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Toward a better understanding of the relation between violent videogame play and different types of antisocial behavior

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  • Respondents are recruited through partner websites, emails with online partners, refer-a-friend, trade shows, client supplied lists of customers, TV advertisements, direct mail, telephone recruitment of targeted populations
  • As the children were recruited from the adults who initially agreed to take the study, the sample selection bias is found primarily in the characteristics of adults who chose to take this survey. Hence, only adults were propensity weighted and the propensity weights that were generated for the adults were applied to the child. The propensity score was derived from key questions in the survey that examined the attitudes and behaviors of the respondents as well as the demographic questions.
  • 28% report 31-1hour 24% report half hour or less 22% report 1-2 hours
  • Why study? A lot of games are being sold and many kids are playing them.
  • … and the kids are often (more than half the time) playing violent video games that reward violent behavior
  • Data (Anderson et al. meta analysis in 2004) have shown that exposure to violent video game play is associated with increased antisocial behavior and decreased prosocial behavior
  • Realism – more graphically advanced, when blood is turned on, using a light gun Identification – sex of the character is consistent with player, player can ‘see’ their character Two studies 1) A study used a level editor to create a central play area that was mirrored, so as the player ran by, they’d see themselves 2) A study found that aggression by males and females was higher when their character was the same sex than when opposite sex
  • Despite the substantial research on exposure to violent media, in general, and violent video games in particular, relatively little is known about the extent to which exposure to violent video games is associated with serious, moderate, and mild forms and antisocial behavior.
  • 28% report 31-1hour 24% report half hour or less 22% report 1-2 hours
  • Amount of game playing increases as level of aggression increases
  • Last 12 months
  • Murder: (i.e., stabbing or shooting someone); Aggravated assult: (i.e., threatening with a weapon; attack requiring medical care) Robbery: (i.e., using a knife, gun, or other weapon to get something from someone) Sexual assault: (i.e., unwanted kissing, touching, or anything else sexual) Note, these questions were randomized for each participant to adjust for response bias due to ordering
  • Breaking rule: (i.e., running away from home; ditching/skipping school) Threatening / Fighting: (i.e., threatened teacher; threatened someone with knife/gun; involved with gang fight) Burglary (i.e., broken into someone house; stolen something valuable) Animal cruelty (i.e., hurt animal on purpose) Based on factor analysis Note, these questions were randomized for each participant to adjust for response bias due to ordering
  • Bullying: (i.e., Excluding people from group; spreading rumors about people) Physical aggression: (i.e., shoving or pushing people; getting into fights) Vandalism: (i.e., damaged someone else’s property) Dishonesty: (i.e., lying to someone to get something or a favor or get out of work) Note, these questions were randomized for each participant to adjust for response bias due to ordering
  • Realistic = They are JUST LIKE or SOMEWHAT LIKE real life Identification = They are JUST LIKE ME or KINDA LIKE me
  • Violent video game = many/most/all of the games they play have violent content Realistic = They are JUST LIKE or SOMEWHAT LIKE real life Identification = They are JUST LIKE ME or KINDA LIKE me
  • Violent videogame play significantly predicted all three outcome variables (p<.05) Model fits the data, though, I do not have the exact fit statistics (they would help, yes?). But clearly, the model is not explaining A LOT of the variance in the 3 outcome variables. Controlling for age, sex, and income (these variables are not shown in the model) The 3 outcome variables are also correlated, but I cannot figure out how to draw the intercorrelations: SVB & ASB: r=0.65 ASB & DB: r=0.45 SVB & DB: r=0.30
  • Violent video game play predicted character identification (p<.01) and character identification predicted the 3 outcome variables (p<.05) Character identification mediates the relation between violent video game play…when character identification is added, the relation between violent video game play and the 3 outcome variables disappears, but character identification is significantly associated with the 3 outcome variables. Model fits the data, though, I do not have the exact fit statistics (they would help, yes?). This model explains more of the variance in the 3 outcome variables. Additional variables in this model include age, sex, income, STAXI, and exposure to community violence (these variables are not shown in the model) The 3 outcome variables are also correlated, but I cannot figure out how to draw the intercorrelations: SVB & ASB: r=0.51 ASB & DB: r=0.23 SVB & DB: r=0.19
  • You don’t know the answer unless you ask – it’s a strength that we have this data and are able to analyze it’s influence Note that to be at risk, you have to have the exposure – i.e., internet use; General population findings may yield different frequencies.
  • The current data highlight some of these unhealthy behaviors, including sexually soliciting others, seeking out violent pornography, and other violent exposures online.
  • Discounting violent video game content…and how we tend to overlook certain aggressive behaviors.
  • Transcript

    • 1. Toward A Better Understanding of the Relation Between Violent Videogame Play and Different Types of Antisocial Behavior Merle Hamburger PhDa Michele Ybarra MPH PhDb Jeffery Hall PhDa Philip J Leaf PhDc Marie Diener-West PhDc aCenters for Disease Control; bInternet Solutions for Kids, Inc.; cJohns Hopkins School of Public Health International Society for Research on Aggression, July 2008, Budapest, Hungary * Thank you for your interest in this presentation. Please note that analyses included herein are preliminary. More recent, finalized analyses may be available by contacting CiPHR for further information.
    • 2. Growing up with Media (GuwM) Methodology  Baseline data were collected August - September, 2006  1,588 households (one caregiver, one child) were surveyed online  Participants recruited from Harris Poll On Line
    • 3. GuwM Eligibility  ADULT  Be the most (or equally) knowledgeable of the youth’s media use in the home  Be a member of HPOL  YOUTH  Aged 10-15 years  Use the Internet at least once in the last 6 months  English speaking
    • 4. Harris Poll On Line  HPOL is a double opt-in panel of millions of respondents.  HPOL data are consistently comparable to data that has been obtained from RDD telephone samples of general populations when sampling and weighting is applied.
    • 5. GuwM RR and Weighting  Response rate was 26%  Propensity scoring was applied  Data were weighted to match the US population of adults with children between the ages of 10 and 15 years
    • 6. GuwM Youth characteristics (n=1,588)  48% Female  Mean age: 12.6 years (SE: 0.05)  71% White, 13% Black, 9% Mixed, 7% Other  19% Hispanic  Median time spent online on a typical day: 31 minutes – 1 hour
    • 7. Why Video Games?  Video games are BIG BUSINESS  ~268 million computer/video games sold in 2007  ~$9.5 BILLION in revenue (for 2007)  Approximately 60% youth (8-18) play video games for about an hour on any given day
    • 8. Violence in Videogames  > 50% of the most popular video games are rated ‘T’ or ‘M’  Teen/Mature rated games  Almost all have violent content  Most (90%) reward injuring characters  Many (~69%) reward killing characters  Youth (8-18) prefer ‘T’ and ‘M’ rated games
    • 9. Exposure to Violent Video Games Exposure related to:  Increased Aggressive behavior, Aggressive affect, and Aggressive cognitions  Decreased prosocial behavior
    • 10. Immersion as a Mediator A player’s sense of ‘presence’ in the game  Realism  Effects more pronounced if game is realistic  Immersion  Effects more pronounced if player identifies with characters
    • 11. Problem Statement Little is known about how exposure to violent video games is associated with: (a) seriously violent behavior; (b) antisocial behavior; and (c) delinquency. What is the association between playing violent video games and concurrent reports of externalizing behavior; To what extent does immersion mediate this association?
    • 12. Characteristics of game video players  N=1,493 (video game players)  48% Female  Mean age: 12.5 years (SE: 0.04)  79% White, 13% Black, 8% Other  12% Hispanic  Median HH income: $50,000-$74,999
    • 13. Game Playing Behavior  Median # of days / week: 3-4  Median time playing/ day: 31-60 min  Overall median exposure: 157 min / week  Median exposure by violent video game  None: 67.5 min / week  Some: 157.5 min / week  Many / Most / All: 287.8 min / week
    • 14. Violent Video Game Play “When you play video, computer, or Internet games, how many show physical fighting, shooting, or killing?” Response alternatives: ‘None’; ‘Some’; ‘Many’; ‘Most/All’
    • 15. Outcome Variables…  Seriously Violent Behavior  Behavior likely resulting in murder  Aggravated assault;  Robbery;  Sexual assault  Alpha = 0.87
    • 16. Outcome Variables…  Antisocial Behavior  Breaking rules  Threatening / fighting with people  Burglary  Animal cruelty  Alpha = 0.85
    • 17. Outcome Variables…  Delinquency  Relational bullying;  Physical aggression;  Vandalism  Manipulative/coercive behavior  Alpha = 0.80
    • 18. Potential Effect Modifiers  Realism  The action in the games is like ‘real life.’  Identification  The people in the games are ‘just like me or people I know’
    • 19. Results Variable # Yes (N = 1,493) % of Sample Serious Violent Behavior 89 6 Antisocial Behavior 400 27 Delinquency 1,028 69.5 Violent Video Game 388 26.3 Realistic 471 49.1 Identification 185 19.4
    • 20. Bivariate Odds Controlling for participant age, sex, and income Variable Violent Behavior Antisocial Behavior Delinquency Violent Video Game Play 1.92 (1.19-3.08) 1.92 (1.45-2.52) 1.62 (1.22-2.17) Realism 1.14 (0.71-1.84) 1.46 (1.09-1.94) 1.22 (0.91-1.64) Identification 3.35 (2.02-5.55) 2.51 (1.76-3.57) 1.68 (1.11-2.54)
    • 21. Path Analysis model
    • 22. Mediational Path analysis model
    • 23. Summary Playing violent video games is common.  Over a quarter of respondents report playing violent video games  Weekly exposure significantly related to playing violent video games
    • 24. Summary Consistent with previous literature reporting associations between violent video games and aggression..  Frequent exposure to violent video games is concurrently associated with serious externalizing behaviors,  Character identification is a mediator
    • 25. Limitations of GuwM Data  Data are cross-sectional  Reliance on self-reports  It is possible that:  Children were monitored by their parents  22% of youth indicated someone was close enough to see the screen during data collection  Parents completed the youth survey.
    • 26. Implications  Need to educate caregivers about the growing evidence for the impact of violent media / video games on serious externalizing behaviors  Character identification appears to be important, over and above the violence in the game itself.
    • 27. Contact Information Dr. Merle Hamburger Centers for Disease Control and Prevention mhamburger@cdc.gov This research was supported by Cooperative Agreement number U49/CE000206 from the Centers for Disease Control and Prevention (CDC). The findings and conclusions in this presentation are those of the authors and do not necessarily represent the official position of the Centers for Disease Control.

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