在贝叶斯式t测试中确定样本大小的可概括方法上
Tsz Keung Wong1, Jorge N Tendeiro2
1Department of Methodology & Statistics, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, The Netherlands. t.k.wong3004@gmail.com.
Behavior research methods
|March 31, 2025
概括
贝叶斯因子设计分析 (BFDA) 提供了一种灵活的方法来确定t测试中的样本大小,超越正常性假设. 一种新方法和Shiny应用程序提高了研究人员的可用性.
科学领域:
- 统计 统计 统计 统计
- 贝叶斯的推理是贝叶斯的推理.
- 心理测量 心理测量 心理测量
背景情况:
- P值通常用于试验零假设,但贝叶斯因子提供了一个潜在的优越替代方案.
- 贝叶斯因子设计分析 (BFDA) 对于优化研究效率和信息性至关重要.
- 现有的BFDA工具通常依赖于计算密集的蒙特卡洛方法.
研究的目的:
- 介绍一种一般化的方法,用于在t测试中进行BFDA用于样本大小的确定.
- 通过不假设效果大小估计的正常性来克服现有的BFDA方法的局限性.
- 开发一个用户友好的Shiny应用程序,以促进BFDA的实施.
主要方法:
- 为BFDA开发了一种基于根查找算法的新方法,将现有方法推广为一般化.
- 该方法允许灵活规范设计和分析先验,没有正常性假设.
- 创建了一个闪亮的应用程序来展示和应用开发的BFDA方法.
主要成果:
- 拟议的方法为BFDA在t测试中提供了一个灵活的框架.
- 闪亮的应用程序促进了BFDA方法的实践应用,以确定样本大小.
- 根据各种先前的规范进行了贝叶斯因子操作特征的探索.
结论:
- 一般化的BFDA方法提高了贝叶斯设计分析的灵活性和适用性.
- 用户友好的Shiny应用程序促进了BFDA在研究中的更广泛采用.
- 这项工作有助于使用贝叶斯方法进行更强大,更有信息的研究设计.
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