Research Report
Examining the effect of competitive vs. cooperative strategy in violent video
games on adolescents’ aggressive affect
Name: Jieun Lee (Jenna) Student number: 11233621
Course: Graduation Project University: University of Amsterdam
Lecturer: Lukas Otto Submission date: 11. 06. 2018
Abstract
Ever since their introduction, violent video games have been widely popular among adolescents. Statistics have shown that the majority of adolescents in US play violent video games on a frequent, if not, daily basis. Considering the prevalence of violent video games in the present-day adolescents’ lives, numerous studies have examined the negative outcomes of playing violent games. From the findings, game characteristics have been suggested as a potential factor that may pose influence on adolescents’ post-game aggression. In the current study, a focus is given on examining the effect of competitive vs. cooperative strategy in violent games on adolescents’ aggressive affect. In so doing, the moderating role of players’ gender is also explored. The study employed a single factor between-subjects experimental design with 52 university students. The participants were randomly assigned to either competitive or cooperative condition and played Unreal Tournament 4 for ten minutes. After the play, their aggressive affect was measured. Results indicated that those who played competitively exhibited significantly higher aggressive affect compared to those who played cooperatively. Yet, gender of the players did not moderate this effect. These findings are consistent with the previous studies, suggesting that competitive strategy could intensify the negative influence of violent video games on adolescents. For future studies, it is advised to further explore the effects of other game characteristics in violent games on adolescents’ post-game aggression.
Key words: video games, violence, competitive, cooperative, aggression, aggressive affect,
gender, adolescent, general aggression model
Introduction
Digital games have become a major part in adolescents’ daily lives: ninety seven percent of adolescents in US play video games, and fifty percent of them play games on a daily basis (Lenhart et al., 2008). Yet, the majority of video games are found to contain violent contents, with half of them including explicit violence against game characters (as cited in Carnagey & Anderson, 2005). Due to this characteristic in the gaming environment, there have been extensive studies on the negative influence of violent video games on adolescents, such as increased aggression or insensitivity to violence. Although these studies explored various consequences of playing violent games, many of them have been criticized for using the excessively simplified categorization of “violent” versus “non-violent” games, overlooking the numerous variables in game characteristics (Williams, 2005). In fact, a
considerable number of studies could not find a causal link between violent video games and increased aggression, despite their existing correlation (Markey & Markey, 2010). Considering this, it is essential to investigate the variables in game characteristics while examining the effect of violent video games on adolescents’ aggression. In their literature review, Barlett, Anderson, and Swing (2009) identified a number of characteristics in violent games that pose influence on adolescents’ aggression: presence of blood, rewarding violence, competitive vs. cooperative play, point of view, sex of character, graphic quality, interface, and realism, to name just a few. Among these variables, competitive vs. cooperative play is a particularly important variable to investigate. In the current game environment, the majority of games are designed with “dual-strategy”: they give players an option to play games in both competitive and cooperative modes (Eden & Eshet-Alkalai, 2014). Unlike other variables, which are difficult to be modified due to user preferences and market value, options for game strategies are already present in the majority of games. Therefore, gaining insight in the effects of competitive vs. cooperative strategy on adolescents’ aggression would provide a rather realistic suggestion in regard to mitigating the negative influence of violent video games. For instance, the information from this research could be used for parental guidance, which could direct children to play with a strategy that could alleviate the negative influence of violent video games. In addition, this research could also contribute to the scientific community by further expanding the study of violent video games, as it explores the effect of game strategies on adolescents’ aggression, which has been often overlooked in the game studies.
With this in mind, the following research question has been developed:
RQ 1: What are the effects of competitive versus cooperative strategy in violent video games on adolescents’ aggressive affect?
Theoretical Framework
Effects of violent video games & General Aggression Model
In the past years, numerous studies have explored the effects of violent video games on adolescents. The findings have been consistent about the negative outcomes of playing violent video games. In the meta-analyses, Sherry found a significantly positive relationship between violent video game play and aggressive outcomes (as cited in Eastin & Griffiths, 2009). Anderson et al. also revealed that violent games generate increased aggressive behavior, cognition, affect, and physical arousal (as cited in Zhang, Liu, Wang, & Piao, 2010). For decades now, these findings have been substantiated by myriads of studies (Barlett
et al., 2009). While a number of theoretical explanations have been developed, one of the most prominent is General Aggression Model (GAM) by Bushman and Anderson (2002). The model combines the situational context of exposure and personal characteristic as a framework to understand the effect of violent games: both the game’s content and the players’ attributes prime the aggressive outcomes in a person’s internal state (Eastin, 2007). While people are playing violent video games, their aggression scripts are activated, which are violent knowledge structures in mind. These knowledge structures guide people’s behaviors in and outside of the game, influencing people to behave more violently in general (Eastin, 2007). In other words, GAM suggests that people may ‘learn’ aggression via exposure to violent video games, as they are constantly reinforced of the idea that hurting other people is an effective solution for conflict (Barlett et al., 2009).
Among the various aggressive outcomes of violent video games that GAM suggests, aggressive affect deserves a special attention. Aggressive affect is defined as people’s inner-state or mood involving negative feelings (e.g. anger, rage, willingness for revenge), which may result in a conflict or aggression towards others in a long term (Waddell & Peng, 2014). It was found that aggressive affect is particularly effective in explaining people’s short-term aggression, which is an important precursor of lasting changes in attitude and aggressive behavior (Schmierbach, 2010). Considering the difficulties in conducting longitudinal studies and measuring people’s actual aggressive behaviors in real-life situations, understanding the short-term aggression via aggressive affect would play a vital role in anticipating and explaining the aggressive behavior caused by violent game play. Therefore, this study focuses on examining the aggressive affect in post-game state.
Game strategies & Aggression
Competition and cooperation are defined based on their varying goal structures. Competition occurs when achieving goal is negatively linked among people: in competitive setting, people obtain their goals only when others do not. On the other hand, cooperation occurs when attainment of goal is positively related among people: people achieve their goals only when others also reach their goals (Eastin, 2007). Due to these varying goal structures between competition and cooperation, the two strategies also elicit different outcomes when applied in violent video games. Previous studies have found that competitive strategy tends to increase players’ post-game aggressive thoughts. Schmierbach (2010) found that players in competitive setting show higher aggressive cognition compared to those in both cooperative and single-player settings. Anderson and Morrow (1995) also found that competitive strategy
activates people’s aggressive knowledge structure significantly more than cooperative strategy. Eden and Eshet-Alkalai (2014) also discovered that participants in competitive strategy show higher aggression level compared to those in cooperative strategy, regardless of the existence of violence. Furthermore, competitive strategy has also been found to increase people’s aggressive behavior. In the study by Hughes and Louw (2013), only participants in competitive condition sent aggressive verbal messages to others, while none was sent in cooperative condition. Zhang et al. (2010) also suggested that participants in competitive game mode punished others with higher intensity in competitive reaction time task, compared to those in cooperative or solo mode. In line with this, Anderson and Morrow (1995) found that people who played in competitive conditions killed a significantly higher number of game creatures than those in cooperative condition.
On the other hand, cooperative strategy has been found to mitigate the post-game aggression and promote cooperative behavior among the players. Numerous studies have found that participants in cooperative condition show a significantly higher level of trust to their partners and more willingness to cooperate, compared to those in competitive condition (Badatala, Leddo, Islan, Patel, & Surapaneni, 2016; Ewoldsen et al., 2012; Velez et al., 2014; Waddell & Peng; 2014). Furthermore, Badatala et al. (2016) and Ewoldsen et al. (2012) found that playing violent games cooperatively promotes higher cooperative behavior among people, compared not only to competitive play but also to not playing violent video games. These findings suggest that cooperative play may not only mitigate the negative influence of playing violent video games, but also solely promote cooperative behavior among the players.
Based on the above findings, the following hypothesis has been developed:
H1: Individuals playing violent video game competitively will exhibit higher aggressive affect compared to those playing cooperatively.
Gender in violent video games
Despite the extensive number of studies on violent video games, only a few have explored a moderating role of players’ gender in the relationship between game strategies and aggression. In fact, even those who explored the role of gender presented mixed results: Eastin and Griffiths (2006) found that only women show difference in post-game aggression due to the game strategies, while Schmierbach (2010) suggested that men are more affected by game mode than women. Due to the limited research in the moderating role of players’ gender, it is difficult to make a clear prediction of whether men or women will be more
affected by game strategies, or if gender would moderate the relationship at all. Considering this, the following research question has been developed:
RQ 2: What is the moderating role of players’ gender in the effect of game strategies in violent video games on adolescents’ aggressive affect?
Conceptual model
The conceptual model visualizes the relationships between the variables in this study. The independent variable is game strategy, consisting of two levels: competitive and cooperative strategy. The dependent variable is aggressive affect. Gender serves as a moderator and contains two categories: male and female. Trait aggression and video game experience are controlled as covariates.
Figure 1. Conceptual model
Method
Design and participants
The study employed a single factor between-subjects design. As the experimental design took a form of two-group random assignment post-test design, it met the criteria for true experiment. Considering that the participants need to engage in an actual game play, the experiment took place offline under the supervision of a researcher. This way, the environment and procedure of each experiment were controlled and stayed consistent. The
pretest was not conducted in order to prevent the pretest sensitization and the familiarization of questions, which could hinder the internal validity of the findings. A between-subjects design was employed in this study, so that the participants would not be affected from the contrast effect or fatigue by being exposed to two different conditions in a row. While there are individual differences present in between-subjects design, they were mitigated by random assignment and controlling for the major covariates: trait aggression and video game experience. A control group was not included in the study, due to the restriction in time and resources in the recruitment process.
In total, 52 participants (26 male, 26 female) were recruited by convenience sampling. There were equal number of male and female, in order to control the individual differences in gender in the stage of data analysis. The participants’ age ranged from 19 to 27 (M = 22.6, SD = 1.69). Roughly 96% of the participants were Bachelor’s student and 2% were Master’s student in the universities in Europe. The remaining 2% did not wish to answer. The participants were mainly university students, since the study aimed to examine the effect of game strategies on adolescents. Yet, the participants had to be older than 17, since the game used for stimulus material was targeted for 17+ users. In addition, the sample largely consisted of participants from the Netherlands (37%), South Korea (23%), and Germany (17%). The participants were informed about the research via social media or word of mouth. The participation did not involve any compensation.
Procedure
Prior to conducting the experiment, a factsheet with the information about the research was sent to the participants via email. Upon arrival, the participants were briefly informed about the purpose and the content of the study. The experiment consisted of three sessions. In the first session, the participants received a Qualtrics link with the informed consent and a set of initial questionnaire about 1) demographics, 2) video game experience, and 3) trait aggression. The second session involved 15 minutes of violent video game play. The participants got a brief explanation about Unreal Tournament 4 and played the basic training for 5 minutes. In the training session, the participants got accustomed to the basic skills in the game, including movement, weapons, and pickups. The basic trainings had similar settings to the actual game (e.g. presence of blood), but they did not involve violent interactions among the players. After the basic training, the participants were randomly assigned to either competitive or cooperative condition. In doing so, the gender of the participants was equally distributed between the conditions. Under the assigned group, the participants received verbal
instructions from the researcher, which explained their manipulated game strategies. In addition, they were informed again about their rights to drop out without providing reason or receiving penalty. Following the instruction, the participants played the assigned mode of game for 10 minutes. The game ended automatically after the allocated time. In the third session, the participants filled in a questionnaire in Qualtrics about 1) manipulation check, and 2) aggressive affect. The session ended with a short thank-you message and a contact information about the researcher.
Stimulus material
A first-person-shooter game Unreal Tournament 4 was used in the experiment. The violence in game is rated as 17+ in Entertainment Software Ratings Board (ESRB) and the game contains animated blood, gore, and violence. Unreal Tournament 4 was suitable for this research, since it contains a highly realistic violence that is prevalent in the present gaming environment, as well as providing both competitive and cooperative settings. Due to these characteristics, a number of studies on game strategies in violent video games have employed this game as well (Eastin, 2007; Eastin & Griffiths, 2009; Velez et al., 2014).
Players in competitive condition played Deathmatch, a multiplayer mode in which every player plays against each other to achieve the highest individual scores possible. Players in cooperative condition played Team Deathmatch, a multiplayer mode in which players work in a team to attack other team members and defend themselves to achieve the highest team scores possible. Both game settings were created in a custom setting, in order to keep the game environments constant except for game strategy. The game settings were set as the following: 1) 10 combatants, 2) chill map, 3) 10 minutes time limit. In Deathmatch, all 10 combatants including the participant played against each other. In Team Deathmatch, two teams were created with 5 combatants in each team. The participants were always assigned to team Blue. Participants in both conditions played against bots. In so doing, the setting of the game stayed consistent for every participant. The levels of both teams were balanced as
average, so that the gaming experience was not extremely easy nor difficult compared to
game plays in real life.
The manipulation between competitive and cooperative condition followed the protocol by Eastin (2007): the key difference between the conditions was the goal structure in the play. Participants in competitive condition received a verbal instruction from a researcher to kill more enemies than all of the other players in the game. Under this setting, the success of the participant’s game play was dependent on the other players’ failure. On the other hand,
participants in cooperative condition were instructed to work in a team with other players. Here, the success of participant was dependent on the other players’ success. Since participants in both conditions played the game in identical settings, with only difference being the game strategy, the potential confounding variables in game characteristics were controlled, such as narration, graphic, or interface.
Measures
Demographics: Age
Since the current study aimed to recruit samples consisting of adolescents, age was measured by an open-ended question. The participants reported the years they were born (e.g. 1995). This way, the confusion from different age perceptions among the cultures (e.g. Korean age counted differently from international age) could be removed.
Demographics: Gender
Taking into account that people’s personal identification may differ from their biological characteristics, the current study measured gender instead of sex. The answer was collected based on three multiple choices: 1) Male, 2) Female, 3) Do not wish to answer.
Demographics: Country of origin
In order to gain an overview of the participants’ cultural backgrounds, their country of origin was measured by an open-ended question. If the participants had multiple cultural identities, they reported the one they feel the most closely to.
Demographics: Education
In order to have insight in the participants’ social characteristics, the level of education was measured based on eight multiple choices: 1) No level of education achieved so far, 2) Primary school, 3) Secondary school, 4) High school, 5) Bachelor’s, 6) Master’s, 7) PhD, 8) Do not wish to answer. Participants were asked to report the level of education they are currently enrolled in.
Dependent variable: Aggressive affect
Aggressive affect was measured by using a State Hostility Scale (SHS), also labelled as Current Mood Scale (Anderson, Deuser, & DeNeve, 1995). Due to its comprehensiveness and accuracy, SHS has been one of the most widely used scales to measure aggressive affect,
tested and substantiated over the last years (Eastin, 2007; Eastin & Griffiths; 2009; Mihan, Anisimowicz, & Nicki, 2015; Waddell & Peng, 2014; Velez et al., 2014; Zhang et al., 2010). The scale consists of 32 items covering a wide range of aggressive affect in four subscales: feeling unsociable, feeling mean, lack of positive feelings, and aggravation. Among the 32 items, 10 items were reverse coded (e.g. I feel sympathetic. See Appendix A for details). The responses were collected based on a scale from 1 (strongly disagree) to 5 (strongly agree). The average of these 32 items was used as a measure of Aggressive affect (M = 2.96, SD = 0.70, Cronbach’s α = 0.96).
Covariates: Video game experience
Previous studies have indicated that experience in video game serves as one of the major covariates which may pose influence on aggressive affect. Funk, Buchman, Jenks, and Bechtodt found that video game experience positively influences post-game aggression (as cited in Eastin, 2007). In fact, a number of studies controlled video game experience as a covariate while examining the effect of game strategies on aggression (Eastin, 2007; Eastin & Griffith, 2009; Schmierback, 2010; Velez et al., 2014). In this study, video game experience was measured by using a questionnaire adapted from Novak, Hoffman, and Yung (2000). While the questionnaire was initially developed for new media experience, the questions were modified to assess video game experience, following the adaptation from Eastin (2007). The questionnaire consists of three items about the participants’ prior experience in video games on different time frames. The answers of the items were collected based on a scale from 1 to 6 (See Appendix A for details). The average of these three items was used as a measure of
Video game experience (M = 2.90, SD = 1.70, Cronbach’s α = 0.90). Covariates: Trait aggression
Trait aggression was also controlled as a confounding variable. Studies have found that trait aggression is a significant precursor of aggressive affect. Anderson and Bushman (2001) suggested that trait aggression has a positive effect on aggression in post-game state. It was also shown that individuals who exhibited higher trait aggression experienced greater aggressive affect after being exposed to violent contents (Eastin & Griffiths, 2009). Considering this, trait aggression was measured by using Buss-Perry Trait Aggression Survey (1992). This measurement has been widely used in the studies on the effect of game strategies involving trait aggression (Eastin, 2007; Eastin & Griffiths, 2009; Ewoldsen et al., 2012; Mihan et al., 2015; Velez et al., 2014; Waddell & Peng, 2014). The survey consists of 29
items, which includes four dimensions in aggression: physical aggression, verbal aggression, anger, and hostility. The responses were collected from a scale of 1 (extremely uncharacteristic of me) to 7 (extremely characteristic of me). Among the 29 items, two items were reverse coded (e.g. I am an even-tempered person. See Appendix A for details). The average of these 29 items was used as a measure of Trait aggression (M = 3.13, SD = 0.69, Cronbach’s α = 0.87).
Manipulation check
After the game play, participants were asked about which game strategy they engaged in. The choice was given in a multiple choice including two options: 1) competitive, and 2) cooperative. This question aimed to ensure that the participants accurately understood the assigned mode of game and the verbal instruction given from a researcher during the game play.
Results
Manipulation check
Participants’ answers in the manipulation check questionnaire were cross-examined with the actual settings they were assigned to. As a result, all 52 participants reported the correct manipulation conditions they were exposed to. Therefore, the data from all participants were included in the final analysis.
Aggressive affect Table 1
Descriptive statistics: aggressive affect as dependent variable
Game strategy Gender Mean Std. Deviation N
Competitive Male 3.1154 .50925 13 Female 3.5192 .68590 13 Total 3.3173 .62667 26 Cooperative Male 2.4663 .44423 13 Female 2.7476 .67706 13 Total 2.6070 .57907 26 Total Male 2.7909 .57335 26 Female 3.1334 .77503 26
Total 2.9621 .69678 52
Table 2
Two-way ANOVA Table
df F η² p Strategy 1 13.696 0.165 0.001 Gender 1 0.071 0.001 0.792 Strategy*Gender 1 0.002 0.000 0.965 Video game experience 1 5.671 0.068 0.021 Trait aggression 1 3.222 0.038 0.079
Note. Significant at the p < 0.05 level.
Figure 2. Interaction effect of game strategy and gender on aggressive affect
Table 1 presents the means and standard deviations for game strategy and gender by aggressive affect, as well as the number of participants in each group. Table 2 displays the degrees of freedom (df), F ratios (F), eta2 (η²), and p value (p) of the variables in the analysis. Figure 2 shows the interaction effect of game strategy and gender on aggressive affect.
A two-way ANOVA was conducted in order to examine the effect of game strategies on aggressive affect (H1), as well as the moderating role of players’ gender (RQ2). All four subgroups were of equal size, and the Levene’s test on homogeneity of variances was not statistically significant, F (3, 48) = 0.86, p = 0.469. Therefore, the main assumptions for conducting a two-way ANOVA were met. From the analysis, video game experience was a significant covariate for aggressive affect, F (1, 46) = 5.67, p < 0.05. On the other hand, trait aggression did not have a statistically significant relation to aggressive affect, F (1, 46) = 3.22, p = 0.079.
H1 posited that individuals playing violent video game competitively will exhibit higher aggressive affect than those playing cooperatively. The result showed that aggressive affect was significantly different for competitive and cooperative strategy, while controlling for trait aggression and video game experience, F (1, 46) = 13.70, p < 0.001, η² = 0.17. As expected in the hypothesis, competitive strategy generated significantly higher aggressive affect (M = 3.32, SD = 0.63), compared to cooperative strategy (M = 2.61, SD = 0.58). Therefore, H1 was supported. On the other hand, players’ gender did not have a significant effect on aggressive affect, F (1, 46) = 0.07, p = 0.792, η² = 0.001. Overall, female showed higher aggressive affect (M = 3.13, SD = 0.78), compared to male (M = 2.80, SD = 0.57). However, this difference does not indicate that gender poses influence on aggressive affect, since the effect of gender was not found to be significant. Lastly, RQ 2 explored how players’ gender moderates the effect of game strategies on aggressive affect. In the analysis, there was no statistically significant interaction effect between game strategy and players’ gender on aggressive affect, F (1, 46) = 0.00, p = 0.965, η² = 0.000. As shown in Figure 2, the lines for male and female are almost parallel to each other. Therefore, it is possible to conclude that the effect of game strategies on aggressive affect is not different for male and female, which answers RQ2. In other words, players’ gender does not moderate the effect of game strategies on aggressive affect.
Conclusion and Discussion
The purpose of this study was to examine the effects of competitive versus cooperative strategy in violent video games on adolescents’ aggressive affect. Based on the previous studies, it was hypothesized that the individuals who play violent video games competitively will exhibit higher aggressive affect than those who play cooperatively. The finding has shown that individuals with competitive strategy do exhibit higher aggressive affect compared to those with cooperative strategy. This result is in line with previous studies,
who found that competitive strategy elicits higher post-game aggressive outcomes than cooperative strategy (Anderson & Morrow, 1995; Eden & Eshet-Alkalai, 2014; Hughes & Louw, 2013; Schmierbach, 2010; Zhang et al., 2010). This finding could be explained by the varying goal structures in competitive and cooperative strategy. Under cooperative setting, the players in a team have an interdependent goal, for which they need to cooperate with each other. This characteristic imbues a feeling of trust and affiliation among the players, which counteracts the aggression generated from being exposed to violent contents (Eastin, 2007). Under competitive setting, however, players do not have partners who share the same goal and to work with; instead, players have to block others from attaining their goals in order to achieve their own. This competitive environment provokes frustration among the players, which intensifies the aggression caused from playing violent games (Eastin, 2007). Therefore, players with competitive strategy are more likely to show higher aggressive affect than those with cooperative strategy.
In addition, this study also explored how gender of the player moderates the effect of game strategies on aggressive affect. Due to the limited studies on this topic, there was no hypothesis posited prior to conducting the research. In the result, it was found that players’ gender does not moderate the effect of game strategies on aggressive affect. In fact, the participants’ post-game aggressive affect between competitive and cooperative settings did not significantly differ between men and women. For this result, the study from Eastin may provide a possible explanation: participants who played violent video game with a character who was the same gender showed more aggressive thoughts than those who played violent video game with a character of the opposite gender (as cited in Barlett et al., 2009). This finding suggests that not the gender of a player, but the match between genders of a player and a character could be a significant moderator in the effect of game strategies on aggressive affect. Taking this into account, it is recommended for future studies to further investigate the moderating role of gender, examining the interaction between player and game characteristics. In fact, this approach is more in line with GAM, which integrates the gaming context and the personal attributes of players to investigate the effect of violent video games.
Furthermore, it is important to note some limitations in this study. The major limitation concerns about the sample. In the process of recruiting participants, a convenience sampling was employed. However, since this sampling method does not randomly select the participants, it hinders the generalizability of the findings to the overall population of violent video game players. Therefore, it is recommended for future studies to employ a random sampling strategy, in order to increase the generalizability of the finding. Furthermore, the
sample in the current study largely consisted of adolescents in the university. While university students indeed take a considerable part in the population of video game players, it is still crucial to include younger samples in the study. In fact, Lenhart et al. (2008) found that ninety seven percent of teenagers in US between the age of 12 and 17 play video games via various means. This statistic highlights the need to include younger adolescents in the sample, as the majority of them actively engage in playing violent video games. Furthermore, younger adolescents may be more susceptible to the negative influence of violent games, since they have “less real-life experience to which they can compare portrayals of violence in video games” (Douglas & Craig, 2006, p. 233). Since younger adolescents are more likely to perceive the violence in games as “real”, it is possible that the negative outcomes from playing violent video games are more severe for them. Therefore, it is advised for future studies to recruit participants also from the younger age groups, such as middle school or high school students.
Based on the findings from this study, parents could develop a possible guidance regarding their children’s violent video game play. As violent video games are pervasive in the current environment for adolescents, simply prohibiting them from playing violent games may not be an effective, or even a realistic solution. Instead, parents are advised to encourage their children to play violent games in a cooperative mode, as it generates significantly lower aggressive affect in the post-game state compared to competitive mode. In addition, the current study also provides a possible implication for the future studies on violent video games. As the effect of game strategies on aggressive affect was found to be significant, this finding supports the claim that variables in game characteristics may play a vital role in the post-game aggressive outcomes (Barlett et al., 2009). Numerous variables in game characteristics, such as interface, narration, graphic, presence of blood, or point of view, may also be a significant factor that pose influence on the aggressive outcomes of violent video game play. Thus, it is advisable for future research to avoid using simple categorization of violent vs. non-violent games in the study of video games. Instead, they are encouraged to take various game characteristics into account.
References
Anderson, C. A., & Bushman, B. J. (2001). Effect of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, and prosocial behavior: A meta-analytic review of the scientific literature.
Psychological Science, 12(5), 353-359. doi: 10.1111/1467-9280.00366
Anderson, C. A., Deuser, W. E., & DeNeve, K. (1995). Hot temperatures, hostile affect, hostile cognition, and arousal: Test of a general model of affective aggression.
Personality and Social Psychology Bulletin, 21(5), 434-448. doi:
10.1177/0146167295215002
Anderson, C. A., & Morrow, M. (1995). Competitive aggression without interaction: Effects of competitive versus cooperative instructions on aggressive behavior in video games. Personality and Social Psychology Bulletin, 21(10), 1020-1030. doi: 10.1177/01461672952110003
Badatala, A., Leddo, J., Islan, A., Patel, K., & Surapaneni, P. (2016). The effects of playing cooperative and competitive video games on teamwork and team performance.
International Journal of Humanities and Social Science Research, 2(12), 24-28.
Barlett, C. P., Anderson, C. A., & Swing, E. L. (2009). Video game effects – Confirmed, suspected, and speculative. Simulations & Gaming, 40(3), 377-403. doi: 10.1177/1046878108327539
Bushman, B. J., & Anderson, C. A. (2002). Violent video games and hostile expectation: A test of the general aggression model. Personality and Social Psychology Bulletin,
28(12), 1679-1686. doi: 10.1177/014616702237649
Buss, A. H., & Perry, M. (1992). The aggression questionnaire. Journal of Personality and
Social Psychology, 63(3), 452-459. doi: 10.1037/0022-3514.63.3.452
Carnagey, N. L., & Anderson, C. A. (2005). The effects of reward and punishment in violent video games on aggressive affect, cognition, and behavior. Psychological Science,
16(11), 882-889. doi: 10.1111/j.1467-9280.2005.01632.x
Douglas, A. G., & Craig, A. A. (2006). Violent video games: The effects on youth, and public policy implications. In N. Dowd, D. G. Singer, & R. F. Wilson (Eds.), Handbook of
children, culture, and violence (pp. 225-246). Thousands Oaks, CA: Sage
Eastin, M. (2007). The influence of competitive and cooperative group game play on state hostility. Human Communication Research, 33(4), 450-466. doi: 10.1111/j.1468-2958.2007.00307.x
Eastin, M. S., & Griffiths, R. P. (2009). Unreal: Hostile expectations from social gameplay.
New Media & Society, 11(4), 509-531. doi: 10.1177/1461444809102958
Eden, S., & Eshet-Alkalai, Y. (2014). The effect of digital games and game strategies on young adolescents’ aggression. Journal of Educational Computing Research, 50(4), 449-466. doi: 10.2190/EC.50.4.a
Ewoldsen, D. R., Eno, C. A., Okdie, B. M., Velez, J. A., Guadagno, R. E., & DeCoster, J. (2012). Effect of playing violent video games cooperatively or competitively on subsequent cooperative behavior. Cyberpsychology, Behavior, and Social
Networking, 15(5), 277-280. doi: 10.1089/cyber.2011.0308
Hughes, M., & Louw, J. (2013). Playing games: The salience of social cues and group norms in eliciting aggressive behavior. South African Journal of Psychology, 43(2), 252-262. doi: 10.1177/0081246313482629
Lenhart, A., Kahne, J., Middaugh, E., Rankin, A., Evans, C., & Vitak, J. (2008). Teens, video
games, and civics: Teens’ gaming experiences are diverse and include significant social interaction and civic engagement. Washington, DC: Pew Internet and
American Life Project.
Markey, P. M., & Markey, C. N. (2010). Vulnerability to violent video games: A review and integration of personality research. Review of General Psychology, 14(2), 82-91. doi: 10.1037/a0019000
Mihan, R., Anisimowicz, Y., & Nicki, R. (2014). Safer with a partner: Exploring the emotional consequences of multiplayer video gaming. Computers in Human
Behavior, 44, 299-304. doi: 10.1016/j.chb.2014.11.053
Novak, T. P., Hoffman, D. L., & Yung, Y. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22-42. doi: 10.1287/mksc.19.1.22.15184
Velez, J. A., Mahood, C., Ewoldsen, D. R., & Moyer-Gusé, E. (2014). Ingroup versus outgroup conflict in the context of violent video game play: The effect of cooperation on increased helping and decreased aggression. Communication
Research, 41(5), 607-626. doi: 10.1177/0093650212456202
Waddell, J. C., & Peng, W. (2014). Does it matter with whom you slay? The effects of competition, cooperation and relationship type among video game players.
Computers in Human Behavior, 38, 331-338. doi: 10.1016/j.chb.2014.06.017
Williams, D. (2005). Bridging the methodological divide in game research. Simulation &
Zhang, X., Liu, C., Wang, L., & Piao, Q. (2010). Effects of violent and non-violent computer video games on explicit and implicit aggression. Journal of Software, 5(9), 1014-1021. doi: 10.4304/jsw.5.9.1014-1021
Appendix A. Measures
1. State Hostility Scale (Anderson, Deuser, & DeNeve, 1995)
(Strongly disagree) (Disagree) (Neither agree nor disagree) (Agree) (Strongly agree)
1) I feel furious 2) I feel aggravated 3) I feel stormy 4) I feel polite* 5) I feel discontented
6) I feel like banging on a table 7) I feel irritated 8) I feel frustrated 9) I feel kindly* 10) I feel unsociable 11) I feel outraged 12) I feel agreeable* 13) I feel angry 14) I feel offended 15) I feel disgusted 16) I feel tame*
17) I feel like I’m about to explode 18) I feel friendly* 19) I feel understanding* 20) I feel amiable* 21) I feel mad 22) I feel mean 23) I feel bitter 24) I feel burned up
25) I feel like yelling at somebody 26) I feel cooperative*
27) I feel like swearing 28) I feel cruel
29) I feel good-natured* 30) I feel disagreeable 31) I feel enraged 32) I feel sympathetic*
*Items were reverse coded. The asterisks were not displayed to the participants during the experiment.
2. Video game experience (Novak, Hoffman, & Yung, 2000)
1. How long have you been playing video games?
(1) This will be my first time (2) Less than a year (3) One to two years (4) Two to three years (5) Three to four years (6) More than four years
(1) Never (2) 1 to 5 times a month (3) 5 to 10 times a month (4) 10 to 15 times a month (5) 15 to 20 times a month (6) More than 20 times a month
3. How often do you play video games daily?
(1) Never (2) Less than 30 minutes per day (3) 30 minutes to 1 hour per day
(4) 1 hour to 2 hours per day (5) 2 hours to 3 hours per day (6) More than 3 hours per day
3. Buss-Perry Trait aggression survey (Buss & Perry, 1992)
Extremely uncharacteristic of me (1) (2) (3) (4) (5) (6) (7) Extremely characteristic of me
1) Once in a while I can’t control the urge to strike another person 2) Given enough provocation, I may hit another person
3) If somebody hits me, I hit back
4) I get into fights a little more than the average person 5) If I have to resort to violence to protect my rights, I will 6) There are people who pushed me so far that we came to blows 7) I can think of no good reason for ever hitting a person*
8) I have threatened people I know
9) I have become so mad that I have broken things 10) I tell my friends openly when I disagree with them 11) I often find myself disagreeing with people
12) When people annoy me, I may tell them what I think of them 13) I can’t help getting into arguments when people disagree with me 14) My friends say that I’m somewhat argumentative
15) I flare up quickly but get over it quickly 16) When frustrated, I let my irritation show
17) I sometimes feel like a powder keg ready to explode 18) I am an even-tempered person*
19) Some of my friends think I’m a hothead
20) Sometimes I fly off the handle for no good reason 21) I have trouble controlling my temper
22) I am sometimes eaten up with jealousy
24) Other people always seem to get the breaks
25) I wonder why sometimes I feel so bitter about things 26) I know that “friends” talk about me behind my back 27) I am suspicious of overly friendly strangers
28) I sometimes feel that people are laughing at me behind my back 29) When people are especially nice, I wonder what they want
*Items were reverse coded. The asterisks were not displayed to the participants during the experiment.
Appendix B. Syntax
*Recode trait aggression
DATASET ACTIVATE DataSet1.
RECODE Q11_7 Q11_18 (1=7) (2=6) (3=5) (4=4) (5=3) (6=2) (7=1) INTO RQ11_7 RQ11_18.
EXECUTE.
*Recode aggressive affect
RECODE Q15_4 Q15_9 Q15_12 Q15_16 Q15_18 Q15_19 Q15_20 Q15_26 Q15_29 Q15_32 (1=5) (2=4) (3=3) (4=2)
(5=1) INTO RQ15_4 RQ15_9 RQ15_12 RQ15_16 RQ15_18 RQ15_19 RQ15_20 RQ15_26 RQ15_29 RQ15_32.
EXECUTE.
*Computing video game experience COMPUTE Cov1=MEAN(Q7,Q8,Q9). EXECUTE.
*Computing trait aggression COMPUTE Cov2=MEAN(Q11_1,Q11_2,Q11_3,Q11_4,Q11_5,Q11_6,RQ11_7,Q11_8,Q11_9,Q11_10,Q 11_11,Q11_12, Q11_13,Q11_14,Q11_15,Q11_16,Q11_17,RQ11_18,Q11_19,Q11_20,Q11_21,Q11_22,Q11_ 23,Q11_24,Q11_25,Q11_26, Q11_27,Q11_28,Q11_29). EXECUTE.
*Computing aggressive affect COMPUTE
DV=MEAN(Q15_1,Q15_2,Q15_3,RQ15_4,Q15_5,Q15_6,Q15_7,Q15_8,RQ15_9,Q15_10,Q 15_11,RQ15_12,
Q15_13,Q15_14,Q15_15,RQ15_16,Q15_17,RQ15_18,RQ15_19,RQ15_20,Q15_21,Q15_22, Q15_23,Q15_24,Q15_25,
RQ15_26,Q15_27,Q15_28,RQ15_29,Q15_30,Q15_31,RQ15_32). EXECUTE.
*Cronbach’s alpha on video game experience RELIABILITY
/VARIABLES=Q7 Q8 Q9
/SCALE('ALL VARIABLES') ALL /MODEL=ALPHA
/STATISTICS=CORR /SUMMARY=TOTAL.
*Cronbach’s alpha on trait aggression RELIABILITY /VARIABLES=Q11_1 Q11_2 Q11_3 Q11_4 Q11_5 Q11_6 Q11_8 Q11_9 Q11_10 Q11_11 Q11_12 Q11_13 Q11_14 Q11_15 Q11_16 Q11_17 Q11_19 Q11_20 Q11_21 Q11_22 Q11_23 Q11_24 Q11_25 Q11_26 Q11_27 Q11_29 Q11_28 RQ11_7 RQ11_18
/SCALE('ALL VARIABLES') ALL /MODEL=ALPHA
/STATISTICS=CORR /SUMMARY=TOTAL.
*Cronbach’s alpha on aggressive affect RELIABILITY /VARIABLES=Q15_1 Q15_3 Q15_2 Q15_5 Q15_6 Q15_7 Q15_8 Q15_10 Q15_11 Q15_13 Q15_14 Q15_15 Q15_17 Q15_21 Q15_22 Q15_23 Q15_24 Q15_25 Q15_27 Q15_28 Q15_30 Q15_31 RQ15_4 RQ15_9 RQ15_12 RQ15_16 RQ15_18 RQ15_19 RQ15_20 RQ15_26 RQ15_29 RQ15_32 /SCALE('ALL VARIABLES') ALL
/MODEL=ALPHA /STATISTICS=CORR /SUMMARY=TOTAL.
*Two-way ANOVA
UNIANOVA DV BY IV Gender WITH Cov1 Cov2 /METHOD=SSTYPE(3)
/INTERCEPT=INCLUDE /PLOT=PROFILE(IV*Gender)
/PRINT=HOMOGENEITY DESCRIPTIVE /CRITERIA=ALPHA(.05)
/DESIGN=Cov1 Cov2 IV Gender IV*Gender.
*Descriptive age
DATASET ACTIVATE DataSet1. DESCRIPTIVES VARIABLES=Age
/STATISTICS=MEAN STDDEV MIN MAX.
*Descriptive covariates and dependent variable
DESCRIPTIVES VARIABLES=Videogameexperience Traitaggression Aggressiveaffect /STATISTICS=MEAN STDDEV MIN MAX.
