强大的统计方法和心理科学的可信度运动
Martina Sladekova1, Andy P Field1
1School of Psychology, University of Sussex, Brighton, East Sussex, United Kingdom.
PeerJ
|October 6, 2025
概括
普通最小平方 (OLS) 在心理学中很常见,但对假设违规很敏感. 强大的方法,如引导,提高参数准确性和统计测试功率,提高研究可重现性.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
背景情况:
- 一般线性模型 (GLM) 在心理学中被广泛使用.
- 普通最小平方 (OLS) 是GLM内部的标准估计方法.
- 当假设得到满足时,OLS提供了公正和最佳的估计.
研究的目的:
- 描述OLS估计及其假设.
- 讨论检测和处理违反假设的方法.
- 引入可靠的估计方法,以提高准确性和可重复性.
主要方法:
- OLS的技术描述及其统计假设.
- 讨论检测和处理违反假设的方法.
- 强大的估计技术的介绍:引导,异种粘度一致的标准误差,M估计器和修剪.
主要成果:
- OLS参数估计和假设测试受到假设违规的影响.
- 目前处理违规行为的方法可以降低研究的可重复性.
- 强大的方法可以提高参数估计准确度和统计测试功率.
结论:
- 违反OLS假设可能会影响心理学研究结果.
- 强大的估计方法可以提高统计分析的可靠性.
- 使用可靠的方法透明地预先注册灵敏度分析,减少了研究人员的自由度,并提高了可重复性.
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