Abstract
Research into Internet gaming disorder (IGD) literature largely uses cross-sectional designs and seldom examines gaming context-related factors. Therefore, the present study combined a cross-sectional and longitudinal design to examine depression and the gamer-avatar relationship (GAR) as risk factors in the development of IGD among emerging adults. IGD behaviors of 125 gamers (64 online gamers, Mage = 23.3 years, SD = 3.4; 61 offline gamers, Mage = 23.0 years, SD = 3.4) were assessed using the nine-item Internet Gaming Disorder Scale Short Form (IGDS-SF9; Pontes and Griffiths Revista Argentina de Ciencias del Comportamiento, 7, 102–118, 2015a; Computers in Human Behavior, 45, 137–143, 2015b). The Self-Presence Scale (Ratan and Dawson Communication Research, 2015) and the Beck Depression Inventory (Beck et al. 1996) were also used to assess gamers’ levels of GAR and depressive symptoms, respectively. Regression and moderation analyses revealed that depression and the GAR act as individual risk factors in the development of IGD over time. Furthermore, the GAR exacerbates the IGD risk effect of depression.
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Notes
The current study is part of a wider project of Federation University Australia that addresses the interplay between individual, Internet, and proximal context factors in the development of Internet gaming disorder symptoms among emerging adults. Instruments used in the data include the following: (1) Internet Gaming Disorder 9-Short Form (Pontes and Griffiths 2015a, b), (2) Beck Depression Inventory—second edition (21 items) (Beck et al. 1996), (3) Beck Anxiety Inventory (21 items) (Beck and Steer 1990), (4) Hikikomori-Social Withdrawal Scale (5 items) (Teo et al. 2015), (5) Attention Deficit Hyperactivity Self-Report Scale (18 items) (Kessler et al. 2005), (6) Ten Item Personality Inventory (Gosling et al. 2003), (7) The Balanced Family Cohesion Scale (7 items) (Olson 2000), (8) Presence Questionnaire (10 items) (Faiola et al. 2013), (9) Online Flow Questionnaire (5 items) (Chen et al. 2000), (10) Self-Presence Questionnaire (Ratan and Hasler 2010), (11) The Gaming-Contingent Self-Worth Scale (12 items) (Beard and Wickham 2016), and (12) Demographic and Internet Use Questions. The battery of questionnaires was utilized for both online and face-to-face data collection. The use of the fitness tracker (Fitbit flex) was used only for face-to-face data collection. Data have not been used in any previous published studies.
To ensure that there were no significant differences between the online and face-to-face samples considering their demographic and Internet use characteristics, as well as the variables used in the present study, independent sample t-tests and chi-square analyses were conducted. Findings did not indicate any significant differences in regards to gender (χ 2 = 0.21, df = 1, p = .89), the type of game genre (i.e., MMOs vs MMORPGs) played (χ 2 = 2.59, df = 1, p = .61), the age of the participants (t = − 0.54, df = 120, p = .59), their years of Internet use (t = 2.35, df = 122, p = .06), their reported level of avatar self-presence (t = 1.09, df = 119, p = .28), and their assessed psychopathological symptoms (depression t = − 0.38, df = 111, p = .70; IGD t = − 0.14, df = 111, p = .89). Therefore, online and face-to-face data (i.e., TP1) were combined (i.e., analyzed together) to investigate cross-sectional questions.
The longitudinal design was assessed for attrition. Assessments’ frequency for each participant varied within a range of 1–3 (Maverage = 2.57). Time point 1 comprised 61 participants, time point 2 comprised 56 participants (8.20% attrition), and time point 3 comprised 43 participants (29.51% attrition). In line with literature recommendations, attrition, in relation to the studied variables, was assessed using Little’s Missing Completely At Random test (MCAR), which was insignificant (MCAR, χ 2 = 1715.79, p = 1.00; Little and Rubin 2014). In order to avoid list-wise deletion, which would reduce the sample’s power, maximum likelihood imputation (five times) of values was applied (Gold and Bentler 2000).
In line with the approval received by the ethics committee of Federation University, the flyers: a) indicated that participants were required to participate on three separate measurement occasions approximately one month apart; b) included an email address to contact the investigators; and c) clearly described the process and stages of the data collection (face-to-face and online). MMO and MMORPG players, aged between 18-29 years old, interested in the study received the Plain Language Information Statement (PLIS). The PLIS clearly indicated that participation was voluntary and that participants could independently decide to withdraw from the study at any point. Individuals who choose to participate were required to provide informed consent.
References
Ackerman, C. M. (2009). The essential elements of Dabrowski’s theory of positive disintegration and how they are connected. Roeper Review, 31(2), 81–95. https://doi.org/10.1080/02783190902737657.
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington: American Psychiatric Publishing, Inc..
Arnett, J. J. (2000). Emerging adulthood: a theory of development from the late teens through the twenties. American Psychology, 55(5), 469–480. https://doi.org/10.1037//0003-066X.55.5.469.
Arnett, J. J. (2005). The developmental context of substance use in emerging adulthood. Journal of Drug Issues, 35(2), 235–254. https://doi.org/10.1177/002204260503500202.
Arnett, J. J., Žukauskienė, R., & Sugimura, K. (2014). The new life stage of emerging adulthood at ages 18–29 years: implications for mental health. The Lancet Psychiatry, 1(7), 569–576. https://doi.org/10.1016/S2215-0366(14)00080-7.
Badrinarayanan, V. A., Sierra, J. J., & Taute, H. A. (2014). Determinants and outcomes of online brand tribalism: exploring communities of massively multiplayer online role playing games (MMORPGs). Psychology and Marketing, 31(10), 853–870. https://doi.org/10.1002/mar.20739.
Batthyány, D., Müller, K. W., Benker, F., & Wölfling, K. (2009). Computer game playing: clinical characteristics of dependence and abuse among adolescents. Wiener Klinische Wochenschrift, 121(15), 502–509. https://doi.org/10.1007/s00508-009-1198-3.
Baumrind, D., & Moselle, K. A. (1985). A development perspective on adolescent drug abuse. Advances in Alcohol & Substance Abuse, 4(3–4), 41–67. https://doi.org/10.1300/J251v04n03_03.
Beard, C. L., & Wickham, R. E. (2016). Gaming-contingent self-worth, gaming motivation, and internet gaming disorder. Computers in Human Behavior, 61, 507–515. https://doi.org/10.1016/j.chb.2016.03.046.
Beck, A. T., & Steer, R. A. (1990). Manual for the Beck anxiety inventory. San Antonio: Psychological Corporation.
Beck, A., Steer, R., & Brown, G. (1996). BDI-II, Beck depression inventory: manual. San Antonio: The Psychological Corporation.
Berkovits, I., Hancock, G. R., & Nevitt, J. (2000). Bootstrap resampling approaches for repeated measure designs: relative robustness to sphericity and normality violations. Educational and Psychological Measurement, 60(6), 877–892. https://doi.org/10.1177/00131640021970961.
Bessiere, K., Seay, A. F., & Kiesler, S. (2007). The ideal elf: identity exploration in World of Warcraft. Cyberpsychology & Behavavior, 10(4), 530–535. https://doi.org/10.1089/cpb.2007.9994.
Billieux, J., Schimmenti, A., & Khazaal, Y. (2015). Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research. Journal of Behavioral Addictions, 4, 119–123. https://doi.org/10.1556/2006.4.2015.009.
Biocca, F. (1997). The cyborg’s dilemma: embodiment in virtual environments. Proceedings of the Second International Conference on Cognitive Technology Humanizing the Information Age, USA (pp 12–26). doi:https://doi.org/10.1109/CT.1997.617676.
Blinka, L. (2008). The relationship of players to their avatars in MMORPGs: differences between adolescents, emerging adults and adults. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 2(1), 1–7.
Buono, F. D., Upton, T. D., Griffiths, M. D., Sprong, M. E., & Bordieri, J. (2016). Demonstrating the validity of the Video Game Functional Assessment-Revised (VGFA-R). Computers in Human Behavior, 54, 501–510. https://doi.org/10.1016/j.chb.2015.08.037.
Caplan, S., Williams, D., & Yee, N. (2009). Problematic internet use and psychosocial well-being among MMO players. Computers in Human Behavior, 25(6), 1312–1319. https://doi.org/10.1016/j.chb.2009.06.006.
Carter, M., Gibbs, M., & Arnold, M. (2012). Avatars, characters, players and users. Proceedings of the 24th Australian Computer-Human Interaction Conference, Australia (pp 68–71). doi:https://doi.org/10.1145/2414536.2414547.
Chen, H., Wigand, R. T., & Nilan, M. (2000). Exploring web users’ optimal flow experiences. Information Technology & People, 13(4), 263–281. https://doi.org/10.1108/09593840010359473.
Cleghorn, J., & Griffiths, M. D. (2015). Why do gamers buy ‘virtual assets’? An insight in to the psychology behind purchase behaviour. Digital Education Review, 27, 98–117.
Dong, G., Lu, Q., Zhou, H., & Zhao, X. (2011). Precursor or sequela: pathological disorders in people with Internet addiction disorder. PloS One, 6(2). https://doi.org/10.1371/journal.pone.0014703.
Douglas, A. C., Mills, J. E., Niang, M., Stepchenkova, S., Byun, S., Ruffini, C., et al. (2008). Internet addiction: meta-synthesis of qualitative research for the decade 1996–2006. Computers in Human Behavior, 24(6), 3027–3044. https://doi.org/10.1016/j.chb.2008.05.009.
Ducheneaut, N., Yee, N., Nickell, E., & Moore, R. J. (2007). The life and death of online gaming communities. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, USA (p 839). doi:https://doi.org/10.1145/1240624.1240750.
Ducheneaut, N., Wen, M. H., Yee, N., & Wadley, G. (2009). Body and mind: a study of avatar personalization in three virtual worlds. Proceedings of the 27th International Conference on Human Factors in Computing Systems, USA (p 1151). doi:https://doi.org/10.1145/1518701.1518877.
Faiola, A., Newlon, C., Pfaff, M., & Smyslova, O. (2013). Correlating the effects of flow and telepresence in virtual worlds: enhancing our understanding of user behavior in game-based learning. Computers in Human Behavior, 29(3), 1113–1121. https://doi.org/10.1016/j.chb.2012.10.003.
Festl, R., Scharkow, M., & Quandt, T. (2013). Problematic computer game use among adolescents, younger and older adults. Addiction, 108(3), 592–599. https://doi.org/10.1111/add.12016.
Galambos, N. L., Barker, E. T., & Krahn, H. J. (2006). Depression, self-esteem, and anger in emerging adulthood: Seven-year trajectories. Developmental Psychology, 42, 350–365. https://doi.org/10.1037/0012-1649.42.2.350.
George, D., & Mallery, P. (2010). SPSS for Windows step by step: a simple guide and reference 17.0 update (10th ed.). Boston: Pearson.
Gold, M. S., & Bentler, P. M. (2000). Treatments of missing data: a Monte Carlo comparison of RBHDI, iterative stochastic regression imputation, and expectation-maximization. Structural Equation Modeling: A Multidisciplinary Journal, 7, 319–355. https://doi.org/10.1207/S15328007SEM0703.
Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003). A very brief measure of the big-five personality domains. Journal of Research in Personality, 37(6), 504–528. https://doi.org/10.1016/S0092-6566(03)00046-1.
Griffiths, M. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4), 191–197. https://doi.org/10.1080/14659890500114359.
Griffiths, M. D. (2010). Computer game playing and social skills: a pilot study. Aloma, 27, 301–310.
Griffiths, M. D. (2011). Adolescent gambling. In B. Bradford Brown & M. Prinstein (Eds.), Encyclopedia of adolescence (Vol. 3, pp. 11–20). San Diego: Academic. https://doi.org/10.1016/B978-0-12-373915-5.00113-3.
Griffiths, M. D. (2015). Gaming addiction and internet gaming disorder. In R. Kowert & T. Quandt (Eds.), The video game debate: Unravelling the physical, social, and psychological effects of video games (pp. 75–93). New York: Routledge.
Griffiths, M. D., Davies, M. N., & Chappell, D. (2004). Demographic factors and playing variables in online computer gaming. Cyberpsychology & Behavior, 7(4), 479–487. https://doi.org/10.1089/cpb.2004.7.479.
Griffiths, M. D., Kuss, D. J., Billieux, J., & Pontes, H. M. (2016a). The evolution of internet addiction: a global perspective. Addictive Behaviors, 53, 193–195. https://doi.org/10.1016/j.addbeh.2015.11.001.
Griffiths, M. D., van Rooij, A. J., Kardefelt-Winther, D., Starcevic, V., Kiraly, O., Pallesen, S.,. . . Demetrovics, Z. (2016b). Working towards an international consensus on criteria for assessing internet gaming disorder: a critical commentary on Petry et al. (2014). Addiction, 111(1), 167–175. doi:https://doi.org/10.1111/add.13057.
Hamari, J., Alha, K., Järvelä, S., Kivikangas, J. M., Koivisto, J., & Paavilainen, J. (2017). Why do players buy in-game content? An empirical study on concrete purchase motivations. Computers in Human Behavior, 68, 538–546. https://doi.org/10.1016/j.chb.2016.11.045.
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. New York: Guilford Press.
Hendriks, V. (1990). Addiction and psychopathology: a multidimensional approach to clinical practice. Rotterdam: Erasmus University Rotterdam Retrieved from http://hdl.handle.net/1765/50810.
Hendry, L. B., & Kloep, M. (2010). How universal is emerging adulthood? An empirical example. Journal of Youth Studies, 13(2), 169–179. https://doi.org/10.1080/13676260903295067.
Higgins, E. T., Horwitz, E. K., Horwitz, M. B., Cope, J., Horwitz, E. K., Horwitz, M. B., & Cope, J. (1987). Self-discrepancy: a theory relating self and affect. Psychological Review, 94(3), 319–340. https://doi.org/10.1037/0033-295X.94.3.319.
Hussain, Z., & Griffiths, M. D. (2009a). The attitudes, feelings, and experiences of online gamers: a qualitative analysis. Cyberpsychology & Behavior, 12(6), 747–753. https://doi.org/10.1089/cpb.2009.0059.
Hussain, Z., & Griffiths, M. D. (2009b). Excessive use of massively multi-player online role-playing games: a pilot study. International Journal of Mental Health and Addiction, 7(4), 563–571. https://doi.org/10.1007/s11469-009-9202-8.
Jeong, E. J., & Kim, D. H. (2011). Social activities, self-efficacy, game attitudes, and game addiction. Cyberpsychology, Behavior, and Social Networking, 14(4), 213–221. https://doi.org/10.1089/cyber.2009.0289.
Kardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: towards a model of compensatory internet use. Computers in Human Behavior, 31(1), 351–354. https://doi.org/10.1016/j.chb.2013.10.059.
Kessler, R. C., Adler, L., Ames, M., Demler, O., Faraone, S., Hiripi, E. V. A., & Ustun, T. B. (2005). The world health organization adult ADHD self-report scale (ASRS): a short screening scale for use in the general population. Psychological Medicine, 35(02), 245–256. https://doi.org/10.1017/S0033291704002892.
Khantzian, E. J. (1997). The self-medication hypothesis of substance use disorders: a reconsideration and recent applications. Harvard Review of Psychiatry, 4(5), 231–244. https://doi.org/10.3109/10673229709030550.
Kim, M. G., & Kim, J. (2010). Cross-validation of reliability, convergent and discriminant validity for the problematic online game use scale. Computers in Human Behavior, 26(3), 389–398. https://doi.org/10.1016/j.chb.2009.11.010.
King, D. L., & Delfabbro, P. H. (2014). Internet gaming disorder treatment: a review of definitions of diagnosis and treatment outcome. Journal of Clinical Psychology, 70(10), 942–955. https://doi.org/10.1002/jclp.22097.
King, D. L., & Delfabbro, P. H. (2016). The cognitive psychopathology of internet gaming disorder in adolescence. Journal of Abnormal Child Psychology, 44, 1635–1645. https://doi.org/10.1007/s10802-016-0135-y.
King, D. L., Delfabbro, P. H., & Griffiths, M. D. (2013a). Video game addiction. In P. M. Miller (Ed.), Comprehensive addictive behaviors and disorders: vol. 1. Principles of addiction (pp. 819–825). San Diego: Academic Press. https://doi.org/10.1016/B978-0-12-398336-7.00082-6.
King, D. L., Haagsma, M. C., Delfabbro, P. H., Gradisar, M., & Griffiths, M. D. (2013b). Toward a consensus definition of pathological video-gaming: A systematic review of psychometric assessment tools. Clinical Psychology Review, 33(3), 331–342. https://doi.org/10.1016/j.cpr.2013.01.002.
Klimmt, C., Hefner, D., & Vorderer, P. (2009). The video game experience as “true” identification: a theory of enjoyable alterations of players’ self-perception. Communication Theory, 19(4), 351–373. https://doi.org/10.1111/j.1468-2885.2009.01347.x.
Ko, C. H. (2014). Internet gaming disorder. Current Addiction Reports, 1(3), 177–185. https://doi.org/10.1007/s40429-014-0030-y.
Ko, C. H., Yen, J. Y., Yen, C. F., Chen, C. S., & Chen, C. C. (2012). The association between internet addiction and psychiatric disorder: a review of the literature. European Psychiatry, 27(1), 1–8. https://doi.org/10.1016/j.eurpsy.2010.04.011.
Kowert, R., & Quandt, T. (Eds.). (2015). The video game debate: unravelling the physical, social, and psychological effects of video games. New York: Routledge.
Kuss, D. J. (2013). Internet gaming addiction: current perspectives. Psychology Research and Behavior Management, 6, 125–137.
Kuss, D. J., & Griffiths, M. D. (2012a). Internet gaming addiction: a systematic review of empirical research. International Journal of Mental Health and Addiction, 10(2), 278–296. https://doi.org/10.1007/s11469-011-9318-5.
Kuss, D. J., & Griffiths, M. D. (2012b). Online gaming addiction in children and adolescents: a review of empirical research. Journal of Behavioral Addictions, 1(1), 3–22. https://doi.org/10.1556/JBA.1.2012.1.1.
Lee, S. Y., & Xia, Y. M. (2006). Maximum likelihood methods in treating outliers and symmetrically heavy-tailed distributions for nonlinear structural equation models with missing data. Psychometrika, 71(3), 565–585. https://doi.org/10.1007/s11336-006-1264-1.
Lemmens, J. S., Valkenburg, P. M., & Peter, J. (2011). Psychosocial causes and consequences of pathological gaming. Computers in Human Behavior, 27(1), 144–152. https://doi.org/10.1016/j.chb.2010.07.015.
Little, R. J. A., & Rubin, D. B. (2014). Statistical analysis with missing data. Hoboken: Wiley.
Masten, A. S. (2001). Ordinary magic. Resilience processes in development. American Psychologist, 56(3), 227–238. https://doi.org/10.1037/0003-066X.56.3.227.
Masten, A. S. (2014). Invited commentary: resilience and positive youth development frameworks in developmental science. Journal of Youth and Adolescence, 43(6), 1018–1024. https://doi.org/10.1007/s10964-014-0118-7.
Meng, J., Williams, D., & Shen, C. (2015). Channels matter: multimodal connectedness, types of co-players and social capital for multiplayer online battle arena gamers. Computers in Human Behavior, 52, 190–199. https://doi.org/10.1016/j.chb.2015.06.007.
Messinger, P., Ge, X., Stroulia, E., Lyons, K., Smirnov, K., Bone, M., & Van Rees, M. (2008). On the relationship between my avatar and myself. Journal of Virtual Worlds Research, 1(1), 1–17. https://doi.org/10.1016/j.bmcl.2006.05.013.
Muller, K. W., Janikian, M., Dreier, M., Wolfling, K., Beutel, M. E., Tzavara, C.,. .. Tsitsika, A. (2015). Regular gaming behavior and internet gaming disorder in European adolescents: results from a cross-national representative survey of prevalence, predictors, and psychopathological correlates. European Child & Adolescent Psychiatry, 24(5), 565–574. doi:https://doi.org/10.1007/s00787-014-0611-2.
Ng, B. D., & Wiemer-Hastings, P. (2005). Addiction to the internet and online gaming. Cyberpsychology & Behavior, 8(2), 110–113. https://doi.org/10.1089/cpb.2005.8.110.
Olson, D. H. (2000). Circumplex model of marital and family systems. Journal of Family Therapy, 22(2), 144–167. https://doi.org/10.1111/1467-6427.00144.
Ortiz de Gortari, A. B., & Griffiths, M. D. (2013). Altered visual perception in game transfer phenomena: An empirical self-report study. International Journal of Human Computer Interaction, 30(2), 95–105. https://doi.org/10.1080/10447318.2013.839900.
Ortiz de Gortari, A. B., & Griffiths, M. D. (2014). Auditory experiences in game transfer phenomena. International Journal of Cyber Behavior, Psychology and Learning, 4(1), 59–75. https://doi.org/10.4018/ijcbpl.2014010105.
Ortiz de Gortari, A. B., & Griffiths, M. D. (2016). Prevalence and characteristics of game transfer phenomena: a descriptive survey study. International Journal of Human Computer Interaction, 32, 470–480. https://doi.org/10.1080/10447318.2016.1164430.
Pandina, R. J., Labouvie, E. W., Johnson, V., & White, H. R. (1990). The relationship between alcohol and marijuana use and competence in adolescence. Journal of Health & Social Policy, 1(3), 89–108. https://doi.org/10.1300/J045v01n03_06.
Petry, N. M., Rehbein, F., Gentile, D. A., Lemmens, J. S., Rumpf, H. J., Mossle, T.,. .. O'Brien, C. P. (2014a). An international consensus for assessing internet gaming disorder using the new DSM-5 approach. Addiction, 109(9), 1399–1406. doi:https://doi.org/10.1111/add.12457.
Petry, N. M., Rehbein, F., Gentile, D. A., Lemmens, J. S., Rumpf, H. J., Mossle, T., et al. (2014b). Moving internet gaming disorder forward: A reply. Addiction, 109(9), 1412–1413. https://doi.org/10.1111/add.12653.
Petry, N. M., Rehbein, F., Ko, C.-H., & O’Brien, C. P. (2015). Internet gaming disorder in the DSM-5. Current Psychiatry Reports, 17, 72. https://doi.org/10.1007/s11920-015-0610-0.
Pontes, H. M., & Griffiths, M. D. (2014). Assessment of internet gaming disorder in clinical research: past and present perspectives. Clinical Research and Regulatory Affairs, 31(2–4), 35–48. https://doi.org/10.3109/10601333.2014.962748.
Pontes, H. M., & Griffiths, M. D. (2015a). Internet gaming disorder and its associated cognitions and cognitive-related impairments: a systematic review using PRISMA guidelines. Revista Argentina de Ciencias del Comportamiento, 7, 102–118 Retrieved from https://revistas.unc.edu.ar/index.php/racc/index.
Pontes, H. M., & Griffiths, M. D. (2015b). Measuring DSM-5 internet gaming disorder: Development and validation of a short psychometric scale. Computers in Human Behavior, 45, 137–143. https://doi.org/10.1016/j.chb.2014.12.006.
Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: theory, methods, and prescriptions. Multivariate Behavioral Research, 42(1), 185–227. https://doi.org/10.1080/00273170701341316.
Pringle, H. M. (2015). Conjuring the ideal self: an investigation of self-presentation in video game avatars. Press Start, 2, 1–20 Retrieved from http://press-start.gla.ac.uk/index.php/press-start/index.
Rao, C. R., & Toutenburg, H. (1995). Linear models. New York: Springer.
Ratan, R. (2013). Self-presence, explicated: Body, emotion, and identity extension into the virtual self. In R. Lippicini (Ed.), Handbook of Research on Technoself (pp. 322–336). Hershey: IGI Global.
Ratan, R. A., & Dawson, M. (2015). When mii is me: a psychophysiological examination of avatar self-relevance. Communication Research. https://doi.org/10.1177/0093650215570652.
Ratan, R., & Hasler, B. S. (2010). Exploring self-presence in collaborative virtual teams. PsychNology Journal, 8(1), 11–31.
Ream, G. L., Elliott, L. C., & Dunlap, E. (2013). Trends in video game play through childhood, adolescence, and emerging adulthood. Psychiatry Journal, 2013, 301460. https://doi.org/10.1155/2013/301460.
Rehbein, F., Psych, G., Kleimann, M., Mediasci, G., & Mößle, T. (2010). Prevalence and risk factors of video game dependency in adolescence: results of a German nationwide survey. Cyberpsychology, Behavior, and Social Networking, 13(3), 269–277. https://doi.org/10.1089/cyber.2009.0227.
Schwartz, S. J., Côté, J. E., & Arnett, J. J. (2005). Identity and agency in emerging adulthood: two developmental routes in the individualization process. Youth & Society, 37(2), 201–229. https://doi.org/10.1177/0044118X05275965.
Scott, J., & Porter-Armstrong, A. P. (2013). Impact of multiplayer online role-playing games upon the psychosocial well-being of adolescents and young adults: reviewing the evidence. Psychiatry Journal, 2013, 464685. https://doi.org/10.1155/2013/464685.
Smahel, D., Blinka, L., & Ledabyl, O. (2008). Playing MMORPGs: connections between addiction and identifying with a character. Cyberpsychology & Behavior, 11(6), 715–718. https://doi.org/10.1089/cpb.2007.0210.
Smith, S. R., & Handler, L. (Eds.). (2007). The clinical practice of child and adolescent assessment. Mahwah: Lawrence Erlbaum Associates.
Stanley, P. (2015). The risk and resilience framework and its implications for teachers and schools. Waikato Journal of Education, 14(1), 139–153. 10.15663/wje.v14i1.248.
Stavropoulos, V., Gentile, D., & Motti-Stefanidi, F. (2016a). A multilevel longitudinal study of adolescent Internet addiction: the role of obsessive–compulsive symptoms and classroom openness to experience. The European Journal of Developmental Psychology, 13(1), 99–114. https://doi.org/10.1080/17405629.2015.1066670.
Stavropoulos, V., Kuss, D., Griffiths, M., & Motti-Stefanidi, F. (2016b). A longitudinal study of adolescent internet addiction: the role of conscientiousness and classroom hostility. Journal of Adolescent Research, 31(4), 442–473. https://doi.org/10.1177/0743558415580163.
Stavropoulos, V., Kuss, D. J., Griffiths, M. D., Wilson, P., & Motti-Stefanidi, F. (2017). MMORPG gaming and hostility predict internet addiction symptoms in adolescents: an empirical multilevel longitudinal study. Addictive Behaviors. https://doi.org/10.1016/j.addbeh.2015.09.001.
Stetina, B. U., Kothgassner, O. D., Lehenbauer, M., & Kryspin-Exner, I. (2011). Beyond the fascination of online-games: probing addictive behavior and depression in the world of online-gaming. Computers in Human Behavior, 27, 473–479. https://doi.org/10.1016/j.chb.2010.09.015.
Stone, A. L., Becker, L. G., Huber, A. M., & Catalano, R. F. (2012). Review of risk and protective factors of substance use and problem use in emerging adulthood. Addictive Behaviors, 37, 747–775. https://doi.org/10.1016/j.addbeh.2012.02.014.
Sussman, S., & Arnett, J. J. (2014). Emerging adulthood: developmental period facilitative of the addictions. Evaluation & the Health Professions, 37(2), 147–155. https://doi.org/10.1177/0163278714521812.
Swendsen, J. D., & Merikangas, K. R. (2000). The comorbidity of depression and substance use disorders. Clinical Psychology Review, 20(2), 173–189. https://doi.org/10.1016/S0272-7358(99)00026-4.
Teo, A. R., Fetters, M. D., Stufflebam, K., Tateno, M., Balhara, Y., Choi, T. Y., & Kato, T. A. (2015). Identification of the hikikomori syndrome of social withdrawal: Psychosocial features and treatment preferences in four countries. International Journal of Social Psychiatry, 61(1), 64–72. https://doi.org/10.1177/0020764014535758.
Volkow, N. D. (2004). The reality of comorbidity: depression and drug abuse. Biological Psychiatry, 56, 714–717. https://doi.org/10.1016/j.biopsych.2004.07.007.
Vollmer, C., Randler, C., Horzum, M. B., & Ayas, T. (2014). Computer game addiction in adolescents and its relationship to chronotype and personality. SAGE Open, 4(1), 1–9. https://doi.org/10.1177/2158244013518054.
Wei, H. T., Chen, M. H., Huang, P. C., & Bai, Y. M. (2012). The association between online gaming, social phobia, and depression: an internet survey. BMC Psychiatry, 12, 92. https://doi.org/10.1186/1471-244X-12-92.
Winer, E. S., Cervone, D., Bryant, J., McKinney, C., Liu, R. T., & Nadorff, M. R. (2016). Distinguishing mediational models and analyses in clinical psychology: atemporal associations do not imply causation. Journal of Clinical Psychology, 72(9), 947–955. https://doi.org/10.1002/jclp.22298.
Wolfendale, J. (2007). My avatar, myself: virtual harm and attachment. Ethics and Information Technology, 9(2), 111–119. https://doi.org/10.1007/s10676-006-9125-z.
Wood, R. T. A., & Griffiths, M. D. (2007). A qualitative investigation of problem gambling as an escape-based coping strategy. Psychology and Psychotherapy: Theory, Research and Practice, 80, 107–125. https://doi.org/10.1348/147608306X107881.
Yee, N. (2006a). The demographics, motivations, and derived experiences of users of massively multi-user online graphical environments. Presence: Teleoperators and Virtual Environments, 15, 309–329. https://doi.org/10.1162/pres.15.3.309.
Yee, N. (2006b). The psychology of massively multi-user online role-playing games: Motivations, emotional investment, relationships and problematic usage. Avatars at Work and Play: Collaboration and Interaction in Shared Virtual Environments, 34, 187–207. https://doi.org/10.1007/1-4020-3898-4_9.
Yee, N. (2014). The Proteus paradox: How online games and virtual worlds change us-and how they don't. Orwigsburg: Yale University Press.
Yen, J. Y., Ko, C. H., Yen, C. F., Wu, H. Y., & Yang, M. J. (2007). The comorbid psychiatric symptoms of Internet addiction: attention deficit and hyperactivity disorder (ADHD), depression, social phobia, and hostility. Journal of Adolescent Health, 41(1), 93–98. https://doi.org/10.1016/j.jadohealth.2007.02.002.
You, S., Kim, E., & Lee, D. (2015). Virtually real: exploring avatar identification in game addiction among massively multiplayer online role-playing games (MMORPG) players. Games and Culture, 1(16). https://doi.org/10.1177/1555412015581087.
Young, K. S. (2009). Internet addiction. American Behavioral Scientist, 4, 402–415.
Young, K. S., & Rodgers, R. C. (1998). The relationship between depression and internet addiction. Cyberpsychology & Behavior, 1(1), 25–28. https://doi.org/10.1089/cpb.1998.1.25.
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TB contributed to the literature review, hypothesis formulation, data collection and analyses, and the structure and sequence of theoretical arguments.
VS contributed to the literature review, hypothesis formulation, data collection and analyses, and the structure and sequence of theoretical arguments.
LL contributed to the data collection and analyses.
BA contributed to the data collection and analyses.
MG contributed to the theoretical consolidation of the current work, and revised and edited the final manuscript.
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Ethical Standards—Animal Rights
All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
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Informed consent was obtained from all individual participants included in the study.
Appendix
Appendix
A1. Analytical assumptions
The presence of outliers in the data was assessed by calculating the Mahalanobis distance (MD). In regard to the cross-sectional analysis, three outlier values were detected with a MD = 21.47, p = .00001; MD = 13.42, p = .00025; and MD = 11.57, p = .00067, respectively. In regard to the longitudinal analysis, two outlier values were detected with a MD = 17.44, p = .00003, and MD = 14.90, p = .00011, respectively. Following literature recommendations, any MD that referred to a probability lower than 0.001 was defined as an outlier, was treated as a missing value, and was substituted with maximum likelihood (five times) imputation based on all the available variables (Lee and Xia 2006).
Assumptions of linearity, multivariate normality, and homoscedasticity were further assessed (Rao and Toutenburg 1995). Given that only one independent variable was used, multicollinearity was not assessed. Linearity assumption was tested with probability-probability plot (PP). All variables used in both cross-sectional and longitudinal data did not violate the assumption of linearity. Normality was checked by measuring skewness and kurtosis with values below − 2 or above + 2 addressed with bootstrapping-1000 resamples (Berkovits et al. 2000). In order to assess homoscedasticity, scatterplots of regression standardized residuals, and the regression standardized predicted values were implemented (George and Mallery 2010). Both cross-sectional and longitudinal data scatterplots did not indicate any violations to the assumption of homoscedasticity.
A2. Correlations and descriptive statistics
Descriptive statistics and bivariate correlations of cross-sectional data can be found in Table 2. The results revealed that higher depression scores and GAR scores are correlated with higher IGD scores.
Descriptive statistics and bivariate correlations, from longitudinal data, can be found in Table 3. The results indicate that higher depression scores are correlated with higher IGD scores. Both depression scores and IGD scores were significantly related to each other on all time points. In addition to this, both GAR scores and IGD scores are also significantly related across all time points.
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Burleigh, T.L., Stavropoulos, V., Liew, L.W.L. et al. Depression, Internet Gaming Disorder, and the Moderating Effect of the Gamer-Avatar Relationship: an Exploratory Longitudinal Study. Int J Ment Health Addiction 16, 102–124 (2018). https://doi.org/10.1007/s11469-017-9806-3
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DOI: https://doi.org/10.1007/s11469-017-9806-3