摘要
网络游戏障碍的诊断标准问题一直受到广泛关注。十项网络游戏障碍测验(IGDT-10)在多个国家具有较高的使用率,并且表现出良好的跨文化适用性。然而IGDT-10中文版在我国的使用率较低,其适用性也有待商榷。本研究对IGDT-10简体中文版量表的信效度进行评估,并采用潜在类别分析和受试者工作特征曲线确定了IGDT-10简体中文版的截断分数及诊断能力。结果发现,IGDT-10简体中文版具有良好的信效度及诊断能力,并且支持了DSM-5关于IGD截断分数的建议(认可五个或更多标准)。此外,本研究样本中的网络游戏障碍流行率为13.4%,且存在性别差异(男性高于女性),但不存在年龄差异。结果表明,IGDT-10简体中文版可作为评估网络游戏障碍的有效工具。
The criteria for identifying internet gaming disorder have been widely acknowledged and discussed.The Ten-Item Internet Gaming Disorder Test(IGDT-10)is frequently utilized in many countries,underscoring its high cross-cultural applicability.However,the Chinese version of IGDT-10 has lacked usage in China,sparking arguments about its applicability.In the current study,the reliability,validity,and diagnostic efficiency were assessed for the simplified Chinese IGDT-10.The cut-off point and diagnostic efficiency were determined through latent class analysis and receiver operating characteristic.The results demonstrated that the simplified Chinese IGDT-10 exhibited strong reliability,validity,and diagnostic efficiency,aligning with the DSM-5 recommendation of recognizing five or more criteria for internet gaming disorder.Moreover,this study revealed a 13.4%prevalence rate of internet gaming disorder in the samples,with a gender difference(higher prevalence among males than females)but no significant age difference.The results suggest that the simplified Chinese IGDT-10 is an effective tool for assessing internet gaming disorder.
作者
杨海波
张馨予
YANG Haibo;ZHANG Xinyu(Key Research Base of Humanities and Social Sciences of the Ministry of Education,Academy of Psychology and Behavior,Tianjin Normal University,Tianjin 300387;Faculty of Psychology,Tianjin Normal University,Tianjin 300387;Tianjin Social Science Laboratory of Students’Mental Development and Learning,Tianjin 300387)
出处
《心理与行为研究》
北大核心
2023年第5期658-666,共9页
Studies of Psychology and Behavior
基金
国家自然科学基金项目(32271140)。
关键词
十项网络游戏障碍测验
信度
效度
受试者工作特征曲线
潜在类别分析
Ten-Item Internet Gaming Disorder Test
reliability
validity
receiver operating characteristic
latent class analysis.