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Prevalence and Correlates of Internet Addiction in Undergraduate Students: Assessing with Two Major Measures

Prevalence and Correlates of Internet Addiction in Undergraduate Students: Assessing with Two Major Measures

Daniel Schoenfeld, Zheng Yan
Copyright: © 2012 |Volume: 2 |Issue: 2 |Pages: 19
ISSN: 2155-7136|EISSN: 2155-7144|EISBN13: 9781466611405|DOI: 10.4018/ijcbpl.2012040102
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MLA

Schoenfeld, Daniel, and Zheng Yan. "Prevalence and Correlates of Internet Addiction in Undergraduate Students: Assessing with Two Major Measures." IJCBPL vol.2, no.2 2012: pp.16-34. http://doi.org/10.4018/ijcbpl.2012040102

APA

Schoenfeld, D. & Yan, Z. (2012). Prevalence and Correlates of Internet Addiction in Undergraduate Students: Assessing with Two Major Measures. International Journal of Cyber Behavior, Psychology and Learning (IJCBPL), 2(2), 16-34. http://doi.org/10.4018/ijcbpl.2012040102

Chicago

Schoenfeld, Daniel, and Zheng Yan. "Prevalence and Correlates of Internet Addiction in Undergraduate Students: Assessing with Two Major Measures," International Journal of Cyber Behavior, Psychology and Learning (IJCBPL) 2, no.2: 16-34. http://doi.org/10.4018/ijcbpl.2012040102

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Abstract

This study determined if two different internet addiction assessments would identify the same individuals as addicted to the internet. A total of 224 undergraduate internet users were surveyed using a stratified sampling plan based on the proportional allocation technique to procure as diverse a sample as possible. The assessments used were Young’s Internet Addiction Test (IAT), Caplan’s Generalized Problematic Internet Use Scale (GPIUS), a demographic questionnaire, and a reasons-for-use questionnaire. Results showed that about 0.9% of the sample could be considered addicted to the internet according to both the IAT and GPIUS, which is a smaller percentage than found in previous studies. There were too few participants identified as addicted to the internet to determine if these two assessments identified the same individuals as addicted; however, over a third of the sample was identified as “at risk” for addiction by one assessment and not the other. These results show that the assessment measure used is of extreme importance when diagnosing internet addiction and more robust sampling procedures may lead to fewer internet addicts identified, which could be a more accurate reflection of internet addiction in the target population.

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