ISSN 1671-3710
CN 11-4766/R
主办:中国科学院心理研究所
出版:科学出版社

心理科学进展 ›› 2017, Vol. 25 ›› Issue (10): 1682-1695.doi: 10.3724/SP.J.1042.2017.01682

• 研究方法 • 上一篇    下一篇

 潜变量建模的贝叶斯方法

 王孟成1,2,3;邓俏文1,2;毕向阳4   

  1.  (1广州大学心理系; 2广州大学心理测量与潜变量建模研究中心; 3广东省未成年人 心理健康与教育认知神经科学实验室, 广州 510006) (4中国政法大学社会学院, 北京 102249)
  • 收稿日期:2016-11-24 出版日期:2017-10-15 发布日期:2017-08-13
  • 通讯作者: 王孟成, E-mail: wmcheng2006@126.com; 毕向阳, E-mail: necessity@126.com E-mail: E-mail: wmcheng2006@126.com; E-mail: necessity@126.com
  • 基金资助:
     国家自然科学基金(31400904); 广州大学“创新强校工程” 青年创新人才类项目(2014WQNCX069); 广州大学青年拔尖人才培养项目。

 Latent variable modeling using Bayesian methods

 WANG Meng-Cheng1,2,3; DENG Qiaowen1,2; BI Xiangyang4   

  1.  (1 Department of Psychology, Guangzhou University; 2 The Center for Psychometric and Latent Variable Modeling, Guangzhou University; 3 The Key Laboratory for Juveniles Mental Health and Educational Neuroscience in Guangdong Province, Guangzhou University, Guangzhou 510006, China) (4 School of Sociology, China University of Political Science and Law, Beijing 102249, China)
  • Received:2016-11-24 Online:2017-10-15 Published:2017-08-13
  • Contact: WANG Meng-Cheng, E-mail: wmcheng2006@126.com; BI Xiangyang, E-mail: necessity@126.com E-mail: E-mail: wmcheng2006@126.com; E-mail: necessity@126.com
  • Supported by:
     

摘要:  贝叶斯统计是统计学的两大流派之一, 近年来贝叶斯统计在社会及行为科学领域日益流行。鉴于国内心理学界对贝叶斯统计应用仍不广泛, 本文尝试从非技术性的角度对贝叶斯统计用于潜变量建模的过程进行简要介绍。主要涉及贝叶斯与频率论在统计学基本概念上的对比; 贝叶斯统计的基本原理和分析过程。最后以一个验证性因子分析为例, 简要介绍贝叶斯统计用于潜变量建模的分析过程。希望本文能为国内心理学者进行潜变量建模提供新的视角。

关键词:  贝叶斯, 频率论, 潜变量建模, Mplus

Abstract:  Bayesian statistical methods is one of the two statistic schools. Recently, Bayesian statistical methods are becoming ever more popular in social and behavioral research. However, domestic psychological and behavioral scholars are not familiar with it. We provide an untechnical introduction about Bayesian statistics, particularly in Bayesian method used in latent variable modeling. Specifically, first we compared the difference between Bayesian methods and Frequentist methods in several basic concepts. Thereafter, the Bayes’ Theorem and its analysis procedure were introduced. Finally, to illustrate Bayesian methods in latent variable modeling (i.e., confirmatory factor analysis), a concrete sample was presented. In the end of the paper, we briefly discussed the future direction of Bayesian methods in psychology research.

Key words:  Bayesian, Frequentist methods, latent variable modeling, Mplus

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