the driving anger expression inventory: a validity study with community college student drivers

The Driving Anger Expression Inventory: A Validity Studywith Community College Student Drivers

Jerry L. Deffenbacher & Calvin C. Kemper &

Tracy L. Richards

Published online: 18 September 2007# Springer Science + Business Media, LLC 2007

Abstract This study explored the relationship of drivinganger expression to driving anger, trait anger, general angerexpression, and aggressive and risky behavior whiledriving. Verbal, physical, and vehicular forms of expressinganger while driving correlated positively with each other,driving and trait anger, anger-in, and anger-out andnegatively with adaptive/constructive driving anger expres-sion and general anger-control. Adaptive/constructive ex-pression formed small negative correlations with thesemeasures, except for a positive correlation with anger-control. Regression models controlling for demographicvariables and driving anger, trait anger, or general angerexpression demonstrated forms of driving anger expressionadded variance to predicting aggressive and risky behavior.Forms of driving anger expression partially mediated theeffects for driving anger, trait anger, and general angerexpression on aggressive and risky behavior. No modera-tion effects were found for age, gender, or miles driven.Findings provided evidence for convergent and incrementalvalidity for the Driving Anger Expression Inventory.

Keywords Driving anger . Driving anger expression .

Aggressive driving . Risky driving

Introduction

Angry, aggressive drivers are a significant social and publichealth concern and have drawn considerable public andmedia attention. Anecdotes abound as nearly every driverhas at one time or another been witness to or a victim of theerratic, impulsive aggressive displays of an enraged driver.There is some evidence that the most extreme examples (i.e.,road rage) increased approximately 7% per year in the US, atleast through the mid-1990s (American Automobile Associ-ation 1997).

Research suggests that, among a variety of factors, aperson’s trait driving anger or propensity to become angeredbehind the wheel is a significant issue. Results from a seriesof studies comparing high and low anger drivers reveal thefollowing (Deffenbacher 2000; Deffenbacher et al. 2000,2003b, 2003c). High anger drivers are angered by three ormore times the number of events on the road and areangered from two-and-a-half to three times more oftenwhen driving. When frustrated or provoked, high angerdrivers experience much more intense anger. High angerdrivers engage in approximately four times as muchaggressive and twice as much risky behavior on the road.In driving simulations, high anger drivers report greateranger and verbal and physical aggression in response tobeing impeded by slow, bumper-to-bumper traffic or beingunable to pass a slow driver on a twisting road(Deffenbacher et al. 2003a). High anger drivers also hadhigher rates of speeding, more erratic driving, moretailgating, shorter times to impact with a vehicle in frontof them, and twice as many crashes in these highimpedance simulations (Deffenbacher et al. 2003a). Theyalso report greater anger and verbal and physical aggressionin response to driving-related interpersonal provocationssuch as someone stealing a parking spot for which the

J Psychopathol Behav Assess (2007) 29:220–230DOI 10.1007/s10862-007-9049-x

J. L. Deffenbacher (*) : C. C. Kemper : T. L. RichardsDepartment of Psychology, Colorado State University,Fort Collins, CO 80523-1876, USAe-mail: [email protected]

J. L. DeffenbacherColorado Injury Control Research Center,Fort Collins, CO, USA

person has been waiting (Deffenbacher 2003; Deffenbacheret al. 2005).

Similar results have been found with other groups aswell. For example, Arnett et al. (1997) found anger to bethe only emotion that correlated with risky drivingbehaviors in adolescents. Elements of trait driving angerin adult British drivers correlated with traffic violationsgenerally (Underwood et al. 1999) and with both aggressiveand non-aggressive offenses (Lajunen et al. 1998). Thus, aperson’s tendency to anger behind the wheel appears animportant factor in understanding anger and aggression onthe road.

How a driver expresses his/her anger, however, may alsomake a significant difference. For example, three driversmight be equally angered by the same event, say being cutoff by another driver. One driver may express his/her angerby yelling and cursing loudly at the other driver, giving theother driver the finger and shaking a fist at them, andrunning up on the other driver’s back bumper with flashinglights and honking loudly and repeatedly. A second drivermay express anger in a very different way. He/she mayseethe, mumble to him/herself, entertaining images ofrevenge and retaliation, ruminate about the injustice andoffense, and experience considerable physiological arousal,but engage in little or no overt aggression. Later, he/shemay continue to be angry and ruminative, but withdrawfrom others and consume more alcohol than usual. Thethird driver may respond not by aggressing, but byattempting to control and reduce anger. He/she mightpurposefully back away from the other driver, engage inrelaxing or distracting activities, and consciously cogni-tively restructure angry thinking by trying to be moreaccepting of poor behavior.

Deffenbacher et al. (2002b) introduced the DrivingAnger Expression Inventory (DAX) to measure drivers’typical ways of expressing anger on the road. Initial studies(Deffenbacher et al. 2001, 2002b) showed that the DAXhad two general factors—aggressive anger expression andadaptive/constructive expression. Adaptive/ConstructiveExpression involves handling anger through behavioralactivities such as purposefully slowing down and backingaway from the offending driver, palliative activities such asengaging in relaxing and distracting behavior, and cognitiveself-instruction to focus on safe driving, problem-solving,and cognitive restructuring relative to provocative, frustrat-ing events. Aggressive Expression involves a variety ofverbal and physical behaviors as well as ways the vehiclemay be used to communicate anger and intimidate, harm orget back at offending drivers and events. The aggressiveexpression factor breaks down into three related, butdifferent forms of anger expression. Verbally AggressiveExpression involves the tendency to shout at, curse andmake denigrating comments about other drivers, and the

like. Personal Physically Aggressive Expression involvesusing one’s physical being to express anger through thingssuch as giving the other driver the finger, making otherangry gestures, and attempting to get out of the vehicle andengage in a physical altercation with another driver. Usingthe Vehicle to express anger involves ways the vehicle ordriving behaviors may be used to express anger. Thisincludes things such as honking horns, flashing lights,tailgating, and cutting someone off when angry.

Initial studies (Deffenbacher et al. 2001, 2002b, 2004)showed that aggressive forms of expression (verbal,physical, and vehicular) correlated positively with eachother and negatively with adaptive/constructive expression.Aggressive forms of expression correlated positively withtrait driving anger, the frequency and intensity of angerbehind the wheel, and the frequency of risky and aggressivebehavior while driving. Adaptive/constructive expressionwas either uncorrelated with or formed small negativecorrelations with these variables. Other research (Deffen-bacher et al. 2003d, 2004) suggested that different forms ofdriving anger expression correlated more highly with sometypes of hostile/aggressive thinking than others. Forexample, using the vehicle to express anger correlatedstronger with revengeful/retaliatory thinking. Verballyaggressive expression was more highly associated withpejorative labeling/verbally aggressive thinking. Adaptive/constructive expression correlated more highly with copingself-instruction. Moreover, there was evidence of incremen-tal validity, as forms of expressing anger contributed toregression models beyond variables such as the person’slevel of trait driving anger. Thus, forms of expressing angerbehind the wheel appear to contribute to understandingimportant driving-related processes and outcomes beyondthat provided by knowing one’s anger level.

Prior research on driving anger expression, however, isnot without limitations. First, studies have primarilysampled young (19-year-old), predominately white non-Hispanic, university freshmen. A significant portion of suchsamples lives in on-campus housing and either does notdrive or drives minimally. Findings, therefore, may belimited by reduced driving. The present study addressedthis problem by sampling community college students whowere older, more diverse, and nearly all of whomcommuted to school, sometimes considerable distances.

Prior studies may also have been confounded by amountof exposure to frustrating, provocative events. If one grouplogs ten times more miles than another group, then the firstgroup might report more aggression and risky behavior, notas a function of their anger level or anger expression style,but because of more exposure to frustration and provoca-tion on the road and therefore more opportunities to engagethe behaviors. The present research, therefore, collectedinformation about frequency of driving and the number of

J Psychopathol Behav Assess (2007) 29:220–230 221

miles driven overall and in heavy traffic to control fordifferential exposure.

Because the present study included a much wider agerange than prior research and because driving anger andrelated variables tend to decrease with age (Schwartz andDeffenbacher 2002), age was entered into regressionmodels to control for age effects. Age, gender, and milesdriven were also explored as possible moderators ofrelationships.

Finally, the construct validity and utility of an instrumentfor things such as assessing effects of interventions fordriving anger reduction would be increased if findings fromone population generalized to another. In summary, thepresent study addressed limitations of prior research andattempted to replicate and extend findings on forms ofdriving anger expression to a new population, namely older,more diverse, commuting community college studentdrivers. The study also explored incremental validity. Thatis, if the DAX and underlying constructs it seeks tomeasure are of value, then they should add somethingbeyond well-validated emotional constructs such as drivingor trait anger or behavioral constructs of forms ofexpressing anger generally.

Materials and Methods

Participants

Participants (M age=27.05 years, SD age=9.92 years, agerange=16 to 60) were 330 (76 male, 254 female) studentsfrom psychology and sociology classes at two sites, anurban and a rural branch of a community college. Of these,3.9% (n=13) were Native American, 3.0% (n=10) AfricanAmerican, 0.9% (n=3) Asian American, 30.3% (n=100)Latino (nearly all Mexican American), 63.0% (n=208)white non-Hispanic, and 4.5% (n=15) of other ethnicbackgrounds (ns are slightly more than the total n, becauseparticipants could select more than one ethnicity). Studentsreceived extra credit (value not exceeding 1% of theirgrade) for completing questionnaires.

Instruments

Demographic Information Participants reported their age,gender, ethnicity (white non-Hispanic, Native American,Asian American, African American, Latino, or other), andweekly frequency of times they drove, number of milesdriven in heavy traffic, and total miles driven. For the latterthree variables, students entered a number into a blankfollowing the question regarding driving behavior. If astudent provided a range, the midpoint was entered as his/her score.

Driving Anger Scale (DAS) On the 33-itemDAS (Deffenbacheret al. 1994) students rated on a 1 to 5 scale (1 = not at all, 5 =very much) the degree of anger experienced when encoun-tering the situation described (e.g., another driver is goingover the speed limit, or a slow driver does not pull over andlet others by). The DAS is internally reliable (α=0.96,current α=0.94) with 10-week test–retest reliability of 0.83(Deffenbacher 2000). Trait driving anger on the DAScorrelates positively with the frequency and intensity ofanger while driving, reported risky and aggressive behavioron the road, and aggressive expression of anger whiledriving and negatively with adaptive and constructive meansof expressing anger while driving (Deffenbacher 2000;Deffenbacher et al. 2002b).

Driving Anger Expression Inventory (DAX) On the 49-itemDAX (Deffenbacher et al. 2001, 2002b, 2004) studentsrated on a 1 to 4 scale (1 = almost never, 4 = almost always)how often they express their driving anger in the mannerdescribed. The DAX yields four general forms of express-ing anger while driving. (1) The 12-item Verbal AggressiveExpression scale (αs=0.88 to 0.90, current α=0.91)measures the individual’s tendency to express angerverbally (e.g., yelling or swearing at another driver). (2)The 11-item Personal Physical Aggressive Expression scale(αs=0.80 to 0.84, current α=0.81) assesses the person’suse of his/her physical being to express anger (e.g.,engaging in a physical altercation or giving another driverthe finger). (3) The 11-item Use of the Vehicle to ExpressAnger scale (αs=0.86 to 0.90, current α=0.88) measureshow the person uses the vehicle to express his/her anger (e.g.,tailgating in anger or speeding up to frustrate anotherdriver). (4) The 15-item Adaptive/Constructive AngerExpression scale (αs=0.89 to 0.91, current α=0.90)assesses the driver’s attempts to reduce anger behind thewheel (e.g., relaxing, listening to calming music, thinkingabout other things to distract one’s self from provocation, orfocusing on safe driving). Verbal, physical, and vehicularmeans of expressing anger correlate positively with eachother, driving anger, hostile forms of driving-relatedthinking, and risky and aggressive behavior. Verbal,physical, and vehicular forms of expressing anger are notcorrelated with or negatively correlated with adaptive/constructive expression. Adaptive/constructive expressioncorrelates positively with positive coping thinking and tendsto form small negative correlations with driving anger,aggression, and risky behavior on the road (Deffenbacheret al. 2002b, 2004).

Aggressive and Risky Behavior Indexes These two meas-ures were drawn from the Driving Survey (Deffenbacher etal. 2000). The Aggressive Behavior Index (αs=0.85 to0.89, current α=0.87) assesses the frequency (0 to 5+ with

222 J Psychopathol Behav Assess (2007) 29:220–230

5+ being treated as a 5 in analyses) that the person reportedengaging in each of 13 aggressive behaviors while drivingin the last 3 months (e.g., yelling at another driver orcutting a driver off in anger). The Risky Behavior Index(αs=0.83 to 0.86, current α=0.86) involves reports of thefrequency (0 to 5+) with which the person engaged in 15risky behaviors while driving in the last 3 months (e.g.,drinking and driving, driving without a seat belt, or speeding20 or more mph over the limit). Aggressive and riskybehaviors correlate positively with each other, driving anger,hostile driving-related thinking, and verbal, physical andvehicular forms of driving anger expression (Deffenbacheret al. 2002a, 2003d, 2004).

Trait Anger Scale (TAS) On the 10-item TAS (Spielberger1988, 1999) students rated on a 1 to 4 scale (1 = almostnever, 4 = almost always) how they generally feel or reactin the manner described (reported αs in the high 0.80 range,current α=0.89). The TAS has 2-week to 2-month test–retest reliabilities from 0.70 to 0.77 (Jacobs et al. 1988;Morris et al. 1996). Trait anger correlates positively withother measures of anger and hostility, anger consequences,and aggression (Deffenbacher et al. 1996; Spielberger 1988,1999) and with driving anger, aggressive anger expression,and aggression on the road (Deffenbacher et al. 2004).

Anger Expression Inventory (AX) On the 24-item AX(Spielberger 1988) students reported on a 1 to 4 scale (1 =almost never, 4 = almost always) how they express theiranger in the manner described. The AX yields three, 8-itemmeasures of general anger expression (reported αs=0.73 to0.84). (1) Anger-In (current α=0.78) assesses suppressinganger, being critical, and harboring grudges (e.g., boiling onthe inside but not showing anger externally). (2) Anger-Out(current α=0.79) measures outward, negative verbal andphysical expression of anger (e.g., striking out at the sourceof provocation). (3) Anger-Control (current α=0.84) meas-ures the person’s attempts to calm down, reduce anger, andcope positively (e.g., calming down). Anger-In correlatesminimally with Anger-Out and Anger-Control, whichcorrelate negatively. Anger-In and Anger-Out correlatepositively with the TAS, whereas Anger-Control correlatesnegatively with the TAS. Anger-In, Anger-Out, and Anger-Control form different patterns of relationships with othermeasures of anger, personality, and physiological variables(Deffenbacher et al. 1996; Spielberger 1988, 1999).

Procedure

This study followed the protocol approved by the universityHuman Research Committee (Institutional Review Board).During class, instructors described the extra credit project

and distributed a large, stamped envelope with the inves-tigator’s return address. The envelope included a writtendescription of the project, instructions for completing andmailing materials, three informed consent forms, question-naires in the order described in the instruments section, asmall stamped return addressed envelope, and a debriefingstatement. Outside of class, students completed theinformed consent forms. They kept one for their records,returned one to their instructor so he/she could documentparticipation and extra credit, and mailed one to theinvestigator in the small envelope. Questionnaires werecompleted anonymously and mailed back in the largeenvelope.

Results

Possible Differences between Urban and Rural Campuses

Gender, age, and driving behaviors were compared toassess whether data from urban and rural sites could becombined. The rural campus contributed 28 male and 94female students, whereas the urban campus contributed 48men and 160 women. Differences in gender distributionswere not significant, χ2(1, N=330)=0.01. Urban (M=26.24, SD=9.56) and rural (M=28.43, SD=10.40) cam-puses also did not differ in terms of age, F(1, 328)=3.78.Students at the urban campus averaged 5.25 times drivingper week (SD=3.06) with an average of 39.40 total miles(SD=39.63) and 16.17 miles in heavy traffic (SD=20.71).Rural students averaged 5.67 incidents of driving (SD=7.28) with an average of 42.16 total miles (SD=37.45) and12.57 miles in heavy traffic (SD=20.45). A MANOVA onthese three driving behaviors revealed no significantmultivariate urban/rural campus differences, F(3, 325)=2.39. Because there were no significant age, gender, anddriving differences between campuses, data across siteswere collapsed and subsequent analyses are for thecombined sample.

Correlations

Pearson correlations between variables are summarized inTable 1. Effect size for correlations were interpreted withinCohen’s (1988) guidelines in which correlations from 0.10to 0.30 are considered small, from 0.30 to 0.50 moderate,and 0.50 and greater as large.

Verbal, physical, and vehicular forms of expressinganger while driving formed large positive correlations witheach other and small negative correlations with adaptive/constructive expression.

Among the demographic variables, neither age noraverage driving behavior correlated with any form of


driving anger expression. Age, however, yielded smallnegative correlations with verbal, physical and vehicularaggressive forms of anger expression and a small positivecorrelation with adaptive/constructive expression.

Verbal, physical, and vehicular forms of expressinganger while driving formed moderate to large positivecorrelations with both trait driving anger (DAS) and generalanger (TAS). Adaptive/constructive anger expression wasuncorrelated with driving anger and demonstrated a smallnegative correlation with general trait anger. The threeaggressive forms of driving anger expression formed smallto moderate positive correlations with general angersuppression, whereas adaptive/constructive expression wasunrelated to general anger suppression. Aggressive forms ofdriving anger expression formed moderate to large positivecorrelations with general outward, negative anger expres-sion and moderate negative correlations with controlledexpression of general anger. Adaptive/constructive expres-sion showed a small negative correlation with anger-outand a moderate positive correlation with controlled expres-sion of general anger. Verbal, physical, and vehicular formsof expressing anger behind the wheel formed largecorrelations with aggressive behavior and moderate to largecorrelations with risky behavior. Adaptive/constructivedriving anger expression, on the other hand, formed smallnegative correlations with aggressive and risky behavior.

Incremental Validity and Potential Moderation

The value of the forms of anger expression while driving(DAX) would be strengthened if these scales added to theprediction of aggression and risky behavior above andbeyond that provided by established measures of anger(DAS and TAS) or general anger expression (AX). Hierar-chical regressions addressed this issue (Tables 2 and 3).

Age, gender, and number of miles driven per week wereentered in Step 1 to control for potentially confoundingeffects of these variables and to remove their effects beforemeasures against which to establish incremental validitywere entered on Step 2. Age was entered, becauseaggressive and risky behaviors tend to decrease with age(Schwartz and Deffenbacher 2002) and because of thesignificant correlations noted in Table 1. Gender wasentered, because gender differences have been found insome studies. Miles driven per week was entered to controlfor exposure effects. The number of times the person droveper week correlated minimally with number of miles drivenper week (r=0.15) or miles driven per week in heavy traffic(r=0.08), whereas total miles and miles driven in heavytraffic correlated highly (r=0.64). Miles driven per weekwas chosen as the variable to enter into regression models,because it provided the most general index of total drivingexposure.

The DAS, TAS, or AX was entered on Step 2 to controlfor driving anger, general anger, and general angerexpression, respectively. These were the anger (DAS andTAS) or general anger expression variables (AX) againstwhich to establish incremental validity for the DAX. DAXscales were entered on Step 3 to see if they addedmeaningful variance after these variables were accounted.

Relevant interactions involving age, gender, and milesdriven with other variables were entered on Step 4. Thisassessed possible interactions with these variables andpossible moderation effects for age, gender, and milesdriven. As suggested by Aiken and West (1991), thesevariables were centered and multiplied to create theinteraction terms.

Given the size of the sample and the number of inter-actions explored, it was important not to over-interpretstatistically significant, but potentially trivial effects. A

Table 1 Correlation of angerexpression with other variables

*p<0.05**p<0.01***p<0.001

Measure VAE PPAE UVE AC

Verbally Aggressive Expression (VAE) 0.51*** 0.56*** −0.11*Personal Physically Aggressive Expression (PPAE) 0.60*** −0.22***Use of the Vehicle to Express Anger (UVE) −0.24***Adaptive/Constructive Expression (AC)Driving Anger Scale 0.46*** 0.41*** 0.46*** −0.08Trait Anger Scale 0.59*** 0.47*** 0.54*** −0.25***Anger-In 0.32*** 0.19** 0.26*** −0.10Anger-Out 0.54*** 0.45*** 0.44*** −0.22***Anger-Control −0.34*** −0.33*** −0.37*** 0.37***Aggressive Behavior (3 months) 0.63*** 0.61*** 0.66*** −0.29***Risky behavior (3 months) 0.37*** 0.33*** 0.52*** −0.19**Gender 0.03 −0.09 −0.05 −0.02Age −0.16** −0.20*** −0.32*** 0.16**Average miles driven per week −0.02 −0.01 −0.02 −0.00


twofold criterion for interpreting a finding as meaningfulwas, therefore, adopted. To be considered meaningful, aneffect had to be statistically significant (p<0.05) and accountfor at least 1% of variance. That is, the effect had to at leastmeet the lower limit of a small effect size (Cohen 1988) tobe considered meaningful and worthy of interpretation.

Interactions of variables with age, gender, and milesdriven on Step 4 did not contribute significantly to modelsfor risky behavior when driving anger (DAS), trait anger(TAS), or general anger expression (Anger-In, Anger-Out,and Anger-Control) was entered on Step 2, ΔR2=0.037, F(18, 293)=0.96, ΔR2=0.042, F(18, 292)=1.13, and ΔR2=0.044, F(24, 285)=0.84, respectively. Interactions were,therefore, not considered significant for models of riskybehavior.

Models for aggression, however, revealed significanteffects for the set of interactions, ΔR2=0.044, F(18, 292)=2.03, p<0.01, ΔR2=0.047, F(18, 291)=2.34, p<0.01, andΔR2=0.049, F(24, 285)=1.67, p<0.05, respectively for

models with the DAS, TAS, and AX on Step 2. When theDAS was entered on Step 2, the Age × Miles driven (β=0.10), Age × DAX Verbal Expression (β=−0.21) and Milesdriven × DAX Physical Expression (β=0.14) interactionswere significant, ts=2.13, 3.86, and 2.46, ps<0.05, 0.001,and 0.05. When the TAS was entered on Step 2, the Age ×DAX Verbal Expression (β=−0.20), Age × DAX PhysicalExpression (β=0.12), and Miles driven x DAX PhysicalExpressions (β=0.16) interactions were significant, ts=−3.68, 1.99, and 2.92, ps<0.001, 0.05, and 0.01. When AXscales were entered on Step 2, the Age × DAX VerbalExpression (β=−0.22), Miles driven × DAX PhysicalExpression (β=0.12), and Miles driven × AX-Out (β=0.21) interactions were significant, ts=−3.77, 2.10, and 2.58,ps<0.001, 0.05, and 0.01.

These interactions met the criterion for statisticalsignificance, but it was not clear if they met the criterionfor a meaningful effect (i.e., accounting for at least 1% ofthe variance). To assess the effect size for significant

Table 2 Hierarchical regressions on aggression with demographic variables on Step 1, Anger/Anger expression on Step 2, and driving angerexpression on Step 3

Step Variables entered β on Step t for β on Step F to enter step ΔR2 for Step

Regression model with Driving Anger Scale on Step 21 Age −0.30 −5.68*** 11.50*** 0.099

Gender −0.09 −1.81Miles driven 0.03 0.47

2 Driving Anger Scale 0.45 9.17*** 84.08*** 0.3133 Adaptive/constructive −0.12 −3.19** 61.73*** 0.315

Verbally aggressive 0.32 6.94***Personal physical 0.20 4.04***Use of vehicle 0.24 4.61***

Regression model with trait anger scale on Step 21 Age −0.31 −5.73*** 11.62*** 0.100


2 Trait anger scale 0.59 13.31*** 177.16*** 0.3253 Adaptive/constructive −0.08 −2.27* 41.34*** 0.200

Verbally aggressive 0.26 5.52***Personal physical 0.20 4.29***Use of vehicle 0.22 4.48***

Regression model with general anger expression on Step 21 Age −0.31 −5.69*** 11.57*** 0.099


2 Anger-In 0.10 2.09* 40.64*** 0.253Anger-Out 0.39 6.74***Anger-Control −0.15 −2.78**

3 Adaptive/constructive −0.09 −2.41* 49.94*** 0.255Verbally aggressive 0.30 6.03***Personal physical 0.20 4.18***Use of vehicle 0.25 4.87***

*p<0.05**p<0.01***p<0.001


interactions, regression models were re-run, and eachsignificant interaction was entered alone on Step 4 toassess if it accounted for at least 1% of the variance. Whenthis was done, no significant interaction accounted for morethan 0.8% of the variance. Thus, none of the interactionsmet the criterion for a meaningful effect size. It wasconcluded that interactions did not contribute meaningfullyto the models for aggression or risky behavior and did notprovide meaningful evidence of moderation effects due toage, gender, or miles driven. Models through Step 3 aresummarized in Table 2 for aggression and Table 3 for riskybehavior.

Of the demographic variables, age consistently contrib-uted to regression models. Older individuals engaged inless aggressive and risky behavior. Gender did notcontribute to models for aggression, but contributed tomodels for risky behavior. Women reported less riskybehavior than men. Miles driven did not contribute tomodels for either aggressive or risky behavior.

When entered on Step 2, driving anger (DAS), traitanger (TAS), and general anger expression (AX) contribut-ed 25 to 33% of variance in aggression and 9 to 16% ofvariance in risky behavior. In all models, greater drivinganger, trait anger, Anger-In, and Anger-Out were associatedwith more aggression and risky behavior, whereas Anger-Control was associated with less aggression and riskybehavior. Thus, driving and general anger and general angerexpression contributed large amounts of variance toaggression and moderate to large amounts of variance inrisky behavior.

The expression of anger while driving (DAX), however,accounted for an additional 20 to 32% of variance inaggression and 6 to 11% of variance in risky behavior whenentered on Step 3. All four forms of expressing anger whiledriving added significantly to models for aggression (Table2). Adaptive/constructive expression was associated withless aggression, whereas verbal, physical, and vehicularforms of expressing anger were positively associated with

Table 3 Hierarchical regressions on risky behavior with demographic variables on Step 1, Anger/Anger expression on Step 2, and driving angerexpression on Step 3

Step Variables entered β on Step t for β on Step F to enter step ΔR2 for Step

Regression model with Driving Anger Scale on Step 21 Age −0.33 −6.27*** 15.71*** 0.130

Gender −0.16 −3.11**Miles driven 0.05 0.87

2 Driving Anger Scale 0.30 5.95*** 35.44*** 0.0883 Adaptive/constructive −0.06 −1.29 13.08*** 0.113

Verbally aggressive 0.12 2.04*Personal physical −0.03 −0.53Use of vehicle 0.32 4.81***

Regression Model with Trait Anger Scale on Step 21 Age −0.33 −6.30*** 15.80*** 0.131


2 Trait anger scale 0.42 8.56*** 73.18*** 0.1643 Adaptive/constructive −0.02 −0.48 7.64*** 0.063

Verbally aggressive 0.05 0.83Personal physical −0.04 −0.66Use of vehicle 0.30 4.60***

Regression model with general anger expression on Step 21 Age −0.33 −6.27*** 15.73*** 0.130


2 Anger-In 0.14 2.62** 13.54*** 0.100Anger-Out 0.18 2.84**Anger-Control −0.12 −2.06*

3 Adaptive/constructive −0.04 −0.69 11.71*** 0.101Verbally aggressive 0.11 1.66Personal physical −0.02 −0.38Use of vehicle 0.32 4.95***

*p<0.05**p<0.01***p<0.001


increased aggression. Using the vehicle to express angerwas the primary contributor to models for risky behavior(Table 3). Using the vehicle to express anger was associatedwith greater risky behavior.

Potential Mediation

Previous analyses showed that driving anger (DAS),general anger (TAS), and general anger expression (AX)all contributed significantly to regression models ofaggressive and risky behavior. Driving anger expression(DAX) added significant variance to models when subse-quently entered into models. This gives rise to thepossibility that the DAX might fully or partially mediatethe effects of the DAS, TAS, and AX on aggressive andrisky behavior. Since both sets of variables were related toaggressive and risky behavior, the first two conditions ofBaron and Kenny’s (1986) test of mediation were estab-lished. If the DAX were entered on Step 2 and eliminatedthe variance contributed by the DAS, TAS, or AX whenentered on Step 3, then conditions for full mediation wouldbe met. If the variance contributed by the DAS, TAS, orAX on Step 3 was still significant, but reduced, thenconditions for partial mediation would be established. Inconducting analyses for possible mediation, the definitionof a meaningful amount of variance was retained (i.e., to beconsidered meaningful a variable must account for at least1% of the variance). That is, if the DAS, TAS, or AX wassignificant on Step 3, but did not contribute at least 1%additional variance, it was not considered a meaningfulamount of variance and was interpreted as evidence for fullmediation. Also, regression models retained age, gender,and miles driven per week on Step 1 so potential mediationwould not be confounded by these variables.

The DAS contributed a significant amount of variance toaggression when entered on Step 2 ( ΔR2=0.190; Table 2).When the DAX was entered on Step 2, the DAS added asignificant, but not meaningful amount of variance whenentered on Step 3, ΔR2=0.008, F(1, 310)=6.64, p<0.05.The DAX thus fully mediated effects of driving anger(DAS) on aggression. Risky behavior yielded similar, butslightly different effects. The DAS was significantlyassociated with risky behavior on Step 2 (ΔR2=0.088; Table3). The contribution of the DAS on Step 3 was greatlyreduced, but still significant, ΔR2=0.010, F(1, 311)=4.75,p<0.05, when the DAX was entered on Step 2. The DAXthus partially mediated effects of driving anger on riskybehavior.

The TAS was significantly related to aggressive (ΔR2=0.325) and risky (ΔR2=0.164) behavior when entered intomodels on Step 2 (Tables 2 and 3). When the DAX wasentered on Step 2, contributions of the TAS on Step 3 weresignificant, but greatly reduced for aggressive, ΔR2=0.025,

F(1, 309)=20.78, p<0.001, and risky behavior, ΔR2=0.035, F(1, 310)=16.98, p<0.001. The DAX, therefore,partially mediated effects of trait anger on aggression andrisky behavior on the road.

The AX was significantly associated with aggressive(ΔR2=0.253) and risky (ΔR2=0.100) behavior on Step 2(Tables 2 and 3). When the DAX was entered on Step 2, thecontribution of the AX on Step 3 was greatly reduced, butstill significant and meaningful for aggressive, ΔR2=0.011,F(1, 308)=2.94, p<0.05, but not risky behavior, ΔR2=0.011, F(1, 308)=1.64. The DAX, therefore, partiallymediated effects of general anger expression on aggressivebehavior and fully mediated effects for general angerexpression on risky behavior.

Discussion

Bivariate Relationships

Aggressive verbal, physical, and vehicular forms ofexpressing anger elicited by driving correlated positivelywith each other (rs=0.51 to 0.60), suggesting that theyassess different, but related aspects of aggressively expressinganger while driving. This degree of correlation betweenaggressive forms of expressing anger while driving isconsistent with relationships reported by others (Deffenbacheret al. 2002a, 2004). These forms of expression also formedsmall negative correlations with adaptive/constructive ex-pression, suggesting that expressing one’s anger aggressivelyand coping constructively with anger are not ends of acontinuum, but are considerably different, nearly orthogonalaspects of dealing with anger behind the wheel. This lowdegree of correlation between aggressive and adaptive/constructive anger expression has also been found by others(Deffenbacher et al. 2002a, 2004).

Aggressive forms of driving anger expression formedmoderate to large positive correlations with trait drivinganger, aggression, and risky behavior. Adaptive/construc-tive expression, on the other hand, was unrelated to drivinganger and formed small negative correlations with risky andaggressive behavior. The relationships between forms ofanger expression and their relationships to driving anger,aggressive and risky behavior are similar to those found foruniversity students (Deffenbacher et al. 2001, 2002a, 2004)and extended those findings and evidence of convergentvalidity for the DAX to an older sample of commuting,community college students.

Aggressive forms of driving anger expression alsocorrelated positively with general trait anger, outwardnegative expression of anger generally and suppression ofgeneral anger and negatively with general anger control.Adaptive/constructive expression of anger behind the wheel


correlated negatively with general trait anger and anger-outand positively with anger-control. Significant correlationswith general anger and general anger expression suggestsome overlap with these more general constructs, but thesize of the correlations suggests considerable independence,as found in prior studies (Deffenbacher et al. 2002a, 2004).

Regression Models, Incremental Validity, Moderation,and Mediation

Regression models failed to provide evidence of modera-tion for age, gender, or miles driven, and yielded differentmain effects for these variables. Age demonstrated aconsistent effect. Older students reported less aggressiveand risky behavior, replicating an age-related decreasereported by Schwartz and Deffenbacher (2002) in a studycomparing university students and their parents.

Gender was related to risky, but not aggressive behavior.Women engaged in less risky behavior than men, but didnot differ from men on aggression, similar to some studies(Deffenbacher et al. 2003a). This finding needs replicationfor two reasons. First, this was the first study withcommunity college students and thus does not establishthe reliability of a finding. Second, gender differences inaggressive and risky behavior tend to have small effectsizes and to be somewhat unstable. For example, somestudies find men report more aggressive and risky behaviorthan women (Deffenbacher et al. 2003b, 2004). Otherstudies, however, report no differences between men andwomen on aggressive or risky behavior on the road(Deffenbacher et al. 2004, 2005).

Miles driven did not relate to either aggressive or riskybehavior. This finding too needs replication, because arecent study with a somewhat similar community collegesample found that miles driven contributed to models forrisky and aggressive behavior. Those who drove morecommitted more acts of aggression and risky behavior(Morrison et al. 2006).

After demographic variables were controlled, anger,either driving anger or general trait anger, and generalanger expression contributed large amounts of variance toaggression and moderate to large amounts of variance torisky behavior. These findings extended relationships foundfor university students (Deffenbacher et al. 2002a, 2004) toa new population of drivers and demonstrated their effectsafter demographic variables were controlled, which had notbeen done in prior studies.

Ways of expressing anger while driving (DAX) addedlarge amounts of variance to the prediction of aggressionwhen effects of demographic variables and emotional (DASand TAS) and general anger expression (AX) had beenaccounted for. Moreover, models for aggression showedthat all four forms of expressing anger on the DAX

contributed significantly to aggression. This suggests thatall four make a significant contribution to understandingaggression on the road, above and beyond effects of othervariables. Regression models thus provided considerableevidence of incremental validity for the DAX in theprediction of aggression.

Although forms of expressing driving anger are nottheoretically related directly to risky behavior, findings forrisky behavior also provided support for incrementalvalidity. The DAX added moderate amounts of varianceto models after demographic and anger and anger expres-sion variables had been entered. A single form ofexpression, using the vehicle to express anger, was theprimary contributor to models of risky behavior. Thissuggests that the dynamics of driving anger expressionmay be different for risky behavior and aggression. Usingone’s vehicle as the instrument of expressing anger was theprimary predictor of risk-taking behind the wheel, whereasthe other three forms of anger expression also aided in theunderstanding of aggressiveness on the road.

Overall, findings suggested the forms of expressinganger on the DAX added something significant beyonddriving-specific or general anger and general anger expres-sion variables. This was particularly impressive in arguablythe most stringent test when general anger expression wasadded to models. That is, if driving anger expression couldbe subsumed under general anger expression, then the DAXshould not have contributed additional variance whengeneral anger expression (AX) had been entered intomodels. However, the DAX contributed an additional25.5% explained variance in aggression with all four formsof expressing anger when driving contributing. Thus, formsof expressing anger on the DAX were adding significantpower to the understanding of aggression, even when theperson’s general anger expression tendencies had alreadybeen taken into account. Moreover, the DAX contributed anadditional 10.1% of variance in risky behavior with usingof the vehicle to express anger contributing significantlyabove and beyond the AX. Thus, the constructs measuredby the DAX appear to capture something additional andunique beyond information provided by driving or generalanger or general anger expression.

Analyses also provided evidence of full or partial media-tion of other effects. The DAX fully mediated effects ofdriving anger on aggression and of general anger expressionon risky behavior. In other analyses of mediation, the amountof variance accounted for by anger and anger expressionvariables was greatly reduced, but still significant, providingconsistent evidence of partial mediation. Findings for full orpartial mediation, like those for convergent and incrementalvalidity, suggest that inclusion of forms of driving angerexpression on the DAX adds significantly to understandingaggressive and risky behavior on the road.


In summary, findings from this study provided evidencefor convergent and incremental validity for the DAX andextended findings to an older, more diverse sample of com-munity college students who may drive more than universitystudents previously studied. Additionally, the study providedevidence of full or partial mediation effects for the DAX inaccounting for aggressive and risky behavior on the road.

Implications for Intervention

Although primary goals of the research were not applied innature, findings offer at least four suggestions for inter-ventions for the reduction of driving anger and aggression.First, forms of anger expression added significantly to levelof driving anger in predicting aggression and riskybehavior, suggesting that intervention design should in-clude components that focus directly on the modification offorms of anger expression, in addition to interventions foranger reduction. Second, given that aggressive and adap-tive/constructive anger expression were nearly independent,it is not safe to assume that decreasing aggressive angerexpression will necessarily increase or enhance positivecoping with anger. Interventions should consider systematicattention to increasing positive, adaptive ways of handlinganger (e.g., engaging in aggression incompatible behavior,focusing on safe driving practices, forgiving the otherdriver’s poor behavior, etc.), as well as lowering aggressiveforms of expressing driving anger. Third, other research (e.g.,Deffenbacher et al. 2003d, 2004) has shown that somecognitive processes are more highly related to some forms ofanger expression (e.g., revengeful and retaliatory thinking ismore highly related to using the vehicle to express anger andpejorative labeling/verbally aggressive thinking is more withverbal aggressive expression). Therefore, if a cognitive-behavioral intervention is chosen, therapists might targetthese cognition-behavioral expressive connections as a unit.Finally, using the vehicle to express anger was the onlyconsistent link to risky behavior. The nature of thisrelationship should be explored more fully so that inter-ventions can be adapted to modify risky behavior as well. Ifsome risky behavior is prompted by being in an angry state,then these conditions could be identified and rehearsalactivities could explicitly target this anger-risky behaviorconnection. However, many risky behaviors (e.g., drinkingand driving, failure to use seat belts, and speeding) may beonly correlated with anger and not be motivated or mediatedby anger. Therefore, intervention design may also seek toassist clients to identify the internal and external prompts ofthese risky behaviors and target those behaviors directly.

Acknowledgments This study was supported, in part, by Grant R49/CCR811509 from the Centers for Disease Control and Prevention. Itscontents are solely the responsibility of the authors and do not

necessarily represent the official views of the Centers for DiseaseControl and Prevention.

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