Online Video Game Addiction

Addiction

RESEARCH REPORT

doi: 1 0.11111j.13 60-0443 .20 1 0.031 04.x

Online video game addiction: identification of addicted adolescent garners

Antonius J. van Rooi]', Tim M. Schoenmakers^', Ad A. Vermulst\ Regina J.J.M. van den Eijnden³ & Dike van de Mheen^l,4

IVO Addiction Research Institute, Rotterdam, the Netherlands.' Behavioural Science Institute. University of Nijmegen, the Netherlands,' Faculty of Social and Behavioral Sciences. Utrecht University. the Netherlands and Department of Public Health, Erasmus Medical Center. the Netherlands'

ABSTRACT

Aims To provide empirical data-driven identification of a group of addicted online garners. Design Repeated cross-sectional survey study, comprising a longitudinal cohort, conducted in 2008 and 2009. Setting Secondary schools in the Netherlands. Participants Two large samples of Dutch schoolchildren (aged 13-16 years). Measurements Compulsive internet use scale, weekly hours of online gaming and psychosocial variables. Findings This study confirms the existence of a small group of addicted online garners (3%), representing about 1.5% of all children aged 13-16 years in the Netherlands. Although these garners report addiction-like problems, relation­ships with decreased psychosocial health were less evident. Conclusions The identification of a small group of addicted online garners supports efforts to develop and validate questionnaire scales aimed at measuring the phenom­enon of online video game addiction. The findings contribute to the discussion on the inclusion of non-substance addictions in the proposed unified concept of Addiction and Related Disorders' for the DSM-V by providing indirect identification and validation of a group of suspected online video game addicts.

Keyword Compulsive internet use, internet addiction, latent class analysis, non-substance addiction, online video games, psychosocial health, video game addiction.

Correspondence to: A. j. van Rooij. IVa Addiction Research Institute. Heemraadsingel194. 3021 DM Rotterdam. the Netherlands. E-mail: [email protected] Submitted 10 March 2010: initial review completed 10 May 2010: final version accepted 10 june 2010

INTRODUCTION

Studies have consistently demonstrated the existence of a small subgroup of video garners that is seemingly 'addicted' to games [1-3]. Although video game addic­tion is not a new phenomenon [4], the introduction of an online component in the current generation of games has probably increased the size and scope of the problem. This online component in gaming led to the initiation of (private and public) treatment programmes targeting gaming addiction [5-7]. Consequently, there is increas­ing focus upon online games when studying video game addiction [8-11].

Both Korean and western researchers report specifi­cally that Massive Multiplayer Online Role Playing Games (MMORPGs) are the main culprits in cases of online video game addiction [12-14]. In an MMORPG the player develops one or more characters (avatars) over time in a persistent virtual world. Examples include World of War­craft, Age of Conan and Runescape. Typically, higher levels require players to cooperate to achieve goals. More-

© 2010 The Authors. Addiction © 2010 Society for the Study of Addiction

over, MMORPGs cannot be completed: due to the regular introduction of new content it is practically impossible to finish all assignments. This places a considerable burden on the player's time, as they are required to continue playing to 'keep up' with the game. Research among a sample of World of Warcraft players identified a group of 10% who played an average of 63 hours per week and showed considerable negative symptoms [15]. Griisser et ai. sampled readers of an online gaming magazine in an online survey and found that 12% of those garners fulfilled diagnostic criteria of addiction concerning their gaming behaviour [2].

These findings demonstrate the existence of a small subgroup of online garners who can potentially be classi­fied as 'online video game addicts'. This group is likely to have various psychological and social problems, as game overuse can be severely disruptive to school, work and 'real-life' social contacts [2,12,16]. Drawing parallels with the internet addiction literature, we hypothesize that this 'flight from reality' may be associated with nega­tive self-esteem, depressive mood, social anxiety and/or

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206 Antonius J. van Rooij et al.

loneliness [17-20]. However, the relationship between psychosocial health and online games is potentially more complicated, as social and psychological benefits from playing online games have also been reported [15,21,22]. Moreover, effects might differ based upon the psychological profile of the gamer, i.e. there may be a group of addicted heavy garners who suffer as a result of their unbalanced life-style, and another group of heavy garners who benefit from having multiple social environ­ments. Given the former, and the fact that the vast major­ityof garners do not report addictive tendencies [1], we hypothesize that a second group of heavy garners is likely to exist. These non-addicted heavy garners will probably not show negative psychosocial outcomes or addictive symptoms, or perhaps to a lesser extent.

Unfortunately, there is no consensus on an opera­tional definition of video game addiction [11,23-25]. Despite the ongoing debate on diagnosis and definition, several methods are used to increase our understanding of game addiction. Researchers construct new scales to measure game addiction [1,3], avoid using standardized scales altogether [2] or approach the specific group of online games indirectly through more established mea­sures of internet addiction [10,26]. Estimates of the size of the group of 'addicted garners' are made subsequently by applying various cut-off points to scales measuring symptoms of video game addiction or internet addiction [1,3,27]. This results in a wide variety of estimates, depending upon the selected cut-off points and composi­tion of the sample. In the absence of consensus on a definition, the absence of a gold standard with which to compare results and the lack of clinical studies using these instruments, these efforts are speculative at best.

The present study contributes to the debate on video game addiction by applying a different approach. It seeks to provide empirical, data-driven evidence for the assumed subgroup of addicted online video garners, using two large-scale samples from the Dutch 'Monitor Study Internet and Youth'. Results provide a basis for data-based scale validation and cut-off scores. Identifica­tion of this group will be conducted through a combina­tion of two indirect measures: game addiction severity and time spent on online gaming.

In the present study, internet addiction is thought to be an appropriate measure of online game addiction severity for several reasons. First, previous work by our group (utilizing an earlier Monitor Study sample) estab­lished cross-sectional and longitudinal relationships between online gaming and internet addiction, referred to as Compulsive Internet Use (CIU) [10]. Secondly, the latter study found low correlations between various inter­net activities and online video gaming among adolescents [28], in line with its immersive nature [29], thus confirm­ing that online gaming is a monolithic activity for adoles-

© 2010 The Authors. Addiction © 2010 Society for the Study of Addiction

cents (these findings were replicated for the samples utilized in the present study). In combination with the inclusion of a measure of time spent on online gaming, this reduces the risk of misidentification (i.e. erroneously measuring addiction to various other applications). Con­sequently, the combination of a high score on CIU with many hours of online gaming per week is hypothesized to identify addicted online garners. Note that we choose to utilize the term 'addiction' for the sake of consistency with other studies: the group is defined more precisely as heavy online garners who score highly on criteria for non-substance addiction. These criteria are theorized to be applicable to online behaviour [1,3], also, see Mea­sures [Compulsive Internet Use Scale (CIUS)].

From this, several research questions emerge. Can the two hypothesized groups of heavy online garners (addicted and non-addicted) be identified using a data­driven approach? If so, how large are these groups? Finally, the present study explores the psychosocial corre­lates for the addicted versus the non-addicted heavy garners, to further elucidate the theoretical relationship between game addiction and psychosocial wellbeing.

METHODS

Procedure

The Dutch 'Monitor Study Internet and Youth' provided data for the current study [10]. This ongoing longitudinal study uses stratified sampling to select schools for partici­pation based upon region, urbanization and education level. Participating classes are included on a school-wide basis, and repeated yearly participation in the study is encouraged. Every year, participating adolescents com­plete a T-hour questionnaire in the classroom, supervised by a teacher.

Written instructions are provided to the teacher, and questionnaires are returned in closed envelopes to ensure anonymity with regard to other students and teachers. Given the non-invasive nature of the study, passive informed consent is obtained from parents every year. More specifically parents receive a letter with infor­mation about the planned questionnaire study on 'Inter­net use and well-being'. If parents do not agree with their child's participation they can inform the school coordinator and/or the researchers, in which case the child is excluded from participation. Children can refuse participation either by informing their parents or their teachers. Refusal by either parents or children rarely occurred.

Sample

The current study utilizes the 2008 (Tl) and 2009 (T2) samples of the Monitor Study. Total response rate was

Addiction. 106. 205-212

Table 1 Demographic information on the subsamples.

Online video game addiction 207

Full sample

Tl

Participating schools

Overall sample size (n)

Gender (% boys)

Dutch ethnicity (%)

Higher education level (%) Average age (years); mean (SD)

12 4559 49% 7S% 66%

14.35 (US)

"Values for T1 are reported. SD: standard deviation.

79% atTl, and 83% atT2. Non-response is mainly attrib­utable to entire classes dropping out due to internal scheduling problems on schools; 13% of all classes did not return any questionnaires at Tl and 12% did not return questionnaires at T2. For the remaining classes, the average per class response rate was 89% at Tl and 92% at T2. Twelve secondary schools participated in the study at Tl and 10 secondary schools participated at T2. Of these schools, eight participated in both years.

Given the aim of the study, i.e. identification of a group of online garners, the full sample is restricted to a subs ample of online game players for both Tl (35%, n = 1572) and T2 (40%, n = 1476). Secondly, a longitu­dinal subs ample, namely a cohort of online garners who were included in both samples, can be identified between Tl and T2 (n = 467). Analyses in the present study span the first four classes of Dutch secondary school (average per year ages of 13, 14, 15 and 16 years, respectively). Table 1 presents demographic information on the sub­samples for gender, ethnicity (Dutch/non-Dutch), higher secondary education (i.e. preparatory college and pre­university education) or lower secondary education (i.e. pre-vocational training), and average age.

Measures

Compulsive internet use

The 14-item version of theCIUS [30] was used to measure CIU, with its Dutch phrasing slightly adjusted for adoles­cents. This questionnaire (employing a five-point scale) covers several core components typical of behavioural addiction: withdrawal symptoms, loss of control, salience, conflict and coping (mood modification) [30], and includes questions such as 'Have you unsuccessfully tried to spend less time on the internet?' and 'Do you neglect to do your homework because you prefer to go on the inter­net?' The CIUS showed good validity [30] and internal reliability [30-32], and showed good reliability in the current samples (Cronbach's a = 0.88 at both Tl and T2).

© 2010 The Authors. Addiction © 2010 Society for the Study of Addiction

Weekly hours online gaming

Hours per week spent on online gaming were calculated by combining results from two questions (answers on a five-point scale) measuring days per week of online gaming [ranging from 'never', '1 day per week or less', '2/3 days per week', '4/5 days per week', to '(almost) daily'], and a seven-point scale measuring average hours of use on a gaming day (ranging from 'don't use', 'less than 1 hour', '1-2 hours', '2-4 hours', '4-6 hours', '6-8 hours' to '8 hours or more'. These questions were recoded to an interval scale and multiplied to obtain an approxi­mation of number of hours per week. Note that although 'online game playing' includes more than just MMORPGs, an open question in the Monitor Study revealed that MMORPGs and First Person Shooters (shooting games utilizing a first person perspective, i.e. Call of Duty or Counterstrike) were the most popular types of online game [33].

Psychosocial outcome measures

The psychosocial measures in the present study were: the Rosenberg's Self-Esteem Scale [20,34], the UCLA Loneli­ness Scale [35,36], the Depressive Mood List [3 7-39] and the Revised Social Anxiety Scale for Children [40-42]. These scales have been used in Dutch studies and demon­strated good reliability in the past [32,43] and in the current samples (Cronbach's a> 0.80). For all four scales, a higher score indicates more reported problems. To facilitate comparison between the scales, the present study reports standardized results.

Statistical analyses

Latent class analysis

Mplus 5.1 was used to perform a latent class analysis (LCA) [44]. LCA is an example of a mixture modelling technique used to identify meaningful groups of people (classes) that are similar in their responses to measured

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208 Antonius J. van Rooij et al.

variables [45]. In the present study, these groups were based on scores for the variables CIU and Weekly Hours Online Gaming.

The present study used LCA in an exploratory manner, aiming to establish the presence of a (small) sub­group of addicted online video garners. Besides fitting with this theoretical expectation, goodness-of-fit indices should be used to select a model of sufficient quality [46]. Two kinds of indices are used: measures of parsimony of the model and statistical tests to evaluate if the k + 1 solution is superior to a k class solution [47]. The pre­ferred measure of parsimony is the Bayesian information criterion (BIC) [48], as shown in simulation studies [45,49]. Lower BIC values indicate a more parsimonious model. Statistical evaluation of model improvement was performed with the bootstrap likelihood ratio test (BLRT) [45]. Significant values for the BLRT indicate that the tested model (k) is superior to the previous model (k - 1). After selecting a solution (see Results), identified class membership was transferred to SPSS version 17 to examine longitudinal transition.

The data were standardized to facilitate interpretabil­ity and comparability of classes (groups). Standardized

psychosocial correlates were explored through a Wald X2 test for mean equality of potential latent class predictors [50], followed by post-hoc tests to test for between-class differences. This test has the advantage of taking the probabilistic nature of class membership into account, leading to less biased estimates.

RESULTS

Latent class identification

Table 2 gives the model fit indicators for the 1-6 latent class models when identifying classes on the basis of CIU and Weekly Hours Online Gaming (Online Gaming). The BLRT consistently reports significant outcomes (P < 0.001) and BIC values are decreasing, indicating that each model is superior to the previous one. Entropy values are consistently high, indicating good classifica­tion quality.

A subgroup of assumed addicted garners, with a higher amount of weekly online gaming and a higher score on CIU, is identified from the three-class solution onwards. This group remains stable in the four- and five-class solu­tions for both time-points (Tl: n=56; n=1572; T2: n = 75, n = 1476). For the three-, four- and five-class solu­tions the relationship between CIU and online gaming seems to have a linear nature: classes are distributed along a straight line, where increases in online gaming are related linearly to simultaneous increases in CIU. The six­class model breaks this trend, as it splits the class with the highest CIU into two groups. Table 3 shows that the first group (class five) has a moderate increase in hours spent on online gaming, while CIU scores remain stable or drop. Thus, class five identifies the non-addicted heavy garners. The second group shows a moderate increase in hours spent on online gaming, accompanied by a disproportion­ate increase in CIU. As this group (class six) identifies the

T1

Table 3 Six latent class model. standardized and unstandardized results for the six classes.

T2

Online gaming (hours per week)

Compulsive internet use scale

%

Z-score hours/week Z-score crus

Class n

%

Z-score hours/week Z-score crus n

Online gaming (hours per week)

Compulsive internet use scale

Online video game addiction 209

Table 4 Six class model classes compared on standardized psychosocial outcome measures within Tl and T2.

hypothesized group of addicted online garners, the six­class model is selected as final model.

Table 3 gives the standardized and unstandardized means for this six-class model, revealing consistent class identification in both years. Unstandardized results are reported to illustrate the actual number of hours played and to support future development of cut-off scores for the CIUS. This result can be attributed partially to repeated measurement. However, the longitudinal cohort represents approximately 30% of the respective samples (Tl and T2). From this, it is assumed that the classes are both stable and replicable. When the data are weighed against national statistics [51] (using learning year, region, gender, ethnicity and education level) to obtain a nationally representative estimate for the Netherlands, the percentage of addicted heavy online garners (i.e. class six) translates to 1.6% of the entire population aged 13-16 years in the Netherlands at Tl and 1.5% at T2.

Examination of psychosocial correlates

Table 4 presents the six-class model through comparison of standardized psychosocial variables across the various classes. Significant overall differences were found for depressive mood (T2, P < 0.05), loneliness (Tl, P < 0.01)

© 2010 The Authors. Addiction © 2010 Society for the Study of Addiction

and negative self-esteem (T2, P < 0.01). Visual inspec­tions of the table shows overall higher mean scores for all four psychosocial variables in class 6 (the most addicted group). Post-hoc tests comparing the most addicted class (6) with the other classes revealed several significant dif­ferences for depressive mood (T2), loneliness (T'L, T2) and negative self-esteem (Tl, T2). Focusing specifically upon the two groups of heavy garners (addicted, class 6 and non-addicted, class 5), only one significant difference was found, i.e. at T2 the addicted garners were more depressed than the heavy garners.

Longitudinal persistence of class membership

Table 5 presents longitudinal (year-to-year) transitions for the various classes. Results show that, apart from the first class, retention for the sixth class is higher than for other classes. In this cohort, although the absolute number of people in the sixth class is low, results indicate that half the addicted online garners at Tl (n = 6) are still addicted at T2 (n = 3).

DISCUSSION

The present study has identified successfully two distinct groups of garners: one group of addicted heavy online

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210 Antonius J. van Rooij et al.

garners and another group of heavy but non-addicted online garners, thus confirming our main hypothesis. The addicted heavy online garners differed only slightly from the non-addicted heavy garners (and various other groups) in terms of psychosocial health. However, some of these addicted garners showed persistence over time, i.e. half the addicted online garners were still addicted 1 year later.

Two large-scale samples from a nationally representa­tive study were used to classify online garners with CIU. Using a data-driven approach, analyses showed the exist­ence of six distinct groups within the data. The vast majority of online garners (95%) are located in four groups, which show a linear increase in CIU as the hours per week of gaming increase. The fifth and sixth groups break this trend. The fifth group is identified as a group of heavy online garners who play many hours per week, but show stability or even a drop in addiction (2008) when compared to the previous groups. This group of non­addicted heavy online garners is relatively small (about 1-2% of the online garners, see Table 3).

The sixth group, which contains about 3% of the online garners in the period 2008/09, spends many hours on online gaming and reports more symptoms of CIU than other groups. Thus it is identified as a group of addicted heavy online video garners. These numbers translate to an average national estimate of 1.5% (2008) and 1.6% (2009) of addicted heavy online garners among all Dutch adolescents in the first four classes of secondary education (aged 13-16 years). These adoles­cents report an average of 55 hours per week on gaming.

Subsequently, psychosocial correlates were examined for the addicted online video garners. Visual inspection of the data shows higher scores on depressive mood, loneli­ness, social anxiety and negative self-esteem for addicted online garners compared to other online garners. However, post-hoc testing revealed that most of the actual bilateral relationships are non-significant from the per­spective of the addicted online garners. When compared to non-addicted heavy garners, only one significant differ­ence was found: in 2009 the addicted heavy garners were more depressed than the non-addicted heavy garners.

These ambiguous results illustrate the complexity of the relationship between online video game use, online video game addiction and psychosocial health. Especially in the case of outcome variables with a strong social element, such as loneliness and self-esteem, video gaming may well have a dualistic effect. First, it expands the horizon of the gamer by offering a second environment in which to experiment [52] and,later on, it may constrain social options in 'real life' when the second life starts to overshadow the first [8]. In this way, depressive symp­toms, loneliness and negative self-esteem might decrease for some garners as they find refuge in online games; on

© 2010 The Authors. Addiction © 2010 Society for the Study of Addiction

the other hand, these correlates may increase for others because relying exclusively on online relationships may fail to provide the full spectrum of social contacts and support the gamer's needs in real life. This hypothesis fits well with earlier theoretical work on 'problematic inter­net use' by Caplan [17,18]. Further examination of these complex relationships in the case of online gaming might benefit from using statistical methods focusing upon modelling, such as structural equation modelling. Clini­cal studies will need to be utilized to establish the actual harm and treatability of the problems associated with 'online video game addiction'.

The identification of a small group of addicted heavy online garners supports future efforts to develop and vali­date questionnaire scales aimed at measuring the phe­nomenon of 'online video game addiction'. It also confirms the existence of the group through an alterna­tive approach, thereby confirming earlier results for the subgroup of online garners [1,3]. Additionally, it provides a basis on which to establish empirically supported cut-off points for scales aiming to measure online video game addiction. Although an addicted group of garners was found, substantial caution should be exercised before the creation of a new 'disorder', due to the modest impair­ment and longitudinal persistence.

The current study has several strengths. It provides a data-driven prevalence estimate for 'video game addic­tion' in the Netherlands, based upon two large-scale samples. Additionally, it provides some of the first longi­tudinal data on the development of this phenomenon over time. However, the study also has some limitations. First, the study uses self-report data, which is known to carry the risk of bias [53]; this should be taken into account when comparing estimates with external outcome variables, such as the number of people report­ing for clinical treatment with game addiction as the main complaint. Secondly, the 'hours per week' variable was the result of a multiplication and might be affected by ceiling effects; as such, it should be viewed as an esti­mate and not as an absolute value. Thirdly, clinical mea­sures were restricted to psychosocial measures and a measure of addiction: future research might benefit from the inclusion of specific clinical measures of, for example, hyperactivity and mania. Finally, different types of online video games are available. Whereas 'online video games' are an advancement of the unified 'video games' approach, future research may benefit from further differentiation, e.g. by distinguishing online First Person Shooter games from online Role Playing Games.

In summary, this study confirms the existence of a small percentage (3%) of addicted online garners. This group represents approximately 1.5% of all children aged 13-16 years in the Netherlands. Although these garners

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report addiction-like problems, relationships with decreased psychosocial health were less evident. While survey-based data cannot determine the exact clinical nature of game addiction, the present findings contribute to the discussion on the proposed unified concept of Addiction and Related Disorders' (which includes non­substance addictions) in the DSM-V [54].

Declarations of interest

None.

Acknowledgements

The authors thank the following organizations for funding data collection of the Monitor Study Internet and Youth: the Netherlands Organization for Health Research and Development (ZonMw, project no. 31160208), the Volksbond Foundation Rotterdam, Addiction Care North Netherlands, the Kennisnet Foundation, Tactus Addic­tion Care and the De Hoop Foundation.

References

1. Gentile D. A. Pathological video-game use among youth ages 8 to 18: a national study. PSYc/lOl Sci 2009; 20: 594- 602.

2. Grusser S. M., Thalemann R., Griffiths M. D. Excessive com­puter game playing: evidence for addiction and aggression? Cyberpsychol Behav 2007; 10: 290-2.

3. Lemmens J. S .. Valkenburg P. M., Peter J. Development and validation of a game addiction scale for adolescents. Med Psycl1012009; 12: 77-95.

4. Keepers G. A. Pathological preoccupation with video games.

JAm Acad Child Psychiatry 1990; 29: 49-50.

5. Lin-Liu J. China's e-Iunkies Head for Rehab. Beijing hospital ward treats Internet addicts (Internet). IEEE Spectrum Online 2006. Available at: http.y/spectrum.Ieee.org/telecom/ internetlchinas-ejunkies-head-for-rehab (accessed 16 Feb­ruary 2010) (archived by WebCite® at http://www. webcitation.org/5na8Qi59k).

6. Sharples T. A New Recovery Center for the Woes of Warcraft.

Time.com (Internet). 2009 27 September. Available at: 3/25/13 article/O, 8 5 9 9, 1925468.00.html (accessed 16 February 2010) (archived by WebCite® at http://www.webcitation.org/5na8oy409).

7. Telegraph (UK). Britain's first computer rehab clinic opens (Internet). Telegraph. co. uk. 2009 3 November. Available at: http://www. telegraph.co. uk/technology /video-games/ 6485888/Britains-first-computer-rehab-c1inic-opens.html (accessed 16 February 2010) (archived by WebCite® at http://www.webcitation.org/ 5na8yQeEN).

8. Hussain Z., Griffiths M. D. The attitudes, feelings, and expe­riences of online gamers: a qualitative analysis. Cyberpsy­cho! Behav 2009; 12: 747-57.

9. Peters C. S.. Malesky A. Problematic usage among highly-engaged players of massively multiplayer online role playing games. Cyberpsyclwl Behav 2008; 11: 481- 4.

10. Eijnden R. J. J. M., Spijkerman R., Vermulst A. A .. Rooij A. J., Engels R. J. J. M. Compulsive Internet use among adoles-

© 2010 The Authors. Addiction © 2010 Society for the Study of Addiction

Online video game addiction 211

cents: bidirectional parent-child relationships. J Almorm Child Psychol2009; 38: 77-89.

11. Wood R. Problems with the concept of video game 'addic­tion': some case study examples. Int J Ment Health Addict 2008; 6: 169-78.

12. Chappell D., Eatough v.. Davies M. N. 0., Griffiths M.

D. EverQuest-it's just a computer game right? An interpre­tative phenomenological analysis of online gaming addic­tion. Int J Ment Health Addict 2006; 4: 205-16.

13. Council on Science and Public Health. Emotional and Behav­ioral Effects, Including Addictive Potential, of Video Games (Internet). 2007. Available at: http://www.ama-assn.org/ amal/pub/upload/mm/467/csaph12a07.doc (accessed 22 February 2010) (archived by WebCite® at http://www. webcitation.org/ 5 njUtlurL).

14. Lee M., Ko Y. H., Song H. S., Kwon K. H., Lee H. S., Nam M. et al. Characteristics of internet use in relation to game genre in Korean adolescents. Cyberpsychol Behav 2007; 10: 278-85.

15. Longman H., O'Connor E .. Obst P. The effect of social support derived from world of warcraft on negative psy­chological symptoms. Cyberpsychol Behav 2009; 12: 563- 6.

16. Wan C .. Chiou W. Why are adolescents addicted to online gaming? An interview study in Taiwan. Cyberpsyc/lOl Behav 2006; 9: 762-6.

17. Caplan S. E. Relations among loneliness, social anxiety, and problematic internet use. CyberpsYc/lOl Behav 2007; 10: 234-42.

18. Caplan S. E. Preference for online social interaction: a theory of problematic internet use and psychosocial well­being. Commun Res 2003; 30: 625-48.

19. Ha J. H., Kim S. Y .. Bae S. C .. Bae S., Kim H .. Sim M. et al.

Depression and internet addiction in adolescents. Psychopa­tiwlogy 2007; 40: 424-30.

20. Kim H., Davis K. E. Toward a comprehensive theory of problematic internet use: evaluating the role of self­esteem, anxiety, flow. and the self-rated importance of internet activities. Comput Hum Behav 2009; 25: 490- 500.

21. Lim S .. Lee J. R. When playing togetherfeels different: effects of task types and social contexts on physiological arousal in multiplayer online gaming contexts. Cyberpsycl101 Behav 2009; 12: 59-61.

22. Wang C. Helping others in online games: prosocial behavior in cyberspace. CyberpsycllOl Behav 2008; 11: 344-6.

23. Blaszczynski A. Commentary: a response to 'Problems With the Concept of Video Game 'Addiction": Some Case Study Examples'. Int J Ment Health Addict 2008; 6: 179-81.

24. Turner N. A comment on 'Problems With the Concept of Video Game 'Addiction": Some Case Study Examples'. Int J Ment Health Addict 2008; 6: 186-90.

25. Wood R. A response to Blaszczynski, Griffiths and Turners' comments on the paper 'Problems With the Concept of Video Game "Addiction": Some Case Study Examples' (this issue). Int J Ment Health Addict 2008; 6: 191-3.

26. Ko C., Yen J .. Chen C., Yeh Y .. Yen C. Predictive values of psychiatric symptoms for internet addiction in adolescents: a 2-year prospective study. Arch Pediatr Adolesc Med 2009; 163: 937-43.

27. Griffiths M. D., Hunt N. Dependence on computer games by adolescents. Psychol Rep 1998; 82: 475-80.

28. Van Rooij A. J., Schoemakers T. M .. Van den Eijnden R. J. J.

M., Van de Mheen D. Compulsive internet use: the role of

Addictiol1. 106. 205-212

212 Antonius J. van Rooij et al.

online gaming and other Internet applications. J Adolesc Health 2010; 47: 51-7.

29. Yee N. Motivations for play in online games. CyberpsycllOl Behav2006;9: 772-5.

30. Meerkerk G. J., Van den Eijnden R. J. J. M., Vermulst A. A., Garretsen H. F. L. The Compulsive Internet Use Scale rcms» some psychometric properties. CyberpsycllOl Behav 2009; 12: 1-6.

31. Meerkerk G. J .. Van den Eijnden R. J. J. M .. Garretsen H. F. L.

Predicting compulsive internet use: it's all about sex! CyberpsycllOl Behav 2006; 9: 95-103.

32. Meerkerk G. J. Pwned by the Internet, Explorative Research into the Causes and Consequences of Compulsive Internet Use. PhD thesis. Rotterdam, the Netherlands: Erasmus University. Rotterdam; 2007.

33. Van Rooij A. J., Schoenmakers T. M., Meerkerk G. J., Van de Mheen D. Monitor Internet En Jongeren. Videogal11es En Ned­erlandse [onqerer: [Monitor Internet and Youth. Video Games and Dutch Youth]. Rotterdam: IVO; 2008.

34. Rosenberg M. Society and the Adolescent SelJ~Il11age, revised edn. Middletown, PA: Wesleyan University Press; 1989.

35. Ghassernzadeh L .. Shahraray M .. Moradi A. Prevalence of internet addiction and comparison of internet addicts and non-addicts in Iranian high schools. CyberpsycllOl Behav 2008; 11: 731-3.

36. Russell D., Peplau L. A., Cutrona C. E. The revised UCLA loneliness scale: concurrent and discriminant validity evi­dence. J Pers Soc Psycl1011980; 39: 472-80.

37. Engels R. C. M. E .. Finkenauer C .. Meeus W., Dekovic M.

Parental attachment and adolescents' emotional adjust­ment: the associations with social skills and relational com­petence. J Couns Psyclw12001; 48: 428-39.

38. Kandel D. B .. Davies M. Epidemiology of depressive mood in adolescents: an empirical study. Arch Gen Psychiatry 1982; 39: 1205-12.

39. Kandel D. B., Davies M. Adult sequelae of adolescent depres­sive symptoms. Arch Gen Psychiatry 1986; 43: 255-62.

40. La Greca A. M., Dandes S. K., Wick P., Shaw K., Stone W.

Development of the social anxiety scale for children: reli­ability and concurrent validity. J Clin Child PSYc/1011988; 17: 84-91.

41. La Greca A. M., Stone W. L. Social anxiety scale for children-revised: factor structure and concurrent validity. J Clin Child Psychol1993; 22: 17-27.

42. Gross E. F. Adolescent internet use: what we expect, what teens report. J Appl Dev PSYc/1012004; 25: 633-49.

© 2010 The Authors. Addiction © 2010 Society for the Study of Addiction

43. Van den Eijnden R. J., Meerkerk G. J .. Vermulst A. A., Spijkerman R .. Engels R. C. M. E. Online communication, compulsive internet use, and psychosocial well-being among adolescents: a longitudinal study. Dev PSycl1012008; 44: 655-65.

44. Muthen L. K., Muthen B. O. Mplus User's Guide, 5th edn. Los Angeles. CA: Muthen & Muthen; 1998.

45. Nylund K. L .. Asparouhov T., Muthen B. O. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Modeling 2007; 14: 535-69.

46. Marsh H., Ludtke 0 .. Trautwein u.. Morin A. Classical latent profile analysis of academic self-concept dimensions: synergy of person- and variable-centered approaches to theoretical models of self-concept. Struct Equ Modeling 2009; 16: 191-225.

47. Van der Vorst H., Vermulst A. A., Meeus W., Dekovic M., Engels R. C. M. E. Identification and prediction of drinking trajectories in early and mid-adolescence. J Clin Child PSycl1012009; 38: 329-41.

48. Schwarz G. Estimating the dimension of a model. Ann Stat 1978; 6: 461-4.

49. Yang C. Evaluating latent class analysis models in qualita­tive phenotype identification. COl11put Stat Data Anal 2006; 50: 1090-104.

50. Asparouhouv T., Muthen B. O. Wald test of mean equality for potential latent class predictors in mixture modeling (Internet). 2007. Available at: http://www.statmodel.com/download/ MeanTest1.pdf (accessed 16 February 2010) (archived by WebCite® at http://www. webcitation.org/ 5na9F6IXo).

51. Statistics Netherlands. CBS StatLine (Internet). 2010. Avail­able at: http://statline.cbs.nllstatweb/?LA=en (accessed 1 June 2010) (archived by WebCite® at http://www. webcitation.org/ 5qA5 rbLeZ).

52. [ansz J. The emotional appeal of violent video games for adolescent males. Coml11un Theor 2005; 15: 219-41.

53. Podsakoff P. M .. MacKenzie S. B .. Lee J. Y., Podsakoff N. P.

Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl PSycl1012003; 88: 879-903.

54. American Psychiatric Assocoation (APA). Substance-Related Disorders (Internet). American Psychiatric Association­DSM-5 Development. 10 February 2010 (available at: http://www.dsm5.org/ProposedRevisions/Pages/ Substance-ReiatedDisorders.aspx (accessed 10 February 2010) (archived by WebCite® at http://www. webcitation.org/5nR8AitLs).

Addiction. 106. 205-212

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