New Perspectives on Violent
Media Use in Adolescence:
Risk, Protection, and the
Moderation and Mediation of
Effects on Behavior
Symposium presented to the 2008 conference of the
Society for Research on Adolescence, Chicago, IL
Paul Boxer, Rutgers University and the University of Michigan
Merle E. Hamburger, Centers for Disease Control
Symposium CoCo-Chairs
Why new perspectives?
„
The current landscape of violent media looks
very different from how it once looked…
Violent characters in
the early days of the
mass visual media
Gunsmoke
Paladin
1
Why new perspectives?
„
The current landscape of violent media looks
very different from how it once looked…
Contemporary violent
characters in the
mass visual media
24
Bourne Identity
Why new perspectives?
„
The current landscape of violent media looks
very different from how it once looked…
Berzerk!
Characters from the
early days of violent
video games
Chuck Norris Superkicks
2
Why new perspectives?
„
The current landscape of violent media looks
very different from how it once looked…
Grand Theft Auto
Characters from
contemporary violent
video games
Bully
Some important gaps to fill
„ Examination
of mediation and moderation
of violent media effects during
adolescence, via demographic and
cognitive/personality factors
„ Expansion of methodology to incorporate
youth samples at varying levels of
developmental risk, with broader range of
behavioral outcomes
3
Papers included
„
Violent Media Effects on Violent and Nonviolent
Antisocial Behavior in Delinquent Adolescents
and High School Students
– Paul Boxer, L. Rowell Huesmann, Maureen J. O’
O’Brien,
and Dominic C. Moceri
„
The Relation of Violent Video Game Play to
Aggressive Behavior and Cognition in
Adolescence: Contextual and Personal
Moderators
– Brad J. Bushman, L. Rowell Huesmann, Craig A.
Anderson, Douglas A. Gentile, and Maureen J. O’
O’Brien
Papers included
„
Family Rules and Parental Monitoring in the
Association between Adolescents’ Violent Video
Game Play and Engagement in Violence
– Merle Hamburger, Michele Ybarra, Philip Leaf, and
Marie DienerDiener-West
„
Developmental and Sex Differences across
Adolescence in Playing Web-Based Violent Video
Games
– Eric F. Dubow,
Dubow, Jason A. Drummond, and Kelly M.
Lister
4
After the conference
„ Please
visit the web link below to download
PDF versions of today’s presentations…
http://rcgd.isr.umich.edu/aggr
5
Violent Media Effects on Violent and
Nonviolent Antisocial Behavior in Delinquent
Adolescents and High School Students
Paul Boxer
Rutgers University and University of Michigan
L. Rowell Huesmann, Maureen J. O’Brien, and Dominic C. Moceri
University of Michigan
Paper presented to the 2008 conference of the Society for Research on Adolescence, Chicago, IL
Acknowledgements
„
Cooperative agreement U49U49-CE000207 from the Centers
for Disease Control
– PI Rowell Huesmann (Michigan); CoCo-PIs Paul Boxer (Rutgers),
Brad Bushman (Michigan), Tom Johnson (Indiana State);
Consultants Craig Anderson (Iowa State), Doug Gentile (Iowa
State)
„
„
„
„
„
„
„
State of Michigan Department of Community Health
Michigan state juvenile detention centers: Maxey Boys
Training School and Adrian Girls Training School
Michigan county juvenile detention centers: Washtenaw
Children’
Children’s Center, Maurice Spear Lenawee County Center
Michigan schools: Milan, Flint, Redford
Aggression Research Program interviewer team
Andrea Kaye and Amelia Deschamps,
Deschamps, data processing
Participating youth, parents, teachers, and facility staff
1
Media Violence and Aggression
From Bushman & Anderson (2001), American Psychologist
The evidence base
„ Meta-analytic
and comprehensive
narrative reviews confirm media violence /
aggression link (Anderson et al., 2003;
Bushman & Huesmann, 2006)
„ Experimental and longitudinal research
suggest that violent media consumption
increases aggression and not vice versa
2
E.g., Oak Park Longitudinal Study, Huesmann, Moise-Titus, Podolski, & Eron, 2003
Where the evidence is lacking
„
Experimental research usually relies on analog
indicators (lab models) to estimate media effects
on behavior
– E.g., noise blasts, shocks
„
Field research usually relies on normative
samples with limited data on confirmed “high
aggressors”
– E.g., violent criminals, juvenile delinquents
„
Thus media effects research has been based on
limited range and scope of actual aggressive or
violent behaviors
3
Typical explanatory models
„
Media violence exposure accounts for short and
long term aggressive outcomes (Bushman &
Huesmann, 2006):
– Situational stimulus effects via arousal, cognitive
priming, and immediate modeling
– LongLong-term, generalized socialization and
desensitization effects via observational learning and
repeated exposures/arousals
„
Individual characteristics such as sex, trait
aggressiveness, and identification with
aggressive actors exacerbate violent media
effects (Anderson et al., 2003)
Goals of this research
„
Advance understanding of how exposure to violent
media relates to subsequent severe aggressive,
delinquent, and criminal behavior in adolescence.
„
Assess violent and nonviolent antisocial behavior in
samples of youth including individuals known to engage
in this behavior.
– Violent antisocial behavior: Acts that cause or have the potential
to cause serious injury.
– Nonviolent antisocial behavior: Antisocial acts that may or may
not be physically aggressive but fall short of causing serious
injury
„
Sample delinquent and normative populations to ensure
wider variability in measures of violent behavior.
4
Methods
„
CrossCross-sectional surveys of youth, their parents/guardians,
and their teachers/staff.
– Incarcerated adolescent delinquents
– High school students from communities of varying risk
„
Individual interviews for youth on computers or paper with
minimal opportunities for anyone to see their answers
„
Guided recall questions inquiring retrospectively about
media use at earlier times
„
Multiple selfself-report socialsocial-cognitive and behavioral
measures of aggression and violence
„
Teacher (staff) and parent reports about the youth’
youth’s
behaviors
Participants
„
Overall N = 820 (60% male; 45% ethnic/racial
minority)
– Juvenile delinquent sample (n = 390)
ƒ By sex: 287 males, 103 females
ƒ By ethnicity: 212 White, 105 Black/Af
Black/Af--Am, 73 other
ƒ Mean age = 15.55 years (SD = 1.53 years; range = 1010-20
years)
ƒ CrossCross-informant reports: 343 staff, 334 parents/guardians
– High school sample (n = 430)
ƒ
ƒ
ƒ
ƒ
By sex: 208 males, 222 females
By ethnicity: 232 White, 164 Black/Af
Black/Af--Am, 34 other
Mean age = 16.83 years (SD = .71 years; range = 1515-20 years)
CrossCross-informant reports: 373 teachers, 390 parents/guardians
– Total of 803 youth with at least one other informant
5
Participants
„ Demographic
differences in sampling by
population type:
– Significantly greater proportion of males in
the delinquent sample
ƒ Χ2 (1)=54.4, p < .001
– High school sample significantly older
ƒ t (536.6) = 15.19, p < .001 (corrected for variance inequality;
greater spread in delinquent group)
– No significant difference in sampling of
white/non-white youth
ƒ Χ2 (1)=.58, p = .81
Key Measures: Aggressive and Delinquent Behavior
Measure
Source/alpha/#
Source/alpha/# items
Sample item
Delinquency Scale (Elliott
& Huizinga, 1980)
Youth, a = .94 (22
items)
“How often since you have been a teenager have
you tried to steal a bike…
bike…?” (0 = never…
never… 4 = 5 or
more times)
BussBuss-Perry Aggression
Questionnaire – Phys Agg
(Buss & Perry, 1995)
Youth, a = .80 (8
items)
“If I have to resort to violence to protect my rights,
I will”
will” (0 = not at all true…
true… 4 = very true of me)
Severe Physical
Aggression Scale
(Lefkowitz et al., 1977)
Youth, a = .80 (4
items; logged)
“How often since you have been a teenager have
you threatened or cut someone with a knife or
threatened or shot at someone with a gun? (0 =
never…
never… 3 = 3 or more times)
Strengths & Difficulties
Questionnaire Conduct
Problems Scale (Goodman,
2000)
Parent, a = .86
Teacher/staff, a = (5
items)
“Often fights with other youth or bullies them”
them” (0 =
not true…
true… 2 = certainly true)
Predictions of Peer Noms
of Aggression (Huesmann
et al., 1994)
Teacher/staff, a = .94
(10 items)
“How many of this youth’
youth’s peers would say that
he/she is someone who pushes or shoves others?”
others?”
(0%... 100%)
Serious Aggressive and
Injurious Behavior
Survey (Huesmann et al.,
2004)
Parent, a = .87, kr20
= .86 (two 15 item
scales)
Teacher/staff, a = .82
(5 items)
“How often does this youth use a weapon against
another youth or an adult?”
adult?” (0 = never…
never… 4 = every
day) [parents add:] “Did this ever result in serious
injury?”
injury?” (yes/no)
6
Key Measures: Exposure to violent media
(Past [Childhood] & Current)
Measure
Computation
Television series violence
consumption
Mean violence ratings of three “favorite”
favorite”
television series
Videos/Films violence
consumption
Violence ratings of three “favorite”
favorite”
videos/movies on TV or in theater
Video game (including
computer) violence
consumption
Violence ratings of three “favorite”
favorite”
video/computer games
Total violent media
consumption
Average of violence scores for favorite TV
series, Video/Movie, and Video Games
Note: Media titles derived through self-report; violence ratings made by trained coders on
scale anchored by 0 (no violence) through 4 (frequent, intense visual interpersonal violence).
Validity of criterion measures: Differences by
sample, females only
Delinquents
High school students
N
M (SD)
N
M (SD)
p
Self
BussBuss-Perry
Severe physical
Delinquency
102
102
102
21.86 (8.87)
.35 (.17)
29.94 (20.35)
222
222
222
14.72 (7.46)
.20 (.14)
10.75 (10.23)
< .001
< .001
< .001
Teacher
Predictions peer noms
SDQSDQ-Conduct problems
Serious Aggression
92
91
92
.24 (.22)
3.11 (2.75)
.51 (.86)
192
192
189
.05 (.09)
1.06 (1.61)
.06 (.23)
< .001
< .001
< .001
Parent
Serious Aggression – Behavior
Serious Aggression – Injury
SDQSDQ-Conduct problems
92
77
91
.41 (.35)
1.56 (2.06)
5.91 (2.87)
200
81
196
.08 (.13)
.22 (.59)
1.44 (1.85)
< .001
< .001
< .001
Measures
7
Validity of criterion measures: Differences by
sample, males only
Delinquents
High school students
N
M (SD)
N
M (SD)
p
Self
BussBuss-Perry
Severe physical
Delinquency
285
284
284
20.40 (7.96)
.35 (.16)
32.16 (22.26)
207
204
206
17.50 (7.62)
.26 (.16)
15.41 (13.41)
< .001
< .001
< .001
Teacher
Predictions peer noms
SDQSDQ-Conduct problems
Serious Aggression
251
250
248
.16 (.18)
3.36 (2.43)
.33 (.72)
181
176
176
.07 (.12)
1.34 (1.89)
.07 (.26)
< .001
< .001
< .001
Parent
Serious Aggression – Behavior
Serious Aggression – Injury
SDQSDQ-Conduct problems
242
192
242
.32 (.37)
1.29 (1.74)
5.05 (2.53)
190
91
186
.08 (.12)
.21 (.51)
1.30 (1.67)
< .001
< .001
< .001
Measures
Cross-informant correlations among criterion
measures, females only
Measure
1
2
3
4
5
6
Self
1 BussBuss-Perry
2 Severe physical
3 Delinquency
.64**
.60**
.69**
-
Teacher
4 Predictions peer noms
5 SDQSDQ-Conduct problems
6 Serious Aggression
.38**
.38**
.29**
.29**
.33**
.34**
.41**
.40**
.38**
.77**
.58**
.66**
-
Parent
7 Serious Aggression – Behavior
8 Serious Aggression – Injury
9 SDQSDQ-Conduct problems
.29**
.33**
.36**
.27**
.32**
.31**
.31**
.29**
.43**
.38**
.32**
.41**
.34**
.40**
.42**
.24**
.27**
.32**
7
8
9
.66**
.75**
.50**
-
Cross-informant r values in gold.
8
Cross-informant correlations among criterion
measures, males only
Measure
1
2
3
4
5
6
Self
1 BussBuss-Perry
2 Severe physical
3 Delinquency
.59**
.53**
.70**
-
Teacher
4 Predictions peer noms
5 SDQSDQ-Conduct problems
6 Serious Aggression
.21**
.25**
.14**
.17**
.23**
.22**
.21**
.30**
.24**
.77**
.50**
.52**
Parent
7 Serious Aggression – Behavior
8 Serious Aggression – Injury
9 SDQSDQ-Conduct problems
-
.15**
.06
.27**
.14**
.12*
.25**
.10*
.09
.34**
.15**
.14*
.24**
.26**
.19**
.41**
.15**
.15*
.23**
7
8
9
.69**
.57**
.38**
-
Cross-informant r values in gold.
Measurement models of violent and nonviolent
antisocial behavior
SPAGL
youth
SAB
teacher
SAB
parent
VIOL
Chi-sq/df = .076
SAI
parent
RMSE = .000 (.000-.060)
Pclose = .920
CFI = 1.00
NFI = 1.00
9
Measurement models of violent and nonviolent
antisocial behavior
BPAG
youth
DELQ
youth
NVIOL
SDCP
parent
Chi-sq/df = 1.299
RMSE = .019 (.000-.065)
SDCP
teacher
Pclose = .835
CFI = .999
NFI = .997
PNOM
teacher
Validity of factor scores generated by
measurement model and regression imputation
„
Violent
Overall model F (3, 816) = 86.979,
p < .001, partial η2 = .242
0.05
0.04
0.03
0.02
0.01
Male
0
Female
-0.01
-0.02
-0.03
-0.04
Driven by substantial main effect of
sample, p < .001, partial η2 =
.227
Delq
HS
Modest sex by sample interaction (p
< .01, partial η2 = .015)
suggests Delq females > males,
reversed trend in HS students
All measurement modeling and imputation performed via AMOS version 7.0.
10
Validity of factor scores generated by
measurement model and regression imputation
„
Overall model F (3, 816) = 214.253,
p < .001, partial η2 = .441
Driven by substantial main effect of
sample, p < .001, partial η2 =
.413
Nonviolent
2.5
2
1.5
1
0.5
Male
0
Female
-0.5
Modest sex by sample interaction (p
< .05, partial η2 = .007)
suggests Delq females > males,
reversed trend in HS students
-1
-1.5
-2
-2.5
Delq
HS
Does exposure to violent
media account for serious
antisocial behavior?
11
Childhood and adolescent violent media preferences
predict violent and nonviolent antisocial behavior
VIOL
Variables/step
Step 1
β
NVIOL
Step 2
β
Step 1
β
Step 2
β
Step 1
Sex (0=female, 1=male)
Age in years
.133***
-.172***
-.002
-.168***
.176***
-.333***
.068+
-.330***
Step 2
Past media violence
Current media violence
(.19**)
(.12**)
.185***
.118**
(.16**)
(.14**)
.134***
.114***
.049***
(.067**)
.045***
.147***
(.064**)
.030***
R2 change for step
( ) = Media violence scores entered without sex/age controls.
Adolescent violent media preference predicts
delinquency status
Delinquent status (0 = HS, 1 = delinquent)
Step 1
OR
Step 1
CI
Step 1
Sex (0=female, 1=male)
Age in years
3.587***
.349***
2.5 - 5.1
.29 - .42
Step 2
Past media violence
Current media violence
(1.350*)
(1.714***)
(1.1 – 1.7) 1.150
(1.4 – 2.2) 1.374*
Variables/step
Step 2
OR
2.691***
.347***
Step 2
CI
1.8 – 4.0
.29 - .42
.86 – 1.5
1.1 – 1.8
( ) = Media violence scores entered without sex/age controls.
12
Does exposure to violent
media account for serious
antisocial behavior in the
presence of major risk
factors?
Key Measures: Other measured risk factors
Measure
Alpha
Sample item
Psychopathology –
Psychoticism and Depression
scales – Brief Symptom
Inventory (Derogatis,
Derogatis, 1994)
PSY a = .76 (5
items);
DEP a = .86 (6
items)
PSY: “How much have you been bothered by
the idea that someone else can control your
thoughts?”
thoughts?” .. DEP: “How much have you been
bothered by feeling no interest in things?”
things?” (0 =
not at all…
all… 4 = extremely)
Neighborhood violence
exposure, lifetime (Attar,
Guerra, & Tolan,
Tolan, 1994)
Kr20 = .75 (8
items)
“Have you seen anyone beaten, shot, or really
hurt?”
hurt?”
CallousCallous-unemotional (CU)
traits – Inventory of CU traits
(Frick, 2004)
a = .81 (24 items)
“I do not care if I get into trouble.”
trouble.” (0 = not at
all true…
true… 4 = definitely true)
Academic skills – Wide Range
Achievement Test, Arithmetic
subtest (Wilkinson, 1993)
NA
Basic addition/subtraction through algebra and
calculus
Note: All self-report.
13
Childhood/adolescent violent media preferences predict
violent behavior in the presence of other risk factors
VIOL
Variables/step
Step 1
Sex (0=female, 1=male)
Age in years
Step 1 β
.140***
-.164***
Step 2
BSIBSI-Psychoticism
BSIBSI-Depression
Neighborhood violence
CallousCallous-Unemotional traits
WRATWRAT-Arithmetic
Past media violence
Current media violence
R2 change for step
NVIOL
Step 2 β
-.010
-.099**
Step 1 β
.191***
-.325***
.247***
.105**
-.247***
-.007
.214***
.255***
.225***
-.148***
.064+
.050
.072
.109*
.264***
.204***
-.065*
.119**
.074*
.048***
Step 2 β
.147***
.255***
Violent media preferences do not predict
delinquency in the presence of other risk factors
Delinquent status (0 = HS, 1 = delinquent)
Variables/step
Step 1
Sex (0=female, 1=male)
Age in years
Step 2
BSIBSI-Psychoticism
BSIBSI-Depression
Neighborhood violence
CallousCallous-Unemotional traits
WRATWRAT-Arithmetic
Past media violence
Current media violence
Step 1
OR
3.849***
.349***
Step 1
CI
Step 2
OR
Step 2
CI
2.7 - 5.5
.29 - .42
5.070***
.336***
3.1 – 8.2
.28 - .41
1.043
1.088**
1.295**
1.271**
.956
.999
1.205+
.97 - 1.1
1.02 - 1.2
1.1 - 1.6
1.1 – 1.5
.94 - .97
.72 – 1.4
.97 – 1.5
14
Childhood violent media preferences predict violent
behavior while controlling nonviolent antisocial behavior
VIOL
Variables/step
Step 1
Sex (0=female, 1=male)
Age in years
Step 1 β
.140***
-.164***
Step 2
NVIOL
Past media violence
Current media violence
R2 change for step
Step 2 β
-.052
.073**
.730***
.088**
.035
.048***
.533***
Does exposure to violent
media account for socialcognitive factors linked to
serious antisocial behavior?
15
Key Measures: Social-cognitive variables
Measure
Alpha
Sample item
Normative beliefs
about aggression
(Huesmann & Guerra,
1997)
a = .91 (20
items)
“In general, is it OK for kids your age
to take their anger out on others
using physical force?”
force?” (1 = really
wrong…
wrong… 4 = perfectly okay)
“Do you sometimes have daydreams
about hitting or hurting somebody
you don’
don’t like?”
like?” (0 = never…
never… 3 =
often)
Aggressive fantasies
a= .81 (4
(Huesmann & Eron,
Eron, 1986) items)
Note: All self-report.
Correlations among social-cognitive variables
and criterion composites
Measure
1
2
SocialSocial-cognitive
1 Normative beliefs
2 Aggressive fantasy
-.34**
--
Criterion
3 Violent behavior
4 Nonviol behavior
.27**
.33**
.39**
.39**
3
4
-.72**
--
16
Childhood/adolescent violent media preferences predict
social-cognitive beliefs in adolescence
NormBel
Variables/step
Step 2
Β
Step 1
Β
Step 1
Sex (0=female, 1=male)
Age in years
.083*
-.073*
Step 2
Past media violence
Current media violence
R2 change for step
AggFant
-.003
-.073*
Step 1
Β
.055
-.003
.197***
-.025
.013**
.029***
Step 2
β
-.052
.002
.149***
.095*
.003
.029***
Normative beliefs mediate effect of past media violence
exposure on current non-violent antisocial behavior
Normative
beliefs
.273***
.187***
Past media
violence
.151***
(.090*)
NVIOL
( ) = Effect after inclusion of mediator. Sex and age controlled. Indirect effect is
significant at p < .001.
17
Normative beliefs mediate effect of past media violence
exposure on current violent antisocial behavior
Normative
beliefs
.216***
.187***
Past media
violence
.207***
(.164***)
VIOL
( ) = Effect after inclusion of mediator. Sex and age controlled. Indirect effect is
significant at p < .001.
Aggressive fantasies mediate effect of past media violence
exposure on current non-violent antisocial behavior
Aggressive
fantasies
.375***
.162***
Past media
violence
.151***
(.088*)
NVIOL
( ) = Effect after inclusion of mediator. Sex and age controlled. Indirect effect is
significant at p < .001.
18
Aggressive fantasies mediate effect of past media violence
exposure on current violent antisocial behavior
Aggressive
fantasies
.360***
.162***
Past media
violence
.207***
(.160***)
VIOL
( ) = Effect after inclusion of mediator. Sex and age controlled. Indirect effect is
significant at p < .001.
Does belief in the realism of
the entertainment media
and identification with
violent media characters
exacerbate the effect of
violent media preference on
violent behavior?
19
Key Measures: Moderator variables
Measure
Alpha
Sample item
Realism of fictional
media content – past
(Huesmann & Eron,
Eron, 1986)
a = .91 (9
items across
three media
types)
“How realistic do you think ___ was
in telling about what life is really
like?”
like?” (0 = not at all…
all… 2=just like it)
Identification with
aggressive media
characters – past
(Huesmann & Eron,
Eron, 1986)
a = .90 (9
items across
three media
types)
“How much do you wish you were
like or really are like ___ from the
show ___?”
___?” (0 = not at all…
all… 2=a lot
like)
Note: All self-report.
Correlations among moderator variables, criterion
composites, and media violence preferences
Measure
1
2
Moderators
1 Media realismrealism-past
2 Media char IDID-past
-.47**
--
Criterion
3 Violent behavior
4 Nonviol behavior
.16**
.16**
-.03
.00
Media violence
5 Past
6 Current
3
4
.13**
.17**
-.72**
--
.11**
.03
.23**
.18**
.21**
.20**
5
-.34**
20
Media realism interacts with past media
violence to predict violent behavior
NVIOL
VIOL
Variables/step
Step 1
β
Step 1
Sex (0=female, 1=male)
Age in years
.134***
-.142***
Step 2
Media realismrealism-past
Past media violence
Realism by media violence
R2 change for step
Step 2
β
.053
-.134***
Step 1
β
Step 2
β
.173***
-.314***
.128**
-.307***
.166***
.203***
.086*
.040***
.069***
.173***
.129**
.062+
.134***
.048***
Belief that violent media “told about life like it
really was” increases effect of violent media
preference on violent behavior
0.06
0.05
0.04
0.03
0.02
VIOL 0.01
0
-0.01
-0.02
-0.03
-0.04
Low MedReal
High MedReal
Low MedVio
High
MedVio
High = > 1 SD above mean; Low = < 1 SD below mean
21
Identification with violent media characters only produces
main effects on violent and nonviolent antisocial behavior
NVIOL
VIOL
Variables/step
Step 1
β
Step 1
Sex (0=female, 1=male)
Age in years
.130***
-.147***
Step 2
Media char identification
Past media violence
Identif by media violence
R2 change for step
Step 2
β
.027
-.150***
Step 1
β
Step 2
β
.171***
-.316***
.096*
-.320***
.111**
.197***
.022
.041***
.046***
.157***
.125**
.013
.135***
.039***
Conclusions
„
Media violence preferences are related to violent
as well as nonviolent antisocial behavior
– Relying on a valid crosscross-informant composite
„
These effects can be observed in isolation and in
the context of other major risk factors for
aggression/violence
„
These effects maintain even when predicting
violence and controlling non-violent antisocial
behavior
22
Conclusions
„
Findings are consistent with theoretical
formulations and previous empirical findings in
more normative samples
– SocialSocial-cognitive factors partially mediate association
between childhood media violence preference and
adolescent violent and nonnon-violent antisocial behavior
– Childhood belief in the realism of violent media
amplifies the relation between childhood media
violence preferences and violent behavior
Limitations
„ Cross-sectional
methods limit inferences
regarding causality and developmental
processes
„ Extensive
but still only partial assessment
of other important risk factors for
aggression
– E.g., family violence, peer antisocial behavior
23
Future directions
„ Incorporate
analysis of other basic
potential moderators of effects (e.g., sex,
developmental level, race/ethnicity)
„ This
study sets a basis for examining
media effects in high-aggressive youth
populations using designs permitting
greater causal inference
– E.g., experimental and longitudinal research
with juvenile delinquent samples
Thank you for your time.
Inquiries:
pboxer@psychology.rutgers.edu
24
AGGRESSION RESEARCH GROUP
The Relation of Violent Video Game Play to
Aggressive Behavior and Cognition in
Adolescence
Brad Bushman, University of Michigan
Rowell Huesmann, University of Michigan
Craig Anderson, Iowa State University
Doug Gentile, Iowa State University
Maureen O’Brien, University of Michigan
Dominic Moceri, University of Michigan
Paul Boxer, University of Michigan & Rutgers University
Presented by Rowell Huesmann at the meetings of the
Society for Research on Adolescence, March 2008.
Acknowledgements
z
z
z
z
z
z
z
NICHD Grant, “Video Game Violence” Brad Bushman
(PI), Huesmann, Boxer, Anderson & Gentile (Co-PIs)
Shirley Huck, Research Associate, Iowa State
Oksana Malanchuk, Research Associate, U of M
Andrea Kaye, Data Processing Assistant, U of M
Amelia Deschamps, Data Processing Assistant, U of M
The Video Game Violence project interviewing staffs, U
of M and Iowa State
The many schools and teachers and staff in Michigan
and Iowa that are participating
Current Research Goals
z
The overall goal of the current research is
to advance
– 1) our understanding of the extent to which
playing violent video games on a daily basis is
related to aggressive behavior and cognitions,
– 2) our understanding of what moderates these
relations, and
– 3) our understanding of what psychological
processes produce these relations.
THEORY: How does observing violence
increase the risk of violent behavior?
(Huesmann, 2003, Developmental Psychology; Huesmann & Kirwil, 2007,
Cambridge Handbook of Violence; Bushman & Huesmann, 2006, Archives
of Pediatrics & Adolescent Medicine)
z
Situational Stimulating Processes (short term)
– 1) By priming aggressive schemas, scripts, and
beliefs.
– 2) By increasing arousal which may be
misattributed to something else
– 3) Because viewers copy ("mimic") behaviors they
see
z
Observational Learning Processes (long term)
– 1) Through the encoding ("imitation") of schemas,
scripts, and beliefs promoting aggression.
– 2) By desensitizing viewers emotionally to violence
THEORY: How does playing violent
games increase risk of violent behavior?
(Huesmann, 2003, Developmental Psychology; Huesmann & Kirwil, 2007,
Cambridge Handbook of Violence; Bushman & Huesmann, 2006, Archives
of Pediatrics & Adolescent Medicine)
z
z
z
Same Situational Stimulating Processes (short
term)
Same Observational Learning Processes (long
term)
Conditioning processes (long term)
– Instrumental conditioning of behaviors and scripts
for aggression
Overview of Current Study
z
A multiple cohort, three wave longitudinal study of
second to eleventh-grade children in urban, suburban,
and rural schools in Michigan and Iowa
z
Initial cohorts of 2nd, 4th, and 9th graders each
interviewed three times at one year intervals
Individual hour long in person interviews for young
children and group interviews for older children.
Teacher assessments of children’s behaviors
Mail and phone interviews of parents about children’s
behaviors and family characteristics
As of March 2008 data are ready to analyze only for
Wave 1 and only for 9th grade cohorts
z
z
z
z
Interview Schedule
Wave 1
2006
Birth Coh
M=Mich
I = Iowa
Spring
1992-M
9th
1993-M
1997-M
4th
Spring Fall
10th
11th
10th
5th
4th
2nd
2000-M
Wave 3
2008
2009
Spring Fall
9th
1998-M
1999-M
Fall
Wave 2
2007
11th
6th
5th
3rd
2nd
Spring
6th
4th
3rd
4th
1991-I
9th
10th
11th
1996-I
4th
5th
6th
1998-I
2nd
3rd
4th
Wave 1 Sample Sizes
2nd
4th
9th
M
F
M
F
M
F
Mich
164
132
181
179
169
143
Iowa
78
70
82
72
79
66
Total
242
202
263
251
248
209
444
514
457
Specific Aims Today
z
z
Examine the relations within the 9th grade Wave 1
data between playing violent video games and
aggressive cognitions and behavior
Examine what moderates the relations
– Demographics
• Gender? Urban/Suburban/Rural? SES?
– Perceptions of violent games?
• Perception of realism? Identification with character?
– Neighborhood violence?
– Personality?
• Psychopathy?
– Parents’ aggression?
– Parenting behaviors?
• Monitoring, discipline & rejection?
Key Measures: Aggressive and Delinquent Behavior
Measure
Computed
Sample item
Delinquency Scale
(Elliott & Huizinga, 1980)
Sum of 22
items
“How often have you tried to steal a
bike…?” (0 = never… 3 = more than
twice)
Buss-Perry Aggression
Questionnaire – Phys
Agg (Buss & Perry, 1995)
Sum of 8
items
“If I have to resort to violence to
protect my rights, I will” (0 = not at all
true… 4 = very true of me)
Severe Physical
Aggression Scale
(Lefkowitz et al., 1977)
Ave of 4
items
“How often have you threatened or
cut someone with a knife or
threatened or shot someone with a
gun? (0 = never… 3 = 3 or more)
Mild Aggression
(Bjorkqvist, et al., 1991)
Ave of 9
items
“How often do you hit other kids?
How often do you call others names?
Teachers Predictions of
Peer-nominated Agg
(Huesmann et al., 1994)
Ave of 10
items
“What percentage of students would
say that this child ‘is someone who
pushes or shoves others?’ “
Delinquency Scale
How often have you:
Never
(a)
Once
(b)
Twice
(c)
More
than
twice
(d)
Thrown rocks or bottles at
people?
0
1
2
3
Sprayed graffiti on walls,
sidewalks, or cars?
0
1
2
3
Stolen or tried to steal a bike,
skateboard, or rollerblades?
0
1
2
3
Been involved in gang fights as
a gang member?
0
1
2
3
Intentionally damaged or
destroyed property that did not
belong to you?
0
1
2
3
Taken something from a store
without paying for it?
0
1
2
3
ETC……………….
0
1
2
3
Severe Physical Aggression
Now we would like to ask you how often you have done certain things.
A few
A lot
times
(d)
(c)
Never
(a)
Once
(b)
How often have you slapped or kicked
someone?
0
1
2
3
How often have you punched or beaten
someone?
0
1
2
3
How often have you choked someone?
0
1
2
3
How often have you threatened or
actually cut another person with a knife or
threatened or shot at another person with
a gun?
0
1
2
3
Key Measures: Aggressive Cognitions
Measure
Computed
Sample item
Normative Beliefs
Approving Aggressive
Retaliation (Huesmann
& Guerra, 1997 )
Sum of 8
items
“ Suppose a boy says something bad
to another boy, John. Do you think it
is OK for John to hit the boy? ( 4 =
perfectly OK, 3 = sort of OK, 2 = sort
of wrong, 1 = really wrong)
Aggressive Fantasy
(Rosenberg, Huesmann,
et al., 1983)
Sum of 4
items
“When you get mad, sometimes do
you daydream about the things you
would like to do to the person your
mad at, like hitting, damaging their
things, or getting them into trouble?”
(0 = never, 1 = sometimes, 3 = lots of
times)
Hostile Attributional
Bias (Based on Dodge et
al.)
Ave of 2
items
“ Pretend a kid knocks into you, and
you drop your books. Why do you
think the kid knocked into you?” (3 =
‘because the kid disliked you and
wanted to be mean to you’)
Key Measures: Video Game Violence
Measure
Computation
Video game (including
computer) violence
consumption
Ave violence ratings of three “favorite”
video/computer games multiplied by
“how often played.”
Realism of violent video
games
Ave rating of “how true to life” three
specific violent games seem to be to the
subject
Identification with violent
video game characters
Ave “identification” of subject with
protagonist in three specific violent
games
Questions for Video Game
Violence Exposure
By video games we mean any game you play on the computer, on video game
consoles (such as PlayStation 2, GameCube, Xbox), on hand-held game devices
(such as GameBoy, Nintendo DS, Playstation Portable (PSP)), or in video arcades.
What is your favorite video game? _________________________
How often do you play it?
Only once in a while =0, A lot, but not always =2, Almost all the time =4
What is your next favorite video game? __________________________
How often do you play it?
Only once in a while =0, A lot, but not always =2, Almost all the time =4
What is your next favorite video game? __________________________
How often do you play it?
Only once in a while =0, A lot, but not always =2, Almost all the time =4
Questions for Realism of Violent Video
Games and Identification with
Protagonists of Violent Video Games
Have you played “Grand Theft Auto?” (If not, go to next program)
How much do you think the video game “Grand Theft Auto” tells about what life
is really like?
‘Not at all like it is’=1, ‘A little bit like it really is’=2, ‘Just like it really is’=3
How much do you think or wish you were like the person or character you
pretend to be in the game “Grand Theft Auto?”
‘Not at all like the person’= 1, ‘A little like the person’=2, ‘A lot like the
person’=3
REPEAT FOR GAMES:
Mortal Combat
James Bond/007
Correlations between 9th graders’ Exposure to Video
Game Violence and their Aggressive /Delinquent
Behavior and Cognitions
Aggression Measure
Delinquent Behavior
Buss-Perry Phys Aggression
Severe Phys Aggression
Mild Aggression
Tch Pred Peer-nom Aggression
Norm Beliefs Approving Agg
Fantasizing about Aggression
Hostile Attributional Biases
Video Game Viol Exposure
All
Female
Male
(n=209)
(n=248)
(n=457)
.16*
.24***
.31***
.16*
.26***
.35***
.21**
.24***
.35***
.29***
.21**
.22***
.12ns
--.15**
.29***
.20***
---
.34***
.27***
.22**
.23***
.15*
---
+p<.10, *p<.05, **p<.01, ***p<.001
Percent of High VG Violence Playing Youth who
have “punched or beaten someone”
Violent
Video
Game
Playing
Hi
(>75th
%tile)
Females
Not Sig
Hi
Diff?
Hi
(>75th
%tile)
Males
Not Sig
Hi
Diff?
75% 47% P<.03 71% 59% P<.065
Percent of High VG Violence Playing Youth who
have “choked someone”
Violent
Video
Game
Playing
Hi
(>75th
%tile)
Females
Not Sig
Hi
Diff?
Hi
(>75th
%tile)
Males
Not Sig
Hi
Diff?
50% 18% P<.006 41% 26% P<.018
Percent of High VG Violence Playing Youth who
have “threatened or actually cut another person
with a knife or threatened or actually shot at
another person with a gun”
Violent
Video
Game
Playing
Hi
(>75th
%tile)
38%
Females
Not Sig
Hi
Diff?
Hi
(>75th
%tile)
Males
Not Sig
Hi
Diff?
10% P<.008 16% 15% n. s.
Correlations between 9th graders’ Exposure to Video
Game Violence and their Aggressive /Delinquent
Behavior and Cognitions
Aggression Measure
Delinquent Behavior
Buss-Perry Phys Aggression
Severe Phys Aggression
Mild Aggression
Tch Pred Peer-nom Aggression
Norm Beliefs Approving Agg
Fantasizing about Aggression
Hostile Attributional Biases
Video Game Viol Exposure
Suburb
Rural
Urban
(n=108)
(n=145)
(n=204)
.17*
.31**
.22**
+
.13
.28**
.38***
.13+
.33***
.29***
.10
.26*
.32***
--.32***
.27**
.11
.15+
---
.32***
.21*
---
.48***
.22**
.14+
+p<.10, *p<.05, **p<.01, ***p<.001
MEAN Scores on Exposure to Video Game Violence and
Aggressive /Delinquent Behavior and Cognitions
for 9th grade Urban, Suburban & Rural Youth
Aggression Measure
Urb
Subur
Rural
(n=204) (n=108) (n=145)
F
test
Delinquent Behavior
5.57
4.05
3.70
5.57**
Buss-Perry Phys Aggression
17.1
12.8
12.1
21.3***
Severe Phys Aggression
0.92
0.64
0.53
20.7***
Mild Aggression
0.88
0.84
0.74
5.18**
Tch Pred Peer-nom Aggression
0.11
0.09
0.09
n.s.
Norm Beliefs Approving Agg
19.3
17.5
17.4
5.89**
Fantasizing about Aggression
2.98
2.24
2.08
9.6***
Exposure to Video Game Viol
10.8
10.2
7.05
4.57*
+p<.10, *p<.05, **p<.01, ***p<.001
Predicting Serious Physical Aggression from Violent Video
Game Playing while Controlling for Household Income
FEMALES
(n=209)
Variables/step
Step 1
Violent VG Play
Step 1 Step 2 Step 3 Step 1 Step 2 Step 3
β
β
β
β
β
β
.40***
Step 2
Household Inc
.31***
.26***
-.28***
-.30***
Step 3
Viol VG Play X
Household Inc
R2 change for step
MALES
(n=248)
.19**
.17**
.17*
-.20***
-.20**
-.10
.161*** .231*** .238***
n.s.
.035*
.073*** .075***
Predicting Serious Physical Aggression from Violent Video
Game Playing while Controlling for Neighborhood Violence
FEMALES
(n=209)
Variables/step
Step 1
Violent VG Play
Step 2
Neighbor Viol
Step 3
Viol VG Play X
Neighbor Viol
R2 change for step
MALES
(n=248)
Step 1 Step 2 Step 3 Step 1 Step 2 Step 3
β
β
β
β
β
β
.35***
.23**
.23**
.39***
.38***
n. s.
.21**
.18**
.18**
.24***
.24***
n. s.
.119*** .256*** .257*** .043** .099*** .099***
Predicting Serious Physical Aggression from Violent Video
Game Playing while Controlling for Identification with
Protagonists in Violent Video Games
FEMALES
(n=209)
Variables/step
Step 1
Violent VG Play
Step 1 Step 2 Step 3 Step 1 Step 2 Step 3
β
β
β
β
β
β
.37***
Step 2
Ident w protag
.22**
.18+
.35***
.33***
Step 3
Viol VG Play X
Ident w protag
R2 change for step
MALES
(n=248)
.20**
.17*
.18**
.20**
.21**
n. s.
n. s.
.136*** .233*** .237*** .042** .080*** .083***
Predicting Serious Physical Aggression from Violent Video
Game Playing while Controlling for Perceived Realism of
Violent Games (“Telling about life like it is.”)
FEMALES
(n=209)
Variables/step
Step 1
Violent VG Play
Step 2
Perceived Real
Step 3
Viol VG Play X
Perceived Real
R2 change for step
MALES
(n=248)
Step 1 Step 2 Step 3 Step 1 Step 2 Step 3
β
β
β
β
β
β
.36***
.31***
.31***
.24**
.23**
n.s.
.20**
.18*
.17**
.13*
.14*
n.s.
.132*** .184*** .191*** .042** .059*** .062***
Predicting Serious Physical Aggression from Violent Video
Game Playing while Controlling for Psychopathy of Youth
FEMALES
(n=209)
Variables/step
Step 1
Violent VG Play
Step 1 Step 2 Step 3 Step 1 Step 2 Step 3
β
β
β
β
β
β
.35***
Step 2
Psychopathy
.29***
.29***
.31**
.29**
Step 3
Viol VG Play X
Psychopathy
R2 change for step
MALES
(n=248)
.21**
.19*
.19**
.29***
.29***
n. s.
n. s.
.119*** .212*** .218*** .043** .126*** .130***
Predicting Serious Physical Aggression from Violent Video
Game Playing while Controlling for Parent’s Aggression
FEMALES
(n=209)
Variables/step
Step 1
Violent VG Play
Step 2
Parent’s agg
Step 3
Viol VG Play X
Parent’s agg
R2 change for step
MALES
(n=248)
Step 1 Step 2 Step 3 Step 1 Step 2 Step 3
β
β
β
β
β
β
.40***
.38***
.37***
.19*
.19*
.18**
.19*
.18**
.11
n. s.
n. s.
.157*** .191*** .197***
n. s.
.031*
.042*
.044*
Predicting Serious Physical Aggression from Violent Video
Game Playing while Controlling for Parent’s “Monitoring” of
the Adolescent
FEMALES
(n=209)
Variables/step
Step 1
Violent VG Play
Step 1 Step 2 Step 3 Step 1 Step 2 Step 3
β
β
β
β
β
β
.41***
Step 2
Monitoring
.37***
.40***
-.17*
-.16*
Step 3
Viol VG Play X
Monitoring
R2 change for step
MALES
(n=248)
.17*
.17*
.17*
n. s.
n. s.
n. s.
.166*** .189*** .193***
n. s.
.029*
.031+
.035
Predicting Serious Physical Aggression from Violent Video
Game Playing while Controlling for Parent’s “Success at
Disciplining” the Adolescent
FEMALES
(n=209)
Variables/step
Step 1
Violent VG Play
Step 2
Disciplining
Step 3
Viol VG Play X
Disciplining
R2 change for step
MALES
(n=248)
Step 1 Step 2 Step 3 Step 1 Step 2 Step 3
β
β
β
β
β
β
.40***
.38***
.37***
-.12
-.11
.18*
.18*
.18*
-.12+
-.14+
n. s.
.157*** .170*** .172***
n. s.
.031*
.046*
.051*
Predicting Serious Physical Aggression from Violent Video
Game Playing while Controlling for “Rejection of Youth” by
Parent
FEMALES
(n=209)
Variables/step
Step 1
Violent VG Play
Step 2
Rejection
Step 3
Viol VG Play X
Rejection
R2 change for step
MALES
(n=248)
Step 1 Step 2 Step 3 Step 1 Step 2 Step 3
β
β
β
β
β
β
.40***
.39***
.40***
.13+
.14+
.18*
n. s.
.157*** .174*** .175***
.16*
.15**
.17*
.16*
.17*
.031*
.058** .087***
Summary
z
z
z
z
z
Habitual playing of violent video games by
adolescents is related to engaging in more
aggressive, delinquent and violent behavior and
being more accepting of aggression.
This effect is somewhat bigger for females than
males.
This effect appears bigger in suburban and rural
adolescents than in urban adolescents, but that
finding may simply be due to the much higher rates
of aggression and violence by urban youth.
These effects seem to be mostly independent of
parent SES, youth’s perception of protagonist,
psychopathic tendencies, neighborhood, parenting
practices, etc.
However, these effects seem to be exacerbated for
males when the adolescent is “rejected” by his
parents.
THEORY: How does age moderate the
effects of media violence on
aggression?
(Huesmann & Kirwil, 2007, Cambridge Handbook of Violence; Bushman &
Huesmann, 2006, Archives of Pediatrics & Adolescent Medicine)
z
z
Short-term effects should be stronger for people with
already established aggressive scripts and cognitions
that can be primed by violent games or scenes and
accessed when person is aroused. Generally these will
be older youth.
Long-term effects should be stronger for youth who do
not have established aggressive scripts and
cognitions. Generally these will be younger youth
Short-Term (Lab) and Long-Term Effects by Age
(Bushman & Huesmann, 2006, Archives of Pediatrics & Adolescent Medicine)
Children
Adults
Correlation
0.3
0.2
0.1
0
Lab
Longitudinal
Study Type
The Association Between
Playing Violent Video Games
and Adolescent’s Concurrent
Reports of Aggression
Mediating Effects of Family Rules
and Parental Involvement
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.
AUTHORS
Merle Hamburger PhDa
Michele Ybarra MPH PhDb
Jeffery Hall PhDa
Philip J Leaf PhDc
Marie Diener­West PhDc
aCenters
for Disease Control; bInternet Solutions for
Kids, Inc.; cJohns Hopkins School of Public Health
1
Why Video Games?
Why Video Games?
„ Video games are BIG BUSINESS
„ ~241 million computer/video games sold
in 2006
„ ~$7.4 BILLION
„ Approximately 60% youth (8-18) play
video games for about an hour on any
given day
2
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
Exposure to Violent
Video Games
„ Exposure related to :
„ Increased
Aggressive behavior,
z Aggressive affect, and
z Aggressive cognitions
z
„ Decreased
prosocial behavior
3
Effect Size of Exposure to Violent
Video Games and Aggression in
Best Practices Studies
0.4
0.35
Viol Video Game
(best pract) vs
Aggression
0.3
0.25
0.2
0.15
0.1
0.05
0
Effect Size of Various Scientifically
Studied Public Health Issues
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Viol Video Game
(best pract) vs
Aggression
Cigarette
Smoking vs Lung
Cancer
School grades vs
Income
Child IQ vs Lead
Exposure
Laryngeal cancer
vs Asbestos
Exposure
4
Problem Statement
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 seriously aggressive or
criminal violence.
Research Questions
„ What is the association between self-
reported use of violent video games and
concurrent reports of perpetrating
seriously violent behavior; and
„ To what extent do various factors
mediate the effects of exposure to
violent video games?
5
Growing up with Media
(GuwM) Methodology
„ Wave 1 of data collection occurred
between August 24 to September 14, 2006
„ Participants recruited from Harris Poll On
Line
„ 1,588 households (one caregiver, one
child) were surveyed online
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.
6
GuwM Eligibility
„ ADULT
„
Be the most (or equally) knowledgeable of
the youth’s media use in the home
„ YOUTH
„ Aged 10-15 years
„ Use the Internet at least once in the last 6
months
„ English speaking
GuwM Data Methods
„ Sample selection was stratified based on
youth age and sex.
„ Sample was also stratified between “novice”
and “experienced” survey participants.
7
GuwM Response Rate
„ Response rate was 26% (typical rates
using online data collection)
„ Propensity scoring was applied to adjust
for the adult’s (i.e., recruitment target)
propensity to be online
„ Data were weighted to match the US
population of adults with children
between the ages of 10 and 15 years
Youth Demographic
Characteristics
„ N=1,496 (video game players)
„ 46% Female
„ Mean age: 12.4 years (SE: 0.05)
„ 79% White, 13% Black, 8% Other
„ 12% Hispanic
„ Median HH income: $50,000-$74,999
8
Seriously Violent Behavior
(α=.90)
„ Behavior likely resulting in murder (i.e., stabbing or
„
„
„
„
shooting someone);
Aggravated assault (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)
Serious property crime (i.e., purposely starting fire to
damage or destroy something)
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’
9
Potential Effect
Modifiers
„ Family rules about video game play
„ Asking
before playing game; not
playing games until after chores done
„ Limits placed on video game play
„ Limited time; limits on game type /
rating
„ Kid’s following family rules / limits
„
Self-reported following rules
Potential Effect
Modifiers (cont)
„ Talking about game content with parent
„ Self-reported frequency
„ Parental monitoring
„ Self-reported frequency of parent knowing
where youth is and who they are with
„ Getting along with parent
„ Self reported frequency
10
Statistical Analyses
„ Logistic regression: Initial test of
mediating effects (SPSS v15)
„ Path analysis: To further test a
mediational measurement model
(AMOS v7)
Results
„ ~6% (n = 88) reported engaging in at
least 1 type of seriously violent behavior
„ No
sex differences
„ Violent video game playing
„ None/Almost None/Some: 73%
„ Many / Most / All : 27%
11
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
Odds of Seriously
Violent Behavior
10
3
1.9
1
Some Violent
Video Game
Most/All Violent
Video Game
Controlling for participant age, sex, and income
12
Adjusted Odds of Reporting
Seriously Violent Behavior
Some VG
Violence
Most/All VG
Violence
Potential
Mediator
AOR (95% CI)
AOR (95% CI)
AOR (95% CI)
Factor
Family Rules
1.9 (1.00-3.65)
2.9 (1.44-5.69)
1.5 (0.94-2.36)
Parental Limits
2.0 (1.02-3.75)
2.8 (1.38-5.48)
2.0 (1.26-3.17)
Follow Rules
2.0 (0.94-4.18)
3.0 (1.37-6.77)
2.0 (1.21-3.44)
Parental Monitoring
1.7 (0.89-3.29)
2.5 (1.24-4.93)
3.2 (1.92-5.24)
Discuss Video Game
2.0 (1.05-3.88)
2.8 (1.39-5.50)
2.0 (1.12-3.73)
Get Along with Parent
1.8 (0.91-3.52)
3.0 (1.46-5.99)
1.6 (1.01-2.60)
Detecting Mediation:
Only Indirect Paths
0,
e1
1
Potential
Mediator
0,
e2
1
Categorical
Violence
Violent Video
Game Play
Age
Male
Income
13
Detecting Mediation:
Both Direct & Indirect Paths
0,
e1
1
Potential
Mediator
0,
e2
1
Categorical
Violence
Violent Video
Game Play
Age
Male
Income
Family Rules about
Video Games
e1
*p<.05; **p<.01; ***p<.001
No
-.13 ***
Familiy Rules
about Video
Games
ta
Violent Video
Game Play
e2
M
ed
-.04
Categorical
Violence
.09 ***
iat
or
.05 *
Δχ2=10.4, df=1, p<.002
Age
.01
Male
.00
Income
14
Parental Limits
Regarding Video Games
e1
Pa
r
-.11***
Violent Video
Game Play
*p<.05; **p<.01; ***p<.001
Parental Limits
on Video
Games
tia
lM
ed
iat
e2
-.06 *
.09***
.05*
Δχ2=9.8, df=1, p<.01
Age
Categorical
Violence
or
.01
.01
Male
Income
Following Family Rules
about Video Games
e1
Pa
r
-.19 ***
Violent Video
Game Play
*p<.05; **p<.01; ***p<.001
Following
Family Rules
tia
lM
ed
iat
e2
-.08**
.08 ***
.06*
Δχ2=8.4, df=1, p<.01
Age
Categorical
Violence
or
.01
Male
.01
Income
15
General Parental
Monitoring
e1
Pa
r
-.21 ***
Violent Video
Game Play
*p<.05; **p<.01; ***p<.001
Parental
Monitoring
tia
lM
ed
iat
e2
-.13 ***
.08**
.04
Δχ2=7.6, df=1, p<.01
Age
Categorical
Violence
or
-.01
Male
.01
Income
Discussing Video Game
Content with Parents
e1
Pa
r
-.09 ***
Violent Video
Game Play
*p<.05; **p<.01; ***p<.001
Kids discuss
VideoGames
with Parents
tia
lM
ed
iat
e2
-.07 **
.09***
.06*
Δχ2=10.2, df=1, p<.01
Age
Categorical
Violence
or
.01
Male
.00
Income
16
Getting Along
with Parents
e1
*p<.05; **p<.01; ***p<.001
No
-.09 ***
Get along
w/parent
ta
Violent Video
Game Play
e2
M
ed
-.05
Categorical
Violence
.10***
iat
or
.06*
Δχ2=11.1, df=1, p<.001
.00
Age
Male
.01
Income
Summary
„ Serious violent behavior relatively rare
„ Over a quarter respondents playing
violent video games
„ Weekly
exposure significantly related to
playing violent video games
„ Engaging in serious violent behavior related
to playing violent video games
17
Identified (Partial)
Mediators
„ Setting limits
„ Following rules
„ Discussing video game content
„ Parental monitoring
Limitations of
GuwM Data
„ Data are cross-sectional
„ No
way to determine directionality of
associations
„ Inclusion of 2nd wave of data shortly
„ Did not query the specific video games
respondents play
„ Reliance on self-reports
18
Limitations (cont)
„ Respondents not observed during data
collection.
„ It is possible that:
„
Children were monitored by their parents
z
„
22% of youth indicated someone was close
enough to see the screen during data
collection
Parents completed the youth survey.
Implications
„ Parent involvement is important
„ Monitoring behavior, in general
„ Limiting use of video games
„ Being willing to discuss video game
content
„ Need to acknowledge the impact of
violent media / video games
19
20
Implications (cont)
„ Important to increase media literacy
„ Importance to actively try and reduce all
violent media exposures
„ Support the development of video
games emphasizing prosocial behavior
„ Darcia Narvaez – General Prosocial
Model
Contact Information
Merle Hamburger
Centers for Disease Control and Prevention
mhamburger@cdc.gov
21
Developmental and Sex Differences
Across Adolescence in Playing WebBased Violent Video Games
Eric F. Dubow, Jason A. Drummond, and
Kelly M. Lister
Department of Psychology
Bowling Green State University
In P. Boxer & M. Hamburger (Chairs), New Perspectives on Violent Media
Use in Adolescence: Risk, Protection, and the Moderation and Mediation of
Effects on Behavior. Presented at the12th biennial meeting for the Society for
Research on Adolescence, Chicago, March 8, 2008.
Recent Statistics on Children’s Media Exposure
• Data come from two recent national largescale surveys in the US
– The Kaiser Family Foundation
• The Generation M: Media in the Lives of 8-18 Year-Olds
survey (Roberts, Foehr, & Rideout, 2005), a schoolbased survey administered to 2,032 students in grades 3
through 12, supplemented by media diaries from 694 of
these students
– The Media in the Home survey (Woodard, 2000),
supported by the Annenberg Public Policy Center,
which was based on telephone interviews of 1,235
parents of children ages 2-17 and 416 children
ages 8-16
1
Some Statistics on Children’s Video Game Playing
• Video game units are present in 83% of homes with
children
• Children spent 49 minutes per day playing video
games
• Each day, 52% of children ages 8-18 years play a
video game
• Boys play far more frequently than girls
• Playing is unrelated to family income level
• Age trends: playing declined from an average of 65
minutes per day for 8-10 year-olds to 33 minutes per
day for 15-18 year-olds
Concerns with Media as a Socializing Influence
• Media replaces “functionally similar” activities
– As children watch more television and play more video games,
their reading time, study time, and library time all decrease, but
there is little change in their time spent in sports or socializing
• Over 70% of parents were at least “somewhat”
concerned with their children’s exposure to TV, the
internet, and music, and 53% were concerned with video
games (Woodard, 2000)
– 80-90% of American parents believe that today's media
contribute to children “becoming too materialistic, using more
coarse and vulgar language, engaging in sexual activity at
younger ages, experiencing a loss of innocence too early, and
behaving in violent or anti-social ways” (Common Sense Media,
2003)
• Engaging with the mass media either alone or with peers
provides learning opportunities that socialize children;
can alter their beliefs, attitudes, and behaviors
2
HOW Does Media Exposure Affect Youth?
• Short-term effects
– Priming
• The human mind acts as an associative network in which ideas are
partially activated, or primed, by stimuli with which they are associated;
the activation produced by an observed stimulus spreads in the
network
• For example, the mere presence of a weapon in a person’s visual field
can increase aggressive thoughts or behavior
– Imitation
• Immediate mimicry of specific behaviors
– Arousal and excitation transfer
• Media portrayals are often high-action sequences that can be very
arousing for youth, as measured by increased heart rate, skin
conductance of electricity.
– When a child has been generally aroused by a media stimulus, the
specific emotion (e.g., anger) generated by a subsequent realworld event (e.g., an insult) may be "felt" as more severe than it is
because some of the lingering media-induced emotional
stimulation is misattributed to the current situation
• Long-term effects: processes that account for lasting
changes in children's cognitions, behaviors, or the links
between emotions and cognitions and behaviors
– Observational learning: encoding lasting behavioral scripts and
cognitions simply as a consequence of observing others
• Identification with actor and viewing rewards to actor increase the likelihood
of encoding the behavioral scripts the actor is using, adopting the schemas
about the world that the actor seems to hold, or acquiring the beliefs that the
observed behaviors seem to imply
– Activation and desensitization of emotional processes
• Repeated exposure to emotionally arousing video games can lead to
habituation of certain natural emotional reactions--"desensitization"
– Violent scenes become less arousing over time, and brief exposure to media
violence can reduce physiological reactions to real-world violence
– Didactic learning processes
• Attitudes and beliefs can be changed by what the child observes through
relatively "automatic" cognitive processes of which the child may be unaware
or through more "controlled and effortful" cognitive processes including
thoughtful elaboration of observed information
3
Goals of the BGSU Study
• Examine the frequency with which adolescents play
violent video games, with a special focus on web-based
games
– Examine sex and age differences across adolescence (7th, 9th,
11th grades)
• Examine the relation between frequency of playing
violent video games and adolescents’ self-reported
behavioral, academic, and social adjustment
• Examine whether these relations vary by sex, age,
context of play (solo play; playing with peers present;
playing with on-line peers), or type/content of game (first
person shooter, strategy, MMO, sports)
Methods of the BGSU Study
• 484 7th (n=90), 9th (n=223), and 11th
(n=171) graders
• 54% were males
• 78% were Caucasian
• 71% had both parents living at home
• The participants completed a 45-minute
survey
4
Survey Measures
•
Frequency of playing violent video games alone, with others present, and with online others
–
–
•
Aggressive and prosocial behavior
–
–
•
Derived from the Direct and Indirect Aggression Scales; Bjorkqvist et al., 1992; α = .86-.95
Sample items: You yell at or argue with another person; You gossip about someone you are angry at;
You compliment someone; You cheer someone up (1 = never to 5 = quite often)
School adjustment
–
–
–
–
•
First-person shooter games, strategy games, massively multi-player on-line games
Grouped into categories of never, once a week/a few times a week, most days/every day
Grade-point average (1=mostly A’s to 9=mostly F’s)
Perceived scholastic competence (Harter, 1988; α = .65); e.g., Some kids do very well at their class
work, but others don’t. Do you do well at your class work? (1 = never to 5 = always)
Negative attitudes toward school (derived from Hawkins et al., 1996; Jenkins, 1997; α = .68); e.g., I
feel bored at school; At school, I try as hard as I can to do my best work (1 = never to 5 = always)
Involvement in 9 school-related activities (Moos et al., 1986); e.g., Went to a meeting of a school club
or group; Elected to some club or office; Took part in a school play or show
Perceived competence in social and intimate relationships
–
–
Harter, 1988; α = .61-.72
Sample items: Some kids have a lot of friends, but others don’t. Do you have a lot of friends?
Some kids are able to make really close friends, but others aren’t. Are you able to make really close
friends? (1 = never to 5 = always)
Frequency of Playing Video Games
HOW OFTEN DO YOU:
Never
Once a
week
A few times
a week
Most
days
Everyday
130. Play First Person Shooters by
yourself (Halo 2, Half-life 2, Battlefield
1942, etc.) *First Person Shooters are
games where the player engages in battle
from a first person point of view*
131. Play First Person Shooters with
another person who is in the room with
you (Halo 2, Half-life 2, Battlefield
1942, etc.)
132. Play First Person Shooters with
someone online who is not in the room
with you (Halo 2, Half-life 2, Battlefield
1942, etc.)
5
HOW OFTEN DO YOU:
Never
Once a
week
A few times a
week
Most
days
Everyday
Once a
week
A few times a
week
Most
days
Everyday
136. Play strategy games by yourself
(Warcraft 3, Starcraft, Civilization 3,
Rome Total War, etc.)
137. Play strategy games with another
person who is in the room with you
(Warcraft 3, Starcraft, Civilization 3,
Rome Total War, etc.)
138. Play strategy games with
someone online who is not in the room
with you (Warcraft 3, Starcraft,
Civilization 3, Rome Total War, etc.)
HOW OFTEN DO YOU:
Never
139. Play Massively Multiplayer
Online Games with at least one other
player in the room with you (Star
Wars Galaxies, World of Warcraft,
Everquest II, Planetside, etc.)
*massive multiplayer online games
are games where there are worlds
created online where players can log
on and play with many other players
140. Play Massively Multiplayer
Online Games with none of the other
players in the room with you (Star
Wars Galaxies, World of Warcraft,
Everquest II, Planetside, etc.)
6
Gender Differences in Playing
Any Violent Video Game
• Boys were more likely than girls to report playing any
type of violent video game, χ2 (2, N=479) = 153.05, p <
.01, regardless of context or game type
Boys
Girls
Never
14%
67%
Once a week to a few
times a week
50%
28%
Most days/every day
36%
5%
Grade Level Differences in Playing Any Violent
Video Game
• Younger students were more likely to play violent video
games than older students, χ2 (4, N=480) = 10.36, p <
.05;
– 7th graders were more likely to play by themselves and with
others; no grade differences for playing with others online
7th
9th
11th
Never
28%
40%
41%
Once a week to a
few times a week
40%
37%
43%
Most days/every
day
32%
23%
16%
7
Gender Differences in Playing
Web-Based Violent Video Games
• Boys were more likely than girls to report playing webbased violent video games, χ2 (2, N=478) = 79.59, p <
.01
Boys
Girls
Never
52%
90%
Once a week to a few
times a week
28%
8%
Most days/every day
20%
3%
Grade Level Differences in Playing Web-Based
Violent Video Games
• Grade level was not significantly associated with playing
web-based violent video games, χ2 (4, N=479) = 5.42,
ns
7th
9th
11th
Never
62%
68%
75%
Once a week to a
few times a week
23%
20%
15%
Most days/every
day
15%
12%
10%
8
Relation Between Frequency of Playing Any Violent Video
Games and Behavioral, Academic, and Social Adjustment
Means
Adjustment
Behavior MANOVA
F-value
Never
1ce/WeekA few
times/week
Most
days/
every day
F (4,950) = 8.56**
Aggression
F (2,476) = 5.36**
2.07a
2.14b
2.32c
Prosocial
F (2,476) = 10.13**
3.71a
3.50b
3.36b
2.60a
3.06b
School Adjustment MANOVA
F (8,940) = 5.28*
Grades
F (2,473) = 7.33**
2.33a
School involvement
F (2,473) = 8.24**
5.54a
5.12
4.70b
Negative attitudes
F (2,473) = 10.39**
2.26a
2.39a
2.63b
Perceived sch. competence
F (2,473) = .63
3.79
3.82
3.72
Social Adjustment MANOVA
F (4,950) = 2.44*
Intimate relations
F (2,476) = 3.93*
3.98a
3.92
3.73b
Social competence
F (2,476) = 3.80*
3.99a
3.92
3.76b
Relation Between Frequency of Playing Web-Based Violent
Video Games and Behavioral, Academic, and Social
Adjustment
Means
Adjustment
Behavior MANOVA
F-value
Never
1ce/WeekA few
times/week
Most
days/
every day
F (4,950) = 6.04**
Aggression
F (2,476) = 1.17
2.12a
2.24
2.17
Prosocial
F (2,476) = 10.40**
3.64a
3.42b
3.25b
2.50
2.76
2.93
School Adjustment MANOVA
F (8,938) = 3.04*
Grades
F (2,472) = 2.42+
School involvement
F (2,472) = 2.48+
5.30
5.06
4.77
Negative attitudes
F (2,472) = 6.70**
2.33a
2.46
2.66b
Perceived sch. competence
F (2,472) = .11
3.78
3.79
3.83
Social Adjustment MANOVA
F (4,948) = 1.79
Intimate relations
F (2,475) = 2.11
3.95
3.84
3.75
Social competence
F (2,475) = 3.33*
3.96a
3.85
3.72b
9
Context of Play
• Boys were more likely to play violent games across all
three contexts (alone, with others present, with others
online)
• Younger students played by themselves and with others
present more often than did older students. No grade
level differences in playing with others online.
F-Values for the Relation between Frequency of
Play in a Given Context and Adjustment
F-Values for Frequency of Play in Each Context
ALONE
Behavior MANOVA
F (4,948) = 7.74**
Aggression
F (2,475) = 5.99**
Prosocial
F (2,475) = 8.25**
School Adjustment MANOVA
F (8,938) = 4.71**
Grades
F (2,472) = 6.64**
School involvement
F (2,472) = 7.35**
Negative attitudes
F (2,472) = 7.76**
Perceived sch. competence
F (2,472) = .37
Social Adjustment MANOVA
F (4,948) = 2.20+
Intimate relations
F (2,475) = 3.39*
Social competence
F (2,475) = 3.55*
10
F-Values for the Relation between Frequency of
Play in a Given Context and Adjustment
F-Values for Frequency of Play in Each Context
ALONE
Behavior MANOVA
F (4,948) = 7.74**
WITH OTHERS
F (4,948) = 6.58**
Aggression
F (2,475) = 5.99**
F (2,475) = 5.32**
Prosocial
F (2,475) = 8.25**
F (2,475) = 6.65**
School Adjustment MANOVA
F (8,938) = 4.71**
F (8,938) = 5.50**
Grades
F (2,472) = 6.64**
F (2,472) = 11.96**
School involvement
F (2,472) = 7.35**
F (2,472) = 6.73**
Negative attitudes
F (2,472) = 7.76**
F (2,472) = 11.46**
Perceived sch. competence F (2,472) = .37
Social Adjustment MANOVA
F (2,472) = 2.48+
F (4,948) = 2.20+
F (4,948) = 1.74
Intimate relations
F (2,475) = 3.39*
F (2,475) = 3.43*
Social competence
F (2,475) = 3.55*
F (2,475) = 1.69
F-Values for the Relation between Frequency of
Play in a Given Context and Adjustment
F-Values for Frequency of Play in Each Context
ALONE
WITH OTHERS
ONLINE
F (4,948) = 7.74**
F (4,948) = 6.58**
F (4,950) = 6.14**
Aggression
F (2,475) = 5.99**
F (2,475) = 5.32**
F (2,476) = .96
Prosocial
F (2,475) = 8.25**
F (2,475) = 6.65**
F (2,476) =
10.93**
F (8,938) = 4.71**
F (8,938) = 5.50**
F (8,938) = 3.35**
Grades
F (2,472) = 6.64**
F (2,472) =
11.96**
F (2,472) = 2.67+
School involvement
F (2,472) = 7.35**
F (2,472) = 6.73**
F (2,472) = 2.86+
Negative attitudes
F (2,472) = 7.76**
F (2,472) =
11.46**
F (2,472) = 7.80**
Behavior MANOVA
School Adjustment MANOVA
Perceived sch. competence F (2,472) = .37
F (2,472) = 2.48+
F (2,472) = .20
F (4,948) = 2.20+
F (4,948) = 1.74
F (4,948) = 2.07+
Intimate relations
F (2,475) = 3.39*
F (2,475) = 3.43*
F (2,475) = 2.07
Social competence
F (2,475) = 3.55*
F (2,475) = 1.69
F (2,475) = 3.86*
Social Adjustment MANOVA
11
No Surprise: Frequency of Play is Related Across
Contexts
• For example: 78% of kids who never play video games with peers
also never play alone;
• 72% of kids who play video games most days/every day with peers
also play most days/every day alone
Frequency of Play Across
Two Contexts
Contingency Coefficients
Alone and with peers
.63
Alone and with others
online
.54
With peers and with others
online
.54
F-Values for the Relation between Frequency of
Play of a Given Content/Type and Adjustment
F-Values for Frequency of Play for Each Content
FPS
Behavior MANOVA
F (4,950) = 9.09**
Aggression
F (2,476) = 5.76**
Prosocial
F (2,476) =
11.43**
School Adjustment MANOVA
F (8,940) = 4.84**
Grades
F (2,473) = 6.90**
School involvement
F (2,473) = 8.87**
Negative attitudes
F (2,473) = 8.44**
Perceived sch. competence
F (2,473) = .31
Social Adjustment MANOVA
F (4,950) = 1.04
Intimate relations
F (2,476) = 2.01
Social competence
F (2,476) = 1.07
12
F-Values for the Relation between Frequency of
Play of a Given Content/Type and Adjustment
F-Values for Frequency of Play for Each Content
Behavior MANOVA
FPS
STR
F (4,950) = 9.09**
F (4,950) = 4.76**
Aggression
F (2,476) = 5.76**
F (2,476) = 1.14
Prosocial
F (2,476) =
11.43**
F (2,476) = 7.73**
School Adjustment MANOVA
F (8,940) = 4.84**
F (8,940) = 4.06**
Grades
F (2,473) = 6.90**
F (2,473) = 3.59*
School involvement
F (2,473) = 8.87**
F (2,473) = 2.55+
Negative attitudes
F (2,473) = 8.44**
F (2,473) = 8.28**
Perceived sch. competence F (2,473) = .31
Social Adjustment MANOVA
F (2,473) = .92
F (4,950) = 1.04
F (4,950) = 3.30*
Intimate relations
F (2,476) = 2.01
F (2,476) = 3.82*
Social competence
F (2,476) = 1.07
F (2,476) = 5.69**
F-Values for the Relation between Frequency of
Play of a Given Content/Type and Adjustment
F-Values for Frequency of Play for Each Content
FPS
STR
MMO
F (4,950) = 9.09**
F (4,950) = 4.76**
F (4,954) = 5.91**
Aggression
F (2,476) = 5.76**
F (2,476) = 1.14
F (2,478) = .20
Prosocial
F (2,476) =
11.43**
F (2,476) = 7.73**
F (2,478) =
11.35**
F (8,940) = 4.84**
F (8,940) = 4.06**
F (8,942) = 3.26**
Grades
F (2,473) = 6.90**
F (2,473) = 3.59*
F (2,474) = 3.48*
School involvement
F (2,473) = 8.87**
F (2,473) = 2.55+
F (2,474) = 5.25**
Behavior MANOVA
School Adjustment MANOVA
Negative attitudes
F (2,473) = 8.44**
F (2,473) = 8.28**
F (2,474) = 4.70**
Perceived sch. competence
F (2,473) = .31
F (2,473) = .92
F (2,474) = .66
F (4,950) = 1.04
F (4,950) = 3.30*
F (4,952) = 2.07+
Intimate relations
F (2,476) = 2.01
F (2,476) = 3.82*
F (2,477) = 2.39+
Social competence
F (2,476) = 1.07
F (2,476) = 5.69**
F (2,477) = 3.75*
Social Adjustment MANOVA
13
No Surprise: Frequency of Play is Related Across
Content of Games
• For example: 64% of kids who never play strategy video games also
never play first person shooter games;
• 55% of kids who play strategy games most days/every day also play
first person shooter video games most days/every day
Frequency of Play Across
Two Contents of Games
Contingency Coefficients
Strategy with First Person
Shooter
.42
Strategy with Massively
Multiplayer Online
.57
Massively Multiplayer
Online with First Person
Shooter
.38
• The negative association between playing
violent video games and adjustment
appears to cut across grade levels (7th,
9th, 11th) and gender
– Interaction terms of grade x frequency of play
and sex x frequency of play did not predict the
behavioral, school adjustment, or social
variables
14
A Silver Lining on Content of Video Games???
F-Values for Frequency of
Playing Sports Video Games
Behavior MANOVA
F (4,948) = 1.33
Aggression
F (2,475) = .24
Prosocial
F (2,475) = 2.55+
School Adjustment MANOVA
a
F (8,938) = .85
Grades
F (2,472) = 1.12
School involvement
F (2,472) = .66
Negative attitudes
F (2,472) = .38
Perceived sch. competence F (2,472) = .16
Social Adjustment MANOVA
F (4,948) = 2.60*
Intimate relations
F (2,475) = 1.20
Social competence
F (2,475) = 3.78*
a
aYouth who played most days/every day had higher prosocial behavior and
higher self perceived social competence than youth who never played sports
games
Association Between Playing Sports Video Games and
Other Types of Video Games
Frequency of Playing
Sports and Another Type
of Game
Contingency Coefficients
Sports with First Person
Shooter
.40
Sports with Strategy
.24
Sports with Massively
Multiplayer Online Games
.19
15
Next Steps
• Youth are not equally affected by even the same media
violent video games
– “Why are there individual differences in the ways in which
exposure to a specific type of media content affects the
development of youths’ attitudes, behaviors, and emotions?”
• Five categories of moderators of the effects of media
exposure
–
–
–
–
–
the user’s motivations for viewing
the user’s characteristics
attributes of the media content
the viewing context
cultural factors
User’s Characteristics
Status:
•Age
•Gender
•SES
User’s Motivations
•Passing time
•Entertainment
•Information seeking
•Social utility
•Escape
•Arousal/affect
Attributes of the
Media Content
•Characterizations
of actors’ behaviors
•Similarity of actors
to viewers
Personal variables:
•Behavioral tendencies
•Existing schemata,
beliefs
-Perceived realism of
content
•Identification with
models
Underlying Processes
•Observational learning
•Priming/schema activation
•Arousal/excitation transfer
•Desensitization
•Didactic learning processes
Viewing Context
Situational factors:
•Presence of coviewing others
Outcomes of
Media Exposure
•Cognitions
•Behaviors
•Emotions
Cultural Factors
•Aspects of cultural context
•Cultural norms
Personal factors:
•Attentional focus
•Pre-existing arousal/mood
Dubow, E. F., Huesmann, L. R., & Greenwood, D. (2007). Media and
youth socialization: Underlying processes and moderators of effects.
In J. E. Grusec & P. D. Hastings (Eds.), Handbook of socialization (pp.
404-430). New York: Guilford Press.
16
The User’s Motivations
• Children engage with the mass media for
multiple reasons
• We believe that in part why children choose to
play video games will be related to how video
games influence children
• For example, we would predict a more negative
effect of playing video games on children’s
social adjustment for those children who
endorse a higher motivation of preferring video
games to friends
Our Most Recent Study (Moyer & Dubow)
• 45-minute survey completed by students
in 5th and 8th grades
• Teacher and student self-reports of
students’ positive and negative social
behaviors; students’ reports of academic
adjustment and perceived social
competence
• Student self-reports of frequency of video
game play and motivations for play
17
Motivations for Video Game Play
Derived from Videogame Questionnaire (Barnett
et al., 1997)
• Preferring video games to friends (“Playing video games is
more exciting than being with my friends.”)
• Video games as companionship (“Playing video games is
like being with a friend.”)
• Achievement/self-esteem (“I get a feeling of achievement
when I play video games.”)
• Agency (“I feel in control of my life when I play video
games.”)
• Escapism (“Playing video games helps me forget my
problems.”)
• Dominance (“I can try to be better than anyone else.”)
• Social Status (“Video games are popular among my
friends.”)
Some Initial Results
• N = 194 (103 males, 91 females; 104 5th graders, 90 8th graders)
• 9% reported never playing video games
Average Minutes Per Day of Video Game Playing
Sex
Day
Weekday
Total
sample
Males
Grade
Females
5th
graders
8th
graders
109
149
64
127
88
(Md=60)
(Md=120)
(Md=30)
(Md=90)
(Md=45)
Weekend
229
day
(Md=137.5)
293
143
259
180
(Md=230)
(Md=60)
(Md=195)
(Md=90)
18
Hypothesis 2a: Overall Sample (N=177)
0.5
0.4
β = -.35**
=
0.2
0
-0.2
-0.2
-0.25
-0.4
β = .13
-0.6
-0.6
=
-0.8
-1 SD
+1 SD
Average Minutes of Video Game Play
High Prefer to Friends
Low Prefer to Friends
Hypothesis 2c: Overall Sample (N=177)
4
3.49
β = -.34**
General Self-Worth
Positive Social Adjustment
0.6
=
3.14
3
2.98
2.98
β = -.02
=
2
-1 SD
+1 SD
Average Minutes of Video Game Play
High Prefer to Friends
Low Prefer to Friends
19
Student Survey Measures
• Self-esteem and perceived self competence [Harter (1985)]
– Cognitive Competence (“Some kids do very well at their class
work…Other kids don’t do very well at their class work.”)
– Social Competence (“Some kids have a lot of friends…Other kids don’t
have very many friends.”)
– General Self-Worth (“Some kids like the kind of person they are…Other
kids often wish they were someone else.”)
• Positive Peer Interactions
– From the Friendship Questionnaire (Bierman & McCauley, 1987)
– Measures the frequency with which children experience positive
interactions with friends and peers
– Sample item: How often is there someone you play with at recess?
• Self-Reported Aggression
– Derived from Peer Nomination of Aggression (Eron et al., 1971) and
Direct and Indirect Aggression Scale (Bjorkqvist et al., 1992)
– Sample items: How often do you call other kids names? How often do
you push someone?
Teacher Survey Measures
• Social Behaviors
– Derived from Teacher Predictions of Peer
Nominations Scale (Huesmann et al., 1994)
– Teachers rate the student on aggression, prosocial
behavior, popularity, peer rejection, and victimization
– Sample item: “What percentage of students would
say [target child] is a child who helps other kids?”
• Perceived Academic and Social Competence
– Derived from Harter Teacher Rating Scale (1985)
– Sample items: “This child believes he/she is very
good at his/her school work,” “This child has a lot of
friends.”
20
Gender Differences in Playing
Any Violent Video Game
• Boys were more likely than girls to report playing any
type of violent video game, χ2 (2, N=479) = 152.49, p <
.01, regardless of game type
Boys
Girls
Never
14%
67%
Once a week or less
26%
18%
More than once a
week
60%
15%
Grade Level Differences in Playing Any Violent
Video Game
• Younger students were more likely to play violent video games than
older students, χ2 (2, N=480) = 13.08, p < .05;
– 7th graders were more likely to play first person shooter and
multiplayer online games than the other students
– Frequency of playing violent video games was highest for 7th
grade boys, with 76% reporting playing more than once a week
7th
9th
11th
Never
28%
40%
41%
Once a week or
less
22%
19%
29%
More than once
a week
50%
41%
30%
21
Gender Differences in Playing
Web-Based Violent Video Games
• Boys were more likely than girls to report playing webbased violent video games, χ2 (2, N=478) = 82.26, p <
.01
Boys
Girls
Never
52%
90%
Once a week or less
14%
6%
More than once a
week
34%
5%
Grade Level Differences in Playing Web-Based
Violent Video Games
• Grade level was not significantly associated with playing
web-based violent video games, χ2 (2, N=479) = 5.90,
ns
7th
9th
11th
Never
62%
68%
75%
Once a week or
less
12%
10%
9%
More than once
a week
26%
22%
15%
22
Relations Between Violent Video Game
Playing and Behaviors
• Frequency of playing violent video games was
associated with aggressive and prosocial behavior
(multivariate F (4,952) = 7.04, p < .01)
– Students who did not play were marginally less aggressive and
significantly more prosocial than those who played
• Frequency of playing also was related to school
adjustment (multivariate F (8,942) = 5.26, p < .01)
– Those who did not play had higher levels of involvement in
school activities, more positive attitudes toward school, and
higher grades than those who played
• Frequency of playing also was related to perceived
social competence (multivariate F (8,952) = 2.46, p < .05)
– Those who did not play had higher levels of perceived social
competence than those who played
more than once a week
Relation Between Frequency of Playing Violent Video
Games and Behavioral, Academic, and Social Adjustment
Means
Adjustment
Behavior MANOVA
F-value
Never
<=
>
1ce/Week 1ce/Week
F (4,952) = 7.04**
Aggression
F (2,476) = 2.43+
2.07
2.22
2.19
Prosocial
F (2,476) = 10.84**
3.71a
3.55
3.39b
School Adjustment MANOVA
F (8,942) = 5.26**
Grades
F (2,473) = 5.39**
2.33a
2.60
2.86b
School involvement
F (2,473) = 9.09**
5.54a
5.28
4.78b
Negative attitudes
F (2,473) = 8.50**
2.26a
2.36
2.54b
Perceived sch. competence
F (2,473) = .10
3.79
3.76
3.80
Social Adjustment MANOVA
F (4,952) = 2.46*
Intimate relations
F (2,476) = 4.05*
3.98
3.98
3.78
Social competence
F (2,476) = 3.77**
3.99
3.97
3.80
23
Relation Between Frequency of Playing Web-Based Violent
Video Games and Behavioral, Academic, and Social
Adjustment
Means
Adjustment
Behavior MANOVA
F-value
Never
<=
>
1ce/Week 1ce/Week
F (4,952) = 5.84**
Aggression
F (2,476) = 1.13
2.12
2.26
2.19
Prosocial
F (2,476) = 10.12**
3.64a
3.46
3.30b
2.94b
School Adjustment MANOVA
F (8,940) = 2.88**
Grades
F (2,472) = 3.04*
2.50a
2.59
School involvement
F (2,472) = 6.44*
5.30a
5.22
4.86b
Negative attitudes
F (2,472) = 8.50**
2.33a
2.42
2.60b
Perceived sch. competence
F (2,472) = .30
3.78
3.86
3.78
Social Adjustment MANOVA
F (4,950) = 3.52**
Intimate relations
F (2,475) = 5.90**
3.96a
4.05
3.68b
Social competence
F (2,475) = 4.89**
3.96a
3.97
3.71b
No Surprise: Frequency of Play is Related Across
Content of Games
•
•
For example: 80% of kids who never play first person shooter video games
also never play strategy games;
45% of kids who play first person shooter video games more than once a
week also play strategy games more than once a week
Frequency of Play Across
Two Contents of Games
Contingency Coefficients
Strategy with First Person
Shooter
.43
Strategy with Massively
Multiplayer Online
.51
Massively Multiplayer
Online with First Person
Shooter
.35
24
Relation Between Frequency of Playing Web-Based Violent
Video Games and Behavioral, Academic, and Social
Adjustment
Means
Adjustment
Behavior MANOVA
F-value
Never
<=
>
1ce/Week 1ce/Week
F (4,952) = 5.84**
Aggression
F (2,476) = 1.13
2.12
2.26
2.19
Prosocial
F (2,476) = 10.12**
3.64a
3.46
3.30b
2.94b
School Adjustment MANOVA
F (8,940) = 2.88**
Grades
F (2,472) = 3.04*
2.50a
2.59
School involvement
F (2,472) = 6.44*
5.30a
5.22
4.86b
Negative attitudes
F (2,472) = 8.50**
2.33a
2.42
2.60b
Perceived sch. competence
F (2,472) = .30
3.78
3.86
3.78
Social Adjustment MANOVA
F (4,950) = 3.52**
Intimate relations
F (2,475) = 5.90**
3.96a
4.05
3.68b
Social competence
F (2,475) = 4.89**
3.96a
3.97
3.71b
F-Values for the Relation between Frequency of
Play in a Given Context and Adjustment
F-Values for Frequency of Play in Each Context
ALONE
Behavior MANOVA
F (4,950) = 6.25**
Aggression
F (2,475) = 2.55+
Prosocial
F (2,475) = 9.03**
School Adjustment MANOVA
F (8,940) = 4.64**
Grades
F (2,472) = 4.04*
School involvement
F (2,472) = 6.90**
Negative attitudes
F (2,472) = 7.76**
Perceived sch. competence
F (2,472) = .15
Social Adjustment MANOVA
F (4,950) = 2.81*
Intimate relations
F (2,475) = 4.18*
Social competence
F (2,475) = 4.74**
25
F-Values for the Relation between Frequency of
Play in a Given Context and Adjustment
F-Values for Frequency of Play in Each Context
ALONE
Behavior MANOVA
F (4,950) = 6.25**
WITH OTHERS
F (4,950) = 4.85*
Aggression
F (2,475) = 2.55+
F (2,475) = 1.93
Prosocial
F (2,475) = 9.03**
F (2,475) = 7.09**
School Adjustment MANOVA
F (8,940) = 4.64**
F (8,940) = 3.59**
Grades
F (2,472) = 4.04*
F (2,472) = 5.64**
School involvement
F (2,472) = 6.90**
F (2,472) = 5.33**
Negative attitudes
F (2,472) = 7.76**
F (2,472) = 6.13**
Perceived sch. competence F (2,472) = .15
Social Adjustment MANOVA
F (2,472) = .17
F (4,950) = 2.81*
F (4,950) = 1.95+
Intimate relations
F (2,475) = 4.18*
F (2,475) = 3.39*
Social competence
F (2,475) = 4.74**
F (2,475) = .95
F-Values for the Relation between Frequency of
Play in a Given Context and Adjustment
F-Values for Frequency of Play in Each Context
ALONE
Behavior MANOVA
WITH OTHERS
ONLINE
F (4,950) = 6.25**
F (4,950) = 4.85*
F (4,952) = 6.38**
Aggression
F (2,475) = 2.55+
F (2,475) = 1.93
F (2,476) = 1.98
Prosocial
F (2,475) = 9.03**
F (2,475) = 7.09**
F (2,476) =
10.57**
F (8,940) = 4.64**
F (8,940) = 3.59**
F (8,940) = 2.73**
Grades
F (2,472) = 4.04*
F (2,472) = 5.64**
F (2,472) = 2.69+
School involvement
F (2,472) = 6.90**
F (2,472) = 5.33**
F (2,472) = 3.78*
School Adjustment MANOVA
Negative attitudes
F (2,472) = 7.76**
F (2,472) = 6.13**
F (2,472) = 5.97**
Perceived sch. competence
F (2,472) = .15
F (2,472) = .17
F (2,472) = .07
F (4,950) = 2.81*
F (4,950) = 1.95+
F (4,950) = 3.92**
Intimate relations
F (2,475) = 4.18*
F (2,475) = 3.39*
F (2,475) = 6.04**
Social competence
F (2,475) = 4.74**
F (2,475) = .95
F (2,475) = 6.28**
Social Adjustment MANOVA
26
F-Values for the Relation between Frequency of
Play of a Given Content/Type and Adjustment
F-Values for Frequency of Play for Each Content
FPS
Behavior MANOVA
F (4,952) = 7.09**
Aggression
F (2,476) = 1.34
Prosocial
F (2,476) =
12.16**
School Adjustment MANOVA
F (8,942) = 5.40**
Grades
F (2,473) = 7.33**
School involvement
F (2,473) = 9.01**
Negative attitudes
F (2,473) = 9.40**
Perceived sch. competence F (2,473) = .40
Social Adjustment MANOVA
F (4,952) = 1.29
Intimate relations
F (2,476) = 2.31
Social competence
F (2,476) = .52
F-Values for the Relation between Frequency of
Play of a Given Content/Type and Adjustment
F-Values for Frequency of Play for Each Content
Behavior MANOVA
FPS
STR
F (4,952) = 7.09**
F (4,952) = 4.90**
Aggression
F (2,476) = 1.34
F (2,476) = 1.18
Prosocial
F (2,476) =
12.16**
F (2,476) = 8.11**
F (8,942) = 5.40**
F (8,942) = 2.98**
Grades
F (2,473) = 7.33**
F (2,473) = 1.43
School involvement
F (2,473) = 9.01**
F (2,473) = 3.03*
Negative attitudes
F (2,473) = 9.40**
F (2,473) = 5.10**
Perceived sch. competence
F (2,473) = .40
F (2,473) = .84
F (4,952) = 1.29
F (4,952) = 3.62**
Intimate relations
F (2,476) = 2.31
F (2,476) = 4.50*
Social competence
F (2,476) = .52
F (2,476) = 6.66**
School Adjustment MANOVA
Social Adjustment MANOVA
27
F-Values for the Relation between Frequency of
Play of a Given Content/Type and Adjustment
F-Values for Frequency of Play for Each Content
FPS
STR
MMO
F (4,952) = 7.09**
F (4,952) = 4.90**
F (4,956) = 5.87**
Aggression
F (2,476) = 1.34
F (2,476) = 1.18
F (2,478) = .26
Prosocial
F (2,476) =
12.16**
F (2,476) = 8.11**
F (2,478) =
11.34**
Behavior MANOVA
School Adjustment MANOVA
F (8,942) = 5.40**
F (8,942) = 2.98**
F (8,944) = 4.54**
Grades
F (2,473) = 7.33**
F (2,473) = 1.43
F (2,474) = 5.43**
School involvement
F (2,473) = 9.01**
F (2,473) = 3.03*
F (2,474) = 3.78*
Negative attitudes
F (2,473) = 9.40**
Perceived sch. competence F (2,473) = .40
Social Adjustment MANOVA
F (2,473) = 5.10**
F (2,474) = 8.87**
F (2,473) = .84
F (2,474) = .16
F (4,952) = 1.29
F (4,952) = 3.62**
F (4,954) = 2.84*
Intimate relations
F (2,476) = 2.31
F (2,476) = 4.50*
F (2,477) = 4.25*
Social competence
F (2,476) = .52
F (2,476) = 6.66**
F (2,477) = 4.47*
A Silver Lining on Content of Video Games???
F-Values for Frequency of
Playing Sports Video Games
Behavior MANOVA
F (4,950) = .52
Aggression
F (2,475) = .02
Prosocial
F (2,475) = 1.03
School Adjustment MANOVA
F (8,940) = 1.36
Grades
F (2,472) = 2.61+
School involvement
F (2,472) = .66
Negative attitudes
F (2,472) = 1.52
Perceived sch. competence F (2,472) = .03
Social Adjustment MANOVA
F (4,950) = 3.50**
Intimate relations
F (2,475) = 1.91
Social competence
F (2,475) = 3.87*a
aYouth
who played more than once a week had higher self perceived social
competence than youth who never played sports games
28