一个关于贝叶斯结构方程建模的教程:原理和应用
Qijin Chen1, Kun Su1, Yonglin Feng1
1Department of Psychology, Sun Yat-sen University, Guangzhou, P. R. China.
概括
贝叶斯结构方程建模 (BSEM) 对复杂心理研究的传统方法具有优势,特别是在样本规模小的情况下. 本指南为研究人员介绍了BSEM概念,应用示例和实施细节.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 量化研究方法 量化研究方法
背景情况:
- 结构方程建模中的频率主义方法可以在复杂的模型和小样本尺寸上受到限制.
- 贝叶斯结构方程建模 (BSEM) 提供了一个具有明显优势的可行替代方案.
- 了解BSEM对于推进心理学研究方法至关重要.
研究的目的:
- 探索贝叶斯结构方程建模 (BSEM) 在心理学中的利用.
- 突出BSEM对复杂模型和小样本大小的频率主义方法的优势.
- 为研究人员提供使用BSEM的实践指导和经验研究的新想法.
主要方法:
- 介绍基本的BSEM概念:事先设定,模型融合和模型合适性评估.
- 通常使用的BSEM应用程序的说明性示例:确认因素分析 (CFA),调解模型和多组CFA.
- 提供经验数据和计算机代码,以促进BSEM的实际实施.
主要成果:
- BSEM有效地处理复杂的模型和小样本大小,与频率主义方法相比具有优势.
- 这篇论文为理解和应用BSEM在心理学研究中提供了一个明确的框架.
- 插图示例和代码有助于采用BSEM技术.
结论:
- 对于心理学研究人员来说,BSEM是一个强大的工具,它能够分析复杂的模型和小型数据集.
- 随着BSEM的不断发展,它有望改善方法,技术和报告标准.
- 研究人员有能力通过采用BSEM克服与传统统计方法相关的挑战.
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