报告时间：2023年4月13日上午10：00-11：00
会议ID：986-980-580
报告嘉宾：袁克海
报告题目：偏最小二乘结构方程模型方法，性质，及应用
英文题目：Partial-least-squares structural equation modeling, its properties and applications
报告摘要：Structural equation modeling (SEM) is a widely used technique for studies involving latent constructs. While covariance-based SEM (CB-SEM) permits estimating the regression relationship among latent constructs, the parameters governing this relationship do not apply to that among the scored values of the constructs, which are needed for prediction, classification and/or diagnosis of individuals/participants. In contrast, the partial-least squares approach to SEM (PLS-SEM) first obtains weighted composites for each case and then estimates the structural relationship among the composites. Consequently, PLS-SEM is a preferred method in predicting and/or classifying individuals. In this talk, I will (1) introduce PLS-SEM methodology, (2) discuss its statistical and psychometric properties, (3) issues in its applications with real data, and (4) recent developments.
个人简介：袁克海，大学和硕士毕业于北京理工大学应用数学系，博士毕业于UCLA数学系，现为圣母大学心理系教授。曾担任Journal of Multivariate Analysis和Psychological Methods 的副主编, 现任多个杂志编委。他在结构方程、路径分析、稳健方法，模型诊断，不完全数据分析等领域都做出了原创性的工作。袁克海发表学术论文180余篇，其方法已被编入多个软件包（EQS、Mplus、SAS Calis、lavaan）。近期，他与合作者阐明了偏最小二乘结构方程模型的部分性质。