Driving Simulation Experiments of Aggressive Driving and Road Rage
Phase: NA
Conditions: Aggression
Sponsor: Ohio State University

Aggressive Driving and Road Rage:  
A Series of Driving Simulation Experiments 
Amended Protocol for Experiment 2 
Amendment Approved August 15, 2022 
ClinicalTrials ID: NCT0340973 
OSU Social and Behavioral Sciences Protocol ID: 2018B0081 
Thomas Kerwin, PhD 
Brad J. Bushman, PhD 

Research Protocol 
I.
Objective
This research will examine the effects of risk and protective factors for aggressive driving in six
driving simulation experiments. We will also conduct an experiment to develop a standardized
measure of aggressive driving.
II.
Background and Rationale
Driving a car is the most dangerous behavior most people engage in every day.1 According to
the National Highway Traffic Safety Administration, over 37,000 Americans were killed in motor
vehicle crashes in 2016.2 They are the leading cause of death among 15 to 29 year olds.3
Although there are several causes of traffic crashes (e.g., texting, alcohol consumption,
inclement weather), the leading cause is aggressive driving. Aggressive driving accounts for
more than half of all traffic fatalities.4 Thus, aggressive driving is an important applied health
topic, especially for young drivers.
It is important to determine the causes of aggressive driving, which requires the experimental
method. Because it is unethical to conduct experimental studies of aggressive driving using real
vehicles on the road, researchers use driving simulators. Previous research has shown that
driving behavior in simulators closely mirrors driving behavior in actual vehicles. A review of the
available evidence concluded that driving simulators “provide a valid tool for assessing a variety
of driving performance measures such as speed, lateral position, brake onset, divided, attention,
and risky traffic behaviors.” (p. 13-1).5
Unfortunately, we could find only six driving simulator experiments,6, 7, 8, 9, 10, 11 and one of those
experiments was conducted in our lab. Thus, additional experimental research on this topic is
sorely needed. The proposed research will use state-of-the-art driving simulator technology to
determine some important causes of aggressive driving and road rage.
III. Procedures
A. Sample
A.1. Experiment 1
Participants in Experiment 1 will be a nationally representative sample of 200 American adults
(n=100 men, n=100 women) in terms of age and race. Participants will be recruited by Qualtrics,
which is an online survey company. Participants will be paid a small amount of money for
completing the survey. The sample size was determined using power analysis.12 A sample this
size will give sufficient power (> .80) to detect relatively small effects (d = 0.30, r = .20).
A.2. Experiments 2-7
Participants in Experiments 2-7 will be adults 18-21 years old, with the exception of Experiment
2, which has no upper age limit, and Experiment 5, which requires participants to be at least 21
years old. They will be recruited from Central Ohio (Franklin County) through advertisements
(e.g., newspaper, Internet) and paid $50 for their participation. All participants must have a
current driver license. Participants who have motion sickness will be excluded. There will be
n=30 participants per group. Driving simulation experiments are quite expensive to conduct
($400 per participant; $350 for the equipment plus $50 for participant payment). According to
NHTSA, the minimum acceptable number of participants for driving simulation experiments
involving driver distraction from in-vehicle devices (e.g., texting) is 24 participants per group.13
Our sample size is 25% larger than the recommended size, and should give us sufficient power

to detect hypothesized effects. Our previous experiment used 30 participants per group, and we 
were able to detect effects ranging from d = 0.43 to d = 0.61.7  
 
 
B. Detailed study procedures 
 
B.1. Measures 
 
B.1.1. Individual Difference Measures 
In addition to reporting their gender and age, participants will complete the following individual 
difference measures after they sign the consent form. 
 
B.1.1.1. Driving experience. Participants will report the number of years they 
have had a driver’s license. Of course, driving experience will be positively related to age, but 
we will measure both. 
 
B.1.1.2. Driving frequency. Consistent with opportunity theory, research has 
shown that the more frequently people drive, the more opportunities they have to be involved in 
acts of aggressive driving and road rage.14 Thus, participants in the proposed research will 
report: (1) the average number of times they drive each week, (2) the average number of miles 
they drive each week, and (3) the average amount of time spent on the road each week. 
 
B.1.1.3. Trait anger. It is useful to distinguish between state and trait anger.15 
State anger is an acute emotional-physiological reaction that ranges from mild irritation to 
intense fury and rage. Trait anger is a personality dimension that reflects the person’s chronic 
tendency to experience the emotion of state anger with greater frequency, intensity, and 
duration. Trait anger is an important antecedent of state anger and aggression.16 There is a 
large body of work linking trait anger to aggressive driving. In the proposed research, trait anger 
will be measured using the Trait Anger Scale,16 which contains 10 items (e.g., “I have a fiery 
temper”) that are rated on a 4-point scale (1 = Almost never; 2 = Sometimes; 3 = Often; 4 = 
Almost always; Cronbach α = .86; see Appendix). 
 
B.1.1.5. Narcissism. The term narcissism comes from the Greek myth about a 
handsome young man named Narcissus who fell in love with his own image reflected in the still 
water. Narcissism is characterized by excessive self-love and a selfish orientation. Narcissists 
think very well of themselves and are willing to take advantage of others. Narcissists are 
egotistical, self-focused, vain, etc. Previous research has shown that narcissism is a risk factor 
for aggressive driving.17 Narcissism will be measured using the Single Item Narcissism Scale 
(SINS): “To what extent do you agree with this statement: ‘I am a narcissist.’ (Note: The word 
“narcissist” means egotistical, self-focused, vain, etc.),” which is scored using a 7-point scale (1 
= Not very true of me to 7 = Very true of me; see Appendix).18 
 
B.1.1.6. Empathy. Empathy involves feeling compassion for others and 
imagining how they feel. We expect empathy to be negatively related to aggressive driving. 
Empathy will be measured using the Single Item Trait Empathy Scale (SITES): “To what extent 
does the following statement describe you: ‘I am an empathetic person.’ (Note: An empathetic 
person understands others' feelings, and experiences care and concern for them.),” which is 
scored using a 5-point scale (Not very true of me to 5 = Very true of me; see Appendix). 
 
B.1.1.7. Mindfulness. Mindfulness is a state and trait characterized by a 
receptive and non-evaluative awareness of present experiences.19  Recent research shows that 
mindfulness is a protective factor for aggressive driving.20 My colleagues and I are developing a 
Single Item Mindfulness Scale21 that will be included in the battery of individual difference 

measures. The item is: “To what extent do you agree with this statement: ‘I am often aware of 
myself, others, and my environment, and I accept things as they are,’” which is rated on a 7-
point scale (1 = Do not agree at all to 4 = Moderately agree to 7 Strongly agree; see Appendix). 
B.1.1.8. Self-reported aggressive and prosocial driving. Although a number
of self-report measures of aggressive driving exist, they are highly correlated with each other. 
The proposed research will use Prosocial and Aggressive Driving Inventory, because it is the 
only scale that measures both prosocial and aggressive driving (see Appendix).22 The prosocial 
driving subscale contains 17 items (e.g., “Drive with extra care around pedestrians” and “Slow 
down in a construction zone”; Cronbach α = .90), whereas the aggressive driving subscale 
contains 12 items (e.g., “Make rude gestures at other drivers when they do something I do not 
like” and “Speed up when another vehicle tries to overtake me”; Cronbach α = .86). All items are 
rated on a 6-point scale (1=never to 6=always). 
B.1.2. State Anger
In the proposed research, state anger will be measured using the State Anger Scale 
(Spielberger, 1988), which contains 15 items (e.g., “I feel angry”) that are rated on a  
4-point scale (1 = Not at all; 2 = Somewhat; 3 = Moderately so; 4 = Very much so; Cronbach α =
.93; see Appendix).
B.1.3. Hostile Appraisals
Research has shown that the hostile attribution bias can influence appraisal and decision 
processes. The hostile attribution bias is the tendency to perceive ambiguous actions by others 
as aggressive.23 For example, if a driver cuts you off in traffic, a hostile attribution would be that 
the driver did it purposely (rather than accidentally). Research has shown that attributing 
causality to an offending driver predicts aggressive driving.24 In the proposed research, we will 
measure whether people assign blame to other drivers using a measure successfully used in 
previous research on aggressive driving.25 For each provocative event, participants will be 
asked whether the actions of the other driver were intentional or accidental. For example, “Do 
you think the other driver deliberately cut you off?” versus “I think the other driver cut me off by 
accident” (1=Not at all to 7=Very much so). In Experiments 2-7, we expect hostile appraisals to 
be positively related to aggressive driving. 
B.1.4. Aggressive Driving
The two primary measures of aggressive driving will be tailgating and speeding. We will use 
three tailgating measures based on the number of seconds between the participant’s car and 
the car in front of them. It is widely recommended that drivers use a 4-second following rule at 
speeds above 30 MPH (48.3 KPH), in heavy traffic, or when there are many obstacles 
(Nationwide, n.d.), as in the present driving scenario. Under normal driving conditions and 
speeds below 30 MPH, a 3-second rule is recommended. For speeds above 30 MPH, a 3-
second rule is considered risky and dangerous. We also will consider a 2-second rule, which is 
considered extremely risky and dangerous. Tailgating will be calculated as the proportion of 
time participants breaks each of the three rules (i.e., 4-second, 3-second, 2-second). 
Average speed is a poor measure of speeding because it depends heavily on random 
influences. Instead, we will use a relatively high-speed cutoff (e.g., 50 MPH) before averaging 
because it removes the variability due to traffic when the participant is not travelling at a high 
speed.   
Other measures of aggressive driving will be combined to reduce the probability of Type 
I errors that could occur by conducting multiple tests for separate measures. These will include 
off-road driving (e.g., crossing the double solid yellow lines into oncoming traffic, driving on the 
shoulder), horn honking, verbal aggression, and aggressive gestures (e.g., giving another driver 
the middle finger). 

B.1.5. Road Rage 
Road rage is an extreme form of aggressive driving and is a criminal offense. The three primary 
measures of road rage will be colliding into other vehicles, bicyclists, and pedestrians. Acts of 
road rage are expected to occur far less frequently than acts of aggressive driving. 
 
B.2. Driving Simulator 
The proposed research will be conducted at the Ohio State Driving Simulation 
Laboratory, which uses a Realtime Technologies Inc. driving simulator. The car is a 2010 Honda 
Accord cab mounted on a 6-degrees of freedom motion-base platform. The vehicle is 
surrounded by a cylindrical projection screen lit by five projectors, which gives a 260° edge-
blended field of view. The rear-view mirror reflects an additional projected screen to the rear of 
the car. Liquid crystal displays (LCD) provide a realistic view in the side mirrors. The interior of 
the vehicle is the same as for the original car (e.g., automatic transmission, gas pedal, brake 
pedal, turn signal, headlights, steering wheel, horn). A simulated dashboard displays speed, 
gear, and turn signal, and headlight information. Three cameras are mounted in the interior of 
the vehicle, to capture both the participant and the simulated driving scenario. The cameras 
allow us to record verbally aggressive comments participants make to other drivers. Speakers 
external to the vehicle provide simulated environmental audio (e.g., engine noise, wind noise, 
passing vehicles).  
 
B.3. Driving Scenario 
To measure aggressive driving and road rage, our research team constructed a driving 
scenario. The Ohio State driving scenario was created using SimCreator (RTI, Inc.) software. 
The driving scenario mimics a two-lane road with occasional curves, and has a posted speed of 
60 miles per hour (MPH) [96.6 kilometers per hour (KPH)]. The simulated traffic is programmed 
to have an average speed of 55 MPH (88.5 KPH) — 5 MPH slower than the posted speed. Five 
frustrating events are programmed to take place at pre-determined spots in the driving scenario: 
(1) a car pulls out in front of the participant from a side-road, (2) traffic jam (i.e., complete road 
blockage with many cars in front of the participant. After the participant stops and waits 10 
seconds, the other cars slowly pull ahead. After the participant starts driving again, the other 
cars stop again for 10 seconds), (3) construction zone (i.e., one lane was closed, and traffic 
slowed down as the lanes merged), (4) a mimic car that copies the participant’s car, and (5) a 
short traffic light (i.e., 60 seconds red and 5 seconds green). In addition, some of the other 
vehicles will honk at the participant. The other driving events are random. Although all the other 
cars are computer generated and controlled, participants will be told that other participants are 
driving some of the other vehicles. This will make the driving situation more realistic, and will 
give participants targets for their anger and aggression. There will also be triggered bicyclists 
and pedestrians on the course to slow participants down. Each participant will practice driving 
for 3-5 minutes to get used to the simulator and to indicate whether he or she experiences 
motion sickness. Next, the participant will drive the simulated scenario, which takes 15-25 
minutes.  
 
B.4. Procedures 
 
B.4.1. Experiment 1: Development of a Standardized Measure of Aggressive 
Driving for Driving Simulation Experiments 
The aim of Experiment 1 is to develop a standardized measure of aggressive driving for driver 
simulation experiments. Participants will be a nationally representative sample of 200 American 
adults (n=100 men, n=100 women) in terms of age and race. Participants will be recruited by 
Qualtrics, which is an online survey company. After giving their consent, participants will 
complete the personal variables described in section B.1.1 above (i.e., gender, age, driving 
experience, driving frequency, trait anger, self-reported aggressive and prosocial driving). Next, 
participants will watch several short videos from our driving simulation lab experiments. 

Because speeding and tailgating are our two primary measures of aggressive driving, the 
videos will differ in terms of how fast the car is traveling over the posted speed limit, and how 
closely the car is following another vehicle. We will also show videos containing less common 
measures of aggressive driving (e.g., driving on shoulder, crossing a solid yellow line to pass 
another vehicle), as well as videos depicting road rage (e.g., hitting another vehicle, cyclist, or 
pedestrian). Participants will indicate whether the driver’s behavior was aggressive (coded 1) or 
nonaggressive (coded 0), and will rate how aggressive it was on an 11-point scale (0=not at all 
aggressive to 10=extremely aggressive). After participants rate the videos, they will be 
debriefed. 
 
B.4.2. Experiment 2: Violent and Nonviolent Racing Video Games 
Experiment 2 tests whether participants actually drive more aggressively after a playing a 
violent or nonviolent racing video game. Participants will be 40 American college students 18 
and older. There will be 20 participants in each of the two conditions. After giving their consent, 
participants will complete the personal variables described in section B.1.1. In Experiment 2, we 
will also ask participants how many hours per week they spend playing video games, whether 
they have played the video games used in this experiment, and what their three favorite video 
games are. Next, participants will be randomly assigned to play one of two types of video 
games for 15 minutes: (1) a racing game that rewards players for engaging in violent actions 
such as hitting other vehicles, or (2) a nonviolent racing video game. The participants in both 
conditions will play the video game Mario Kart 8: Deluxe on the Nintendo Switch to control for 
other dimensions that could be related to aggressive driving (e.g., competition, frustration). All 
participants will play the game using the same character and vehicle and they will race on the 
same courses. In condition 1, players will have the option to use items to attack other racers, 
while in condition 2 the items will be turned off. After videogame play, participants will rate the 
game they played on several dimensions (i.e., how absorbing, action-packed, “addicting,” 
arousing, boring, challenging, difficult, enjoyable, entertaining, exciting, frustrating, fun, 
involving, stimulating, stressful, and violent it was). The violence rating will be used as a 
manipulation check. The other ratings will be used as possible covariates.  After participants 
complete the driving scenario, the state anger measure (section B.1.2), and the hostile appraisal 
measure (section B.1.3), they will be thoroughly debriefed. 
 
B.4.3. Experiment 3: Racial Tension on the Road 
Experiment 3 tests the impact of racial bumper stickers on aggressive driving in black and white 
motorists. Participants will be 120 adults (n=60 black, n=60 white) from a community sample. 
After giving their consent, participants will complete the personal variables described in section 
B.1.1. In Experiment 3, participants will also complete the race Implicit Association Test (IAT) 
from the Project Implicit website (https://implicit.harvard.edu/implicit/). In this IAT, photos of 
White and Black male and female faces are paired with “good” words (e.g., joy, love, peace) or 
“bad” words (e.g., terrible, horrible, evil). Slower responses to the “White / Bad” and “Black / 
Good” pairings than to “White / Good” and “Black / Bad” pairings are considered to be indicative 
of more negative attitudes about black people than white people. Participants will also report 
their political party (i.e., Republican, Democrat, Neither/Independent). Some of the other cars in 
the driving scenario will contain bumper stickers. Experiment 3 contains four conditions: (1) 
white participants / “All Lives Matter” bumper sticker, (2) black participants / “All Lives Matter” 
bumper sticker, (3) white participants / “Black Lives Matter” bumper sticker, (4) black 
participants / “Black Lives Matter” bumper sticker. Assignment to the bumper sticker condition 
will be random. After participants complete the driving scenario, the state anger measure, and 
the hostile appraisal measure. Finally, participants will report their attitudes toward the Black 
Lives Matter and All Lives Matter movements (-10=extremely unfavorable to 10=extremely 
favorable). A debriefing will follow. 
 
B.4.4. Experiment 4: Political Tension on the Road 

Experiment 4 tests the impact of political bumper stickers on aggressive driving in Republicans 
versus Democrats. Participants will be 120 adults (n=60 Republicans, n=60 Democrats) from a 
community sample. After giving their consent, participants will complete the personal variables 
described in section B.1.1. Some of the other cars in the driving scenario will contain bumper 
stickers. Experiment 4 contains four conditions: (1) Republican participants / “Donald Trump for 
President 2016” bumper stickers, (2) Republican participants / “Hillary Clinton for President 
2016” bumper stickers, (3) Democrat participants / “Donald Trump for President 2016” bumper 
stickers, (4) Democrat participants / “Hillary Clinton for President 2016” bumper stickers. After 
participants complete the driving scenario, the state anger measure, and the hostile appraisal 
measure. Finally, participants will report their attitudes toward Donald Trump and Hillary Clinton 
(-10=extremely unfavorable to 10=extremely favorable), and who they voted for in the 2016 
presidential election (if they voted). A debriefing will follow. 
 
B.4.5. Experiment 5: Alcohol Cues 
Experiment 5 will test whether the mere presence of alcohol-related cues can increase 
aggressive driving. Participants will be 60 adults (aged 21+) from a community sample. After 
giving their consent, participants will complete the personal variables described in section B.1.1. 
Next, participants will be randomly assigned to one of two conditions: (1) a twelve pack 
cardboard container of beer cans on the passenger seat, or (2) a twelve pack cardboard 
container of sparkling water on the passenger seat. In actuality, both containers will hold water 
cans to avoid bringing alcohol into the lab. Participants will be told that the object on the seat is 
part of a different experiment that the other experimenter forgot to clean up, and that they 
should ignore it. After participants complete the driving scenario, the state anger measure, and 
the hostile appraisal measure, they will be thoroughly debriefed. 
 
B.4.6. Experiment 6: Violent Music 
Experiment 6 will test the effects of music with aggressive versus prosocial lyrics on aggressive 
driving. The tempo of the music will also be manipulated because it might influence arousal 
levels. Participants will be 150 adults from a community sample. After giving their consent, 
participants will complete the personal variables described in section B.1.1. Music will be played 
over the car’s sound system. Participants will be randomly assigned to one of five conditions: (1) 
violent lyrics / upbeat tempo, (2) violent lyrics / calm tempo, (3) prosocial lyrics / upbeat tempo, 
(4) prosocial lyrics / calm tempo, or (5) no music control. After participants complete the driving 
scenario, the state anger measure, and the hostile appraisal measure, they will be thoroughly 
debriefed. 
 
B.4.7. Experiment 7: Roadside Objects 
Experiment 7 tests whether roadside vegetation can decrease aggressive driving, and whether 
roadside trash can increase aggressive driving. Participants will be 90 adults from a community 
sample. After giving their consent, participants will complete the personal variables described in 
section B.1.1. Next, they will complete the Enjoyment of Nature Scale, which contains 7 items 
(e.g., “I like to see wild flowers in nature” and “Being in the natural environment makes me feel 
peaceful”), which are scored using a 5-point scale (1=strongly disagree to 5= strongly disagree; 
Cronbach α = .87; see Appendix).26 Next, participants will be randomly assigned to one of three 
driving scenarios: (1) roadside vegetation, (2) trash, or (3) control (no roadside vegetation / no 
trash). After participants complete the driving scenario, the state anger measure, and the hostile 
appraisal measure, they will be thoroughly debriefed. 
 
C. Internal Validity 
 
The present research is high in internal validity because all studies use experimental designs in 
which participants are randomly assigned to conditions 
 

D. Data Analysis 
 
The data will be analyzed in R. 
 
 
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Effects of Video Game Violence on 
Driving Aggression 
Analysis plan 
 
1 RESEARCH QUESTIONS 
1.1 HYPOTHESES 
1. It is predicted that drivers who have just played a video game with cartoon violence will have 
higher measures of aggressive driving than drivers who have just played a video game without 
cartoon violence. 
2. It is predicted that state anger mediates the relationship between the engaging in video game 
violence and aggressive driving.  
2 DESIGN PLAN 
2.1 STUDY TYPE 
This is an experiment with a mixed between-subjects, within-subjects design.  
2.2 BLINDING 
The participants are not told of the true purpose of the study before it is completed. The experimenters 
are not blinded as to which group the participants belong to. 
2.3 STUDY DESIGN 
The experiment is a between-subjects, two group design. In the violent video game group, participants 
will be asked to play the video game Mario Kart with standard settings before driving in the simulator. In 
the non-violent video game group, participants will be asked to play the video game Mario Kart with 
attack items turned off before driving in the simulator. Each participant will drive the same scenario in 
the driving simulator. The driving scenario mimics a two-lane road with a posted speed limit of 60 mph 
(96.6 km per hour). The simulated traffic was programmed to have an average speed of 55 mph (88.5 
kph). Five frustrating events were programmed to take place at pre-determined spots in the driving 
scenario: (1) a car pulled out in front of the participant from a side-road, (2) a traffic jam, (3) a 
construction zone, (4) a mimic car that copied the participant's car, and (5) a short traffic light. Each 
frustrating event occurred once. The frustrating events were programmed to take place at pre-
determined locations in the driving scenario.  

3 SAMPLING PLAN  
3.1 DATA COLLECTION PROCEDURES 
Participants will need to hold a driving license. They will also be screened for car or simulator sickness.  
Participants will come to the Driving Simulation Lab, read and sign the consent form, and be assigned to 
play the violent or nonviolent video game. They will then drive on the simulator, where driving data will 
be recorded. 
3.2 SAMPLE SIZE 
The target participant size is 60, with 30 participants in the violent video game group and 30 participants 
in the non-violent video game group. This sample size was based on a previous study.1 With driving 
simulation studies, simulator sickness is a possibility, and individuals who decide to stop due to 
simulator sickness will be replaced.  
3.3 STOPPING RULE 
Data will stop being collected when full data for 30 participants in both groups has been collected. 
4 VARIABLES  
4.1 MANIPULATED VARIABLES 
The only manipulation is the level of violence in the video game played before the participants drive in 
the scenario. 
4.2 MEASURED VARIABLES 
4.2.1 Driving behavior 
A primary measure of driving aggression is following distance. A very close following distance (tailgating) 
can indicate aggressive driving. Following distance is often measured in headway time, meaning the 
number of seconds it takes for the driver’s car to get to the position of the vehicle ahead of it. This 
corresponds to the greater safety margins that are needed when driving at higher rates of speed. Four 
variations of following distance metrics will be used: the proportion of time driving over 30 MPH where 
a participant had a following distance of less than one second, two seconds, three seconds, or four 
seconds. 
 
1 Bushman, B. J., Kerwin, T., *Whitlock, T., & Weisenberger, J. M. (2017). The weapons effect on wheels: Motorists 
drive more aggressively when there is a gun in the vehicle. Journal of Experimental Social Psychology, 73, 82-85. 
DOI: 10.1016/j.jesp.2017.06.007   

A second primary measure of driving aggression is speed. Faster speeds are often associated with higher 
aggression. We will use mean speed of all speeds above 50 MPH to exclude parts of the drive where the 
participant is stopped or going very slowly. 
A third measure of driving aggression is collisions with other vehicles. We expect the number of 
collisions to be small overall, but they will be measured. 
A fourth measure of driving aggression are grouped into “other aggressive actions.” These include 
commonly accepted aggressive driving actions that do not fit into another category. These are off-road 
driving (e.g., crossing the double solid yellow lines into oncoming traffic, driving on the shoulder), 
attempting to honk the horn, verbal aggression (e.g., “This guy's a dickhead”), and aggressive gestures 
(e.g., giving another driver the middle finger). 
All of these measures will be calculated in regions. These regions are defined as between the time points 
for the start of the scenario, each frustrating event, and the end of the scenario, resulting in five regions.  
4.2.2 State anger 
After driving in the simulator, participants will complete a state anger measure. We test whether it 
mediates the link between type of video game played and aggressive driving.  
4.2.3 Individual differences 
Before driving in the simulator, participants will fill out a Trait anger survey, the Prosocial and Aggressive 
Driving Inventory (PADI), a single item narcissism scale, a single item empathy scale and a single item 
mindfulness scale. They will also report gender.  These self-report items will be included in the analysis 
as control variables.  
4.2.4 Game-related differences 
After driving in the simulator, participants will answer some questions related to the video game they 
played. This includes: 
1. A measure of general exposure to violent video games, determined by the number of violent 
games listed as their three favorites. A violent video game is one that the ESRB has rated as 
violent.  
2. A measure of general exposure to violent racing video games, determined by the number of 
violent games listed as their three favorites. A violent video game is one that the ESRB has rated 
as violent and that is in the racing genre. 
3. Identification with the character of the game. 
4. Indication of realism of the game played. 
5. Hours playing video games. 
6. Prior experience with the Mario Kart game series. 

5 ANALYSIS PLAN  
5.1 STATISTICAL MODELS 
5.1.1 Manipulation check 
We expect that the survey result of game violence will differ significantly between the two game 
options. Ordered logistic regression using the `polr` function in the `MASS` R package will be used to 
report the results of a chi-square test for goodness of fit and the 95% confidence interval for the log 
odds for the formula `violence_rating~Game`. 
5.1.2 Primary hypothesis testing 
We will conduct a linear mixed model analysis of the main aggressive driving effects using R, the `afex` 
package and the `lme4` package. In the `lme4` package syntax, our proposed models are of the form: 
DV ~ Game*Region + individual_covariates + game_covariates + (1|Participant) 
individual_covariates = Gender + trait_anger + prococial_driving + aggressive_driving + 
narcissism + empathy + mindfulness  
game_covariates = exposureToViolentGames + exposureToViolentRacingGames + 
identificationWithCharacter + gameRealism + hoursPlayingGames + experienceWithMarioKart 
(1|Participant) designates Participant identity as a random effect in the model. DV is the dependent 
variable, which in this case are the following distance and speeding variables. Game is which game they 
played (violent or non-violent). Region refers to the previously defined aggravating event regions. 
We will have a similar model extended to a generalized linear mixed model with a Poisson function for 
the incidence of collisions and other aggressive actions, since that is count data.  
For hypothesis 1, we will use `afex`2 and `lme4`3 to report a type III sum of squares ANOVA-like analysis 
for each of the linear mixed models, including the beta and p-values for all predictors. 
For hypothesis 2, we will use `mediation`4 and `lme4` to compare models for each dependent variable 
and including state anger with a model that has state anger as the dependent variable: 
DV ~ Game*Region + individual_covariates + game_covariates + StateAnger + 
(1|Participant) 
StateAnger ~ Game*Region + individual_covariates + game_covariates + (1|Participant) 
We will report on the average causal mediation effect (ACME) value and if it is significant or not. 
 
2 Henrik Singmann, Ben Bolker, Jake Westfall, Frederik Aust and Mattan S. Ben-Shachar (2021). 
  afex: Analysis of Factorial Experiments. R package version 1.0-1.  https://CRAN.R-project.org/package=afex 
3 Bates D, Mächler M, Bolker B, Walker S (2015). “Fitting Linear Mixed-Effects Models Using lme4.” Journal of 
Statistical Software, 67(1), 1–48. doi: 10.18637/jss.v067.i01. 
4   Dustin Tingley, Teppei Yamamoto, Kentaro Hirose, Luke Keele, Kosuke Imai (2014). mediation: 
  R Package for Causal Mediation Analysis. Journal of Statistical Software, 59(5), 1-38. URL 
  http://www.jstatsoft.org/v59/i05/. 

5.2 INFERENCE CRITERIA 
p-values will be used to determine if the values seen are statistically significant from those expected if 
the null hypothesis is correct. The alpha level for all analyses is set to .05. Adjustment for multiple 
hypothesis testing will be done by the false discovery rate adjustment method by Benjamini and 
Hochberg.5 
5.3 TRANSFORMATIONS 
Numerical predictor variables (Trait Anger and PADI scores) will be centered on the mean.  
5.4 MISSING DATA 
If we do not have full driving data, due to a participant stopping before the end of the scenario or due to 
equipment error, we will exclude data from that participant. 
 
 
5 Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to 
multiple testing. Journal of the Royal Statistical Society Series B, 57, 289–300. doi: 10.1111/j.2517-
6161.1995.tb02031.x. https://www.jstor.org/stable/2346101.