Skip to main content
SpringerLink
Log in

Psychometric Validation of the Chinese Compulsive Internet Use Scale (CIUS) with Taiwanese High School Adolescents

  • Original Paper
  • Published:
Psychiatric Quarterly Aims and scope Submit manuscript

Abstract

The recent development of internet infrastructure has fuelled a popular concern that young Asian internet users are experiencing Internet addiction due to excessive Internet use. In order to understand the phenomenon, psychometric validation of a 14-item Compulsive Internet Use Scale (CIUS), with 417 Chinese adolescents has been performed. Compared to other instruments for use with Chinese populations, e.g. the 20-item Internet Addiction Test (IAT) and the 26-item Chen Internet Addiction Scale, the CIUS is relatively concise, and easy to use for measuring and diagnosing Internet addiction. The present psychometric validation has found good factorial stability with a one-factor solution for the CIUS. The internal consistency and model fit indices were very good, and even better than any previous CIUS validations. The Chinese CIUS is a valid and reliable self-reporting instrument for examining compulsive Internet use among Chinese adolescents. Other findings included: male adolescents tend to experience more compulsive Internet use than their female counterparts, and CIUS scores were positively correlated with the daily Internet use time and negatively correlated with the academic performance of the participants. No significant relationships between the CIUS, ICT accessibility, family economic condition, parental occupation or religion were found.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. http://psico.fcep.urv.es/utilitats/factor/index.html.

References

  1. Cao F, Su L: Internet addiction among Chinese adolescents: prevalence and psychological features. Child 33:275–81, 2007.

    CAS  Google Scholar 

  2. Cao F, Su L, LiuT, Gao X: The relationship between impulsivity and internet addiction in a sample of Chinese adolescents. European Psychiatry 22:466–71, 2007.

    Article  PubMed  Google Scholar 

  3. Ha JH, Kim SY, Bae SC, Bae S, Kim H, Sim M, Lyoo IK, Cho SC: Depression and Internet addiction in adolescents. Psychopathology 40:424–30, 2007.

    Article  PubMed  Google Scholar 

  4. Jang KS, Hwang SY, Choi JY: Internet addiction and psychiatric symptoms among Korean adolescents. Journal of School Health 78:165–71, 2008.

    Article  PubMed  Google Scholar 

  5. Ko CH, Yen JY, Chen SH, Yang MJ, Lin HC, Yen CF: Proposed diagnostic criteria and the screening and diagnosing tool of Internet addiction in college students. Comprehensive Psychiatry 50:378–384, 2009.

    Article  PubMed  Google Scholar 

  6. Asia Internet Use. http://www.internetworldstats.com/stats3.htm. Accessed 10 May, 2014.

  7. Internet Usage in Asia. 2013. http://www.internetworldstats.com/asia.htm. Accessed 10 May, 2014.

  8. Chen YF, Peng SS: University students’ internet use and its relationships with academic performance, interpersonal relationships, psychosocial adjustment, and self-evaluation. CyberPsychology and Behavior 11:467–69, 2008.

    Article  PubMed  Google Scholar 

  9. Leung L: Net-generation attributes and seductive properties of the internet as predictors of online activities and Internet addiction. CyberPsychology and Behavior 7:333–48, 2004.

    Article  PubMed  Google Scholar 

  10. Nalwa K, Anand AP: Internet addiction in students: a cause of concern. CyberPsychology and Behavior 6:653–56, 2003.

    Article  PubMed  Google Scholar 

  11. Chou C: Internet heavy use and addiction among Taiwanese college students: an online interview study. CyberPsychology and Behavior 4:573–85, 2001.

    Article  CAS  PubMed  Google Scholar 

  12. Suhail K, Bargees Z. Effects of excessive internet use on undergraduate students in Pakistan. CyberPsychology and Behavior 9:297–307, 2006.

    Article  PubMed  Google Scholar 

  13. Young KS: Internet addiction: a new clinical phenomenon and its consequences. American Behavioral Scientist 48:402–15, 2004.

    Article  Google Scholar 

  14. Shapira NA, Goldsmith TD, Keck Jr PE, Khosla UM, McElroy SL: Psychiatric features of individuals with problematic internet use. Journal of Affective Disorders 57:267–72, 2000.

    Article  CAS  PubMed  Google Scholar 

  15. Ghassemzadeh L, Shahraray M, Moradi AR: Prevalence of internet addiction and comparison of internet addicts and non-addicts in Iranian high schools. CyberPsychology & Behavior 11:731–3, 2008.

    Article  Google Scholar 

  16. Young KS: Caught in the net: how to recognize the signs of internet addiction—and a winning strategy for recovery. Wiley, New York, pp. 1–245, 1998.

    Google Scholar 

  17. Greenfield DN: Psychological characteristics of compulsive internet use: a preliminary analysis. CyberPsychology & Behavior 2:403–12, 1999.

    Article  CAS  Google Scholar 

  18. Meerkerk GJ, Van Den Eijnden RJ Vermulst AA, Garretse HF: The Compulsive Internet Use Scale (CIUS): some psychometric properties. Cyberpsychology and Behavior 12:1–6, 2009.

    Article  PubMed  Google Scholar 

  19. Caplan SE: Problematic Internet use and psychosocial well-being: Development of a theory-based cognitive-behavioral measurement instrument. Computers in Human Behavior 18:553–7, 2002.

    Article  Google Scholar 

  20. Davis RA: A cognitive-behavioral model of pathological Internet use. Computers in Human Behavior 17:187–95, 2001.

    Article  Google Scholar 

  21. Lu HY: Sensation-seeking, internet dependency, and online interpersonal deception. CyberPsychology & Behavior 11:227–31, 2008.

    Article  CAS  Google Scholar 

  22. Barke A, Nyenhuis N, Kröner-Herwig B: The German version of the Generalized Problematic Internet Use Scale 2 (GPIUS2): a validation study. Cyberpsychology, Behavior, and Social Networking 17(7):474–82, 2014.

    Article  Google Scholar 

  23. Chen S, Weng L, Su Y, Wu H, Yang P: Development of a Chinese internet addiction scale and its psychometric study. Chinese Journal of Psychology 45:279–94, 2003.

    Google Scholar 

  24. Widyanto L, McMurran M: The psychometric properties of the internet addiction test. Cyberpsychology & Behavior 7:443–50, 2004.

    Article  Google Scholar 

  25. Widyanto L, Griffiths MD, Brunsden VA: Psychometric comparison of the internet addiction test, the Internet-related problem scale, and self-diagnosis. Cyberpsychology, Behavior, and Social Networking 14:141–49, 2011.

    Article  Google Scholar 

  26. Jelenchick LA, Becker T, Moreno MA: Assessing the psychometric properties of the internet addiction test (IAT) in US college students. Psychiatry Research 196:296–301, 2012.

    Article  PubMed Central  PubMed  Google Scholar 

  27. Ferraro G, Caci B, D’Amico A, Blasi MD: Internet addiction disorder: an Italian study. Cyberpsychology & Behavior 10:170–5, 2004.

    Article  Google Scholar 

  28. Chong Guan N, Isa SM, Hashim AH, Pillai SK, Harbajan Singh MK: Validity of the Malay version of the Internet Addiction Test: a study on a group of medical students in Malaysia. Asia-Pacific Journal of Public Health 20(10):1–12, 2012.

    Google Scholar 

  29. Lee K, Lee HK, Gyeong H, Yu B, Song YM, Kim D: Reliability and validity of the Korean version of the Internet Addiction Test among college students. Journal of Korean Medical Science 28:763–8, 2013.

    Article  PubMed Central  PubMed  Google Scholar 

  30. Roseline YKF: The reliability and validity of three Internet Addiction instruments in the Japanese Population, University of Tokyo publications. 2013. http://repository.dl.itc.u-tokyo.ac.jp/dspace/bitstream/2261/55608/1/H24_4149_yong.pdf. Accessed 04, May 2014.

  31. Tsimtsiou Z, Haidich A-B, Kokkali S, Dardavesis T, Young KS, Arvanitidou M: Greek version of the Internet Addiction Test: a validation study. Psychiatric Quarterly 85(2):187–95, 2014.

    Article  PubMed  Google Scholar 

  32. Chang MK, Law SPM: Factor structure for Young’s Internet addiction test: a confirmatory study. Computers in Human Behavior 24:2597–619, 2008.

    Article  Google Scholar 

  33. Lai CM, Mak KK, Watanabe H, Ang RP, Pang JS, Ho RC: Psychometric properties of the Internet addiction test in Chinese adolescents. Journal of Pediatric Psychology 38(7): 794–807, 2013.

    Article  PubMed  Google Scholar 

  34. Ngai SS: Exploring the validity of the Internet Addiction Test for students in grades 5–9 in Hong Kong. International Journal of Adolescence & Youth 13:221–37, 2007.

    Article  Google Scholar 

  35. Hawi NS. Arabic validation of the internet addiction test. Cyberpsychology, Behavior & Networking 16(3):200–4, 2013.

    Article  Google Scholar 

  36. Korkeila J, Kaarlas S, Jääskeläinen M, Vahlberg T, Taiminen T: Attached to the web—harmful use of the Internet and its correlates. European Psychiatry 25:236–41, 2010.

    Article  CAS  PubMed  Google Scholar 

  37. Khazaal Y, Chatton A, Horn A, Achab S, Thorens G, Zullino D, Billieux J: French validation of the Compulsive Internet Use Scale (CIUS). Psychiatric Quarterly 83(4): 397–405, 2012.

    Article  PubMed  Google Scholar 

  38. Bai Y, Fan F: A study on the Internet dependence of college students: the revising and applying of a measurement. Psychological Development and Education 4:99–104, 2005.

    Google Scholar 

  39. Kwok-Kei M, Ching-Man, Lai Ko CH, Chou C, Kim DII, Watanabe H, Ho RCM: Psychometric properties of the Revised Chen Internet Addiction Scale (CIAS-R) in Chinese adolescents. Journal of Abnormal Child Psychology, 42(7):1237–45, 2014.

    Article  Google Scholar 

  40. American Psychological Association: Diagnostic and statistical manual of mental disorders. American Psychiatric Publishing, Washington, DC, 1994.

    Google Scholar 

  41. Griffiths M: Internet addiction: fact or fiction? Psychologist 12:246–50, 1999.

    Google Scholar 

  42. Brown RIF: Some contributions of the study of gambling to the study of other addictions. In: Eadington WR, Cornelius JA (Eds.), Gambling behavior and problem gambling. University of Nevada, Reno, pp. 241–72, 1993.

    Google Scholar 

  43. Marks I: Behavioural (non-chemical) addictions. British Journal of Addiction 85:1389–94, 1990.

    Article  CAS  PubMed  Google Scholar 

  44. Khazaal Y, Chatton A, Atwi K, Zullino D, Khan R, Billieux J: Arabic validation of the Compulsive Internet Use Scale (CIUS). Substance Abuse Treatment, Prevention, and Policy 6:32, 2011.

    Article  PubMed Central  PubMed  Google Scholar 

  45. Wartberg L, Petersen KU, Kammerl R, Rosenkranz M, Thomasius R: Psychometric validation of a German version of the compulsive Internet use scale, Cyberpsychology Behavior & Social Networking 17(2): 99–103, 2014.

    Article  Google Scholar 

  46. Alavi SS, Jannatifard F, Eslami M, Rezapour H: Validity, reliability and factor analysis of compulsive internet use scale in students of Isfahan’s universities. Health Information Management 7:724, 2011.

    Google Scholar 

  47. Kaiser HA: A second generation little jiffy. Psychometrika 35:401–15, 1970.

    Article  Google Scholar 

  48. Vander Aa N, Overbeek G, Engels RCME, Scholte RHJ, Meerkerk GJ, Vanden Eijnden RJJM: Daily and compulsive internet use and well-being in adolescence: a diathesis-stress model based on big five personality traits. Journal of Youth Adolescence 38:765–76, 2009.

    Article  PubMed  Google Scholar 

  49. Little RJA, Rubin DB: Statistical analysis with missing data. Wiley, New York, 1987.

    Google Scholar 

  50. Acock AC: Working with missing values. Journal of Marriage and Family 67:1012–28, 2005.

    Article  Google Scholar 

  51. Fichman M, Cummings JN: Multiple imputations for missing data: making the most of what you know. Organizational Research Methods 6(3),282–95, 2003.

    Article  Google Scholar 

  52. Schafer JL, Graham JW: Missing data: our view of the state of the art. Psychological Methods 7:147–77, 2002.

    Article  PubMed  Google Scholar 

  53. Allison PD: Missing data. Sage Publishers, Thousand Oaks, 2001.

    Google Scholar 

  54. Pigott TD: A review of methods for missing data. Educational Research and Evaluation 7:353–83, 2001.

    Article  Google Scholar 

  55. Van Dam NT, Earleywine M, Borders A. Measuring mindfulness? An item response theory analysis of the Mindful Attention Awareness Scale. Personality and Individual Differences 49;805–10, 2010.

    Article  Google Scholar 

  56. Tabachnick BG, Fidell LS: Using Multivariate Statistics. Harper Collins, New York pp. 66–70, 1996.

    Google Scholar 

  57. Stevens J: Applied multivariate statistics for the social sciences. Lawrence Erlbaum Associate, Mahwah, 1996.

    Google Scholar 

  58. Byrne MB: Structural equation modeling with AMOS. Basic concepts, applications, and programming. Taylor & Francis, New York, 2001.

    Google Scholar 

  59. George D, Mallery P: SPSS for Windows step by step: a simple guide and reference. 11.0 update (4th ed.). Allyn and Bacon, Boston, 2003.

  60. Hair JF, Anderson RE, Tatham RL, Black WC: Multivariate data analysis. Prentice Hall, London, 1998.

    Google Scholar 

  61. DeVellis RF: Scale development: theory and applications, applied social research methods. Sage Publications, Thousand Oaks, pp. 1–216, 2003.

  62. Streiner D: Starting at the beginning: an introduction to co-efficient alpha and internal consistency. Journal of Personality Assessment 80:99–103, 2003.

    Article  PubMed  Google Scholar 

  63. Tavakol M, Dennick R: Making sense of Cronbach’s alpha. International Journal of Medical Education 2:53–55, 2011.

    Article  PubMed Central  Google Scholar 

  64. Bartlett MS: A note on multiplying factors for various Chi square approximations. Journal of the Royal Statistical Society 16:296–8, 1954.

    Google Scholar 

  65. O’Connor B: SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, Instruments, & Computers 32:396–402, 2000.

    Article  Google Scholar 

  66. Velicer WF: Determining the number of components from the matrix of partial correlations. Psychometrika 41:321–327, 1976.

    Article  Google Scholar 

  67. Catell RB. The Scree Test for number of factors. Multivariate Behavioral Research 1;245–76, 1966.

    Article  Google Scholar 

  68. Schumacker RE, Lomax RG: A beginner’s guide to structural equation modeling. Routledge, New York, 2004.

    Google Scholar 

  69. Kline RB: Principles and practice of structural equation modeling, 3rd edn. Guilford Press, New York, 2011.

    Google Scholar 

  70. Browne MW, Cudeck R: Alternative ways of assessing model fit. In: Bollen KA, Long JS (Eds.), Testing structural equation models. Sage, Beverly Hills, pp. 136–62, 1993.

  71. Hu LT, Bentle PM: Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling 6:1–55, 1999.

    Article  Google Scholar 

  72. Choi K Son H, Park M, Han J, Kim K, Lee B, Gwak H: Internet overuse and excessive daytime sleepiness in adolescents. Psychiatry and Clinical Neurosciences 63:455–62, 2009.

    Article  PubMed  Google Scholar 

  73. Khazaal Y, Billieux J, Thorens G, Khan R, Louati Y, Scarlatti E, Theintz F, Lederrey J, Van der Linden M, Zullino D: French validation of the internet addiction test. Cyberpsychology Behavior 11: 703–706, 2009.

    Article  Google Scholar 

Download references

Acknowledgments

This research was conducted in the Future Industrial Services (FutIS) research program (Project No. 2113194), managed by the Finnish Metals and Engineering Competence Cluster (FIMECC), and funded by the Finnish Funding Agency for Technology and Innovation (TEKES), research institutes and companies. Their support is gratefully acknowledged. The support received from Academy of Finland in the form of researcher’s mobility grant to Taiwan (Decision No. 265969) and South Africa (Decision No. 277571) is acknowledged. Additionally, we would like to acknowledge the support received from Ministry of Science and Technology, Taiwan, under grant number NSC 102-2628-S-011-001-MY4.

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Institute of Behavioral Psychology, University of Helsinki, Helsinki, Finland

    Amandeep Dhir

  2. Department of Computer Science and Engineering, Aalto University, Espoo, Finland

    Amandeep Dhir

  3. Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taipei, Taiwan

    Sufen Chen

  4. Department of Computer Science and Engineering, Aalto University, Espoo, Finland

    Marko Nieminen

Authors
  1. Amandeep Dhir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sufen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marko Nieminen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amandeep Dhir.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dhir, A., Chen, S. & Nieminen, M. Psychometric Validation of the Chinese Compulsive Internet Use Scale (CIUS) with Taiwanese High School Adolescents. Psychiatr Q 86, 581–596 (2015). https://doi.org/10.1007/s11126-015-9351-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11126-015-9351-9

Keywords

Search

Navigation