Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators

Transportation Research Part F xxx (2016) xxx–xxx

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Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators David J. Bumgarner ⇑,1, Jon R. Webb 1, Chris S. Dula 1 East Tennessee State University, Department of Psychology, Box 70649, Johnson City, TN 37614, United States

a r t i c l e

i n f o

Article history: Received 18 May 2015 Received in revised form 14 July 2016 Accepted 24 July 2016 Available online xxxx Keywords: Forgiveness Driving anger Aggressive driving Driving outcomes

a b s t r a c t Purpose: In the United States, motor-vehicle crashes are the leading cause of death for individuals 18–24 years of age. Multiple factors place young drivers at an increased risk including risky and aggressive driving behaviors. Aggressive driving has been shown to account for more than half of the driving fatalities in the United States. Driving anger is predictive of aggressive driving and adverse driving outcomes. Research outside the context of driving has demonstrated associations between multiple dimensions of forgiveness and anger, aggressive behaviors, and health outcomes. A very small body of research suggests a modest relationship between forgiveness and both driving anger and aggressive driving. The current study expands on previous research to examine the impact of multiple dimensions of forgiveness on adverse driving outcomes. Methods: Undergraduate students (N = 446) completed, self-report measures of forgiveness, driving anger, driving anger expression, aggressive driving behaviors, and aversive driving outcomes. Results: Bivariate correlations indicated a significant negative relationship between each dimension of forgiveness and driving anger, driving anger expression, and aggressive driving. Forgiveness (of others and of uncontrollable situations) was found to have a significant indirect only effect on traffic violations through the mediators of driving anger and aggressive driving. Discussion: Current findings support and expand on previous research examining the association of forgiveness with adverse driving outcomes. Forgiveness of others and forgiveness of uncontrollable situations, but not forgiveness of self, were shown to indirectly impact traffic violations/warnings, but not crashes, within the past five years through reduced driving-related anger, anger expression, and/or aggression. Implications, limitations, and future research are discussed. Ó 2016 Published by Elsevier Ltd.

1. Introduction 1.1. Health impact of adverse driving outcomes In the United States alone, motor vehicle crashes (MVCs) account for more than 33,000 deaths and 2.5 million hospital emergency room visits annually (National Highway and Traffic Safety Association [NHTSA], 2013). As a result, the annual ⇑ Corresponding author at: Gulf Coast Veterans Health Care System, 400 Veterans Ave, Biloxi, MS 39531, United States. Fax: +1 228 523 5958. 1

E-mail addresses: [email protected] (D.J. Bumgarner), [email protected] (J.R. Webb), [email protected] (C.S. Dula). Fax: +1 423 439 5695.

http://dx.doi.org/10.1016/j.trf.2016.07.017 1369-8478/Ó 2016 Published by Elsevier Ltd.

Please cite this article in press as: Bumgarner, D. J., et al. Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators. Transportation Research Part F (2016), http://dx.doi.org/ 10.1016/j.trf.2016.07.017

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financial cost of medical treatment and loss of productivity has been estimated to be in excess of $80 billion (Centers for Disease Control and Prevention [CDC], 2014a). Fatal and non-fatal crashes directly affect the victim’s physical and psychological health, decrease quality of life, and indirectly affect family members and society at large (Cobiac, Vos, Doran, & Wallace, 2009). The World Health Organization has therefore deemed road traffic injuries as a ‘‘global health and development problem” (World Health Organization [WHO], 2009, p. iv), with fatal crashes being deemed an ‘‘ultimate” health outcome (Wagenaar, Erickson, Harwood, & O’Malley, 2006). Of note, motor-vehicle related fatalities are the leading cause of death for individuals aged 18–24 (CDC, 2016). As MVCs are generally considered to be a low frequency occurrence, traffic violations and/or citations are often used as an additional measure of adverse driving outcomes (e.g., Emerson et al., 2012). Therefore, according to the definition of health proposed by the WHO, as ‘‘a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity” (Preamble of the Constitution of the WHO, 1948, p. 100), and the significant health implications of automobile crashes and traffic violations, this study defines adverse driving outcomes as a health outcome. 1.2. Factors affecting driving Driving, to an experienced driver, is a skill that develops almost to the point of being automatic; however, driving is a dynamic process that requires the continuous coordination of multiple perceptual, cognitive, and muscular systems (Ma & Kaber, 2007). The driver must continuously perceive, comprehend, and project to the future status to compensate for the dynamic nature of the environment (Endsley, 1995). Using terminology defined by Posner (1980), both exogenous (i.e., external or outside the driver) and endogenous (i.e., internal or originating from driver’s thoughts or cognitive activity) factors can impact driving performance and outcomes. Research has demonstrated that weather conditions (Andrey, Mills, Leahy, & Suggett, 2003), low-light conditions (Plainis & Murray, 2002), mobile phone use (both hand-held and hands-free) (Horrey & Wickens, 2006), text messaging while driving (Owens, McLaughlin, & Sudweeks, 2011), and aggressive driving behaviors (Chliaoutakis et al., 2002) all contribute to adverse driving behaviors and/or outcomes. Moreover, several factors place young drivers at an elevated risk for crash-related injuries such as inexperience (Kass, Cole, & Stanny, 2007), risky driving behaviors (Rhodes & Pivik, 2011), and distracted driving (Wilson & Simpson, 2010). Although approximately 90–95% of all MVCs are attributed to human error (U.S. Department of Transportation, 2009), the majority of research has focused on exogenous factors related to driving (e.g., mobile phone use, in-vehicle displays, and advertisement billboards), with less work published regarding the potential impact of endogenous factors (Recarte & Nunes, 2003). 1.3. Driving anger Endogenous factors such as personality traits (Dahlen, Edwards, Tubré, Zyphur, & Warren, 2012), driver inattention (Yanko & Spalek, 2013), and anger while driving (Deffenbacher, Lynch, Oetting, & Swaim, 2002) have been found to impact driving performance and driving outcomes. Driving anger has been shown to be a significant predictor of aggressive driving, ‘close calls’, and traffic violations (e.g., Deffenbacher, Deffenbacher, Lynch, & Richards, 2003; Deffenbacher et al., 2002; Nesbit & Conger, 2012; Zhang & Chan, 2016). Anger has been defined as both a state and trait variable (e.g., Spielberger, Jacobs, Russell, & Crane, 1983). State anger is a strong emotion in response to a situation that is generally accompanied by autonomic arousal, muscle tension, negative cognitions, and behavioral expressions (see Deffenbacher et al., 1996). In contrast, trait anger is more of a stable personality-like factor that influences the propensity, intensity, and duration of the state anger experienced (see Deffenbacher et al., 1996). Trait anger in particular has been found to impact aggressive driving, risky driving, driving errors, near misses, and MVCs (Zhang & Chan, 2016). Driving anger has been shown to be moderately to strongly correlated (r = .25–51) with aggressive driving (Dahlen, Martin, Ragan, & Kuhlman, 2005). In addition to aggressive driving, driving anger may impact driving performance through increased physiological arousal (Deffenbacher et al., 2002), inattention (Dula, Martin, Fox, & Leonard, 2011), and anger rumination (Suhr & Nesbit, 2013). Anger experienced while driving may be generated by stimuli directly (e.g., being cut-off in traffic) and/or indirectly (e.g., distressing phone call, work stress) related to driving (Deffenbacher, Lynch, Filetti, Dahlen, & Oetting, 2003). For example, participants in a driving simulator were more likely to engage in dangerous driving behaviors when attending to an emotionally charged cell phone call rather than those on a mundane call (Dula et al., 2011). This impact on performance has been shown to persist for up to 5 min after hang-up (Redelmeier & Tibshirani, 1997). Therefore, research suggests that anger experienced while driving is associated with aggressive driving behaviors and has a significant impact, both initially and delayed, on driving performance and outcomes. 1.4. Aggressive driving One of the primary factors associated with MVCs and driving-related fatalities is aggressive driving (AAA Foundation for Traffic Safety [AAA], 2009). Aggressive driving has been implicated as a factor in approximately 56% of all fatal crashes (AAA, 2009). Risky and aggressive driving are key factors related to the increased risk of injury and death among young drivers (Reason, Manstead, Stradling, Baxter, & Campbell, 1990). Broadly defined, aggressive driving includes verbal, physical, or vehicular behaviors that result from anger experienced while driving (Deffenbacher et al., 2002). For example, swearing, tailgating, or shaking one’s fist at another driver. These aggressive behaviors, according to Deffenbacher et al. (2002), are Please cite this article in press as: Bumgarner, D. J., et al. Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators. Transportation Research Part F (2016), http://dx.doi.org/ 10.1016/j.trf.2016.07.017

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routes of expression for feeling angry or furious while driving. Based on this theory, anger can be expressed through multiple behaviors whether adaptive or maladaptive. However, this definition of aggressive driving may be limited. The term ‘‘aggressive driving” has been used in the lay and professional literature to describe multiple distinct constructs including reckless driving, risky driving, and road rage (Dula & Geller, 2003). Dula and Ballard (2003) argue that aggressive driving is distinct from risky driving behavior and negative emotions while driving (e.g., anger). For example, a driver may derive pleasure from harming or infringing on the rights of others, and therefore, drive aggressively in the absence of anger. While the nuanced definitional aspects of aggressive driving are beyond the scope of this article (for further detail see Dula & Geller, 2003), this study adheres to the definition of aggressive driving purposed by Dula and Ballard (2003), which defines aggressive driving as ‘‘any behavior emitted by a driver while driving, that is intended to cause physical and/or psychological harm to any sentient being” (p. 565). Furthermore, aggressive driving is considered an overt, observable behavior which has been shown to impact the health and safety of motorists and pedestrians (Deffenbacher, Lynch, et al., 2003; Dula & Ballard, 2003). Aggressive driving, as defined by Dula and Ballard (2003), conforms to the definition of health behavior proposed by Gochman (1988, 1997). Therefore, aggressive driving is herein conceptualized as a health-risk behavior (i.e., health behavior). Due to the threat of aggressive driving to the safety of society, researchers have worked to develop and examine interventions to reduce aggressive driving behaviors (for review see Galovski, Malt, & Blanchard, 2006). For example, Galovski and Blanchard (2002) demonstrated the effectiveness of a four week cognitive-behavioral program on driving anger, trait anxiety, and driving behaviors for court-ordered offenders. This intervention included psychoeducation, relaxation training, and cognitive components. In addition to broad cognitive-behavioral approaches to addressing aggressive driving, forgiveness, or a lack thereof, has been theorized as a potentially important component of aggressive driving behavior (Takaku, 2006). More recently, trait forgiveness of others has been shown to be a contributing factor in lower levels of driving anger and in turn, driving aggression (Kovácsová, Rošková, & Lajunen, 2014; Moore & Dahlen, 2008). 1.5. Forgiveness and driving as a health-related outcome Forgiveness is defined as an inter- and intra-personal coping strategy that can be utilized in response to a perceived transgression (Toussaint & Webb, 2005; Worthington & Scherer, 2004). In response to a transgression and/or transgressor, the process of forgiveness works to reframe negative thoughts, behaviors, and emotions to a more neutral or positive position (Thompson et al., 2005; Toussaint & Webb, 2005; Worthington, 2003). Forgiveness is conceptualized as both a state and trait variable (Lawler et al., 2005; Toussaint & Webb, 2005) that can occur as a decisional and/or emotional process (Worthington, 2003). Forgiveness is not excusing, condoning, pardoning, or forgetting (Enright, 1996). Forgiveness, in general, has been shown to impact health-related outcomes both directly and indirectly through various mediators (for review see Webb, Toussaint, & Conway-Williams, 2012; see also Toussaint, Worthington, & Williams, 2015). 1.5.1. Direct effect of forgiveness on health Forgiveness has been theorized to directly impact health through the amelioration of (un)forgiveness. Unforgiveness, according to Worthington and Wade (1999), is conceptualized as a cluster of negative emotions (e.g., hostility and anger) experienced after rumination on a perceived wrong done toward another, the self, or a higher power. According to Worthington, Berry, and Parrott (2001), the direct connection between (un)forgiveness and health is greatest in those that are chronically unforgiving or have a disposition (i.e., trait) toward (un)forgivingness. Similar to other chronic stress conditions, chronic (un)forgiveness is thought to act through a hyper-arousal stress response (Harris & Thoresen, 2005) and is positively correlated with sympathetic nervous system arousal, facial tension, skin conductance, and cardiovascular reactivity (see Worthington, Witvliet, Pietrini, & Miller, 2007). Research outside the context of driving has shown a negative association between forgiveness and anger (e.g., Berry, Worthington, O’Connor, Parrott, & Wade, 2005; Carson et al., 2005). Anger, according to Fitzgibbons (1986), involves the presence of strong feelings of displeasure and antagonism after a perceived wrong or injury which leads to a desire for revenge, that is not resolved until it is recognized and released. The decision and process of forgiveness is one way in which anger can be released or ameliorated (Worthington & Scherer, 2004). Outside the context of driving, forgiveness is negatively correlated with state and trait anger (Carson et al., 2005), anger rumination (Barber, Maltby, & Macaskill, 2005), and aggressive behaviors (Webb, Dula, & Brewer, 2012), and positively correlated with positive other-oriented emotions (Worthington et al., 2007). In sum, after experiencing a perceived wrong, an individual recognizes (un)forgiveness (e.g., anger), then can make the decision to forgive which leads to decreased anger rumination (cognitive) and physiological arousal (biological), which in turn leads to a reduced desire to retaliate with aggression (behavioral), over time this process leads to decreased negative emotions and increased positive emotions (emotional), making it more likely that this process will occur again in the future (behavioral/dispositional). 1.5.2. Indirect effect of forgiveness on health Indirectly, based on the theoretical model developed by Worthington et al. (2001), forgiveness has been shown to impact health-related outcomes through various mediators (for review see Webb, Toussaint, et al., 2012; see also Toussaint et al., 2015). Research, outside the context of driving, has demonstrated consistent support for this indirect model of forgiveness on health outcomes. For example, the indirect effect of forgiveness on health has been demonstrated for physical and mental health (Webb, Hirsch, Visser, & Brewer, 2013), and alcohol/substance-related outcomes (Webb, Robinson, & Brower, 2011). Furthermore, the relationship between forgiveness and health, as mediated by social support, interpersonal functioning, and Please cite this article in press as: Bumgarner, D. J., et al. Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators. Transportation Research Part F (2016), http://dx.doi.org/ 10.1016/j.trf.2016.07.017

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health behaviors, among others, has been supported (e.g., Webb et al., 2013). Therefore, using this well-established theoretical model of forgiveness and health (i.e., Worthington et al., 2001), this study proposes that the relationship between forgiveness and driving may be studied within this theoretical and empirically supported context. More specifically, forgiveness is hypothesized, based on previous findings outside the context of driving (e.g., Webb et al., 2012) and definitions proposed in the present study (see above), to impact health (i.e., adverse driving outcomes) directly and indirectly through the mediator of health-risk behavior (i.e., aggressive driving), see Fig. 1. 1.6. Empirical support for forgiveness, driving anger, and aggressive driving To date, only three peer-reviewed publications have explicitly examined the potential forgiveness–driving relationship (Kovácsová et al., 2014; Moore & Dahlen, 2008; Takaku, 2006). Two of these three studies empirically examined forgiveness as a variable; with Takaku (2006) only discussing the potential theoretical implications of forgiveness on driving. The two empirical articles (i.e., Kovácsová et al., 2014; Moore & Dahlen, 2008) have shown significant, yet modest support for the relationship between trait forgiveness of others and driving related variables (e.g., driving anger and aggressive driving). Due to the limited research in this area, a full review of the studies, including a brief critique, is presented below. 1.6.1. Moore and Dahlen (2008) Moore and Dahlen (2008) examined the relationship between trait forgiveness of others, consideration of future consequences, driving anger, driving anger expression, and aggressive driving (U.S. undergraduate students aged 18–38 Mdn = 20; N = 316). Bivariate analyses indicated significant correlations, in the expected direction, for forgiveness of others and each of the driving anger and anger expression variables. Hierarchical multiple regression analysis indicated that the overall effect of tendency to forgive others and consideration of future consequences, entered simultaneously, were negatively correlated with aggressive driving and driving anger expression, above and beyond that of driving anger (DR2 = .05–.09, p < .01). Although not reported in the publication, based on Cohen’s f2 calculations this DR2 is considered to be a small effect size (Cohen, 1992). Furthermore, trait forgiveness of others was shown to be a significant predictor of each of the negative aggressive driving behavior and anger expression subscales (b =
.18 to b =
.21, p < .01). Replication of these findings was conducted as part of the first authors dissertation (Bumgarner, 2015) and demonstrated comparable results . . . In addition, results were similar when the Dula Dangerous Driving Index-aggressive driving subscale (DDDI-A; Dula &

Fig. 1. A model (and summary) of the association of forgiveness with adverse driving outcomes: driving anger, driving anger expression, and aggressive driving behaviors as mediators.

Please cite this article in press as: Bumgarner, D. J., et al. Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators. Transportation Research Part F (2016), http://dx.doi.org/ 10.1016/j.trf.2016.07.017

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Ballard, 2003) was substituted for the Driving Anger Expression Inventory (DAX; Deffenbacher et al., 2002). This replication (Bumgarner, 2015) provided additional support for the findings of Moore and Dahlen (2008) using a different sample and initial support for the comparable utility of the DDDI-A as a more precise measure of aggressive driving (given the aforementioned definitional concerns) in the context of forgiveness. 1.6.2. Kovácsová et al. (2014) Using self-report measures, Kovácsová et al. (2014) examined the relationship between trait forgivingness, driving anger, aggressive driving, hostility, and aggression using a community and university sample (i.e., Slovak and Czech drivers aged 18–69, M = 33.19, SD = 11.59; N = 612). Bivariate correlations indicated a significant relationship, in the expected direction, between trait forgiveness of others and each of the other variables studied (e.g., driving anger, aggressive driving). Hierarchical regression analyses did not demonstrate a significant effect for trait forgiveness of others (step 3) on aggressive driving over and above that of age, sex, annual mileage (step 1) and driving anger (step 2). A path analysis was then conducted, and after removing age and sex, where trait forgiveness of others was shown to have a significant effect on driving anger, which in turn had a significant impact on aggressive driving. Based on these findings, mediation analysis, utilizing Hayes’ techniques (see Hayes, 2013), confirmed that driving anger was a mediator of the relationship between trait forgiveness of others and aggressive driving. A total model R2 was not reported for the mediation analyses and thus an effect size (e.g., Cohen’s f2; Cohen, 1992) cannot be calculated. 1.6.3. Critique of Moore and Dahlen, and Kovácsová et al. Although each of the two empirical studies (i.e., Kovácsová et al., 2014; Moore & Dahlen, 2008) indicated significant relationships between trait forgiveness of others and driving-related variables (e.g., driving anger and aggressive driving), they are not without limitation. First, both studies showed statistically significant results, however, neither study directly reported effect sizes. Reporting effect sizes is important for the interpretation of results with regard to practical significance (Cumming et al., 2007). While not reporting an effect size themselves, Moore and Dahlen (2008) provided data that allowed for effect size estimation which indicated small effects sizes (Cohen, 1992), and thus the practical utility of their findings may be limited. Second, both studies employed broad, potentially conceptually limited measures of driving aggression. Third, neither study included measures of multiple dimensions of forgiveness (e.g., of others, of self, of uncontrollable situations), although research has shown that health-related outcome variables correlate differently based on the specific dimension of forgiveness being studied (e.g., Webb et al., 2011). Fourth, neither study reported on how forgiveness may be linked to adverse driving outcomes and therefore, are limited in their ability to describe how forgiveness may ultimately impact driving outcomes and subsequently health. Finally, a lack of grounding in a theoretical model in these previous studies limits the current understanding of the forgiveness–driving association. 1.7. Purpose and hypotheses The purpose of this study was to expand on previous findings (i.e., Kovácsová et al., 2014; Moore & Dahlen, 2008), that is, (1) to include multiple dimensions of forgiveness, (2) to include adverse driving outcomes as a dependent variable, (3) to utilize a more precise measure of aggressive driving, and (4) to test the utility of Worthington et al.’s (2001) model of forgiveness and health as applied to driving. It was hypothesized that (a) forgiveness would be correlated with driving anger, negative driving anger expression, aggressive driving behavior, and adverse driving outcomes, (b) multiple dimensions of forgiveness would impact adverse driving outcomes both directly and indirectly through the mediators of driving anger, negative driving anger expression, and aggressive driving behavior, and (c) the relationship between forgiveness and adverse driving outcomes would be varied based on dimension(s) of forgiveness examined.

2. Method 2.1. Participants Approval for this study was obtained from the Institutional Review Board and cross-sectional data was collected, through a secure online survey system, from undergraduate students at a four-year regional university in southern Appalachia. Students received modest course credit for their participation. No specific time restrictions were placed on the completion of the set of self-report questionnaires and students were allowed to withdraw from the study at any point without penalty. In total, 759 participants responded to the set of questionnaires, but only participants that were 18–24 years of age at the time of the survey were included in the study. In addition, participants were excluded if they provided unrealistic/impossible answers (e.g., number of days driven per week = 19) and/or if they failed and/or chose not to answer one or more individual items within a given scale. After exclusion, 446 participants were included in the current study with an age range of 18–24 years (M = 19.80, SD = .08). The sample was largely female (69%, n = 308; 30% male, n = 135; and <1% transgender, n = 3) and ethnicity/race was primarily Caucasian (83%; n = 368). On average, participants reported driving approximately 148 miles per week (M = 147.97, SD = 8.42, Range = 0–2000).

Please cite this article in press as: Bumgarner, D. J., et al. Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators. Transportation Research Part F (2016), http://dx.doi.org/ 10.1016/j.trf.2016.07.017

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2.2. Measures 2.2.1. Demographic and driving-related information Information relevant to the current study (e.g., miles driven per week, type of vehicle, and number of years as a licensed driver) was collected utilizing questions sampled from the Unsafe Driving Behaviors Questionnaire (Boyle, Dienstfrey, & Sothoron, 1998). Participants reported the number of MVCs as the driver and number of traffic tickets and/or warnings (i.e., traffic violations) received, both within the past five years. A high level of accuracy (83% and 85%) has been observed between self-reported and police reported traffic violations and crashes, respectively (Boufous et al., 2010). Furthermore, traffic violations have been shown to be positively correlated with MVCs (De Winter & Dodou, 2010) and therefore one or both are commonly used as indicators of adverse driving outcomes (e.g., Dahlen & White, 2006; Dula & Ballard, 2003; González-Iglesias, Gómez-Fraguela, & Luengo-Martín, 2012). As in previous research (e.g., Dahlen et al., 2012), the total number of MVCs, as a driver, were calculated regardless of perceived fault. 2.2.2. Driving anger The Deffenbacher Driving Anger Scale short-form (DAS; Deffenbacher, Oetting, & Lynch, 1994) measures the tendency to become angry while driving. Participants are asked to imagine a driving situation in which each of the statements was actually happening and to rate the degree of anger that it would provoke using a Likert scale from ‘‘1 = not at all” to ‘‘5 = very much.” The DAS-short consists of 14 items that have shown adequate internal consistency (a = .80) and have been shown to be a valid predictor of aggressive driving behaviors and crash-related outcomes (Deffenbacher, Huff, Lynch, Oetting, & Salvatore, 2000). Analysis from the present study demonstrated excellent internal consistency (a = .91). 2.2.3. Driving anger expression The Driving Anger Expression Inventory (DAX; Deffenbacher et al., 2002) is a 49-item measure that assesses a participant’s mode of expressing anger while driving. Participant’s are asked to rate ‘‘how often you generally react or behave in the manner described when you are angry or furious while driving.” Participants rate the frequency of such on a Likert scale from ‘‘0 = Almost Never” to ‘‘3 = Almost Always.” The measure includes a total score, and four subscales [Verbal, Physical, and Vehicular Anger Expression, and Adaptive Anger Expression (not included in the total negative expression score)]. Independently, the four subscales have been shown to have adequate internal consistency (a = .80–.90) and to correlate in the expected direction with trait anger, aggression, and angry or risky driving behaviors (Deffenbacher et al., 2002). Excellent internal consistency was observed for the total negative expression scale (DAX-N) in the current study (a = .95). 2.2.4. Aggressive driving Aggressive driving behaviors were measured using the Aggressive Driving subscale of the Dula Dangerous Driving Index (DDDI-A; Dula & Ballard, 2003). The DDDI is a 31-item self-report measure developed to assess a driver’s likelihood to drive dangerously. It is comprised of three subscales (i.e. Aggressive Driving, Negative Emotional Driving, and Risky Driving) and also provides a total dangerous driving behaviors score. Participants are asked to rate, using a 5-point Likert scale (A = never, B = rarely, C = sometimes, D = often, and E = always), the frequency with which they engage in specific driving behaviors. Initial findings in a sample of undergraduate students (Dula & Geller, 2003) suggested good internal consistency overall (a = .92) and for each of the subscales (a = .83–.85). Regarding validity, the subscales were found to be correlated with other measures of similar constructs (i.e. trait anger, aggression, anger expression, and negative emotions) in the predicted direction and to account for differences between traffic offenders and non-offenders (Willemsen, Dula, Declercq, & Verhaeghe, 2008). Results from the current study indicated good internal for the Aggressive Driving subscale (a = .88). 2.2.5. Multiple dimensions of forgiveness The Heartland Forgiveness Scale (HFS; Thompson et al., 2005) was used to assess multiple dimensions of trait forgiveness. The HFS is an 18-item self-report measure that consists of three subscales with six items each: forgiveness of self (FS), forgiveness of others (FO), and forgiveness of uncontrollable situations (Fsit). Each item is scored on a 7-point Likert scale from ‘‘1 = Almost Always False of Me” to ‘‘7 = Almost Always True of Me.” Good psychometric properties have been shown in multiple samples of college students at a large, public, mid-western university (Thompson et al., 2005). Internal consistency for the individual subscales and the total score were as follows: FS (a = .72–.75), FO (a = .78–.81), FSit (a = .79–.82), and Total (a = .86–.87). In addition, acceptable test-retest reliability was observed at a 3-week interval. Finally, in support of its validity, the HFS was found to be significantly correlated with other measures of forgiveness, psychological variables, and personality factors in an expected manner (Thompson et al., 2005). The current study demonstrated excellent internal consistency for the complete measure (a = .92) and good internal consistency for each of the subscales: FS (a = .83), FO (a = .84) and Fsit (a = .85). 2.3. Statistical analyses 2.3.1. Multiple serial mediation analyses For the primary analyses of this study (see Fig. 1), multivariable analyses were conducted using the statistical mediation methods described by Hayes (2013). In contrast to Baron and Kenny (1986), Preacher and Hayes (2008) argue that an initial Please cite this article in press as: Bumgarner, D. J., et al. Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators. Transportation Research Part F (2016), http://dx.doi.org/ 10.1016/j.trf.2016.07.017

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direct effect does not have to exist between the independent variable (IV) and the dependent variable (DV) for an indirect effect to be tested. In sum, in the absence of a direct effect of the IV on the DV, the IV can significantly affect the DV entirely through a mediator as an indirect only effect. Indeed, the Baron and Kenny method might lead to an increase in the potential for a Type-II error (i.e. not finding a significant relationship, when one actually exists). The methods developed by Hayes and colleagues (see Hayes, 2013) use bootstrapping when assessing for indirect effect (s), which relies on a method of resampling the data k times (k P 10,000) to better estimate the distribution of the population under investigation. This process both negates the need to assume normality in the shape of the sampling distribution and enhances statistical power for detecting indirect effects (Hayes, Preacher, & Myers, 2011). For each adverse driving outcome (i.e., MVCs and traffic violations) a separate serial mediation analysis, controlling for age, sex, and miles driven per week, was conducted for each dimension of forgiveness, with driving anger (first-order mediator) and driving anger expression or aggressive driving behaviors (second-order mediator – depending on the model) as mediating variables. Consistent with Hayes’ (2013) methods, when multiple IVs are included in the overall analysis, each, in turn, is placed in the IV role and the others are included as covariates in a series of individual analyses that are integrated into the overall analysis. This method allows for analysis of each IV in the context of one another and thereby, an integrated interpretation. Of note, the HFS, DAS, DAX-N, and DDDI-A were all scored such that higher scores would be indicative of higher levels of each variable (e.g., higher forgiveness, etc.).

3. Results 3.1. Bivariate correlations Bivariate correlations were calculated to examine the zero-order associations among all variables of interest (Table 1). Regarding control variables, age was correlated with FS and traffic violations (r = .15 & .28, respectively), sex with aggressive driving (r = .12), such that males reported more aggressive behaviors on the DDDI-A; and miles driven per week with FS, Fsit, and traffic violations (r = .10; .11; & .17, respectively). Each dimension of forgiveness (i.e., FS, FO, Fsit) was found to be negatively correlated with the DAS (r =
.23;
.29;
.34), the DAX-N (r =
.21;
.27;
.25), and DDDI-A (r =
.22;
.33;
.26), respectively. In addition, the DAS was found to be correlated with the DAX-N (r = .52) and the DDDI-A (r = .49). Correlations were also found between traffic violations and MVCs (r = .28), as well as each of the adverse driving outcomes and both the DAX-N (r = .17 & .13) and the DDDI-A (r = .20 & .13), respectively; but not the DAS. All of the significant correlations were in the expected direction based on previous research and hypotheses. The bivariate correlations of the present study replicate, support, and expand on those reported by both Moore and Dahlen (2008) and Kovácsová et al. (2014); however, contrary to previously stated hypotheses, no significant bivariate correlations were observed between any dimensions of forgiveness and the adverse driving outcomes.

Table 1 Bivariate correlations: dimensions of forgiveness, driving anger, driving anger expression, and adverse driving outcomes (n = 446). Variable

Age

Sex

Miles per week

FS

FO

Fsit

DAS

DAX-N

DDDI-A

Tick 5yr

Age Sex Miles per week FS FO Fsit DAS DAX-N DDDI-A Tick 5yr MVC 5yr

.08 .05 .15** .05 .08 .00
.04
.01 .28** .06


.01
.04
.04 .03
.05 .04 .12* .02
.02

.10* .02 .11*
.04
.03 .01 .17** .05

.47** .70**
.23**
.21**
.22**
.02 .03

.60**
.29**
.27**
.33**
.07 .03


.34**
.25**
.26**
.05 .03

.52** .49** .08 .02

.80** .17** .13**

.20** .13**

.28**

Sex: 0 = female; 1 = male. FS: Heartland Forgiveness Scale-forgiveness of self subscale. FO: Heartland Forgiveness Scale-forgiveness of others subscale. Fsit: Heartland Forgiveness Scale-forgiveness of uncontrollable situations subscale. DAS: Deffenbacher Driving Anger Scale. DAX-N: Deffenbacher Driving Anger Expression Inventory-total negative expression scale. DDDI-A: Dula Dangerous Driving Index-aggressive driving subscale. Tick 5yr: Tickets and Warnings received in the last five years. MVC 5yr: Motor Vehicle Crashes as the Driver in the last five years. * p 6 .05. ** p 6 .01.

Please cite this article in press as: Bumgarner, D. J., et al. Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators. Transportation Research Part F (2016), http://dx.doi.org/ 10.1016/j.trf.2016.07.017

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3.2. Serial mediation analyses The total or overall explanatory power of the serial mediation models, including control and active variables, was found to be statistically significant only when traffic violations were entered as the dependent variable (as opposed to the overall models for MVCs), as such only results within the context of traffic violations are reported. Significant models were observed for traffic violations when both the DAX-N (Table 2 and Fig. 1; R2 = .14, f2 = .16) and the DDDI-A (Table 3 and Fig. 1; R2 = .14, f2 = .17) were entered separately (as the second-order mediator) into the serial mediation models. Medium effect sizes (f2) were observed (Cohen, 1992). In contrast to stated hypotheses, none of the three dimensions of forgiveness measured were found to have a total effect (c) or a direct effect (c0 ) on either adverse driving outcome. 3.2.1. Individual pathways Each individual pathway (i.e., a1, a2, a3, b1, b2, c, and c0 ) is represented by an unstandardized regression coefficient (i.e., unstandardized B), see Tables 2 and 3, and Fig. 1. Individual mediation analysis indicated that FO and Fsit, but not FS, were associated with the DAS in a negative fashion (a1 =
.35; a1 =
.47, respectively). Only FO was associated with the DAX-N (a2 =
.41) or the DDDI-A (a2 =
.26). Both the DAX-N (a3 = .70) and DDDI-A (a3 = .22) were found to be associated with the DAS. Both the DAX-N (b2 = .03) and the DDDI-A (b2 = .10) were found to be significantly associated with traffic violations; however, no direct association was observed between the DAS (b1) and traffic violations. 3.2.2. Indirect only effects Indirect only effects were observed through the proposed mediators (i.e., if the 95% Confidence Interval (95CI) did not cross zero), despite the absence of significant total (c) and direct (c0 ) effects of the forgiveness dimensions on adverse driving outcomes. A total indirect effect was observed only for FO on traffic violations for both overall models; that is, whether including as the second-order mediator, the DAX-N (ab =
.0181, 95CI =
.0406 to
.0053) or the DDDI-A (ab =
.0318, 95CI =
.0579 to
.0147). Similarly, a specific indirect only effect was observed only for FO in association with traffic violations through either the DAX-N alone (a2b2 =
.0131, 95CI =
.0320 to
.0029) or DDDI-A alone (a2b2 =
.0267, 95CI =
.0507 to
.0120). That is, higher levels of FO were associated with lower levels of driving anger expression (Table 2) or aggressive driving behaviors (Table 3), as the second-order mediator, which in turn were associated with fewer traffic violations.

Table 2 The association of forgiveness with tickets/warnings in the last 5 years: driving anger and driving anger expression as mediators (n = 446). Forgiveness of self

Forgiveness of others

Forgiveness of uncontrollable situations

R2 = .1370⁄⁄⁄⁄, f2 = .16 Coefficient

p value

Coefficient

p value

Coefficient

p value

a1 a2 a3 b1 b2

.04
.17 .70
.01 .03

ns


.35
.41 .70
.01 .03

⁄


.47 .10 .70
.01 .03

⁄⁄⁄

c c0


.01
.01

ns


.03
.02

ns


.02
.01

ns

ab a1b1 a2b2 a1a3b2

ns ⁄⁄⁄⁄ ns ⁄⁄

ns

Effect

95CI


.0048
.0003
.0054 .0009


.0162
.0058
.0170
.0036

.0029 .0015 .0013 .0068

⁄ ⁄⁄⁄⁄ ns ⁄⁄

ns

Effect

95CI


.0181⁄ .0028
.0131⁄
.0078⁄


.0406
.0038
.0320
.0210


.0053 .0158
.0029
.0014

ns ⁄⁄⁄⁄ ns ⁄⁄

ns

Effect

95CI


.0036 .0038 .0031
.0105⁄


.0190
.0065
.0046
.0224

.0093 .0171 .0132
.0035

Analyses controlled for: Age, Gender, and Miles Driven per Week. a1 = basic association of Forgiveness with Driving Anger. a2 = basic association of Forgiveness with Driving Anger Expression. a3 = basic association of Driving Anger with Driving Anger Expression. b1 = basic association of Driving Anger with Traffic Violations. b2 = basic association of Driving Anger Expression with Traffic Violations. ab = total indirect effect. a1b1 = specific indirect effect of Forgiveness on Traffic Violations through Driving Anger. a2b2 = specific indirect effect of Forgiveness on Traffic Violations through Driving Anger Expression. a1a3b2 = specific indirect effect of Forgiveness on Traffic Violations through Driving Anger and Driving Anger Expression, in serial. c = total effect of Forgiveness on Traffic Violations, without accounting for any Mediator Variables. c0 = direct effect of Forgiveness on Traffic Violations, after accounting for all Mediator Variables. 95CI = Bias-corrected 95% Confidence Interval. * p 6 .05; ** p 6 .01; *** p 6 .001; **** p 6 .0001; y 6 .10; ns = non-significant. f2 = effect size (.02 = small, .15 = medium, .35 = large; Cohen, 1992).

Please cite this article in press as: Bumgarner, D. J., et al. Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators. Transportation Research Part F (2016), http://dx.doi.org/ 10.1016/j.trf.2016.07.017

9

D.J. Bumgarner et al. / Transportation Research Part F xxx (2016) xxx–xxx Table 3 The association of forgiveness with tickets/warnings in the last 5 years: driving anger and aggressive driving behaviors as mediators (n = 446). Forgiveness of self

Forgiveness of others

Forgiveness of uncontrollable situations

R2 = .1429⁄⁄⁄⁄, f2 = .17 Coefficient

p value

Coefficient

p value

Coefficient

p value

a1 a2 a3 b1 b2

.04
.04 .22
.01 .10

ns


.35
.26 .22
.01 .10

⁄


.47 .03 .22
.01 .10

⁄⁄⁄

c c0


.01
.01

ns


.03
.00

ns


.02
.01

ns

ab a1b1 a2b2 a1a3b2

ns ⁄⁄⁄⁄ ns ⁄⁄⁄

ns

Effect

95CI


.0031
.0003
.0036 .0009


.0147
.0060
.0155
.0037

.0070 .0015 .0055 .0064

⁄⁄⁄⁄ ⁄⁄⁄⁄ ns ⁄⁄⁄

ns

Effect

95CI


.0318⁄ .0029
.0267⁄
.0079⁄


.0579
.0037
.0507
.0196


.0147 .0154
.0120
.0014

ns ⁄⁄⁄⁄ ns ⁄⁄⁄

ns

Effect

95CI


.0035 .0038 .0032
.0105⁄


.0208
.0063
.0068
.0214

.0109 .0167 .0154
.0039

Analyses controlled for: Age, Gender, and Miles Driven per Week. a1 = basic association of Forgiveness with Driving Anger. a2 = basic association of Forgiveness with Aggressive Driving Behaviors. a3 = basic association of Driving Anger with Aggressive Driving Behaviors. b1 = basic association of Driving Anger with Traffic Violations. b2 = basic association of Aggressive Driving Behaviors with Traffic Violations. ab = total indirect effect. a1b1 = specific indirect effect of Forgiveness on Traffic Violations through Driving Anger. a2b2 = specific indirect effect of Forgiveness on Traffic Violations through Aggressive Driving Behaviors. a1a3b2 = specific indirect effect of Forgiveness on Traffic Violations through Driving Anger and Aggressive Driving Behaviors, in serial. c = total effect of Forgiveness on Traffic Violations, without accounting for any Mediator Variables. c0 = direct effect of Forgiveness on Traffic Violations, after accounting for all Mediator Variables. 95CI = Bias-corrected 95% Confidence Interval. * p 6 .05; ** p 6 .01; *** p 6 .001; **** p 6 .0001; y 6 .10; ns = non-significant. f2 = effect size (.02 = small, .15 = medium, .35 = large; Cohen, 1992).

When the first- and second-order mediators were examined in serial (i.e., a1a3b2), specific indirect only effects were observed for both FO and Fsit. That is, FO was associated indirectly with traffic violations through both the DAS and DAX-N in sequence (a1a3b2 =
.0078, 95CI =
.0210 to
.0014), and the DAS and DDDI-A in sequence (a1a3b2 =
.0079, 95CI =
.0196 to
.0014). Also, Fsit was associated indirectly with traffic violations through both the DAS and DAX-N in sequence (a1a3b2 =
.0105, 95CI =
.0224 to
.0035), and the DAS and DDDI-A in sequence (a1a3b2 =
.0105, 95CI =
.0214 to
.0039). Higher levels of FO or Fsit were associated with lower levels of driving anger (first-order mediator), which in turn were associated with lower levels of driving anger expression (Table 2) or aggressive driving behaviors (Table 3), as the second-order mediator, which in turn were associated with fewer traffic violations. 3.3. Summary of findings Consistent with previous research, bivariate correlations indicated a significant salutary relationship between each dimension of forgiveness (i.e., FS, FO, and Fsit) and driving anger (DAS), negative driving anger expression (DAX-N), and aggressive driving behaviors (DDDI-A). However, none of the dimensions of forgiveness were directly associated with either adverse driving outcome (MVCs or traffic violations), whether at the bivariate or multivariable levels of analysis. Serial mediation analyses revealed (1) an overall effect of the models for forgiveness, with medium effect sizes, only in the context of traffic violations (models for MVCs were non-significant), (2) a total indirect effect for FO on traffic violations in both overall models (regardless of which second-order mediator was included), (3) specific indirect only effects for FO through each second-order mediator alone (i.e., DAX-N or DDDI-A, independently), (4) specific indirect only effects for each of FO and Fsit on traffic violations operating in serial through driving anger (DAS) and each second-order mediator (i.e., DAX-N or DDDI-A, independently), and (5) no relevant differences in results were observed when using either the DAX-N or DDDI-A as a second-order mediator in the analyses. 4. Discussion Previous research (i.e., Kovácsová et al., 2014; Moore & Dahlen, 2008) has suggested a negative relationship between forgiveness of others and both driving anger and aggressive driving. The purpose of the current study was to expand on previous research, using an adaptation of an empirically supported theory of forgiveness and health (i.e., Worthington et al., 2001) and to examine the impact of multiple dimensions of forgiveness on adverse driving outcomes through the mediators Please cite this article in press as: Bumgarner, D. J., et al. Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators. Transportation Research Part F (2016), http://dx.doi.org/ 10.1016/j.trf.2016.07.017

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D.J. Bumgarner et al. / Transportation Research Part F xxx (2016) xxx–xxx

of driving anger and two measures of aggressive driving. We hypothesized that (1) multiple dimensions of forgiveness would be significantly related to driving anger, negative driving anger expression, aggressive driving behaviors, and adverse driving outcomes, (2) that the relationship between forgiveness and adverse driving outcomes would be mediated by driving anger, negative driving anger expression, and aggressive driving behaviors, and (3) that the relationships would be varied based on the dimension of forgiveness under consideration. Initial hypotheses were partially supported. 4.1. Effects of forgiveness on driving outcomes Empirical support, outside the context of driving, has demonstrated the utility of using Worthington et al.’s (2001) model to examine the direct and indirect relationships between forgiveness and health outcomes (see Toussaint et al., 2015; Webb, Toussaint, et al., 2012). The current study is the first to test the utility of this model to examine the relationship between forgiveness and driving. Specifically, the current study suggests an indirect only relationship between forgiveness (i.e., of others and of uncontrollable situations) and traffic violations—health outcome—through driving anger and aggressive driving—health behavior. These findings are consistent with research outside the context of driving, which has demonstrated an indirect only relationship between forgiveness and health outcomes (e.g., Webb et al., 2011, 2013). In sum, this study further supports the use of Worthington et al.’s model to examine forgiveness and health, and provides initial support for the usefulness of the model in examining the relationship between forgiveness and driving. 4.1.1. Direct effects of forgiveness on driving outcomes In contrast to the stated hypotheses, no direct relationships were observed between forgiveness and adverse driving outcomes (i.e., traffic violations or MVCs within the past five years), however, a significant basic, bivariate negative correlation was observed between each dimension of forgiveness and driving anger. In addition, the basic relationship between forgiveness (i.e., of others and of uncontrollable situations) and driving anger was shown to persist in the context of the mediation analyses. These findings suggest that individuals with a higher propensity to forgive others and/or uncontrollable situations are likely to experience less driving anger when presented with a potential anger provoking situation (e.g., being cut off in traffic). Such is consistent with previous basic negative correlations between forgiveness and driving anger (i.e., Kovácsová et al., 2014; Moore & Dahlen, 2008). Nevertheless, such findings do not support the extended notion that the association of forgiveness with adverse driving outcomes operates indirectly through the amelioration of the negative emotions (e.g., anger) associated with unforgiveness following a provoking experience while driving (Worthington & Scherer, 2004). There are many possible explanations for the lack of a direct relationship between forgiveness and adverse driving outcomes including the complex nature of driving (Ma & Kaber, 2007), delayed impact of unforgiveness on health outcomes (Worthington & Scherer, 2004), and a low incidence of adverse driving outcomes in the current sample. The impact of forgiveness on health outcomes has been theorized to occur in a similar fashion to other chronic stress conditions (Harris & Thoresen, 2005) and to be greatest in those that are chronically unforgiving (Worthington et al., 2001). As with other health outcomes (e.g., heart attack, cancer), adverse driving outcomes are a low probability event (particularly MVCs) that occur across time and may not be directly impacted by forgiveness. As such, other measures of potential risk may be better indicators of the impact of forgiveness on driving performance and outcomes. 4.1.2. Indirect effects of forgiveness on driving outcomes Serial mediation analyses from the current study suggested an overall effect of forgiveness on traffic violations, but not MVCs, within the past five years through the mediators of driving anger and two measures of aggressive driving. The current models each accounted for approximately 14% of the variance (i.e., medium effect size; Cohen, 1992) in reported number of traffic violations. Specific indirect only relationships were found for each of forgiveness of others and forgiveness of uncontrollable situations through driving anger and the measures of aggressive driving in sequence, and for forgiveness of others through aggressive driving alone. These findings suggest that individuals that are more forgiving of others and of uncontrollable situations are more likely to experience less driving anger, which may lead to less aggressive driving, which in turn may lead to fewer traffic violations. In addition, forgiveness of others may impact traffic violations only through aggressive driving, independent of driving anger. Again, these results are consistent with previous research, outside the context of driving, which has demonstrated indirect only effects of forgiveness on health outcomes through various mediators (e.g., Webb et al., 2011, 2013). One hypothesis for the association of forgiveness with health (e.g., adverse driving outcomes) operating through health behaviors (e.g., driving-related anger and aggression) is that forgiveness may free up cognitive, behavioral, and emotional resources to more actively engage in health promoting behaviors (Temoshok & Wald, 2005). Worthington et al. (2007) state that negative emotions (e.g., anger) may interrupt reasoning and lead to ‘‘emotive responding” (p. 294). In support, driving performance has been shown to be impacted by the emotional valence of auditory content (Chan & Singhal, 2015; Dula et al., 2011) as well as relatively high or low levels of arousal (Fischer, Langner, Birbaumer, & Brocke, 2008). In addition, previous research has consistently shown a direct relationship between driving anger and driving aggression (e.g., Deffenbacher et al., 2002). In sum, forgiveness may decrease negative emotional valence and arousal, leading to a decrease in anger and aggression, which in turn may reduce the likelihood of adverse driving outcomes. These relationships are complex and although the current cross-sectional findings provide support for this reasoning, longitudinal and intervention-based research will be necessary before causal inference can be supported. Please cite this article in press as: Bumgarner, D. J., et al. Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators. Transportation Research Part F (2016), http://dx.doi.org/ 10.1016/j.trf.2016.07.017

D.J. Bumgarner et al. / Transportation Research Part F xxx (2016) xxx–xxx

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4.2. Multiple dimensions of forgiveness in association with adverse driving outcomes The current study expands on previous forgiveness-driving research to include multiple dimensions of forgiveness (i.e., of others, of self, and of uncontrollable situations). Each dimension of forgiveness was found to be negatively correlated with driving anger and each measure of driving aggression. As predicted, results were varied according to the dimension of forgiveness studied. In particular, forgiveness of others and of uncontrollable situations were found to have an indirect only relationship with traffic violations, through the mediators of driving anger and/or driving aggression. These findings are consistent with the dynamic nature of driving (Endsley, 1995) which includes other drivers as well as potentially problematic situations (e.g., weather conditions, traffic congestion). Contrary to the stated hypotheses, forgiveness of self was not found to be directly or indirectly predictive of adverse driving outcomes. One possible explanation is that forgiveness of self has been shown to be correlated with rumination on angry memories (Barber et al., 2005), and therefore, may be more internally focused. Assessing driving performance using only external indicators (i.e., traffic violations and MVCs) may not be an accurate indicator of the impact of forgiveness of self on driving. It is probable that multiple dimensions of forgiveness, including forgiveness of self, may impact cognitive and other internal indicators of driving performance. For example, research has demonstrated that driving performance is impacted by anger rumination (Suhr & Nesbit, 2013), off-task thinking associated with negative emotions (e.g., Smallwood, Fitzgerald, Miles, & Phillips, 2009), and/or cognitive attributions of perceived wrong (Wickens, Wiesenthal, Flora, & Flett, 2011). Further research is needed to assess these cognitive routes of forgiveness on driving performance. 4.3. Measurement in the forgiveness-driving relationship The current study provides support for the utility of: (1) more precise measurement of aggressive driving, (2) the assessment of multiple dimensions of forgiveness, and (3) the assessment of actual driving-related outcomes. First, the construct of ‘aggressive driving’ has had limited research utility due to a lack of conceptual clarity in that it has been used to describe multiple distinct constructs (e.g., reckless driving and road rage) and has been erroneously conflated with driving anger and risky driving (Dula & Ballard, 2003; Dula & Geller, 2003). The current study indicated a strong correlation between driving anger and each of the aggressive driving measures (i.e., DAX-N and DDDI-A) as well as between the DAX-N and DDDI-A. Furthermore, results were found to be similar when using either the DAX-N or the DDDI-A, in the mediation analyses. These findings suggest that the DDDI-A is at least comparable to the DAX-N when examining forgiveness and adverse driving, and that the DDDI-A may provide more specific findings (e.g., distinct from risky driving and negative emotions while driving). Second, as predicted, multiple dimensions of forgiveness were related to driving anger, aggressive driving, and adverse driving outcomes. That is, forgiveness of uncontrollable situations, and not just forgiveness of others, was found to be a significant predictor of traffic violations. These findings support previous research which demonstrates the need to examine multiple dimensions of forgiveness in relationship to health (e.g., Webb et al., 2012). Finally, MVCs were positively correlated with aggressive driving and traffic violations. These results are consistent with previous research which has demonstrated a direct correlation between MVCs and both traffic violations (De Winter & Dodou, 2010) and aggressive driving (e.g., Deffenbacher, Lynch, et al., 2003). However, in contrast to the stated hypotheses, no direct or indirect effects were found between any dimension of forgiveness and MVCs. One possible explanation for this lack of significant findings was the low number of reported MVCs by the current sample. In our sample of young adult college students (18–24 years of age), only 45% reported at least one MVC as the driver, and only 10% reported two or more MVCs as the driver. Consistent with previous research (e.g., Emerson et al., 2012), the insufficient statistical variance observed for MVCs in our sample further suggests that MVCs may be limited in their utility as a DV in research seeking to differentiate dangerous drivers from less dangerous drivers and to identify effective preventive predictors of adverse driving outcomes. As such, alternative measures of external adverse driving outcomes are increasingly necessary. In sum, the current findings support the utility of using the DDDI aggressive driving subscale to assess aggressive driving; the need to measure multiple dimensions of forgiveness; and the need for additional consideration regarding the use of alternative measures of adverse driving outcomes. 4.4. Limitations Results from the current study suggest a significant indirect relationship between dimensions of forgiveness and traffic violations; however, the findings are not without limitations. Limitations of the current study include: the use of crosssectional, correlational data from college students; analysis of variables using self-report measures; medium effect sizes; and limited control of extraneous variables. First, the current study used cross-sectional, correlational data collected from college students at a regional university located in a small metropolitan area from a predominately rural region (Centers for Disease Control & Prevention, 2014b). Based on the current design and sample, causality and directionality cannot be inferred and the results may not generalize to a broader population. In defense, cross-sectional data is a commonly used and potentially useful way to collect data on a large number of individuals with regards to multiple variables. Also, cross-sectional, correlational data may be particularly useful when conducting preliminary or exploratory research on new or novel research topics. Importantly, college students are often considered a sample of convenience; however, based on the elevated risk for young drivers, the current sample is a sample of specific relevance. Please cite this article in press as: Bumgarner, D. J., et al. Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators. Transportation Research Part F (2016), http://dx.doi.org/ 10.1016/j.trf.2016.07.017

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D.J. Bumgarner et al. / Transportation Research Part F xxx (2016) xxx–xxx

Second, self-report measures are a common means of collecting driving and assessment information (e.g. Dula & Ballard, 2003), however, they rely heavily on an individual’s memory, honesty, and their accuracy of reporting, and may be susceptible to socially desirable responding (Lajunen, Corry, Summala, & Hartley, 1997). Therefore, self-report measures are considered a limitation to the field of driving research (Boyce & Geller, 2002) and in the current study. In defense, selfreported driving outcomes have been shown to be significantly correlated with actual traffic records (Boufous et al., 2010). In addition, self-reported driving behaviors have been correlated with actual driving performance (e.g., Matthews et al., 1998). Third, effect sizes were not directly reported in previous forgiveness-driving research (Kovácsová et al., 2014; Moore & Dahlen, 2008). Calculation of the effect sizes for these previous studies, based on their published data, revealed only a small effect of forgiveness of others on aggressive driving. In contrast, the current study indicated medium overall effects for the models in the context of traffic violations within the past five years; accounting for approximately 14% of the variance. As adverse driving outcomes have serious health and financial implications, understanding even 14% of the variance in the occurrence of traffic violations could have a potentially meaningful impact. Moreover, while effect sizes in the context of forgiveness-health research are typically modest, they are nonetheless consistently observed and cannot be overlooked (see Webb, Toussaint, Kalpakjian, & Tate, 2010). Fourth, due to the complex nature of driving, multiple factors have been shown to negatively impact driving performance (e.g., Andrey et al., 2003; Horrey & Wickens, 2006). Therefore, it is possible that a third variable(s) could better account for the variance described in the current models. In defense, the current models are rooted in a well-established model of forgiveness and health; based on previous forgiveness-driving research; and were designed to account for known extraneous factors (i.e., age, sex, and miles driven per week). In sum, the current study is limited by the nature of the design, sample composition, effect size, and potential third-variable(s), yet provides useful support for the impact of forgiveness on driving outcomes. 4.5. Future research Future research should be conducted to further understand and expand on current findings to include: the use of a multimeasure approach, control of other potentially extraneous variables, and the development and testing of driving interventions which incorporate forgiveness. The use of self-report measures has plagued the field of driving research (Boyce & Geller, 2002). Based on current findings, future research should utilize additional measures of driving performance, behaviors, and outcomes. For example, researchers should consider using driving simulators to assess real-time driving performance. Driving simulation would allow for consistency/standardization of the driving environment, the ability to track various driving behaviors, the ability to measure physiological responses, and the ability to increase the potential likelihood and frequency of adverse driving outcomes. Researchers should also begin to examine and control for additional factors related to driving performance (e.g., risky driving behavior, mobile phone use, personality factors). Driving is a complex process that involves multiple internal and external factors. By understanding how these complex variables impact each other, researchers will be better able to provide possible solutions. Lastly, future research should examine the potential utility of forgiveness-based interventions as applied to driving anger and aggressive driving. Research outside the context of driving has shown support for increased forgiveness and decreased unforgiveness through group interventions, in as little as 6 h (Wade, Worthington, & Meyer, 2005; Worthington, 2006). Similar to Galovski and Blanchard (2002), targeting individuals high on aggressive driving behaviors or in programs to specifically address aggressive driving may be a particularly effective strategy. Therefore, it is possible that specific forgiveness-based components may be extracted, modified, or combined with existing aggressive driving interventions, to increase forgivingness and in turn, decrease aggressive driving behaviors/outcomes. Future research should focus on multiple measures of driving-related behaviors, include real-time assessment of driving performance, and develop forgiveness-based interventional components to augment existing driving intervention programs. 5. Conclusion Driving is a complex skill that develops over time and occurs in a dynamic environment. Young adult drivers are at particular risk to experience adverse driving outcomes. Multiple factors place young drivers at risk including a higher incidence of risky and aggressive driving, as compared to other age groups. In particular, aggressive driving has been shown to be a factor in over half of all fatal crashes. Aggressive driving has been shown to be one way in which driver’s respond to driving anger. Forgiveness has been shown to be negatively correlated with driving anger, driving aggression, and now the adverse driving outcome of traffic violations. The current findings, in concert with previous research, suggest that forgiveness, particularly forgiveness of others and of uncontrollable situations, are important factors associated with traffic violations, driving anger, and aggressive driving. Given that adverse driving outcomes, such as MVCs and traffic violations, can have significant personal and social impacts it is imperative that researchers continue to examine variables that can potentially hinder or improve driving behaviors and outcomes. Therefore, further research is warranted to better understand the direct and indirect relationships between forgiveness and adverse driving outcomes.

Please cite this article in press as: Bumgarner, D. J., et al. Forgiveness and adverse driving outcomes within the past five years: Driving anger, driving anger expression, and aggressive driving behaviors as mediators. Transportation Research Part F (2016), http://dx.doi.org/ 10.1016/j.trf.2016.07.017

D.J. Bumgarner et al. / Transportation Research Part F xxx (2016) xxx–xxx

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Acknowledgements This study was conducted as part of the first author’s dissertation, as such this work was partially supported by resources and use of facilities at East Tennessee State University. In addition, this material is based upon work supported (or supported in part) by the Department of Veterans Affairs. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government. This work was supported in part by the Office of Academic Affiliation (OAA) through educational grants which have funded the Rural & Underserved Interprofessional Postgraduate Fellowship. Additionally, this study is the result of work supported with resources and the use of facilities at the Gulf Coast Veterans Health Care System (GCVHCS), located in Biloxi, MS. A special thanks to Ms. Elizabeth Polinsky, LMSW, for her work in proofreading this article.

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