Psychometric Validation of the Chinese Compulsive Internet Use Scale (CIUS) with Taiwanese High School Adolescents
- 333 Downloads
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.
KeywordsAdolescents Compulsive Internet Use Scale Cross-sectional survey Psychometric validation
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.
- 1.Cao F, Su L: Internet addiction among Chinese adolescents: prevalence and psychological features. Child 33:275–81, 2007.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.
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 49.Little RJA, Rubin DB: Statistical analysis with missing data. Wiley, New York, 1987.Google Scholar
- 53.Allison PD: Missing data. Sage Publishers, Thousand Oaks, 2001.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.Google Scholar
- 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.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
- 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.Google Scholar