随机调节模型的贝叶斯估计:效果大小,覆盖范围,测试功率和I型错误
Dan Wei1,2, Peida Zhan3
1Faculty of Psychology, Beijing Normal University, Beijing, China.
Frontiers in psychology
|July 19, 2023
概括
与传统的最大概率估计相比,贝叶斯估计为随机调节模型 (RMM) 提供了更准确,更可靠的方法. 这种方法在调节分析中提供了更好的效果大小准确性和受控的错误率.
科学领域:
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
- 量化心理学 量化心理学
背景情况:
- 随机调节模型 (RMM) 在调节分析中使用两级回归来解决异构复杂性.
- 基于正常分布的最大概率 (NML) 估计此前已为RMM开发.
研究的目的:
- 评估贝叶斯估计的有效性,作为RMM的NML替代方案.
- 使用Mplus软件探索一种更实用的RMM估计方法.
主要方法:
- 为了研究RMM,进行了一项模拟研究.
- 对RMM的贝叶斯估计与NML和默认最大概率 (ML) 估计在Mplus.
主要成果:
- 贝叶斯估计在估计适度效应大小方面表现出更高的准确性.
- 贝叶斯的方法为真正的调节效应提供了更高的95%可信度区间覆盖率.
- 第一种类型的错误率得到了良好的控制,并且在贝叶斯估计中更稳定,而统计能力仍然与ML方法相比.
结论:
- 与NML和默认ML相比,贝叶斯估计是估计RMM的更优越和更实用的替代方案.
- 这些发现支持使用贝叶斯方法在复杂的统计建模中进行更强大的调节分析.
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