Violent Video Games and Physical Aggression: Evidence for a Selection Effect Among Adolescents

Abstract

Longitudinal studies investigating the relationship of aggression and violent video games are still scarce. Most of the previous studies focused on children or younger adolescents and relied on convenience samples. This paper presents data from a 1-year longitudinal study of N = 276 video game players aged 14 to 21 drawn from a representative sample of German gamers. We tested both whether the use of violent games predicts physical aggression (i.e., the socialization hypothesis) and whether physical aggression predicts the subsequent use of violent games (i.e., the selection hypothesis). The results support the selection hypotheses for the group of adolescents aged 14 to 17. For the group of young adults (18–21), we found no evidence for both the socialization and the selection hypothesis. Our findings suggest that the use of violent video games is not a substantial predictor of physical aggression, at least in the later phases of adolescence and early adulthood. The differences we found between the age groups show that age plays an important role in the relationship of aggression and violent video games and that research in this area can benefit from a more individualistic perspective that takes into account both intraindividual developmental change and interindividual differences between players.

Full text

Psychology of Popular Media Culture
Violent Video Games and Physical Aggression: Evidence
for a Selection Effect Among Adolescents
Johannes Breuer, Jens Vogelgesang, Thorsten Quandt, and Ruth Festl
Online First Publication, February 16, 2015. http://dx.doi.org/10.1037/ppm0000035
CITATION
Breuer, J., Vogelgesang, J., Quandt, T., & Festl, R. (2015, February 16). Violent Video Games
and Physical Aggression: Evidence for a Selection Effect Among Adolescents. Psychology of
Popular Media Culture. Advance online publication. http://dx.doi.org/10.1037/ppm0000035

Violent Video Games and Physical Aggression:
Evidence for a Selection Effect Among Adolescents
Johannes Breuer
University of Münster Jens Vogelgesang
University of Erfurt
Thorsten Quandt
University of Münster Ruth Festl
University of Münster and University of
Hohenheim
Longitudinal studies investigating the relationship of aggression and violent video
games are still scarce. Most of the previous studies focused on children or younger
adolescents and relied on convenience samples. This paper presents data from a 1-year
longitudinal study of N⫽276 video game players aged 14 to 21 drawn from a
representative sample of German gamers. We tested both whether the use of violent
games predicts physical aggression (i.e., the socialization hypothesis) and whether
physical aggression predicts the subsequent use of violent games (i.e., the selection
hypothesis). The results support the selection hypotheses for the group of adolescents
aged 14 to 17. For the group of young adults (18–21), we found no evidence for both
the socialization and the selection hypothesis. Our findings suggest that the use of
violent video games is not a substantial predictor of physical aggression, at least in the
later phases of adolescence and early adulthood. The differences we found between the
age groups show that age plays an important role in the relationship of aggression and
violent video games and that research in this area can benefit from a more individu-
alistic perspective that takes into account both intraindividual developmental change
and interindividual differences between players.
Keywords: video games, violence, aggression, adolescents, young adults
From the earliest investigations into the rela-
tionship of video game
1
use and aggression in
the 1980s (Cooper & Mackie, 1986; Dominick,
1984; Silvern & Williamson, 1987; Winkel,
Novak, & Hopson, 1987) until today, hundreds
of experimental and correlational studies have
been conducted. Despite the large number of
studies, the debate about the link between video
games and aggression is ongoing, not only in
politics and the mass media, but also within
academia (Bushman & Huesmann, 2014; Elson
& Ferguson, 2014a, 2014b; Krahé, 2014; War-
burton, 2014). While all of the available meta-
analyses (Anderson et al., 2010; Ferguson,
2007; Ferguson & Kilburn, 2009; Sherry, 2001,
2007) found a relationship between aggression
and the use of (violent) video games, the size
and interpretation of this connection differ
largely between these studies; as do the defini-
tions and measurement of violent content and
1
We use the term video games as an umbrella term that
includes all types of digital games, whether they are played
on a PC, home consoles, handhelds, or mobile devices. We
decided to use “video game” because it is the most common
term in the literature and it is easier to read than the
composite “computer and video games” or the more aca-
demic denomination “digital games.”
Johannes Breuer, Department of Communication, Uni-
versity of Münster; Jens Vogelgesang, Department of Com-
munication, University of Erfurt; Thorsten Quandt, Depart-
ment of Communication, University of Münster; Ruth Festl,
Department of Communication, University of Münster, and
Department of Communication, University of Hohenheim.
The research leading to these results has received funding
from the European Union’s Seventh Framework Pro-
gramme (FP7/2007–2013) under grant agreement number
240864 (SOFOGA).
Correspondence concerning this article should be ad-
dressed to Johannes Breuer, Department of Communication,
University of Münster, Bispinghof 9-14, 48143 Münster,
Germany. E-mail: johannes.breuer@uni-muenster.de
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2015, Vol. 3, No. 3, 000 2160-4134/15/$12.00 http://dx.doi.org/10.1037/ppm0000035
1

aggression in the studies that were included in
these meta-analyses. In addition, some meta-
analyses only found a relationship for aggres-
sive thoughts or feelings, but not for aggressive
behavior. There is also a controversy about
what exactly causes this link and, most impor-
tantly, about the direction of the (potential) ef-
fects.
Experimental research on video games and
aggression has been criticized for a lack of
ecological validity and the unstandardized use
of measures of aggression that have not been
properly validated (Ferguson & Rueda, 2009;
Ferguson, Smith, Miller-Stratton, Fritz, & Hei-
nrich, 2008; Ritter & Eslea, 2005; Tedeschi &
Quigley, 1996). The issue of the real-world
implications of findings from laboratory studies
is further complicated by the fact that they can
only investigate short-term effects that often
only last for a few minutes (Barlett, Branch,
Rodeheffer, & Harris, 2009). Cross-sectional
correlational research, on the other hand, typi-
cally has larger samples, but is unsuitable for
making any claims about the direction of the
effect. Longitudinal studies combine the advan-
tages of cross-sectional and experimental stud-
ies, as they use larger samples than most exper-
imental studies and allow to sort out the
temporal precedence between the variables of
interest. Although it is still possible that addi-
tional variables are responsible for the temporal
order, given a sound control of potentially rel-
evant third variables, panel studies allow to
make claims about long-term effects that both
cross-sectional and experimental research do
not allow. Nonetheless, while panel data can
help to determine direction and strengths of
effects by testing for covariation and controlling
for temporal order, only controlled experiments
provide the means to actually prove causality
(Finkel, 1995). Compared with the abundance
of cross-sectional survey studies and experi-
mental research, panel studies on video games
and aggression are still scarce. The meta-
analysis by Anderson et al. (2010), for example,
included 34 effect sizes from longitudinal stud-
ies
2
and Ferguson and Kilburn (2009) used data
from five longitudinal studies. While several
longitudinal studies use a composite score for
media violence that includes video games (e.g.,
Ferguson, Ivory, & Beaver, 2013; Gentile,
Coyne, & Walsh, 2011; Krahé, Busching, &
Möller, 2012; Krahé & Möller, 2010; Ostrov,
Gentile, & Crick, 2006), there are relatively few
that look specifically at the effects of video
games. Among those studies that explicitly in-
vestigate video games, some only look at rela-
tively brief periods of several months, and al-
most all studies rely on convenience samples
and focus on children or adolescents.
In longitudinal research on media violence
and aggression, there are two seemingly com-
peting hypotheses. The socialization hypothesis
states that the repeated use of violent media
leads to an increase of aggression over time,
whereas the selection hypothesis is based on the
idea of selective exposure (Zillmann & Bryant,
1985) and posits that individuals who are more
aggressive will tend to choose (more) violent
media content. The downward spiral model
(Slater, Henry, Swaim, & Anderson, 2003)
combines these hypotheses by proposing that
individuals higher in trait aggression will
choose more violent media content, which, in
turn, increases their level of aggression. As with
the experimental and cross-sectional studies,
evidence from longitudinal studies on the rela-
tionship between (violent) video games and ag-
gression is mixed at best. Some studies found a
media effect (Anderson et al., 2008; Hopf, Hu-
ber, & Wei␤, 2008; Möller & Krahé, 2009),
while others report selection effects (von
Salisch, Vogelgesang, Kristen, & Oppl, 2011),
provide evidence for both (Slater et al., 2003),
or found no effects (Ferguson, 2011; Ferguson,
Garza, Jerabeck, Ramos, & Galindo, 2013; Fer-
guson, San Miguel, Garza, & Jerabeck, 2012;
Wallenius & Punamäki, 2008; Williams &
Skoric, 2005).
A limitation of the previous longitudinal
studies is that almost all of them rely on con-
venience samples that are mostly composed of
students from elementary schools, high schools,
or colleges located in the areas where the re-
spective researchers are based. Most studies
also focus on specific grades, thereby reducing
the age range of participants. In addition, even
longitudinal studies often only test one direction
of effects; mostly the socialization hypothesis.
The goal of the current study was to address
2
Anderson et al. (2010) do not report the number of
longitudinal studies in their paper. This number should be
substantially lower than the number of effect sizes, as most
longitudinal studies include cross-sectional and longitudinal
effects (often also for different dependent variables).
2 BREUER, VOGELGESANG, QUANDT, AND FESTL
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some of these issues by testing both the social-
ization and the selection hypothesis and com-
paring these relationships for adolescents and
young adults, as these groups differ with regard
to their developmental stage as well as their
access to (violent) video games.
Theories Explaining Long-Term Effects of
Video Games on Aggression
In the field of media violence research, there
are three comprehensive theoretical models that
aim at explaining the relationship between vio-
lent video games and aggression. The most pop-
ular is the General Aggression Model (GAM;
Anderson & Bushman, 2002). The GAM com-
bines the assumptions of social learning (Ban-
dura, 1977), excitation transfer (Zillmann,
1983), and cognitive neoassociation (Berkow-
itz, 1990). Long-term effects of video game
violence are explained mainly by mechanisms
of social learning and cognitive neoassociation.
Put briefly, the GAM posits that the repeated
use of violent media causes a learning, re-
hearsal, and reinforcement of aggressive be-
liefs, attitudes, perceptual and expectation sche-
mata, and behavioral scripts, as well as an
emotional desensitization to violence. In their
combination, all of these processes can lead to
an increase in aggressive personality and, ulti-
mately, affect the likelihood to (re-) act aggres-
sively in social encounters in the real world.
Although the GAM allows to formulate spe-
cific hypotheses about the effects of violent
video games and has been widely used in pre-
vious research, it has been criticized for its
overreliance on social learning, the neglect of
biological factors, the conceptualization of me-
dia use(r)s as passive, and the insufficient dis-
tinction between real and fictional violence
(Ferguson & Dyck, 2012). An alternative theory
that focuses more on genetic factors and attri-
butes of the social environment is the Catalyst
Model (Ferguson et al., 2008). In essence, the
Catalyst Model suggests that the roots of (vio-
lent) criminal and aggressive behavior are ge-
netic and proximal social factors, such as family
and peer influences, and their interaction,
whereas distal social influences, such as media
violence, only have a negligible effect (Fergu-
son, Ivory, et al., 2013). In this model, violent
media are considered as stylistic catalysts in-
stead of sources of aggression. This means that
individuals with an increased tendency for ag-
gressive behavior may model violent acts they
have seen in the media, whereas the actual
inclination to (re-) act aggressively is not influ-
enced or caused by violent media. The main
limitation of the Catalyst Model is that it is
difficult to test, as the measurement of genetic
and proximal social risk factors poses substan-
tial challenges to the methods of social science
research. To date only three studies have sys-
tematically tested the Catalyst Model and found
support for its main assumptions (Ferguson,
Ivory, et al., 2013; Ferguson et al., 2008;
Surette, 2013).
The Downward Spiral Model by Slater et al.
(2003) is a theory that accounts for both the
socialization and the selection hypothesis. The
Downward Spiral Model has also been called
the negative feedback loop model by its authors
(Slater, 2003) and describes a reciprocal rein-
forcement of aggressive personality and prefer-
ence for violent media content. Basically, the
model assumes a circular relationship between
current and future aggressive tendencies and use
of violent media. While the inclusion of both
socialization and selection effects is a strength
of this model, it does not make any detailed
statements about the role of other variables,
such as personal experiences with violence, that
could potentially moderate the relationship be-
tween media use and aggression. As the down-
ward spiral can only be studied in longitudinal
designs that ideally also include more than two
waves, there have been few studies that actually
tested this model (Ferguson, 2011; Möller &
Krahé, 2009; Slater et al., 2003; von Salisch et
al., 2011; Willoughby, Adachi, & Good, 2012)
and only one of these studies provided some
empirical support for it (Slater et al., 2003).
Longitudinal Studies on Video Games and
Aggression
As mentioned before, the number of longitu-
dinal studies on video games and aggression is
still relatively small. As the present study was
concerned with video games, the overview in
this section will focus on studies that were pub-
lished in peer-reviewed journals and explicitly
looked at the relationship between aggression
and video games, and not violent media content
in general. One of the earliest studies focusing
on video games was the short-term longitudinal
3VIOLENT VIDEO GAMES AND PHYSICAL AGGRESSION
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field study by Williams and Skoric (2005). This
study investigated the effect of one particular
massively multiplayer online role-playing game
(MMORPG) on aggressive cognitions and be-
haviors. Later studies looked at longer periods
(typically between 1 and 2 years) and video
games in general or at least specific genres or
types of games (mostly “violent” games; with
varying definitions of what “violent” means). In
our review of the literature, we found 11 journal
publications that present longitudinal data from
studies dealing specifically with the relationship
of aggression and video games. Table 1 sums up
their methods (sample, design, and measures)
and main findings. Overall, there are vast dif-
ferences between these studies with regard to
both the direction (socialization vs. selection)
and the size of the effects they found. A big part
of the inconsistencies in the results can be at-
tributed to major methodological discrepancies
between the individual studies. The longitudinal
studies differ from one another in various re-
spects, including size, origin, and composition
of the sample; measures of aggression and ex-
posure to violent video games; control vari-
ables; and number of and time lag between
waves (Table 1). While the differences in some
crucial categories, such as the measures for ag-
gression and exposure to violent content, are
quite substantial, other features are much more
homogeneous across studies. Although sample
sizes vary between N⫽143 (Ferguson, Garza,
et al., 2013; Möller & Krahé, 2009) and N⫽
1,492 (Willoughby et al., 2012), almost all of
them are convenience samples and the large
majority include only children and/or adoles-
cents (Table 1).
Summing up the comparisons in Table 1, it
can be noted that both the methods and results
of longitudinal studies on the link between ag-
gression and video game use are very heteroge-
neous. This heterogeneity of findings and mea-
sures is somewhat contrasted by a relative
homogeneity in the age and recruitment of the
samples.
Aggression, Violent Video Games, and Age
Our review of previous longitudinal studies
on video games and aggression revealed that the
majority of them worked with convenience
samples of children and teenagers, with the ex-
ception of the study by Williams and Skoric
(2005) that used a self-selected online sample
that also included adult players. Accordingly,
the age range of the samples typically only
spans a few years (M⫽4.8 years for the nine
studies in Table 1 that report the age range of
their sample). Due to the limited age range of
most studies, few of them have investigated the
role of participant age in detail. While control-
ling for participant sex is done in most studies,
only a few control for age (Wallenius & Pu-
namäki, 2008; Williams & Skoric, 2005) or
specifically look at potential differences in the
size and direction of effects between age groups
(Anderson et al., 2008; Ferguson, Garza, et al.,
2013; Willoughby et al., 2012).
3
Most of the
studies that did compare between age groups
also found differences in terms of effect size. In
the study by Willoughby et al. (2012), there
were only small socialization effects from
grades 9 to 10 (␤⫽.06) and 11 to 12 (␤⫽.08),
but not from grades 10 to 11, when controlling
for all of the measured third variables. Ander-
son et al. (2008) found stronger socialization
effects for the younger samples (␤⫽.15) than
for the older sample (␤⫽.08). However, the
study by Ferguson, Garza, et al. (2013) that
found no effect of exposure to video game vi-
olence on aggression, bullying, and delinquency
also found no differences between the groups of
late childhood (ages 10–11), preadolescence
(12–13), and adolescence (14–17). With regard
to age differences, von Salisch et al. (2011)
suggest that the selection effect they found in
their study with third and fourth graders may be
replaced by socialization effects once media
preferences have become more stable at an
older age.
In a review of the literature on violent video
games and aggression, Kirsh (2003) laments the
absence of a developmental perspective. For the
case of video game violence and aggression,
this is especially problematic, as research has
shown that video game preferences differ be-
tween age groups and also change over time
(Greenberg, Sherry, Lachlan, Luca, & Holm-
strom, 2010). Genres that typically include
large amounts of violence, such as action and
3
While Willoughby et al. (2012) compared the effect
sizes across three waves for the same sample, Anderson et
al. (2010) calculated a combined model that distinguished
between younger and older participants with the data from
the two Japanese studies and the one American study.
4 BREUER, VOGELGESANG, QUANDT, AND FESTL
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Table 1
Overview of Longitudinal Studies on Video Games and Aggression
Study Country Sample Sample type
Measure of
video game
violence Measure(s) of
aggression Control variables Waves
Time
lag
between
waves Socialization
effect Selection effect
Williams &
Skoric
(2005)
USA N⫽213;
Age 14–68
(M⫽27.7)
Self-selected Self-reported
hours of
play for
MMORPG
Asheron’s
Call 2
Normative
Beliefs in
Aggression
general scale
(Huesmann &
Guerra, 1997);
two behavioral
questions on
aggressive
social
interactions
Sex, age 2 1 month Normative beliefs
about
aggression:
B⫽.25, n.s.;
Arguments
with friends:
B⫽⫺1.63,
n.s.;
Arguments
with partner:
B⫽⫺.04, n.s.
Not tested (field
study)
Wallenius &
Punamäki
(2008)
Finland N⫽316;
Age (T2)
12–15
(M⫽13.8)
Convenience Self-reported
ratings of
violence
in games
played
and
frequency
of playing
action,
fighting,
and
shooting
games
Ten items from
the Direct &
Indirect
Aggression
Scale
(Björkqvist,
Lagerspetz, &
Österman,
1998)
Sex, age-group,
parent–child
communication
2 2 years ␤⫽.01, n.s. Not tested
(table continues)
5VIOLENT VIDEO GAMES AND PHYSICAL AGGRESSION
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Table 1 (continued)
Study Country Sample Sample type
Measure of
video game
violence Measure(s) of
aggression Control variables Waves
Time
lag
between
waves Socialization
effect Selection effect
Shibuya et al.
(2008) Japan N⫽591;
Age (T1)
10–11
(mean age
not
reported)
Unclear (most
likely
convenience)
Dichotomous
variable
(violent
yes/no)
for three
favorite
video
games and
21
contextual
variables
about the
type of
violent
content in
each game
Aggression Scale
for Children
(Buss & Perry,
1992; Sakai et
al., 2000); anti-
violence
norms; recent
aggressive
behavior
Sex, area of living,
weekly video game
use
2 1 year Hostility: ␤⫽
.13, p⬍.05
for boys and
n.s. for girls;
Physical and
verbal
aggression,
antiviolence
norms, and
aggressive
behavior: all
n.s. for both
girls and boys
(effect sizes
not reported)
Not tested
Hopf et al.
(2008) Germany N⫽314;
Mean age
(T1) ⫽12
Convenience Frequency of
playing
violent
games
from a list
of 19
popular
titles
Items on violence
beliefs,
delinquency,
verbal
aggression,
physical
aggression, and
deviance in
school
(Tillmann,
Holler-
Novitzki,
Holtappels,
Meier, & Popp,
1999)
School and classroom
climate, well-being
in school, self-
regulation, self-
efficacy in school,
emotional reactions
to violence,
materialistic value
orientations, media
education by
parents, poverty,
aggressiveness,
peaceableness
2 2 years Aggressive
behavior in
school:
␤⫽.18
(significance
level not
reported);
Delinquency:
␤⫽.29
(significance
level not
reported)
Not tested
(table continues)
6 BREUER, VOGELGESANG, QUANDT, AND FESTL
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Table 1 (continued)
Study Country Sample Sample type
Measure of
video game
violence Measure(s) of
aggression Control variables Waves
Time
lag
between
waves Socialization
effect Selection effect
Anderson et
al. (2008) USA N⫽364;
Age 9–12
(mean age
not
reported)
Unclear (most
likely
convenience)
Self-reported
amount of
violent
content ⫻
frequency
of play for
three
favorite
video
games
Index of teacher,
peer, and self-
reports of
physical
aggression
Sex, physical
aggression at time
1
2 5 to 6
months ␤⫽.16 (95%-
CI: .08, .23) Not tested
Japan N⫽181;
Age 12–15
(mean age
not
reported)
Unclear (most
likely
convenience)
Frequency of
playing
five
violent
video
game
genres:
fighting
action,
action,
action
role-
playing,
shooting,
adventure
Six-item version
of the physical
aggression
scale by Buss
& Perry (1992)
Sex, physical
aggression at time
1
24
months ␤⫽.14 (95%-
CI: .03, .25) Not tested
(table continues)
7VIOLENT VIDEO GAMES AND PHYSICAL AGGRESSION
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Table 1 (continued)
Study Country Sample Sample type
Measure of
video game
violence Measure(s) of
aggression Control variables Waves
Time
lag
between
waves Socialization
effect Selection effect
Japan N⫽1,050;
Age 13–18
(mean age
not
reported)
Unclear (most
likely
convenience)
Video game
play in hrs
per
week ⫻
violence
ratings for
most
favorite
genre and
three
additional
favorite
genres
assigned
by the
authors
Single item self-
report on
frequency of
physical
aggression in
the last month
Sex, physical
aggression at time
1
2 3 to 4
months ␤⫽.08 (95%-
CI: .02, .13) Not tested
Möller &
Krahé
(2009)
Germany N⫽143;
Mean age
(T1) ⫽
13.3
Convenience Frequency of
play ⫻
expert
violence
ratings for
a list of
popular
games
Seven items from
the physical
aggression
subscale by
Buss & Perry
(1992) ⫹
seven items on
relational
aggression
based on the
indirect
aggression
scale by Buss
& Warren
(2000)
Sex, normative
beliefs about
aggression, hostile
attribution bias
230
months Physical
aggression:
␤⫽.27, p⬍
.001;
Relational
aggression:
␤⫽.08, n.s.
Physical aggression:
␤⫽⫺.02, n.s.;
Relational
aggression: ␤⫽
⫺.09, n.s.
(table continues)
8 BREUER, VOGELGESANG, QUANDT, AND FESTL
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Table 1 (continued)
Study Country Sample Sample type
Measure of
video game
violence Measure(s) of
aggression Control variables Waves
Time
lag
between
waves Socialization
effect Selection effect
Ferguson
(2011) USA N⫽302;
Age (T1)
10–14
(M⫽12.3)
Convenience
(snowball
sampling)
ESRB
ratings ⫻
frequency
of play for
three
favorite
video
games
Child Behavior
Checklist
(Achenbach &
Rescorla,
2001) filled out
by the
participants
and their
primary
caregivers;
Olweus
Bullying
Questionnaire
(Olweus,
1996); general
delinquency
subscale of the
Negative Life
Events
questionnaire
(Paternoster &
Mazerolle,
1994)
Sex, antisocial
personality,
neighborhood
problems, negative
relations with
adults, family
attachment,
delinquent peers,
family
environment,
family violence,
depressive
symptoms
2 1 year Self-reported
serious
aggression:
␤⫽.⫺03,
n.s.;
Other-reported
serious
aggression:
␤⫽⫺.01,
n.s.;
Violent crime:
␤⫽.07, n.s.;
Bullying: ␤⫽
.12, n.s.;
n.s. (effect size for
aggressive
behavior not
reported)
von Salisch et
al. (2011) Germany N⫽324;
Age (T1)
8–12 (M⫽
8.9)
Convenience Average of
expert
violence
ratings for
up to six
favorite
computer
or video
games
Peer and teacher
nominations
for verbally
and physically
aggressive
behavior
Sex, neighborhood of
residence, parents’
migration status,
presence of an
older brother,
school
achievement, self-
perceived
competence
2 1 year ␤⫽⫺.01, n.s. ␤⫽.26, p⬍.01
(table continues)
9VIOLENT VIDEO GAMES AND PHYSICAL AGGRESSION
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Table 1 (continued)
Study Country Sample Sample type
Measure of
video game
violence Measure(s) of
aggression Control variables Waves
Time
lag
between
waves Socialization
effect Selection effect
Willoughby et
al. (2012) Canada N⫽1,492;
Age not
reported
(T1:
Canadian
ninth-
graders)
Complete
sample of all
high schools
in one
school
district in
Ontario,
Canada
Dichotomous
variables
(yes/no)
for action
and
fighting
video
games
(sustained
play: sums
for all
waves) &
frequency
of playing
action and
fighting
games for
grades 11
and 12
Overt aggression
assessed by a
composite of
two scales
(Little, Jones,
Henrich, &
Hawley, 2003;
Marini, Spear,
& Bombay,
1999)
Sex, nonviolent video
game play,
academic marks,
depressive
symptoms, delay of
gratification, peer
deviance, sports
involvement,
friendship quality,
parent–adolescent
relationship
quality, parental
control, school
culture
4 1 year Grades 9–10:
␤⫽.06, p⬍
.05;
Grades 11–12:
␤⫽.08, p⬍
.01
␤s not reported, but
all n.s.
Ferguson et al.
(2012) USA N⫽165;
Age (T1)
10–14
(M⫽12.3)
Convenience ESRB
ratings ⫻
frequency
of play for
three
favorite
video
games
Child Behavior
Checklist
(Achenbach &
Rescorla,
2001) filled out
by the
participants
and their
primary
caregivers
Sex, antisocial
personality traits,
family attachment,
delinquent peers,
family violence,
depression
3 1 year
and 2
years
Self-reported
serious
aggression:
␤⫽.03, n.s.;
Other-reported
serious
aggression: ␤⫽
⫺.03, n.s.;
Dating violence:
␤⫽⫺.05, n.s.;
Not tested
(table continues)
10 BREUER, VOGELGESANG, QUANDT, AND FESTL
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Table 1 (continued)
Study Country Sample Sample type
Measure of
video game
violence Measure(s) of
aggression Control variables Waves
Time
lag
between
waves Socialization
effect Selection effect
Ferguson,
Garza, et al.
(2013)
USA N⫽143;
Age (T1)
10–17
(M⫽12.8)
Convenience ESRB
ratings ⫻
frequency
of play for
three
favorite
video
games
Child Behavior
Checklist
(Achenbach &
Rescorla,
2001) filled out
by the
participants’
primary
caregivers;
Olweus
Bullying
Questionnaire
(Olweus,
1996); general
delinquency
subscale of the
Negative Life
Events
questionnaire
(Paternoster &
Mazerolle,
1994)
Sex, depressive
symptoms,
antisocial
personality, family
attachment,
delinquent peers,
parental
supervision,
parental depression
2 1 year Aggression: ␤⫽
⫺.02, n.s.;
Delinquency:
␤⫽.02, n.s.;
Bullying: ␤⫽
.05, n.s.
Not tested
Note. ESRB ⫽Entertainment Software Rating Board.
11VIOLENT VIDEO GAMES AND PHYSICAL AGGRESSION
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first-person shooter games, are particularly pop-
ular among younger players (Quandt, Breuer,
Festl, & Scharkow, 2013). At the same time, the
age of a player also affects her or his access to
video games. Most games that feature very ex-
plicit and graphical depictions of violence are
rated 18⫹and should, hence, not be legally
available to minors. Teenage players usually
also have a very limited amount of personal
income that they can spend on video games, and
parents are more likely to monitor the media use
of their children when they are younger (Wal-
lenius & Punamäki, 2008). But, age not only
determines the accessibility and use of violent
video games, it is also related to (physical)
aggression. Developmental researchers found a
curvilinear relationship between aggression and
age, with peaks in early adolescence (Linde-
man, Harakka, & Keltikangas-Järvinen, 1997;
Loeber & Stouthamer-Loeber, 1998). Wil-
loughby et al. (2012) also suggest that “the
long-term relation between violent video game
play and aggression may be different for ado-
lescents (e.g., 12 to 19 years) and adults (e.g.,
25 years and older), due to changes in the brain
during adolescence and young adulthood” (p.
12). Following this suggestion, the present
study was carried out to investigate whether the
size and maybe even the direction of effects
differ for adolescents and young adults. Adoles-
cents and young adults are an interesting target
demographic for this line of research because
they have been shown to be the heavy users of
video games (Greenberg et al., 2010). A recent
survey among adolescents aged 12 to 19 in
Germany showed that 81% play video games
and that 34% regularly play games with violent
content (Medienpädagogischer Forschungsver-
bund Südwest, 2012).
Method
Participants and Procedure
Our review of the existing literature showed
that most studies rely on convenience samples
(Table 1), typically drawn from local schools.
Only the study by Willoughby et al. (2012) can
be seen as potentially representative, at least for
high school students in the province of Ontario,
Canada. To arrive at more generalizable results,
our study used data from a representative panel
study of German gamers aged 14 and older.
Recruiting for this study was a two-step proce-
dure. First, a representative sample of 50,012
persons aged 14 and older were asked about
their use of video games in an omnibus tele-
phone survey. This sample was recruited in
accordance with the German Arbeitskreis
Deutscher Markt- und Sozialforschungsinstitute
(ADM) telephone sampling system (von der
Heyde, 2013): First, private households with
phones (mostly landline plus a small amount of
mobile phone numbers) were selected ran-
domly. Second, within the household, the indi-
vidual whose last birthday was closest to the
date of the call was selected for the telephone
interview. Approximately 25% (N⫽12,587) of
the participants were identified as gamers (i.e.,
individuals who currently play video games at
least occasionally). From this group, we re-
cruited a stratified random sample of 4,500
gamers for the first wave of the main study. This
sample was composed of 3,500 respondents
who play digital games with others (colocated,
online, or via local networks) and 1,000 gamers
who only play solo. Due to this stratified sam-
pling, the proportion of gamers who play with
others was higher in the main study (77.8%)
than in the omnibus survey (68.4%).
The computer-assisted telephone interviews
were conducted by a professional German mar-
ket research institute. At the end of the inter-
view, respondents were asked, if they were will-
ing to participate in the second wave of the
study 1 year later. Because of financial con-
straints and in anticipation of panel mortality,
we recruited a random subset of about 50% of
the respondents from wave 1 of the main study
for the second wave. Thus, of the 4,500 gamers
from wave 1, N⫽2,199 were interviewed in
wave 2. As we were only interested in the
longitudinal relationship of violent video games
and aggression among adolescents and young
adults, we focused our analysis on those respon-
dents who participated in both waves and were
aged 14 to 21 when they were first interviewed.
This subsample included n⫽332 individuals.
There was no difference in average age between
respondents who took part in both waves and
those who were interviewed only in the first
wave (M
2
⫽17.5 years compared with M
1
⫽
17.7, t(883) ⫽1.15, p⫽.25). The second wave
sample contained slightly more females than the
first wave sample (29% compared with 26%,
␹
2
(1) ⫽0.74, p⫽.39). After listwise deletion,
12 BREUER, VOGELGESANG, QUANDT, AND FESTL
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the final sample (see Data Analysis section)
comprised n⫽276 respondents (i.e., 83% of
the subsample).
4
Little’s (1988) likelihood ratio
test showed (␹
2
(18) ⫽23.32, p⫽.18) that the
missing data of the variables of interest (phys-
ical aggression and use of violent video games)
are missing completely at random. Respondents
of the final sample that was used for our anal-
yses had an average age of 17.6 (SD ⫽1.9) and
19.2% (n⫽53) of them were female.
Measures
Demographic factors. Participant sex, age,
and education were all measured with single
items. The education item asked respondents
about their highest educational degree. The an-
swering option reflected the German educa-
tional system and ranged from 0 (no school
leaving certificate)to5(university degree).
Physical aggression. We decided to focus
on physical aggression, as this is the type of
aggression most commonly featured in violent
video games (Lachlan, Smith, & Tamborini,
2005; Smith, Lachlan, & Tamborini, 2003) and
both socialization and selection effects are more
likely to occur, if the behavior presented in the
game and the one exhibited in real life are
similar (Möller & Krahé, 2009). We used two
items from the German translation (Herzberg,
2003) of the physical aggression subscale from
the Aggression Questionnaire by Buss and
Perry (1992). The two items were “There are
people who pushed me so far that we came to
blows” (phys aggr 1) and “Given enough prov-
ocation, I may hit another person” (phys aggr
2). Participants indicated on a 5-point scale to
what degree these statements apply to them
(ranging from 1 ⫽does not apply at all to5⫽
fully applies). Cronbach’s alphas for physical
aggression were satisfactory and stable across
both waves for the subsample under investiga-
tion (t
1
:␣⫽.75; t
2
:␣⫽.74).
Use of violent video games. Participants
were asked to name their favorite game plus up
to five additional games that they currently play.
All games were then coded for the age rating
assigned by the German age rating system Un-
terhaltungssoftware Selbstkontrolle (USK) us-
ing their online database (see www.usk.de/en).
The coding scheme used the USK age rating
system (0⫹,6⫹,12⫹,16⫹,18⫹) with the
additional category of “no clearance.” No clear-
ance means that the game did not receive an
official age rating from the USK because it is
deemed harmful to minors. In such cases, the
games are examined by the Federal Review
Board for Media Harmful to Minors (BPjM:
Bundesprüfstelle für jugendgefährdende Me-
dien;seehttp://www.bundespruefstelle.de/bpjm/
information-in-english.html). If the BPjM ar-
rives at the decision that a video game is
potentially harmful to minors, the game is not
allowed to be advertised in Germany and can
only be sold “under the counter” in stores to
which minors have access. Eventually, this
means that the game is less publicly visible and
much harder to (legally) acquire, but it might
also increase its appeal as a “forbidden fruit”
(Bijvank, Konijn, Bushman, & Roelofsma,
2009).
For each wave, we computed a mean age
rating score for every participant who named at
least one game. We used the USK rating as an
indicator for violent content, as this character-
istic is one of the main reasons for the assign-
ment of age ratings in Germany (Hyman, 2005;
MacMillan & Wedell, 2013). If games are not
given an age rating, this is mostly due to ex-
treme and explicit depictions of violence (see
http://www.usk.de/fileadmin/documents/USK_
Broschuere_ENG.pdf). Previous content analy-
ses have also shown that games with higher age
ratings tend to include more frequent and more
graphic portrayals of violence (Haninger &
Thompson, 2004; Thompson, Tepichin, &
Haninger, 2006). Age ratings have already been
used as a proxy for violent content in several
previous studies (Ferguson, 2011; Ferguson et
al., 2012; Olson et al., 2009) and they were
clearly correlated with ratings of violent content
in the studies by Busching et al. (2013); Möller
and Krahé (2009), and Ferguson (2011). While
Busching et al. (2013) suggest that age ratings
are valid and reliable measures of violent con-
tent, they caution researchers that “they should
only be used in the country in which they were
4
The relatively high number of excluded respondents is
mostly due to missing values in the age ratings for the
games they played (details see Measures section). In several
cases, the games could not be clearly identified because of
unintelligible answers by the respondents or typos by the
interviewers or because there were no age ratings available
for the game in the USK database (e.g., for games played
over social networking sites).
13VIOLENT VIDEO GAMES AND PHYSICAL AGGRESSION
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developed” (p. 13) because of potential inter-
cultural differences in the reasons for assigning
age labels.
Overall video game use. Overall use of
video games was measured in self-reported
hours per day.
Data Analysis
For our main analysis, we performed param-
eter estimation using the mean- and variance-
adjusted maximum likelihood (MLMV) proce-
dure in Mplus (Version 6.0; Muthén & Muthén,
1998–2010). MLMV provides chi-square val-
ues and estimates with standard errors that are
robust to non-normality. Model fit is assessed
using the probability of the mean- and variance-
adjusted chi-square value (pⱖ.05), root-mean
square error of approximation (RMSEA ⱕ.06),
the comparative fit index (CFI ⱖ.9), and
weighted root mean square residual (WRMR ⱕ
.9). Model comparison and selection was per-
formed using MLMV difference testing (Asp-
arouhov & Muthén, 2006). For the comparison
of age groups, we distinguished between ado-
lescents (aged 14–17 at t
1
) and young adults
(18–21 at t
1
). The age of 18 was chosen as a
cutoff because this is the age at which you can
legally buy video games that are labeled 18⫹,
which is the highest age rating assigned by the
German USK (see previous section on Mea-
sures). We opted for a group comparison in-
stead of using age as a continuous control vari-
able because we were interested more in the
differences between the two populations of ad-
olescents and young adults and less in the in-
fluence of age on physical aggression and the
use of (violent) video games.
Results
Preliminary Analyses
Table 2 shows the intercorrelations of the
variables included in the structural equation
model as well as their means, standard devia-
tions, skewness, and kurtosis.
As the descriptive statistics for the two age
groups differed, we examined mean differences
in the measures of video game use and physical
aggression. Although the mean differences were
in the expected directions, with older respon-
dents (18–21) playing more violent games
(M⫽2.85), while reporting fewer hours of
overall video game play (M⫽1.17) and lower
levels of mean physical aggression (M⫽1.77)
than the respondents aged 14 to 17 (Ms⫽2.65;
1.24; 1.97), separate t-tests revealed no signifi-
cant differences between the groups at Time 1
(all p⬎.1, r⬍.1).
5
Although the differences
between the groups were not significant, they
mirror the descriptive data from the Jugend,
Information, (Multi-) Media (JIM) study of ad-
olescents and media use in Germany that found
a decrease of overall gaming frequency, but an
increase in the use of violent games with age
(Medienpädagogischer Forschungsverbund
Südwest, 2012). Looking at the cross-sectional
association between the use of violent games
and physical aggression, we found a significant
correlation only for the younger group (r⫽.34,
p⬍.001).
6
Long-Term Relationships Between Use of
Violent Video Games and Physical
Aggression
Longitudinal research rests on the assump-
tion that the meaning of the constructs involved
does not change over time. To test this, the
factor loadings of the measurement model were
constrained to be time-invariant over the two
waves. The overall model fit indicates that the
validity of the physical aggression measurement
model does not change over time (␹
2
(6, N⫽
276) ⫽12.39, p⫽.05, CFI ⫽.98, RMSEA ⫽
.06, WRMR ⫽.62). In the next step, we further
tested the assumption that the physical aggres-
sion measurement model is group-invariant
(i.e., the validity of physical aggression mea-
surement model is the same for individuals aged
14–17 years and 18–21 years). The overall
model fit indicates that this assumption is valid
(␹
2
(15, N
14–17
⫽140, N
18–21
⫽136) ⫽17.92,
5
We refrained from comparisons between female and
male respondents due to their uneven distribution in our
sample. Most of the previous studies, however, have found
males to report higher levels of both aggression and use of
violent video games (e.g., Anderson et al., 2008; Ferguson,
Garza, et al., 2013; Möller & Krahé, 2009; Shibuya et al.,
2008; von Salisch et al., 2011; Wallenius & Punamäki,
2008; Willoughby et al., 2012).
6
After controlling for measurement error in the cross-
lagged structural equation model, the cross-sectional path
turned out to be even stronger with r⫽.4 (see next section
and Figure 1).
14 BREUER, VOGELGESANG, QUANDT, AND FESTL
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p⫽.27, CFI ⫽.99, RMSEA ⫽.04, WRMR ⫽
.48). In sum, the aforementioned tests show that
physical aggression has been measured time-
and group-invariant.
Figure 1 shows the results of the cross-lagged
structural equation model. The upper values of
the arrows in Figure 1 represent the parameter
estimates for the younger age-group (14–17),
while the lower values show the parameter es-
timates for the older age-group (18–21). On the
left side, the two-sided arrow shows the cross-
sectional correlation between physical aggres-
sion and use of violent games. The horizontal
one-sided arrows between the same variables
represent the stability estimates (autoregres-
sion). The standardized autoregression coeffi-
cient is a number between ⫺1 and ⫹1. A high
positive value indicates that interindividual dif-
ferences over time do not change, which is
commonly referred to as covariance stability.
The one-sided arrows between different vari-
ables represent the cross-lagged effects. The
two-sided arrow on the right side of Figure 1
shows the cross-sectional residual correlation of
the dependent variables after controlling for au-
toregressive and cross-lagged effects.
Parameter estimation suggests that physical
aggression self-reports were highly stable over
time. A ␹
2
-difference test indicated that the
autoregressive effects of physical aggression are
not statistically different between the two
groups (⌬␹
2
(1, N
14–17
⫽140, N
18–21
⫽136) ⫽
1.01, p⫽.32, one-tailed). It can therefore be
concluded that physical aggression is highly
time-invariant in both groups. By contrast, the
two age groups differ with respect to the autore-
gression of violent game use (⌬␹
2
(1, N
14–17
⫽
140, N
18–21
⫽136) ⫽8.96, p⬍.01, one-
tailed). The use of violent video games is far
more stable among respondents aged 18 to 21.
Table 2
Intercorrelations Between Items for Participants Aged 14 to 17 Years and 18 to 21 Years
Item 1234 5 6 7 8 9
Adolescents aged 14–17 years (n⫽140)
1. Phys aggr 1 t
1
— .56 .58 .52 .31 .29 ⫺.22 ⫺.15 .01
2. Phys aggr 2 t
1
— .49 .55 .29 .37 ⫺.25 ⫺.09 .07
3. Phys aggr 1 t
2
— .62 .13 .27 ⫺.19 ⫺.16 ⫺.02
4. Phys aggr 2 t
2
— .25 .28 ⫺.27 ⫺.23 ⫺.08
5. Violent game use t
1
— .46 ⫺.33 ⫺.01 .28
6. Violent game use t
2
—⫺.34 ⫺.06 .24
7. Participant sex t
1
a
— .03 ⫺.17
8. Education t
1
—⫺.13
9. Gaming frequency t
1
—
Mean 1.86 2.08 1.58 1.41 2.65 2.63 .22 2.85 1.24
SD 1.24 1.25 .98 .70 1.26 1.26 .42 1.12 1.29
Skewness 1.33 1.16 1.86 1.91 .22 .27 1.36 ⫺.12 2.16
Kurtosis .62 .39 3.02 4.52 ⫺1.14 ⫺.98 ⫺.16 ⫺1.62 6.30
Young adults aged 18–21 years (n⫽136)
1. Phys aggr 1 t
1
— .64 .59 .54 .03 ⫺.04 ⫺.15 ⫺.04 .08
2. Phys aggr 2 t
1
— .64 .53 .02 ⫺.01 ⫺.21 ⫺.08 .08
3. Phys aggr 1 t
2
— .65 .08 .07 ⫺.21 ⫺.05 .09
4. Phys aggr 2 t
2
— .10 .06 ⫺.21 .01 .24
5. Violent game use t
1
— .65 ⫺.21 ⫺.06 .17
6. Violent game use t
2
—⫺.21 ⫺.05 .25
7. Participant sex t
1
a
— .06 ⫺.19
8. Education t
1
— .05
9. Gaming frequency t
1
—
Mean 1.68 1.85 1.56 1.41 2.85 2.63 .16 3.41 1.17
SD 1.10 1.13 .97 .75 1.19 1.22 .37 .95 1.11
Skewness 1.77 1.36 1.97 2.19 ⫺.04 .16 1.86 ⫺1.15 1.25
Kurtosis 2.42 .99 3.53 5.30 ⫺.91 ⫺.97 1.47 ⫺.08 .84
Note.
a
0⫽male, 1 ⫽female.
15VIOLENT VIDEO GAMES AND PHYSICAL AGGRESSION
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The cross-sectional positive correlation be-
tween physical aggression and violent video
game use is statistically significant (r⫽.40,
p⬍.01, one-tailed) for the younger group.
However, this contemporary correlation only
indicates covariation. To test the temporal or-
der, cross-lagged parameters were estimated.
Comparing all cross-lagged effects showed that
only one parameter estimate between physical
aggression self-reports at Time 1 and the use of
violent games at Time 2 was statistically signif-
icant. Participants aged 14 to 17 who are more
physically aggressive at Time 1 nominated
more violent games at Time 2 (␤⫽.30, p⬍
.01). By contrast, this relationship was not
found for young adults (aged 18–21). A ␹
2
-
difference test proved that the effect size differ-
ence between the two groups was statistically
significant (⌬␹
2
(1, N
14–17
⫽140, N
18–21
⫽
136) ⫽8.04, p⬍.01, one-tailed). Accordingly,
our data suggest that physical aggression pre-
dicts the use of violent video game among ad-
olescents, while the reverse does not seem to be
true.
The cross-lagged structural equation model
depicted in Figure 1 is only testing the bivariate
relationship between physical aggression and
violent video game use. It is important, though,
to control for spurious effects of third variables
on the bivariate relationship of interest (Slater,
2007). Following the procedure by von Salisch
et al. (2011), we estimated three additional
models. In each model, a third variable was
introduced in the structural equations (Figure
2). The candidate set of third variables consisted
of participant sex, level of education, and gam-
ing frequency measured at Time 1.
As can be seen in Table 3, all three additional
two-group models fitted the data excellent. All
␹
2
tests were nonsignificant. When controlling
for sex, level of education, and gaming fre-
quency separately, physical aggression at Time
1 was still a significant predictor of violent
video game use at Time 2 in the younger age-
group. The selection effect varied between ␤⫽
.26 (p⬍.01) and ␤⫽.34 (p⬍.01). The size
of the cross-lagged socialization effects was
again not statistically significant in any of the
additional models. In sum, the longitudinal re-
lations between physical aggression and violent
video game use were not influenced by sex,
education, or gaming frequency.
Discussion
The results of our study provide some ev-
idence for a selection effect in the adolescent
group aged 14 to 17. Essentially, this corrob-
orates the findings from von Salisch et al.
(2011), who found a selection effect in their
Physical
Aggression
Physical
Aggression
.97 **
.88 **
.40 **
ns
.30 **
ns
ns
ns
R² = .84
R² = .79
Use of
Violent Games
Use of
Violent Games
R² = .28
R² = .43
ns
ns
.34 **
.65 **
2 emiT1 emiT
Figure 1. Cross-lagged structural equation model: Relationships between physical aggres-
sion and use of violent games. Note. Upper row: standardized coefficients of adolescents aged
14 to 17 years, lower row: standardized coefficients of young adults aged 18 to 21 years. N⫽
276, MLMV estimation, ␹
2
(15, N14-17 ⫽140, N18-21 ⫽136) ⫽17.92, p⫽.27, CFI ⫽.99,
RMSEA ⫽.04, WRMR ⫽.48,
ⴱ
pⱕ.05,
ⴱⴱ
pⱕ.01.
16 BREUER, VOGELGESANG, QUANDT, AND FESTL
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sample of children aged 8 to 12, for an older
sample. For the group of young adults aged
18 to 21, however, we found no indication of
either a socialization or a selection effect.
These findings pertain, even when controlling
for participant sex, education, and overall fre-
quency of video game play. This is in line
with several previous longitudinal studies
(Ferguson, Garza, et al., 2013; Ferguson et
al., 2012; von Salisch et al., 2011; Wallenius
& Punamäki, 2008; Williams & Skoric,
2005), while it also contradicts others (An-
derson et al., 2008; Möller & Krahé, 2009).
With regard to the theories that explain the
relationship between violent video games and
aggression, our results fit best with the Cata-
lyst Model (Ferguson et al., 2008) that does
not predict a substantial influence of violent
media on real-life aggression. The idea of
violent media as a stylistic catalyst for indi-
viduals with a tendency for aggression is
compatible with the selection effect we found
for adolescents aged 14 to 17. The absence of
socialization effects in the present study con-
tradicts the assumptions of the GAM (Ander-
son & Bushman, 2002), according to which a
repeated use of violent media leads to an
increase in aggressive behavioral tendencies.
While von Salisch et al. (2011) speculated
that the selection effect they found for their
sample of third and fourth graders might be
the beginning of a downward spiral (Slater et
al., 2003), our study found no such relation-
ship for adolescents and young adults. As the
sample in our study was limited to adoles-
cents and young adults, however, it might be
Physical
Aggression
Physical
Aggression
Use of
Violent Games
Use of
Violent Games
2 emiT1 emiT
Third variable
Figure 2. Cross-lagged structural equation model with third variable control.
Table 3
Influence of Third Variables on the Cross-Lagged Effects
Third variable
Selection effect
with third
variable control
Socialization
effect with third
variable control Model fit
14–17 18–21 14–17 18–21
␹(df)p␤␤␤␤
Participant sex (n⫽276) .26
ⴱⴱ
NS NS NS 20.69 .35
Education (n⫽247) .34
ⴱⴱ
NS NS NS 18.54 .49
Gaming frequency (n⫽273) .31
ⴱⴱ
NS NS NS 23.38 .22
Note. MLMV estimation.
ⴱ
pⱕ.05.
ⴱⴱ
pⱕ.01.
17VIOLENT VIDEO GAMES AND PHYSICAL AGGRESSION
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possible that socialization effects occur at a
younger age when media preferences and es-
pecially personality traits are more malleable.
As both physical aggression (Lindeman et
al., 1997; Loeber & Stouthamer-Loeber,
1998) and the use of (violent) video games
(Greenberg et al., 2010) change with age, it is
not surprising that the same should be true for
the relationship between these two variables.
Both the peak in aggression and the height-
ened interest in violent video games have
been suggested to be part of normal (i.e.,
healthy) developmental phases, especially
among boys (Ferguson, 2010; Lenhart et al.,
2008; Olson, 2010). Accordingly, the selec-
tion effect we found for adolescents could be
interpreted as a sign of selective exposure to
violent content in a phase of life that goes
along with a general peak in aggressive be-
havioral tendencies. It may well be that phys-
ical aggression, the use of violent games, and
the selective exposure effect for adolescents
can be explained by another underlying fac-
tor, such as sensation-seeking. Similar to the
developmental change in aggression, a study
by Steinberg et al. (2008) found a curvilinear
relationship between age and sensation-
seeking, with peaks between age 10 and 15.
Previous studies have also linked sensation-
seeking with both a preference for violent
media (Slater, 2003) and aggression (Joire-
man, Anderson, & Strathman, 2003).
As stated before, some of the differences in
the findings can be attributed to differences in
the methods used. Unlike other studies that
mostly relied on convenience samples, we
used data from a representative sample of
German gamers aged 14 and older. We also
compared the effects for adolescents (aged
14–17) and young adults (18–21) to take into
account both developmental change and the
access to video games. The differences we
found between the age groups suggest that
age is an important variable that needs to be
considered when investigating the relation-
ship between aggression and video game use.
From our data it appears that the selection
effect disappears once media preferences
have solidified and appeal of the “forbidden
fruit” is diminished. The higher autoregres-
sion coefficient for the use of violent games
among the older age-group (␤⫽.65 vs. ␤⫽
.34) indicates that video game preferences
stabilize at the beginning of adulthood. It
seems that the phase of preference formation
that von Salisch et al. (2011) report for their
sample of 8- to 12-year-olds continues into
adolescence and begins to stabilize once play-
ers turn 18 and all types of games are legally
available to them. In addition, parental con-
trol of video game use tends to decrease with
age (Wallenius & Punamäki, 2008). This not
only enables players to more freely choose
the games they play, but likely also reduces
the “forbidden fruit effect” (Bijvank et al.,
2009).
7
Unfortunately, it is impossible to dis-
entangle the effect of the solidification of
media preferences and the forbidden fruit ef-
fect in our current data. Hence, to arrive at a
more detailed understanding of what causes
the disappearance of the selection effect in
early adulthood, more research into the con-
tribution and relationship of the factors of
legal availability and the solidification of
video game preferences would be necessary.
For now, we can only assume that both con-
tribute to some extent to the change in the
relationship between video game use and
(physical) aggression.
Physical aggression was extremely stable
across waves for both age groups (␤⫽.97 vs.
␤⫽.88). This stability of trait aggression in the
transition from adolescence to early adulthood
might be part of the reason why we found no
media effects. Put simply, the use of violent
video games cannot explain a change in physi-
cal aggression, if physical aggression does not
change at all. Again, this is in line with the
Catalyst Model (Ferguson et al., 2008), which
proposes that the use of violent media is not a
strong enough influence to alter fundamental
personality traits. Genetic influences and prox-
imal social factors, such as family violence
(Ferguson, Ivory, et al., 2013; Ferguson et al.,
2012), are likely to shape aggressive personality
traits already before the later phases of adoles-
cence that were the focus of the current study.
7
Despite the relatively strict regulations in Germany,
however, it is not uncommon for minors to play games that
are not suitable for them according to the USK labels. In our
wave 1 sample, 14% of gamers aged 14 and 15 played at
least one game labeled 16⫹and 29% reported to currently
play at least one game with an 18⫹USK rating. Of the
respondents who were 16 or 17 years old in the first wave,
35% indicated that they play one or more games rated 18⫹.
18 BREUER, VOGELGESANG, QUANDT, AND FESTL
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Although we did cover a larger age range
than most other longitudinal studies in this field
and compared the relationship between violent
video games and physical aggression for ado-
lescents and young adults, our findings are not
generalizable to other player populations, such
as primary school students or older adults. Of
course, our results do not mean that violent
video games are completely harmless and do
not have an effect on any player. Especially
younger children may be negatively affected by
these games, but then again, age rating systems
and parental control should prevent access to
violent games at a young age. Hence, if children
play games that are not suitable for them, the
undesired effects this may have are, ultimately,
attributable to a lack of parent–child communi-
cation or parental care. The results may also be
different for other countries. Germany has some
of the strictest laws with regard to the protection
of minors from potentially harmful media con-
tent. Some violent games are only available as
localized low-violence versions that are typi-
cally less explicit and graphic in their depiction
of violent acts. A more important limitation of
the present study, however, is the reliance on a
very brief self-report measure of physical ag-
gression. The inclusion of only two items on
physical aggression was due to the design of the
survey that featured questions on large variety
of topics. And while the additional inclusion of
peer, teacher, or parent reports of aggressive
behavior is desirable (Ferguson, 2011; Fergu-
son, Garza, et al., 2013; Ferguson et al., 2012;
Gentile & Bushman, 2012; Krahé et al., 2012;
von Salisch et al., 2011), this is not feasible for
large-scale telephone surveys, especially if they
also include adults. It is also possible that the
effects we found are different for other types of
aggression, such as verbal, relational, or indirect
aggression (Möller & Krahé, 2009).
Another limitation of this study is that our use
of age ratings as a proxy for violent content
might be too crude. Even though violent content
is one of the major criteria for the German USK
age ratings (Hyman, 2005; MacMillan &
Wedell, 2013), there are certainly others, such
as sexual content or the complexity of the game
mechanics (Busching et al., 2013). And while
games with higher age ratings tend to feature
more violent acts (Thompson et al., 2006),
many games for younger audiences also contain
some forms of violence (Thompson &
Haninger, 2001). These types of violence usu-
ally differ from another on several dimensions,
such as graphicness, realism, and justification
(Tamborini, Weber, Bowman, Eden, & Skalski,
2013). The combination of a thorough content
analysis and a longitudinal survey design by
Shibuya, Sakamoto, Ihori, & Yukawa (2008)
showed that the characteristics of video game
violence, such as its justification, realism,
graphicness, or punishment, seem to be more
important than just the amount. However, most
subjective ratings of violent content depend
mostly or even exclusively on the graphicness
of the portrayals (Gentile et al., 2011; Potter,
1999). Accordingly, self-reports of how violent
a game is (Anderson et al., 2008; Gentile &
Bushman, 2012) can be problematic, also be-
cause there are interindividual differences in
what is perceived as violent. Expert ratings, on
the other hand, can be expected to be less bi-
ased, but still bring about the difficulty of dif-
ferences in expertise and, at the same time,
strongly depend on the training of the coders
and the stimulus material that is used, such as
video recordings of a game or game reviews
(Busching et al., 2013). To avoid the issue of
interindividual differences in the evaluation of
violent content, we opted for age ratings, but we
acknowledge that there may be other measures
of violent content that are more precise.
Games and genres that are violent, such as
first-person shooters or fighting games, typi-
cally also differ from others on more dimen-
sions than just violent content, including
competitiveness or pace of action (Adachi &
Willoughby, 2011; Elson, Breuer, van Looy,
Kneer, & Quandt, 2013). Apart from game
characteristics, it might be that there are other
variables affecting the relationship between
aggression and the use of violent video games
that we did not control for in this study, such
as academic achievement (Krahé et al., 2012;
von Salisch et al., 2011; Willoughby et al.,
2012), relationship with parents (Ferguson,
Garza, et al., 2013; Wallenius & Punamäki,
2008; Willoughby et al., 2012), family vio-
lence (Ferguson et al., 2012; Ferguson, Garza,
et al., 2013), or peer delinquency (Ferguson,
Garza, et al., 2013; Ferguson et al., 2012;
Willoughby et al., 2012). Finally, longitudinal
studies remain correlational data, even though
they can identify the temporal precedence be-
tween two or more variables. While we tried
19VIOLENT VIDEO GAMES AND PHYSICAL AGGRESSION
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to control for potentially influential third vari-
ables, such as respondent sex, education, and
overall use of video games, there may be
other variables that influenced the temporal
relationship of physical aggression and use of
violent games, such as those mentioned ear-
lier.
Despite these limitations, we believe that
our study shows that individual differences
between video game players need to be taken
into account when studying the relationship
between aggression and video game use. One
important variable in this context is age, as it
is closely related to both developmental
change and the access to violent games. Be-
cause media preferences and personality traits
tend to stabilize with age, both socialization
and selection effects should be less likely for
older players. Our replication of the findings
by von Salisch et al. (2011) with data from an
older and representative sample lends further
support to the assumption of the Catalyst
Model (Ferguson et al., 2008) that violent
media do not have a substantial impact on
aggressive personality or behavior, at least in
the phases of late adolescence and early adult-
hood that we focused on. To more fully in-
vestigate the role of developmental change
and age differences, future studies should in-
clude more and potentially also more fine-
grained age groups; consider additional mod-
erator variables, such as family violence or
sensation-seeking; and look at longer periods
than just 1 year. To explain the relationship
between video game use and aggression, it is
necessary to abandon monocausal and unidi-
rectional models and to understand that video
games are more than just stimuli that affect
everybody in the same way. Media users are
more active and media effects are more indi-
vidual than most theoretical models would
suggest.
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Received November 20, 2013
Revision received February 27, 2014
Accepted March 4, 2014 䡲
24 BREUER, VOGELGESANG, QUANDT, AND FESTL
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