Two widely used scales of Internet addiction (IA), the Internet Addiction Test (IAT) and the Chen Internet Addiction Scale (CIAS), were compared and a new scale of IA was assembled from their items with improved reliability in terms of classification consistency. A total of 467 Chinese college students participated in the study. Items were calibrated using the Muraki's Generalized Partial Credit Model. Most items had higher item information on medium levels of addiction, but much lower item information on the two ends of the latent trait continuum. The average item information of the CIAS was significantly larger compared with IAT on most of the latent trait levels. A new scale assembled using the cutoff points of IAT had a larger classification consistency than the original IAT. It was shown that the classification consistency of the IA measurement could be improved by selecting items to optimize test information around cutoff points. Implications for test and item development of IA were discussed.
Get full access to this article
View all access options for this article.
References
1.
Chinese Internet Network Information Center. 2011. 28th China Internet development statistics report. www.cnnic.net.cn/dtygg/dtgg/201101/t20110118_20250.html. 2011Jul.
2.
ChouC, HsiaoM. Internet addiction, usage, gratification, and pleasure experience: the Taiwan college students' case. Computers and Education, 2000; 35:65–80.
3.
MooreD. 1995. The emperor's virtual clothes: the naked truth about the Internet culture. Alogonquin, NC: Chapel Hill.
4.
GoldbergI. 1996. Internet Addiction Disorder. www.rider.edu/∼suler/psycyber/supportgp.html. 1996Aug.
5.
YoungKS. A case that breaks the stereotype. Psychological Reports, 1996; 79:899–902.
6.
YoungKS. 1999. Internet addiction: symptoms, evaluation, and treatment. VandeCreekL, JacksonT. Innovations in clinical practice: a source book, 17. Sarasota, FL: Professional Resource Press, 19–31.
7.
DavisRA. A cognitive-behavioral model of pathological Internet use. Computers in Human Behavior, 2001; 17:187–195.
8.
BeardK, WolfE. Modification in the proposed diagnostic criteria for internet addiction. Cyberpsychology and Behavior, 2001; 4:377–383.
9.
SchererK. College life online: healthy and unhealthy Internet use. Journal of College Student Development, 1997; 38:655–665.
10.
HallA, ParsonsJ. Internet addiction: college student case study using best practices in cognitive behavior therapy. Journal of Mental Health Counseling, 2001; 23:312–327.
11.
YoungKS. 1996. Internet addiction: the emergence of a new clinical disorder. Paper presented at the 104 American Psychological Association Annual Convention, Toronto, Canada.
12.
GroholJM. Too much time online: Internet addiction or healthy social interactions?Cyberpsychology and Behavior, 1999; 2:395–401.
13.
YoungKS. 1998. Caught in the Net: how to recognize the signs of Internet addiction—and a winning strategy for recovery. New York: Wiley, 257–258.
14.
ChenS, WengL, SuYet al.Development of a chinese Internet addiction scale and its psychometric study. Chinese Journal of Psychology, 2003; 45:279–294.
15.
BaiY, FanF. A Study on the Internet dependence of college students: the revising and applying of a measurement. Psychological Development and Education, 2005; 4:99–104.
16.
WidyantoL, McMurranM. The psychometric properties of the Internet addiction test. Cyberpsychology and Behavior, 2004; 7:443–450.
17.
WidyantoL, GriffithsMD, BrunsdenV. A psychometric comparison of the Internet addiction test, the Internet-related problem scale, and self-diagnosis. Cyberpsychology, Behavior, and Social Networking, 2011; 14:141–149.
18.
CananF, AtaogluA, NicholsLAet al.Evaluation of psychometric properties of the internet addiction scale in a sample of Turkish high school students. Cyberpsychology, Behavior, and Social Networking, 2010; 13:317–320.
19.
EmbretsonSE, ReiseSP. 2000. Item response theory for psychologists. Mahwah, NJ: Erlbaum.
20.
HambletonRK, GruijterDNM. Application of item response models to criterion-referenced test item selection. Journal of Educational Measurement, 1983; 20:355–367.
21.
ChristakisD, MorenoM, JelenchickLet al.Problematic internet usage in US college students: a pilot study. BMC Medicine, 2011; 9:77–82.
22.
FletcherRB, HattieJA. An examination of the psychometric properties of the physical self-description questionnaire using a polytomous item response model. Psychology of Sport and Exercise, 2004; 5:423–446.
23.
FraleyRC, WallerNG, BrennanK. An item response theory analysis of self-report measures of adult attachment. Journal of Personality and Social Psychology, 2000; 78:350–365.
24.
MokkenRJ. 1971. A theory and procedure of scale analysis. Berlin, Germany: De Gruyter.
25.
van der ArkLA. Mokken scale analysis in R. Journal of Statistical Software, 2007; 20:1–19.
OrlandoM, ThissenD. Further investigation of the performance of SX2: an item fit index for use with dichotomous item response theory models. Applied Psychological Measurement, 2003; 27:289–298.
28.
OrlandoM, ThissenD. Likelihood-based item fit indices for dichotomous item response theory models. Applied Psychological Measurement, 2000; 24:50–64.
29.
LeeWC. Classification consistency and accuracy for complex assessments using item response theory. Journal of Educational Measurement, 2010; 47:1–17.
IrwinDE, StuckyB, LangerMMet al.An item response analysis of the pediatric PROMIS anxiety and depressive symptoms scales. Quality of Life Research, 2010; 19:595–607.
32.
WeiserEB. The functions of Internet use and their social and psychological consequences. Cyberpsychology and Behavior, 2001; 4:723–743.
