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对心理测量网络的节点参数聚合.

K B S Huth1,2,3, B DeLong4, L Waldorp1

  • 1Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.

Multivariate behavioral research
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概括
此摘要是机器生成的。

用节点回归估计心理测量网络需要仔细聚合回归系数,以获得准确的边缘权重. 平均系数可以对连续变量的结果产生偏差; 替代方法确保真正的部分相关性恢复.

关键词:
网络分析 网络分析具有非对称的属性.节点回归的节点回归部分相关性 部分相关性的回归系数回归系数.

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科学领域:

  • 心理测量 心理测量 心理测量
  • 网络分析 网络分析
  • 统计建模 统计建模

背景情况:

  • 心理测量网络对于理解变量之间的复杂关系是有价值的.
  • 节点回归是估计这些网络的一种方法,特别是当直接计算具有挑战性时.
  • 这些网络中的边缘权重代表了变量之间的条件关联.

研究的目的:

  • 为了研究节点回归在估计边缘权重的准确性与连续变量心理测量网络.
  • 确定回归系数当前聚合方法中的潜在偏差.
  • 提出和验证改进的方法,从节点回归获得真实部分相关性.

主要方法:

  • 利用节点回归,将一般化的线性模型与每个节点作为结果相匹配.
  • 检查了每个环节的两个回归系数的聚合,以导出边缘权重.
  • 介绍并评估了两种新的聚合技术:乘法系数和取平方根,并通过剩余方差进行重新缩放.

主要成果:

  • 回归系数的标准平均值可以导致异常偏差的部分相关性估计,特别是当预测因应与控制变量的相关性不同时.
  • 这种偏差在变量与其他节点有异质相关性的网络中是明显的.
  • 提出的方法 (乘以系数/平方根并通过余方差进行重新缩放) 成功地恢复了真实的网络结构和边缘权重.

结论:

  • 节点回归系数的聚合方法对于使用连续变量准确的心理测量网络估计至关重要.
  • 简单的平均值是不够的,可以引入偏差.
  • 乘以系数 (用平方根) 或通过余方差进行重新缩放是获得准确的部分相关性和网络结构的可靠替代方案.