心理网络中的统计证据
Karoline B S Huth1,2,3,4, Jonas M B Haslbeck5,6, Sara Keetelaar5
1Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands. k.huth@uva.nl.
Nature human behaviour
|October 9, 2025
概括
社会科学中的心理测量网络模型往往缺乏报告的关联的强有力的统计证据. 研究人员应谨慎解释网络发现,因为数据支持较弱或不确.
科学领域:
- 心理学和社会科学 心理学和社会科学
- 统计建模 统计建模
背景情况:
- 心理测量网络模型越来越多地用于分析变量之间的复杂关系.
- 将网络边缘解释为条件关联是常见的,但统计证据往往没有被评估.
研究的目的:
- 评估已发表的心理测量网络模型中支持边缘的统计证据.
- 在大量研究样本中评估网络发现的可靠性.
主要方法:
- 从126篇发表的论文中重新分析了293个网络.
- 应用贝叶斯方法来量化每个网络边缘的证据,使用包容贝叶斯因子 (BF10).
主要成果:
- 网络边缘的很大一部分是由弱或不确的证据支持的.
- 三分之一的边缘显示不确的证据 (1/3 < BF10 < 3),大约一半显示弱的证据.
- 不到20%的边缘具有强有力的支 (BF10 > 10或BF10 < 1/10);较大的样本大小与更强大的发现相关.
结论:
- 许多报告的心理测量网络发现是基于不充分的统计证据.
- 在解释单个网络边缘时建议谨慎,因为结果可能无法完全得到数据的支持.
- 未来的研究应该优先考虑对网络结构的强有力的统计评估.
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