›› 2019, Vol. ›› Issue (2): 387-394.

• 社会、人格与管理 • 上一篇    下一篇

大学新生的羞怯、自尊与网络依赖的关系:交叉滞后分析

高峰强1,张淑洁2,隋怡1,王鹏2,石洁茹2,孟维璇1,司英栋1   

  1. 1. 山东师范大学心理学院
    2. 山东师范大学
  • 收稿日期:2018-01-09 修回日期:2018-08-02 出版日期:2019-03-20 发布日期:2019-03-20
  • 通讯作者: 王鹏

Associations between Shyness, Self-esteem and Internet Dependence in College Freshmen: A Cross-lagged Analysis

  • Received:2018-01-09 Revised:2018-08-02 Online:2019-03-20 Published:2019-03-20

摘要: 对361名大一新生进行为期3个月的追踪测量,以考察羞怯、自尊与网络依赖的发展变化及相互关系。结果发现:(1)大一新生的羞怯、自尊和网络依赖的水平具有一定程度的横向稳定性。(2)羞怯与网络依赖正相关,自尊与网络依赖负相关。(3)T1的网络依赖正向预测T2的羞怯,T2的羞怯正向预测T3的网络依赖;T1和T2的网络依赖分别负向预测T2和T3的自尊。结论:羞怯与网络依赖之间存在恶性循环,并且网络依赖会导致自尊水平的下降。

关键词: 大一新生, 羞怯, 自尊, 网络依赖, 交叉滞后分析

Abstract: With the development of science and technology, the network popularity has rapidly mounted, college students have also became the main force of Internet use. The network brings college students much benefiting, but over reliance on the Internet also brings a lot of psychological problems to the college students, such as increasing shyness and lowering self-esteem. Shyness refers to a temperament, attitude or state of inhibition. It often feels inferior and can not express itself in public occasions, and shows an inappropriate response to external changes. Previous studies showed that shyness preferred to use network to build and develop their intimacy, and as the level of shyness increases, their dependence on the Internet was also higher. On the other hand, some researches suggest that the formation of reasonable self-esteem can reduce the possibility that individuals form Internet dependence. Self-esteem may serve as a protective mechanism, making individuals more willing to pursue the true meaning of life, so as to reduce the possibility of the formation of network dependence, and low self-esteem are eager to achieve seeking their own value in the virtual network, therefore more prone to behavior problems such as network dependence. In order to explore the mutual predictive relationship between shyness and Internet dependence, and self-esteem and Internet dependence in college freshmen, four kinds of theoretical models are proposed by the method of cross lag analysis. The models are compared by 2(df), TLI, CFI, RMSEA and the optimal model is determined. Test started from the first month after the military training and the participants were freshmen of two universities in Shandong Province. The total measurement time was three months; each time interval was one month. Each participant completed the The College Students' Shyness Scale, The Chinese version of Self Esteem Scale and The Chinese version of Internet Dependency Scale. After deleting invalid questionnaire, a total of 361 valid questionnaires were obtained. Finally, data analysis using SPSS17.0 and AMOS22.0, including repeated measurement of variance analysis, correlation analysis and path analysis of cross lag model. The results were as follows: (1) Shyness, self-esteem, and Internet dependence were stable cross time. (2) Pearson correlation analysis showed that, the correlation between shyness at T1、T2 and T3 was significant, so is self-esteem and Internet dependence. (3) At three time points, there was a positive correlation between shyness and Internet dependence, and a negative correlation between self-esteem and Internet dependence. (4) Cross lagged analysis show that, Internet dependent at T1 can positively predict their shyness at T2, while the level of shyness can further positively predict their Internet dependent at T3. Shyness plays a partial mediating role between the relationship of Internet dependent at T1 and T3. (5) The Internet dependence at T1 and T2 can negatively predict the self-esteem at T2 and T3 respectively. Therefore, it is necessary to intervene in the freshmen as soon as possible, reducing their shyness, improving their self-esteem, and encouraging students to actively participate in various activities in the extracurricular time, enriching students' spare time to reduce the use of network.

Key words: freshmen, shyness, self-esteem, Internet dependence, cross-lagged analysis