一种简单的蒙特卡洛方法,用于估计多层设计中的功率
Craig K Enders1, Brian T Keller2, Michael P Woller1
1Department of Psychology, University of California, Los Angeles.
Psychological methods
|November 13, 2023
概括
估计多层模型的统计能力现在更简单了. 本教程介绍了复杂的多层回归模型的灵活蒙特卡洛模拟方法,提高了功率分析的准确性.
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:
- 估计多层模型的统计能力,由于复杂性和多个变异来源,提出了挑战.
- 现有的蒙特卡洛模拟方法通常专注于更简单的模型或需要广泛的参数规范.
研究的目的:
- 介绍一种灵活的蒙特卡洛模拟方法,用于在广泛的多级回归模型中进行功率分析,并提供连续结果.
- 引入R包mlmpower,用于自动化复杂的功率估计程序.
主要方法:
- 开发了一种蒙特卡洛模拟策略,可以容纳多个预测因素,相互作用和多个级别的随机系数.
- 实施了一种新的方法,使用差异解释效应大小来推导种群参数,消除了对试点数据的需求.
- 设计了mlmpower R包,以自动化数据生成和分析以进行功率估计.
主要成果:
- 该方法支持具有非对角预测因素和多重相互作用效应的复杂模型.
- 差异解释效果大小方法提供了一种直观的方式来指定模型参数.
- 该mlmpower套件简化了多层模型的功率分析过程.
结论:
- 这种灵活的蒙特卡洛方法显著提高了复杂的多层模型的功率估计.
- mlmpower R包为进行功率分析的研究人员提供了一个实用的工具.
- 该方法在多层次研究中促进了更准确,更有效的统计能力估计.
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