准确度参数估计和模拟方法用于样本大小规划,考虑项目效应
Erin M Buchanan1, Mahmoud M Elsherif2, Jason Geller3
1Analytics, Harrisburg University of Science and Technology, 326 Market St, Harrisburg, PA, 17101, USA. ebuchanan@harrisburgu.edu.
Behavior research methods
|January 23, 2026
概括
本研究引入了一种用于研究的样本大小规划的新方法,它结合了参数估计 (AIPE) 和模拟方法的准确性. 这种灵活的策略可以确保对研究项目进行充分和精确的测量,即使没有特定的假设测试.
科学领域:
- 心理学科学 心理学科学
- 量化心理学 量化心理学
- 研究方法研究方法研究方法学
背景情况:
- 传统的样本大小规划通常依赖于在特定的功率和效果大小下实现统计显著性.
- 当研究缺乏特定的假设测试或单一的预定义分析时,这种方法可能是限制性的.
研究的目的:
- 探索参数估计精度 (AIPE) 和模拟用于样本大小规划的组合.
- 为没有特定假设测试或考虑多元分析的研究提供灵活的框架.
- 为了指导使用多个项目的研究的样本大小的确定,确保充分和精确的测量.
主要方法:
- 该研究结合了参数估计精度 (AIPE) 和模拟技术.
- 它专注于规划涉及多项项目的研究的样本大小.
- 为实际应用提供了代码插图和包装功能.
主要成果:
- 联合AIPE和模拟方法提供了超越传统功率分析的灵活性.
- 这种方法允许基于对项目的充分和精确测量进行样本大小规划,无论具体的统计测试是什么.
- 研究人员可以将提供的工具调整为自己的测量计划.
结论:
- 结合AIPE和模拟,在各种研究场景中为样本大小规划提供了可靠的方法.
- 这种方法提高了多项项研究中测量的精度和充分性.
- 该教程为研究人员提供了实用工具,帮助他们做出明智的样本大小决定.
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