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贝叶斯结构方程模型在运动与锻炼心理学中的应用
引用本文:晏宁,李英,李玉磊,郭璐,毛志雄.贝叶斯结构方程模型在运动与锻炼心理学中的应用[J].北京体育大学学报,2018,41(9):75-82.
作者姓名:晏宁  李英  李玉磊  郭璐  毛志雄
作者单位:北京体育大学心理学院,北京 100084;北京联合大学心理素质教育中心,北京 100101,北京中法实验学校,北京 100095,北京体育大学心理学院,北京 100084,北京体育大学心理学院,北京 100084,北京体育大学心理学院,北京 100084
摘    要:摘要:目的:旨在介绍贝叶斯结构方程模型的特点及其使用方法。方法:首先讨论了贝叶斯结构方程模型的优势,然后以运动员训练状态检测量表(32×7)的测评数据,分别采用最大似然估计和贝叶斯估计进行二阶验证性因素分析。结果:纳入交叉载荷和残差相关等小方差先验信息的贝叶斯估计模型拟合良好,而采用最大似然估计的模型拟合不理想。分析造成上述差异的原因,并总结贝叶斯结构方程模型的优势和不足。

关 键 词:关键词:贝叶斯方法  结构方程模型  验证性因素分析  交叉载荷  残差相关
收稿时间:2017/11/15 0:00:00

Application of Bayesian Structural Equation Modeling in Sport and Exercise Psychology
YAN Ning,LI Ying,LI Yu-Lei,GUO Lu and MAO Zhi-Xiong.Application of Bayesian Structural Equation Modeling in Sport and Exercise Psychology[J].Journal of Beijing Sport University,2018,41(9):75-82.
Authors:YAN Ning  LI Ying  LI Yu-Lei  GUO Lu and MAO Zhi-Xiong
Institution:Psychology College, Beijing Sport University, Beijing 100084, China; Beijing Union University, Psychological Services Center, Beijing, 100101, China,Beijing Sino-French Experimental School, Beijing 100095, China,Psychology College, Beijing Sport University, Beijing 100084, China,Psychology College, Beijing Sport University, Beijing 100084, China and Psychology College, Beijing Sport University, Beijing 100084, China
Abstract:Abstract: Purpose: To introduce the characteristics of Bayesian structural equation model and its usage. Methods: Firstly, the advantages of the Bayesian structural equation modeling were discussed in this paper, and then, a second-order confirmatory factor analysis (CFA) was carried out by using maximum likelihood estimation and Bayesian estimation with the data from the Athlete Training State Test Scale (32×7). Results: The Bayesian estimation model incorporating small variance prior information such as cross-load and residual correlation fitted well, but the model fitting using maximum likelihood estimation was not ideal. This study analyzed the reasons for the above differences and summarized the advantages and disadvantages of Bayesian structural equation modeling.
Keywords:Keywords: Bayesian analysis  structure equation model  confirmatory factor analysis  cross-loading  residual correlation
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