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Communication Studies

ISSN: 1051-0974 (Print) 1745-1035 (Online) Journal homepage: http://www.tandfonline.com/loi/rcst20

Ready, Aim, Fire! Violent Video Game Play and Gun Controller Use: Effects on Behavioral Aggression and Social Norms Concerning Violence

Kirstie M. Farrar, Matthew A. Lapierre, Rory McGloin & Joshua Fishlock

To cite this article: Kirstie M. Farrar, Matthew A. Lapierre, Rory McGloin & Joshua Fishlock (2017) Ready, Aim, Fire! Violent Video Game Play and Gun Controller Use: Effects on Behavioral Aggression and Social Norms Concerning Violence, Communication Studies, 68:4, 369-384, DOI: 10.1080/10510974.2017.1324889

To link to this article: http://dx.doi.org/10.1080/10510974.2017.1324889

Published online: 24 May 2017.

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Ready, Aim, Fire! Violent Video Game Play and Gun Controller Use: Effects on Behavioral Aggression and Social Norms Concerning Violence Kirstie M. Farrar, Matthew A. Lapierre, Rory McGloin, & Joshua Fishlock

In addition to individual risk factors, recent findings surrounding the effects of violent video game play have provided compelling evidence that the contextual features of games also contribute to increased outcome aggression. The current study focuses on the relationship between violent video game play and the use of gun controllers on both social norms related to aggression and real-life behavioral aggression. As predicted, both violent video game play and gun controller use were positively related to behavioral aggression. The implications of these findings are discussed along with their influence on future research in this area.

Keywords: Aggression; Mental Models; Model Matching; Social Norms; Violent Games; Weapons Effect

Research shows that Americans spent over $21 billion on video games in 2014 (Entertainment Software Association, 2014). The same research also reported that the two most popular genres of video games are shooter games and adventure games, which are known for featuring violence as a fundamental part of the storyline

Kirstie M. Farrar (PhD, University of California, Santa Barbara, 2001) is an Associate Professor and Rory McGloin (PhD, University of Connecticut, 2011) is an Assistant Professor in the Department of Communication at the University of Connecticut. Matthew A. Lapierre (PhD, University of Pennsylvania, 2013) is an Assistant Professor in the Department of Communication at the University of Arizona. Joshua Fishlock (PhD, The University of Connecticut, 2016) is a faculty member at Arizona State University Colleges at Lake Havasu City. Correspondence to: Kirstie M. Farrar, Department of Communication, University of Connecticut, 337 Mansfield Road, Storrs, CT 06269-1259, USA. E-mail: [email protected]

Communication Studies Vol. 68, No. 4, September–October 2017, pp. 369–384

ISSN 1051-0974 (print)/ISSN 1745-1035 (online) © 2017 Central States Communication Association DOI: 10.1080/10510974.2017.1324889

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(Entertainment Software Association, 2014). Researchers studying the effects of vio- lent video games on aggression have found evidence that games increase cognitive (Anderson et al., 2004), affective (Farrar, Krcmar, & Nowak, 2006), and behavioral (Anderson & Dill, 2000) aggression. Moreover, these findings hold across different ages (Anderson, Gentile, & Buckley, 2007) and across various research methodologies (Greitemeyer & Mugge, 2014).

However, it should be noted that not all researchers in the field accept the link between violent video game exposure and aggression. Ferguson, for example, argues that the types of aggression being measured in field and laboratory studies of violent video games are not relevant to real-life aggression (Ferguson, 2015). Ferguson and Dyck (2012) also suggest that the body of literature affirming aggressive effects from video games is plagued by publication bias. Other researchers in the field have countered these claims (see Bushman & Pollard-Sacks, 2014).

In addition, longitudinal research triangulates with experimental work in this area and supports a link between violent video game play and aggression. Research conducted in both Japan and the United States found that habitual violent video game play at the beginning of the school year predicted subsequent aggression in both samples, even when gender and previous levels of aggression were controlled (Anderson et al., 2008). Möller and Krahe (2009) reported that exposure to violent games significantly predicted physical aggression 30 months later in a sample of German adolescents. Willoughby, Adachi, and Good (2012) found that sustained violent game play throughout the high school years was associated with a steeper increase in aggressive behavior in a sample of Canadian adolescents. Additionally, there is mounting research evidence that violent game play does hold up as a risk factor for aggressive behavior when measures of real-life aggressive behavior are used outside of laboratory settings. DeLisi, Vaughn, Anderson, Gentile, and Shook (2012) found that violent video game play and a preference for violent games were related to both violent and nonviolent acts of delinquency amongst a sample of male and female juvenile offenders, and this relationship held even after controlling for other factors known to predict delinquency, including psychopathy.

Gentile and Bushman (2012) argue for a reframing of the debate on the relationship between media violence and aggression in which media violence is seen as a public health concern, deserving neither special denials nor special acclaim in terms of its influence on aggression. In their longitudinal work, Gentile and Bushman found that exposure to media violence was the second strongest predictor of physical aggression among an elementary school sample. In addition to personality-level risk factors for aggression, research has also identified unique risk factors that are specific to the contextual features of video games (e.g., Eastin, 2007; Farrar et al., 2006) such as realistic natural mapping controllers (McGloin, Farrar, & Fishlock, 2015; McGloin, Farrar, & Krcmar, 2013).

The goal of this research is to extend the work of McGloin et al. (2015) by investigating the effects of violent game play and the use of realistic gun controllers via survey methodology. This allows us to assess a broader window of exposure regarding participants’ games of choice and their actual use of these controllers. For example, while experiments have many advantages in terms of exerting control and determining causality, subjects who participate in video game experiments may not

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generally choose to play violent games with immersive controllers on their own time. Surveys allow us to look at people who choose to play these games and use these types of controllers. Studying this topic with a survey also allows us to focus on real-life indicators of behavioral aggression. The lack of these measures in experimental work has been a primary criticism of the work on violent video games and aggression (e.g., Ferguson, 2015). In this study, behavioral aggression is defined as aggressive behaviors that could result in physical injury to others (Orpinas & Frankowski, 2001). Based on this approach, participants were asked to report on the frequency of behaviors that might have resulted in physical harm, including physical fights, punching, kicking, and slapping others, and encouraging others to get into physical fights.

Violent Game Play and Aggression

Recent research has taken a more nuanced approach by focusing on the contextual features of games and game play that may encourage or inhibit aggression. For example, recent research has found that a number of different contextual features can influence/ increase aggressive outcomes, including the type of controller used (i.e., gun; McGloin et al., 2015; Whitaker & Bushman, 2014), the immersiveness of the virtual environment (Persky & Blascovich, 2007, 2008), the appearance of enemy characters (i.e., human vs. nonhuman; Krcmar, Farrar, & McGloin, 2011), and the presence of blood (Barlett, Harris, & Bruey, 2008; Farrar et al., 2006). Thus, the impact of violent video games is not just limited to the personality-level risk factors of each gamer but may also be a byproduct of an interaction with the game’s contextual features.

One explanation for these findings that ties in with the research on individual differences and contextual features of violent games is the mental models approach. The mental models approach has the ability to explain the type of evolving and interactive behavior that occurs when individuals play video games (McGloin, Farrar, Krcmar, Park, & Fishlock, 2016). Given the influence that media can have on devel- oping an individual’s mental models of the real world, the following section examines the relationship between mental models and normative beliefs surrounding aggressive behaviors and behavioral aggression.

Mental Models and Behavioral Aggression

A mental model is a type of cognitive script that develops from things that we see and experience both in real life and in mediated contexts (Roskos-Ewoldsen, Roskos- Ewoldsen, & Dillman Carpentier, 2009). Mental models are flexible and constantly adapting, even though they often arise during early development. They can be detailed and may include specific people, contexts, and concepts. They can represent real- world situations (e.g., handling a conflict) and hypothetical ones (e.g., surviving a physical attack) and can be formed by media consumption as well as applied to media use (Krcmar & Curtis, 2003; Roskos-Ewoldsen et al., 2009). Therefore, mental models

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can influence both how we interpret stimuli (Roskos-Ewoldsen et al., 2009) and our response to it (Krcmar & Curtis, 2003).

In terms of violent video games, the mental models approach has been used to explain the effects of exposure on aggression (Farrar et al., 2006). Farrar et al. argue that the repetition inherent in the repeated playing of violent video games could lead to the development of more accessible and elaborate mental models of aggression. This rehearsal and repetition of violent scenes may solidify aggressive models in the player’s mind and make them more accessible. When aggressive models are more accessible, aggressive behavior may be more likely. Thus, we hypothesize the following:

H1a: Participants who report playing more violent video games will report more behavioral aggression.

While the gender gap is closing in terms of overall video game play, research suggests that men still play more first-person shooter games than women (Green- berg, Sherry, Lachlan, Lucas, & Holmstrom, 2010). Therefore, we hypothesize the following:

H1b: Men will play more violent video games than women. H1c: Men will use gun controllers more frequently than women.

Recent crime statistics also indicate that men engage in more behavioral aggression than women (Federal Bureau of Investigation, 2013). Thus, we predict the following:

H1d: Men will engage in more behavioral aggression than women.

In addition to behavioral aggression, another outcome of violent video game play suggested by the mental models approach may be the belief that aggression is normative.

Mental Models and Normative Beliefs about Aggression

Normative beliefs about aggression reflect individuals’ ideas about how often aggressive behaviors occur (Anderson et al., 2007). As mentioned, the majority of today’s most popular video games contain violence and aggression as major themes (Entertainment Software Association, 2014). Aggression is often the primary way to solve a problem, to advance to the next level, or to avoid harm. The mental models approach would suggest that repeated exposure to the notion that violence and aggression solve problems could lead to a more elaborate and accessible model of violence as a way to handle problems in real life. Anderson et al. (2007) surveyed 189 high-school students and found support for an effect of violent game play on normative beliefs about aggression. They reported that adolescents who played more violent video games believed that violence was more typical in everyday life. Therefore, we predict the following:

H2a: Participants who report playing more violent games will score higher on normative beliefs about aggression.

Since perceived social norms are also important predictors of behavior (Ajzen, 1991; Anderson, Noar, & Rogers, 2013), we also predict the following:

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H2b: Individuals who believe aggression is more socially normative will be more likely to exhibit behavioral aggression.

Since men and women differ in both their physical aggressiveness and their experience of violent behavior, men and women may differ in their normative beliefs about aggression. Given that women are more afraid of victimization (Ferraro, 1996; Hale, 1996), they may perceive that aggression is normative and relatively common. Thus, we predict the following:

H2c: Women will score higher on a measure of the social normativity of violence.

Recent research has linked the mental models perspective with the concept of model matching (McGloin et al., 2015; McGloin, Farrar, & Krcmar, 2011). Researchers (e.g., McGloin et al., 2015) argue that natural mapping motion-capturing controllers help reduce the gap between a user’s mental model of a real-life behavior (e.g., aim a gun and pull the trigger) and the same behavior in a video game.

Model Matching

In the context of video games, model matching is the process in which a player’s existing models fit or match a game’s models to help the player overcome the game’s challenges (Boyan & Sherry, 2011). In the past, video games have utilized a variety of mechanisms to help facilitate the model-matching process, including increased visual realism and sound fidelity. Today’s consoles now feature the ability to capture and integrate player movement through natural mapping motion-capturing controllers such as realistic gun controllers that are designed to mimic real-life weapons. Realistic firearm controllers allow the player to rely on existing mental models for how a gun works (e.g., previous gun usage, television shows, movies, or games that feature gun usage, etc.) and also help to develop accessible gun-related models for players (e.g., when to pull the trigger, how to deal with recoil, how to aim, etc.).

The accurate simulation of movements and behaviors by the player using a gun controller could create and/or reinforce their mental models of firearm use in both the real and gaming world and this could have a significant impact on the gaming experience. In fact, previous research has found that natural mapping controllers increase immersion (Downs & Oliver, 2009; McGloin et al., 2013; Skalski, Tamborini, Shelton, Buncher, & Lindmark, 2011) and can increase the perceived realism of a game (McGloin et al., 2011, 2013; Skalski et al., 2011), as players are using natural motions to enact real-world behaviors. Immersion (Eastin & Griffiths, 2006; Nowak, Krcmar, & Farrar, 2006; Persky & Blascovich, 2007) and perceived realism (Barlett & Rodeheffer, 2009; Farrar et al., 2006; Jeong, Biocca, & Bohil, 2012; Krcmar et al., 2011; Lin, 2013) are important, given that both concepts have been tied to outcome aggression in previous research.

It seems likely then, that gamers who play violent games using realistic natural mapping gun controllers would be more immersed and perceive the game as more realistic thus possibly leading to increased aggression. Research on the “weapons effect” suggests another avenue through which using a gun controller might lead to increases in aggression.

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The weapons effect According to the weapons effect, seeing a picture of a gun can increase aggression in the viewer (Anderson, Benjamin, & Bartholow, 1998; Berkowitz & LePage, 1967; Leyens & Parke, 1975). This is important given that there is more repetitive and extensive gun violence in video games compared to other screen media (Smith et al., 2004). In addition, new gun controllers, designed to mimic the look and feel of real-life firearms, are often marketed as a way for the user to feel more “in” the game, and research finds that these controllers can increase immersion (McGloin et al., 2015). The popularity of shooter games, the prevalence of gun violence in video games, and the use of these realistic gun controllers are also of potential concern, given the weapons effect.

Research on the weapons effect (see Carlson, Marcus-Newhall, & Miller, 1990) has demonstrated that most individuals have a strong mental link between guns and aggression. Given the prevalence of gun violence in movies, television, and video games, it seems likely that most people understand how to fire a gun even if they have never fired one in real life. Thus, holding a realistic looking and feeling weapon controller should be very salient for most individuals given existing mental models of firearms. The frequent priming of aggression experienced via the weapons effect, when participants play with realistic gun controllers, may lead to more chronically accessible models of aggression. Two studies have found support for the weapons effect in the context of violent video game play and gun controller use. Barlett, Harris, and Baldas- saro (2007) found that playing a violent game with a realistic looking light gun controller instead of a standard controller increased aggression and hostility. McGloin et al. (2015) reported an effect size from gun controller use to cognitive aggression that was nearly double that reported in most other research examining violent game play with regular controllers and cognitive aggression. McGloin et al. argued that using gun controllers to play realistic violent shooter games is a “triple whammy” in terms of cognitive aggression as it combines engaging in video game violence on the screen, seeing a weapon on the screen, and using a realistic weapon controller to play the game.

Experimental designs on the weapons effect, such as Barlett et al. (2007) and McGloin et al. (2015), provide essential information about causality and allow researchers to control for other known variables of interest in order to isolate relationships. However, for all they offer in terms of internal validity, there are issues related to external validity and whether experimental findings extend to the “real world.” This study allows us to continue the work of McGloin et al. and Barlett et al. by surveying participants about their natural gaming behavior, as well as their use of gun controllers outside the laboratory. This also allows us to rely on measures of self- reported, real-life, behavioral aggression as opposed to laboratory measures that may lack validity (e.g., sound blasts). Using these varying methodologies helps researchers get a more complete picture of how these variables interact. Thus, if our results converge on the experimental work of McGloin et al. and Barlett et al., this will further strengthen the body of research linking violent video games broadly and weapons controllers, specifically, to aggression.

Therefore, we hypothesize the following:

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H3: Participants who report using gun controllers more frequently will report more behavioral aggression.

A path model containing each of the hypothesized relationships is presented in Figure 1.

Method

Recruitment

Students from introductory communication courses were recruited from a large northeastern university and directed to an online survey hosted by Qualtrics. Students were told that they were participating in a study about video games and political attitudes, that their participation was voluntary, and that they were free to end their participation at any time and to skip any questions that made them uncomfortable.

Sample

The sample was initially comprised of 748 students. Due to extensive missing data or a failure to respond appropriately to attention checks, 239 subjects were dropped resulting in a final sample of 509.

The sample was 44.8% male (n = 228) and ranged in age from 17 to 31 years, with an average age of 19.10 (SD = 1.19), and 78.6% of the sample reported their ethnicity as White (n = 400).

Normative beliefs

about aggression

Behavioral

Aggression

Gun Controller Use

Frequency of VVG

Play

Gender

M = 1, F = 0

H 1c

(+)

H 1b

(+)

H 2c

(-)

H 3

(+)

H 2a

(+)

H 2b

(+)

H 1a

(+)

H 1d

(+)

Figure 1 Hypothesized path model and predicted direction of relationships.

Violent Video Games and Behavioral Aggression 375

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Measures

Video game exposure measures Participants were asked on an 8-point scale (0 days a week—7 days a week) how many days during an average week they play video games on a computer or console (M = 2.57, SD = 2.13). They were next asked if they had ever played a video game using a gun controller that replicates a gun or firearm. More than half of participants reported that they had (55.1%, n = 429). Next, they were asked to indicate how frequently they play console games using gun controllers on a 7-point scale, with 1 indicating Never and 7 indicating Daily (M = 1.35, SD = 0.69).

Violent video game exposure was measured by asking participants on a 7-point scale, with 1 corresponding to Never and 7 corresponding to Very Frequently, how often they play different genres of video games, with exemplars provided. Action games, first-person shooters, other shooters, role-playing games, fighting games, and massive multiplayer online games were averaged into a single index of exposure to violent video games (α = .88; M = 2.03, SD = 1.22). Content ratings indicate these genres are violent and this index has been successfully used in other studies, given its reliability and ability to predict aggression-related outcomes (see Farrar, Krcmar, & McGloin, 2013; Nowak et al., 2006).

Social norms related to violence. Participants were asked 13 questions from the normative aggressive beliefs scale by Anderson et al. (2007). Items from the scale relating to verbal aggression were omitted and several distractor topics were also included (e.g., drug use, pornography use) to mask the purpose of the study. They were asked, for each question, to indicate what percentage of the U.S. population they believe engage in each behavior. Questions included items such as the percentage of American adults who have been the victim of a violent crime or the percentage of American adults who get in a physical fight at least once a year. All of the violence- related items were summed into a single scale and were averaged with higher numbers indicating the perception that more Americans are involved in aggression as either a perpetrator or victim (α = .88, M = 27.66, SD = 13.97).

Behavioral aggression Based on work by Orpinas and Frankowski (2001), behavioral aggression was defined as aggressive behaviors that could result in physical injury to others. Participants responded to 11 questions asking about their behavior over the last 30 days. Items were taken from Orpinas and Frankowski (2001) and the National Youth Survey (Elliott, 1987) and were modified to fit college students. Factor analysis returned a three-factor solution: physical violence (α = .82), verbal aggres- sion (α = .69), and anger (α = .65). Physical violence (M = 1.30, SD = 0.67) was retained due to the emphasis on real-world physical aggression. Example prompts for the physical violence subscale included “I got into a physical fight,” “I pushed or shoved others,” and “I slapped, kicked or punched someone.” Participants indicated on how many days out of the last 30 (from 0 days to 6 or more days) they had engaged in the behavior.

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Results

We tested the hypothesized model using path modeling techniques in AMOS 22. Previously established criteria were used to evaluate model fit and statistics. First, each path needed to be in the hypothesized direction and significant at the p ≤ .05 level. Model fit statistics also needed to reach acceptable predetermined statistical cut-offs for each of the major fit indices. The hypothesized model was tested and provided a good fit to the data, χ2 (1) = 0.948, p = .33 (see Figure 2). The RMSEA for the tested model was .001 (.90 CI), the CFI was 1.000, the TLI was 1.000, and the standardized RMR was .001. Each of these statistics indicates the tested model (see Figure 2) was a good fit of the data according to previously determined goodness of fit cut-offs as outlined by Hu and Bentler (1999) and Byrne (1998). Table 1 contains a zero-order correlation matrix for each of the variables included in the tested model.

Hypothesis 1a predicted that participants who play more violent video games would report more behavioral aggression. The path from violent game play to behavioral aggression was both positive and significant (β = 0.11, p = .048). Partici- pants who played more violent video games reported more behavioral aggression over the past 30 days. Thus, hypothesis 1a was supported.

Hypothesis 1b predicted that men would play more violent video games than women (men coded as “1,” women as “0”). This hypothesis was supported (β = 0.55, p < .001). Hypothesis 1c predicted that men would play more games using gun controllers and this hypothesis was also supported (β = 0.31, p < .001). Hypothesis 1d predicted that men would report engaging in more behavioral aggression over the past 30 days than women.

Normative beliefs

about aggression

Behavioral

Aggression

Gun Controller Use

Frequency of VVG

Play

Gender

M = 1, F = 0

.31***

.15**

.11*

.11*

.06

.55***

-.30***

Chi-Sq. = .948, p = .33

RMSEA = .001

CI = .90

CFI = 1.00

TLI = 1.00

SRMR = .001

.08

Figure 2 Tested path model with standardized path coefficients *p < .05. **p < .01. ***p < .001.

Violent Video Games and Behavioral Aggression 377

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This hypothesis was not supported (β = 0.08, p = .13). In sum, more violent game play was positively related to more aggressive behavior; men played more violent video games than women and used more realistic gun controllers; however, there were no differences between men and women in aggressive behavior over the past 30 days.

Hypothesis 2a predicted that participants who reported playing more violent video games would score higher on a measure of the social normativity of aggressive behavior. This path was nonsignificant (β = 0.06, p = .20). Hypothesis 2b predicted that perceptions of the social normativity of violence would be positively related to behavioral aggression. This hypothesis was supported (β = 0.11, p = .01). Hypothesis 2c predicted that women would have higher perceptions of the social normativity of violence. This hypothesis was supported (β = –0.30, p < .001) as females did have stronger/higher perceptions of the social normativity of violence. Thus, the only significant predictor of violent social norms in this study was gender.

Hypothesis 3 predicted that participants who play more video games using gun con- trollers would report more behavioral aggression. Hypothesis 3 is supported (β = 0.15, p < .01). Use of gun controllers was positively linked to more behavioral aggression.

Discussion

The purpose of this study was to examine the effects of violent video game play and gun controller use on a measure of real-world behavioral aggression and perceptions of the social normativity of aggression. Experimental research has demonstrated that violent game play can lead to increases in aggression and that playing violent games with gun controllers impacts cognitive aggression (McGloin et al., 2015) and hostility (Barlett et al., 2007). However, it is challenging to measure “real-world” aggression in a laboratory setting; thus, this study attempts to add to the body of literature on gun controller effects and violent games by focusing on self-reported acts of physical aggression using a survey

Table 1 Summary of Correlations for Gender, Violent Video Game Play, Gun Controller Use, Violent Social Norms, and Violent Behavior (One-Tailed)

Gender

Violent

VG Play

Gun

Controller

Use

Normative Beliefs

About Aggression

Behavioral

Aggression

Gender 1 – – – –

Violent VG Play .55** 1 – – –

Gun Controller Use .20** .33** 1 – –

Normative Beliefs

About Aggression

−.26** −.10* .02 1 –

Behavioral Aggression .16** .21** .11** .08* 1

Note. Gender coded as: Male = 1, Female = 0. *p < .05. **p < .01.

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method. This allows us to capture real-life reported acts of physical aggression while also measuring participants’ typical engagement with violent games and immersive controllers. This study also follows up on McGloin et al.’s experimental work on the link between gun controller use and cognitive aggression by extending the model-matching hypothesis to self- reported behavioral aggression.

In sum, results found that young adults play violent video games and many of them have used gun controllers. Men were more likely to play violent games and to use gun controllers than women. In contrast, women reported higher perceptions of the social normativity of violence. As predicted, violent video game play was positively related to behavioral aggres- sion. Gun controller use also positively correlated with self-reported behavioral aggression.

Implications

In this study, we identified a positive relationship between increased violent game play and behavioral aggression. Similar findings have emerged in recent experimental studies (Eastin, 2007; Farrar et al., 2006; Krcmar et al., 2011), although some researchers have been critical of these findings (e.g., Ferguson, 2015). However, the findings from this cross-sectional study appear to support these experimental findings as well as those of other survey and longitudinal studies that have identified similar relationships between violent video game play and outcomes related to aggression (Anderson & Dill, 2000; Anderson et al., 2008; DeLisi et al., 2012; Gentile, Lynch, Linder, & Walsh, 2004; Möller & Krahe, 2009; Wil- loughby et al., 2012). While DeLisi et al. found violent game play to be related to aggressive delinquency among juvenile offenders, this study provides evidence that relationships with behavioral aggression can be seen even in nonoffending college populations. The results of this study, therefore, corroborate previous findings, both experimental and cross-sectional, suggesting a relationship between violent game play and aggression.

The theory of mental models provides one explanation for the findings in this study. Given that the development of mental models occurs over time and through a wide range of experiences, including media (Krcmar & Curtis, 2003; Roskos-Ewoldsen et al., 2009), it is not surprising that individuals who play more violent video games exhibit more aggressive behavior given that they are developing models from violent video games that promote or reward violence. These individuals have more practice enacting violence as a way to solve problems in the game world. The repetition that comes from exposure to violent games can make this aggressive problem-solving mental model more accessible and well established.

However, it is also worth acknowledging that our data cannot imply causality given the cross-sectional design. For example, it is possible that more aggressive people choose to play more violent games. However, it should be noted that recent long- itudinal studies have failed to find significant support for a selection bias in which more aggressive individuals are more likely to choose violent games (Möller & Krahe, 2009; Willoughby et al., 2012). Thus, despite the limitations of inferring causality from a cross-sectional design such as this one, our findings do corroborate the findings from recent experimental studies that identify a similar link between violent game play and aggression (e.g., Barlett et al., 2007; Eastin, 2007; Farrar et al., 2006; Jeong

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et al., 2012; McGloin et al., 2015). The current findings, coupled with the results of recent and related research, seem to suggest that violent video game play should at least be considered a risk factor for aggression.

Despite the link between violent game play and behavioral aggression, we did not find that exposure to violent video games increases perceptions of the social normativity of aggression. Unlike the findings from Anderson et al. (2007), participants in this study who played more violent games did not perceive that higher numbers of Americans get into physical fights or have experienced a violent home invasion, for example. In fact, the only predictor of the social normativity of violence in this study was female gender. That noted, we did not ask people to list their most frequently played games. As such, it is possible that participants are seeing violent themes when playing violent games that did not resonate with the questions they were asked about regarding social norms. It is also possible that research using more demographically diverse samples, as opposed to college students, could find different results in terms of video game play and the social normativity of aggression. Regardless, more work needs to be done to fully understand the potential relationship between video game play and social perceptions.

Finally, the current study helps to expand the body of research on gun controllers (Barlett et al., 2007; McGloin et al., 2015). McGloin et al. found an effect size for cognitive aggression that was double or nearly double the effect size reported in other experimental studies using more traditional controllers. They concluded that these stronger effects may be the new norm with increasing game realism and increasing availability of realistic gun controllers. However, it is possible that the experimental setting and the types of laboratory aggression measures used may reduce the external validity of these experimental findings. Therefore, the current study sought to determine if the relationship between using gun controllers and aggression existed outside of the laboratory. The findings from this study identified that people who play more violent games using gun controllers exhibit more behavioral aggres- sion. Thus, both survey and experimental work suggest that gun controllers are worthy of further consideration in the context of violent video games and aggression.

Limitations

The cross-sectional nature of these data does not allow us to conclude that playing violent games or using gun controllers causes behavioral aggression. It is possible, for example, that those individuals who are more aggressive are more likely to play violent games and to use gun controllers; however, as mentioned, longitudinal research has not uncovered much support for the selection hypothesis (Möller & Krahe, 2009; Willoughby et al., 2012). However, the cross-sectional nature of these data does not allow us to rule out this possibility completely. On the substantive significance of correlations, Cohen, Cohen, West, and Aiken (2003) point out, “Correlation does not prove causation; however, the absence of correlation implies the absence of a causal relationship” (p. 7, italics in original). Thus, the presence of a correlation between exposure to violent games and gun controllers and behavioral aggression indicates an underlying relationship between these variables.

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Second, the sample was comprised entirely of college students. This sample may not be representative of the broad spectrum of video game players. However, today’s college students have grown up immersed in the world of video gaming and our results found that they do play violent video games.

Third, while approximately half of the participants in this study reported having used a gun controller they did not report a very high frequency of gun controller use. This “floor effect” may have limited our ability to fully understand the relationship between gun controller use and aggression. The frequency of gun controller use may increase as more gun controllers become available.

Future Research

Research has recently focused on individual differences that may mediate or moderate effects of violent video games (e.g., Farrar et al., 2006; 2013). For example, recent research found that preferences for first-person shooter games were based on nonverbal sensitivity, rather than just gender (Jung, Oh, Sng, Kwon, & Detenber, 2015). Hart- mann, Möller, and Krause (2015) found that preferences for violent games differ based on expected guilt and enjoyment experienced from play. Additional work could exam- ine players who spend more time with violent video games as some research indicates that more skilled players play differently and may experience different outcomes (Matthews, 2015). Future research should also attempt to replicate these findings and the findings of McGloin et al. (2015) with children, as research suggests children may be more vulnerable to the effects of violent video games (see Anderson et al., 2007).

Finally, several short-term longitudinal studies have demonstrated effects over time of violent game play and aggression. Continued work in this area would prove valuable, possibly starting with children at a very young age, as Huesmann, Moise- Titus, Podolski, and Eron (2003) did with their work on television violence.

Conclusion

This study adds to the current literature on violent video games and aggressive outcomes by finding a relationship between self-reported amounts of violent game play and self-reports of aggressive behavior among young adults. In addition, this study supports the work by McGloin et al. (2015) that playing violent games with realistic gun controllers can contribute to outcome aggression. The findings from this cross-sectional study support extant experimental findings, as well as those of other survey and longitudinal studies that have identified similar relationships between violent video game play and outcomes related to aggression.

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