Reassessing Media Violence Effects Using a Risk and ResilienceApproach to Understanding Aggression

Douglas A. GentileIowa State University

Brad J. BushmanThe Ohio State University and VU University

Public discourse about the effect of violent media on aggression has become conten-tious. We propose that violent media effects can best be understood within a risk andresilience framework that considers multiple factors that facilitate or inhibit aggression.In a prospective study, 430 third through fourth grade children, their peers, and theirteachers were surveyed twice during the school year, about 6 months apart. Sixrisk/protective factors for aggression were measured: media violence exposure (TV,movies, video games), physical victimization, participant sex, hostile attribution bias,parental monitoring, and prior aggression. Each Time 1 risk factor (including mediaviolence exposure) was associated with an increased risk of physical aggression atTime 2, whereas protective factors were associated with a decreased risk. There wasalso a Gestalt-type effect, where the combination of risk factors was a better predictorof aggression than the sum of their individual parts. The results offer strong support fora risk and resilience framework for aggression. Results also suggest that the effects ofmedia violence exposure may be underestimated by standard data analysis procedures.Exposure to media violence acts similarly to other risk factors for aggression andtherefore deserves neither special acclaim nor dismissal as a risk factor.

Keywords: aggression, risk factors, media violence, media, longitudinal study

“As Hillary [Clinton] pointed out in her book, the morechildren see of violence, the more numb they are to thedeadly consequences of violence. Now, video gameslike ‘Mortal Kombat,’ ‘Killer Instinct,’ and ‘Doom,’the very games played obsessively by the two youngmen who ended so many lives in Littleton, make ourchildren more active participants in simulated vio-lence.”

—Bill Clinton, former U.S. president. State-ment made April 24, 1999, in President’s radio

address following the Columbine High Schoolshooting in Littleton, Colorado.

“Playing a violent game won’t turn you into a psycho,a murderer or a serial killer. Most studies show thatvery clearly on the contrary violent games allow play-ers to express themselves. It’s like an outlet for them ina way. All these violent actions that are said to havebeen inspired by playing violent video games are noth-ing but the expressions of issues unrelated to videogames.”

—Guillaume de Fondaumiere, former presi-dent of the French National Video Game Asso-ciation. Statement made November 16, 2009,interview with Digital Games on digital-games.fr.

Public debate on the link between violentmedia and aggressive and violent behavior isoften contentious. Media violence effects areusually only discussed publicly in response toextreme events such as school shootings. Thisresults in a culprit mentality, in which peopleseek to identify “the cause” of the tragedy.However, violent behavior is extremely com-plex, and no single factor can predict it. Wesuggest that media violence effects on aggres-sion and violence can be best understood within

Douglas A. Gentile, Department of Psychology, IowaState University; Brad J. Bushman, School of Communica-tion, The Ohio State University, and Communication Sci-ence, VU University, Amsterdam, the Netherlands.

A portion of these results were presented by the firstauthor at the Media Violence Workshop, Potsdam, Ger-many, June 2007, and additional analyses are availableelsewhere (Gentile et al., 2011).The data collection wassupported by a grant from the Laura Jane Musser Fund.

D. G. and B. B. were both involved in data analysis, datainterpretation, and drafting this article. D. G. was responsi-ble for study design and data collection.

Correspondence concerning this article should be addressed toDouglas Gentile, Department of Psychology, Iowa State Univer-sity, W112 Lagomarcino Hall, Ames, IA 50011. E-mail:dgentile@iastate.edu

Psychology of Popular Media Culture © 2012 American Psychological Association2012, Vol. ●●, No. ●, 000–000 2160-4134/12/$12.00 DOI: 10.1037/a0028481

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a risk and resilience approach that considersseveral risk factors.

Several theoretical models describe the psy-chological mechanisms through which mediaviolence can influence later behavior (Bandura,1977; Berkowitz, 1984; Dodge, 1980; Hues-mann, 1986). Fundamentally, the psychologicalprocesses all rely on learning. With repeatedexposure to media violence, one can predictperceptual, cognitive, and emotional responses(Maier & Gentile, 2012). An example of a per-ceptual response is the tendency to perceiveambiguous actions by others as hostile—calleda hostile attribution bias. Research shows thatwith repeated exposure to violent media, am-biguous events are perceived as being morehostile (Gentile, Coyne, & Walsh, 2011). Cog-nitive responses include beliefs, such as thebelief that aggression is normal (Huesmann &Guerra, 1997), and scripts, such as expectingthat retaliation is the most typical response tobeing provoked (Anderson et al., 2004). Emo-tional responses include feelings of anger(Bushman & Geen, 1990). Most data and theoryfocus on these psychological-level mechanismsand effects. The public discussion, however, isoften at the macro level, such as focusing onpopulation-level violent crime statistics. Thishas allowed the public debate to focus on issuessuch as whether parents are good or bad parents,rather than on understanding the multicausalnature of aggression. In our view, aggressionshould be viewed as a public health issue, withviolent media being viewed as just one of manypotential risk factors.

Medical science overcomes similar difficul-ties in describing complex multicausal systemsby focusing on the pattern of risk and protectivefactors. This macro-level theoretical approachis sometimes called a risk and resilience ap-proach. For example, scientists consider a num-ber of risk factors for heart disease (e.g., smok-ing, alcohol consumption, high-fat diet, exer-cise, family history of heart disease), althoughthe standard statistical analyses usually focus oneach factor independently.

Although there have been several calls toconsider aggression within a risk-factor publichealth approach (Browne & Hamilton-Giachrit-sis, 2005; Centers for Disease Control & Pre-vention, 2008; Dodge & Pettit, 2003; Gentile &Sesma, 2003; U.S. Surgeon General, 2001), onecritic has recently dismissed this approach, say-

ing that it is “nonscientific” because it is “fun-damentally unfalsifiable” (Ferguson, 2009, p.118), although he does endorse a multivariateapproach elsewhere (Ferguson, San Miguel, &Hartley, 2009). The present research demon-strates that testable and falsifiable hypothesescan be generated from a risk and resiliencemodel. This article does not claim to provide anovel theory but to provide novel tests of therisk and resilience approach, as well as demon-strating whether media violence exposure actssimilarly to other known risk factors for aggres-sion.

The U.S. Surgeon General’s report on youthviolence (U.S. Surgeon General, 2001) statesthat “the bulk of research that has been done onrisk factors identifies and measures their predic-tive value separately, without taking into ac-count the influence of other risk factors. Moreimportant than any individual factor, however,is the accumulation of risk factors” (p. 59). Notethat this approach requires a change in how weunderstand causes of behavior. Rather than re-lying on the overly simplistic Humean “neces-sary and sufficient” views of causality (Haus-man, 1998), modern science has moved to astochastic understanding of causality. For ex-ample, some people with high cholesterol donot have heart disease, so cholesterol is not asufficient cause. Furthermore, some people whohave heart disease never had high cholesterol,so it is not a necessary cause. Therefore, cho-lesterol is neither a necessary nor sufficientcause of heart disease. Medical science does notdismiss cholesterol as unimportant, however,because it is one predictable, and therefore im-portant, risk factor for heart disease. In otherwords, high cholesterol is causally related toheart disease, even if it is neither a necessarynor sufficient cause. Similarly, some peoplewho are aggressive do not consume violent me-dia, and some people who consume violent me-dia are not very aggressive. Nonetheless, expo-sure to violent media is one predictable, andtherefore important, risk factor for aggression,as we show in the present research.

Other researchers have noted that there areseveral documented risk factors at multiple lev-els of analysis for aggression and that “no singlefactor predicts a high proportion of the variancein outcomes” (Dodge & Pettit, 2003, p. 354).Instead, cumulative risk models suggest that thetotal number of risk factors is a better predictor

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than any one factor, although risk factors can beinteractive as well as additive. Therefore, todemonstrate the full value of a risk and resil-ience approach, one should demonstrate notonly the increment in risk due to individual riskfactors but also the decrement in risk due toindividual protective factors. It would also beimportant to test whether cumulative risk fac-tors have an additive, linear effect or whetherthey have a multiplicative, interactive effect.

Another issue that clouds the dialogue is alack of clarity about whether the research fo-cuses on aggression or violence broadly (ag-gression is typically defined as any behavior,physical, verbal, or relational, that is intended toharm others, whereas violence is typically de-fined as an extreme subtype of physical aggres-sion that is likely to result is severe bodilyharm). More extreme outcomes usually requiremany more risk factors and are even harder topredict. Therefore, there are very few studiesthat show any link between exposure to mediaviolence and violent criminal behaviors, such ashomicide or aggravated assault. Nonetheless,media violence exposure can predict aggressivebehaviors, such as fighting or bullying behav-iors. Although many critics of the research fo-cus on extreme but rare violent criminal behav-iors, we focus on more common aggressivebehaviors, which can also be harmful.

Risk Factors for Aggression

The present research examines six potentialrisk factors for aggression. Each was selectedon the basis of previous research and theorysuggesting that each is related to increased riskof aggression. Most of these are noted as riskfactors in the U.S. Surgeon General (2001) andCenters for Disease Control and Prevention(2010) reports on youth violence. The risk fac-tors include:

Hostile Attribution bias

Children who have a bias toward attributinghostility to others’ actions are far more likely tobehave aggressively (Crick & Dodge, 1994,1996; Crick, Grotpeter, & Bigbee, 2002). Ameta-analysis of 41 studies involving morethan 6,000 children showed a strong relation-ship between hostile attribution of intent and

aggressive behavior (Orobio de Castro, Veer-man, Koops, Bosch, & Monshouwer, 2002).

Prior Involvement in a Physical Fight

Studies have repeatedly shown that the singlebest predictor of future aggression is past ag-gression (Centers for Disease Control & Pre-vention, 2010). Aggression is quite stable overtime and across situations, almost as stable asintelligence (Olweus, 1979). Although this is astrong risk factor, it does not provide explana-tory power. In a sense, it is a measure of prioraccumulated risk, which is why it is often usedas a covariate to control for earlier risk factors.

Prior Physical Victimization

Having been bullied or physically victimizedis also a risk factor for future aggression (Cen-ters for Disease Control & Prevention, 2010;Mercer, McMillen, & DeRosier, 2009). Theremay be many reasons that victims become of-fenders (and vice versa), although the mostcommon is retaliation (U.S. Surgeon General,2001).

Participant Sex

Being male is a strong risk factor for physicalaggression, and, conversely, being female is aprotective factor (U.S. Surgeon General, 2001).

Media Violence Exposure

Five decades of scientific data led to theconclusion that exposure to violent media in-creases aggression (for a review, see Bushman& Huesmann, 2012). Findings are similaracross studies that use very different methodol-ogies. Each research method has its uniquestrengths and weaknesses, yet across the differ-ent methods, there is a convergence of evidencein meta-analytic reviews of each methodology(e.g., Anderson & Bushman, 2002; Anderson etal., 2010; Comstock & Scharrer, 2003; Paik &Comstock, 1994). Experimental studies demon-strate that exposure to media violence causespeople to behave more aggressively immedi-ately afterward. Experimental studies have beencriticized for their somewhat artificial nature,but field experiments have produced similar re-sults in more realistic settings. However, it isnot so much the immediate effects of media

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violence exposure that are of concern, but ratherthe aggregated long-term effects. Longitudinalstudies offer evidence of a relationship betweenmedia violence exposure as a child and aggres-sive and violent behavior many years later as anadult.

Parental Involvement in Media

If exposure to media violence is a risk factorfor aggression, then parental monitoring of chil-dren’s media use should act as a protectivefactor (Austin, 1993; Nathanson, 2001). Con-versely, a lack of parental monitoring of chil-dren’s media should act as a risk factor foraggression (Anderson, Gentile, & Buckley,2007). Previous research has shown that violentmedia effects are larger when parents do notmonitor what media their children consume andwhen they do not discuss the content with them(Anderson et al., 2007).

The present research uses prospective datawith multiple informants (participants, peers,and teachers) to test three hypotheses: (1) thepresence of any individual risk factor at Time 1should be associated with an increase in thelikelihood of aggression at Time 2; (2) the pres-ence of any individual protective factor atTime 1 should be associated with a decrease inthe likelihood of aggression at Time 2, even inthe presence of other risk factors; and (3) thepresence of multiple risk factors at Time 1should be associated with an increase in thelikelihood of aggression at Time 2 to a greaterextent than any individual risk factor. In addi-tion, we tested whether a linear or curvilinearmodel fit the data better. Note that answeringthese questions requires a different approach toanalysis than the standard approach seekingsimply to ask whether media violence exposurepredicts future aggression, although that level ofanalysis has also been conducted with these data(Gentile et al., 2011).

Method

Participants

Participants were 430 children (51% male;Mage � 9.7 years, SD � 1.03, range � 7 to 11;86% Caucasian, which is representative of theregion). To obtain a diverse sample, studentswere recruited from five Minnesota schools,

including one suburban private school (n �138), three suburban public schools (n � 265),and one rural public school (n � 27). Parentalconsent was over 70% for all classrooms; childassent was 100%.

Procedure

Children and teachers were surveyed twice ina school year, 6 months apart for most partici-pants. All participants were treated in accor-dance with American Psychological Associa-tion’s ethical guidelines.

Assessment of Aggression

Physical aggression was measured using self-reports, peer-nominations, and teacher-reports.

Self-report. Participants were asked toreport if they had been involved in a physicalfight in the past year (used as a dichotomousvariable).

Peer nominations. A peer nomination in-strument was used to assess peer perceptions ofaggression (Crick, 1995; Crick, Bigbee, &Howes, 1996; Crick & Grotpeter, 1995). Chil-dren were provided with a roster of classmates,with each student given a number. Studentswere asked to nominate three students for eachof several items, by writing the student numberson the answer form. Confidentiality wasstressed to maximize truthful responding andminimize the risk of hurt feelings. Items mea-sured peer acceptance and rejection (2 items),physical aggression (2 items), relational aggres-sion (3 items), prosocial behavior (2 items), andverbal aggression (1 item). Only the PhysicalAggression subscale was analyzed in the currentstudy (Cronbach’s alpha � .92), as our focuswas on physical aggression rather than aggres-sion more broadly defined. The two items askedchildren to nominate which students in theirclasses “hit, kick, or punch others” and “pushand shove other kids around.” Each child wasgiven a z-score, standardized within classrooms,based on the number of nominations he or shereceived.

Teacher ratings. Teachers completed asurvey assessing the frequency of each child’sobserved aggression (Anderson et al., 2007).Teachers rated four physically aggressive be-haviors for each child: the child hits or kickspeers, threatens to hit or beat up other children,

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pushes or shoves peers, and initiates or gets intophysical fights with peers (1 � never true to5 � almost always true). Responses to the fouritems were summed to create a total score foreach child (Cronbach’s alpha � .92).

Composite measure of physical aggression.A composite aggression measure was created byfirst standardizing the peer nominations ofphysical aggression, the teacher ratings of phys-ical aggression, and the self-reports of involve-ment in a physical fight, and then averaging thethree standardized z-scores. The resulting com-posite physical aggression score yielded highinternal reliability at both Time 1 (Cronbach’salpha � .87) and Time 2 (Cronbach’s alpha �.89). Table 1 displays the intercorrelations be-tween these variables at both times. All corre-lations were positive and significant, demon-strating that self- and other-perceptions of ag-gressive behavior were concordant.

Assessment of Physical Victimization

Teachers rated three victim behaviors (e.g.,gets hit or kicked by peers) for each child (1 �never true to 5 � almost always true; Cron-bach’s alpha � .90; Anderson et al., 2007).

Hostile Attribution Bias

Hostile attribution bias was measured with awidely used scenario-based instrument that in-cludes 10 stories, each describing an instance ofprovocation in which the intent of the provoca-teur is ambiguous (Crick, 1995; Crick et al.,2002; Nelson & Crick, 1999). The stories weredeveloped to reflect common situations thatchildren might encounter (e.g., a peer spillsmilk on you in the lunch room). Participants

answer two questions following each story. Thefirst question presents four possible reasons forthe peer’s behavior, two reflecting hostile intentand two reflecting benign intent. The secondquestion asks whether the provocateur(s) in-tended to be mean or not. Each measure wasscored as 1 if the participant selected a hostileintent, or as 0 if not. Responses were summedwithin and across the stories for each provoca-tion type (Fitzgerald & Asher, 1987). Possiblescores ranged from 0 to 20, with higher scoresindicating a greater hostile attribution bias(Cronbach’s alpha � .85).

Assessment of Media Variables

Media violence exposure. Participantslisted their three favorite TV shows, videogames, and movies (Anderson & Dill, 2000;Anderson et al., 2007; Gentile, Lynch, Linder,& Walsh, 2004). For each, participants ratedhow frequently they watched or played it (1 �almost never to 5 � almost every day) and howviolent it was (1 � not at all violent to 4 � veryviolent). An overall violence exposure scorewas computed for each participant by multiply-ing the violence rating by the frequency ofviewing/playing, and then averaging across thenine responses (i.e., 3 TV programs � 3 videogames � 3 movies; Cronbach’s alpha � .80).This approach to measuring exposure to mediaviolence has been used successfully with chil-dren in other studies (Anderson et al., 2007;Gentile et al., 2004). Importantly, this approachhas been validated in research showing thatchild ratings correlate .75 with expert ratings(Gentile et al., 2009).

Total screen time. Participants providedthe amount of time they spent watching TV andplaying video games during three time periods(from when they wake up until lunch, fromlunch until dinner, from dinner until bedtime)separately for weekdays and weekends. Weeklyamounts were calculated by (a) summing theweekday times and multiplying by 5, (b) sum-ming the weekend times multiplied by 2, and (c)summing these products. TV and video-gametimes were summed to provide the total amountof screen time (as separate from violent content,and used as a covariate to control for amount,such that analyses of media violence exposureare interpretable as about violent content ratherthan media exposure broadly).

Table 1Intercorrelations Among Physical AggressionVariables

1 2 3

1. Peer report of aggression(continuous) — .53 .30

2. Teacher report of aggression(continuous) .57 — .28

3. Self-report of fights(dichotomous) .21 .23 —

Note. Below the diagonal are Time 1 intercorrelations;above the diagonal are Time 2 intercorrelations. All corre-lations statistically significant at p � .001.

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Parent involvement in children’s mediahabits. Participants reported how frequentlytheir parents watched TV with them and howfrequently they discussed content with their par-ents (1 � never to 5 � always). These twoitems were significantly correlated (r � .32, p �.001) and were averaged to create a composite(Cronbach’s alpha � .45). Although this reli-ability coefficient is lower than might be ex-pected, recent validity analyses have demon-strated that both parent reports and child reportsof parental monitoring are valid for predictingtheoretically predicted child variables, such asmedia violence exposure and school perfor-mance (Gentile, Nathanson, Rasmussen, Re-imer, & Walsh, 2012).

Results

The goal of this research is not to test whethermedia violence exposure itself is a significantpredictor but instead to demonstrate how riskfactors (including media violence exposure) in-dividually and collectively are associated withaggression. Therefore, our analysis strategydoes not rely specifically on traditional signifi-cance-testing approaches, although that type ofanalysis strategy demonstrates that media vio-lence exposure is a significant predictor of fu-ture aggressive behavior (Gentile et al., 2011).Instead, the analysis approach was to look athow well aggression could be predicted frommultiple risk factors and how media violenceexposure compares with other risk factors.

Hypothesis 1: The presence of any individ-ual risk factor at Time 1 should be associ-ated with an increase in the likelihood ofaggression at Time 2.

Our first hypothesis was that the presence ofany individual risk factor should predict aggres-sion at Time 2, even after controlling for ag-gression at Time 1 and total screen time for TV,videos and video games. We measured this twoways, utilizing different criterion variables.First, we created logistic regression equations(on the full sample) predicting the likelihood ofinvolvement in a physical fight at Time 2 fromTime 1 variables (including the Time 1 fightvariable). Using a dichotomous fight outcomevariable allows us to calculate probabilities ofinvolvement in a fight at Time 2, given certain

starting values of the Time 1 predictor vari-ables, based on the regression equation for thefull data set. For this analysis, low risk wasdefined as scoring at the 5th percentile on agiven risk factor, high risk was defined as scor-ing at the 95th percentile, and average risk wasdefined as scoring at the 50th percentile (me-dian). These risk definitions are arbitrary but arechosen to illustrate the difference betweenclearly low risk and clearly high risk amounts ofeach variable. High, low, and median valueswere entered into the regression equation, andthe resulting probability of involvement in aphysical fight at Time 2 was graphed for each.What is graphed is the result of the logisticfunction—a predicted distribution based on thedata rather than a description of our specificsample characteristics—therefore reporting thenumber of people at each level and confidenceintervals would be inappropriate. Figure 1shows likelihood of involvement in a fight atTime 2, based on whether a person is either lowor high risk on each Time 1 factor, holding allother risk factors constant at the median value.Thus, having a high hostile attribution bias atTime 1 is associated with an increased in the riskof involvement in a physical fight at Time 2 from35% to 42% (odds ratio � 1.35), holding all otherrisk factors constant and controlling for totalscreen time. That is, the shift from low to high riskis associated with a 34.5% increase in the likeli-hood of physical fights. Similarly, being a boyincreases the risk from 33% to 45% (odds ra-tio � 1.66); and high media violence exposureincreases the risk from 31% to 62% (odds ra-tio � 3.63), holding all other risk factors constantand controlling for total screen time.

Our second approach used multiple regres-sion to predict the Time 2 composite (self-report, peer nomination, and teacher report) ag-gression score from the six risk factors, againcontrolling for total screen time. Collectively,these accounted for 53% of the variance inTime 2 composite aggression, F(7,365) � 58.82, p � .001. The relative impor-tance of each of the predictors cannot be deter-mined by simply examining the size of betacoefficients, as risk factors are collinear (John-son, 2001, 2004). Johnson’s (2001; Johnson &LeBreton, 2004) relative weights analysis wasconducted to test the unique contribution ofeach risk factor to the overall R2. This analysisestimates the proportionate contribution each pre-

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dictor makes to the overall R2 while consideringboth its unique contribution and its contributionwhen combined with other predictors, thus parti-tioning the variance accounted for by each predic-tor. As shown in Figure 2, consumption of violentmedia explained 8.1% of the variance in the com-posite measure of aggression at Time 2, control-ling for all other factors.

Hypothesis 2: The presence of any individ-ual protective factor at Time 1 should beassociated with a decrease in the likelihoodof aggression at Time 2, even in the pres-ence of other risk factors.

Greater parental involvement in children’smedia habits has been suggested as a protectivefactor for aggression (Anderson et al., 2007;Gentile et al., 2004; Singer et al., 1999). To testwhether protective factors are associated with adecreased risk of aggression, even in the pres-ence of risk factors, we split participants intolow, median, or high cumulative risk groups,based on involvement in a prior fight, sex, andphysical victimization. Low risk was defined byentering the 5th percentile value for all the threerisk factors into the regression equation,whereas the 95th percentile value was entered todemonstrate high risk. As displayed in Figure 3,

parent involvement acts as protective factor forinvolvement in a physical fight at Time 2, re-gardless of whether participants’ profile is low,median, or high on the other risk factors. Thispattern is maintained even when low, median,and high media violence exposure is added as afourth risk factor.

Figure 1. Effect of each risk factor on involvement in a fight at Time 2, holding othersconstant (controlling for Time 1 total screen time).

Figure 2. Predicting Time 2 physical aggression (compos-ite of self-report, peer-nomination, and teacher-report) fromTime 1 risk and protective factors. Percentages are varianceaccounted for.

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Hypothesis 3: The presence of multiplerisk factors at Time 1 should be associatedwith an increase in the likelihood of ag-gression at Time 2 to a greater extent thanany individual risk factor.

We tested this hypothesis using two ap-proaches. In the first, we hypothesized that ifrisk factors are cumulative, then the likelihoodof aggression at Time 2 should be positivelyassociated with each additional risk factor, andthat exposure to media violence should furtherincrease the risk over and above other factors.As displayed in Figure 4, the individuals mostlikely to behave aggressively at Time 2 are boyswith high hostile attribution bias who have pre-viously been involved in a fight, who consumeviolent media, and whose parents are not in-volved in their media exposure. Note that expo-sure to media violence increases the risk ofaggression over and above the other risk factors.

We recoded the risk factors to indicate di-chotomized risk, following standard approachesto combining multiple risk factors (Boxer,Huesmann, Bushman, O’Brien, & Moceri,

2009; Sameroff, 2000). Risk was defined forthese analyses as being at or above the 75thpercentile (coded 1), or below the 75th percen-tile (coded 0). This approach is consistent withthe procedures established by investigatorsworking in the risk and resilience tradition(Appleyard, Egeland, van Dulmen, & Sroufe,2005; Boxer et al., 2009). Figure 5 shows theincrease in the likelihood of aggression basedon the number of risk factors present. Figure 6shows that the relation between the number ofrisk factors and the likelihood of aggression hasboth a quadratic and linear component, R2 �.39, F(2, 401) � 125.73, p � .001, and R2 �.31, F(1, 402) � 178.51, p � .001, respectively.

Discussion

Using prospective data, we demonstrated thatexposure to media violence is associated withincreased risk of later aggression, that parentalmonitoring of media can decrease the risk, andthat the greatest risk occurs when multiple riskfactors are present. The best single predictor offuture aggression in this sample of elementary

Figure 3. Effect of parent involvement as a protective factor.

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schoolchildren was past aggression, followed byviolent media exposure, followed by having beena victim of aggression (Figures 1 and 2). The orderand relative size of each risk factor is not, how-ever, the main point. The main point is that riskfactors increase and protective factors decrease thelikelihood of aggression in a predictable manner.

Perhaps most interesting is the finding thatmultiple risk factors are not simply additive butcan be multiplicative (see Figures 5 and 6). Thelikelihood of aggression increases more as thenumber of risk factors increase. Most cumula-tive risk models have an assumption of equifi-nality, in that the same outcome can result fromany combination of disparate sources and thatadditive models will therefore work well(Dodge & Pettit, 2003). In contrast, interactivemodels assume that some combination of riskfactors may increase the magnitude of effects toa greater extent, and, therefore, additive models

will not suffice. Our data provide evidence tosupport both positions. The quadratic model fitsbetter than the linear model, but the linearmodel provides a very good approximation andfits almost as well. Note that, in this sample,once a child has five of the six risk factors, onecan predict, with 84% accuracy, whether he orshe will be involved in a physical fight by theend of the school year.

This approach to analysis also provides data onan issue that has been suggested but rarely test-ed—that traditional effect-size estimates of mediaviolence may overestimate or underestimate theactual amount of variance in aggressive behavior(Anderson et al., 2007; Ferguson, 2010). Moststudies use regression to test violent media effects,controlling for several relevant variables (thisanalysis approach yields � � .16 with the presentdata, suggesting 2.6% of variance accounted for inour composite aggression measure). However, be-

Figure 4. Cumulative risk factors for aggression: hostile attribution bias, prior aggression,sex, and media violence exposure.

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cause violent media exposure is collinear withprior aggression, participant sex, and other predic-tors of aggression, controlling for these other fac-tors may underestimate the effect of violent mediaon aggression. The relative weights analysis, incontrast, provides a more accurate estimate of howmuch variance in the outcome each predictor ac-counts for, with violent media exposure account-ing for 8.1% of the variance—more than would befound using standard multivariate regression anal-ysis.

Implications

These findings have several important impli-cations. The first is that the risk and resilienceapproach is a valuable approach for understand-ing media violence effects. Violent media ex-posure is not the only factor associated withaggression, or even the most important, but it isone important risk factor. The risk and resil-ience approach yields testable hypotheses andallows for understanding how media violencemay work in combination with other risk factorsto predict future aggression. Importantly, it may

allow the rhetoric surrounding the heated “de-bate” over media violence to cool down. Ifmedia violence is understood as just one riskfactor among many for aggression, then we canview it as similar to other public health risks.Media violence has somehow achieved whatappears to us to be a type of special status. Thepresent analyses demonstrate that it deservesneither special acclaim as a risk factor for ag-gression nor special denials as being unrelatedto aggression. It acts similarly to other knownrisk factors for predicting aggression and can bemoderated by protective factors such as parentalinvolvement (see Figure 3).

This approach allows for a more balancedunderstanding of the causes of aggression. Fig-ure 7 displays a metaphorical aggression ther-mometer, in which the “cold” end signifies re-spectful behavior and the “hot” end signifiesviolent behavior. No single risk factor can heatit up all the way. In fact, any individual riskfactor can probably only move the thermometerone or two notches. This yields two importantinsights. First, to get to an aggressive behavior

Figure 5. Predicted likelihood of involvement in a fight (Time 2) from number of Time 1risk factors present.

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that is easily visible (such as violent criminalbehavior) requires many risk factors at once andvery few protective factors. Second, this helpsto explain why most people can say, correctly,

that they have consumed a lot of media violenceand have never committed a violent crime. Mostpeople have only a few risk factors or also haveseveral protective factors. This means that nomatter how much media violence they consume, itcan never push the thermometer all the way to thetop. Note, however, that this is different fromsaying that it has no effect. It is having an effect(likely on psychological level variables such asattitudes, desensitization, aggressive normativebeliefs, hostile attribution bias, etc.), but not onethat will be easily observable in behaviors.

Limitations and Future Directions

The present research has several limitations,which suggest directions for future research.That our self-report of fights is a single item anddoes not distinguish between being involved asa perpetrator or victim is one limitation, al-though it is balanced by peer nominations andteacher reports of aggression, which providesnot only multiple informants but also concep-tual replication between multiple types of ag-gression measures. It also would be valuable toexplore additional measures, including parentreports or direct observation of the risk factors.

Figure 6. Linear and quadratic relationship between the number of risk factors present at Time 1and aggression at Time 2 (self-, peer, and teacher reports). Values on the Y-axis are z-scores.

Figure 7. Metaphorical aggression thermometer (adaptedfrom Gentile & Sesma, 2003).

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It is likely that the relative sizes of some of therisk factors would change if measured differ-ently, and this type of replication is definitelyneeded. We note, however, that the goal of thisanalysis was not to estimate specific values forindividual risk factors but instead to demon-strate that each risk factor predictes aggressionin a systematic way. Another limitation is thenonexperimental nature of the design, whichprevents us from drawing causal inferencesbased on these data, although they are prospec-tive and are concordant with causal theories.Finally, several studies of parental monitoringof media have demonstrated that there are moreaspects to monitoring than the two that weremeasured here (Gentile et al., 2012; Nathanson,2001; Valkenburg, Krcmar, Peeters, & Mar-seille, 1999). Future studies should use morecomprehensive measures, which would yielda scale with a higher reliability than our two-item scale, and may yield larger effect sizesfor parental monitoring than were found inthis sample.

Conclusion

Although this study provides support for arisk and resilience approach to understandingmedia violence, it should not supplant detailedpsychological theories of how media violenceor other risk factors can influence aggression.The risk and resilience approach is a macroleveltheory and therefore does not speak to why riskfactors have their effects, nor how they mayhave effects on different types of psychologicalmechanisms (e.g., cognition, arousal, affect).Psychological theories are still needed to under-stand the role of these mechanisms, and severalwell-tested theories exist (Carnagey & Ander-son, 2003). A risk-factor approach, however,may have additional benefits, by communicat-ing the research to a lay audience in a mannerthat is more understandable and is thereforevaluable for discourse both within the scientificcommunity as well as among the general public.A risk-factor approach may also reduce some ofthe contention in current debates about violentmedia effects. Exposure to violent media is notthe only risk factor for aggression, or even themost important risk factor, but it is one impor-tant risk factor.

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Received August 15, 2011Revision received March 29, 2012

Accepted April 3, 2012 �

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