) 中山大学心理学系, 广州 510006
收稿日期:2018-07-24出版日期:2019-10-31发布日期:2019-09-23通讯作者:潘俊豪E-mail:panjunh@mail.sysu.edu.cn基金资助:* 国家自然科学基金项目(31871128);教育部人文社会科学研究规划基金项目(18YJA190013);中山大学2018年大学生创新训练计划项目(201810558015)Bayesian structural equation modeling and its current researches
ZHANG Lijin, LU Jiaqi, WEI Xiayan, PAN Junhao() Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China
Received:2018-07-24Online:2019-10-31Published:2019-09-23Contact:PAN Junhao E-mail:panjunh@mail.sysu.edu.cn摘要/Abstract
摘要： 在心理学研究中结构方程模型(Structural Equation Modeling, SEM)被广泛用于检验潜变量间的因果效应, 其估计方法有频率学方法(如, 极大似然估计)和贝叶斯方法两类。近年来由于贝叶斯统计的流行及其在结构方程建模中易于处理小样本、缺失数据及复杂模型等方面的优势, 贝叶斯结构方程模型发展迅速, 但其在国内心理学领域的应用不足。主要介绍了贝叶斯结构方程模型的方法基础和优良特性, 及几类常用的贝叶斯结构方程模型及其应用现状, 旨在为应用研究者介绍新的研究工具。
图/表 3
图1科学成就的结构方程模型图(改编自Kaplan(2009))
图1科学成就的结构方程模型图(改编自Kaplan(2009))
图2非条件潜变量增长曲线模型(以英语词汇接受能力为例)
图2非条件潜变量增长曲线模型(以英语词汇接受能力为例)
图3研究概念模型图
图3研究概念模型图参考文献 80
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