对具有依赖效果大小的元分析进行功率分析:共同指导方针和POMADE R包的介绍
Mikkel Helding Vembye1, James Eric Pustejovsky2, Therese Deocampo Pigott3
1Department of Quantitative Methods, The Danish Center for Social Science Research, VIVE, Aarhus, Denmark.
Research synthesis methods
|September 18, 2024
概括
现在可以更容易地计划一个具有依赖效果大小的元分析. 本研究介绍了实用指导和POMADE R包,用于进行复杂研究合成的功率分析.
科学领域:
- 统计 统计 统计 统计
- 研究方法研究方法研究方法学
- 社会科学 社会科学 社会科学
背景情况:
- 样本大小和统计能力对于研究综合计划至关重要.
- 传统的功率分析方法仅限于具有独立效果大小的简单数据结构.
- 分析通常涉及多个相互依赖的效果大小估计,特别是在社会科学研究中.
研究的目的:
- 为在具有依赖效果大小的元分析中进行功率分析提供实用指导.
- 推出POMADE R包,旨在促进复杂的元分析数据结构的功率分析.
- 为应对应用近期功率近似公式用于规划研究综合的实际挑战.
主要方法:
- 为具有依赖效果大小的元分析开发和应用功率近似公式.
- 介绍了用于进行功率分析的POMADE R包.
- 资源的介绍,以确定必要的研究设计特征和模型参数.
- 使用详细的工作示例来说明POMADE包的应用.
- 重点是图形工具,包括一个新的"交通信号灯功率图",用于展示功率分析结果.
主要成果:
- 波马德R套件提供了一个实用的工具,用于对效果大小依赖的元分析中的功率分析.
- 提供了全面的指导和示例,用于在复杂的研究综合计划中应用功率分析.
- 图形工具,如交通信号灯功率图,增强了功率分析结果和假设的沟通.
结论:
- 这项工作促进了先进的功率分析技术的应用,用于涉及依赖效应大小的元分析.
- 波马德 (POMADE) 套件和实践指导使研究人员能够更好地规划他们的合成,提高统计能力.
- 对功率分析结果的有效可视化有助于理解和传达假设的确定性.
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