Assessing Internet Addiction Using the Parsimonious Internet Addiction Components Model—A Preliminary Study

  • Daria J. Kuss
  • Gillian W. Shorter
  • Antonius J. van Rooij
  • Mark D. Griffiths
  • Tim M. Schoenmakers


Internet usage has grown exponentially over the last decade. Research indicates that excessive Internet use can lead to symptoms associated with addiction. To date, assessment of potential Internet addiction has varied regarding populations studied and instruments used, making reliable prevalence estimations difficult. To overcome the present problems a preliminary study was conducted testing a parsimonious Internet addiction components model based on Griffiths’ addiction components (Journal of Substance Use, 10, 191–197, 2005), including salience, mood modification, tolerance, withdrawal, conflict, and relapse. Two validated measures of Internet addiction were used (Compulsive Internet Use Scale [CIUS], Meerkerk et al. in Cyberpsychology & Behavior, 12(1), 1–6, 2009, and Assessment for Internet and Computer Game Addiction Scale [AICA-S], Wölfling et al. 2010) in two independent samples (ns = 3,105 and 2,257). The fit of the model was analysed using Confirmatory Factor Analysis. Results indicate that the Internet addiction components model fits the data in both samples well. The two sample/two instrument approach provides converging evidence concerning the degree to which the components model can organize the self-reported behavioural components of Internet addiction. Recommendations for future research include a more detailed assessment of tolerance as addiction component.


Internet addiction Behavioural addiction Addiction components Classification Diagnosis 


Conflict of Interest

The authors report no conflicts of interest.


  1. Aboujaoude, E., Koran, L. M., Gamel, N., Large, M. D., & Serpe, R. T. (2006). Potential markers for problematic Internet use: a telephone survey of 2,513 adults. Cns Spectrums, 11(10), 750–755.PubMedGoogle Scholar
  2. American Psychiatric Association. (2012). DSM-5: The future of psychiatric diagnosis. DSM-5 development. Retrieved 28.04.2012, from
  3. American Psychiatric Association. (2000). Diagnostic and statistical manual for mental disorders IV, text-revision. Washington: American Psychiatric Association.Google Scholar
  4. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5). Arlington: American Psychiatric Association.Google Scholar
  5. Andreassen, C. S., Griffiths, M. D., Hetland, J., & Pallesen, S. (2012a). Development of a work addiction scale. Scandinavian Journal of Psychology, 53(3), 265–272.PubMedCrossRefGoogle Scholar
  6. Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012b). Development of a Facebook addiction scale. Psychological Reports, 110(2), 1–17.CrossRefGoogle Scholar
  7. Andrews-Hanna, J. R., Mackiewicz Seghete, K. L., Claus, E. D., Burgess, G. C., Ruzic, L., & Banich, M. T. (2011). Cognitive control in adolescence: neural underpinnings and relation to self-report behaviors. Plos One, 6(6), e21598.PubMedCentralPubMedCrossRefGoogle Scholar
  8. Armstrong, L., Phillips, J. G., & Saling, L. L. (2000). Potential determinants of heavier internet usage. International Journal of Human-Computer Studies, 53(4), 537–550.CrossRefGoogle Scholar
  9. Beard, K. W. (2005). Internet addiction: a review of current assessment techniques and potential assessment questions. Cyberpsychology & Behavior, 8(1), 7–14.CrossRefGoogle Scholar
  10. Blaszczynski, A. (2006). Internet use: in search of an addiction. International Journal of Mental Health and Addiction, 4, 7–9.CrossRefGoogle Scholar
  11. Blum, K., Cull, J. G., Braverman, E. R., & Comings, D. E. (1996). Reward deficiency syndrome. American Scientist, 84(2), 132–145.Google Scholar
  12. Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling—A Multidisciplinary Journal, 7(3), 461–483.CrossRefGoogle Scholar
  13. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford.Google Scholar
  14. Cao, H., Sun, Y., Wan, Y., Hao, J., & Tao, F. (2011). Problematic Internet use in Chinese adolescents and its relation to psychosomatic symptoms and life satisfaction. Bmc Public Health, 11.Google Scholar
  15. Christakis, D. A. (2010). Internet addiction: a 21st century epidemic? Bmc Medicine, 8(61).Google Scholar
  16. Clark, M., & Calleja, K. (2008). Shopping addiction: a preliminary investigation among Maltese university students. Addiction Research & Theory, 16(6), 633–649.CrossRefGoogle Scholar
  17. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.PubMedCrossRefGoogle Scholar
  18. Conner, B. T., Stein, J. A., Longshore, D., & Stacy, A. W. (1999). Associations between drug abuse treatment and cigarette use: evidence of substance replacement. Experimental and Clinical Psychopharmacology, 7(1), 64–71.PubMedCrossRefGoogle Scholar
  19. Dong, G., Zhou, H., & Zhao, X. (2011). Male Internet addicts show impaired executive control ability: evidence from a color-word Stroop task. Neuroscience Letters, 499(2), 114–118.PubMedCrossRefGoogle Scholar
  20. El-Guebaly, N., Patten, S. B., Currie, S., Williams, J. V. A., Beck, C. A., Maxwell, C. J., et al. (2006). Epidemiological associations between gambling behavior, substance use & mood and anxiety disorders. Journal of Gambling Studies, 22(3), 275–287.PubMedCrossRefGoogle Scholar
  21. Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491.PubMedCentralPubMedCrossRefGoogle Scholar
  22. Glasner-Edwards, S., & Rawson, R. (2010). Evidence-based practices in addiction treatment: review and recommendations for public policy. Health Policy, 97(2–3), 93–104.PubMedCentralPubMedCrossRefGoogle Scholar
  23. Gray, L., Thomas, N., & Lewis, L. (2010). Teachers’ use of educational technology in U.S. public schools: 2009 (NCES 2010-040). Washington: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.Google Scholar
  24. Griffiths, M. (1993). Tolerance in gambling: an objective measure using the psychophysiological analysis of male fruit machine gamblers. Addictive Behaviors, 18, 365–372.PubMedCrossRefGoogle Scholar
  25. Griffiths, M. D. (2005). A “components” model of addiction within a biopsychosocial framework. Journal of Substance Use, 10, 191–197.CrossRefGoogle Scholar
  26. Griffiths, M. D. (2010). The use of online methodologies in data collection. International Journal of Mental Health and Addiction, 8(1), 8–20.CrossRefGoogle Scholar
  27. Griffiths, M. D., Szabo, A., & Terry, A. (2005). The exercise addiction inventory: a quick and easy screening tool for health practitioners. British Journal of Sports Medicine, 39(6), e30.PubMedCentralPubMedCrossRefGoogle Scholar
  28. Hayduk, L. A., & Glaser, D. N. (2000). Jiving the four-step, waltzing around factor analysis, and other serious fun. Structural Equation Modeling, 7(1), 1–35.CrossRefGoogle Scholar
  29. Hellman, M., Schoenmakers, T. M., Nordstrom, B. R., & Van Holst, R. J. (2012). Is there such a thing as online video game addiction? A cross-disciplinary review. Addiction Research & Theory, (online first).Google Scholar
  30. Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.Google Scholar
  31. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling—A Multidisciplinary Journal, 6(1), 1–55.CrossRefGoogle Scholar
  32. International Telecommunication Union. (2012). Internet users. Retrieved 21.11.2012, from
  33. Jöreskog, K. G. (2005). Structural equation modeling with ordinal variables using LISREL. Scientific Software International. from
  34. Kaltiala-Heino, R., Lintonen, T., & Rimpela, A. (2004). Internet addiction? Potentially problematic use of the Internet in a population of 12–18 year-old adolescents. Addiction Research & Theory, 12(1), 89–96.CrossRefGoogle Scholar
  35. King, D. L., & Delfabbro, P. H. (2013). Video-gaming disorder and the DSM-5: some further thoughts. Australian and New Zealand Journal of Psychiatry, 47(9), 875–876.PubMedCrossRefGoogle Scholar
  36. Ko, C. H., Yen, J. Y., Chen, C. S., Yeh, Y. C., & Yen, C. F. (2009). Predictive values of psychiatric symptoms for Internet addiction in adolescents: a 2-year prospective study. Archives of Pediatrics & Adolescent Medicine, 163(10), 937–943.CrossRefGoogle Scholar
  37. Koob, G. F., & Le Moal, M. (1997). Drug abuse: hedonic homeostatic dysregulation. Science, 278(5335), 52–58.PubMedCrossRefGoogle Scholar
  38. Koob, G. F., & Le Moal, M. (2001). Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology, 24, 97–129.PubMedCrossRefGoogle Scholar
  39. Kuss, D. J. (2012). Substance and behavioral addictions: beyond dependence. Journal of Addiction Research and Therapy, S6, e001.Google Scholar
  40. Kuss, D. J., & Griffiths, M. D. (2012a). Internet and gaming addiction: a systematic literature review of neuroimaging studies. Brain Sciences, 2, 347–374.CrossRefGoogle Scholar
  41. Kuss, D. J., & Griffiths, M. D. (2012b). Internet gaming addiction: a systematic review of empirical research. International Journal of Mental Health and Addiction, 10(2), 278–296.CrossRefGoogle Scholar
  42. Kuss, D. J., Griffiths, M. D., & Binder, J. F. (2013a). Internet addiction in students: prevalence and risk factors. Computers in Human Behavior, 29(3), 959–966.CrossRefGoogle Scholar
  43. Kuss, D. J., van Rooij, A., Shorter, G. W., Griffiths, M. D., & van de Mheen, D. (2013b). Internet addiction in adolescents: prevalence and risk factors. Computers in Human Behavior, 29(5), 1987–1996.CrossRefGoogle Scholar
  44. Larkin, M., & Griffiths, M. D. (2002). Experiences of addiction and recovery: the case for subjective accounts. Addiction Research & Theory, 10(3), 281–311.CrossRefGoogle Scholar
  45. Lemmens, J. S., Valkenburg, P. M., & Peter, J. (2009). Development and validation of a game addiction scale for adolescents. Media Psychology, 12(1), 77–95.CrossRefGoogle Scholar
  46. Leung, L., & Lee, P. S. N. (2012). Impact of Internet literacy, Internet addiction symptoms, and Internet activities on academic performance. Social Science Computer Review, 30(4), 403–418.CrossRefGoogle Scholar
  47. Lin, F., Zhou, Y., Du, Y., Qin, L., Zhao, Z., Xu, J., et al. (2012). Abnormal white matter integrity in adolescents with Internet Addiction Disorder: a tract-based spatial statistics study. Plos One, 7(1), e30253.PubMedCentralPubMedCrossRefGoogle Scholar
  48. Littel, M., Luijten, M., van den Berg, I., van Rooij, A., Keemink, L., & Franken, I. (2012). Error-processing and response inhibition in excessive computer game players: an ERP study. Addiction Biology, 17(5), 934–947.PubMedCrossRefGoogle Scholar
  49. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data. New York: Wiley & Sons.Google Scholar
  50. Liu, C.-Y., & Kuo, F.-Y. (2007). A study of Internet addiction through the lens of the interpersonal theory. Cyberpsychology & Behavior, 10(6), 799–804.CrossRefGoogle Scholar
  51. Liu, J., Gao, X. P., Osunde, I., Li, X., Zhou, S. K., Zheng, H. R., et al. (2010). Increased regional homogeneity in internet addiction disorder: a resting state functional magnetic resonance imaging study. Chinese Medical Journal, 123(14), 1904–1908.PubMedGoogle Scholar
  52. Lopez-Moreno, J. A., Gonzalez-Cuevas, G., Moreno, G., & Navarro, M. (2008). The pharmacology of the endocannabinoid system: functional and structural interactions with other neurotransmitter systems and their repercussions in behavioral addiction. Addiction Biology, 13(2), 160–187.PubMedCrossRefGoogle Scholar
  53. MacCallum, R. C. (1986). Specification searches in covariance structure modeling. Psychological Bulletin, 100(1), 107–120.CrossRefGoogle Scholar
  54. McLellan, A. T., & Meyers, K. (2004). Contemporary addiction treatment: a review of systems problems for adults and adolescents. Biological Psychiatry, 56(10), 764–770.PubMedCrossRefGoogle Scholar
  55. Meerkerk, G. J., Van Den Eijnden, R. J., Vermulst, A. A., & Garretsen, H. F. L. (2009). The Compulsive Internet Use Scale (CIUS): some psychometric properties. Cyberpsychology & Behavior, 12(1), 1–6.CrossRefGoogle Scholar
  56. Müller, K. W., Ammerschläger, M., Freisleder, F. J., Beutel, M. E., & Wölfling, K. (2012). Addictive Internet use as a comorbid disorder among clients of an adolescent psychiatry—prevalence and psychopathological symptoms. [Suchtartige Internetnutzung als komorbide Störung im jugendpsychiatrischen Setting.]. Zeitschrift für Kinder- und Jugendpsychiatrie und Psychotherapie, 40(5), 331–339.PubMedCrossRefGoogle Scholar
  57. Murali, V., & George, S. (2007). Lost online: an overview of internet addiction. Advances in Psychiatric Treatment, 13(1), 24–30.CrossRefGoogle Scholar
  58. Muthén, L. K. (2012). Model fit index WRMR. Retrieved 23.11.2012, from
  59. Muthén, B., & Asparouhov, T. (2002). Latent variable analysis with categorical outcomes: Multiple-group and growth modeling in Mplus. Unpublished manuscript. Retrieved 22.11.2012, from
  60. Muthén, L. K., & Muthén, B. O. (2011). Mplus user’s guide (6th ed.). Los Angeles: Muthén & Muthén.Google Scholar
  61. Nichols, L. A., & Nicki, R. (2004). Development of a psychometrically sound Internet addiction scale: a preliminary step. Psychology of Addictive Behaviors, 18(4), 381–384.PubMedCrossRefGoogle Scholar
  62. Niemz, K., Griffiths, M., & Banyard, P. (2005). Prevalence of pathological Internet use among university students and correlations with self-esteem, the general health questionnaire (GHQ), and disinhibition. Cyberpsychology & Behavior, 8(6), 562–570.CrossRefGoogle Scholar
  63. Paulhus, D. L., & Vazire, S. (2009). The self-report method. In R. W. Robins, R. C. Fraley, & R. F. Krueger (Eds.), Handbook or research methods in personality psychology (pp. 224–239). New York: Guilford.Google Scholar
  64. Pies, R. (2009). Should DSM-V designate “Internet addiction” a mental disorder? Psychiatry, 6(2), 31–37.PubMedCentralPubMedGoogle Scholar
  65. Rumpf, H. J., Meyer, C., Kreuzer, A., & John, U. (2011). Prävalenz der Internetabhängigkeit (PINTA). Bericht an das Bundesministerium für Gesundheit. Greifswald: Universität zu Lübeck, Universitätsmedizin Greifswald.Google Scholar
  66. Shaffer, H. J., Hall, M. N., & Vander Bilt, J. (2000). “Computer addiction”: a critical consideration. American Journal of Orthopsychiatry, 70(2), 162–168.PubMedCrossRefGoogle Scholar
  67. Shaffer, H. J., LaPlante, D. A., LaBrie, R. A., Kidman, R. C., Donato, A. N., & Stanton, M. V. (2004). Toward a syndrome model of addiction: multiple expressions, common etiology. Harvard Review of Psychiatry, 12(6), 367–374.PubMedCrossRefGoogle Scholar
  68. Smith, G. W., Farrell, M., Bunting, B. P., Houston, J. E., & Shevlin, M. (2001). Patterns of polydrug use in Great Britian: findings from a national household population survey. Drug and Alcohol Dependence, 113(2–3), 222–228.Google Scholar
  69. Solomon, R. L. (1980). The opponent-process theory of acquired motivation: the costs of pleasure and the benefits of pain. American Psychologist, 35(8), 691–712.PubMedCrossRefGoogle Scholar
  70. Starcevic, V. (2013). Video-gaming disorder and behavioural addictions. Australian and New Zealand Journal of Psychiatry, 47(3), 285–286.PubMedCrossRefGoogle Scholar
  71. Steiger, J. H. (2000). Point estimation, hypothesis testing, and interval estimation using the RMSEA: some comments and a reply to Hayduk and Glaser. Structural Equation Modeling, 7(2), 149–162.CrossRefGoogle Scholar
  72. Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Personality & Individual Differences, 42(5), 893–898.CrossRefGoogle Scholar
  73. Treuer, T., Fabian, Z., & Furedi, J. (2001). Internet addiction associated with features of impulse control disorder: is it a real psychiatric disorder? Journal of Affective Disorders, 66(2–3), 283–283.PubMedCrossRefGoogle Scholar
  74. Tsai, C. C., & Lin, S. S. J. (2003). Internet addiction of adolescents in Taiwan: an interview study. Cyberpsychology & Behavior, 6(6), 649–652.CrossRefGoogle Scholar
  75. van Rooij, A. J., Schoenmakers, T. M., Vermulst, A. A., van den Eijnden, R. J. J. M., & van de Mheen, D. (2011). Online video game addiction: identification of addicted adolescent gamers. Addiction, 106(1), 205–212.PubMedCrossRefGoogle Scholar
  76. Volkow, N. D., Fowler, J. S., & Wang, G. J. (2003). The addicted human brain: insights from imaging studies. Journal of Clinical Investigation, 111(10), 1444–1451.PubMedCentralPubMedCrossRefGoogle Scholar
  77. Volkow, N. D., Fowler, J. S., Wang, G.-J., Swanson, J. M., & Telang, F. (2007). Dopamine in drug abuse and addiction—results of imaging studies and treatment implications. Archives of Neurology, 64(11), 1575–1579.PubMedCrossRefGoogle Scholar
  78. Wölfling, K., Müller, K., & Beutel, M. (2010). Diagnostic measures: Scale for the assessment of internet and computer game addiction (AICA-S). In D. Mücken, A. Teske, F. Rehbein, & B. te Wildt (Eds.), Prevention, diagnostics, and therapy of computer game addiction (pp. 212–215). Lengerich: Pabst Science.Google Scholar
  79. World Health Organization. (1992). ICD 10: The ICD-10 classification of mental and behavioral disorders: Clinical descriptions and diagnostic guidelines. Geneva: World Health Organization.Google Scholar
  80. 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.PubMedCrossRefGoogle Scholar
  81. Young, K. (1999). Internet addiction: Symptoms, evaluation, and treatment. In L. V. T. L. Jackson (Ed.), Innovations in clinical practice. Sarasota: Professional Resource Press.Google Scholar
  82. Young, K. S. (2004). Internet addiction—a new clinical phenomenon and its consequences. American Behavioral Scientist, 48(4), 402–415.CrossRefGoogle Scholar
  83. Young, K. (2010). Internet addiction over the decade: a personal look back. World Psychiatry, 9(2), 91–91.PubMedCentralPubMedGoogle Scholar
  84. Yu, C. F. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. Dissertation. University of California. Los Angeles. Retrieved from
  85. Yuen, C. N., & Lavin, M. J. (2004). Internet dependence in the collegiate population: the role of shyness. Cyberpsychology & Behavior, 7(4), 379–383.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Daria J. Kuss
    • 1
  • Gillian W. Shorter
    • 2
    • 3
  • Antonius J. van Rooij
    • 4
    • 5
  • Mark D. Griffiths
    • 6
  • Tim M. Schoenmakers
    • 4
    • 5
  1. 1.Birmingham City UniversityBirminghamUK
  2. 2.Bamford Centre for Mental Health and WellbeingUniversity of UlsterLondonderryUK
  3. 3.MRC All Ireland Trials Methodology HubUniversity of UlsterLondonderryUK
  4. 4.IVO Addiction Research InstituteRotterdamThe Netherlands
  5. 5.Erasmus University Medical CenterRotterdamThe Netherlands
  6. 6.International Gaming Research UnitNottingham Trent UniversityNottinghamUK

Personalised recommendations