Youth Violence and Video Games: A Data Analysis – Psychology and Video Games (2023)

The debate on whether video games influence violent behaviour has persisted for several decades. Critics such as Jack Thompson argue that violent video games encourage young people to enjoy the act of killing (Benedetti, 2007). It is argued that violent characters portrayed through video games encourage young people to become more aggressive and emulate violent acts:

Young children possess an instinctive desire to imitate actions they observe…there is deep concern that playing violent video games…will cause children to become more aggressive toward other children and become more tolerant of, and more likely to engage in, real-life violence.

– McAfee (1994)

To aid our understanding of how video games can influence violence, academics have conducted their own research into gaming and violent behaviour. However, this research has been subject to its own debate primarily for two reasons.

The first reason involves the participants used in these studies. It is common for studies into video games and violence to be conducted with around 30-150 university students (Hollingdale & Greitemeyer, 2014; Hasan et al., 2013). It is argued that this specific population does not allow us to apply these findings to other populations (e.g. teenagers). Using small samples in research also increases the chance of producing false-positive results and overestimating the strength of a finding (Hackshaw, 2008).

The second reason regards the methods used to conduct video game research. It is unethical to encourage people to hurt one another in the name of research. This means that ‘violence’ and ‘aggression’ have been measured using methods such as playing a loud noise to a computer and measuring a portion of hot sauce for someone who does not like spice (Hasan et al., Hollingdale et al.). These methods are low in what is known as ecological validity – they do not represent violence in everyday life.

To combat this lack of ecological validity, researchers have explored violence that has already occurred and how it relates to gaming. In research conducted by DeCamp and Ferguson (2017), young people were asked if they hit others with the intention of hurting them or if they participated in group fights. The study found that there were much larger predictors of youth violence than playing video games. The researchers concluded that family and social factors need to be addressed when reducing youth violence rather than addressing video game use.

In this article, I will conduct my own video game research that is high in ecological validity and sample size. This study will use methodology similar to DeCamp and Ferguson’s by exploring youth violence through fighting. The study will be conducted with a dataset of 214,080 participants, making this, to my knowledge, the world’s largest data analysis into violence and video games.

The research question being addressed in this study is once again drawn from DeCamp and Ferguson’s research: Do other social and environmental factors predict violent behaviour more than video games?

As usual, there will be a summary at the bottom if you do not wish to read everything. Thank you and I hope you enjoy.


    1. The Data
    2. Data Analysis Strategy and Checking
    3. Results
    4. Discussion
    5. Critique
    6. Summary
    7. References

The Data

This analysis will be conducted using the 2014 wave of the Health Behaviour in School-Aged Children (HBSC) study. The HBSC is a large-scale endeavour to improve young peoples’ wellbeing by researching their health behaviours and social context. Data is gathered for this study via an anonymous questionnaire. The 2014 wave of the study includes a total of 214,080 11-, 13- and 15-year old participants from 41 countries.

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This dataset is open-access and is open for all researchers to analyse, providing that they abide by the License Agreement. In accordance with the License Agreement, I must copy and paste the acknowledgement text below. Feel free to move on to the next section.

HBSC is an international study carried out in collaboration with WHO/EURO. The International Coordinator of the 2013/14 survey was Prof. Candace Currie and the Data Bank Manager was Prof. Oddrun Samdal. The 2013/14 survey was conducted by Principal Investigator Candace Currie in 41 countries. For details, see

Data Analysis Strategy and Checking

The data in the current study will be analysed similarly to DeCamp and Ferguson’s research, using what is known as a regression analysis.

Simply put, regression analyses are used to explain why something happens (known as ‘variance explained’). For example, when trying to explain number of ice creams sold on a day, significant predictors may include variables such as temperature, time of year etc.

As regression analyses produce a lot of statistics, I have decided to report on the variance explained by the model and β values, t values and significance values for each significantly contributing variable. While β values can be thought of as how much the variable contributes to explaining a behaviour, t values demonstrate the strength of the relationship.

Using HBSC data grants us rich data into social, contextual and wellbeing factors for young people. To help readers understand the variables that have been entered into the regression analysis, I have grouped variables together into easy-to-understand categories.

The dependent variable (what we’re looking to explain) is how often a participant has been involved in a physical fight in the past 12 months; answers range from ‘none’ to ‘four or more’. The categories that will be used to explore why young people get into fights include:

Individual factors: This category includes the age and gender of the participant.

Free time: This category involves how young people choose to spend their free time. Variables include: hours spent watching TV (weekdays and weekend), hours spent using a computer (weekdays and weekend), hours spent playing video games (weekdays and weekend), number of hours of exercise per week, and number of times spent hanging out with friends after school.

Risk factors: The HBSC study identifies the following variables as risk factors for young people: smoking, drinking, getting drunk, cannabis use and underage sex. All variables apart from underage sex looked at how often these behaviours occurred in the 30 days prior to the study. For underage sex, young people were simply asked to report ‘Yes’ or ‘No’.

Bullying: As bullying may involve physical violence, both being bullied and being a bully in the two months prior to the study were included in the analysis.

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Home life: As home life variables were significant predictors of youth violence in DeCamp and Ferguson’s study, all variables regarding home and family life from the HBSC were included in the analysis. These variables include: presence of mother in the home, presence of father in the home, ease of communication with parents, how helpful young people view their family to be, how supportive they view them to be, and how helpful their family is in making life decisions.

Social life: As friendship circles can be an influential factor in bullying and violence (O’Connell et al., 1999; Pepler et al., 2010), all friendship variables from the HBSC were included in the analysis. These include: how helpful their friends are, how dependable they are, whether the young person can rely on their friends, how much joy they share with their friends, and whether they can talk to their friends about problems.

Health: As mental and physical health problems can be an influential factor in youth violence (Resnick et al., 2004; Patel et al., 2007), all mental and physical health variables from the HBSC were included in the analysis. These include: general self-reported health, how often the young person feels low, how often they have temper issues, how often they feel nervous and their general life satisfaction.

School life: As academic performance and school problems are linked with school violence (Resnick et al.), all school life variables from the HBSC were included in the analysis. These include: whether the young person likes school, what their academic performance is like, whether students like to be together, whether students are helpful, whether students accept the young person, and whether the young person feels pressured by school.

Socioeconomic status (SES): As SES was a significant contributor to youth violence in DeCamp and Ferguson’s research, several SES variables were included in the analysis. These variables include: number of cars in the household, how often the family go on holiday together, number of computers, whether the young person has their own bedroom, and whether they consider their family to be wealthy.

Before conducting the regression analysis, I wanted to ensure that the regression results would be accurate by checking a number of data assumptions. Please feel free to ignore anything written in red, this is all statistical talk.

The multicollinearity of variables was assessed using both Tolerance and Variance Inflation Factor (VIF) measures. No VIF value exceeded 5 and each Tolerance value exceeded 0.2, suggesting that variables are sufficiently independent for the analysis. The influence of undue cases was assessed using Mahalanobis Distance, Cook’s Distance and Centered Leverage Values. 40 participants had a Centered Leverage Value that exceeded three times the mean; these participants were removed from the analysis. Multiple participants had a Mahalanobis Distance value which exceeded 15. However, Stevens (2002) recommends that the Cook’s Distance value should be consulted before removing participants with a Mahalanobis Distance value exceeding 15. Due to the very small size of Cook’s Distance values in the analysis (Maximum: 0.001) which is to be expected from having a very large sample size, no further participants were removed as part of this check.


After removing participants who did not answer the fighting question and those with a high Centered Leverage Value, the final participant pool for the data analysis was 203,121. This pool included 103,653 females and 99,468 males.

The variables included in the analysis explained 23.7% of why young people get into fights (Adjusted R-Squared = .237). 27 variables emerged as significant predictors in this model. To minimise losing readers in a sea of statistics, I will be organising variables based on how much they explain youth violence in accordance with their β values. This means that the first variable listed explains the most variance in youth violence, while the final variable explains the least.

1st-10th Largest Contributors: Being male (β = 0.23, t = 56.96, p < .001); engaging in underage sex (β = 0.16, t = 41.47, p < .001); being younger (β = 0.16, t = 38.97, p < .001); being a bully (β = 0.15, t = 38.86, p < .001); underage cannabis use (β = 0.07, t = 17.83, p < .001); having temper problems (β = 0.07, t = 14.10, p < .001); being a victim of bullying (β = 0.06, t = 15.69, p < .001); underage alcohol use (β = 0.05, t = 11.91, p < .001); not enjoying school (β = 0.04, t = 10.66, p < .001); exercising often (β = 0.04, t = 9.75, p < .001).

11th-20th Largest Contributors: Not having their own bedroom (β = 0.04, t = 9.66, p < .001); poorer academic achievement (β = 0.03, t = 8.65, p < .001); spending more time after school with friends (β = 0.03, t = 8.86, p < .001); feeling nervous more often (β = 0.03, t = 7.27, p < .001); going on family holidays (β = 0.03, t = 6.86, p < .001); using a computer at the weekend (β = 0.02, t = 3.15, p = .002); having difficulties talking to their mother (β = 0.02, t = 4.95, p < .001); not having a family car (β = 0.02, t = 5.07, p < .001); watching TV on weekdays (β = 0.02, t = 3.71, p < .001); watching TV at the weekend (β = 0.02, t = 3.58, p < .001).

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21st-27th Largest Contributors: Playing games on weekdays (β = 0.02, t = 2.90, p = .004); having fewer computers at home (β = 0.02, t = 3.91, p < .001); underage smoking (β = 0.02, t = 3.78, p < .001); getting drunk more often (β = 0.01, t = 2.50, p = .012); not viewing school peers as helpful (β = 0.01, t = 2.12, p = .034); having peers that like to spend time together (β = 0.01, t = 2.07, p = 0.38); feeling pressured by schoolwork (β = 0.01, t = 2.18, p = .029).

For researchers and academics, more in-depth statistics and the coding structure for significant variables can be read here and here respectively.


This study set out to explore whether other social and environmental factors explain youth violence more than video games. The analysis found 20 factors that explain more of youth violence than video games. Video gaming was weakly and inconsistently associated with youth violence: playing games on weekdays had a β value of 0.02, while playing at the weekend was not associated at all. For the sake of comparison, gender explains almost 1200% more of youth violence in the model than video games, while being a bully explains almost 800% more.

In data analysis, regressions are helpful as they go beyond simple correlations that show us a relationship between x and y. Regression analyses allow us to identify variables that are most helpful in explaining why something happens. This is beneficial for reducing dangerous behaviours as money and resources can be invested in addressing factors that are most related to the behaviour.

This analysis shows that focusing on video games to reduce youth violence would be a suboptimal way to use resources. In fact, using resources to reduce video gaming as a method of lowering violence could be considered a social injustice for two reasons.

Firstly, the weak and inconsistent relationship between gaming and youth violence suggests that reducing gaming will not be massively effective in reducing violence. This would continue to put young people at risk of injuries such as concussions. Secondly, this analysis suggests that placing resources into other factors will not only be better at reducing youth violence, but will also be more beneficial to young people. A few data-driven examples from the current analysis include:

  • Teaching young males healthier ways of dealing with conflict and emotions. This would be helpful as this population experience many social, emotional and hormonal changes at this age (Currie et al., 2001).
  • Providing more funding to anti-bullying initiatives that address factors such as physical bullying and gang violence.
  • Further funding for initiatives that try to reduce risk-taking behaviours such as underage drinking and drug use.

An interesting finding of the regression was the significance of several free time variables such as exercise and watching TV. I would like to offer an explanation for why this is the case using this study’s findings. Engaging in youth violence was significantly associated with poorer academic achievement and disliking school. These young people are also more likely to have an unsupportive home life (being unable to turn to their mother for help) and less likely to have their own bedroom.

These findings paint a picture of young people who do not have the space/support to study or thrive academically. Due to lack of space, quiet and support, they may give up on trying to do schoolwork at home. These young people may also be less willing to stay after-school for help due to their dislike of school. Lack of studying results in an abundance of free time that may be spent playing sports, watching TV, playing video games or getting involved in dangerous social situations.

Dedicating resources to reducing free time behaviours (such as reducing exercise or gaming) instead of improving young people’s academic engagement may be seen as socially irresponsible. This recommendation, combined with the three recommendations above, offer four helpful and data-driven ideas for reducing youth violence and improving their quality of life. Due to the social good that these interventions are capable of, they should be prioritised over trying to reduce video gaming.


All good research provides self-critique so that other researchers can improve upon the methodology. Before concluding, I would like to offer a few self-critiques of this study:

  • While this study includes data from an impressive 41 countries, I would like to have had more data from North America and from Asian countries.
  • This study looks at ‘video games’ rather than ‘violent video games’. However, my justification for this involves a recent tragic event that occurred at a non-violent video game event (for which you can donate to here). As the discussion is now changing from ‘violent video games’ to simply ‘video games’, I would like to provide data on video games as a whole.
  • Young people may underreport being a bully for two reasons. Firstly, young people may not acknowledge that they’re engaging in bullying and may simply think that the other person ‘deserves it’. Secondly, they may not be able to admit to themselves morally that they are bullying someone, so they will not admit to it in a questionnaire.
  • As both being a bully and being bullied were related to youth violence, asking young people whether they have been in a fight may capture both perpetrators and victims. To minimise this in the future, I recommend using DeCamp and Ferguson’s phrasing: ‘hitting someone with the intent to hurt them’.
  • Using a sample population of 11-15 year olds may limit this study’s applicability to other populations. However, I specifically wished to look at young people due to the combination of video game research often using university samples and adults frequently worrying about the effect of video games on young people.


  • Research has been carried out to understand whether video games increase youth violence and aggression. However, this research has been criticised for relying on small samples of university students and using inaccurate measures of violence (such as giving someone a portion of hot sauce). When using more accurate measures of violence in a youth population, evidence shows that there are much larger social and environmental contributors to youth violence than video games.
  • Inspired by this research, I conducted the world’s largest data analysis into video games and youth violence using a dataset of 214,080 11-15 year olds. The research question for this study is as follows: Do other social and environmental factors predict violent behaviour more than video games?
  • Data from the 2014 wave of the Health Behaviour in School-Aged Children (HBSC) study was used; this study explores youth wellbeing in 41 countries. The following categories of variables were entered into a regression analysis to try to explain why young people get into fights: age and gender; free time; risk factors; bullying; home life; social life; health; school life and socioeconomic status. After removing undue cases and those who did not answer the fighting question, the final sample size was 203,121.
  • The model explained 23.7% of why young people get into fights and included 27 significant predictors; 20 variables explained more of youth violence than video games. Video gaming was weakly and inconsistently related to youth violence: playing games on weekdays had a β value of 0.02, while playing at the weekend was not associated at all. For the sake of comparison, gender explains almost 1200% more of youth violence in the model than video games, while being a bully explains almost 800% more.
  • These findings demonstrate that focusing on video games as a method of reducing youth violence would be cost-ineffective, would produce few benefits and would continue to place young people at risk of physical injuries. Four recommendations are made for reducing youth violence: teaching healthier coping mechanisms, further investments in anti-bullying schemes, further investments in minimising risk-taking behaviours, and improving academic engagement in young people lacking study space and support. Due to the social good that these interventions are capable of, they should be prioritised over trying to reduce video gaming.

Thank you all very much for reading! This hard work would not be possible without the support of my wonderful Patrons. I would particularly like to thank my Platinum Patrons: Matt Demers, Albert S Calderon, Kyle T, Kyle Ryan, Andrew Shirvis, redKheld, DigitalPsyche, Brent Halen, Colton Ballou, Dimelo ‘Derp’ Waterson, Hagbard Celine and Senpai. Thank you!

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Benedetti, W. (2007). Were video games to blame for massacre? Retrieved July 20th, 2018 from

Currie, C., Samdal, O., Boyce, W., & Smith, B. (2001). Health behaviour in school-aged children: A world health organization cross-national study, research protocol for the 2001/02 survey. Edinburgh: Child and Adolescent Health Research Unit

DeCamp, W., & Ferguson, C. J. (2017). The impact of degree of exposure to violent video games, family background, and other factors on youth violence. Journal of Youth and Adolescence, 46(2), 388-400.

Hackshaw, A. (2008). Small studies: strengths and limitations. European Respiratory Journal, 32, 1141-1143.

Hasan, Y., Bègue, L., Scharkow, M., & Bushman, B. J. (2013). The more you play, the more aggressive you become: A long-term experimental study of cumulative violent video game effects on hostile expectations and aggressive behavior. Journal of Experimental Social Psychology, 49(2), 224-227.

Hollingdale, J. & Greitemeyer, T. (2014) The Effect of Online Violent Video Games on Levels of Aggression. PLOS ONE, 9(11): e111790.

McAfee, R. E. (1994). Testimony before House Energy and Commerce Committee Subcommittee on Telecommunications and Finance.

O’Connell, P., Pepler, D., & Craig, W. (1999). Peer involvement in bullying: Insights and challenges for intervention. Journal of Adolescence, 22, 437–452.

Patel, V., Flisher, A. J., Hetrick, S., & McGorry, P. (2007). Mental health of young people: a global public-health challenge. The Lancet, 369(9569), 1302-1313.

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Pepler, D., Craig, W., & O’Connell, P. (2010). Peer processes in bullying: Informing prevention and intervention strategies. In S. R. Jimerson, S. M. Swearer, & D. L. Espelage (Eds.), Handbook of bullying in schools: An international perspective (pp. 469–479). New York, NY: Routledge.

Resnick, M. D., Ireland, M., & Borowsky, I. (2004). Youth violence perpetration: what protects? What predicts? Findings from the National Longitudinal Study of Adolescent Health. Journal of Adolescent Health, 35(5), 424-e1.

Stevens, J. P. (2002). Applied multivariate statistics for the social sciences (4th ed.). Hillsdale, NJ: Erlbaum