The virtual census: Representations of gender, race and age in video games


A large-scale content analysis of characters in video games was employed to answer questions about their representations of gender, race and age in comparison to the US population. The sample included 150 games from a year across nine platforms, with the results weighted according to game sales. This innovation enabled the results to be analyzed in proportion to the games that were actually played by the public, and thus allowed the first statements able to be generalized about the content of popular video games. The results show a systematic over-representation of males, white and adults and a systematic under-representation of females, Hispanics, Native Americans, children and the elderly. Overall, the results are similar to those found in television research. The implications for identity, cognitive models, cultivation and game research are discussed.
New Media & Society
DOI: 10.1177/1461444809105354
2009; 11; 815 New Media Society
Dmitri Williams, Nicole Martins, Mia Consalvo and James D. Ivory
The virtual census: representations of gender, race and age in video
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The virtual census:
representations of gender,
race and age in video
University of Southern California, USA
Indiana University, USA
Ohio University, USA
Virginia Polytechnic Institute and State University, USA
A large-scale content analysis of characters in video games was
employed to answer questions about their representations of
gender, race and age in comparison to the US population. The
sample included 150 games from a year across nine platforms,
with the results weighted according to game sales. This
innovation enabled the results to be analyzed in proportion
to the games that were actually played by the public, and thus
allowed the first statements able to be generalized about the
content of popular video games. The results show a systematic
over-representation of males, white and adults and a systematic
under-representation of females, Hispanics, Native Americans,
children and the elderly. Overall, the results are similar to those
found in television research. The implications for identity,
cognitive models, cultivation and game research are discussed.
new media & society
Copyright © 2009 SAGE Publications
Los Angeles, London, New Delhi, Singapore and Washington DC
Vol 11(5): 815–834 [DOI: 10.1177/1461444809105354]
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Key words
age • comparison • consoles • content analysis • gender • identity
• race • population • ratings • videogames
Video games have become a widely popular and highly profitable
medium, with more than 40 percent of Americans now playing them
regularly (Slagle, 2006). A majority of adults age 18 and older (53%) play
video games and approximately one in five adults (21%) play every day or
almost every day (Lenhart et al., 2008). In fact, video games surpass television
in terms of time spent among some populations (Sherry et al., 2006). It
follows that if games are a significant portion of the media diet, they need
to be understood as important systems of symbols which might have a
broad social impact. In the same vein that television has been thought to
create cultivation effects (Gerbner et al., 1994) and to have an impact on
the cognitive modeling of social identity formation (Mastro et al., 2007),
games also may be influencing players’ impressions of social groups, including
their own (Comstock and Cobbey, 1979). Content analyses of mainstream
media have demonstrated where portrayals of gender, race and age have
diverged from actual group proportions in the US population (Harwood and
Anderson, 2002). However, despite the popularity of video games, there is
a gap in our understanding of such portrayals across the wide range of game
titles and as understood from the consumer’s viewpoint. Past research has
focused on convenience samples of game titles, but never in proportion
to what is actually played. Sampling and weighting games according to
popularity will allow a connection between research and actual social
The existing content analytic work done on video games has focused on
two topics of special interest to communication researchers: violence (Dietz,
1998; Heintz-Knowles et al., 2001; Schierbeck and Carstens, 2000; Shibuya
et al., 2004; Smith et al., 2003; Thompson and Haninger, 2001; Thompson
et al., 2006), and gender and sexuality (Braun and Giroux, 1989; Dietz, 1998;
Downs and Smith, 2005; Heintz-Knowles et al., 2001; Janz and Martis,
2007). Although these studies are important steps in examining videogame
content, there is still much left to discover, including a more basic study
of representation. In the work presented here, this study seeks to obtain
a baseline measure of race, gender and age distribution across the current
universe of videogame characters. Because media character demographics
and portrayals of social groups may influence players’ likelihood of attending
to and learning from game characters (Bandura, 1994), as well as players’
perceptions of social reality (Gerbner et al., 1994; Shrum, 1999), establishing
sound baseline measures of videogame character demographics is a necessary
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step in applying theories of influence, identity construction and perceived
social reality.
Prior studies of game content have been concerned with the sensitive
topics of violence and sexuality, while typically not focusing on the
generalizability of the findings to the universe of actual content. Although
violence is outside the scope of this article, there are strengths and weaknesses
in this body of literature that inform the present study. Most notably, these
concern the gender and race breakdowns of prior samples, the inclusion
of the Entertainment Software Rating Board (ESRB) ratings system as an
independent variable and the sampling frame used to collect the games.
For example, Dietz (1998) examined violence and gender stereotyping in
her content analysis of 33 popular Nintendo and Sega Genesis videogames.
She found that the most common portrayal for female characters was the
complete absence of females. In fact, there were no female characters in more
than 40 percent of the games that she sampled. Heintz-Knowles et al. (2001)
also examined violence and gender stereotyping in their study of 70 video
games. The results revealed that of the 874 characters coded, 73 percent
were male and 12 percent were female. When females did appear, they were
likely to be seen in secondary roles. Dill and colleagues (2005) coded the
role of each character encountered in their content analysis of 20 top-selling
PC video games of 1999. The results revealed that across all 20 games,
70 percent of the primary characters were male and 10 percent were female.
For secondary characters, 55 percent were male and 31 percent were female.
Dill and colleagues (2005) also found that more than two-thirds of the main
characters were white (68%), followed by Latino (15%) and black (8%).
Game ratings have received attention in the literature and should be
examined as a possible source of content variation. Created in 1994, the
ESRB rates videogames with age-based symbols and content descriptors.
Games rated ‘E’ (for everyone) have been deemed suitable for players aged
six years and older. In contrast, games rated ‘AO’ are suitable for adults only.
Studies of the ESRB ratings system suggest that games with different rating
levels do have different kinds of content (Thompson et al., 2001), yet not all
content analyses of games take ratings into account.
Sampling has proven to be a barrier in prior content analyses of
video games, with many studies examining limited subsets of games or
games released for systems that never became popular. For example, the
comprehensive ‘Children Now’ study (Heintz-Knowles et al., 2001) featured
games played on the Sega Dreamcast, a system that flared briefly and then
sputtered in the late 1990s. To make things more complex, most studies rely
on one or two systems, but in the current environment this severely limits
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a sample. In the present very competitive games market there are no fewer
than nine major and viable systems representing PCs, consoles and hand-
held options for players. Any comprehensive content analysis would need to
include each of these.
A key obstacle in prior work has been linking the small samples used to
the games that are actually consumed by the wider public. In the absence
of a Nielsen-like system, researchers have been unable to connect content
data with the actual practices of players. The Heintz-Knowles et al. (2001)
study examined the 10 top-selling video games for each of the six video
game consoles available at the time (Dreamcast, Game Boy Advance, Game
Boy Color, Nintendo 64, Playstation, Playstation 2 and PC), resulting in a
total sample size of 60 games. Other pioneering studies have limited their
samples to games from a handful of platforms. Dill et al. (2005) analyzed 20
top-selling games, but they were for PCs only. Downs and Smith (2005)
had one of the largest samples when they analyzed 60 top-selling games for
the Microsoft Xbox (N = 20), Nintendo Game Cube (N = 20) and Sony
Playstation 2 (N = 20), yet none of these studies considered any of the games
to be more influential than any other. However, when the most popular
game (Madden ’06) sells over 6 million copies and the least popular (game
#150 in the present study’s sampling frame, BeyBlade) 15,000 copies, it is
safe to assume that one game will be played significantly more than another.
Thus, if the goal is to measure what the public is actually consuming, content
from the two should not be given equal weight in the analysis.
A final limitation concerns the absence of handheld game systems from
content analytic samples. With the exception of the ‘Children Now’ study,
no content analysis to date has examined character portrayals on handheld
games. This is an important omission to note, because these systems are
marketed to young children and adolescents. The present study sought to
address these previous limitations by employing a large sample, including
games from every major platform, utilizing ratings schemes and sampling
with a scheme that includes the actual popularity of games in the resulting
content analysis.
Why game representations matter
There are several reasons why the presence, absence or type of portrayal
of social groups matter in a diverse society, ranging from social justice and
power imbalance to models of effects and stereotype formation. Harwood and
Anderson (2002) have suggested that representation on television is at heart a
proxy for other social forces – that is, groups who appear more often in the
media are more ‘vital’ and enjoy more status and power in daily life. Their use
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of ethnolinguistic vitality theory argues that the media work as a mirror for
existing social forces as much as a causal agent of them. Therefore, measuring
the imbalances that exist on the screen can tell us what imbalances exist in
social identity formation, social power and policy formation in daily life.
Moving past the media as a mirror for social power relations, several
theories offer models and explanations for the reason why the consumers of
media may be affected by them. Cultivation theory posits that the world
of media exerts a broad, ‘gravitational’ pull on the viewer, systematically
shaping their worldview to match that of the symbolic one on TV (Gerbner
et al., 1994). This work has remained highly contested and controversial
(Hirsch, 1981; Potter, 1994). Moreover, an experiment of cultivation in a
video game (Williams, 2006b) has shown that the mechanism was precise
and targeted rather than broad and spreading, supporting Shrum’s (2002)
cognitive processing version of the theory. In other words, it was a specific
set of symbols that yielded cultivation effects rather than a broader set of
values or cultures.
The theoretical mechanism in Shrum’s approach suggests that the
presence (or absence) of a set of images in media causes a set of impressions
in viewers (or players) through well-studied cognitive mechanisms. Price
and Tewksbury (1997) reviewed this literature on cognitive associations,
priming and framing and generated a parsimonious model for the impact of
media imagery. Viewing (or in this case, playing) media creates objects in
what Price and Tewksbury term the ‘knowledge store’, which they describe
as ‘a network of constructs, including information about social objects and
their attributes’ (1997: 186). The frequency with which social objects will
be recalled and used depends in large part on chronic accessibility. At the
simplest level, constructs are accessible when they are reinforced repeatedly
and recently. Thus, imagery that is viewed or played repeatedly is more
accessible when a person is attempting to recall information about that class of
social objects. This is consistent with Shrum’s (2002) approach to cultivation,
i.e. that a set of ideas about the real world are in large part based on the
accessibility of constructs, which in turn are influenced by how often those
constructs are viewed in media. In other words, social objects, like types of
people, can be viewed or played in media and this action makes them more
likely to be recalled later if they were more prevalent.
Theoretically, a media environment in which a particular type of person
is highly represented will result in a viewer or player who is more likely to
recall that type of person rather than a different type of person. The outcomes
of such a system are very similar to the outcomes suggested by traditional
cultivation, even while the causal mechanisms differ. Recently, work by
Mastro and colleagues (2007) has made this connection with the mental
models approach for the cultivation of Latinos on television. This work
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reveals that a medium’s general depiction of a group does have an impact
on its users’ perceptions of that group, albeit moderated by their real-world
experiences. If such a consistent pattern of representation on television can
have effects as Mastro et al. (2007) show, a consistent pattern in other media
may do as well. This is especially relevant as games begin to displace prior
media as the dominant symbol sets for many Americans. For gaming, groups
repeatedly seen or seen in particular roles, will begin to be more accessible to
the viewer or player. In keeping with prior video game content analyses as
well as the Harwood and Anderson television work, the key group variables
here are gender, race and age.
This is also relevant to the populations themselves, as representation
can have identity and self-esteem effects on individuals from those groups
(Comstock and Cobbey, 1979; McDermott and Greenberg, 1984). Tajfel’s
social identity theory (1978) suggests that groups look for representations
of themselves and then compare those representations with those of other
groups. The presence of the group – including within games (Royse et al.,
2007) – serves as a marker for members to know that they carry weight
in society. Conversely, the absence of portrayals should lead to a feeling
of relative unimportance and powerlessness (Mastro and Behm-Morawitz,
2005). These effects may be more or less likely if those populations play
games at higher or lower rates. Thus, population figures can be used as an
expected value baseline for comparison with the actual numbers of characters.
In addition, real-world demographic player data can suggest which groups
might be accessing games at higher rates than others.
Gender is the first example: the US population is 50.9 percent female
and 49.1 percent male (Smith and Spraggins, 2001). In contrast, 60 percent
of gamers are male and 40 percent are female across all age brackets
(Entertainment Software Association, 2009).
For race, the US population is increasingly multicultural, but still
dominated by one major racial group. In the 2000 census, the ethnic
breakdown was 75.1 percent white, 12.5 percent Hispanic/Latino, 12.3
percent African American, 4 percent Asian/Pacific Islander, 0.9 percent
Native American and 2.4 percent biracial (Grieco and Cassidy, 2001).
African American and Hispanic youths play games at the highest rates
(Rideout et al., 2005).
For age, children (under 13) are 21.41 percent of the population, teens
(13–19) are 7.18 percent, adults (20 to 64) are 58.97 percent and the elderly
(65+) are 12.43 percent. Players skew slightly younger than the general
population, but not as much as they did 10 years ago (Williams, 2006a).
According to industry statistics, the average game player is currently 35 years
old (Entertainment Software Association, 2009).
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The appearance of groups in the media environment can be contrasted with
the appearance of that group in daily life. Exposure to different groups varies
greatly according to socioeconomic status and geography, but simple census-
level data provide a national comparative baseline. If a group appears more in
a game than in real life, it is over-represented, which could make such groups
more accessible to players recalling group-based schemas. Additionally, Price
and Tewksbury’s (1997) model implies a converse possibility: social objects
which are not chronically accessible will be recalled less often. Demographic
groups of people who are not represented are slowly rendered invisible by
virtue of their relative inaccessibility in the knowledge store. For example,
mainstream television shows rarely portray actors or characters from the
Hmong ethnic group of South-East Asia. If asked to list ethnic groups,
theoretically the people who viewed mainstream television would be less
likely to name the Hmong because the social object ‘Hmong’ would be
relatively inaccessible compared to the easily accessible objects ‘white’,
‘black’, etc. (that is, except for those viewers whose personal experiences,
knowledge or location made the ‘Hmong’ social object accessible; Mastro
et al., 2007). Hmong would be unlikely to be the object of recall for both
positive associations (e.g. groups that are seen as trustworthy, hardworking
or make good leaders) and negative associations (e.g. groups that are seen as
untrustworthy, lazy or make bad leaders). In this sense, groups who appear
relatively less often in media may be rendered invisible to the viewer.
Therefore, with the data on users and the general population in place as a
comparative baseline, we can formulate the following research question:
RQ1: How frequently are different gender, race and age groups represented
in games?
Just as in other media, all characters are not equally important to the story or
action. Some appear briefly, while others remain on the screen for the entire
game session. To simplify for the very broad sample used here, the most basic
level of role is used: those who are played by the player and those that are
part of the game world. Played characters, called ‘primary’ characters in the
analysis, drive the action in games. Non-player characters are the ‘secondary’
characters in the analysis. They may drive some of the action, for example
when they talk to or interact with the player, and some may feature strong
artificial intelligence, but ultimately their role is secondary, subservient or
simply as targets. For the mental models or accessibility and recall approaches,
the groups which typically appear in secondary roles may be associated with
having secondary roles in daily life. Within the game session, there is evidence
to suggest that players regard secondary characters very differently than
primary ones. Magnetic Resonance Imaging (MRI)-based research shows
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that players react far differently to other characters when they think they are
controlled by the computer rather than another real person (Reeves, 2007).
Therefore, it is important to establish who appears in primary and secondary
roles. Once these baseline measures are established, future research can use
them to test their impact rigorously in causal models. Therefore:
RQ2: Is there a difference between groups’ appearance in primary and
secondary roles?
As noted in the introduction, there has been a practical gap in research
between the games which have been studied and the games actually played
by the public. This is an issue of external validity; if research has occurred on
media that are not used, it is of less practical import. Investigating the possible
difference between all games and the games most played allows for two kinds
of analysis. First, it creates a test of prior content analytic work, testing to see
if those projects have been overestimating or underestimating outcomes in
proportion to what games are actually played. Second, it creates a test with
implications for player research. If there is a systematic difference between
content in the games that are most played and the games that are made, it
offers insight into player tastes and preferences. In past work, developers have
been imputed as the agents driving content, but examining popularity places
some of the causal agency in the hands of the player. If players systematically
choose games with particular forms of social identity, developers are
economically incentivized to create those games, creating a cycle of social
identity creation and perpetuation. Therefore:
RQ3: Is there a difference in character representation between the typical game
made and the most popular games?
As noted earlier, ratings systems have been used in previous content
analyses. The present study seeks to replicate and extend those findings with a
larger sample which can be generalized:
RQ4: Is there a difference in characters’ social group representation between
games with different ESRB ratings?
A large-scale content analysis of video game characters was undertaken to
address the research questions.
Sales data were obtained from the research firm The NPD Group for a
calendar year from March 2005 to February 2006 for the nine major
game systems sold in the USA during that timespan: Xbox 360, Xbox,
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PlayStation 2 (PS2), PlayStation (PS), Nintendo Gamecube, PlayStation
Portable (PSP), Nintendo Gameboy Advance (GBA), Nintendo Dual Screen
(DS) and PC. Of these, the PSP, GBA and DS are portable systems, PCs
are standard computer systems and the rest are TV-based console systems.
The sampling frame included the top 150 games across all platforms, with
a minimum of 15 titles per system. On multiple systems 17 games were
available, leaving 133 games to be tested. In these duplicate system cases, the
system with the most advanced graphical processor was used.
These 133 titles
constituted a highly representative frame for the universe of games as made by
developers by accounting for more than 95 percent of all game sales within
the sampling period.
In order to determine the relative use and popularity of the games, sales
figures were used as weights during the analysis. Each game was weighted
by the number of copies that it sold, meaning that a game selling 4 million
copies figured twice as heavily in the computations as a game selling 2 million
copies. In this way, the sample could be weighted to represent not only the
games made by developers, but also the games that were actually purchased
and played. Giving each title equal weight would inaccurately represent
the time spent on content across the country, and invalidate the theoretical
approaches outlined earlier that depend on the volume of impressions as
consumed, not as produced.
Each game was played by an expert game player – who was not one of
the coders – for 30 minutes on the default difficulty setting, typically ‘low’
or ‘easy’. These 30-minute segments were recorded digitally and stored on a
high-end desktop computer for coding. The 30-minute segments were the
largest unit of analysis and were used for the research questions that called
on game-level data. Measures of representation as portrayed on games for
a given system required a game-level number for computation. Similarly,
portrayals by ratings code required a rating-level value of different titles. In
these cases, the weighted data were used to create collapsed mean values for
games rather than including all characters as equal units of analysis. Such an
approach would have skewed the results, given that some games feature more
characters than others.
Data analysis
The bulk of the analysis concerned characters within games. Every character
in the sample was recorded as an individual unit and coded, for a total of
8572 characters. Each character was coded for their status as either a primary
(player-controlled) or secondary (computer-controlled) figure prior to
coding. Not all games featured visible primary characters. In the event that
a game provided the option to choose from a list of primary characters, such
characters were selected randomly so that characters from both genders, all
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ethnicities and all age groups had an equal chance of being selected. Then, all
characters were coded on the variables of gender, race and age. Because prior
work has shown that viewers heavily discount social objects that they cannot
confirm as human (Hoffner and Cantor, 1991; Reeves and Nass, 1996), in
addition the characters were coded as either human or one of several non-
human or quasi-human options. Only the human characters were retained for
analysis (N = 4966).
To gain comparative baseline data from the actual US population, US
census data were collected from the government census website (www. and from special reports derived from the 2000 census (Grieco
and Cassidy, 2001; Hetzel and Smith, 2001; Smith and Spraggins, 2001).
Two research assistants coded all of the 30-minute recordings in the
sample. Prior to coding, all of the assistants participated in 10 hours of
training sessions. Coding rates were assessed at regular intervals throughout
the study. Cohen’s kappas (Cohen, 1960) were .93 for gender, .92 for age,
.89 for race and .97 for human/non-human.
The first question addressed what groups appear in games and which are
over-represented and under-represented in relation to the actual population.
These were addressed with respect to gender, race and age. Figure 1 shows
the weighted data for gender representation across the universe of games.
Figure 1 also addresses RQ2, which was concerned with the role (primary or
secondary) played by the character.
As the figure shows, male characters are vastly more likely to appear
than female character in general. The overall difference of 85.23/14.77
percent is also a large contrast with the 50.9/49.1 percent distribution in the
actual population. This difference is heightened among the primary ‘doer’
characters, where males are even more likely to appear. As a general rule
then, males appear more frequently in games than females, and even more so
as drivers of the action. When females do appear, they are more likely to be
in secondary roles than primary ones.
Figure 2 shows the weighted data for race representation across the
universe of games, with comparison bars from the US population.
Contrasts between the proportion of characters appearing in games and
those appearing in the general population can be observed for every race.
Whites and Asians are over-represented and all other groups are under-
represented. In proportional figures relative to their actual population, whites
are 6.59 percent and Asians are 25.75 percent over-represented. All others
are under-represented: blacks by 12.68 percent, Hispanics by 78.32 percent,
biracials by 42.08 percent and Native Americans by 90 percent. When
primary roles are considered, all groups appear less often except for whites,
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who appear more often than overall. White characters account for 84.95
percent of all primary characters, black 9.67 percent, biracial 3.69 percent
and Asian 1.69 percent. Hispanics and Native Americans did not appear as a
primary character in any game, they existed solely as secondary characters.
Figure 3 shows the weighted data for age representation across the universe
of games, with comparison bars from the US population.
t-tests for male vs. female: for all characters, t = 224.31, df = 4343, p<.001; for primary characters, t = 22.05,
df = 96, p<.001; for secondary characters, t = 175.331, df = 4231, p<.001
Figure 1 Gender breakdowns by role
All characters,
All characters,
White Black Hispanic Biracial Native
% Characters in
% of actual people in
US Census
Figure 2 Over-representation and under-representation by race
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Contrasts between the proportion of characters appearing in games and
those appearing in the general population can be observed by age bracket.
Again, some groups differ systematically. Almost all of the differences can be
explained by the over-representation of adult characters, who appear at a rate
in games 47.33 percent higher than their prevalence in the actual population.
Teens appear at a rate very similar to the population, but children and the
elderly appear at substantially lower rates.
When primary and secondary roles are considered, these patterns change.
Among primary characters, children rise to 8.9 percent, teens rise to 13.3
percent, adults drop to 76.5 percent and the elderly drop to 1.34 percent.
These numbers remain substantially different from the actual population, but
with the exception of the elderly, all ages move towards population values
among primary characters. It is the great bulk of secondary characters that
makes the universe of games appear less age representative, but regardless
of the role, adults appear the most often and at the expense of children and
the elderly.
The next research question asked whether representations varied by
ESRB rating. There were no games rated AO, leaving the ESRB categories
E = Everyone, E10 (everyone 10 or older), T = Teen and M = Mature
(18+) (see Table 1).
The gender differences between ratings were statistically significant due to
the large number of characters, but were substantively negligible. In contrast,
there was a substantive race difference for the E-rated games, which featured
far fewer white characters and included far more black and biracial characters.
This was due almost entirely to the presence of very popular sports titles,
Figure 3 Over-representation and under-representation by age
Children Teens Adults Elderly
% Characters in games % of actual people in US Census
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which contained a much higher proportion of black and biracial characters –
all actual athletes drawn directly from real-world sporting leagues. Age
differences showed the E and E10 rated titles having more child characters
than the older titles, with a decrease in the ranks of adult characters.
RQ4 involved the possible difference in content between the games that
were made and those that were most popular. Using the unweighted data,
an examination of game representations by gender shows that the split is
81.24/18.76 percent male/female. This is in contrast to the weighted data,
which showed an 85.23/14.77 split.
In other words, gamemakers created
games that heavily featured male characters, but the games that were actually
purchased were even more heavily male. This finding varied by ratings, with
smaller gender imbalances between games made and games bought occurring
as the ratings increased (E-10 rated games were omitted because there were
too few in the sample year for substantive analysis): E-rated games featured
79.03 percent male characters as made, but 86.19 percent as purchased (7.16
percent difference); T-rated games featured 81.69 percent male characters
as made, but 84.71 percent as purchased (3.02 percent difference); M-rated
games featured 88.07 percent male characters as made, but 86.99 percent as
purchased (-1.08 percent difference). In other words, games for young or
general audiences had more gender equity, but the games actually purchased
by players (and assumedly by parents) largely negated it.
Table 1 Representations by ESRB rating
Percentage of all characters, male 86.09 79.10 85.03 86.55
Percentage of all characters, female 13.91 20.90 14.97 13.45
Race as a percentage of all characters:
White 59.53 75.07 79.60 72.39
Black 32.64 18.78 9.09 11.68
Hispanic 1.63 1.61 1.70 7.59
Biracial 5.30 0.53 3.14 0.15
Native American 0.00 0.00 0.11 0.71
Asian/Pacific Islander 0.89 4.00 6.36 7.47
Age as a percentage of all characters:
Child 5.14 16.61 2.14 0.41
Teen 10.76 0.00 3.66 1.62
Adult 81.74 83.06 93.27 96.15
Elderly 2.37 0.33 0.93 1.82
Total game N = 68 for Everyone, 8 for E10, 38 for Teen and 19 for Mature. 95% confidence
interval tests performed at the character level show that none of the values in the table overlap
with another column (with the exception of the 0 values for Native Americans and the E and
E10 values for Hispanics).
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The goal of the study was to determine whether there were systematic
patterns of representation for videogame characters for gender, race and age
across all games and within ratings categories. The large sample – nearly
the universe of titles for a year – and the weighting approach allow for the
first statements which can be generalized about the characters contained
in popular games. The results show that males, whites and adults are
over-represented in comparison to the actual US population. These over-
representations come at the expense of women, some minority groups –
chiefly Latinos and Native Americans – and children and the elderly. The
most popular games are less representative than the typical game produced
by developers, indicating that players also play a role in the cycle of creation
and consumption. In addition, the mere difference in numbers between
the weighted and unweighted data suggests a likelihood that prior content
analyses have been systematically off if the goal was to analyze the games
that the public actually plays. These several outcomes have implications for
theories of game effects and identity and for comparative analyses of media.
The differences between the game world, the player base and the US
population have implications for both self-identities and considerations
of other groups. The Latino case provides one strong example from the
data. Although there are no race data for older game players, we do know
that Latino children play more video games than white children, so it is
conceivable that they play more as teenagers and adults. Nevertheless,
Latinos are unlikely to see representations of their ethnic group among game
characters and never as primary characters. According to social identity theory
(Tajfel, 1978), this lack of appearance is a direct signal to Latinos that they
are relatively unimportant and powerless compared to more heavily present
groups. In addition, perceptions about Latinos may change for members
of other groups. The data here show that – unlike television, which shows
modest gains for Latinos (Mastro and Behm-Morawitz, 2005) – members of
these two ethnic groups continue to be most present in secondary roles.
The same issues are present for blacks, Native Americans, females and
children, all of whom are under-represented compared to the population and
among game players. The data on elderly players are not reliable, but given
that the average age of game players continues to rise steadily, it is a safe
assumption that more older players are picking up the control pad over time.
When they do, they will find a game universe which does not feature them.
For children, the stakes may be slightly higher than for television. Many have
suggested that games function as crucial gatekeepers to interest in technology,
which translates into education and careers in mathematics and science-
related fields (Lin and Lepper, 1987; Williams, 2006a). If Latinos or any
other groups become disenchanted with games due to poor representation,
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subsequently they may have less interest in technology and its opportunities
for class advancement. Ironically, they would be less likely to become
gamemakers themselves, helping to perpetuate the cycle.
For those outside the groups, representations have been shown consistently
to lead to effects (Dixon and Linz, 2000; Pan and Kosicki, 1996), although
the valence of the effects depends on the context on the portrayals
(Bodenhausen et al., 1995) and the viewers’ other real-world exposure to that
group in relation to the amount of media consumed (Mastro et al., 2007).
In the case of the game data, the under-represented groups were both scarce
and, when present, featured in secondary roles. The Price and Tewksbury
(1997) ‘knowledge store’ and Shrum’s (2002) cultivation recall model both
suggest that this makes those groups seem less visible, while social identity
theory additionally suggests that they will be seen as less important. Of
course, a content analysis cannot show a link between images and resulting
opinion or behavioral change. However, it is a necessary condition which has
now been demonstrated and can be tested for causality.
Comparing games with prior media and tracking the causes
The findings have a striking similarity to those typically found in content
analyses of television, suggesting that whatever causal forces are at work
stretch across media and are not unique to games. With regard to race,
the findings here nearly mirror Harwood and Anderson’s television data,
which found 82.9 percent white, 2.6 percent Latino,11.4 percent black and
2.6 percent Asian characters. It follows that Latinos and Native Americans
can be said to be systematically under-represented across media. This is a
particularly surprising finding for Latinos, who are a growing minority ethnic
group off-screen. According to the Census Bureau (Grieco and Cassidy,
2001), Latinos are projected to double in population over the next 20 years.
Any theoretical effects arising from their invisibility in media could be
expected to worsen unless the basic causes of under-representation change.
The age outcome disparity is nearly identical to what Harwood and Anderson
found for television, i.e. that adults are over-represented at the expense of
children and the elderly. The only exception to the TV rule is with gender,
where the disparity is much larger for games than for television.
There are several possible explanations as to why game characters are
disproportionate to the general population. First, the most obvious is that
game titles are driven by consumer demand, an explanation that finds support
in the previous analysis; the most popular games were the ones with larger
imbalances. Williams (2006a) has suggested that games and gender work as
a cycle: games feature more males and so attract more young males to play.
Those males grow up and are more likely to become gamemakers than
women, perpetuating the role of males in game creation, etc. What is new in
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these data is the recognition that the patterns could be far more imbalanced if
they were driven entirely by sales. After all, if the most popular games contain
more males than the average game does, there is an economic incentive
for developers to make games with 85 percent males, not 81 percent; if
developers were aware of these data and were interested in matching their
clientele with representations accurately, they would add slightly more males
to game titles in general – that is, if their goal was only to satisfy the current
audience rather than to expand it.
Second, ethnolinguistic vitality theory offers a further explanation for the
disparity: that games are merely a mirror for underlying systematic social
inequalities. However, this line of thinking ignores the process by which
media are created by individuals and groups with their own identities and
influences. Third, creators of media simply make media that reflect their
own identity. Because the game industry studies diversity within its ranks,
this ecological hypothesis can receive some basic investigation. With the
exception of African Americans, the representation in games bears a strong
resemblance to the game developer workforce itself. Game developers
are 88.5 percent male and 11.5 percent female (Gourdin, 2005). They are
31 years old on average, with nearly all between the ages of 20 and 40.
Ethnically, they are 83.3 percent white, 7.5 percent Asian, 2.5 percent
Hispanic and 2.0 percent black. This would explain the low numbers of
Hispanic game characters, who have few internal advocates, but not the
black characters. Blacks are far more represented in games than their numbers
among developer ranks would suggest. However, this may be simply an
artifact of the prevalence of black characters in sport-based games, a pattern
which is reflected in the larger culture of professional sports. Indeed, outside
of sports games, the representation of African Americans drops precipitously,
with many of the remaining featured as gangsters and street people in Grand
Theft Auto and 50 Cent Bulletproof. Not coincidentally, the popularity of
sports games is also a primary driver of the maleness of characters, since the
sports titles are all game versions of real-world men’s sports leagues (Madden
Football, NCAA Football, NBA Live, MVP Baseball, WWE Wrestling), with
no games derived from women’s sports leagues. The developer demographic
explanation is made stronger by considering that the number of female
characters (15%) comes much closer to the number of female gamemakers
(11.5%) than the number of female players (38%). If the process were entirely
player-driven, there would be far more female characters, especially among
the primary, playable characters (where the proportion is nearly identical to
the developers’ ranks). Research on gender among game developers would
shed light on this process.
Still, the most likely cause for the representation patterns in this study is
a combination of developer demographics and perceived ideas about game
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players among marketers. The stereotype of game players as only young,
white males who want to be powerful white adults may be driving the
content-creation process, even as the player base becomes older and more
diverse. From a business and marketing viewpoint, game developers would
be missing substantial opportunities for making games for different audiences.
Women, at 38 percent of game players but only 15 percent of characters, are
the most underserved. Latinos, who play more per day than whites and form
12.5 percent of the population, are only 2 percent of characters. These two
groups would be profitable existing groups to serve better. This rests on the
assumption that those underserved groups do in fact want to play characters
like themselves. It is possible that those relatively marginalized populations
would prefer to play the more empowered groups: women preferring to
play men, blacks preferring to play whites, etc. However, research on female
gamers generally has found the opposite: that female players in educational
(DeJean et al., 1999), online (Taylor, 2006) and more general game contexts
seek out feminine, sexy and strong characters to play (Royse et al., 2007).
As with any project, there are limitations in the current work. Most notably,
the representations here do not give context beyond primary and secondary
roles, merely appearance. Groups systematically shown in negative contexts
in media can become stereotyped by other groups (Dixon and Linz, 2000).
Future research could explore this in more depth, with the appearance of
blacks as either athletes or gangsters as an obvious starting point. The measure
of exposure (units sold) is also a proxy rather than a perfect time log-based
one. However, there is no such system available, making this the best
available option. Lastly, while this study examined the largest sample of
top-selling games to date, we have only one massively multiplayer online
role-playing game (MMORPG) within the year sampling frame: World
of Warcraft. MMORPGs are becoming increasingly popular and regularly
give players multiple options for choosing gender, race, age and other
increasingly fine-grained identity markers. This highlights the fact that if
the level of player choice continues to grow, researchers should pay special
attention to the players themselves as important sources of character variation.
Nevertheless, the current study demonstrates that the world of game
characters is highly unrepresentative of the actual population and even of
game players. For developers, this is a missed opportunity. For players, it is a
potential source of identity-based problems.
The authors would like to thank the University of Illinois at Urbana-Champaign for
funding the research with a grant to the first author and L.Leo Xiong, Sarah Pica and
Andrew Beharelle for their help.
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1 Note that the Hispanic/Latino classification system used by the census allows for
overlapping entries among Latinos who self-classify as Latino in one scale and another
race elsewhere, making the total not a perfect 100 percent.
2 An exception was made for Nintendo DS games, which were substantively different
due to the unique dual screen. The DS games were all measured, whether duplicated
or not. The master game list is available from the first author.
3 When measured at the character level, the 99 percent confidence intervals for the
weighted and unweighted values do not overlap for any of the measures in this
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DMITRI WILLIAMS is an assistant professor at the Annenberg School for Communication,
University of Southern California. His research interests include community and networked
: Annenberg School for Communication, University of Southern California, 734 W.
Adams Boulevard, Los Angeles, CA 90089, USA. [email: dcwillia
NICOLE MARTINS is an assistant professor in the Department of Telecommunications, Indiana
University. Her research interests include the social and psychological effects of the mass
media on youth.
MIA CONSALVO is an associate professor in the School of Media Arts and Studies, Ohio
University. Her research interests include the influence of Japan on the digital games industry,
women and games, and fashion in virtual worlds.
JAMES D. IVORY is an assistant professor in the Department of Communication, Virginia
Polytechnic Institute and State University. His research interests include the content and
physiological, psychological and social effects of new communication technologies, particularly
videogames and virtual environments.
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Video game entertainment, as well as animation, may be a symbol of Japanese culture for children and adolescents around the world. While children eagerly devote their time to playing video games, parents and teachers express their concerns, particularly about their violent contents, which may adversely affect children. After the schoolyard massacre at Columbine High School in Colorado, these concerns have been increasingly growing in the United States. On the effect of violent video games, more than 40 empirical studies have been reported (for reviews, see Dill and Dill 1998; Griffiths 1999; Shibuya 2001). Although recent studies indicate playing violent video games is likely to increase aggressive behavior (Anderson and Dill 2000; Sherry 2001; Sakamoto et al. 2001a; Ihori et al. 2003; Katori 2001), some results were inconsistent (Yukawa and Yoshida 2000). This inconsistency in experiments suggests that the effect of violent video games on aggression depends on the quantity and context of violent scenes, as well as the personality, sex, and age of the individuals. On the basis of television violence studies, Table 1 shows the predictions for which variables are likely to increase and decrease learning aggressive behavior. Among 17 variables, the effects of 12 variables (X1-12) were based on Wilson et al. (1997), and those of the other five variables (X13-17) were suggested by video game studies (e.g., Ballard and Wiest 1996; Brooks 1999; Calvert and Tan 1994;Yukawa and Yoshida 2000; for a review, see Shibuya and Sakamoto 2002). Table 1 also reports the effects of graphicness, reality, rewards, and competition which were empirically assessed in video games experiments (Ballard and Lineberger 1999; Sakamoto et al. 2001b; Anderson and Morrow 1995). Content analysis has been conducted on the quantity and context of popular video games in the United States (Lachlan et al. 2000), but no content analysis with category definitions has been reported in Japan. The previous content analysis analyzed best-selling games, which may not be popular among children or adolescents, and its categorization did not consider interactive video game features. This study analyzes the quantity and context of violence of popular video games selected by children, and the contexts of analysis include interactive video game features. This study posed the following research questions: 1. What is the prevalence of violence in childrens' favorite video games in Japan? 2. Among those games that contain violence, (1) How frequently is each variable found? (2) Is violence justified rather than unjustified? (3) Is violence rewarded rather than punished? (4) Are violent acts performed competitively rather than cooperatively?
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This study examines how individual differences in the consumption of computer games intersect with gender and how games and gender mutually constitute each other.The study focused on adult women with particular attention to differences in level of play, as well as genre preferences.Three levels of game consumption were identified. For power gamers, technology and gender are most highly integrated.These women enjoy multiple pleasures from the gaming experience, including mastery of game-based skills and competition. Moderate gamers play games in order to cope with their real lives.These women reported taking pleasure in controlling the gaming environment, or alternately that games provide a needed distraction from the pressures of their daily lives. Finally, the non-gamers who participated in the study expressed strong criticisms about game-playing and gaming culture. For these women, games are a