摘要
目的:了解大学生网络成瘾的潜在类别及其与人口学变量的关系。方法:选取559名大学生(男208人,女351人,年龄17~28岁),采用大学生网络成瘾类型问卷(TIASU)进行测试,对大学生网络成瘾特征进行潜剖面分析。采用R3STEP法的多项式logistic回归分析探讨大学生网络成瘾类型与人口学变量的关系。结果:潜剖面分析结果支持3个潜类别的模型,分别定义为“高危型”(21.6%)、“中间型”(53.0%)和“低危型”(25.4%)。logistic回归分析发现,相较于低危型而言,网龄每大一岁属于中间型的发生比要高出5.8%;相较于低危型而言,平均每天上网时间每多一小时属于中间型和高危型的发生比分别要高出21.3%和23.6%。结论:大学生网络成瘾存在3种不同潜类别;网龄、平均每天上网时间有助于预测大学生所属的类别组。
Objective:To explore the latent classes of college students’Internet addiction,and its relationsip with demographic variables.Methods:Totally 559 college students(208 males,351 females,aged 17-28 years)were selected with the Different Type of Internet Addiction Scale for Undergraduates(TIASU).Latent profile analysis was used to analyze characteristics of college students’Internet addiction.R3 STEP of multinomial logistic regression analysis was used to analyze the relationship between the types of Internet addiction and the demographic variables.Results:The latent profile analysis showed that three latent classes were supported,including high-risk type(21.6%),intermediate type(53.0%)and low-risk type(25.4%).Logistic regression analysis found,compared with"low-risk type",the incidence of intermediate type was 5.8%higher when the older the net age was.Compared with"low-risk type",the incidence of intermediate type and high-risk type was 21.3%and 23.6%higher separately when the longer the average online time per day was.Conclusion:There may be three latent classes of Internet addiction among college students.Net age and average online time per day are helpful to predict the category group of college students.
作者
杨宏
金童林
刘振会
李鑫
乌云特娜
YANG Hong;JIN Tonglin;LIU Zhenhui;LI Xin;Wuyuntena(School of Psychology,Inner Mongolia Normal University,Hohhot 010022,China)
出处
《中国心理卫生杂志》
CSSCI
CSCD
北大核心
2020年第6期539-542,共4页
Chinese Mental Health Journal
基金
国家社会科学基金(BMA170035)
2019年度内蒙古师范大学研究生科研创新基金(CXJJS19011)。
关键词
大学生
网络成瘾
网龄
平均每天上网时间
潜剖面分析
college student
Internet addiction
net age
average online time per day
latent profile analysis
