对线性和日志线性增长的纵向干预研究的贝叶斯样本大小的确定
Ulrich Lösener1, Mirjam Moerbeek2
1Department of Methodology and Statistics, Utrecht University, Utrecht, Netherlands. u.c.losener1@uu.nl.
Behavior research methods
|July 28, 2025
概括
本研究引入了一种用于测试贝叶斯假设的样本大小确定 (SSD) 的新方法,用于纵向研究. 它在多层模型中为SSD提供了一个R函数,这对于准确的试验设计至关重要.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 心理测量 心理测量 心理测量
背景情况:
- 样本大小确定 (SSD) 对于高效,强大的研究至关重要,并且经常被伦理委员会和资助机构要求.
- 基于NHST的SSD面临批评;使用贝叶斯因子的贝叶斯假设评估提供了一个替代方案.
- 目前的贝叶斯式SSD工具仅限于简单的模型,不包括复杂的纵向数据,其中观察在个体内嵌套.
研究的目的:
- 为使用纵向数据的多层模型在贝叶斯假设测试中提供样本大小确定 (SSD) 的工具.
- 为在复杂的研究设计中实施贝叶斯式SSD提供必要的理论背景和实践示例.
- 通过启用SSD用于嵌套数据结构来解决现有软件的局限性.
主要方法:
- 该研究在贝叶斯框架内提出了基于模拟的SSD方法.
- 它侧重于多层模型的应用,以处理纵向实验中固有的嵌套数据结构.
- 开发了一个开源的R函数,以方便研究人员进行定制SSD模拟.
主要成果:
- 开发的R函数允许研究人员在贝叶斯语境下对多层模型执行SSD.
- 该工具支持纵向数据分析,如果观察不是独立的.
- 这为设计具有复杂数据结构的研究提供了实用解决方案.
结论:
- 这项工作为进行需要测试贝叶斯假设的纵向研究的研究人员提供了宝贵的资源.
- 提供的R函数简化了复杂的多层模型的样本大小确定过程.
- 使用此工具准确的SSD提高了涉及纵向数据的研究设计的严谨性和效率.
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