Toward A Better Understanding of the RelationBetween Violent Videogame Play and Different Typesof Antisocial BehaviorMerle...
Growing up with Media(GuwM) Methodology Baseline data were collected August -September, 2006 1,588 households (one careg...
GuwM Eligibility ADULT Be the most (or equally) knowledgeable of theyouth’s media use in the home Be a member of HPOL ...
Harris Poll On Line HPOL is a double opt-in panel ofmillions of respondents. HPOL data are consistently comparableto dat...
GuwM RR andWeighting Response rate was 26% Propensity scoring was applied Data were weighted to match the USpopulation ...
GuwM Youthcharacteristics (n=1,588) 48% Female Mean age: 12.6 years (SE: 0.05) 71% White, 13% Black, 9% Mixed, 7%Other...
Why Video Games? Video games are BIG BUSINESS ~268 million computer/video games soldin 2007 ~$9.5 BILLION in revenue (f...
Violence in Videogames > 50% of the most popular videogames are rated ‘T’ or ‘M’ Teen/Mature rated games Almost all hav...
Exposure to ViolentVideo GamesExposure related to: IncreasedAggressive behavior,Aggressive affect, andAggressive cogni...
Immersion as a MediatorA player’s sense of ‘presence’ in thegame Realism Effects more pronounced if game isrealistic Im...
Problem StatementLittle is known about how exposure to violent videogames is associated with: (a) seriously violentbehavio...
Characteristics of gamevideo players N=1,493 (video game players) 48% Female Mean age: 12.5 years (SE: 0.04) 79% White...
Game Playing Behavior Median # of days / week: 3-4 Median time playing/ day: 31-60 min Overall median exposure: 157 min...
Violent Video Game Play“When you play video, computer, orInternet games, how many showphysical fighting, shooting, orkilli...
Outcome Variables… Seriously Violent Behavior Behavior likely resulting in murder Aggravated assault; Robbery; Sexual...
Outcome Variables… Antisocial Behavior Breaking rules Threatening / fighting with people Burglary Animal cruelty Alp...
Outcome Variables… Delinquency Relational bullying; Physical aggression; Vandalism Manipulative/coercive behavior Al...
Potential EffectModifiers Realism The action in the games is like ‘real life.’ Identification The people in the games ...
ResultsVariable# Yes(N = 1,493)% ofSampleSerious Violent Behavior 89 6Antisocial Behavior 400 27Delinquency 1,028 69.5Viol...
Bivariate OddsControlling for participant age, sex, and incomeVariableViolentBehaviorAntisocialBehaviorDelinquencyViolent ...
Path Analysis model
Mediational Pathanalysis model
SummaryPlaying violent video games is common. Over a quarter of respondents reportplaying violent video games Weekly exp...
SummaryConsistent with previous literaturereporting associations between violentvideo games and aggression.. Frequent exp...
Limitations ofGuwM Data Data are cross-sectional Reliance on self-reports It is possible that: Children were monitored...
Implications Need to educate caregivers about thegrowing evidence for the impact ofviolent media / video games on serious...
Contact InformationDr. Merle HamburgerCenters for Disease Control and Preventionmhamburger@cdc.govThis research was suppor...
Toward a better understanding of the relation between violent videogame play and different types of antisocial behavior
Upcoming SlideShare
Loading in …5
×

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

149 views
87 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
149
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
1
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • 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.
  • Toward a better understanding of the relation between violent videogame play and different types of antisocial behavior

    1. 1. Toward A Better Understanding of the RelationBetween Violent Videogame Play and Different Typesof Antisocial BehaviorMerle Hamburger PhDaMichele Ybarra MPH PhDbJeffery Hall PhDaPhilip J Leaf PhDcMarie Diener-West PhDcaCenters for Disease Control; bInternet Solutions for Kids, Inc.;cJohns Hopkins School of Public HealthInternational Society for Research on Aggression,July 2008, Budapest, Hungary* Thank you for your interest in this presentation. Please note thatanalyses included herein are preliminary. More recent, finalizedanalyses may be available by contacting CiPHR for furtherinformation.
    2. 2. Growing up with Media(GuwM) Methodology Baseline data were collected August -September, 2006 1,588 households (one caregiver, onechild) were surveyed online Participants recruited from Harris Poll OnLine
    3. 3. GuwM Eligibility ADULT Be the most (or equally) knowledgeable of theyouth’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 6months English speaking
    4. 4. Harris Poll On Line HPOL is a double opt-in panel ofmillions of respondents. HPOL data are consistently comparableto data that has been obtained fromRDD telephone samples of generalpopulations when sampling andweighting is applied.
    5. 5. GuwM RR andWeighting Response rate was 26% Propensity scoring was applied Data were weighted to match the USpopulation of adults with childrenbetween the ages of 10 and 15 years
    6. 6. GuwM Youthcharacteristics (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 typicalday: 31 minutes – 1 hour
    7. 7. Why Video Games? Video games are BIG BUSINESS ~268 million computer/video games soldin 2007 ~$9.5 BILLION in revenue (for 2007) Approximately 60% youth (8-18) playvideo games for about an hour on anygiven day
    8. 8. Violence in Videogames > 50% of the most popular videogames 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’ ratedgames
    9. 9. Exposure to ViolentVideo GamesExposure related to: IncreasedAggressive behavior,Aggressive affect, andAggressive cognitions Decreased prosocial behavior
    10. 10. Immersion as a MediatorA player’s sense of ‘presence’ in thegame Realism Effects more pronounced if game isrealistic Immersion Effects more pronounced if playeridentifies with characters
    11. 11. Problem StatementLittle is known about how exposure to violent videogames is associated with: (a) seriously violentbehavior; (b) antisocial behavior; and (c)delinquency.What is the association between playing violent videogames and concurrent reports of externalizing behavior;To what extent does immersion mediate thisassociation?
    12. 12. Characteristics of gamevideo 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. 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. 14. Violent Video Game Play“When you play video, computer, orInternet games, how many showphysical fighting, shooting, orkilling?”Response alternatives:‘None’; ‘Some’; ‘Many’; ‘Most/All’
    15. 15. Outcome Variables… Seriously Violent Behavior Behavior likely resulting in murder Aggravated assault; Robbery; Sexual assault Alpha = 0.87
    16. 16. Outcome Variables… Antisocial Behavior Breaking rules Threatening / fighting with people Burglary Animal cruelty Alpha = 0.85
    17. 17. Outcome Variables… Delinquency Relational bullying; Physical aggression; Vandalism Manipulative/coercive behavior Alpha = 0.80
    18. 18. Potential EffectModifiers Realism The action in the games is like ‘real life.’ Identification The people in the games are ‘just like meor people I know’
    19. 19. ResultsVariable# Yes(N = 1,493)% ofSampleSerious Violent Behavior 89 6Antisocial Behavior 400 27Delinquency 1,028 69.5Violent Video Game 388 26.3Realistic 471 49.1Identification 185 19.4
    20. 20. Bivariate OddsControlling for participant age, sex, and incomeVariableViolentBehaviorAntisocialBehaviorDelinquencyViolent VideoGame Play1.92 (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. 21. Path Analysis model
    22. 22. Mediational Pathanalysis model
    23. 23. SummaryPlaying violent video games is common. Over a quarter of respondents reportplaying violent video games Weekly exposure significantly related toplaying violent video games
    24. 24. SummaryConsistent with previous literaturereporting associations between violentvideo games and aggression.. Frequent exposure to violent video gamesis concurrently associated with seriousexternalizing behaviors, Character identification is a mediator
    25. 25. Limitations ofGuwM 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 closeenough to see the screen during data collection Parents completed the youth survey.
    26. 26. Implications Need to educate caregivers about thegrowing evidence for the impact ofviolent media / video games on seriousexternalizing behaviors Character identification appears to beimportant, over and above the violencein the game itself.
    27. 27. Contact InformationDr. Merle HamburgerCenters for Disease Control and Preventionmhamburger@cdc.govThis research was supported by Cooperative Agreement numberU49/CE000206 from the Centers for Disease Control and Prevention(CDC). The findings and conclusions in this presentation are those ofthe authors and do not necessarily represent the official position of theCenters for Disease Control.

    ×