communication with parents

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Author's personal copy Journal of Applied Developmental Psychology 29 (2008) 286–294

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Journal of Applied Developmental Psychology

Digital game violence and direct aggression in adolescence: A longitudinal study of the roles of sex, age, and parent–child communication☆ Marjut Wallenius ⁎, Raija-Leena Punamäki Department of Psychology, University of Tampere, FIN-33014 University of Tampere, Finland Research Unit of Pirkanmaa Hospital District, Tampere University Hospital, University of Tampere, FIN-33014 University of Tampere, Finland

a r t i c l e

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Article history: Available online 8 May 2008 Keywords: Adolescents Digital game violence Direct aggression Parent–child communication Longitudinal study

a b s t r a c t This study investigated the roles of sex, age, and parent–child communication in moderating the association between digital game violence and direct aggression in a two-year longitudinal study. Finnish 12- and 15-year-old adolescents (N = 316) participated in the follow-up survey. As hypothesized, digital game violence was linked to direct aggression both longitudinally and synchronously, and the link was moderated by parent–child communication in interaction with sex and age. Results suggest that the moderating role of parent–child communication changes with increasing age. Poor parent–child communication may be one of the factors in an adolescent's development that may strengthen the negative effects of digital game violence, but even good parent–child communication does not necessarily protect the adolescent in the long run. Digital game violence seems to be one of the risk factors of increased aggressive behavior. © 2008 Elsevier Inc. All rights reserved.

1. Introduction The digital games industry is growing faster than any other entertainment industry and game playing is spreading from younger to older age groups (ESA, 2006). Early adolescence is the most intensive period in digital game playing. About 90% of teenaged boys and 60% of teenaged girls in Finland play digital games (e.g., Wallenius, Rimpelä, Punamäki, & Lintonen, in press) and figures are similar in other countries (e.g., Gentile & Walsh, 2002). In general, digital game playing is oriented primarily to male abilities and needs for social inclusion (e.g., Lucas & Sherry, 2004). Violence is common in games: as much as 89% of games played by adolescents contain violence designed to cause injury or death to another person (Smith, Lachlan, & Tamborini, 2003). A substantial body of both correlational and experimental research has found relations between digital game violence and adolescents' aggressive behavior (see Anderson, 2004; Sherry, 2001). However, little is known about the long-term effects of digital game violence and determinants and moderators of its impact on aggressive behaviour. The present study addresses this gap by examining the role of child sex, age, and parent–child communication in moderating the link between digital game playing and aggression in the transitions from middle childhood to early adolescence and from early adolescence to adolescence. Anderson and colleagues' General Aggression Model (GAM) addresses how digital game violence influences behavior (e.g., Buckley & Anderson, 2006). The model describes a cyclical interaction between a person and the environment. Both person factors (e.g., age, trait hostility) and situational variables (e.g., media, other people) influence an individual's cognition, affect and arousal, leading to the

☆ This study was supported by the grant from the Academy of Finland (201669), the Information Society Institute of the University of Tampere and the Tampere University of Technology (16-01), and the Competitive Research Funding of the Pirkanmaa Hospital District (9G211). We are grateful to the schools for cooperation and to the children and adolescents who participated in the study. We thank Arja Rimpelä and Clas-Håkan Nygård for their contribution to planning the study and Minna Rantanen, Riikka Haakana, Marja Vajaranta, Marjatta Radecki, Susanna Rainio, Lasse Pere, and Tomi Lintonen for assistance in baseline data collection, and Hanna Lavikainen, Kirsi Wiss, Anne-Marie Rigoff, Hanne Kivimäki and Susanna Rainio for assistance in follow-up data collection. We further thank Mrs. Virginia Mattila, University of Tampere, for revising the language. ⁎ Corresponding author. Department of Psychology, FIN-33014, University of Tampere, Finland. Tel.: +35 8 3 215 111; fax: +35 8 3 215 7345. E-mail address: marjut.wallenius@elisanet.fi (M. Wallenius). 0193-3973/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.appdev.2008.04.010

Author's personal copy M. Wallenius, R.-L. Punamäki / Journal of Applied Developmental Psychology 29 (2008) 286–294

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onset of thoughtful or impulsive action. Violent games may increase aggression by teaching aggressive behavior models (Bandura, 2001), priming hostile thoughts (Gentile, Lynch, Linder, & Walsh, 2004), and increasing aggressive affect and arousal (e.g., Schneider, Lang, Shin, & Bradley, 2004). Violent digital games promote learning of aggressive knowledge structures and emotional desensitizing to fearful, violent stimuli. These influence how individuals perceive, interpret and react to the world. According to GAM, repeated exposure to violent digital games leads to well-rehearsed and automatized aggressive knowledge structures, the enactment of which depends on how relevant they are in a specific situation (Buckley & Anderson, 2006). Empirical findings are congruent with GAM. Meta-analyses (e.g., Anderson & Bushman, 2001; Sherry, 2001) reveal that digital game violence and aggressive behavior are significantly and positively correlated, with small to moderate average effect sizes (r = .15–.20). Thus, studies support the link between digital game violence and aggression. Recently, longitudinal results have suggested that digital game violence may also predict an increase in future aggressive behavior (Anderson, Gentile, & Buckley, 2007). However, some studies show a negative correlation between time spent playing violent games and aggression, suggesting a possible decline in the initial arousal effect (Sherry, 2001). Recent neuroscientific research supports the link between game violence and aggression by showing that violent digital games activate the brain processes characteristic for aggression (e.g., Weber, Ritterfeld, & Mathiak, 2006). Findings that the brain function of violent game players shows reduced aversive reaction to violence, which is further associated with higher levels of aggression, support the desensitization hypothesis (Bartholow, Bushman, & Sestir, 2006). Further, amount of media and game violence is related to executive functioning, the ability of the individual to inhibit, regulate, direct, and plan behavior (Kronenberger et al., 2004). Both executive functioning and aggressive behavior are regulated by the frontal brain region. Some researchers suggest that digital game playing may influence brain development in adolescence through the pruning of the grey matter of the brain, in which neural connections that are not used are eliminated, whereas those that are used will survive (Giedd, 2002). During adolescence changes occur in virtually every aspect of development, including aggression. Age-related curvilinear patterns for physical direct aggression peak between 13 and 15, then decrease (Steinberg & Morris, 2001). Generally boys express higher levels of direct aggression than girls (Peets & Kikas, 2006), but there is stability in high levels of aggression, which tends to continue and predict subsequent violence (Loeber & Hay, 1997). Predictors of continuity of aggressive behavior, (e.g., family conflict) are similar for males and females (Harachi et al., 2006). Increases in aggression in early adolescence and exposure to violent digital games appear to interact and negatively bias an adolescent's internal state by reinforcing aggressive cognitions, hostile affects, and increasing aggression-related arousal. Family interactions are a major socializing force, affecting how children perceive, interpret, and respond to events in the physical and social environments (see Chen et al., 2004). Adolescent–parent relationships change and less time is spent with parents. Conflicts are at their highest in early adolescence, simultaneous with the peak in aggression (Smetana, Campione-Barr, & Metzger, 2006). Thus, changes in parent relations and aggressive behavior intertwine in adolescent development. Parent–child relations play an important role in the development of aggression and as moderators between exposure to violence and aggressive responses (e.g., Rutter, Giller, & Hagell, 1998). Evidence suggests that good family relations and authoritative parenting practices can prevent aggressive development among children living in violent communities (Proctor, 2006; Quouta, Punamäki, Miller, & El Sarraj, 2008). Moreover, domestic violence is not associated with aggressive behavior if the child has good relations at least with one parent (Rutter et al., 1998). Little is known about the connection between family communication and violent digital game play. Results show that increased playing of violent electronic games is related to family conflict, more among younger than older (ages 9–12) children (Vandewater, Lee, & Shim, 2005). Moreover, when parent–child communication is poor, game violence is related to higher aggression, especially among boys in middle childhood (Wallenius et al., 2007). Parental involvement in game playing time and content reduces shortterm effects of violent games on aggressive behavior (Anderson et al., 2007). According to the cumulative risk model, development is a result of interaction of multiple risk factors and protective factors (Masten, 2001). Risk of problematic behavior is increased with each additional risk factor and decreases with each protective factor. Thus, risk factors may affect development through several possible pathways. Poor parent–child communication and digital game violence may interact and additively contribute to the development of negative world views, aggressive knowledge structures, and a hostile attributional style (Burks et al., 1999). Thus, poor parent–child communication may be a crucial factor affecting children's and adolescents' vulnerability to the negative effects of violent games and consequently aggressive behavior (Kirsch, 2003). We investigated whether digital game playing fosters aggression in adolescents and whether child sex, age and parent–child communication act as possible moderators of this relationship. We hypothesized a positive relationship between digital game violence and aggression. We expected a positive relationship between digital game violence and aggression more in boys than in girls; the intensity of digital game playing is higher in boys than in girls, which means that boys are exposed to game violence more than girls. Additionally, level of aggression is higher in boys than in girls. Because aggression peaks and the quality of parent–child communication deteriorate in early adolescence, which also is the most intensive time of game playing, we expected that the influence of digital game violence on aggression would be most pronounced in early adolescence. We hypothesized that the quality of parent–child communication moderates the link between digital game violence and aggression such that the positive relation is stronger when parent–child communication is poor, whereas good parental communication may protect adolescents from the effects of violent games on aggression. We took a more exploratory approach to the interaction between the three moderating variables. The design was longitudinal, enabling us to examine both longitudinal and synchronous effects. We studied 10 and 13 year olds initially, and conducted a 2-year follow-up study because of the developmental saliency in the transition from middle childhood to early adolescence and further to adolescence (Steinberg & Morris, 2001).

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2. Method 2.1. Participants In spring 2004 (Time 1), 689 pupils from five elementary and two middle schools in a city in Finland were recruited for the study. Participants were 222 fourth graders and 256 seventh graders, mean (SD) ages = 10.27 (.47) and 13.28 (.46), respectively; 54.4% were girls. There were no age group differences between boys and girls, χ2(1, N = 478) = 1.31, p = .86. Two years later (Time 2), 316 of the 478 participants in the first wave of the study returned the follow-up questionnaire: 132 (59.5%) were from sixth grade and 184 (71.9%) from ninth grade, mean (SD) ages = 12.27 (.47) and 15.28 (.46), respectively; 57% were girls. There were no age differences between boys and girls, χ2(1, N = 316) = 1.23, p = .27. A comparison of the Time 1 responses from participants who dropped out versus those who participated in the follow-up study showed one significant difference: more younger than older children dropped out, χ2(1, N = 478) = 8.18, p = .004. Drop outs reported somewhat higher intensity of digital game playing, M (SD) = 1.11 .97 (.87), than participants who continued on in the follow-up study, M (SD) = .97 (.76), although this difference failed to reach the level of significance, t(476) = 1.78, p = .076. Finally, participants who continued on in the follow-up study reported slightly poorer parent–child communication, M (SD) = 2.51 (.55) than participants who dropped out, M (SD) = 2.61 (.56), although this difference also only approached significance, t(476) = 1.76, p = .079. 2.2. Study procedure Consent to participate was obtained from all adolescents willing to participate and from their parent/guardian if the adolescent was under 15 years. Participants in the first wave completed two questionnaires with a one-week interval between administrations, and completed one questionnaire in the second wave. Each session took place in the classroom during a lesson, lasted about 1 h, and was preceded by instructions from one of the researchers who also remained present to answer any inquiries. 2.3. Measures 2.3.1. Violence in digital games Participants indicated how often the digital games they played contained violence (such as killing, fighting, attacks, kicking) using a 4-point scale: 0 = not at all, 1 = sporadically, 2 = often, and 3 = very often (Slater, Henry, Swain, & Andersson, 2003). Using the same 4-point scale, participants reported how often they played the game genre ‘Action, fighting, shooting’ (involving physical violence, shooting and killing). Internal consistency estimates were acceptable, Time 1 α = .90 and Time 2 α = .86. 2.3.2. Parent–child communication Barnes and Olson's Parent–Adolescent Communication Scale (PACS) (see Bradbury & Fincham, 1990) is a 20 item Likert-type questionnaire that assesses positive and negative characteristics of parent–child interaction, and contents and processes of communication. In this study, a 14 item version was used, in which participants estimated how well the descriptions fit their parent–child interaction and communication using a 5-point scale (0 = not at all, 1 = somewhat, 2 = rather well, 3 = well, and 4 = very well). Participants completed the form twice, once for each parent. The PACS assesses two dimensions of communication. Open mother/father communication (7 items) describes positive and encouraging interactions (e.g., ‘Mother is always a good listener’, ‘Father tries to understand my opinions’). Problem mother/father communication (7 items) focuses on negative and conflicting communication (e.g., ‘Mother hurts me when she is angry’, ‘Father tells me things that make me feel bad’). The parent–child total communication score was the mean of all the responses after the problem scale items were reverse scored. High scores indicate good and open communication quality. Internal consistency was α = .87 for both Times 1 and 2. 2.3.3. Direct aggression Aggression was measured by the 10 item included in the Direct & Indirect Aggression Scale (DIAS; Björkqvist, Lagerspetz, & Österman, 1998), which describes direct physical aggression toward others (e.g., ‘I might hit a person when I'm irritated’, ‘I kick and hit’, ‘I take away things from the other person’). Using a 5-point scale, participants estimated how often they themselves showed such behavior with their peers (0 = never, 1 = very seldom, 2 = sometimes, 3 = rather often, or 4 = very often). A mean score was computed. Internal consistency at Time 1 α = .87 and at Time 2 α = .88 (overall α = .86). 3. Results 3.1. Preliminary analysis Table 1 provides descriptive statistics for Time 1 and Time 2 variables by sex and age. A 2 (sex) × 2 (age) multivariate analysis of variance performed on these variables yielded significant effects for participant sex, participant age, Fs(6, 305) = 62.15 and 5.78 respectively, ps b .01, and a sex × age interaction, F(6, 305) = 2.48, p b .05. Follow-up univariate analyses specified that boys reported playing violent digital games and more direct aggression than girls at both Times 1 and 2. Older students showed higher


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Table 1 Mean (SD) scores for all variables by participant sex and age group at two times of measurement Variable

Girls

Boys

Younger

Older

Younger

Older

M

SD

M

SD

M

SD

M

SD

Time 1 Direct aggressiona, b Game violencea, b Parent–child communicationab

.28 .24 2.74

.34 .50 .61

.36 .42 2.31

.33 .65 .54

.65 1.37 2.58

.62 .90 .52

.76 1.86 2.49

.49 .85 .44

Time 2 Direct aggressiona, b Game violencea, b Parent–child communicationb

.33 .75 2.52

.34 .40 .59

.44 .88 2.22

.48 .58 .58

.66 1.82 2.51

.52 .88 .49

.81 2.20 2.46

.57 .82 .49

Note. Range = 0–4, except for game violence = 0–3. Values in excess of 3 SD of the mean were excluded to reduce the impact of outliers (one younger girl and one older boy were excluded based on direct aggression at Time 2). a p b .01 for sex difference. bp b .01 for age difference. abp b .05 for sex × age interaction.

frequencies of direct aggression and of playing violent digital games than younger students. Time 1 parent–child communication was poorer among older compared to younger girls; Time 2 parent–child communication was poorer among older than younger children. Repeated measures analyses of variance comparing responses at Times 1 and 2 revealed that digital game violence at Time 2 was higher than at Time 1, Ms (SDs) = 1.35 (.92) vs. .91 (.99), respectively, F(1, 312) = 106.29, p b .001, η2 = .254. The quality of parent–child communication was also poorer at Time 2 than at Time 1, Ms (SDs) = 2.41 (.56) and 2.51 (.55), respectively, F(1, 312) = 11.25, p b .001, η2 = .035. Table 2 shows intercorrelations among variables at Time 1 and Time 2, reported separately for boys (above diagonal) and girls (below diagonal). Correlations showed moderate stability of direct aggression, digital game violence, and parent–child communication (all ps b .001). Moreover, digital game violence correlated positively with direct aggression at Time 1 among boys and at Time 2 among girls. Negative correlations between good parent–child communication and direct aggression were significant for both sexes. A negative correlation between positive parent–child communication and digital game violence occurred for girls. 3.2. Digital game violence predicting direct aggression Regression analyses using a destructive testing approach were conducted to test whether digital game violence predicted direct aggression longitudinally and synchronously (Anderson & Anderson, 1996). This approach first examines whether a predicted relation between the two target variables is statistically significant. Then the relation is tested by systematically adding theoretically relevant covariates to the statistical model until the link between the two target variables is broken or the selected covariates have all been used. If the inclusion of several theoretically relevant covariates fails to break the link, confidence in the validity of that link is high. A destructive testing analysis was applied to the longitudinal link between digital game violence at Time 1 and direct aggression at Time 2. Relevant competitor variables were sex, age, and parent–child communication at Time 1. Next a destructive testing analysis was conducted on the synchronous link between digital game violence at Time 2 and direct aggression at Time 2. Competitor variables were sex, age, and parent–child communication at Time 2. In both analyses of Time 2 direct aggression, the Time 1 aggression measure was included as the final competitor variable added to the destructive testing set. This allowed us to examine the change in direct aggression scores at Time 2 that was uniquely associated with digital game violence. When digital game violence was the only predictor, it was positively related to direct aggression longitudinally and synchronously; in each case accounting for about 12% of the variance in aggression (see Table 3). Partialling out the variation with

Table 2 Correlations between variables at Time 1 and at Time 2 for boys' scores (above diagonal, n = 136) and for girls' scores (below diagonal, n = 180) Variable Time 1 1. Direct aggression 2. Game violence 3. Parent–child communication Time 2 4. Direct aggression 5. Game violence 6. Parent–child communication ⁎p b .05. ⁎⁎p b .01. ⁎⁎⁎p b .001.

1

2

3

4

5

6

---.12 − .36

.28⁎⁎⁎ ---− .16⁎

− .40⁎⁎⁎ − .10 ----

.41⁎⁎⁎ .19⁎ − .07

.10 .56⁎⁎⁎ − .11

− .23⁎⁎ .01 .53⁎⁎

.35⁎⁎ .06 − .28⁎⁎⁎

.10 .27⁎⁎⁎ − .11

− .24⁎⁎⁎ − .21⁎⁎ .56⁎⁎⁎

---.23⁎⁎ − .35⁎⁎⁎

.13 ---− .21⁎⁎

− .21⁎ .03 ----



Table 3 Summary of destructive testing of key theoretical links between violent digital game playing, direct aggression, sex, age, and parent–child communication Link tested (dependent variable/target predictor)

Variables in model DGV

+ Sex

+ Age

+ PCC(T1/T2)

Direct aggression (T2)/DGV (T1) Digital game violence slopes % unique variance explained by DGV t value of DGV effect

+ DA(T1)

.12 11.60 6.41⁎⁎⁎

.10 2.00 2.73⁎⁎

.09 1.40 2.30⁎

.08 1.20 2.13⁎

.03 .20 .96

Direct aggression (T2)/DGV (T2) Digital game violence slopes % unique variance explained by DGV t value of DGV effect

.20 12.20 6.59⁎⁎⁎

.12 2.40 2.95⁎⁎

.10 1.80 2.60⁎⁎

.09 1.50 2.41⁎

.08 1.00 2.13⁎

Note. DGV = digital game violence; PCC = parent–child communication; DA = direct aggression. ⁎p b .05. ⁎⁎p b .01. ⁎⁎⁎p b .001.

child sex reduced the amount of variance attributable to digital game violence, as was predicted on the basis of boys' higher level of digital game violence and direct aggression. After controlling for child age and parent–child communication, the positive link between digital game violence and direct aggression remained significant. Finally, partialling out Time 1 direct aggression broke the longitudinal link between digital game violence and Time 2 direct aggression but left the synchronous link significant. Hierarchical multiple regression analyses were conducted to test whether child sex, age and parent–child communication moderated the association between digital game violence and direct aggression (Baron & Kenny, 1986). Because direct aggression has been shown to be stable over time (e.g., Harachi et al., 2006), the dependent variable at Time 1 was entered first. Controlling for the dependent variable at Time 1 also controls for the possible confounding effect of a stable personality characteristic, because this should already be contained in the Time 1 value. Thus, changes in direct aggression from Time 1 to Time 2 were examined. Sex, age, digital game violence and parent–child communication were entered in the second step. In the third step, the 2-way interactions of game violence with moderator variables of sex, age and parent–child communication were entered. Finally, in the fourth step, the cross-product terms of digital game violence, parent–child communication and first sex and then age were entered. The interactions were based on the centered scores to ensure that multicollinearity between the main effects and the corresponding interaction effects would not distort the analysis (Aiken & West, 1991). Two regression models were formed. The first concerned the longitudinal prediction of direct aggression by digital game violence at Time 1. In the longitudinal prediction, separate models were formed by first entering Time 1 parent–child communication scores and then Time 2 parent–child communication scores. The longitudinal prediction including parent–child communication at Time 1 showed that earlier parent–child communication was neither associated with direct aggression two years later at Time 2 nor did it interact with other moderating variables. Thus, the results are based on the longitudinal model including current parent–child communication at Time 2. The second analysis concerned the synchronous effect of Time 2 digital game violence on direct aggression at Time 2. When controlling for aggression at Time 1 this model went beyond mere crosssectional analysis (Zarf, Dormann, & Frese, 1996). The results of the regression analyses are summarized in Tables 4 and 5.

Table 4 Summary of hierarchical regression analyses for longitudinal data predicting direct aggression (N = 315) Predictor variables Step 1 Dependent T1 Step 2 Sex Age group Game violence T1 PCC T2 Step 3 Game violence T1 × sex Game violence T1 × age Game violence T1 × PCC T2 Step 4 Violence T1 × sex × age Violence T1 × PCC T2 × sex Violence T1 × PCC T2 × age Adjusted R2 F model(df)

B

SE B

ΔR2

β

.224⁎⁎⁎ .36

.06

.33⁎⁎⁎

.23 .12 − .01 − .28

.07 .07 .04 .06

.22⁎⁎ .11 .01 − .30⁎⁎⁎

.02 .03 − .02

.03 .03 .03

.03 .05 − .04

− .04 .11 − .06

.04 .04 .03

− .08 .20⁎⁎ − .11⁎

.069⁎⁎⁎

.001

.032⁎⁎

.301 13.28(11, 303)⁎⁎⁎

Note. β = standardized regression coefficient; ΔR2 = change in R2 in the last step; PCC = parent-child communication. ⁎p b .05. ⁎⁎p b .01. ⁎⁎⁎p b .001.


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Table 5 Summary of hierarchical regression analyses for synchronous data predicting direct aggression (N = 315) Predictor variables Step 1 Dependent T1 Step 2 Sex Age group Game violence T2 Parent–child communication (PCC) Step 3 Game violence T2 × sex Game violence T2 × age Game violence T2 × PCC T2 Step 4 Violence T2 × sex × age Violence T2 × PCC T2 × sex Violence T2 × PCC T2 × age Adjusted R2 F model(df)

B

SE B

ΔR2

β

.224⁎⁎⁎ .36

.06

.33⁎⁎⁎

.20 .10 .06 − .25

.08 .07 .05 .07

.18⁎ .09 .11 − .27⁎⁎⁎

− .02 − .01 .01

.04 .03 .03

− .03 − .02 .01

− .04 .08 − .01

.04 .04 .03

− .07 .15⁎ − .01

.077⁎⁎⁎

.010

.014

.300 13.22(11, 303)⁎⁎⁎

Note. β = standardized regression coefficient; ΔR2 = change in R2 in the last step; PCC = parent-child communication. ⁎p b .05. ⁎⁎⁎p b .001.

Consistent with earlier results, the main effects revealed that poor parent–child communication was associated with higher levels of direct aggression. In the fourth step, first digital game violence × sex × current parent–child communication interaction predicted direct aggression both longitudinally and synchronously. Second, the interaction of digital game violence, age and current parent–child communication had a significant longitudinal effect on direct aggression. To illustrate the nature of the interactions, we followed the procedure outlined by Aiken and West (1991), which allows the comparison of the slope of the regression line at 1 SD above and below the mean score of the moderating variable. Fig. 1 represents the regression of direct aggression on digital game violence at both good (+ 1 SD above the sample mean) and poor (− 1 SD below the sample mean) current parent–child communication separately for girls and boys. Among girls, digital game violence longitudinally predicted increased direct aggression, as hypothesized, when current parent–child communication was poor. Yet, synchronously at Time 2 there was a significant link between digital game violence and direct aggression regardless of whether the girls reported poor or good parent–child communication. For boys, digital game violence predicted increased direct aggression both longitudinally and synchronously when their current reported parent–child communication was good, contrary to our hypothesis.

Fig. 1. The interaction of digital game violence and parent–child communication in the prediction of direct aggression by participant sex: Longitudinal (A) and synchronous (B) analysis.



Fig. 2. The interaction of digital game violence and parent–child communication in the prediction of direct aggression by age in girls. Longitudinal analysis.

Fig. 2 represents the regression of Time 2 direct aggression on Time 1 digital game violence in girls at ages 12 and 15. The interaction of digital game violence, age and parent–child communication specified the longitudinal results such that digital game violence was associated with direct aggression only among older girls who reported poor parent–child communication. 4. Discussion We investigated whether digital game playing is related to increased direct aggression in adolescents and whether sex, age and current parent–child communication moderate this relationship. The results suggest both longitudinal and synchronous relations between digital game violence and direct aggression. As hypothesized, moderating and joint effects of sex, age and parent–child communication were found. The observed positive relationship between digital game violence and direct aggression concurs with predictions based on GAM (Anderson & Bushman, 2001; Buckley & Anderson, 2006) and earlier results (Anderson, 2004; Anderson et al., 2007; Sherry, 2001). The strength of this relationship was within the range found in other studies. Supporting our hypothesis and earlier findings, results showed that digital game violence predicts aggressive behavior in adolescents both in the long term and in the short term. This is the first longitudinal study with as long a time span as two years. Interestingly, the effect of violent game playing two years earlier seems to be of approximately the same size as the effect of current playing. Our results also specified the link between digital game violence and direct aggression with moderating variables. In line with prior findings (Anderson et al., 2007; Peets & Kikas, 2006) boys expressed higher levels of both direct aggression and digital game violence at both measurement points. We hypothesized that for these reasons game violence may be associated with aggression more strongly in boys than in girls. A sex difference was found in the younger age group: in the longitudinal prediction digital game violence two years earlier was associated with current direct aggression in boys but not in girls. Presumably the sex difference is based on girls' lower level of game violence at age ten compared to boys of the same age who already played violent digital games more intensively. Second, we hypothesized that the association of digital game violence with direct aggression is strongest in early adolescence. The Time 2 age difference between 12- and 15-year-old girls in the longitudinal prediction of direct aggression partly supports our age hypothesis. Earlier digital game violence was associated two years later with direct aggression in older but not younger girls. Again, the level of digital game violence in the Time 1 ten-year-old girls may have been too low to predict subsequent aggression at the age 12. At Time 1, the older 13-year-old girls already expressed a higher level of digital game violence and direct aggression. Principally, however, age-related increase in aggression and digital game violence do not seem to interact. Likewise, previous studies usually show no age differences in relations between digital game violence and direct aggression (Anderson et al., 2007). Third, results substantiated our hypothesis that parent–child communication moderates the link between digital game violence and direct aggression. Poorer parent–child communication was related to a higher level of direct aggression, supporting earlier findings (Rutter et al., 1998). Among girls reporting poor parent–child communication, Time 1 digital game violence in the older age group, and current game violence in both age groups predicted increased Time 2 direct aggression. Also Time 1 results showed that for 10-year-old boys reporting poor parent–child communication, digital game violence was associated with direct aggression (Wallenius et al., 2007). These results suggest that digital game violence and poor parent–child communication may interact and additively contribute to the development of the risk of aggressive behavior as proposed in the cumulative risk model (Masten, 2001). The cumulative risk model assertion that every additional protective factor reduces the risk of problematic behavior was also supported. Concurring with earlier findings (e.g., Proctor, 2006) our results suggest that good parent–child communication may have a critical role in protecting a child or adolescent against adverse effects of digital game violence on increased direct aggression. Perhaps good parent–child communication includes higher parental involvement, including setting limits on the amount and content of games played, which has been found to reduce effects of digital game violence on aggression (Anderson et al., 2007). We were surprised that digital game violence also predicted increased direct aggression when parent–child communication was good, for all participants synchronously and for boys also longitudinally. The moderating role of good parent–child communication emerges after initial stages of violent digital game playing, thus later among girls than boys. One possibility is that adolescents reporting good parent–child communication may have started their violent game playing later, due to better parental


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monitoring, than adolescents reporting poor child–parent communication. Good parent–child communication may also have protective effects (Masten, 2001) such that effects of digital game violence on adolescents emerge more slowly. Possibly, repeated playing of violent digital games (Buckley & Anderson, 2006) and brain development (Giedd, 2002) converge such that aggressionrelated scripts are established and good parent–child communication no longer prevents their effects on increased direct aggression. The growing autonomy of adolescents also decreases the importance of parental monitoring although parent–child communication may otherwise be good. Although the level of direct aggression among adolescent boys reporting poor parent–child communication was high, digital game violence experience did not exacerbate it. Perhaps in the absence of parental involvement these boys started violent digital game playing earlier than others. Results suggest that the arousing effect of game violence may decrease over time and lead to emotional desensitization (e.g., Bartholow et al., 2006). Thus game violence may lose its impact on direct aggression among adolescents reporting poor parent–child communication. More study may reveal if a delayed ceiling effect emerges later among girls. Overall, the results suggest that the moderating role of parent–child communication in the link between digital game violence and direct aggression changes with age and the change follows the same pattern in both boys and girls. The change occurs later in girls than in boys, possibly because playing violent digital games begins later among girls. The moderating role of parent–child communication quality in the present time seems to be more important than earlier communication. During earlier years, game violence and poor parent–child communication may jointly reinforce aggression-related cognitions and affects, perhaps additively contributing to the development of negative world view and aggressive knowledge structures (Burks et al., 1999) and hence aggressive behavior. Good parent–child communication may reduce the risk of aggressive behavior. In the long term, good parent– child communication may lose its ability to counterbalance violent scripts and cognitive structures that develop in tandem with exposure to violent digital game contents. Our research lacks conceptualization and measures of adolescents' scripts and interpretations of digital game violence and their playing activity. Knowledge about their attributional models and representations of aggression, parental relations and digital virtual realities would be informative. As also emphasized by Sigel (1984) situational, cultural and developmental aspects are essential in understanding children's thinking, behaving and sensemaking. A number of limitations of this study include reliance on self-reports, subject to issues of shared variance, accuracy, etc. Devising objective measures for the time spent on violent digital games is not easy. Diary methods yield more exact estimates (Anderson et al., 1985), but are restricted to quite short time periods, and reliability depends on participants' adherence to instructions. Recently an exposure measure in which the amount of game violence in each game is multiplied by the amount of time spent playing it and then summing the products has shown promise (Anderson et al., 2007). While the longitudinal design in two age groups enabled investigation of changes in the relation of violent game play and aggression, the two-year follow-up may not be sufficient to explicate the curvilinear patterns in the development of direct aggression (Steinberg & Morris, 2001) and parent–child communication (Smetana et al., 2006). A third follow-up study would better reveal the changes during the age-transitions from middle childhood to early adolescence and further to adolescence. Although synchronous and longitudinal effects on changes in aggression were identified, such effects do not necessarily reflect causal relations. Nevertheless, a synchronous model goes well beyond a simple cross-sectional analysis as the Time 1 value of the dependent variable, aggression, is controlled. In addition, alternative explanations for our findings are possible. 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