The development of the Problematic Online Gaming Questionnaire (POGQ).
ABSTRACT Online gaming has become increasingly popular. However, this has led to concerns that these games might induce serious problems and/or lead to dependence for a minority of players.
The aim of this study was to uncover and operationalize the components of problematic online gaming.
A total of 3415 gamers (90% males; mean age 21 years), were recruited through online gaming websites. A combined method of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) was applied. Latent profile analysis was applied to identify persons at-risk.
EFA revealed a six-factor structure in the background of problematic online gaming that was also confirmed by a CFA. For the assessment of the identified six dimensions--preoccupation, overuse, immersion, social isolation, interpersonal conflicts, and withdrawal--the 18-item Problematic Online Gaming Questionnaire (POGQ) proved to be exceedingly suitable. Based on the latent profile analysis, 3.4% of the gamer population was considered to be at high risk, while another 15.2% was moderately problematic.
The POGQ seems to be an adequate measurement tool for the differentiated assessment of gaming related problems on six subscales.
- SourceAvailable from: Halley M Pontes[Show abstract] [Hide abstract]
ABSTRACT: Background: Over the last decade, there has been growing concern about 'gaming addiction' and its widely documented detrimental impacts on a minority of individuals that play excessively. The latest (fifth) edition of the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders (DSM-5) included nine criteria for the potential diagnosis of Internet Gaming Disorder (IGD) and noted that it was a condition that warranted further empirical study. Aim: The main aim of this study was to develop a valid and reliable standardised psychometrically robust tool in addition to providing empirically supported cut-off points. Methods: A sample of 1003 gamers (85.2% males; mean age 26 years) from 57 different countries were recruited via online gaming forums. Validity was assessed by confirmatory factor analysis (CFA), criterion-related validity, and concurrent validity. Latent profile analysis was also carried to distinguish disordered gamers from nondisordered gamers. Sensitivity and specificity analyses were performed to determine an empirical cut-off for the test. Results: The CFA confirmed the viability of IGD-20 Test with a six-factor structure (salience, mood modification, tolerance, withdrawal, conflict and relapse) for the assessment of IGD according to the nine criteria from DSM-5. The IGD-20 Test proved to be valid and reliable. According to the latent profile analysis, 5.3% of the total sample were classed as disordered gamers. Additionally, an optimal empirical cut-off of 71 points (out of 100) seemed to be adequate according to the sensitivity and specificity analyses carried. Conclusions: The present findings support the viability of the IGD-20 Test as an adequate standardised psychometrically robust tool for assessing internet gaming disorder. Consequently, the new instrument represents the first step towards unification and consensus in the field of gaming studies.PLoS ONE 09/2014; · 3.53 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: More and more college students are using microblogs, with some excessive users demonstrating addiction-like symptoms. However, there is currently no published scale available for use in assessing excessive use of these microblogs, a significant impediment to advancing this area of research. We collected data from 3,047 college students in China and developed a Microblog Excessive Use Scale (MEUS) for Chinese college students, comparing it with criteria used for assessing Internet addiction. Our diagnostic scale featured three factors, two of which-"withdrawal and health problem" and "time management and performance"-are already included in Internet addiction assessment scales. The third factor, "social comfort," does not appear in Internet addiction assessment scales. Our study found that females have significantly higher MEUS scores than males, and that total MEUS scores positively correlated with scores from "self-disclosure" and "real social interaction" scales. These findings differ from results obtained in previous investigations into Internet addiction. Our results indicate that some characteristics of the excessive use of microblogs are different to those of Internet addiction, suggesting that microblog overuse may not correspond exactly to the state of Internet addiction.PLoS ONE 01/2014; 9(11):e110960. · 3.53 Impact Factor
- Addiction Research and Theory 12/2014; 5(4):e124. · 1.03 Impact Factor
The Development of the Problematic Online Gaming
Zsolt Demetrovics1*, Ro ´bert Urba ´n1, Katalin Nagygyo ¨rgy1,3, Judit Farkas1,3, Mark D. Griffiths2*,
Orsolya Pa ´pay1,3, Gyo ¨ngyi Ko ¨ko ¨nyei1, Katalin Felvinczi1, Attila Ola ´h1
1Eo ¨tvo ¨s Lora ´nd University, Institute of Psychology, Budapest, Hungary, 2Nottingham Trent University, Psychology Division, Nottingham, United Kingdom, 3Doctoral
School of Psychology, Eo ¨tvo ¨s Lora ´nd University, Budapest, Hungary
Background: Online gaming has become increasingly popular. However, this has led to concerns that these games might
induce serious problems and/or lead to dependence for a minority of players. Aim: The aim of this study was to uncover and
operationalize the components of problematic online gaming.
Methods: A total of 3415 gamers (90% males; mean age 21 years), were recruited through online gaming websites. A
combined method of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) was applied. Latent profile
analysis was applied to identify persons at-risk.
Results: EFA revealed a six-factor structure in the background of problematic online gaming that was also confirmed by a
CFA. For the assessment of the identified six dimensions – preoccupation, overuse, immersion, social isolation, interpersonal
conflicts, and withdrawal – the 18-item Problematic Online Gaming Questionnaire (POGQ) proved to be exceedingly
suitable. Based on the latent profile analysis, 3.4% of the gamer population was considered to be at high risk, while another
15.2% was moderately problematic.
Conclusions: The POGQ seems to be an adequate measurement tool for the differentiated assessment of gaming related
problems on six subscales.
Citation: Demetrovics Z, Urba ´n R, Nagygyo ¨rgy K, Farkas J, Griffiths MD, et al. (2012) The Development of the Problematic Online Gaming Questionnaire
(POGQ). PLoS ONE 7(5): e36417. doi:10.1371/journal.pone.0036417
Editor: Jerson Laks, Federal University of Rio de Janeiro, Brazil
Received January 26, 2012; Accepted March 31, 2012; Published May 10, 2012
Copyright: ? 2012 Demetrovics et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the Hungarian Scientific Research Fund (grant number: 83884) (http://otka.hu). The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com (ZD); firstname.lastname@example.org (MG)
The popularity of online gaming has spread at an increasing
rate since its introduction . However, given the increasing
number of studies on the phenomenon of problematic gaming
[2,3,4,5,6], there have been growing concerns about negative
consequences for a small minority. These gamers spend more time
with gaming than planned while ignoring other important
activities causing negative effects on their performance [4,7],
social relationships [8,9], and withdrawal symptoms [10,11].
There are different names present regarding this phenomenon in
scientific literature. The phenomenon is referred to as addiction
[11,12], problematic use [13,14], excessive game use  or
engagement . However, all these authors – irrespective of the
name – agree in that there exists an excessive form of online
gaming that shows a problematic pattern and that is related to
behavioral addictions [16,17]. The present authors propose to use
the name problematic gaming. This term describes both the
quintessence of the phenomenon (i.e., that the behavior is not only
excessive but gaming-related problems are also expected to be
present), while avoiding the notion of dependency (as the exact
definition and diagnostic criteria have not yet been clarified or
The issues outlined above also suggest that precise assessment
and screening of the phenomenon is an urgent matter. In order to
measure the problematic nature of gaming, some authors have
developed questionnaires based on the general phenomenon of
internet addiction , while others have attempted to operatio-
nalize the behavioral addiction model of Griffiths [16,18].
However, the limitation of these current questionnaires is that
many of them typically target users of Massively Multiplayer
Online Role-Playing Games (MMORPG) [4,9,13,14,19]. Though
this type of game is the most popular among online games, the
total population of gamers is more diverse and it is therefore
necessary to develop a measure that is suitable for the assessment
of other genres and gamer populations such as those who play
online Real-Time Strategy (RTS) games and online First Person
Shooter (FPS) games.
The aim of our study was therefore twofold. Firstly, to explore
what components comprise problematic online gaming. Secondly,
to make these dimensions measurable, to develop such a scale on
which the identified dimensions can be assessed. Contrary to
earlier studies, the objective here was to create a questionnaire
PLoS ONE | www.plosone.org1 May 2012 | Volume 7 | Issue 5 | e36417
suitable for all types of Massively Multiplayer Online Games. A
further intention was to carry out an empirical based analysis to
ensure that all components of problematic gaming remain in focus.
The study was approved by the Institutional Review Board of
the Eo ¨tvo ¨s Lora ´nd University. Informed consent was obtained via
our online system from all subjects. After introducing the goals of
the study in details the subjects were asked to tick into a box if they
agreed to continue and participate in the study.
Sample, Procedure and Participants
All Hungarian websites that facilitate the playing online games
were identified (n=18). All 18 sites were contacted by the research
team and were asked information about the number of visitors,
and requested their cooperation in the planned study. All sites
responded. Based on this information, the number of (ever)
registered users was estimated to be approximately 30,000.
However, many of these users may have simultaneously registered
on multiple sites. Furthermore, it is likely that many formerly
registered users were not currently active. All sites agreed to
publish a call for participation in the present study on their home
sites or via a newsletter. In the call for participation, gamers were
asked to visit the study website, to sign in with a password provided
by the researchers, and to complete a questionnaire. A total of
4390 questionnaires were started but not everyone completed the
whole survey. This left 3415 completed questionnaires. In addition
to answering the general questions regarding online gaming habits,
the respondents were asked about online gaming problems.
Major socio-demographic characteristics of the gamers (gender,
age, qualification, marital status, school, work) and characteristics
regarding their online gaming activities were recorded. Addition-
ally, the survey contained a 26-item questionnaire that listed
several problems regarding online gaming. The 26 items were
created by means of (i) a comprehensive literature review
supplemented with (ii) interviews with online gamers. Firstly, in
relation to the literature review, a full search was carried out in the
databases Web of Science, Science Direct, PsycINFO, and
Medline using the following keywords: online gam*, MMO,
MMORPG, multiplayer, FPS, First Person Shooter, RTS, Real
Time Strategy. A total of 199 hits were found. However, 115 was
excluded because they were irrelevant regarding specific aspects
and characteristics of online gaming. The remaining 84 papers
were read carefully and all items were listed out that could be
considered as reflecting problematic aspects of online gaming. A
total of 42 characteristics were identified this way. Secondly (and
concurrently with the literature review), 15 online gamers were
asked to list problems they had noticed in themselves and/or
others as result of online gaming. These gamers listed 32 problems.
Following exclusion of duplicates and similar items, the list was
reduced to 26 items.
Statistical analysis comprised an exploratory factor analysis
(EFA) with robust maximum-likelihood estimation (MLR) in
MPLUS 6.0. The goodness of fit was assessed by the root-mean-
square error of approximation (RMSEA) and its 90% confidence
interval (90% CI), and p value smaller than 0.05 for test of close fit
(Cfit..05). The factor solution was selected based on fit statistics
and interpretability of factors.
The factor structure based on EFA was confirmed through
confirmatory factor analyses (CFA) with independent samples.
CFA was performed with robust maximum-likelihood estimation
(MLR) in MPLUS 6.0. The goodness of fit was evaluated using
RMSEA and its 90% confidence interval (90% CI), p value
smaller than 0.05 for test of close fit, standardized root-mean-
square residual (SRMR), comparative fit index (CFI), and Tucker-
Lewis Fit Index (TLI). As Brown (2006) and Kline (2005)
recommended, multiple indices were selected in order to provide
different information for evaluating model fit.
To carry out the above analyses, four non-overlapping groups
from the sample were randomly selected. Sample 1 (n=600) was
used to perform an initial EFA on the original 26 items. Sample 2
(n=600) was used to conduct a separate EFA to cross-validate the
factor structure found in the first analysis. Sample 3 (n=600) was
used to conduct CFA analysis. After the inspection of modification
indices, we also cross-validated the final CFA model with sample 4
In order to identify the groups of users with high risk of
problematic use of online gaming a person-oriented statistical
framework was selected, seeking subtypes of gamers that exhibited
similar patterns of symptoms of problematic use. Therefore a
latent profile analysis was performed with 1 to 6 classes with the
full sample (n=3415). The latent profile analysis [23,24] is a latent
variable analysis with a categorical latent variable – in this case
problematic gamers – and continuous manifest indicators such as
factor scores of POGQ. In the process of determining the number
of latent classes, the Bayesian information criteria parsimony index
was used, alongside the minimization of cross-classification
probabilities, entropy and the interpretability of clusters. In the
final determination of the number of classes, the likelihood-ratio
difference test (Lo-Mendell-Rubin Adjusted LRT Test) was also
used. This compares the estimated model with a model having one
less class than the estimated model . A low p value (,.05)
indicates that the model with one less class is rejected in favor of
the estimated model.
To determine the cut-off point for POGQ a sensitivity analysis
based on membership in the most problematic group in the latent
profile analysis was carried out. Considering membership in this
group as a gold standard, the sensitivity and specificity values for
all POGQ cut-off points was calculated. Thus, the accuracy of the
POGQ by calculating the proportion of participants classified as
being at high risk for problematic gaming versus other gamers
could be assessed. The sensitivity (i.e., the proportion of true
positives belonging to the most problematic group based on LPA)
and specificity (i.e., the proportion of true negatives) was defined as
suggested by Altman and Bland  and Glaros and Kline .
In order to explore the probability that the POGQ would give the
correct ‘‘diagnosis’’, the positive predictive values (PPV), the
negative predictive values (NPV), and the accuracy values for each
possible POGQ cut-off points was calculated. PPV was defined as
the proportion of participants with positive test results who are
correctly diagnosed [27,28]. The NPV was defined as the
proportion of patients with negative test results who are correctly
90% of our sample (n=3072) was male. Mean age was 21 years
(SD=5.85 years). Slightly more than one-tenth of the participants
had graduate education, while 39.4% had secondary education.
The majority (61.9%) were primarily students, but approximately
one-quarter worked full time (24.3%). Almost two-thirds of the
Problematic Online Gaming Questionnaire (POGQ)
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participants were single (64.9%), and a further 24.3% were in a
relationship but did not live with their partner (see Table 1).
Slightly more than one-third (35.7%) played a maximum
14 hours per week and approximately the same number played
between 15 and 28 hours per week. One in ten played more than
42 hours a week (6 hours per day on average) (Table 1).
Approximately a half spent money on gaming, although most did
not spend more than $25(US) per month. The majority of the
sample participants were individual gamers (38.6%) but there was
also a relatively high ratio of different level organized gamers
Exploratory Factor Analyses
An exploratory factor analysis was performed with maximum-
likelihood estimation which is robust to non-normality and
promax rotation to evaluate the factor structure of 26 items on
Sample 1 (n=600). Acceptability of the factor solution was based
on goodness of fit index (RMSEA ,0.08, Cfit (90% CI) ,0.08,
pclose .0.05), the interpretability of the solution and salient factor
loadings (.0.30). A total of 1–8 factor solutions was examined.
The six-factor solution provided the first adequate RMSEA value
basedon the criteria(x2=409.8,
RMSEA=0.045 [0.039–0.051] pclose .0.90). The exploratory
factor analysis on Sample 2 (n=600) was repeated. As in Sample
1, a six-factor solution also provided the first adequate and
interpretable factor solution (x2=457.7 df=184 p,0.0001;
RMSEA=0.050 [0.044–0.056] Cfit=0.514). Factor loadings are
presented in Table 2.
For the further development of this scale, items with the
following rules were selected. First, items were excluded that had
factor loadings lower than 0.40 at least in one of the two analyses.
Second, items with salient cross loadings were excluded. If a cross
loading only in one of the two parallel EFAs was identified, the
cutoff 0.50 was used. In case of more than two cross-loadings, a
0.30 as a cutoff was used to exclude items from further analyses.
The excluded items are crossed out in Table 2. As result of the
above criteria, 18 of the original 26 items were retained (see
Confirmatory Factor Analysis
Based on the previous analyses on the samples 1 and 2, a six-
factor solution was tested on Sample 3 (n=600) with confirmatory
factor analysis. This model provided an optimal fit to the data
RMSEA=0.043 [0.036–0.051] Cfit.0.90; SRMR=0.037). We
cross-validated this model with Sample 4 (n=1615) and found
adequate level of fit (x2=512.8 df=120 p,0.0001; CFI=0.962;
SRMR=0.036). The factor loadings, factor reliabilities, internal
consistencies, means, and SDs are presented in Table 3.
Professional vs. Non-professional Gamers
The possible bias stemming from the inclusion of professional
gamers in the total sample was checked. The level of fit of the
measurement models without professional gamers (N=2857) was
TLI=0.956; RMSEA=0.043 [0.040–0.046]; SRMR=0.034).
The level of fit of the measurement models only among
professional gamers (N=528) was also satisfactory (x2=290.0
df=120 p,0.0001; CFI=0.948; TLI=0.934; RMSEA=0.052
[0.044–0.059]; SRMR=0.034). Furthermore, a multi-group
(N=528) gamers was performed. In this analysis, the factor
loadings and intercepts were set equal in both groups. The level of
fit was satisfactory (x2nonprofessional=758.6, x2profession-
RMSEA=0.042 [0.040–0.045]; SRMR=0.036) and the means
of latent variables were not statistically different in either group.
Table 1. Demographics and gaming characteristics.
Age, years; Mean (SD)21.01 (5.85)
Gender (Males, %) 90
Less than high school (%)49.5
High school graduate (%)39.4
College graduate or more (%)11.1
Employed fulltime (%)24.3
Employed part-time (%) 10.4
Neither employed nor student (%)3.4
Place of residence
Budapest (%) 26.9
Other cities (%)54.0
In relationship but not living together (%)22.3
In relationship and living together (%) 7.3
Married (%) 4.9
Divorced or widowed (%)1.0
Subjective economic status
Better than average (%)47.9
Average (%) 41.8
Less than average (%)10.3
Gaming related characteristics
Time spent with gaming
Less than 7 hours a week (%)11.8
Between 7–14 hrs a week (%)23.9
15–28 hrs a week (%) 34.8
29–42 hrs a week (%)20.2
More than 42 hrs a week (%)9.3
Money spent on gaming
None (%) 51.8
Maximum 25 USD per month) (%) 36.8
More than 25 USD per month (%) 11.4
Type of players – level of organization
Problematic Online Gaming Questionnaire (POGQ)
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Table 2. Exploratory factor analyses of the generated items.
Factor 1Factor 2 Factor 3Factor 4Factor 5Factor 6
Y1 When you are not gaming, how often do you
think about playing a game or
think about how would it feel to play at that
0.89 0.93 0.05
Y4 How often do you daydream about gaming?0.82 0.82 0.03 0.07
Y16 How often do you dream about gaming?
18.104.22.168 0.270.26 0.04 0.100.04 0.10
Y3 How often do you feel that you
should reduce the amount of time you spend
Y6 How often do you unsuccessfully try to
reduce the time you spend on gaming?
0.01 0.070.870.84 0.10 0.06
Y12 How often do you feel that gaming causes
problems for you in your life?
20.07 20.09 0.470.63
20.070.19 0.05 0.23 0.150.03 0.13
Y11 How often do you neglect your
studies, work or other important duties because of
0–04 20.07 0.390.380.11 0.260.12
Y2 How often do you neglect your tasks at home in
order to play games more?
0.30 0.380.21 0.270.03
Y29 How often do you think about getting
professional help to reduce your time
20.17 20.16 0.300.40
22.214.171.124.25 0.110.01 0.25
Y9 How often do you try to keep the amount
of time you spent on gaming secret
from people around you?
0.40 0.210.03 0.19
20.06 0.030.180.26 0.190.09
Y20 How often do you lose track of time when
20.05 20.04 0.04 0.040.75 0.76
Y23 How often do you play longer than
20.04 20.08 0.090.100.690.70
Y28 How often do you feel time stops while
20.09 20.02 0.02
Y22 How often are you so immersed in
gaming that you forget to eat?
20.05 20.08 20.06
20.09 0.610.710.04 0.02
Y5 How often do you play games when you
should be sleeping?
Y15 How often do you think to yourself ‘‘just a
few minutes more and then I’ll stop’’?
Y19 How often do you fail to meet up with a
friend because you were gaming?
20.04 20.14 0.000.01
Y24 How often do you neglect other activities
because you would rather game?
20.080.03 0.130.07 0.720.80 0.10
Y17 How often do you choose gaming
over going out with someone?
20.020.02 0.000.70 0.79
Y26 How often do you lose concentration on
other tasks because you are preoccupied
0.22 0.150.04 0.110.08 0.030.240.18 0.10 0.180.19 0.25
Y25 How often do you argue with your parents
and/or your partner because of gaming?
Y14 How often do the people around you
complain that you are gaming too much?
0.16 0.110.06 0.130.06 0.08
Y10 How often do you get restless or irritable
if you are unable to play games
for a few days?
Y13 How often do you feel depressed or irritable
when not gaming only for these
feelings to disappear when you start playing?
0.09 0.06 0.02
Y7 How often do you get irritable, restless or
anxious when you cannot play games
as much as you want?
0.09 0.13 0.060.14 0.010.03
Problematic Online Gaming Questionnaire (POGQ)
PLoS ONE | www.plosone.org4 May 2012 | Volume 7 | Issue 5 | e36417
Labels of Factors
In the first factor, two items belonged that referred to obsessive
thinking and daydreaming on the online game. This dimension
was named preoccupation. The second factor contained items
concerning the excessive use of online games. The three items
belonging here referred to noticing gaming related problems,
elongated gaming time, and the difficulties in controlling time
spent on gaming. This factor was named overuse. The third factor
was named immersion as these four items indicated dealing
excessively with online games, immersion in gaming, and losing
track of time. The fourth factor indicated damage to social
relationships, and the preference of gaming over social activities.
This three-item dimension was named social isolation. The two
items of the fifth factor referred to the comments of the player’s
social environment on overuse of online games and the related
conflicts, so this factor was named interpersonal conflicts. Finally,
the four items of the sixth factor concerned the appearance of
withdrawal symptoms in cases when players experienced difficul-
ties in gaming as much as they wanted. This dimension got the
Latent Profile Analysis
A latent profile analysis was performed on the dimensions of
problematic online gaming, and, a four-class solution was found
according to the decision criteria. As Table 4 demonstrates that
the AIC, BIC and sample-size adjusted BIC continued to decrease
as more latent classes were added. However, a leveling-off after the
four-latent-class solution was noted. In inspection of entropy, the
two-class solution reached the maximum level, but the four-class
solution also provided also an adequate level of entropy. Based on
the L-M-R test, the four-class solution was accepted.
The features of each class are presented in Figure 1. The first
class represents those gamers (47.8% of the total sample) that
scored on dimensions of problematic use below the average. The
second class of gamers (33.7%) represents the low risk of
problematic use. The third class (15.2%) represents the medium
risk of problematic use. Finally, the fourth class (3.4%) represents
the high risk of problematic use. In this latter group, the ‘social
isolation’ factor and ‘withdrawal symptoms’ factor especially
showed an elevated level compared to other dimensions.
Determination cut-off score to be classified a problematic
gamer: Sensitivity and specificity analyses.
membership in the fourth class (i.e., being at high risk for
problematic gaming) as a ‘‘gold standard’’, the sensitivity,
specificity, PPV as well as NPV, and accuracy of the POGQ at
all possible cut-off points (Table 5) were calculated.
Based on this analysis, a cut-off score of 65 points is suggested as
an ideal cut-off to be classed as problematic gamer. In this case,
specificity is 100%, while sensitivity is 96%. This means that
practically none of the negative (i.e., non-problematic) cases are
considered as problematic, while only 4% of the true problematic
cases are not recognized. Accuracy, as well as NPV in this case is
100%, while PPV is 90%. Increasing of the cut-off score would
result in the growing number of the false negative cases, whereas
decreasing would lead to more false positive cases.
Due to the growing number of indicated problems concerning
online gaming it has become an absolute necessity to develop a
tool with adequate psychometric characteristics for the measure-
ment of the extent of gaming-related problems. The Problematic
Online Gaming Questionnaire (POGQ) developed in this study,
based on the results of the analyses, appears to fulfill those
requirements that are expected from a measure like this. The
POGQ was created in a way that it is applicable for all types of
online games and its empirical basis makes it possible to cover all
problems experienced by the players.
The results of these empirically-based analyses are at the same
time very much supported by the fact that the six dimensions
identified in the background of problematic online gaming fit
closely to the available theoretical frameworks. Griffiths 
proposed a ‘‘components’’ model for addictions that assumes the
six classical symptoms for addiction behaviors in general that is
salience, mood modification, tolerance, withdrawal, conflict, and
relapse. The withdrawal and preoccupation components can be
identified with our equally named factors, while conflict dimension
is partly covered by the interpersonal conflicts factor and partly by
the factor overuse (intrapersonal conflicts). It is interesting though
that items explicitly representing salience dimension fell out during
analysis (see item 2, 5, 11 in Table 2.), but the component is still
present in overuse, preoccupation, and social isolation factors. The
relapse component appears in the overuse dimension while mood
modification dimension is primarily present in the withdrawal
factor (item 13 in Table 2).
In another approach, the DSM-IV criteria for psychoactive
substance use dependence by the American Psychiatric Associa-
tion  that are generally regarded as the definitional basis of
behavioral addictions can be considered. These dimensions –
withdrawal, lack of control (unsuccessful attempt to quit), much
time spent on the activity, behavior continues despite knowledge of
adverse consequences, more intensive use and for longer period
than intended – are all clearly reflected in the POGQ. The more
than adequate psychometric properties of the POGQ and the wide
empirical content it is based on, is reassuring regarding the future
use of the scale. However, further tasks include the cross-cultural
validation of the POGQ and clinical validation of the scale.
It is an issue in relation to all behavioral addictions not present
in DSM-IV-TR whether those individuals who engage in a specific
behavior excessively should be regarded as having a disorder, and
Table 2. Cont.
Factor 1 Factor 2Factor 3 Factor 4Factor 5Factor 6
Y21 How often do you get irritable or upset
when you cannot play?
20.11 20.08 20.03
20.12 0.010.080.13 0.08 0.09 0.220.68 0.68
Note: Excluded items (16, 11, 2, 29, 9, 5, 15, 26) are in italic. Factor loadings $30 are in bold.
Problematic Online Gaming Questionnaire (POGQ)
PLoS ONE | www.plosone.org5 May 2012 | Volume 7 | Issue 5 | e36417
Table 3. Confirmatory factor analyses of POGQ with two independent samples.
Y1 When you are not gaming, how often do you think about playing a game or think
about how would it feel to play at that moment?
Y4 How often do you daydream about gaming?
Y3 How often do you feel that you should reduce the amount of time you spend gaming?
Y6 How often do you unsuccessfully try to reduce the time
you spend on gaming?
Y12 How often do you feel that gaming causes problems
for you in your life?
Y20 How often do you lose track of time when gaming?
Y23 How often do you play longer than originally planned?
Y28 How often do you feel time stops while gaming?
Y22 How often are you so immersed in gaming that you forget to eat?
Y19 How often do you fail to meet up with a friend because you were gaming?
Y24 How often do you neglect other activities because you would rather game?
Y17 How often do you choose gaming over going out with someone?
Y25 How often do you argue with your parents and/or your partner because of gaming?
Y14 How often do the people around you complain that you are gaming too much?
Y10 How often do you get restless or irritable if you are unable to play games for
a few days?
Y13 How often do you feel depressed or irritable when not gaming only for these
feelings to disappear when you start playing?
Y7 How often do you get irritable, restless or anxious when you cannot play games
as much as you want?
Y21 How often do you get irritable or upset when you cannot play?
Problematic Online Gaming Questionnaire (POGQ)
PLoS ONE | www.plosone.org6 May 2012 | Volume 7 | Issue 5 | e36417
with which criteria we should identify individuals functioning on
other pathologic levels. Considering the lack of consensus
regarding definitions, the authors of the present study insisted on
using the expression ‘problematic gaming’ instead of the more
ambiguous gaming dependence. However, the latent profile
analysis performed also indicated that a segment of the online
gaming population (3.4% in this study) significantly exceeded the
whole population and characteristically showed more problems
than others. A further 15.2% of the population also showed
moderately elevated level of problems. One of the most important
tasks in future research is the detailed analysis of this at-risk
population and to explore which background factors may carry
high risk concerning problematic gaming. For forthcoming studies,
the results here highlight two significant dimensions, withdrawal
and social isolation, that showed elevated levels in case of these
gamers, while obsession and overuse seemed to be the less
indicative dimensions. Therefore, it seems that intensive actual
(overuse) or imaginary (obsessive) gaming is less indicative of
problematic gaming in itself. These results coincide with the results
observed concerning problematic internet use . In contrast,
neglecting social relationships and especially the presence of
withdrawal symptoms (feeling depressed or irritable, getting
restless, anxious or upset when not able to game) appear to carry
the highest risks. Furthermore, it is important to note that both
dimensions include reducing or neglecting other activities which
are key characteristics of addictions according to the results of
many other studies.
One limitation of the present study is that it was carried out
among Hungarian gamers thus results should be cautiously
generalized for other cultures. However, it is hoped that future
studies can confirm the findings presented here in other cultures.
Another important issue is that current results were based on self-
report data. It is again a challenge for future studies to investigate
Figure 1. Latent profile analysis on the three factors of the POGQ. The latent profile analysis resulted four classes. The first class represents
those gamers that scored on POGQ below the average, while the second class represents the low risk, the third class represents medium risk of
problematic use. The fourth class (3.4%) represents the high risk of problematic use.
Table 4. Fit indices for the latent profile analysis of the POGQ.
classes AIC BIC SSABICEntropy
2 classes 36625 3674136681 0.9249567
3 classes3267532834 327520.9093896
4 classes3088831090 309860.8921769
5 classes30079 30325301980.863 8080.171
Note: AIC: Akaike Information Criteria; BIC: Bayesian Information Criteria;
SSABIC: Sample size adjusted Bayesian Information Criteria. L-M-R Test: Lo-
Mendell-Rubin adjusted likelihood ratio test value; p: p-value associated with L-
Problematic Online Gaming Questionnaire (POGQ)
PLoS ONE | www.plosone.org7 May 2012 | Volume 7 | Issue 5 | e36417
and confirm the identified problem-dimensions in clinical and/or
observational studies. In conclusion, and based on all these
assumptions, it is hoped that creation of the POGQ will facilitate
and enhance further research, and that the instrument will serve as
a valid and reliable tool in future studies.
Problematic Online Gaming Question-
Conceived and designed the experiments: ZD. Performed the experiments:
ZD KN JF OP KF GK. Analyzed the data: RU AO. Wrote the paper: ZD
UR KN JF MDG KF.
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Problematic Online Gaming Questionnaire (POGQ)
PLoS ONE | www.plosone.org9 May 2012 | Volume 7 | Issue 5 | e36417