), 董书阳2, 袁帅3, 胡传鹏4,5() 1 清华大学人文学院, 北京 100084
2 Department of Developmental Psychology, Utrecht University, Utrecht,3584CS, Netherland
3 Department of Methodology and Statistics, Tilburg University, Tilburg, 5037AB, Netherland
4 German Resilience Center, Mainz, 55131, Germany
5 Neuroimaging Center (NIC), University Medical Centre of the Johannes Gutenberg University, Mainz, 55131, Germany
收稿日期:2019-05-27出版日期:2020-01-15发布日期:2019-11-21通讯作者:蔡玉清,胡传鹏E-mail:cyq_2016@outlook.com;hcp4715@hotmail.com基金资助:清华大学学术推进计划资助Network analysis and its applications in psychology
CAI Yuqing1(), DONG Shuyang2, YUAN Shuai3, HU Chuan-Peng4,5() 1 Department of Humanity, Tsinghua University, Beijing 100084, China
2 Department of Developmental Psychology, Utrecht University, Utrecht,3584CS, Netherland
3 Department of Methodology and Statistics, Tilburg University, Tilburg, 5037AB, Netherland
4 German Resilience Center, Mainz, 55131, Germany
5 Neuroimaging Center (NIC), University Medical Centre of the Johannes Gutenberg University, Mainz, 55131, Germany
Received:2019-05-27Online:2020-01-15Published:2019-11-21Contact:CAI Yuqing,HU Chuan-Peng E-mail:cyq_2016@outlook.com;hcp4715@hotmail.com摘要/Abstract
摘要： 变量间的网络分析模型近年来被广泛应用于心理学研究。不同于将潜变量作为观测变量的共同先导因素的潜变量模型, 网络分析模型将观测变量作为初级指标, 采用图论的方法建立观测变量之间的关系网络, 其中变量为网络的节点, 而变量间的关系是节点之间的连线。因此网络分析可以突显观测变量之间的联系以及观测变量相互影响而形成的系统。通过变量网络中基于各个节点特征的指标(如中心性)以及基于整体结构特征的指标(如小世界性), 网络分析为研究各种心理现象提供了新的可视化的描述方式和理解视角。近10年来, 网络分析的方法已在人格心理学、社会心理学和临床心理学等领域得到一定的应用。未来研究应继续发展和完善网络分析模型的理论和方法, 使之运用到更多的数据类型和更广的研究领域中。
图/表 2
图1简短大五人格问卷中宜人性(agreeableness)、神经质(neuroticism)和外向性(extroversion)三个维度的偏相关网络模型。测量相同特质的问卷条目由相同颜色的节点表示(数据、代码及问卷条目见在线补充材料或osf.io/g74dz/)。条目与条目之间的联系由连线表示, 绿色连线代表正相关, 红色连线代表负相关, 连线越粗代表相关性越强。左：偏相关网络模型;右：GLASSO网络模型。数据来源：Liu等(2019)。
图1简短大五人格问卷中宜人性(agreeableness)、神经质(neuroticism)和外向性(extroversion)三个维度的偏相关网络模型。测量相同特质的问卷条目由相同颜色的节点表示(数据、代码及问卷条目见在线补充材料或osf.io/g74dz/)。条目与条目之间的联系由连线表示, 绿色连线代表正相关, 红色连线代表负相关, 连线越粗代表相关性越强。左：偏相关网络模型;右：GLASSO网络模型。数据来源：Liu等(2019)。
图2包含4个节点的加权网络示意图, 各连线间加权值如各连线上数值所示
图2包含4个节点的加权网络示意图, 各连线间加权值如各连线上数值所示参考文献 71
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