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ГОДИШЊАК ЗА ПСИХОЛОГИЈУ Pub Date : 2021-12-13 DOI:10.46630/gpsi.18.2021.04

Teodora Pavićević

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摘要

本研究的目的是确定应对策略在预测网络成瘾维度中的作用。样本是方便的，它包括597名受试者(63%的女性)。受试者年龄18 ~ 70岁，平均年龄35.58岁(SD=11.29)。青少年网络成瘾测试(简表)使用YIAT-SF，对遇到的问题进行简要的应对指导;简短的COPE被用来衡量人们如何处理问题和压力生活事件。进行了两次单独的分层回归分析。在这两个分析中，第一步的预测变量是社会人口学变量，第二步是十种应对策略(自我分心、积极应对、否认、使用情感支持、使用工具支持、行为脱离、发泄、积极重构、接受和自责)，而标准变量是网络成瘾的不同维度(失去控制/时间管理和渴望/社会问题)。结果表明，两种模型均具有统计学显著性。第一步失控/时间管理维度的显著预测因子是年龄(β = -)。13, p = .001)，第二步是自我分心(β = .09, p = .018);积极应对(β = -)15, p = .001);行为脱离(β = .24, p < .001)和自责(β = .13, p = .002)。当涉及到渴望/社会问题时，这一变量的第一步显著预测因子是年龄(β = -)。11, p = 0.006)，其次是性别(β = 0.07, p = 0.041);积极应对(β = -)09, p = 0.044);否认(β = .14, p = .001);使用情感支持(β = -)。16, p = .002)和行为脱离(β = .25, p < .001)。因此，基于这些结果，我们可以得出结论:基于自我分心、积极应对、行为脱离和自责，我们可以预测网络使用引起的日常功能困难，但我们也可以基于否认、使用情感支持和行为脱离来预测过度使用网络对社会关系和对网络的渴望的影响。验证性因子分析表明，青少年网络成瘾量表具有较好的结构效度。关键词:应对策略，网络成瘾，网络使用

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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STRATEGIJE PREVLADAVANJA STRESA KAO PREDIKTORI INTERNET ZAVISNOSTI

The aim of this study was to determinate the role of coping strategies in predicting internet addiction dimensions. The sample is conveniente and it consists of 597 subjects (63% women). The age of the subject ranges from 18 to 70 years, with an average age of 35.58 (SD=11.29). To measure a severity of compulsive internet use Young Internet Addiction Test-Short Form; YIAT-SF was used, and Brief Coping Orientation to Problems Experienced; Brief COPE was used to measure how people cope with problems and stressful life events. Two separate hierarchical regression analyses were conducted. In both analyses, in first step the predictors were sociodemographic variables, and in second were ten types of coping strategies (Self-distraction, Active coping, Denial, Use of emotional support, Use of instrumental support, Behavioral disengagement, Venting, Positive reframing, Acceptance and Self-blame), while the criteria variables were different dimensions of internet addiction (Loss of control/Time management and Craving/Social problems). The results showed that both models are statistically significant. Significant predictor of dimension Loss of control/Time management in the first step is age (β = -.13, p = .001), and in second step are Self-distraction (β = .09, p = .018); Active coping (β = -.15, p = .001); Behavioral disengagement (β = .24, p < .001) and Self-blame (β = .13, p = .002). When it comes to Craving/Social problems, significant predictor of this variable in first step is age (β = -.11, p = .006), and in second are gender (β = .07, p = .041); Active coping (β = -.09, p = .044); Denial (β = .14, p = .001); Use of emotional support (β = -.16, p = .002) and Behavioral disengagement (β = .25, p < .001). So, based on the results, we can conclude that based on Self-distraction, Active coping, Behavioral disengagement, and Self-blame we can predict the difficulties in everyday functioning caused by internet use, but we can also predict an effect that excessive internet use could have on social relations and craving for the Internet based on Denial, Use of emotional support and Behavioral disengagement. The application of Confirmatory Factor Analysis suggested good construct validity of Young Internet Addiction Test-Short Form. Keywords: coping strategies, internet addiction, internet use

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ГОДИШЊАК ЗА ПСИХОЛОГИЈУ

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