Unpopular, Overweight, and Socially Inept:
Reconsidering the Stereotype of Online Gamers
Rachel Kowert, MA,
Ruth Festl, MA,
and Thorsten Quandt, PhD
Online gaming has become an activity associated with a highly speciﬁc, caricatured, and often negative
image. This ‘‘stereotype’’ has permeated the collective consciousness, as online gamers have become common
caricatures in popular media. A lack of comprehensive demographic inquiries into the online gaming
population has made it difﬁcult to dispute these stereotypical characteristics and led to rising concerns about
the validity of these stereotypes. The current study aims to clarify the basis of these negative characteriza-
tions, and determine whether online video game players display the social, physical, and psychological
shortcomings stereotypically attributed them. Sampling and recruiting was conducted using a two-stage
approach. First, a representative sample of 50,000 individuals aged 14 and older who were asked about their
gaming behavior in an omnibus telephone survey. From this sample, 4,500 video game players were called
for a second telephone interview, from which the current data were collected. Only those participants who
completed all of the questions relating to video game play were retained for the current analysis (n=2,550).
Between- and within-group analyses were enlisted to uncover differences between online, ofﬂine, and
nongame playing communities across varying degrees of involvement. The results indicate that the stereo-
type of online gamers is not fully supported empirically. However, a majority of the stereotypical attributes
was found to hold a stronger relationship with more involved online players than video game players as a
whole, indicating an empirical foundation for the unique stereotypes that have emerged for this particular
subgroup of video game players.
For numerous reasons, perhaps in part because of its
rapid growth, online gaming is an activity that has come
to be associated in popular culture with a highly speciﬁc,
caricatured, and often negative image.
has been reﬂected in numerous television shows, news re-
ports, current affairs programs, and other sources of popular
and is epitomized in the following quote from
Williams et al.: ‘‘[online] game players are stereotypically
male and young, pale from too much time spent indoors and
socially inept. As a new generation of isolated and lonely
‘couch potatoes,’ young male game players are far from as-
An empirical inquiry by Kowert
found that the stereotype of online gamers revolves
around four themes: (un)popularity, (un)attractiveness, idle-
ness, and social (in)competence. The researchers also found
evidence to suggest that these negative characterizations have
become personally endorsed as accurate representations of
the online gaming community.
The validity of the stereotype of online gamers has fre-
quently been disputed as ﬁction, citing demographic data
that have provided little support for these stereotypic attri-
However, the majority of the demographic
examinations of the online gaming community have been
conducted in the context of broader assessments, which has
led to a vast, but shallow, pool of information about the
online gaming community. The work of Grifﬁths
Williams et al.
are two exceptions to this, as they attempted
to evaluate the stereotype of online gamers systematically
through large-scale demographic assessments. However,
these studies are also limited in their ability to support, or
discredit, the validity of the stereotype, as they provide in-
formation on some aspects of the stereotype (such as age and
gender), while others remained completely unexamined
(such popularity and sociality). Furthermore, these studies
(as well as the majority of the others) focused their demo-
graphic inquiries on players of massively multiplayer online
role-playing games ( MMORPG) rather than online gaming
communities more generally. However, this subgroup of
¨r Kommunikationswissenschaft, University of Mu
Kommunikationswissenschaft, University of Hohenheim, Stuttgart, Germany.
CYBERPSYCHOLOGY,BEHAVIOR,AND SOCIAL NETWORKING
Volume 17, Number 3, 2014
ªMary Ann Liebert, Inc.
players has been found to hold different stereotypes, and are
perceived more negatively, and stereotypically, than online
gamers as a whole,
making it difﬁcult to generalize the
ﬁndings across the broader population of online game
The current study aims to evaluate the accuracy of the
stereotype of online gamers systematically and clarify whe-
ther members of the online video gaming community display
the social, physical, and psychological shortcomings stereo-
typically attributed them. To do so, telephone interviews
were undertaken across a representative German sample,
whereby participants were asked to provide information
about a wide range of outcome variables related to the ste-
reotype of online gamers (the primary variables of interest are
outlined in the following section). Differences in outcomes
between online, ofﬂine, and nonplayers will be examined in
two ways. First, broad differences in outcomes will be ex-
amined between groups to determine the presence of differ-
ences between those who choose to engage in online game
play speciﬁcally, game players who do not (i.e., online play-
ers), and the nongame playing community. Second, the linear
relationships between video game involvement and the out-
come measures will be assessed across different game playing
categories (i.e., all players, ofﬂine players, online players).
This kind of analysis was chosen, as it will be able to elucidate
any differences, and strength of these differences, across dif-
ferent game playing categories, demonstrating the extent to
which the stereotypical attributes vary with involvement
As there is little empirical evidence relating to the broader
online gaming playing population, and the validity of the
stereotype of this group, this study is largely exploratory.
However, if one were to endorse the ‘‘kernel of truth’’ hy-
and assume the stereotype is grounded in fact, one
would expect online game players to display more stereo-
typic qualities than ofﬂine video game players or nonplayers.
These patterns should also be magniﬁed amongst more in-
volved online game players.
Materials and Methods
The present study draws from a large representative
(N=5,000) of computer and console game players in
Germany who took part in a telephone interview between
March and April 2011. Only those participants who com-
pleted all of the questions relating to the variables of interest
were retained for the current analysis (n=2,551). Of these
participants, 2,051 were identiﬁed as active players (i.e.,
playing for more than 1 minute a day), 896 online players (i.e.,
participants who reported to play online video games more
often than ‘‘never’’), and 1,155 ofﬂine only players (i.e., active
players of video games who reported never engaging in on-
line play). The remaining 500 participants were nonplayers
(i.e., no history of video game play). A summary of the de-
mographic information of these game playing categories can
be found in Table 1.
As seen in Table 1, the gender distribution varied across
categories, with online players showing a more dispropor-
tionate ratio of male to female participants. Online gamers
were also found to be signiﬁcantly younger
and to play
than the subgroup of ofﬂine players.
Variables of interest
Assessment of stereotypes. The selection of the stereo-
typical attributes was guided by the research of Kowert
who identiﬁed four core components to the stereotype
of online gamers (see Table 2). Due to the time constraints of
telephone interviews, considerable semantic overlap between
some of the subcomponents of this model (e.g., athletic and
overweight), and difﬁculty in assessing the more subjective
components of the stereotype (e.g., fashionable, unattractive),
each subcomponent was not individually assessed.
For instance, within the ‘‘Popularity’’ component of the
stereotype, only the subcomponents of popular and athletic
were evaluated, as these two subcomponents could be clearly
measured (i.e., popularity was measured through a report of
how many good friends participants’ had, while athleticism
was quantiﬁed by frequency of exercise), while the other two
facets (fashionable and well groomed) would only be quan-
tiﬁable by subjective measures of participants’ perceptions.
As this kind of evaluation would likely be more inﬂuenced by
self-perception variables and, consequently, more biased,
than a report of the size of one’s friendship circle or frequency
of participating in a particular activity, a direct assessment of
popularity and athleticism was prioritized.
However, as can be seen in Table 2, subcomponents from
all four of the primary stereotype dimensions were examined
(e.g., popularity, attractiveness, idleness, and sociality).
The measures enlisted to evaluate the stereotypical di-
mensions varied. While some constructs were measured with
a single item (such as a frequency report), others were as-
sessed with shortened versions of validated scales. This var-
iation should not be considered problematic, however, as the
evaluation of each subcomponent was tailored to the partic-
ular construct in question based on previous research. The
assessed subcomponents, and the measures enlisted to
quantify them, are discussed in more detail below.
Table 1. Mean and Standard Deviations
of Demographic Variables Across All Gaming Groups
n=1,155 n=896 n=500
Gender 45.9% male 70.0% male 39.6% male
Age 45.45 (14.75) 33.57 (13.28) 48.56 (17.12)
(minutes per day)
33.68 (61.61) 74.19 (83.66) —
Table 2. Four Components of the Stereotype of Online
Game Players According to Kowert et al.
Popularity Attractiveness Idleness Sociality
Well-groomed Unattractive *Young** *Socially inept
Fashionable *Overweight *Underachiever Lazy
*Popular *Loner *Isolated *Reclusive
*Athletic *Obsessive Pale Introverted
Note. *Assessed subcomponent. **While what constitutes young is
debatable, the average age of online gamers will be assessed in
relation to the stereotypical ‘‘teenage male,’’ which is stereotypically
portrayed as the primary consumer of online games.
142 KOWERT ET AL.
Popularity. To assess popularity, participants reported the
size (i.e., number of important persons) and quality (i.e., num-
ber of good friends) of their social circle, with a greater number
of friends of a greater quality signifying popularity.
leticism subcomponent of this factor was assessed by asking
participants to report how often they exercise in their leisure
time (on a 0–4 scale ranging from ‘‘never’’ to ‘‘daily’’), with less
frequent exercise signifying a lower level of athleticism.
Attractiveness. The two subcomponents of loner and
obsessive were examined. ‘‘Loner’’ was quantiﬁed by exam-
ining participants’ social motivations for play, as greater so-
cial motivation would indicate a preference to be in the
company of others (when playing) rather than alone (i.e.,
To assess social motivations, participants responded
to six statements adapted from Yee’s
scale. Reponses were given on a 5-point Likert scale ranging
from 1 =‘‘does not apply at all’’ to 5 =‘‘applies completely.’’
These items examined the extent to which players engaged in
online game play to be part of the community (‘‘I use video
games to be part of a community’’; ‘‘I use video games to
enter into an exchange with others’’), to gain social capital (‘‘I
use video games to ﬁnd new friends’’; ‘‘I use video games to
get to know other gamers’’), and to be part of a team (‘‘I use
video games to play with other people’’; ‘‘I use video games
to play on the same team as others’’). This scale was found to
be highly reliable (Cronbach’s a=0.85). Outcomes were av-
eraged to provide a mean score for each participant.
To evaluate obsessiveness, the short form of the Game
Addiction Scale developed by Lemmens et al.
istered (for an in-depth analysis of this scale see [Festl,
Scharkow, and Quandt, 2013]
). Participants indicated how
often they experienced each of the seven situations described
in scale within the last 6 months. Responses were given on a
5-point Likert scale ranging from 1 =‘‘never’’ to 5 =‘‘very of-
ten.’’ This scale was found to be highly reliable (Cronbach’s
a=0.89). Outcomes were averaged to provide a mean score
for each participant.
Idleness. The underachiever and isolated subcompo-
nents of idleness were evaluated. To assess underachieve-
ment, participants were asked to rate how successful they are
in school/university/occupational settings compared to their
peers on a 5-point Likert scale ranging from 1 =‘‘not at all
successful’’ to 5 =‘‘very successful’’ (see Turban and Dough-
for a similar approach). A lower outcome would sug-
gest a greater degree of underachievement. As these
categories are mutually exclusive, outcomes for each of these
questions will be assessed independently.
The outcomes from an abridged version of the Berlin Social
were used to quantify isolation. The four
items included in this assessment represent the highest
loading items from each of the two subscales of this measure
(instrumental and emotional support; Cronbach’s a=0.87).
This scale was found to be highly reliable (Cronbach’s
a=0.84). The outcomes from this measure were averaged to
compute a mean score.
Sociality. Two of the four components of sociality were
directly assessed: socially inept and reclusive. Social compe-
tence was measured using two items adopted from the Cali-
fornia Psychological Inventory (CPI; i.e., ‘‘I get on very well
with others’’ and ‘‘I set high standards formyself and others’’).
Reclusiveness was assessed alongside ‘‘loner’’ by examining
participants’ social motivations to play. Greater social motiva-
tion to play would suggest players are not reclusive but instead
engage in this activity in order to participate with others.
An overview of the assessed subcomponents and their
measures can be found in Table 3.
Differences in stereotypical attributes between online and
ofﬂine players and between online and nonplayers were ex-
amined with two separate MANOVA analyses. It was pre-
dicted that online players would exhibit the stereotypical
Table 3. Assessed Subcomponents of the Stereotype
of Online Gamers, as Identiﬁed by Kowert et al.
Popular Number of important persons
Number of good friends
Athletic Frequency of exercise
Loner Social motivations to play
Obsessive Game Addiction Scale (short form)
Underachiever Academic and work success
Isolated Number of important persons
Berlin Social Support Scale
Socially inept CPI Sociability subscale
Reclusive Social motivations to play
Table 4. Mean Differences in Outcomes
for Assessments of Stereotypical Attributes
(n=881) (n=500) (n=1136)
Number of important
persons (P, I)
5.80 (5.94) 5.71 (8.35) 5.29 (3.99)
Number of good
6.08 (6.95) 5.57 (6.64) 5.07 (4.59)
exercise (P, A)
2.26 (1.23) 2.14 (1.27) 2.07 (1.25)
Social motivations (A) 8.09 (2.71) — 5.04 (2.24)*
Problematic use (A) 1.17 (1.2) — 0.64 (3.11)*
School success (I) 3.54 (0.72) 3.76 (0.69) 3.72 (0.83)
University success (I) 3.57 (0.79) 3.33 (0.49) 3.70 (0.67)
3.67 (0.89) 3.86 (0.81) 3.77 (0.78)
Social support (I, S) 4.42 (0.58) 4.36 (0.68) 4.38 (0.62)
Sociability (S) 4.14 (0.67) 4.32 (0.64) 4.15 (0.64)
Note. **As success categories were mutually exclusive, the number
of participants responding to each question varied across groups.
School success: ofﬂine players, n=75; online players, n=183;
nonplayers, n=41. University success: ofﬂine players, n=27; online
players, n=76; nonplayers, n=12. Occupational success: ofﬂine
players, n=1,047; online players, n=630; nonplayers, n=442.
RECONSIDERING THE STEREOTYPE OF ONLINE GAMERS 143
attributes to a greater extent than ofﬂine and nonplayers.
These predictions were not supported. After controlling for
multiple comparisons (Bonferroni adjustment, p<0.001), no
signiﬁcant differences between online and nonplayers were
found. However, signiﬁcant differences between online
players and ofﬂine players were found, with online players
reporting greater problematic play behavior (F=16.35,
p<0.001, partial g
=0.008) and greater social motivations for
play (F=436.57, p<0.001, partial g
=0.178) than ofﬂine
players. These results can be seen in Table 4. No other sig-
niﬁcant differences were found.
Hierarchical regression analyses were conducted whereby
video game involvement was regressed by each of the variables
across each gaming group. In line with previous research,
measure of play duration was employed as the measure of vi-
deo game involvement. This was quantiﬁed as average daily
play time (minutes), with a higher rate of play indicating greater
interaction, or involvement, with video game environments.
A potential problem in this type of analysis is the correla-
in which two variables (i.e., involvement and
social motivations) are correlated because they share variance
with a third variable (i.e., gender). This can lead to an erroneous
interpretation that the two variables are directly related when
in fact they are not. The diversity of the current sample makes
this problem particularly likely, as there are several variables
that could potentially co-vary with both gaming involvement
and the various outcome measures, such as gender and age.
Therefore, the effects of age and gender were partialled out in
step 1 and are not reported. As can be seen in Table 5, signiﬁ-
cant linear relationships between video game involvement and
the outcome measures were found across game playing cate-
gories. To ease interpretation, the stereotypic factor(s) that each
item relates to is listed next to each outcome variable.
Lower exercise frequency, poorer occupational success,
lower social support, and a greater social motivation to play
were all found to be predictive of increased levels of in-
volvement, all of which were magniﬁed amongst the subgroup
of online players. Increased problematic use also emerged as a
signiﬁcant individual predictor of online video game in-
volvement, suggesting that increased online video game play
is a particularly strong predictor of problematic use.
This study examined the validity of the stereotype of online
gamers by evaluating differences in outcomes on a range of
variables associated with the cultural stereotype of this pop-
ulation. Contrary to predictions, broad differences were not
found between online and nonplayers. The only signiﬁcant
difference to emerge between these groups was age, as online
players were found to be signiﬁcantly younger than ofﬂine or
nonplayers. However, the average online player was found to
be in their 30s, rather than their teenage years, disputing the
anecdotal prototype and conﬁrming previous demographic
Additional differences did emerge between on-
line and ofﬂine players. However, these differences were
limited to problematic play and social motivations, with on-
line players generating higher outcomes on both of these
measures. The lack of overarching differences between on-
line, ofﬂine, and nonplayers signiﬁes that most the compo-
nents of the stereotype are not empirically supported. Online
players do not seem to be more lazy, overweight, or unath-
letic than ofﬂine or nonplaying participants, as they all re-
ported similar levels of exercise, nor are particularly
unpopular, socially inept, isolated, or reclusive, as online
players reported equivalent levels of quality friendships
sociability as compared to the other groups, as well as a
greater social motivation to play than ofﬂine players.
However, regression analyses did uncover signiﬁcant in-
verse relationships between involvement and frequency of
exercise, occupational success, and social support, suggesting
that more involved online video game players are more un-
athletic, underachieving in their occupational pursuits in re-
lation to their peers, and less socially supported than the
broader video game playing population or the subgroup of
ofﬂine players. A positive relationship between involvement
and problematic play amongst online players also emerged,
indicating that the greater involvement one has in online
gaming as an activity, the greater likelihood one will exhibit
the qualities associated with problematic play (e.g., salience,
tolerance, mood modiﬁcation, relapse, withdrawal, conﬂict,
and problems). While these results indicate that variation in
outcomes correspond with increased involvement, it remains
unclear whether the emergence of linear relationships sig-
niﬁes a causal chain or represents preexisting differences. It is
possible that changes in athleticism, occupational success,
and social support are cultivated through involvement,
whereas the relationships between involvement and prob-
lematic play reﬂect differences that existed prior to engage-
ment, as no broad differences were found between groups
with the former but were evident for the latter. However,
additional research, particularly, longitudinal, would be
needed to clarify the mechanisms underlying these ﬁndings.
Limitations and future research
While this work provides the ﬁrst systematic examination
of the validity of the stereotype of online gamers, there are
limitations to consider. First, the current sample was limited
Table 5. Total R
and Beta Weights for Individual
Predictors Among Online Game Players
Step 2 B SE (B)
Number of important persons (P, I) 20.017* 0.008
Number of good friends (P) 0.001 0.007
Frequency of exercise (P, A) 20.239* 0.039
Social motivations (A) 0.482* 0.050
Problematic use (A) 0.839* 0.191
School success (I) -0.141 0.136
University success (I) -0.355 0.180
Occupational success (I) 20.361* 0.063
Social support (I, S) 20.170* 0.081
Sociability (S) 0.013 0.070
Note.*p<0.001. **As success categories were mutually exclusive,
the number of participants responding to each question varied.
School success: n=182. University success: n=76. Occupational
144 KOWERT ET AL.
to residents of Germany. As such, replications are needed to
determine if these effects remain across other Western pop-
ulations. Second, due to the limitations of computer assisted
telephone interviewing, it was not possible to evaluate every
subcomponent of the stereotype or provide an in-depth
analysis of each of the attributes that was assessed. As such,
some differences may have gone undetected. For instance, it
is possible that online game players exhibit greater degrees of
introversion than the ofﬂine or nongaming population. It is
also possible that the measure of sociability was too broad to
uncover any substantial differences between online gamers
and nonplayers. These two contentions seem particularly
likely, as researchers have identiﬁed considerable differences
in sociability within online gaming populations
suggested that online video game play may be particularly
desirable for introverted individuals.
The stereotype of online gamers centers around themes of
(un)popularity, (un)attractiveness, idleness, and social compe-
conjuring up images of socially inept teenage boys,
hypnotically engaged in their gaming worlds. While this char-
acterization has permeated the collective consciousness, both
anecdotally and individually, there has yet to be a systematic
evaluation into the validity of these stereotypical attributions.
The current research attempted to provide this information by
examining the demographic proﬁle of online game players and
its alignment with popular stereotypes. The results indicated
that the stereotype of this population is not fully supported
empirically. While more involved online video game players do
seem to differ from their less involved counterparts on a variety
of stereotypically ascribed attributes, particularly in relation to
the psychological (e.g., problematic use, occupational success,
social support) components of the stereotype, most aspects of
the stereotype did not garner empirical support. As broad dif-
ferences did not emerge between online, ofﬂine, and nonplaying
populations for most of the stereotypical constructs (i.e., un-
popular, unathletic, loner, isolated, socially inept, reclusive), it
epitomize the stereotypical mold anecdotally attributed to them.
a. This sample was drawn from a representative sample of
50,000 individuals aged 14 and older who were asked
about their gaming behavior in an omnibus telephone
survey using the German standard computer assisted
telephone interviewing (CATI) sampling procedure.
b. Univariate ANOVA analysis, controlling for age: F(1,
2047) =116.26, p<0.001, g
c. Univariate ANOVA analysis controlling for age and
gender: F(1, 2048) =396.22, p<0.001, g
d. While an inverse relationship was found for the general
size of their social circle (e.g., number of important
persons), this relationship was relatively small in mag-
nitude, suggesting that this difference is not substantial.
The research leading to these results received funding from
the European Union’s Seventh Framework Programme (FP7/
2007–2013) under grand agreement no. 240864 (SOFOGA).
Author Disclosure Statement
No competing ﬁnancial interests exist.
1. Kowert R, Grifﬁths MD, Oldmeadow JA. Geek or chic:
emerging stereotypes of online gamers. Bulletin of Science,
Technology, & Society 2012; 32:471–479.
2. Williams D. (2005). A brief social history of game play. In
Vorderer P and Bryant J, eds. Playing Computer Games: Mo-
tives, Responses, and Consequences. Mahwah, NJ: Lawrence
Erlbaum. pp. 197–212.
3. Williams D, Yee N, Caplan S. Who plays, how much, and
why? Debunking the stereotypical gamer proﬁle. Journal of
Computer-Mediated Communication Monographs. 2008;
4. Day F. (Writer), Morgan JS (Director). (2007) Wake up call.
In F. Day (Producer), The Guild.
5. Lorre C, Molaro S, Kaplan M (Writers). (2008) Barbarian
sublimation. In F. Belyeu (Producer), The big bang theory.
Warner Brothers Studios.
6. Parker T, Stone M. (2006) Make love, not warcraft. In Parker
T, Stone M, eds. South park. South Park Studios.
7. Truly M. (Writer). (2010) Bullseye. In W. Films (Producer),
Law and order: special victims unit. NBC Studios.
8. Axelsson A, Regan T. (2002) How belonging to an online group
affects social behaviour—a case study of Asheron’s Call. 2nd ed.
Redmond: Microsoft Research.
9. Cole H, Grifﬁths MD. Social interactions in massively mul-
tiplayer online role-playing games. CyberPsychology & Be-
havior 2007; 10:575–583.
10. Grifﬁths MD, Davies M, Chappell D. Breaking the stereo-
type: the case of online gaming. CyberPsychology & Beha-
vior 2003; 6:81–91.
11. Williams D, Ducheneaut N, Xiong L, et al. From tree house
to barracks. Games & Culture 2006; 1:338–361.
12. Yee N. The demographics, motivations, and derived experiences
of users of massively-multi-user online graphical environ-
ments. Teleoperators & Virtual Environments 2006; 15:309–329.
13. Yee N. Motivations of play in online games. Journal of Cy-
berPsychology & Behavior 2007; 9:772–775.
14. Kowert R, Oldmeadow JA. (2012) The stereotype of online
gamers: new characterization or recycled prototype. Tampere,
15. Prothro TE, Melikian LH. Studies in stereotypes: familiarity
and the kernel of truth hypothesis. The Journal of Social
Psychology 1955; 41:3–10.
16. Parker J, Asher S. Friendship and friendship quality in
middle childhood: links with peer group acceptance and
feelings of loneliness and social dissatisfaction. Develop-
mental Psychology 1993; 29:611–621.
17. Lemmens J, Valkenburg P, Peter J. Development and vali-
dation of a game addiction scale for adolescents. Media
Psychology 2009; 12:77–95.
18. Festl R, Scharkow M, Quandt T. Problematic computer game
use among adolescents, younger and older adults. Addic-
tion, 2013; 108:592–599.
19. Turban D, Dougherty T. Role of prote
´personality in re-
ceipt of mentoring and career success. Academy of Man-
agement Journal 1994; 37:688–702.
20. Schulz U, Schwarzer R. Social support and coping with ill-
ness: The Berlin Social Support Scales (BSSS). Diagnostica
21. Gough HG, Bradley P. (1996) CPI manual. 3rd ed. Palo Alto,
CA: Consulting Psychologists Press.
RECONSIDERING THE STEREOTYPE OF ONLINE GAMERS 145
22. Barnett M, Vitaglione G, Harper K, et al. Late adolescents’
experiences with and attitudes towards videogames. Journal
of Applied Social Psychology 1997; 27:1316–1334.
23. Colwell J, Kato M. Investigation of the relationship between
social isolation, self-esteem, aggression and computer game
play in Japanese adolescents. Asian Journal of Social Psy-
chology 2003; 6:149–158.
24. Grifﬁths MD. Computer game playing and social skills: a
pilot study. Aloma 2010; 27:301–310.
25. Kolo C, Baur T. Living a virtual life: social dynamics of
online gaming. International Journal of Computer Game
Research 2004. www.gamestudies.org/0401/kolo (accessed
Mar. 5, 2013).
26. Lo S, Wang C, Fang W. Physical interpersonal relationships
and social anxiety among online game players. CyberPsy-
chology & Behavior 2005; 8:15–20.
27. Senlow G. Playing videogames: the electronic friend. Journal
of Communication 1984; 34:148–156.
28. Shen C, Williams D. Unpacking time online: connecting In-
ternet and massively multiplayer online game use with
psychological well-being. Communication Research 2010;
29. Smyth J. Beyond self-selection in video game play. Cy-
berPsychology & Behavior 2007; 10:717–721.
30. Huff D. (1954) How to lie with statistics. New York: Norton.
31. Kowert R, Oldmeadow J. (A)social reputation: exploring the
relationship between online video game involvement and
social competence. Computers in Human Behavior 2013;
32. Lemmens J, Valkenburg P, Peter J. Psychosocial causes and
consequences of pathological gaming. Computers in Human
Behavior 2011; 27:114–152.
33. Liu M, Peng W. Cognitive and psychological predictors of
the negative outcomes associated with playing MMOGs
(massively multiplayer online games). Computers in Human
Behavior 2009; 25: 306–1311.
34. Caplan S, Williams D, Yee N. Problematic Internet use and
psychosocial well-being among MMO players. Computers
in Human Behavior 2009; 25:1312–1319.
Address correspondence to:
The University of Mu
Rektorat- DerKanzler—Abteilung 3.3
146 KOWERT ET AL.