接收器在曲线 (或平均值) 下的特征区域作为效果大小
1Research School of Psychology, Australian National University.
Psychological methods
|July 13, 2023
概括
收音机操作员特征 (ROC) 曲线下面的区域 (AUC) 是一种多功能效果尺寸尺度. 这项研究证明了其应用超出了两组比较,用于各种具有不同预测器和结果类型的各种一般线性模型.
科学领域:
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
- 数据分析 数据分析
背景情况:
- 作为效果大小的措施,建议使用接收机操作员特征 (ROC) 曲线下的区域 (AUC) 和平均行程.
- 这些措施比传统的效果大小具有优势,包括基准利率不敏感性和对异常值的稳定性.
- 目前的应用主要局限于对两组进行比较,限制了它们更广泛的实用性.
研究的目的:
- 为了证明AUC作为一般效应大小指标的实用性.
- 扩大AUC的应用范围,不仅仅是简单的群组比较,还包括各种统计模型.
- 为在统计分析中实施AUC提供实际指导和资源.
主要方法:
- 该研究探讨了AUC作为一般线性模型中的效果大小的应用.
- 演示包括线性回归,顺序逻辑回归,玛回归和β回归.
- 这篇文章讨论了AUC与分类和连续预测指标以及各种依赖变量级别 (序列,区间,比率) 的使用.
主要成果:
- AUC可以有效地作为广泛的统计模型中的一般效果大小.
- AUC适用于顺序,区间或比率等级的依赖变量.
- 该研究证实了AUC的稳定性和可解释性作为复杂模型中的效果大小.
结论:
- AUC是一个强大而通用的效应大小测量方法,适用于基本组比较之外的众多统计模型.
- 研究人员可以利用AUC来更全面地了解各种回归环境中的效果大小.
- 提供了额外的资源,包括R软件代码和数据,以促进AUC的采用.
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