许多异常,一个模拟:不同的数据生成算法是否会影响研究结果?
Amanda J Fairchild1, Yunhang Yin2, Amanda N Baraldi3
1Department of Psychology, University of South Carolina, Columbia, SC, USA. afairchi@mailbox.sc.edu.
Behavior research methods
|February 23, 2024
概括
对最大概率 (ML) 强度与非正常性进行模拟研究的复制发现了一般一致性,但突出了跨数据生成算法概括性的问题. 方法建议可能不是普遍有效的.
科学领域:
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
- 超级科学是一个超级科学.
背景情况:
- 蒙特卡洛模拟研究对于统计方法学至关重要,但往往缺乏复制.
- 模拟中的可复制性失败可能源于各种因素,影响统计指导的可靠性.
- 很少有模拟研究被复制,这限制了其结果的验证.
研究的目的:
- 复制一项备受引用的1996年模拟研究,该研究涉及基于最大概率 (ML) 的正常理论的稳定性,在多变量非正常性下进行千平方匹配统计.
- 检查不同非正常数据生成算法的原始研究结果的概括性.
- 评估不同数据生成方法对基于ML的匹配统计数据的可靠性结论的影响.
主要方法:
- 通过复制特定的模拟研究 (Curran等人,1996) 进行了一项超科学研究.
- 采用多个非正常数据生成算法来测试原始发现的概括性.
- 将复制结果与原始结果进行比较,并分析了算法之间的差异.
主要成果:
- 复制结果通常与原始研究一致,但注意到了几个差异.
- 概括性结果是混合的;所有检查的算法中只有两个发现.
- 独立生成器 (IG) 算法产生了截然不同的结果,表明测试的因子模型的ML稳定性与非正常性.
结论:
- 在存在多个数据生成算法时,来自模拟的现有方法建议可能无法普遍适用.
- 研究人员应该考虑多种数据生成方法,以提高模拟研究结果的概括性.
- 数据生成算法的选择可以显著影响ML等统计方法的稳定性结论.
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