Annotated Bibliography 7

E M P I R I C A L R E S E A R C H

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

Christopher J. Ferguson • Cheryl K. Olson

Received: 18 April 2013 / Accepted: 17 July 2013 / Published online: 24 August 2013

� Springer Science+Business Media New York 2013

Abstract The issue of children’s exposure to violent

video games has been a source of considerable debate for

several decades. Questions persist whether children with

pre-existing mental health problems may be influenced

adversely by exposure to violent games, even if other

children are not. We explored this issue with 377 children

(62 % female, mixed ethnicity, mean age = 12.93) dis-

playing clinically elevated attention deficit or depressive

symptoms on the Pediatric Symptom Checklist. Results

from our study found no evidence for increased bullying or

delinquent behaviors among youth with clinically elevated

mental health symptoms who also played violent video

games. Our results did not support the hypothesis that

children with elevated mental health symptoms constitute a

‘‘vulnerable’’ population for video game violence effects.

Implications and suggestions for further research are

provided.

Keywords Video games � Aggression � Violence � Mental health

Introduction

Whether violent video games do or do not contribute to

behavioral aggression and societal violence among youth

has been debated, at the time of this writing, for three

decades. By societal violence, we refer to a range of

behaviors, from bullying and physical fighting to criminal

assault and even homicide, which are of concern to law-

makers and parents. We contrast societal violence with the

measures of relatively mild aggression (or perhaps com-

petition) often used in laboratory studies of college stu-

dents, which arguably do not tap well into the issue of

societal violence (Kutner and Olson 2008). Caution is

required in generalization of laboratory aggression mea-

sures to societal violence as the potential for misinformation

is considerable (Ferguson et al. 2011). To date, no con-

sensus has been reached on the matter of whether violent

games and societal violence are linked: some scholars argue

that violent games contribute to behavioral aggression

(Fraser et al. 2012) or even societal violence (Strasburger

2007), while others suggest that video games have a neg-

ligible influence on aggression (Puri and Pugliese 2012) or

may even reduce aggression (Colwell and Kato 2003).

Existing societal concerns about video games have

intensified after the 1999 Columbine High School massacre

(Ferguson 2013) and other well-publicized school shootings.

The tragic 2012 Sandy Hook Elementary School murders in

Newtown, Connecticut resurrected these debates amid

reports that the 20-year-old shooter was an avid gamer (e.g.,

Henderson 2012). The Newtown shooting also brought

renewed attention to wide discrepancies in opinion regarding

whether violent video games influence criminal behavior.

The Brown v EMA (2011) Supreme Court decision, in which

the Court ruled that a California law restricting the sale or

rental of violent games to minors was an unconstitutional

violation of the First Amendment, highlighted the limitations

of existing studies of violent video games and the difficulty

of applying this pool of research to policy-relevant questions.

A series of appellate court rulings made similar points (see

Brown v EMA 2011, p. 12). Given these court rulings, and

C. J. Ferguson (&) Department of Psychology, Stetson University,

DeLand, FL 32729, USA

e-mail: CJFerguson1111@Aol.com

C. K. Olson

Reston, VA, USA

123

J Youth Adolescence (2014) 43:127–136

DOI 10.1007/s10964-013-9986-5

the recurring media focus on video games, researchers need

to do more to answer the questions of greatest public concern

regarding video games and any potential harm to youth. The

recurrence of these concerns with each school shooting or

court ruling points to the need for studies that can mean-

ingfully inform policy and legal debates.

Video Game Violence Research: What is the Evidence?

Much speculation focuses on the issue of whether violence

in video games or other entertainment media, such as

television, can contribute to real-life violence. Evidence to

date is scant. For instance, in a recent meta-analysis that

focused on criminal aggression, Savage and Yancey (2008)

found that exposure to media violence shared only trivial

amounts of variance with criminal aggression. Similarly, in

a large sample of youth aged 10–15, Ybarra et al. (2008)

found that violent media exposure did not predict violence

once other confounding variables were controlled. It is also

noteworthy that the explosion in popularity and availability

of video games has coincided with a precipitous decline in

youth violence, not a rise (see Ferguson 2013 for

discussion).

There exists a large pool of studies examining video

game violence effects in college students using laboratory

methods and measures of relatively mild aggression. The

validity of these measures has been debated within the

research community (e.g. Giancola and Zeichner 1995;

Ritter and Eslea 2005). One point of contention is the lack

of clear correspondence between these measures and the

types of aggressive behaviors of interest to policy makers

and parents. For instance, such studies have examined

outcomes such as filling in the missing letters of words,

where ‘‘kill’’ rather than ‘‘kiss’’ is considered more

aggressive (Farrar et al. 2013); self-ratings of hostile

feelings (Williams 2011); or administering non-painful

bursts of annoying noise to consenting opponents in a

reaction-time test (Anderson and Dill 2000). Taken at face

value, such studies may be generalizable to competitive-

ness rather than aggression, or perhaps to mild aggressive

acts (the equivalent of children sticking tongues out at each

other), but cannot be generalized to societal violence. Even

these studies produce mixed results, however, and have

been criticized for methodological issues such as failing to

match violent and non-violent video game play conditions

carefully (Adachi and Willoughby 2011), using unstan-

dardized outcome measures that may allow researchers to

pick and choose outcomes fitting their hypotheses (Fergu-

son 2013), and high potential for demand characteristics.

By contrast, studies of video game effects on violent

behaviors among children, conducted outside laboratory

settings, remain relatively few in number. Such studies

differ in quality and standardized approach to measure-

ment. One study (Anderson et al. 2008) found weak links

between video game violence and aggression in US and

Japanese children, although interpretation of results is

complicated by the use of non-standard measures of

aggression and inadequate control for other variables. A

later German study tying media violence, including video

game play, to aggression in children (Krahé et al. 2012)

also did not use standardized assessments. That study may

have been compromised by the introduction of a media

education program into the schools mid-way through the

longitudinal period (e.g., Möller et al. 2012) introducing

demand characteristics (i.e., advertising the study hypoth-

eses to prime respondents to answer surveys in a particular

way, not representative of how they actually behave).

Another recent study that links violent games with

aggression, by Willoughby et al. (2012), carefully con-

trolled for important ‘‘third’’ variables. With other vari-

ables controlled, exposure to violent video games

correlated with later aggression with an effect size equiv-

alent to r = .07, indicating that violent game use was

associated with approximately half a percent increase in

aggressive behavior. The authors noted, however, that it

may be competitive qualities of the games, not violent

content, which led to this increase (see Adachi and Wil-

loughby 2011). In a follow-up longitudinal study (Adachi

and Willoughby 2013), the authors confirmed that com-

petition predicts later aggression, irrespective of violent

game exposure history.

Few other studies of children and video games have

made a solid case for a connection to aggression or violent

outcomes. Several have suggested that use of violent video

games might reduce aggression (Colwell and Kato 2003;

Shibuya et al. 2008 1 ). Others indicate that, with other

factors controlled, effects are null (Ferguson 2011; von

Salisch et al. 2011; Wallenius and Punamäki 2008; Ybarra

et al. 2008) or that effects may be idiosyncratic among

children (Unsworth et al. 2007). Meta-analyses (e.g.,

Sherry 2007) have found weaker effects in studies of

children than for college students, the opposite of what

might be expected developmentally. Thus, overall, it is

1 We note the issue that some research reports insinuate links

between violent games and aggression, where their data fail to support

such insinuations. We note that in Shibuya et al. 2008, in their

Table 2, the video game exposure by violence presence variable is

associated with a reduction in aggression in boys, but not girls. For

Ybarra et al. (2008) the null effect for violent video games is noted in

their Figure 2, although they largely ignore their own results to imply

links between violent games and youth aggression. These papers

highlight the need to closely examine research results when under-

standing the true implications of a research study. The rhetoric

employed by scholars in their abstracts and discussion sections does

not always match their data.

128 J Youth Adolescence (2014) 43:127–136

123

difficult to make clear conclusions about links between

video game violence and childhood aggression or violence.

Post-Sandy Hook, a view emerged, typified by the report

of the US House of Representatives Gun Violence Pre-

vention Task Force (2013), that current research probably

did not support concerns that the average child was harmed

by video game violence. Rather, attention should be

focused on prevention and early intervention with ‘‘at-risk

youth,’’ with particular emphasis on mental health. This is

a reasonable hypothesis, but one that has not been studied

extensively. Several studies of college students by Patrick

Markey found that violent video games may interact with

preexisting anger symptoms in some young adults to

increase hostility, although he has been cautious about

extending these findings to violence in children (Giumetti

and Markey 2007; Markey and Markey 2010; Markey and

Scherer 2009). These warnings are consistent with those of

criminologists who warn against generalizing laboratory

aggression measures to criminal violence (Savage 2008).

One recent analysis with children (Ferguson 2011) was

unable to confirm the hypothesis that children with pre-

existing antisocial traits were adversely influenced by

violent video games. However, more research would cer-

tainly be welcome.

The Current Study

The current study is intended to address gaps in the existing

literature by considering the impact of exposure to violence

in video games on criminal delinquency and bullying

behaviors in a sample of children with clinically elevated

mental health symptoms. It is important to note at the

outset that the vast majority of children with mental health

symptoms do not engage in violent behavior. Although

some symptoms of mental health problems such as

depression (Ferguson 2011) and attention deficit disorder

(Wymbs et al. 2012) have been identified as risk factors for

aggressive or violent behavior, this occurs only in combi-

nation with other significant risk factors, not as a direct

result of the mental health symptoms. Thus, scholars must

exercise caution not to further stigmatize mental illness by

insinuating links with violence.

Rather, our analyses are intended to address the

hypothesis that children with clinically elevated mental

health symptoms consistitute a ‘‘vulnerable’’ population of

individuals who may be susceptible to video game violence

effects even if clinically ‘‘normal’’ children are not. We

thus test two main hypotheses. First, it was hypothesized

that children with clinically elevated symptoms of

depression will demonstrate a correlation between violent

video game exposure and criminal delinquency and bul-

lying behavior-related outcomes. Second, it was

hypothesized that children with clinically elevated atten-

tion deficit symptoms will demonstrate a correlation

between violent video game exposure and criminal delin-

quency and bullying behavior related outcomes.

Methods

Participants

The current study includes a subset of participants from a

large federally funded project examining video game vio-

lence effects on youth. Details related to the initial devel-

opment and recruitment for this project can be found at

Kutner and Olson (2008). Only children who scored in the

clinically significant range on clinically validated scales

related to depressive or attention deficit symptoms (scales

discussed below) were included in the current analyses.

These included 377 children: 182 with clinically elevated

attention deficit symptoms, and 284 with clinically ele-

vated depressive symptoms. Clinically elevated symptoms

were comorbid for 89 (23.6 %) children. There were 234

females in the sample and 140 males (3 chose not to report

their gender). The mean age of the children was 12.93

(SD = .76). Children were recruited from both an urban

and suburban school. The ethnic makeup of students in the

urban school was 50 % white, 43 % black, 2 % Asian, 5 %

Hispanic and\1 % other. The ethnic makeup of students in the suburban school was 90 % white, 4 % black, 4 %

Asian, 1 % Hispanic and 1 % other (individual students

were not asked to report their ethnic background).

Measures

Depression/Attention Symptoms

Symptoms of depression and attention-deficit/hyperactivity

problems were assessed using the relevant subscales of the

youth self-report version of the Pediatric Symptom Check-

list—17 (PSC; Gardner et al. 1999). This instrument is a

validated, brief screening device for mental health problems

in children, and provides clinical cut-offs to identify children

whose symptoms merit further evaluation. Participants were

asked to rate whether they experienced particular mental

health symptoms ‘‘never,’’ ‘‘sometimes’’ or ‘‘often.’’ With

the current sample, coefficient alpha for the ADHD subscale

was .75 and for the depression subscale .80. The sample

reported mean was 5.41 and standard deviation was 2.28.

Trait Aggression

The Attitudes Toward Conflict scale (ATC; Dahlberg et al.

1998) consists of eight Likert items related to potential

J Youth Adolescence (2014) 43:127–136 129

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aggressive responses to various hypothetical situations.

Sample items include, ‘‘It’s OK for me to hit someone to

get them to do what I want’’ and ‘‘I try to talk out a

problem instead of fighting.’’ Due to the stability in trait

aggression it is commonly regarded as an important control

variable and we include it here for this reason. Trait

aggression correlated with video game exposure at r = .24

for youth with elevated attention deficit symptoms and .23

for youth with elevated depressive symptoms. However,

predictive relationships between exposure to video game

violence and trait aggression became non-significant in

regression equations with gender, parental involvement,

stress and family/peer support controlled. Thus, we are

confident that our use of trait aggression as a control var-

iable does not miss relationships between video game

violence and trait aggression with other factors controlled.

Coefficient alpha for the current sample for the ATC was

.76. The sample reported mean was 16.48 and standard

deviation was 4.60.

Parental Involvement

To measure parents’ involvement with their children’s

media use, sharing media consumption with children and

making media consumption decisions for them, a nine-item

Likert-scale was created for this study. Examples of

questions included in this scale are ‘‘My parents play

electronic games with me,’’ and ‘‘My parents tell me I can’t

play a particular electronic game.’’ Coefficient alpha for

the current sample was .68. The sample reported mean was

18.48 and standard deviation was 4.12.

Support from Others

We compiled a sixteen item Likert-scale measure of per-

ceived support from peers and family. This measure was

based on two existing measures (Lerner et al. 2005; Phillips

and Springer 1992) of peer support and family support.

Overall coefficient alpha for the resultant scale was .87.

The sample reported mean was 44.35 and standard devia-

tion was 10.22.

Stress

The Stressful Urban Life Events scale (SULE; Attar et al.

1994), a 19 item yes/no scale, was used to measure total

stress that children in the current sample had experienced

during the past year. The SULE addressed stressors such as

getting suspended from school, getting poor grades on

one’s report card, or experiencing the death of a family

member. Coefficient alpha for the total stress scale was .67

for the current sample. The sample reported mean was 4.82

and standard deviation was 2.96.

Exposure to Video Game Violence

In the current study, we used Entertainment Software

Ratings Board (ESRB) video game ratings as an estimate

of exposure to violence in video games. Respondents were

asked to write the names of five video games that they had

‘‘played a lot’’ in the past 6 months. ESRB ratings were

then obtained for each game, and ordinally coded (a

maximal score of 5 for ‘‘Mature,’’ 4 for ‘‘Teen,’’ etc.). The

sample reported mean was 29.97 and standard deviation

was 30.09.

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.

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.

Common violence-containing games named by participants

included those in the Halo, Grand Theft Auto, and Mortal

Kombat series. The ratings were summed across the 5

games listed, then multiplied by the number of hours per

week that the child reported playing video games. As with

all attempts to assess game content exposure, this is only an

estimate; however, it removes some of the subjectivity

inherent in previous methods. This approach has been

found to be reliable and valid in previous research (Fer-

guson 2011; Lenhart et al. 2008).

Delinquency

A six-item Likert scale of general delinquency was com-

piled from several existing delinquency scales (Brener

et al. 2002; Elliot et al. 1985; Leffert et al. 1998). Ques-

tions addressed physical aggression (been in a physical

fight; hit or beat up someone) as well as more general

delinquency (stole something from a store; got into trouble

with the police; damaged property just for fun, such as

breaking windows, scratching a car, or putting paint on

walls; skipped classes or school without an excuse). Par-

ticipants were asked to report how often these behaviors

occurred within the previous twelve months. Coefficient

alpha for the resultant scale was .75 for the current sample.

The sample reported mean was 3.00 and standard deviation

was 3.95.

Bullying

The Revised Olweus Bully/Victim Questionnaire (Olweus

1996) was used to assess bullying behaviors. The bullying

perpetration scale consisted of 9 items in which partici-

pants were asked to rate how often they had engaged in

bullying behaviors over the past couple of months. Items

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inquire about physical aggression, verbal aggression,

threats and social exclusion. A coefficient alpha of .86 was

obtained for the current sample. The sample reported mean

was 2.68 and standard deviation was 4.27.

Procedure

All procedures described within this study were approved

by local IRB and designed to comport with APA standards

for ethical human research. An ‘‘opt out’’ procedure was

used for student involvement, with parents notified of the

study through school newsletters and notices sent home to

students. Youth assent for participation was obtained for all

participants. Teachers were not present during data col-

lection, which occurred during the school day.

Primary data analysis used for the testing of the study

hypotheses were OLS multiple regressions. Gender,

parental involvement, trait aggression, stress, family/peer

support and exposure to video game violence, as well as

the interaction between exposure to violent video game and

trait aggression, were entered simultaneously in the

regression equation. In keeping with the recommendations

of Simmons et al. (2011), we certify that this analysis

approach was selected in advance and was not altered to

produce particular results. An interaction between trait

aggression and exposure to video game violence was tested

by first centering the variables to avoid multicollinearity.

Collinearity diagnostics for all regressions revealed

absence of any concerns with all VIFs below 2.0. Youth

with depressive or attention deficit symptoms will be

considered separately.

Results

Video Game Exposure

Children in our sample were generally very familiar with

electronic games. Of our sample, 84.4 % reported playing

video games on a computer, 81.2 % on a console and

50.4 % on a handheld device in the previous 6 months.

Only 6.1 % reported playing no games at all during that

time. Similarly, only 11.4 % of our sample had no expo-

sure to violent video games. Boys had considerably more

exposure to violent video games than did girls

[t(189.24) = 9.07, p \ .001, r = .46, 95 % CI = .38, .54]. Kurtosis and skew were acceptable, suggesting a normal

distribution of scores.

Video Game Influences

With the sample of children with clinically elevated

depressive symptoms and regarding delinquent criminality

as an outcome only stress (b = .30) and trait aggression (b = .42) were predictive of delinquent criminality. Nei- ther exposure to video game violence nor the interaction

between trait aggression and exposure to video game vio-

lence were predictive of delinquent outcomes. The adjusted

R 2

for this regression equation was .36. These results are

presented in Table 1.

With the same sample of children with clinically ele-

vated depressive symptoms but considering bullying

behaviors as an outcome, once again only stress (b = .23) and trait aggression (b = .28) were predictive of bullying behaviors. Neither exposure to video game violence nor the

interaction between exposure to video game violence and

trait aggression were predictive of bullying related out-

comes. The adjusted R 2

for this regression equation was

.22. These results are presented in Table 2.

With the sample of children with clinically elevated

attention deficit symptoms and regarding delinquent crim-

inality, as with the sample of children with clinically

Table 1 Delinquency regression: beta weights and significance of entered variables for adolescents with clinical elevated depressive

symptoms

Variable b 95 % confidence

interval

t test Significance

Gender .06 0.92 .36

Parental involvement -.01 -0.05 .96

Stress .30 (.19, .40) 4.73 .001*

Family/peer support -.07 -0.96 .34

Trait aggression .42 (.32, .51) 6.08 .001*

VGV .04 0.55 .59

VGV 9 trait aggression .04 0.64 .53

VGV exposure to video game violence

Table 2 Bullying regression: beta weights and significance of entered variables for adolescents with clinical elevated depressive

symptoms

Variable b 95 % confidence

interval

t test Significance

Gender -.11 -1.74 .14

Parental involvement -.01 -0.09 .92

Stress .23 (.12, .34) 3.24 .001*

Family/peer support -.05 -0.67 .50

Trait aggression .28 (.17, .38) 3.74 .001*

VGV -.07 -0.95 .34

VGV 9 trait aggression -.02 -0.23 .82

VGV exposure to video game violence

J Youth Adolescence (2014) 43:127–136 131

123

elevated depressive symptoms only stress (b = .32) and trait aggression (b = .38) were predictive of delinquent criminality. Neither exposure to video game violence nor

the interaction between trait aggression and exposure to

video game violence were predictive of delinquent out-

comes. The adjusted R 2

for this regression equation was

.37. These results are presented in Table 3.

Finally, with the sample once again of children with

clinically elevated attention deficit symptoms and with

regards to bullying behavior only trait aggression (b = .41) was predictive of bullying behaviors along with the inter-

action between trait aggression and exposure to violent

games (b = -.22) suggesting that highly trait aggressive children who also played violent video games were less

likely to engage in bullying behaviors. Exposure to Video

game violence was not a significant predictor of bullying

behaviors. The adjusted R 2

for this regression equation was

.19. These results are presented in Table 4.

Discussion

The 2011 Supreme Court (Brown v EMA 2011) case

seemed to have briefly cooled speculation about video

game violence effects on children. The tragic 2012 shoot-

ing of young children in Newtown, Connecticut by a

20-year-old male reportedly fond of playing violent video

games put the issue back on the front burner (Gun Violence

Prevention Task Force 2013). The consensus from the

government (e.g., Gun Violence Prevention Task Force

2013) seems to have been that current research does not

consistently link exposure to video game violence with

aggression or societal violence, but more research is nec-

essary to assess effects on potentially vulnerable subgroups

of children. The current study is an attempt to fill that gap

by considering correlational violent video game effects in a

sample of youth with clinically elevated mental health

symptoms. Our results did not provide support for the

hypotheses that exposure to violent video games would be

associated with increased delinquency or bullying behav-

iors in children with elevated mental health symptoms.

Our results indicated that violent video games were

associated with neither delinquent criminality nor bullying

behaviors in children with either clinically elevated

depressive or attention deficit symptoms. Nor did we find

support for the belief that trait aggression would interact

with video game violence within this sample of youth. That

is a particularly interesting finding given that a combina-

tion of mental health symptoms and long-term aggressive

traits are common elements to attackers who carried out

school shootings (US Secret Service and US Department of

Education 2002). Our results cannot, of course, be gen-

eralized to mass homicides. We do note that our findings

with more general forms of youth violence are similar to

those of the Secret Service report, in that trait aggressive-

ness and stress were risk factors for negative outcomes

where exposure to video game violence was not. The only

exception was our finding that, for children with elevated

attention deficit symptoms, trait aggression and video game

violence interacted in such a way as to predict reduced

bullying. This could be considered some small correla-

tional evidence for a cathartic type effect, although we note

it was for only one of four outcomes and small in effect

size. Thus we caution against overinterpretation of this

result.

None of the hypotheses related to video game violence

effects on vulnerable youth were supported. Although this

is only one piece of evidence, this early result does not

support the belief that certain at-risk populations of youth,

at least related to clinically elevated depression and

attention deficit symptoms and trait aggression, demon-

strate negative associations between violent video games

and aggression related outcomes. It may be that the

Table 3 Delinquency regression: beta weights and significance of entered variables for adolescents with clinical elevated attention

deficit symptoms

Variable b 95 % Confidence

interval

t test Significance

Gender .06 0.71 .48

Parental involvement .06 0.70 .49

Stress .32 (.18, .44) 4.21 .001*

Family/peer support -.15 -1.69 .10

Trait aggression .38 (.25, .50) 4.23 .001*

VGV .04 0.45 .65

VGV 9 trait aggression .03 0.39 .70

VGV = exposure to video game violence

Table 4 Bullying regression: beta weights and significance of entered variables for adolescents with clinical elevated attention

deficit symptoms

Variable b 95 % confidence

interval

t test Significance

Gender -.06 -0.61 .54

Parental involvement .06 0.65 .52

Stress .12 1.38 .17

Family/peer support .01 0.02 .99

Trait aggression .41 (.28, .52) 4.17 .001*

VGV .06 0.60 .55

VGV 9 trait

aggression

-.22 (-.08, -.35) -2.27 .03*

VGV exposure to video game violence

132 J Youth Adolescence (2014) 43:127–136

123

influence of media is simply too distal to impact children,

even those with mental health symptoms. We do note that

our results do not rule out motivational models of media

use, wherein effects are driven by user motivations rather

than automatic modeling of content. However, we found

little evidence to support beliefs in reliable probabilistic

models of automatic media modeling of violence in chil-

dren with elevated depressive or attention deficit

symptoms.

We note that our results differ from those of Patrick

Markey (Giumetti and Markey 2007; Markey and Markey

2010; Markey and Scherer 2009). There are several pos-

sible explanations for the differing results. For example,

Markey’s work considered hostile feelings in the short term

as outcome. It may be that such feelings do not persist or

do not extend to actual violent behavior. Markey’s work

also examined college students, whereas ours look at

youth. Differences between laboratory-based work and

correlational work also may help explain the differences in

findings.

Developmental and Theoretical Perspectives

Across youth and across outcomes, the current levels of

stress and trait aggression were the most consistent pre-

dictors of negative outcomes in youth. These results are

consistent with a model of aggression known as the Cata-

lyst Model, which is basically a diathesis stress model of

violence (Ferguson et al. 2008). Although we did not

specifically set out to test the Catalyst Model, our results

are a good fit for this theory’s predictions that violence is

the product of crystallized personality traits coupled with

stressful triggers from the environment.

From a developmental perspective, the Catalyst Model

suggests that such personality traits results from a combi-

nation of genetic propensity coupled with harsh upbring-

ing, although these were variables beyond our current

dataset. However, the Catalyst Model generally assumes

that exposure to media violence is a normative rather than

deviant experience (see also Olson 2010). This may differ

from the perspective of many commentators concerned

about harmful media influences. For instance, much

attention has focused on whether Adam Lanza (the New-

town, Connecticut shooter) had significant exposure to

violent video games (e.g. Henderson 2012). It is worth

noting that, statistically speaking, it would be more unusual

if he did not play violent video games, given that the

majority of youth and young men play such games at least

occasionally (Lenhart et al. 2008; Olson et al. 2007). Thus,

it may be a mistake to take the perspective that exposure to

violent video games or other media is a developmentally

abnormal experience. Our results support that generally

accepted thinking, even for children with elevated mental

health systems, may need to be changed.

The Catalyst Model has the advantage of acknowledging

that not all learning opportunities are equal. That is to say,

proximal influences, such as family environment, are

considered to have a greater impact than distal influences,

such as electronic media. We believe that this is superior to

traditional social cognitive models of aggression that

equate all learning opportunities and thus lack nuance and

an acknowledgement of developmental trends in which

children are known to process different sources of infor-

mation differently (Woolley and Van Reet 2006). The

Catalyst Model also relies less on the assumption that

aggressive cognitions and behaviors are based primarily on

cognitive aggressive scripts, which does not appear to be an

effective approach to understanding serious aggression.

The Catalyst Model fits best with our observations of stress

and trait aggression as the primary predictors of delin-

quency and bullying in youth, although as a correlational

study our findings can not address the causal assumptions

of the Catalyst Model.

In addition to looking at violence from more of a

diathesis-stress approach, there may be value in viewing

media use from more of a motivational perspective, such as

the uses and gratifications approach (Sherry et al. 2006) or

Self-Determination Theory (Przybylski et al. 2010; Ryan

et al. 2006). These theoretical approaches have in common

the value of taking the user experience as a primary driving

factor of the relationship between the user and media,

rather than presuming that content drives the relationship.

In the typical ‘‘hypodermic needle model’’ of media

effects, effects are traditionally conceptualized as Stimu-

lus/Response, or perhaps Stimulus/Organism/Response if

the individual is considered as a moderating variable (see

Ferguson and Dyck 2012 for discussion). There may be

greater value in considering the relationship from more of

an Organism/Stimulus/Response arrangement, with the

organism rather than the stimulus as the primary driving

force of the relationship between media and behavior. That

is to say, individuals may select certain kinds of media in

order meet needs they have or reach desired emotional

states. Even specific forms of media may have idiosyn-

cratic effects on users dependent upon how they consume

and process media.

Limitations and Conclusions

As with all studies, ours has limitations that are important

to consider. First, our sample includes children with mental

health symptoms above clinical cut-off points on a vali-

dated screening tool, but screening results do not constitute

official diagnoses of mental health disorders. Further,

J Youth Adolescence (2014) 43:127–136 133

123

although we considered mental health and trait aggression,

it is possible that other issues may place some children in

vulnerable populations that we did not identify. Our study

involves concurrent correlational data; thus, it is not pos-

sible to make causal inferences or to test the directionality

of observed relationships. Reliabilities of the stress and

parental involvement scales were also lower than ideal.

These two scales appear to tap into a broad array of issues,

which may explain this result; future researchers may wish

to consider more narrowly constructed scales. Lastly,

although our delinquency scale was compiled from existing

well-validated scales, it would be valuable to see our

results replicated using clinical outcomes such as the Child

Behavior Checklist or criminological outcomes such as the

Negative Life Events scale (Paternoster and Mazerolle

1994).

Our results suggest that the association between violent

video games and aggression related outcomes in children,

even those with clinically elevated mental health symp-

toms, may be minimal. Our research contributes to the field

of youth and media by providing evidence that a timely,

policy-relevant, and seemingly reasonable hypothesis—

that mentally vulnerable children may be particularly

influenced by violent video games—does not appear to be

well supported. However, more research on this popula-

tion, and on others likely to be at increased risk (such as

children exposed to violence in their homes or neighbor-

hoods), is needed to guide parents, health professionals and

policymakers. It may be valuable for future researchers to

consider alternate models of youth’s media use, particu-

larly those that focus on motivational models in which

users, rather than content, drive experiences. Content-based

theoretical models do not appear to be sufficient for a

sophisticated understanding of media use and effects.

A Word of Caution

Scholarship produced in the emotional and politicized

environment that follows a national tragedy (see Ferguson

2013) can give the appearance of a ‘‘wag the dog’’ effect,

with research commissioned based upon, and then used to

support, an a priori political agenda. As Hall et al. (2011)

noted in their article on the Supreme Court and video

games, a rush to judgment grounded in legislators’ inter-

pretations of ‘‘unsettled science’’ may damage the credi-

bility of the scientific process. Scholars would be wise to

proceed carefully, with close attention to sound method-

ology and discussion of limitations, as they design and

conduct the next wave of studies. Studies which move

beyond traditional social cognitive automatic processes to

consider how youth select, interpret and involve media in

their identity development as active consumers of media

would be of particularly high value.

Author contributions CJF conducted the main analyses for the paper and wrote the initial draft. CO collected the data an contributed

to revising drafts of this paper. Both authors participated equally in

conceiving and designing the analyses. Both authors read and

approved of the final manuscript.

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Author Biographies

Dr. Christopher J. Ferguson is associate professor and department chair at Stetson University. His research interests focus on media

effects on children and adolescents, particularly violent media, and

thin images on body dissatisfaction.

Dr. Cheryl K. Olson is currently working as a consultant. Her research interests have focused on public health and policy related to

media issues.

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