0.40 0.692 −0.02 0.81 Extraversion 47.68 7.78 49.04 7.54 −2.78 0.006 −0.18 0.85 Neuroticism 32.06 9.06 36.05 9.28 −6.84 0.001 −0.44 0.90 Sensation seeking 37.66 8.62 34.87 8.46 5.12 0.001 0.32 0.84 SCSRQ-20 Sensitivity to reward 21.53 5.56 20.31 5.09 4.53 001 0.23 0.79 Sensitivity to −8.37 001 −0.43 0.82 21.51 5.51 23.91 5.80 punishment GDMS Rational 4.06 0.64 3.98 0.63 1.97 0.049 0.12 0.86 Intuitive 3.58 0.88 3.77 0.85 −3.46 0.001 −0.22 0.90 Dependent 3.31 0.83 3.49 0.79 −3.58 0.001 −0.22 0.83 Avoidant 2.20 0.93 2.31 0.91 −1.90 0.053 −0.12 0.92 Spontaneous 2.24 0.86 2.25 0.88 −0.24 0.088 −0.01 0.87 DBQ Lapses 20.45 12.13 25.85 15.32 −6.20 0.001 −0.40 0.84 Errors 8.55 6.70 8.86 7.61 −0.66 0.507 −0.04 0.81 Ordinary violations 17.27 13.31 13.14 11.62 5.15 0.001 0.33 0.84 Aggressive violations 12.58 11.53 9.59 9.52 4.39 0.001 0.28 0.81 ZKA-PQ/SF: Zuckerman-Kuhlman-Aluja Personality Questionnaire shortened form. SPSRQ-20: Sensitivity to Punishment and Sensitivity to Reward Questionnaire shortened 20-item form. GDMS: General Decision-Making Scale. DBQ: Driving Behavior Questionnaire. Cohen’s d: 0.10: very small, 0.20: small, 0.50: medium, 0.80: large, 1.20: very large. Frontiers in Psychology 06 frontiersin.org Aluja et al. 10.3389/fpsyg.2023.1058927 59 *** 57 55 *** ** *** *** DBQ T-score 53 *** *** *** *** * 51 49 47 45 43 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 Lapsus Errors Ordinary violations Aggressive violations Males Females All subjects 1 > 4, 5 1 > 4, 5 1 > 2, 3, 4 > 5 1, 2 > 3, 4, 5 FIGURE 1 ANOVA mean comparison of age ranges and Driver Behavior Questionnaire (DBQ): 1: 30 years old and below; 2: 31–40; 3: 41–50; 4: 51–60, and 5: more than 60 years old. Scheffe post-tests comparisons (p < 0.05). T-test of gender comparisons: *p < 0.05, **p < 0.01, ***p < 0.001. score of the youngest men. Similarly, differences on Aggressive more sensitive to reward (0.33 and 0.30) and tended to have a more violations were associated with gender (p < 0.001), and age (p < 0.001). spontaneous decision-making style (0.32 and 0.37) and more ordinary The interaction was also significant (p < 0.003). Again, this interaction violations (0.28 and 0.30). The subjects with higher scores on effect is mainly due to the higher mean score of youngest men. Aggressiveness commit more aggressive driving violations (0.43 and However, it should be mentioned that the interaction effect, when 0.38) and have a more spontaneous decision-making style (0.36 and significant, presented an eta squared (η2) of about 0.01 indicating a 0.35). People more Sensitive to Reward presented more aggressive rather small effect on the DBQ scales. Age presented η2 higher than driving violations as well (0.36 and 0.28). gender in all but the Lapses variable. The largest η2 obtained were for As can be seen in Table 1, the zero-order correlations matrices the age factor on Ordinary (0.10) and Aggressive violations (0.07). have many variables that correlate with each other. In addition to the Both values suggest a medium effect size. strong effect of age already mentioned, some personality variables also correlated strongly with certain decision-making scales. To eliminate the excessive effect of these correlations, a partial correlation was 3.3. Inter-correlations analysis separately performed, controlling all variables. The use of partial correlations for gender, and empirical network partial allowed a clearer interpretation of the real and direct relationships correlations between scales. Figure 2 shows the empirical network as well as the partial correlations for the entire sample. Although correlations Table 1 shows two intercorrelation matrices (one for each gender) decreased in intensity, they retained the same pattern depicted above between age and all the psychometric variables included in the present for the zero-order correlation matrices for men and women. study. Correlations equal to or greater than 0.15 were statistically significant, but this result is due to the large size of the sample. We think it is more appropriate to focus only on correlations greater 3.4. Factor convergence and regression than 0.30. It should be remarked that the pattern of correlations was analysis highly congruent across genders, although men tended to have higher correlations. Age was strongly and negatively related to Sensation Another way to study the relationships among risky driving scales, Seeking for men and women (−0.37 and −0.36, respectively). Age was personality, and decision-making variables is to perform a Principal also negatively related to ordinary violations (−0.40 and −0.26), and Components Analysis (PCA) with Varimax rotation (Table 3). Three aggressive violations (−0.34 and −0.17). As it has been stated, factors were extracted according to Horn’s parallel analysis method correlations tended to be lower for women. Extraverted people were (Horn, 1965). The Kaiser-Meyer-Olkin Measure of Sampling less avoidant (−0.33 and −0.26). People with high scores on Adequacy (KMO) was 0.750, with an approximate Chi-Square of Neuroticism were more dependent (0.34 and 0.37), and avoidant (0.50 5113.83 (p < 0.001). The variance accounted for by the three factors and 0.41), and people high on Sensitive to Punishment were also more was 50.03%. The first factor grouped the four scales of DBQ and, with Dependent (0.32 and 0.35), Avoidant (0.49 and 0.39) and Spontaneous a lower loading, the Rational decision-making scale in negative. As the decision-making (0.34 and 0.28) styles. Those more Sensitive to four DBQ scales present high and positive correlations and load on Punishment were more aggressive (0.38 and 0.30) and commit more the same factor, it sounds psychometrically appropriate to compute a violent violations while driving (0.36 and 28). Sensation seekers were single score. This result allows us to compute a regression analysis on Frontiers in Psychology 07 frontiersin.org Aluja et al. 10.3389/fpsyg.2023.1058927 FIGURE 2 Empirical network with the age, DBQ, GDMS, SPSRQ-20, and ZKA-PQ/SF scales (partial correlations). Nodes represent scales. The edges represent the relationship among scales. The thicker the edge, the greater is the relationship between scales. Green and red lines represent positive and negative relationships, respectively. SR, Sensitivity to Reward; SP, Sensitivity to Punishment; AG, Aggressiveness; AC, Activity; Ex, Extraversion; NE, Neuroticism; SS, Sensation Seeking; RA, Rational; IN, Intuitive; DE, Dependent; AV, Avoidant; SPO, Spontaneous; LA, Lapses; ER, Errors; OVI, Ordinary violations; AVI, Aggressive violations. TABLE 3 Principal component analysis including DBQ, ZKA-PQ/SF, decision-making scales related to Neuroticism (Avoidant and SPSRQ-20, and GDMS scales. Dependent) and Aggressiveness. The third was a factor of Sensation Seeking, Sensitivity to Reward (as a measure of impulsivity according I II II to Gray’s personality model) and Activity. The most disinhibited Errors 0.85 0.07 −0.06 decision-making styles -Spontaneous and Intuitive- and Activity trait Ordinary violations 0.83 −0.11 0.08 were also included. It is important to remark that Aggressiveness Aggressive violations 0.82 −0.07 0.16 loaded on the three factors, and that the loading on its own factor is quite similar to the loading on the risky driving factor (the first). Lapses 0.73 0.17 −0.07 A multiple linear regression analysis was also carried out Rational −0.29 −0.16 −0.10 separately for men and women with the aim of identifying the best Neuroticism 0.16 0.83 0.04 predictors of total DBQ score using age, personality traits and Sensitivity to decision-making styles as independent variables (Table 4). Age (−), 0.00 0.81 −0.18 punishment Aggressiveness (+), Sensitivity to Reward (+) and Rational decision- making style (−) explained 25% of risky driving (DBQ) in men. In Avoidant 0.15 0.70 0.01 women, the variables that entered the equation were age (−), Dependent −0.09 0.54 0.03 Aggressiveness (+), Sensitivity to Reward (+), Rational decision- Extraversion −0.08 −0.50 0.44 making styles (−) and Sensation Seeking (+), explaining 18% of the Aggressiveness 0.40 0.42 0.34 variance of risky driving (DBQ). The pattern of predictors is, therefore, Sensation seeking 0.23 −0.16 0.65 highly congruent across genders. Table 5 shows the results of predicting every DBQ scale from age, Sensitivity to reward 0.30 0.20 0.61 personality scales and decision-making styles. It was observed that all Spontaneous 0.27 0.24 0.57 variables accounted for between 11 and 29% of the variance of the Intuitive −0.13 −0.05 0.53 DBQ scales. It should be remarked that aggressive violations were Activity −0.07 −0.11 0.52 better predicted than lapses and errors. This better prediction came mainly from the Aggressiveness and Sensitivity to reward traits. Factor loadings equal to or greater than 0.30 are in boldface. Lapses and error presented a different pattern according to gender. For men, the best predictor was age, and for women Aggressiveness, the DBQ single score what is more informative about the personality Neuroticism (although significant for lapses only), and Sensation correlates of the risky driving behavior than using each DBQ Seeking and Sensitivity to Reward (significant for errors only). separately. The second factor grouped the Neuroticism scales, Decision-making styles barely contributed to the variance, with a lack including Sensitivity to Punishment, Extraversion (−), the of stability in the pattern observed across DBQ scales. Frontiers in Psychology 08 frontiersin.org Aluja et al. 10.3389/fpsyg.2023.1058927 TABLE 4 Standardized coefficients, t-test and significance of the variables included in the equation of the multiple regression analysis With regard to the role of independent variables, most studies separately for genders, considering age, ZKA-PQ/SF, SPSRQ-20 and indicate that age, and to a lesser extent gender, are strong predictors decision-making styles as independent variables and DBQ total score as for risky driving behavior (McCartt et al., 2009; Lucidi et al., 2010; dependent variable. Perepjolkina and Renge, 2011; Barr et al., 2015; Zeyin et al., 2022). The Standardized results are in strong agreement with the literature, since age was also t Sig. coefficients negatively associated with risky driving (McCartt et al., 2009; Zeyin Men. R2 adjusted: 0.25 et al., 2022). In fact, the results suggest that age is the best predictor of risky driving behavior in men. Age −0.273 −6.996 0.001 More centrally to the aims of the study, personality traits proved Aggressiveness 0.216 5.207 0.001 to be associated with risky driving in both men and women. The Sensitivity to reward 0.170 4.102 0.001 personality trait most associated with risky driving was Aggressiveness Rational −0.103 −2.659 0.008 in both genders. These results for Aggressiveness and driving are consistent with the findings of Iancu et al. (2016), but not entirely with Women R adjusted: 0.18 2 those of Šeibokaitė et al. (2014), who found that Aggressiveness was a Age −0.122 −2.636 0.009 predictor of risky driving only in men. Along with Aggressiveness, Aggressiveness 0.258 5.635 0.001 Sensitivity to Reward (Impulsiveness) predicted risky behavior in both Sensitivity to reward 0.129 2.734 0.007 genders, and Sensation Seeking also seemed to play a role (Lucidi Rational −0.116 −2.629 0.009 et al., 2019). Considering the theoretical relationship between Sensation Seeking and Sensitivity to Reward, results of the present Sensation seeking 0.100 2.055 0.040 study suggest that an approach behavior in response to incentives (Corr, 2004) could be responsible for some risky driving behaviors. These incentives could be from arriving earlier to some kind of 3.5. Non-parametric and multiple linear competitive behavior. Considering the relationships among DBQ and regression analysis personality scales, Neuroticism was also observed to be associated with committing more lapses, hence providing some confirmation Figure 3 shows a nonparametric LOESS graphical analysis for that being prone to anxiety and worries leads to a larger amount of local regression for men and women together. Based on correlation errors (especially in women) (Alavi et al., 2017; Wang et al., 2018). and factor analysis results, total DBQ scores were used. They were Sensitivity to Punishment played a less relevant role, and in women converted into T-scores, and GDMS (A) and ZKA-PQ/SF and only. A possible explanation is that the criteria are focused on risky SPSRQ-20 into Z-scores (B). The mean of the T-score was 50 and the behavior, where the need to obtain something is clearly more mean of the Z-scores was 0, therefore the domain lines of the GDMS important than any possible punishment resulting from negative and ZKA-PQ/SF and SPSRQ-20 coincided on the graph at these outcomes of risky behavior. points. As subjects progressed in DBQ T-scores, the lines representing A contribution of this study is the fact that decision-making styles the domains of the GDMS, and the two personality questionnaires add predictive power to risky driving beyond personality traits. spread out on the graph. In the case of the GDMS scales, the most Congruent with the literature, rational style is the decision-making predictive were spontaneous (positive), and rational, (negative). In the style most related with risky driving behavior. Note that the case of ZKA-PQ and SPSRQ-20, Aggressiveness and Sensitivity to relationship is negative, so this decision-making style seems to act as Reward presented the most positive prediction, followed by Sensation a protector in risky driving, in agreement with other studies (Chen, Seeking and Neuroticism. The rest presented flat lines, suggesting a 2013). This result (albeit with a small sample size) suggests that an lack of prediction of total DBQ score. objective analysis of the situation could mediate the impact of the emotion (whether aimed at getting reward such as impulsiveness, fighting someone such as aggressiveness and anger, or feeling anxiety). 4. Discussion On the other hand, Spontaneous style is the one most closely related to Ordinary and Aggressive violations in both genders. Avoidant style This study analyzes altogether the role of age, gender, personality is also associated with Lapses and Errors, although to a lesser extent traits (Zuckerman’s and Gray’s models) and decision-making styles on in women. risky driving in a large sample of middle social position and gender Our results corroborate the findings of previous research that has parity, and with an age distribution similar to that of the general studied the relationships between personality and decision-making population. Our purpose was to contribute to this field of research by scales and risky driving separately. In our study, we have tried to deepening our knowledge about the role of individual difference investigate their relationships in a more integrated way. Furthermore, variables such as personality traits and decision-making styles on to the best of our knowledge, this is the second study to use the motor vehicle driving behavior. First, this study replicates the gender ZKA-PQ in driving behavior research. Note that most studies have differences on risky behavior scales. Women scored higher on lapses, used the FFM. In addition, the inclusion of a personality measure and men on ordinary violations in all age ranges (Berdoulat et al., according to Gray’s model makes it possible to analyze the role of 2013; Lajunen et al., 2022). Men also scored higher on Aggressive impulsivity (defined as Sensitivity to Reward) from a complementary violations than women, except for the group between 31 and perspective, more inspired by the biological tradition of 40 years old. personality research. Frontiers in Psychology 09 frontiersin.org Aluja et al. 10.3389/fpsyg.2023.1058927 TABLE 5 Standardized coefficients, beta,a t and significance of the variables by gender included in the equation of the multiple regression analysis, considering age, ZKA-PQ/SF, SPSRQ-20, and decision-making styles as independent variables and DBQ domains as dependent variables. Ordinary violations (R2 Aggressive violations Lapses (R2 adj: 0.13) Errors (R2 adj: 0.12) Men adj: 0.22) (R2 adj: 0.30) β T Sig. β T Sig. β t Sig. β t Sig. Age −0.172 −3.780 0.001 −0.112 −2.456 0.014 −0.275 −6.352 0.001 −0.237 −5.799 0.001 Aggressiveness 0.126 2.467 0.014 0.075 1.472 0.142 0.102 2.110 0.035 0.322 7.019 0.001 Activity −0.056 −1.252 0.211 0.026 0.584 0.559 −0.055 −1.296 0.195 0.053 1.306 0.192 Extraversion −0.016 −0.329 0.742 −0.061 −1.223 0.222 −0.057 −1.191 0.234 −0.033 −0.733 0.464 Neuroticism 0.080 1.297 0.195 0.106 1.712 0.087 0.048 0.820 0.412 −0.052 −0.938 0.348 Sensation seeking −0.011 −0.234 0.815 −0.003 −0.064 0.949 0.130 2.786 0.006 −0.021 −0.484 0.628 Sensitivity to 0.087 1.761 0.079 0.213 4.320 0.001 0.153 3.278 0.001 0.199 4.500 0.001 reward Sensitivity to 0.050 0.881 0.379 −0.024 −0.424 0.672 −0.084 −1.545 0.123 −0.071 −1.383 0.167 punishment Rational −0.088 −1.860 0.063 −0.065 −1.375 0.170 −0.026 −0.587 0.558 −0.110 −2.584 0.010 Intuitive 0.011 0.245 0.807 −0.088 −1.987 0.047 −0.063 −1.508 0.132 −0.032 −0.795 0.427 Dependent 0.040 0.852 0.395 −0.008 −0.180 0.858 −0.052 −1.187 0.236 −0.079 −1.898 0.058 Avoidant 0.067 1.281 0.201 0.089 1.692 0.091 0.017 0.344 0.731 0.025 0.539 0.590 Spontaneous −0.052 −1.015 0.311 −0.090 −1.760 0.079 0.010 0.204 0.838 −0.006 −0.125 0.901 Lapses (R adj: 0.10) 2 Errors (R adj: 0.11) 2 Ordinary violations (R adj: 0.16) 2 Aggressive violations (R adj: 0.19) 2 Women β T Sig. β t Sig. β t Sig. β t Sig. Age −0.057 −1.150 0.251 −0.035 −0.714 0.475 −0.183 −3.799 0.001 −0.118 −2.483 0.013 Aggressiveness 0.166 3.061 0.002 0.183 3.394 0.001 0.184 3.512 0.001 0.323 6.251 0.001 Activity −0.037 −0.765 0.445 −0.055 −1.161 0.246 −0.011 −0.243 0.808 0.010 0.215 0.830 Extraversion 0.069 1.266 0.206 −0.006 −0.103 0.918 −0.034 −0.646 0.518 0.029 0.565 0.572 Neuroticism 0.128 1.896 0.059 0.071 1.051 0.294 −0.037 −0.563 0.574 0.020 0.311 0.756 Sensation seeking 0.060 1.063 0.288 0.092 1.649 0.100 0.162 2.980 0.003 −0.010 −0.181 0.856 Sensitivity to 0.080 1.553 0.121 0.098 1.912 0.057 0.084 1.691 0.092 0.169 3.457 0.001 reward Sensitivity to 0.025 0.384 0.701 −0.076 −1.154 0.249 −0.119 −1.869 0.062 −0.116 −1.848 0.065 punishment Rational −0.114 −2.108 0.036 −0.063 −1.164 0.245 −0.085 −1.630 0.104 −0.016 −0.303 0.762 Intuitive −0.037 −0.760 0.448 −0.047 −0.976 0.330 −0.042 −0.902 0.368 −0.047 −1.018 0.309 Dependent −0.001 −0.018 0.986 0.026 0.504 0.615 0.073 1.463 0.144 −0.033 −0.676 0.500 Avoidant −0.004 −0.079 0.937 0.044 0.830 0.407 0.043 0.846 0.398 −0.019 −0.374 0.709 Spontaneous 0.017 0.290 0.772 0.062 1.043 0.297 0.001 0.022 0.983 0.054 0.949 0.343 Significant (p < 0.05) beta coefficients are in boldface. a Considering the linear results observed in the present paper, protective factor, increases with age (Pellerone et al., 2016). The results drivers with high scores on risky driving could be defined as young, of the present paper would suggest that changes in personality traits more aggressive, high in Neuroticism (and Sensitivity to Punishment), and decision-making styles could contribute to lower risk driving as Sensation seekers, and low in rational, and high in avoidant and age increases. (2) In Spain, we have a points-based driver’s license. spontaneous decision-making styles. It prompts the following Drivers who lose their driving license because they have run out of considerations: (1) It provides some insight into the phenomenon that points are forced to take a driver awareness and re-education course the overall crash rate steadily decreases as driver age increases. There to recover it. The present results suggest that this course should is ample evidence that age is strongly negatively associated with consider psychological aspects to increase its efficacy and reduce the disinhibited personality traits such as Sensation Seeking, likelihood of traffic accidents in the future, especially in the case of Aggressiveness, and Sensitivity to Reward (Zuckerman, 1994, 2005; young people. In this sense, courses should include psychosocial Jonah, 1997; Jonah et al., 2001; Iancu et al., 2016; Scott-Parker and interventions that have been successful to reduce aggressiveness and Weston, 2017; Zhang et al., 2019). In addition, the decision-making aggressive behaviors (McGuire, 2008). In addition, interventions style most related with risky driving (Rational), and which acts as a aimed to promote self-control (that is to say, less impulsive behavior) Frontiers in Psychology 10 frontiersin.org Aluja et al. 10.3389/fpsyg.2023.1058927 A B DBQZ-Scores T-Scores FIGURE 3 LOESS plots for GDMS (A), and ZKA-PQ/SF and SPSRQ-20 (B). RA, Rational; IN, Intuitive; DE, Dependent; AV, Avoidant; SPO, Spontaneous; AG, Aggressiveness; AC, Activity; Ex, Extraversion; NE, Neuroticism; SS, Sensation Seeking; SR, Sensitivity to Reward; SP, Sensitivity to Punishment. would also be advisable. This intervention would help to develop self- accidents, experience as a driver, and so forth, to complement self- regulatory skills that would inhibit risky behavior (De Ridder report measures. For instance, not considering the exposure of the et al., 2020). driver to risk, the frequency of driving or the usual mileage of the As a secondary aim of the present study, we replicated the participant as covariates is a limitation of the present study (Lucidi observed relationships among variables analyzed in the present study. et al., 2010). These variables could be a key confounder for the effect As has been commented above, the negative relationship among age of age, gender, personality traits and decision-making styles. Another and some personality traits was replicated. Gender differences in limitation is that the sample is composed of habitual drivers of motor personality traits match those expected according to the literature. of 2 or 4-wheel vehicles, but we have not recorded the normal vehicle Women score higher on Neuroticism and Sensitivity to Punishment, used. People who ride motorcycles could present a larger number of and men on Sensation seeking and Sensitivity to Reward (Aluja and risky driving behaviors (Peek-Asa and Kraus, 1996; Hassanzadeh Blanch, 2011; Cross et al., 2011, 2013; Rossier et al., 2016). There were et al., 2020). A future study should compare both groups of drivers of no gender differences in Aggressiveness. This is due to the new 2 or 4-wheel vehicles to test if relationships observed remain invariable conceptualization of Zuckerman’s trait in the ZKA-PQ (Aluja et al., across vehicle type. 2010). It includes four facets (Physical aggression, Verbal Aggression, Summing up, the psychological description of risky drivers Anger, and Hostility) with different patterns on gender differences obtained in our large and representative sample of drivers is (Aluja et al., 2018; Björkqvist, 2018). With regard to decision-making mainly characterized by young subjects, high scores in styles, and in line with the findings of Delaney et al. (2015) and Aggressiveness, Neuroticism, Sensation Seeking, Sensitivity to Bayram and Aydemir (2017), women had higher scores on the Reward and Punishment, and decision-making styles Dependent and Intuitive styles. characterized by low rationality, and high spontaneity. The This study has strengths and limitations. Among the strengths is present study demonstrates that personality traits and decision- the large sample of anonymous drivers with gender parity and a wide making styles play a complementary role in the prediction of age range, the use of questionnaires validated in the same sociocultural risky behavior. Finally, the present study closely replicates in the context, and the good internal consistency of the different scales. The Spanish population the association of age with risky driving, as basic limitation is the fact that the study was carried out with data well as the different pattern of risky behaviors observed in men obtained through self-reported questionnaires. Cross-sectional and women in other countries. surveys have an inherent method bias. Independent and dependent variables are measured at the same time, which makes it difficult to distinguish between cause and effect. In this sense, it would Data availability statement be desirable to use longitudinal designs using, for instance, a panel design. Thus, both individual and situational factors for risky driving The datasets presented in this article are not readily available could be better detected. It would also be advisable to use objective because they are subject to ongoing research. Requests to access the data on risky driving such as loss of points, number of sanctions or datasets should be directed to the corresponding author. Frontiers in Psychology 11 frontiersin.org Aluja et al. 10.3389/fpsyg.2023.1058927 Ethics statement Acknowledgments The studies involving human participants were reviewed and We thank the voluntary participants and the undergraduate students approved by the Ethical Commission of the University of Lleida. The who participated in the data collection in the general population. patients/participants provided their written informed consent to participate in this study. Conflict of interest Author contributions The authors declare that the research was conducted in the absence of any commercial or financial relationships that could AA and FB designed this study and conducted the statistical be construed as a potential conflict of interest. analyses. All authors collected the data, contributed to the writing and editing of the manuscript, and approved the final version. Publisher’s note Funding All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, This research was funded by a grant from the Spanish Ministry of or those of the publisher, the editors and the reviewers. Any product Economy, Industry and Competitiveness (PID2019-103981RB-I00). that may be evaluated in this article, or claim that may be made by its All the authors took part in this project as researchers. manufacturer, is not guaranteed or endorsed by the publisher. References Alacreu-Crespo, A., Fuentes, M. C., Abad-Tortosa, D., Cano-Lopez, I., González, E., Bucsuházy, K., Matuchová, E., Zůvala, R., Moravcová, P., Kostíková, M., and and Serrano, M. A. (2019). Spanish validation of general decision-making style scale: Mikulec, R. (2020). Human factors contributing to the road traffic accident occurrence. sex invariance, sex differences and relationships with personality and coping styles. Trans. Res. 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