The risk factors of Internet addiction—A survey of university freshmen
Abstract
This study was designed to explore the risk factors of Internet addiction in 1360 freshmen of the National Cheng Kung University in Taiwan in 2003. The test battery included a self-administrated structured questionnaire, the Chinese Internet Addiction Scale-Revision (CIAS-R), the 12-item Chinese Health Questionnaire (CHQ-12), the Measurement of Support Functions (MSF), and the neuroticism subscale of the Maudsley Personality Inventory (MPI). Of the total study population, there were 680 college freshmen (17.9%) in the Internet addiction group, as defined by high CIAS-R scores. Using logistic regression analyses, we found positive relationships between Internet addiction and male gender, neuroticism scores and the CHQ score. In addition, the freshmen who skipped breakfast and those who had poorer social support also had a higher probability of Internet addiction. Internet addiction is prevalent among university freshmen in Taiwan. Risk factors included male gender, habit of skipping breakfast, mental health morbidity, deficient social support; and neurotic personality characteristics.
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A previous latent class analysis in two large Dutch school-based samples found that a 6-class model had the best fit.
“In two large school-based samples, respondents filled out the Compulsive Internet Use Scale (CIUS) [19] , a 14-item questionnaire covering core criteria of problematic Internet use. A LCA revealed a 6-class model to have the best fit.”
From Occurence of Internet addiction in a general population sample: A Latent Class Analysis (2014) · cites this paper as [18]
The CIUS has shown good validity and Cronbach's alpha reliability between 0.88 and 0.90.
“The questionnaire showed good validity and a Cronbach's α as measure of reliability ranging from 0.88 to 0.90 [18, 19]”
From Occurence of Internet addiction in a general population sample: A Latent Class Analysis (2014) · cites this paper as [18]
In that previous latent class analysis, two classes had characteristics making it very likely that participants were heavy users or had problems with online gaming.
“According to the data, 2 of these classes showed characteristics that made it very likely that participants were experiencing heavy use or problems with online gaming”
From Occurence of Internet addiction in a general population sample: A Latent Class Analysis (2014) · cites this paper as [18]
In that previous study, the dependent-user group made up 3% of the sample and about 1.5% of all children aged 13-16 in the Netherlands.
“This latter group constituted 3% of the sample and about 1.5% of all children aged 13-16 years in the Netherlands.”
From Occurence of Internet addiction in a general population sample: A Latent Class Analysis (2014) · cites this paper as [18]
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- Authors
- Tsai, H. F., Cheng, S. H., Yeh, T. L., Shih, C.-C., Chen, K. C., Yang, Y. C., & Yang, Y. K.
- Affiliations
- National Cheng Kung University Hospital (TW)
- National Cheng Kung University (TW)
- Corresponding author
- Yang, Yi Ching
- ORCID
- Yang, Yi Ching
- Venue
- Psychiatry Research
- Publisher
- Elsevier BV
- Published
- 2009-05
- Volume
- 167
- Issue
- 3
- Pages
- 294-299
- ISSN
- 0165-1781 (ISSN-L)
- OpenAlex
- W2134599820
- Cited by
- 368
- References
- 40
- Citations by year
- 2025: 9 · 2024: 12 · 2023: 16 · 2022: 25 · 2021: 26 · 2020: 33 · 2019: 36 · 2018: 41 · 2017: 29 · 2016: 28 · 2015: 25 · 2014: 30 · 2013: 30 · 2012: 17
- FWCI
- 30.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychiatry Research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Cheng Kung University
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Psychology › Clinical Psychology › Sexuality, Behavior, and Technology
- Social Sciences › Psychology › Clinical Psychology › Gambling Behavior and Treatments
- Keywords
- internet addiction, risk factors, first-year students, neuroticism, social support, breakfast skipping, mental health, gender differences
- MeSH terms
- Female, Health Surveys, Humans, Life Style, Male, Neurotic Disorders, Personality Inventory, Surveys and Questionnaires, Risk Factors, Social Support, Students, Taiwan, Universities, Prevalence, Behavior, Addictive, Video Games, Internet, Asian People
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