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EMPIRICAL RESEARCH

Video Games and Youth Violence: A Prospective Analysis in Adolescents

Christopher J. Ferguson

Received: 24 September 2010 / Accepted: 9 November 2010 / Published online: 14 December 2010

� Springer Science+Business Media, LLC 2010

Abstract The potential influence of violent video games

on youth violence remains an issue of concern for psy-

chologists, policymakers and the general public. Although

several prospective studies of video game violence effects

have been conducted, none have employed well validated

measures of youth violence, nor considered video game

violence effects in context with other influences on youth

violence such as family environment, peer delinquency,

and depressive symptoms. The current study builds upon

previous research in a sample of 302 (52.3% female)

mostly Hispanic youth. Results indicated that current levels

of depressive symptoms were a strong predictor of serious

aggression and violence across most outcome measures.

Depressive symptoms also interacted with antisocial traits

so that antisocial individuals with depressive symptoms

were most inclined toward youth violence. Neither video

game violence exposure, nor television violence exposure,

were prospective predictors of serious acts of youth

aggression or violence. These results are put into the

context of criminological data on serious acts of violence

among youth.

Keywords Computer games � Mass media � Aggression � Violence � Adolescence

Introduction

Concerns about the potential influence of violent video

games on serious acts of youth aggression and violence

have been debated in the general public, among policy

makers and among social scientists for several decades. At

present, a general consensus on video game violence

effects has been elusive, with great debate occurring among

scholars in this field. Some scholars have concluded that

strong video game violence effects on aggression have

been conclusively and causally demonstrated in wide

segments of the population (e.g., Anderson et al. 2008;

Anderson 2004). Others have concluded that video game

violence may have only weak effects on youth aggression,

or may only influence some youth, particularly those

already at-risk for violence (e.g., Giumetti and Markey

2007; Kirsh 1998; Markey and Scherer 2009). Still others

have concluded that video game violence effects on youth

aggression are either essentially null, or that the field of

video game violence studies has difficulties with method-

ological problems to such an extent that meaningful con-

clusions cannot be made about the existing research (e.g.,

Durkin and Barber 2002; Kutner and Olson 2008; Olson

2004; Savage and Yancey 2008; Sherry 2007; Unsworth

et al. 2007). For instance, as some have noted (e.g., Olson

2004), the increased popularity of video game play among

youth has been correlated with a societal reduction in youth

violence rather than an increase in youth violence.

The divergence in findings may be understood as a

function of methods used. As has been found for television

research (Ferguson and Kilburn 2009; Savage and Yancey

2008; Paik and Comstock 1994), studies of video games

that use well validated measures of aggression or violence

find less evidence for harmful effects, as do studies that

employ greater statistical controls for third variables

Although several prospective studies of video game effects refer to

themselves as ‘‘longitudinal’’, none use multiple assessment periods

over years that typically mark longitudinal designs. Rather they are

short-term prospective studies by and large.

C. J. Ferguson (&)

Department of Behavioral, Applied Sciences and Criminal

Justice, Texas A&M International University,

Laredo, TX 78045, USA

e-mail: [email protected]

123

J Youth Adolescence (2011) 40:377–391

DOI 10.1007/s10964-010-9610-x

(Ferguson and Kilburn 2009). Thus, put generally, it

appears that more careful controls are correlated with

weaker effects, which essentially was the conclusion of

Ferguson and Kilburn (2009) in their review of the

research. For example, Ybarra et al. (2008) found weak

bivariate correlations between video game violence expo-

sure and youth violence. However, as indicated in their

Fig. 2, these correlations vanished once other relevant

factors were controlled, such as family environment and

personality. Similarly, Ferguson and colleagues (Ferguson

et al. 2008) found that controlling for ‘‘third’’ variables in a

correlational study, and using a well-standardized aggres-

sion measure in an experimental design (as opposed to ad

hoc unstandardized measures often used as discussed in

Ferguson et al. 2008) resulted in no correlational or

experimental evidence for harmful effects.

Prospective Studies of Violent Video Game Effects

At present, a small number of prospective designs have

examined video game violence influences on player

aggression. Thus far, results have been mixed and arguably

limited by use of aggression measures that do not neces-

sarily tap well into serious aggression or violence, nor use

sophisticated controls for third or confounding variables.

As such, the generalizability of existing prospective

designs to behavioral outcomes of most interest, namely

serious/pathological aggression and criminally violent

behavior, may be limited (see Gauntlett 1995; Savage and

Yancey 2008 for a discussion of aggression measure

validity issues). Below, a review of prospective studies of

video game violence appearing in peer-reviewed journals

follows.

The first prospective study of video game violence was

by Williams and Skoric (2005). This study was unusual

in that it employed an experimental design, randomly

assigning 213 volunteers to either play a violent on-line

game Asheron’s Call 2, or to a control group that did not

play the game (none of the participants had previously

played the game). Outcome measures included a scale of

normative beliefs in aggression (NOBAGS) as well as a

self-report measure of engaging in verbal aggression such

as arguments and name calling with others. Results indi-

cated that, controlling for previous game exposure, ran-

domized exposure to the violent game did not influence

players’ normative beliefs in aggression, nor frequency of

verbal altercations. However, this study has some signifi-

cant weaknesses. First, the prospective period was fairly

short (1 month). Second, the outcome measures are more

relevant for mild or non-serious aggression (i.e., intention

physical assaults were not measured) and cannot be gen-

eralized to more serious aggressive acts. Further the out-

come measures related to constructs such as ‘‘normative

beliefs’’ in aggression are among those criticized for not

predicting actual aggressive behavior effectively (Savage

and Yancey 2008).

Anderson et al. (2008) reported on several prospective

studies, two occurring with Japanese samples and one with

an American sample, all involving youth. The prospective

periods in these studies ranged from 3 to 6 months. The

authors found small but statistically significant prospective

effects (ranging from .075 to .152, suggesting the covari-

ance between video game violence exposure and aggres-

sion may range between .5 and 2.3% when time 1

aggression is controlled). Although the authors interpret

these findings as highly significant and generalizable to

serious youth violence, it is not clear how to interpret such

small effects (falling mainly near or below Cohen’s 1992

guidelines for trivial findings). None of these prospective

results control for third variables, thus it is possible that the

actual effects may even be lower than reported here.

Finally, the aggression measures used in this study again

fall under the category of those that have been criticized in

the past for validity problems (Gauntlett 1995; Savage and

Yancey 2008), particularly when generalizing to serious

aggression or violence.

Shibuya et al. (2008) report a prospective study of 591

fifth-grade Japanese youth with a prospective period of

1-year. Gender and living area (urban or rural) were con-

trolled as third variables, but other variables known to be

predictive of youth violence (peer delinquency, depressive

symptoms, family environment, etc.) were not. The out-

come measure was trait aggression, once again not clearly

well-validated as a predictor of serious youth aggression

and violence (Gauntlett 1995; Savage and Yancey 2008).

Interestingly in this study, time spent playing violent video

games (exposure to violent games 9 time spent playing

interaction) was related to reduced trait aggression (b =

-.15) in boys, but had no influence on girls. Weaknesses of

this study are similar to those above. Although the authors

did control for gender and living area, other third variables

were not controlled, nor was a well-validated measure of

serious aggression employed.

Finally, Moller and Krahe (2009) provide a prospective

analysis of 143 German youth with a 30 month prospective

period. Outcome measures included normative beliefs

about aggression (NOBAGS. similar to Williams and

Skoric 2005), hostile attribution bias and a measure of trait

aggression (divided into physical and relational aggression

subscales). Results of this study were inconsistent. At Time

1, video game violence exposure was not related to phys-

ical aggression (b = .09, NS), but was slightly related to

relational aggression (i.e., arguing, spreading rumors,

similar to Williams and Skoric 2005, b = .19). In the

prospective analyses, exposure to violent video games did

not have direct effects on either physical aggression

378 J Youth Adolescence (2011) 40:377–391

123

(b = .11, NS) or relational aggression (b = .02, NS), but

did potentially indirectly influence physical aggression

through a small moderating relationship with normative

aggressive beliefs (b = .26). This indirect relationship was

not found for relational aggression.

In summary, among existing prospective studies of

video game violence on aggression, two do not find evi-

dence of effects or (in the case of Shibuya et al. 2008)

suggest violent game exposure may reduce aggression for

boys. One study (Moller and Krahe 2009) finds inconsistent

evidence for an indirect relationship between video game

violence and physical but not relational aggression, but no

evidence for direct effects, and the last finds consistent

effects but of small magnitude. Arguably, across these

studies, prospective analyses of video game violence

effects raise little cause for alarm.

Despite whether individual prospective studies appear to

support or not support causal beliefs in negative video

game violence effects, these studies display several con-

sistent flaws including the failure to consider and control

for third variables (family environment, peer delinquency,

etc.) and reliance on outcome measures that are not well

validated as measures of pathological youth aggression and

violence. To qualify in the latter category, it would be

desirable for outcome measures to demonstrate high pre-

dictive validity coefficients (.3–.4 or above) with patho-

logical outcomes. Otherwise, it is unclear if research

studies are merely examining minor fluctuations in normal,

even healthy levels of aggression (see Hawley and Vaughn

2003). The intent here is not to be overly critical of the

above studies, it is merely to argue that much remains to be

known about the prospective influences of violent video

games on pathological aggression.

Three Theoretical Views of the Video

Game Violence/Serious Aggression Relationship

There are three basic views of the potential relationship

between video game violence exposure and serious

aggressive behavior among youth. Quite simply, these are:

first, video game violence exposure has a learning-based

causal influence on subsequent serious aggression; second,

individuals with high levels of a priori aggression are

subsequently drawn to video game violence or; third that

any correlation between the video game playing and

aggression is due to underlying third variables. Each of

these views present different hypotheses for the ways in

which video game violence and serious aggression/youth

violence relate.

The ‘‘causal’’ view, namely that video game violence

exposure causes subsequent serious aggression in players,

has roots in Bandura’s social learning experiments in

which children modeled aggressive behavior of adults in

experimental videos (e.g., Bandura et al. 1961, 1963),

although elements of the same view can be traced back at

least to the Payne Fund studies of movie violence (Blum-

mer 1933) or even Plato’s concerns that Greek plays would

cause rebelliousness and licentiousness in youth who

watched them (Griswold 2004). As noted above, much of

the debate on video game violence focuses on whether this

theoretical perspective is ‘‘true.’’ Proponents of this view

tend to express considerable certitude (e.g., Anderson

2004; Huesmann 2007) where as detractors suggest that

existing evidence is not sufficient to support this view

(Cumberbatch 2008; Mitrofan et al. 2009; Olson 2004;

Savage and Yancey 2008) or suggest the causal view relies

on outdated tabula rasa theories (Pinker 2002).

The second view, that a priori aggression leads to

extensive video game violence use, is most often offered as

a counterargument by skeptical scholars (e.g., Freedman

2002; Gauntlett 1995) to the causal view. However, this

basic position is likely consistent with both social and

biological theories that emphasize influences more proxi-

mal to youth than media effects, such as family environ-

ment, peer influences and evolutionary and biological

influences (e.g., Beaver et al. 2007, 2009; Buss and

Shackelford 1997; Pinker 2002). Similarly, research has

indicated that exposure to and selection of different forms

of media is not a passive process but that individuals

actively seek out certain forms of media and these prefer-

ences are correlated with pre-existing personality profiles

(e.g., McCown et al. 1997; Rentfrow and Gosling 2003). In

relation to video game violence, two models have emerged

that typify this view to varying degrees. First the ‘‘catalyst’’

model developed by Ferguson et al. (2008) suggests that

serious aggression and violence results from a combination

of genetic and proximal environmental influences (such as

family and peers) but that distal environmental factors such

as media, have little influence on behavior. Patrick Markey

(Giumetti and Markey 2007; Markey and Scherer 2009)

has developed a somewhat different view in which a priori

personality traits such as psychoticism interact with violent

video game exposure to produce serious aggression.

Finally, it could be argued that video game violence use

and serious aggression have little real influence on each

other. Some correlation between aggression and video

game violence use may exist, but such correlations are

expected to be rather small in size, and due to underlying

third variables rather than any direct relationship between

aggression and video game violence. For example, boys

play more violent video games and are more inclined

toward aggressive and violent behavior than girls. As such,

gender is an obvious and important ‘‘third’’ variable,

although one still overlooked in some studies. Similarly,

aggressive or antisocial personality traits may direct indi-

viduals to be more inclined to violent games and violent

J Youth Adolescence (2011) 40:377–391 379

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behavior. Peer and family influences may have a similar

impact, and individuals with certain mental health prob-

lems may be both more inclined toward aggression and

seek violent games as a form of cathartic release (Olson

2010). This perspective appears to be endorsed by research

indicating that video game use, including the use of violent

games, is widespread among even non-violent youth, par-

ticularly boys (e.g., Lenhart et al. 2008; Kutner and Olson

2008; Olson et al. 2007). It is important to note that tem-

poral sequencing cannot rule out this possibility. For

instance, maturational processes that lead to increased

violent video game use in early childhood may not nec-

essarily produce increased aggression until later in ado-

lescence. Thus, the temporal sequence of video game

violence use and the emergence of aggression, even if

correlated, does not rule out the influence of third variables.

The Current Study

The current study intends to improve upon past designs in

several ways. First, the present study will focus to a much

greater extent on clinical and criminological measures that

are well validated as outcome measures for pathological,

serious aggression and rule-breaking (i.e., parent and youth

report versions of the Child Behavior Checklist; CBCL),

bullying other children (the Olweus Bullying Question-

naire; OBQ) and criminologically violent behavior (Neg-

ative Life Events, NLE). A focus on these clinical and

criminological outcome measures will help illuminate the

potential impact of violent game exposure on serious levels

of aggression and violent crime among youth. Second,

most previous prospective studies have employed only

basic controls and have not considered the potential influ-

ence of third variables.

Several hypotheses will be tested in the current article.

First, it is hypothesized that exposure to violent content in

video games will be consistent across time (H1). Second,

the frequency of exposure to violent content in video

games at Time 1 will predict serious aggressive behavior

across outcome measures 1-year later once third variables

have been controlled (H2). Third, aggression level (com-

posite across aggression measures) at Time 1 will be pre-

dictive of video games exposure at Time 2 (H3).

As a note, H2 and H3 essentially are opposing per-

spectives, both presented in the affirmative. Finding evi-

dence for H2 but not H3 would support the overarching

theory that video game violence exposure comes first in the

temporal pattern, where as finding evidence for H3 but not

H2 would suggest that aggressive tendencies come first in

the temporal sequence. Finding support for H2 and H3

would suggest the relationship is bidirectional, whereas

finding evidence for neither H2 nor H3 would suggest that

the interaction between violent video game exposure and

aggression is limited (meaning that children’s choice to

play violent video games is not dependent upon their

aggressiveness nor vice versa).

Methods

Participants

Participants in the current study were recruited from a prior

study of youth violence (Ferguson et al. 2009). This study

examined cross section data on correlates of youth violence

in a sample of 603 mainly Hispanic youth. Results from

this study indicated that depressive symptoms and peer

delinquency were the best predictors of concurrent

aggression and violence, as were antisocial traits and

parental psychological aggression. Video game and tele-

vision violence were not strong correlates of youth vio-

lence. The present study presents prospective data not

included in the prior study, thus there is no resubmission of

prior existing data (i.e., data presented here do not overlap

with that presented in the previous study). 536 children

(89%) from the original sample volunteered to participate

in this prospective design at Time 1 (T1). As with the

discussion of the T2 dropout below, the sample who vol-

unteered for the prospective study did not systematically

differ from those who did not. As this sample was drawn

from a small Hispanic-majority city population on the

border of Mexico, this sample of youth were almost all

(519; 96.8%) Hispanic. Proportions of Caucasian, African

American, Asian American and other ethnic groups were

all at 1% or less. This ethnic composition is consistent with

the ethnic composition of the city from which the sample

was drawn and represents a ‘‘convenience’’ sample,

meaning that Hispanics were not specifically recruited for a

theoretical reason. However, to date, no prospective (and

few cross sectional or experimental) studies of video game

violence have considered Hispanic majority samples. As

such, examining such a sample may help generalize this

research to ethnic groups beyond Causasians and Japanese.

All participants were between the ages of 10 and 14 at T1

(M = 12.34, SD = 1.33) as this age was viewed as that

likely to see high rates of video game play (Griffiths and

Hunt 1995; Lenhart et al. 2008; Olson et al. 2007) yet

young enough that developmental processes may still be

strong and easily observable. About an equal number of

boys (275, 51.3%) and girls (261, 48.7%) were included in

the study. Children included in this study were from the

general community, not specifically at-risk children for

serious aggression.

380 J Youth Adolescence (2011) 40:377–391

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Recruitment

Recruitment of a representative community sample of

youth was obtained using a modified multimethod

‘‘snowball’’ approach. Snowball sampling, like other forms

of non-random sampling, is not without the potential for

certain kinds of biases. At the same time snowball sam-

pling has been shown to be an effective sampling approach

under most conditions and is better at detecting ‘‘hidden

populations’’ as may be the case with violent youth, than

are institutional sampling techniques (Goodman 1961;

Salganik and Heckathorn 2002). In snowball sampling,

respondents for a sample are drawn from associates nom-

inated by an initial group of study participants. Several

variations on this approach were used in this study in an

attempt to achieve as representative a sample as possible.

First an approach similar to that used by McCrae et al.

(2002) in which college students at a local university

nominated relatives or associates within the targeted age

range for inclusion in exchange for extra credit, was

employed. Second, several community social organizations

were approached for nominations of children to be inclu-

ded in the study. Third, the study was advertised in the

local newspaper and on several popular local FM radio

stations (catering to both English and Spanish language

music), including interviews between the DJ and lead

investigator on several radio stations during prime (i.e.,

morning traffic) listening hours. These interviews were

very brief, requesting participants for a study of ‘‘youth

health.’’ No discussion of video games or youth violence

took place during any of these media appeals. Families

were encouraged to nominate themselves for the study. No

compensation was offered for participation.

Analysis of T2 Nonresponse/Drop-Out

All participants who volunteered at T1 were contacted

again approximately 12 months later for the Time 2 (T2)

assessment. T2 assessments were conducted via phone

interview with a trained research assistant using a stan-

dardized scripted interview comprised mainly of items

taken from the outcome assessments (CBCL, OBS, NLE)

and video game use. At T2 302 children and their families

completed the follow up assessment representing a com-

pletion rate of 56%. This figure is reasonably representative

of dropout rates typical in prospective studies although at

greater issue is whether drop-out is selective or random

(Wolke et al. 2009). In particular, were children with

greater rates of serious aggression or violent behaviors to

drop from the study than children without these problem

behaviors, results obtained in this study would potentially

be confounded. To examine for this potential t-test com-

parisons on all outcome variables (CBCL parent and child

report, OBQ, NLE violent and non-violent crime subscales,

all of which are described below) were conducted. All

t-test comparisons were non-significant (p [ .05) lending

confidence to the conclusion that drop-out in this study was

random rather than selective. Gender (52.3% female), age

and ethnicity composition of the final T2 sample of 302

children was essentially identical in proportion to that

reported above for the T1 original sample. Given that the

local city includes a fairly high proportion of both migrant

workers and transient government employees (e.g., Border

Patrol, FBI. DEA. etc.,), some degree of dropout was

expected. Retention rates for the current study reflect the

general pattern from other prospective studies of video

game violence. Williams and Skoric (2005) report a

retention rate of approximately 75% at 3 months, Shibuya

et al. (2008) report a retention rate of 62% at 1-year,

whereas Moller and Krahe (2009) report a retention rate of

48% at 30 months. Anderson et al. (2008) do not report

retention rates.

Measures

With exceptions noted below, all materials used Likert-

scale items and demonstrate psychometric properties suit-

able for use in multiple regression and path analyses. All

measures were included in the T1 assessment. For the T2

follow up, only the media exposure, depressive symptoms

and outcome variables were reassessed. Alphas reported

are for T1; T2 alphas did not differ greatly.

Media Violence Questionnaire

Child participants were asked to list their 3 favorite tele-

vision shows and video games and estimate how often they

play or view the media in question. Many media studies in

the past asked respondents to rate violence levels in media

they watched, although this runs the risk of variable esti-

mates between respondents. In the current study, I took a

slightly different approach, using existing Entertainment

Software Ratings Board (ESRB) video game ratings as an

estimate of video game violence exposure. ESRB ratings

were obtained for each game reported by the respondent,

and ordinally coded (a maximal score of 6 for ‘‘Adults

Only,’’ 5 for ‘‘Mature,’’ 4 for ‘‘Teen,’’ etc.). This ordinal

coding system was designed to correspond to the levels of

the ESRB rating system. The ESRB system has been

supported by the Federal Trade Commission (2009) and

the Parent Teacher Association (2008) as effective and

reliable.

Many factors go into an ESRB rating, including lan-

guage, sexual content, and use of (or reference to) drugs or

gambling. However, among those factors that determine

the age-based rating, violence appears to take priority. Of

J Youth Adolescence (2011) 40:377–391 381

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the 30 ‘‘content descriptors’’ that accompany ratings, ten

concern violence. Descriptors of listed games were

reviewed to ensure that high ratings had not been obtained

primarily for sexual content; this was not the case for any

of the games reported by youth. The ESRB rating system

was also tested by pulling a random sample of ten com-

mercially available games (Lego Star Wars II: The Original

Trilogy, Call of Duty 4, F.E.A.R., Bioshock, Race Pro,

Baja: Edge of Control, Sonic Unleashed, Spiderman 3,

Silent Hill: Homecoming. Lego Indiana Jones). Each of the

games were played (for approximately 45 min each) by

two independent student RAs (one male, one female, nei-

ther heavy gamers). The RAs had not played any of the

games previously, and was not aware of the ESRB ratings

for each game. The RAs were provided with and trained on

a standardized 5-point violence assessment ranking system

and asked to code each game on this system after playing.

Each RA was alone while playing and ranking the games

and did not know of each others’ ratings. Interrater reli-

ability was high (kappa = .95). The RAs’ rankings, which

focused exclusively on violence, were then correlated with

the categorical ESRB ratings for each game. The correla-

tion between the mean RA rankings and the ESRB ratings

was .98, providing external evidence for validity of the

ESRB ratings as estimates of violent content.

The ESRB ratings were multiplied against the respon-

dents’ reported time spent playing each game then summed

across the 3 games listed. For television ratings a similar

approach was employed using the TV Parental Guidelines

System (PGS; i.e., TV-Y through TV-MA). As with the

video game ratings, the television ratings were checked for

violent content using the external check process described

above. The sampled television shows were Wizards of

Waverly Place, Hannah Montana, Spongebob Squarepants,

South Park, Zoey 101, Heroes, CSI, Chowder, WWE

Superstars and Robot Chicken, all shows reported by youth

in our current database as among those watched. Interrater

reliability between the RAs for rating violent content in the

shows was kappa = .88. The correlation between the mean

RA rating and the PGS was .89, lending evidence to the

validity of using the PGS system as an estimate of violent

content in television shows.

This general approach has been used with success in the

past (Olson et al. 2009). As with all attempts to assess

game or television content exposure, this is only an esti-

mate; however, it removes some of the subjectivity inher-

ent in previous methods.

Negative Life Events

The Negative Life Events instrument is a commonly used

and well validated measure of youth behaviors used in

criminological research (NLE; Paternoster and Mazerolle

1994) and includes the following scales used in this study

as third variables:

1. Neighborhood problems (e.g., How much of a problem

are each of the following in your neighborhood?

Vandalism, traffic, burglaries, etc.; alpha in current

sample = .86).

2. Negative relations with adults (e.g., My parents think I

break rules, My parents think I get in trouble, etc.;

alpha = .95)

3. Antisocial personality (e.g., It’s important to be honest

with your parents, even if they become upset or you

get punished, To stay out of trouble, it is sometimes

necessary to lie to teachers, etc.; alpha = .70)

4. Family attachment (e.g., On average, how many after-

noons during the school week, from the end of school or

work to dinner, have you spent talking, working, or

playing with your family, etc.; alpha = .86)

5. Delinquent peers (e.g., How many of your close

friends purposely damaged or destroyed property that

did not belong to them, etc.; alpha = .84).

This measure tapped multiple constructs related to family,

peer and school environment as well as delinquent

behavior and beliefs. Scales described here are used as

predictor third variables, although two scales (violent

crimes and non-violent crimes) related to delinquent

behaviors (described below) function as outcome variables.

There are no item overlaps between subscales.

Family Environment

The Family Environment Scale (FES; Moos and Moos

2002) is a 90-item true–false measure designed to assess

styles of family interaction and communication. Research

on this instrument has demonstrated good internal consis-

tency and test–retest reliability, as well as validity in dis-

tinguishing between functional families and families

experiencing a variety of dysfunctions including psychiat-

ric and substance abuse problems and physical abuse. The

family conflict subscale (alpha = .57) was used in the

current project. Sample items include ‘‘We fight a lot in our

family’’ and ‘‘Family members sometimes get so angry

they throw things.’’

Family Violence

The child’s primary guardian was asked to fill out the

Conflict Tactics Scale (CTS; Straus et al. 2003), a measure

of positive and negative behaviors occurring in marital or

dating relationships. The CTS has been shown to have

good reliability and corresponds well to incidents of dating

and family violence. It is used here to get a measure of

conflict and aggression occurring between the primary

382 J Youth Adolescence (2011) 40:377–391

123

caregiver and their spouse or romantic partners and thus a

sense of the child’s exposure to domestic violence. Sub-

scales related to physical assaults (e.g., ‘‘I beat up my

partner’’; ‘‘I pushed or shoved my partner’’; alpha = .88)

and psychological aggression (‘‘I insulted or swore at my

partner’’; ‘‘I called my partner fat or ugly’’; alpha = .81)

were used in the current study. The physical assaults sub-

scale was found to have a significantly skewed distribution

and a square-root transformation was conducted to produce

a normalized distribution.

Depressive Symptoms

The withdrawal/depression scale of the Child Behavior

Checklist Youth Self-Report (YSR; Achenbach and Resc-

orla 2001) indicated child depressive symptoms. This scale

has no item overlaps with the aggression/rule breaking

scales described below. Depressive symptoms were reas-

sessed at T2 and this variable, current depressive symp-

toms, is used in the regression equations described below.

Coefficient alpha of the scale with the current sample was

.80. Sample items include ‘‘I feel sad’’ and ‘‘I would rather

be alone.’’

Serious Aggression

Regarding mental health, youth and their primary care-

givers filled out the Child Behavior Checklist (CBCL,

Achenbach and Rescorla 2001). The CBCL consists of a

youth self-report and parent report on problematic behav-

iors which may represent psychopathology. The CBCL is a

well researched and validated tool for measuring behav-

ioral problems in children and adolescents. Research

indicates the CBCL is highly valid in diagnosing serious

externalizing behavior problems in children including

conduct disorder (Hudziak et al. 2004; Tackett et al. 2003).

Caregivers filled out the parental version of the CBCL,

whereas children filled out the YSR on themselves. These

indices were used to indicate outcomes related to delin-

quency and aggressiveness. All alphas with the current

sample were above .70. Sample items for the aggression

scale (from the child prospective, parents items are simply

reworded) include ‘‘I attack people’’ and ‘‘I threaten oth-

ers’’ and for the rule breaking scale ‘‘I lie or cheat’’ and ‘‘I

skip school.’’

Bullying

The Olweus Bullying Questionnaire (OBQ; Olweus 1996)

was used to measure bullying behaviors in the current

study. This measure is commonly used and well researched

with high reliability and validity reported. With the current

sample, alpha was .83. Sample items include ‘‘In the past

month I have called another kid ‘‘stupid, fat, ugly’’ or other

mean names’’ and ‘‘In the past month I have Forced

another kid to do something they didn’t want to do.’’

Delinquent Behavior

The NLE questionnaire, described above has a subscale

related to general delinquency (e.g., How many times in

the following year have you stolen something worth more

than $50, etc.). The general delinquency scale can be fur-

ther divided into non-violent (alpha = .96) and violent

(alpha = .98) criminal activities. As indicated above, these

scales are widely used in criminological research and do

not overlap in items with the third variable predictor scales

described above.

Statistical Analyses

Main analyses consisted of hierarchical multiple regression

equations. Separate hierarchical multiple regressions were

run for each of the outcome measures related to patho-

logical aggression (parent and child versions of the CBCL

aggression and rule-breaking scales, violent and non-

violent crime commission as reported on the NLE, and

bullying behavior). In each case, gender, depressive

symptoms and T1 pretest score for the specific scale were

entered on the first step, NLE variables (neighborhood,

negative adult relationships, antisocial personality, family

attachment and delinquent peers) were entered on the

second step, the FES conflict scale was entered on the third

step, CTS psychological aggression and physical assault

were entered on the fourth step and television and video

game violence exposure entered on the fifth step. Lastly,

interaction terms between antisocial traits and depressive

symptoms and media violence exposure (a composite of

television and video games) were included on the final

step. The antisocial, depressive symptoms and media vio-

lence terms were first centered before creating the inter-

action terms to avoid multicollinearity. This hierarchy was

designed theoretically to extend from most proximal vari-

ables outward (e.g., Bronfenbrenner 1979). Out of concern

that placing video game violence exposure in the last step

may artificially reduce the predictive value of this variable

on youth aggression, each regression equation was then

rerun with video game violence exposure included as a step

1 variable. Multicollinearity was examined using tolerance

and VIF statistics and found to be acceptable in all cases.

Highest VIF values were 1.9, and lowest tolerance values

were .54, which fall within most recommended acceptable

guidelines (Keith 2006). Secondary analyses involved the

use of path analysis to test alternate causal models

regarding the development of pathological youth

J Youth Adolescence (2011) 40:377–391 383

123

aggression as well as temporal relationships between video

game violence exposure and youth violence outcomes.

Power Analysis

A post-hoc power analysis was conducted to examine the

sensitivity of the current design and sample to pick up

small effects. Results indicated that the current design is

capable of detecting effects as statistically significant at or

just below the r = .14 level, close to Cohen’s threshold for

trivial effects (Cohen 1992).

Results

Prevalence of Violent Game Exposure and Criminal

Activity

At T2 75% of children reported playing some video games

on computer, console or other devices in the preceding

month. 40.4% of children reported playing games with

violent content as indicated by their own self-ratings of

violence in games. Using the ESRB ratings, 20.9% repor-

ted playing an M-rated game in the preceding month.

Consistent with past research (Griffiths and Hunt 1995;

Olson et al. 2007), boys were more likely to play violent

video games than girls [t(234) = 6.65, p B .001, r = .40,

.30 B r B .49]. Video game violence exposure was not

correlated with age of the child r = .02, nor reported GPA

of the child (r = -.02), nor did hours spent playing video

games predict GPA (r = -.09).

As for criminal activity, at T2 22 children (7.3%)

reported engaging in at least one criminally violent act over

the previous 12 months based specifically on the results

from the NLE. Most common violent crimes were physical

assaults on other students and strong-arm robbery (i.e.,

using physical force to take an object or money from

another person). Regarding non-violent crimes, 52 (19.2%)

of children reported engaging in at least one non-violent

crime over the past 12 months based on the NLE. Most

common non-violent crimes include thefts of small objects

(i.e., shoplifting) and thefts occurring on school property.

The commission of violent and non-violent crimes was

highly correlated (r = .51, p B .01, .42 B r B .59).

Consistency Among Parent and Child Reports

of Aggression on the CBCL and YSR

One intended strength of the current research design is that

it includes both parent and child report based outcome

assessments. Consistency between child and parent report

on the CBCL/YSR rule-breaking scales was r = .57

(.49 B r B .64), and for aggressive behavior, r = .52

(.43 B r B .60). Paired samples t-tests indicated that chil-

dren tended to report both higher levels of rule-breaking

[t(301) = 8.16, r = .43, .34 B r B .52] and aggression

[t(301) = 6.62, r = .36, .26 B r B .46]. Taken together,

these results suggest that parents have a good idea of the

‘‘gist’’ of how problematic the behavior of their children is

relative to other children, but generally are unaware of the

full scope of children’s behavior problems.

Consistency in Video Game Violence Exposure Over

Time (H1)

Table 1 presents bivariate correlations between video game

violence exposure at time 1 and time 2.

Video game violence exposure at T1 was significantly

correlated with video game violence exposure at T2

(r = .33, p B .01, .23 B r B .43); however, the effect size

was small, allowing a considerable amount of variance

across time in video game violence exposure, probably as

children put away older games and pick up news games

that are different in genre and violence content.

Long-Term Relationships Between Aggression

and Video Game Violence Exposure (H2, H3)

Bivariate Correlations Between Video Game Violence

Exposure at T1 and Violence and Aggression Related

Outcomes

Table 1 presents bivariate correlations between video game

violence exposure at T1 and aggression related outcomes at

T1 and T2. A Bonferroni correction due to multiple com-

parisons of p = .004 was applied. As can be seen, bivariate

correlations between T1 video game violence exposure

were significant only for bullying at T1, and T2, but not for

the other six outcome variables. Those results that were

significant were still small in size with none reaching

r = .2.

Table 1 T1 Video game violence bivariate correlations with

aggression and violence related outcomes at T1 and T2

Outcome variable Time 1

outcome

Time 2

outcome

CBCL rule breaking (parent report) .05 .05

YSR rule breaking (child report) .12 .10

CBCL aggression (parent report) .06 .01

YSR aggression (child report) .12 .06

OBQ .18* .18*

NLE violent crimes .06 .09

NLE non-violent crimes .03 .07

* p B .004

384 J Youth Adolescence (2011) 40:377–391

123

Prospective Hierarchical Multiple Regressions (H2)

Seven sets of hierarchical multiple regressions were run

with the steps described above in the procedure section.

These results are presented in Table 2. Steps in the hier-

archical model are broken down by double solid lines in the

Table, with delta R2 reported at each step. Standardized

regression coefficients (beta-weights) presented are for the

final model in each case, as all model steps were statisti-

cally significant. A representation of the depressive

symptoms/antisocial personality interaction (using a com-

posite of the aggression/violence/bullying measures) is

provided in Fig. 1. Both variables were split into four

categories (i.e., ‘‘quartiles’’) based on mean and standard

deviation scores to make visualization easier; however, it

should be clearly stated that continuous scores were used in

the regression model. Quartiles based on means and stan-

dard deviations were viewed as more clinically meaningful

than percentile splits. As can be seen, the influence of

depressive symptoms on violence was most severe for

individuals with preexisting antisocial personality traits. In

each case, reversing the step on which the video game

violence variable was entered did not influence results.

For the child-report aggression YSR outcome variable,

current level of depressive symptoms predicted aggres-

siveness and this was a strong predictor (b = .66) of T2

aggression as was the interaction between antisocial traits

and depressive symptoms (b = .15). Video game violence

exposure was not predictive of T2 aggression.

For the child-report rule-breaking YSR outcome vari-

able, current level of depressive symptoms predicted rule

breaking and this was a strong predictor (b = .62) of T2

rule breaking whereas peer delinquency at T1 was a sig-

nificant but weaker predictor (b = .12) as was the antiso-

cial/depressive symptoms interaction (b = .12). Video

game violence exposure was not predictive of T2 rule-

breaking.

For the parent-report aggression CBCL outcome vari-

able, T1 CBCL aggression (b = .22), current depressive

symptoms (b = .54), the antisocial/depressive symptoms

interaction (b = .14) and parental level of psychological

abuse in relationships (b = .15) were all predictive of T2

aggression. Video game violence exposure was not pre-

dictive of T2 aggression.

For the parent-report rule-breaking CBCL outcome

variable, T1 CBCL rule breaking (b = .20), current

depressive symptoms (b = .52), and parental level of

psychological abuse in relationships (b = .15) were all

predictive of T2 rule-breaking. Video game violence

exposure was not predictive of T2 rule-breaking.

For NLE non-violent crimes at T2, T1 commission of

nonviolent crimes (b = .26) was significant predictive of

T2 commission on non-violent crimes as was the

interaction of antisocial traits and depressive symptoms

(b = .12) and between antisocial traits and media violence

(b = .18). An examination of this latter interaction sug-

gested that individuals who were low in antisocial traits,

but who were exposed to more violent media committed

fewer non violent crimes than their peers. However, the

most antisocial youth who also consumed the most violent

media committed more non-violent crimes than their peers.

Direct video game violence exposure was not predictive of

T2 non-violent criminal behavior.

For NLE violent crimes at T2, attachment to family at

T1 served as a protective factor (b = -.15) at T2, whereas

the interaction between antisocial traits and depressive

symptoms (b = .17) and between antisocial traits and

media violence (b = .14). An examination of this latter

interaction suggested that individuals who were low in

antisocial traits, but who were exposed to more violent

media committed fewer violent crimes than their peers.

However, the most antisocial youth who also consumed the

most violent media committed more violent crimes than

their peers. No other variables were significant predictors

of T2 violent criminal behavior. Video game violence

exposure was not predictive of T2 violent criminal

behavior.

For the OBQ at T2, only current depressive symptoms

(b = .32) and T1 antisocial personality (b = .12) were

significant predictors. Video game violence exposure was

not predictive of T2 bullying behavior.

The above regressions were rerun with T1 depressive

symptoms replacing current (T2) depressive symptoms on

step 1. T1 depressive symptoms did not prove to be pre-

dictive of T2 aggressive or violent outcomes in any of the

equations. As such, current depressive symptoms rather

than a past history of depressive symptoms is most pre-

dictive of violent outcomes. In each of these regressions

with T1 depressive symptoms, T1 violent video game

exposure remained non-significant as a predictor of T2

aggression and violence outcomes.

Prospective Video Game Violence Analysis (H3)

To examine the temporal sequence between aggression and

video game violence use, a hierarchical multiple regression

was run with video game violence use at T2 as the

dependent variable. Ordering of variables was the same as

described for the regressions above, with the exception that

video game violence exposure at T1 was entered on step 1

(just as aggression T1 variables were included on step 1 for

the aggression regressions). T1 aggression was entered

along with T1 television violence exposure on step 5 (this

gave T1 aggression the same positioning in this regression

as T1 video game exposure had in the aggression regres-

sions). In order to avoid multicollinearity, a composite

J Youth Adolescence (2011) 40:377–391 385

123

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386 J Youth Adolescence (2011) 40:377–391

123

aggression measure was created from the sum of the

seven individual aggression measures. This composite

measure showed high consistency (alpha = .81). The

resulting regression equation was statistically significant

[F(15,250) = 6.20, R = .52, adj R2 = .23] through the last

step. Male gender (b = .31, .20 B r B .41), current (T2)

level of depressive symptoms (b = .30, .19 B r B .40) and

T1 video game use (b = .16, .05 B r B .27) were all sig-

nificant predictors of T2 video game use. Aggressive

behavior at T1 was not predictive of video game use at T2.

Adding aggression to step 1 rather than step 5 of the

regression did not change the outcome.

Path Analysis of Temporal Sequencing of Video Game

Violence Exposure and Aggression (H2, H3)

Path analysis can be used to test the temporal sequence of

video game violence exposure and aggressive behavior,

using each variable and T1 and T2. If video game violence

exposure at T1 is predictive of aggression at T2, but

aggression at T1 is not predictive of video game violence

exposure at T2 this lends support to causal beliefs that

video game violence exposure leads to subsequent

aggression as the alternative hypothesis (that aggression

leads to subsequent video game violence use) is ruled out

(however the data remains correlational, and alternate

explanations based on third variables cannot be ruled out).

The basic path analysis was based on that used by

Moller and Krahe (2009), and is represented in Fig. 2.

Using path analysis, goodness of fit can be evaluated both

through a non-significant chi-squared analysis, as well as

by several goodness of fit indices such as the ‘‘Adjusted

Goodness of Fit Index’’ or root mean squared error of

approximation (RMSEA).

Separate path analyses were run with T1 video game

exposure leading to T2 aggression and T1 aggression

leading to T2 video game exposure (these paths are rep-

resented by the divided arrows in Fig. 2). Aggression was

measured by the T1 and T2 composite measures described

above. Neither of these proved to be good fits to the data,

nor did a combined path analysis with T1 aggression and

video game violence exposure both leading to T2 aggres-

sion and video game violence exposure.

Next, a path model was developed based on the

regression results with aggression pre-score, current

depressive symptoms, and the antisocial/depressive symp-

toms interaction each functioning as separate, direct con-

tributors to the composite youth aggression measure at T2.

Although close to the criteria described above, this model

did not prove a good fit. Antisocial personality traits were

then added to the model as a contributor to T1 aggression.

This model proved to be a good fit to the data [v2(6) =

23.8, p C .05, NFI = .91, CFI = .92, RMSEA = .09] and

is presented in Fig. 3.

Discussion

The issue of video game violence exposure remains

a pressing one in Western society. The US State of

California, as well as nations ranging from Australia and

Switzerland to China and Venezuela, are considering

efforts to restrict youth access to violent video games. As

of yet, the empirical understanding of the long-term

influences of video games on youth violence remain

murky. Although several short-term prospective studies of

youth violence have been published (Anderson et al. 2008;

Moller and Krahe 2009; Shibuya et al. 2008; Williams and

Skoric 2005), these have been inconsistent in results and

have been limited by the low clinical validity of the

aggression/violence measures used, and paucity of statis-

tical controls for other relevant variables. The current study

represents the first prospective study to employ well-vali-

dated clinical measures of aggression and violence, and to

control carefully for a number of other relevant factors that

may influence youth violence.

Interaction Effect of Depression and Antisocial Personality on Composite Aggression Score

0

10

20

30

40

50

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Antisocial 1st Quartile

Antisocial 2nd Quartile

Antisocial 3rd Quartile

Antisocial 4th Quartile

C o

m p

o si

te A

g g

re ss

io n

Fig. 1 Depressive symptoms/antisocial interaction

Fig. 2 Initial time sequenced path model

J Youth Adolescence (2011) 40:377–391 387

123

Several important conclusions can be made from the

current study. First, hypothesis H1, that video game use

would be consistent over time, was moderately supported

by the current data with a stability coefficient at 1 year of

r = .33, as indicated in the bivariate correlations. This

indicates moderate stability in video game violence expo-

sure over time, but this stability coefficient is far smaller,

for instance, than that seen in personality research (McCrae

et al. 2002). This suggests that children’s video game genre

selection may be reasonably variable over time.

Relevant to H2, that video game violence exposure at T1

would prospectively predict serious acts of aggression at

T2, no evidence was found to support this hypothesis either

in the regression analyses for the seven outcome measures,

or for the path analysis using the composite aggression

score. No evidence across any of the outcome measures

supported H2. This remained true whether video game

violence exposure was entered on step 1 or step 5 of the

hierarchical multiple regressions. It would be reasonable to

express the concern that, despite a reasonable level of

power in the current analysis, small effects might have

been missed. However, with the exception of bullying

(b = .12), all of the effects for video game violence

exposure were at or below Cohen’s (1992) suggested

threshold of r = .10 for trivial effects (the effect for bul-

lying nonetheless fell below Ferguson’s 2009 recommen-

dations for interpretation of practical significance). The

effect for bullying was slightly larger than for other out-

comes. It is important not to overinterpret this, as the

bullying finding remained non-significant and very small in

effect size. Nonetheless, it may be simply that less serious

forms of aggression show slightly higher relations with

video game violence than do more serious forms of

aggression, an observation made previously in the literature

(Ferguson and Kilburn 2009).

It appears reasonable to conclude that, in the current

sample, little evidence supported a significant predictive

relationship between violent video game exposure and

serious user aggression. Results of the current study are, in

fact, not out of league with previous prospective studies, all

of which have found only small effects (hovering on either

side of r = .10) of video game violence on subsequent

aggression. What seems to vary between reports is the

language used in interpreting these effects ranging from

attempts to generalize findings to serious acts of youth

violence (Anderson et al. 2008) to the conclusion that such

small effects effectively represent null findings (Williams

and Skoric 2005). It may be prudent for scholars to be more

temperate and conservative in their interpretations in the

future, particularly where effect sizes have tended to be

generally weak.

In the current study, results by and large are at or below

r = .10 with confidence intervals that, as such, cross the

zero mark and thus, irrespective of statistical significance,

do not provide support for H2. It may be argued that some

scholars have, in the past, been overzealous in arguing for

strong, consistent and general effects, when evidence

backing such conclusions is limited (see Sherry 2007 for a

similar conclusion). The current study, however, is the first

prospective study to carefully examine pathological/serious

youth aggression and violent behavior using well validated

clinical measures. Thus, generalizability to serious youth

aggression is more possible with the current study than

with those previously mentioned.

For criminal behaviors (both violent and non-violent),

although no direct effects of video games or television

violence were seen, total media violence consumption

interacted with antisocial traits. Interestingly, for children

with low antisocial traits, media violence exposure was

associated with less criminal behavior. Only for the most

Fig. 3 Final ‘‘good fit’’ path

model

388 J Youth Adolescence (2011) 40:377–391

123

antisocial children was media violence exposure associated

with more violent crimes. There are two possible expla-

nations for this phenomenon. First, antisocial children who

are most inclined toward criminal behavior may also be

those most likely to select violent media. This is the

explanation favored by Ferguson et al. (2008) based on

similar findings as well as by Kutner and Olson (2008).

However, Giumetti and Markey (2007) alternatively sug-

gest that, although violent video games are harmless for the

vast majority of children, for those with preexisting high

antisocial traits, video game violence may exacerbate these

traits. More data is needed to ascertain which of these

possibilities is correct. These findings also should be tem-

pered by their small effect size and the fact that the media

interaction term was not a good fit for the path analysis.

Related to H3, that a priori aggressiveness predicts T2

video game use, no greater support for this view was found

in either the regression analyses or path analysis than for

H1. Indeed, aggressiveness and video game violence use do

not seem to be highly predictive of one another, at least

prospectively. Of the theoretical perspectives discussed

earlier in the article, the ‘‘third variable’’ perspective that

aggression and video game violence have little causal

impact on each other, is best supported by the results of the

current study.

Of the third variables that predicted T2 serious aggres-

sion and violence, by far the best predictor was current

(T2) depressive symptoms in both the regression and path

analyses. As such, this variable warrants some discussion.

The effect size for the T2 depressive symptoms variable on

pathological aggression was, by the standards of social

science, large (Cohen 1992), ranging between .5 and .62

for the CBCL outcomes, and .32 for bullying (but non-

significant for criminal behavior). Also depressive symp-

toms and antisocial traits appeared to interact, such that

individuals with high antisocial traits who also were

depressed were most likely to engage in aggressive and

criminal acts. By contrast, T1 depressive symptoms were

not predictive of T2 serious aggression. These results

suggest that current mood states may be more important in

the etiology of aggressiveness than historical influences, at

least for children and young adolescents. Although some

T1 third variables, such as peer delinquency and parental

psychological aggression in romantic relationships, were

predictive of some serious aggression outcomes, these

effects were generally small and inconsistent across mea-

sures. Therefore, in the current analysis, depressive

symptoms stand out as particularly strong predictors of

youth violence and aggression.

Some research has indicated that low serotonergic

functioning is related both to increased levels of depressive

symptoms and serious aggressive behavior (Carver et al.

2008) and results of the current study may reflect this.

Similarly a US Secret Service and US Department of

Education (2002) evaluation of adolescent and young adult

‘‘school shooters’’ (a group often linked with violent video

games in the popular press) found that 78% had a history of

feeling suicidal prior to their assault, and 61% had a history

of significant depressive symptoms or despondency,

although this often went undiagnosed (the figure above

reflects psychological autopsy results in which diaries or

blogs of shooters reflected serious depressive symptoms

that was not brought to the attention of mental health

professionals). Thus, current levels of depressive symp-

toms may be a key variable of interest in the prevention of

serious aggression in youth.

Results from the current study suggest that long-term

prediction of youth violence remains spotty at best and

practitioners may need to be careful not to ‘‘profile’’ youth

who have not committed serious aggressive acts. Predictive

results based on sociological variables (or video game use)

may run the risk of significant overidentification of ‘‘at

risk’’ status. Practitioners and policy makers may be eager

to identify and intervene with at-risk youth, but where

long-term prediction remains unreliable, the potential for

damage as well as good should temper and restrain efforts

in this realm.

No study is without flaws, and it is important to docu-

ment them in a research report. It should be reemphasized

that the current sample is non-random. Although efforts

were made to get the most representative sample possible,

generalizations from a non-random sample should be

undertaken only with caution. The current sample also was

a Hispanic-majority sample. Although this represents an

important extension of prospective designs into a previ-

ously neglected ethnic group, generalization to other ethnic

groups and cultures may be unwarranted. Furthermore, it is

not possible for a single research design to consider all

possible third variables. Important third variables that were

not considered in the current study but which have been

identified as important in other research (e.g., Pratt and

Cullen 2005) include poverty, substance abuse, school

influences, self-control and genetics. Further research

designs may wish to consider these predictor variables in

the future. The aggression related outcome measures used

here were designed to tap into more serious forms of

aggression, than in previous prospective studies. However,

it is reasonable to note differences even between these

measures. Arguably the severely violent criminal behaviors

referenced by the NLE differ from bullying behaviors

tapped by the OBQ. Thus, caution is warranted in gener-

alizing across these outcomes.

In conclusion, the current study finds no evidence to

support a long-term relationship between video game vio-

lence use and subsequent aggression. Although debates

about video game violence effects on player aggression are

J Youth Adolescence (2011) 40:377–391 389

123

likely to continue for some time, it is suggested that the

degree of certainty and statements regarding the strength of

causal effects should be revised in a conservative direction

(similar calls have been made by other scholars, e.g.,

Cumberbatch 2008, Freedman 2002; Olson 2004, Savage

and Yancey 2008; Sherry 2007). A reasonable argument

and debate for small influences could probably still be

made (e.g., Markey and Scherer 2009), although statements

reflecting strong, broad effects generalizable to serious acts

of youth violence are at current, likely unwarranted. This is

particularly important to note given that, as video games

have become more widespread over the past few decades,

the incidence rate of criminal youth violence has declined

sharply; it has not increased as feared (Childstats.gov

2009). Naturally, video games are an unlikely cause of this

youth violence decline (to conclude otherwise would be to

indulge in the ecological fallacy), however these results

suggest a mismatch between public fears of violent video

games and actual trends in youth violence (i.e., fears of

juvenile superpredators never materialized, see Muschert

2007). It is argued here that scientists must be cautious to

remain conservative in their conclusions lest the public be

misinformed. A continued debate over violent video games

will likely be positive and constructive, but such a debate

must be made with restraint. It is hoped that the current

article will contribute to such a debate.

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Author Biography

Christopher J. Ferguson is an associate professor of clinical and

forensic psychology at Texas A&M International University. His

research has focused on the positive and negative effects of violent

video games, and on youth violence more broadly. He lives in Laredo,

TX with his wife and young son.

J Youth Adolescence (2011) 40:377–391 391

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• Video Game Violence Use Among ‘‘Vulnerable’’ Populations: The Impact of Violent Games on Delinquency and Bullying Among Children with Clinically Elevated Depression or Attention Deficit Symptoms
• Abstract
• Introduction
• Video Game Violence Research: What is the Evidence?
• The Current Study
• Methods
• Participants
• Measures
• Depression/Attention Symptoms
• Trait Aggression
• Parental Involvement
• Support from Others
• Stress
• Exposure to Video Game Violence
• Delinquency
• Bullying
• Procedure
• Results
• Video Game Exposure
• Video Game Influences
• Discussion
• Developmental and Theoretical Perspectives
• Limitations and Conclusions
• A Word of Caution
• Author contributions
• References