1. Using Video Game Habits to Measure Gender &
Racial Discrimination and Sexism
Amanda M. Jones, BA & Shirley Ogletree, Ph.D.
Texas State University
Introduction
 Women and racial/ethnic minorities are regularly absent from video
games, with 14.77% of video game characters from 2005 being female and
19.95% being non-white (Williams et al., 2009).
When presented in games, women are often portrayed as oversexualized
(Miller & Summers, 2007) with unrealistic body proportions and partial to
full nudity (Downs & Smith, 2009). Minorities are frequently portrayed with
harmful stereotypes (Behm-Morawitz & Ta, 2014).
Male participants reported having objectifying thoughts toward females
after viewing sexually stimulating females in a game (Yao, Mahood & Linz,
2010). Players behave more aggressively toward black people after playing
a violent video game as a black avatar, reinforcing harmful racial
stereotypes (Yang et al., 2014).
The Ambivalent Sexism Inventory (Glick & Fiske, 1996) has measured
hostile and benevolent sexism of gamers (Stermer & Burkley, 2015).
How do college gamers identify with their favorite video game character
based on gender and ethnicity? How do sexism scores compare among
gamers versus non-gamers and males versus females?
References:
• Behm-Morawitz, E., & Ta, D. (2014). Cultivating virtual stereotypes?: The impact of video
game play on racial/ethnic stereotypes. Howard Journal Of Communications, 25(1), 1-15.
doi:10.1080/10646175.2013.835600
• Down, E., & Smith, S.L. (2010). Keeping abreast of hypersexuality: A video game character
content analysis. Sex Roles, 62(11-12), 721-733. Doi: 10.1007/s11199-009-9637-1
• Glick, P. & Fiske, S.T. (1996) The Ambivalent Sexism Inventory: Differentiating Hostile and
Benevolent Sexism. Journal of Personality and Social Psychology, 70(3). 491-512.
• Miller, M. K., & Summers, A. (2007). Gender differences in video game characters' roles,
appearances, and attire as portrayed in video game magazines. Sex Roles, 57(9-10), 733-
742.
• Stermer, S. P., & Burkley, M. (2015). SeX-Box: Exposure to sexist video games predicts
benevolent sexism. Psychology Of Popular Media Culture, 4(1), 47-55.
doi:10.1037/a0028397
• Williams, D., Martins, N., Consalvo, M., & Ivory, James D. The virtual census:
representations of gender, race and age in video games. New Media & Society 11(5), 815-
834. Doi: 10.1177/1461444809105354
• Yang, G.S., Gibson, B., Lueke, A.K., Huesmann, L.R., & Bushman, B.J. (2014) Effects of
Avatar Race in Violent Video Games on Racial Attitudes and Aggression. Social Psychology
and Personality Science, 5 (6) 698-704. doi: 10.1177/1948550614528008
• Yao, M. Z., Mahood, C., & Linz, D. (2010). Sexual priming, gender stereotyping, and
likelihood to sexually harass: Examining the cognitive effects of playing a sexually-explicit
video game. Sex Roles, 62(1-2), 77-88. doi:10.1007/s11199-009-9695-4
Email: amj3@txstate.edu
Method
 There were 273 participants (86 male, 183 female) aged 18-39 (M =
18.71)
 Most common race/ethnicity was white (41.6%) followed by
Hispanic/Latino (32.8%), African-America/Black (13.9%) and multiple
races (6.9%)
 Participants either never play video games (31.4%), play 1-10 hours a
week (36.1%), 11 or more hours a week (4.3%) or did not report habits
(28.1%).
Discussion
Participants were more likely to
choose a character that matched
their ethnicity.
However, both genders picked
male characters more than female
characters
Males have higher hostile and
benevolent sexism scores than
females, regardless of gaming
habits.
Gaming status only significantly
affected benevolent sexism, in which
the more participants played video
games, the lower their score.
Some limitations include:
28% of participants did not
report gaming habits.
Gaming habits were loosely
defined.
Not enough male participants
Future Research
Trends of sexism based on
other measures of gaming habits
Results – Ambivalent Sexism Inventory
Results – Identify with Characters
Ambivalent
Sexism
Inventory
Favorite
Gaming
Character
Gaming
Industry
Statements
22 statements
about attitudes
toward women
Gender, race,
appearance
and role
Diversity,
representation
& sexualization
White and Hispanic participants are
significantly more likely to choose white
characters. Black participants are
significantly more likely to choose black
characters, χ2 (df = 4) = 39.54, p < .001
0
5
10
15
20
25
30
35
40
Black Hispanic White
NumberofParticipants
Ethnicity of Participant
Frequency Counts of Favorite
Character - Ethnicity
Black Character Hispanic Character White Character
0
10
20
30
40
50
60
70
Male Female
NumberofParticipants
Gender of Participant
Frequency Counts of Favorite
Character - Gender
Male Character Female Character
Both male and female participants are
significantly more likely to choose a male
character over female character,
χ2 (df = 1) = 22.34, p < .001
32
34
36
38
40
42
Never 1-5 hours 6 + hours
BenevolentSexismScores
Frequency of Gaming Habits
Benevolent Sexism Scores
Male Female
33
34
35
36
37
38
39
40
41
Never 1-5 hours 6 + hours
HostileSexismScores
Frequency of Gaming Habits
Hostile Sexism Scores
Male Female
Gender has a main effect on benevolent sexism
scores, F(1, 176) = 5.26, p = .023. Gaming status
also has a main effect on scores, F(2, 176) =
3.06, p = .049. The more women play video
games, the lower their benevolence score.
However, the more men play video games, the
higher their score.
Gender has a marginal main effect on hostile
sexism scores, F(1, 176) = 3.84, p = .052.
Gaming status had no effect on scores, F(2, 176)
= .562, p = .571.