Prevalence and Correlates of Internet Addiction in Undergraduate Students: Assessing with Two Major Measures
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 sam...
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Veröffentlicht in: | International journal of cyber behavior, psychology, and learning psychology, and learning, 2012-04, Vol.2 (2), p.16-34 |
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description | 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. |
doi_str_mv | 10.4018/ijcbpl.2012040102 |
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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.</description><identifier>ISSN: 2155-7136</identifier><identifier>EISSN: 2155-7144</identifier><identifier>DOI: 10.4018/ijcbpl.2012040102</identifier><language>eng</language><publisher>Hershey: IGI Global</publisher><subject>Addictions ; Allocations ; Analysis ; Assessments ; College students ; Demographics ; Internet ; Internet addiction ; Learning ; Prevalence studies (Epidemiology) ; Questionnaires ; Reflection ; Sampling ; Students ; Surveys ; Undergraduate study</subject><ispartof>International journal of cyber behavior, psychology, and learning, 2012-04, Vol.2 (2), p.16-34</ispartof><rights>COPYRIGHT 2012 IGI Global</rights><rights>Copyright © 2012, IGI Global. 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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.</description><subject>Addictions</subject><subject>Allocations</subject><subject>Analysis</subject><subject>Assessments</subject><subject>College students</subject><subject>Demographics</subject><subject>Internet</subject><subject>Internet addiction</subject><subject>Learning</subject><subject>Prevalence studies (Epidemiology)</subject><subject>Questionnaires</subject><subject>Reflection</subject><subject>Sampling</subject><subject>Students</subject><subject>Surveys</subject><subject>Undergraduate study</subject><issn>2155-7136</issn><issn>2155-7144</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kVFPHCEUhSeNTWqsP8A3kr7Uh10ZGGagb5uNWhNNTdRnwsBly2YWVmA0_vuyTtNNW4UHyM137j25p6pOajxvcM3P3Fr322FOcE1wKWDyoTokNWOzrm6agz9_2n6qjlNa43JYw2veHVbDbYQnNYDXgJQ3aBlihEFlSChYdOUzRA8ZLYxxOrvgkfPowRuIq6jMWDh0l0cDPqdvaJESpOT8Cj27_BPdPwd0o9YhohtQaYyQPlcfrRoSHP9-j6qHi_P75ffZ9Y_Lq-XieqYp5XmmWyU0Ybw3LXTCGq210RSzlgLDYDBhuvgX2va8JYJ2HPeMdaKjXEBvjKVH1dep7zaGxxFSlhuXNAyD8hDGJMuGCO-IaERBv_yDrsMYfXEnS-ua7ybwPbUqq5LO25Cj0rumctEQKhjuyI6av0GVa2DjdPBgXan_JagngY4hpQhWbqPbqPhSHMpdsnJKVu6TLZrTSeNWbu_1P05uX_dw-QY7pS1L2nKftgz2_aGE_gKsTLzJ</recordid><startdate>20120401</startdate><enddate>20120401</enddate><creator>Schoenfeld, Daniel</creator><creator>Yan, Zheng</creator><general>IGI Global</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope></search><sort><creationdate>20120401</creationdate><title>Prevalence and Correlates of Internet Addiction in Undergraduate Students: Assessing with Two Major Measures</title><author>Schoenfeld, Daniel ; Yan, Zheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-c6a9c258bd6e79fdcccdc30563e50ed025c0549cfb86293780b55797389ebddf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Addictions</topic><topic>Allocations</topic><topic>Analysis</topic><topic>Assessments</topic><topic>College students</topic><topic>Demographics</topic><topic>Internet</topic><topic>Internet addiction</topic><topic>Learning</topic><topic>Prevalence studies (Epidemiology)</topic><topic>Questionnaires</topic><topic>Reflection</topic><topic>Sampling</topic><topic>Students</topic><topic>Surveys</topic><topic>Undergraduate study</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Schoenfeld, Daniel</creatorcontrib><creatorcontrib>Yan, Zheng</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest One Psychology</collection><jtitle>International journal of cyber behavior, psychology, and learning</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Schoenfeld, Daniel</au><au>Yan, Zheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prevalence and Correlates of Internet Addiction in Undergraduate Students: Assessing with Two Major Measures</atitle><jtitle>International journal of cyber behavior, psychology, and learning</jtitle><date>2012-04-01</date><risdate>2012</risdate><volume>2</volume><issue>2</issue><spage>16</spage><epage>34</epage><pages>16-34</pages><issn>2155-7136</issn><eissn>2155-7144</eissn><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.</abstract><cop>Hershey</cop><pub>IGI Global</pub><doi>10.4018/ijcbpl.2012040102</doi><tpages>19</tpages></addata></record> |
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subjects | Addictions Allocations Analysis Assessments College students Demographics Internet Internet addiction Learning Prevalence studies (Epidemiology) Questionnaires Reflection Sampling Students Surveys Undergraduate study |
title | Prevalence and Correlates of Internet Addiction in Undergraduate Students: Assessing with Two Major Measures |
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