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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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在计数回归模型中隐性变量之间的相互作用.

Christoph Kiefer1, Sarah Wilker2, Axel Mayer3

  • 1Methods and Evaluation, Department of Psychology, Bielefeld University, Universitätsstraße 25, D-33501, Bielefeld, Germany. christoph.kiefer@uni-bielefeld.de.

Behavior research methods
|August 26, 2024
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概括
此摘要是机器生成的。

研究人员经常忽视计数回归模型中的测量误差,导致结果偏差. 一个新的隐性变量计数回归模型 (LV-CRM) 准确地估计了系数,并改善了统计推理,即使有隐性相互作用.

关键词:
计算结果的数量.潜伏相互作用 潜伏相互作用普森回归是一种回归式.

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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:

  • 计算结果变量在心理学和社会科学中很常见.
  • 计数数据的通用线性模型 (GLM) 通常忽略预测器中的测量误差,导致减弱偏差.
  • 现有的方法很少解决在计数回归中涉及潜在变量的相互作用.

研究的目的:

  • 引入一个隐性变量计数回归模型 (LV-CRM),该模型包含隐性预测因素及其相互作用.
  • 与基于GLM的计数回归模型相比,评估LV-CRM的估计准确性和统计推断.
  • 展示LV-CRM在临床心理学中的实际应用.

主要方法:

  • 开发了一个潜在变量计数回归模型 (LV-CRM).
  • 进行了三项模拟研究,将LV-CRM与基于GLM的计数回归模型进行比较.
  • 在各种条件下调查估计准确性和统计推断.

主要成果:

  • 基于GLM的模型显示了回归系数的严重偏差,即使具有高预测器可靠性.
  • LV-CRM提供了几乎无偏见的回归系数,即使样本大小适度.
  • 对于LV-CRM来说,统计推断通常是可以接受的,而基于GLM的模型显示出混合的结果 (覆盖率低,可接受的检测率).

结论:

  • LV-CRM有效地考虑了隐性预测器中的测量误差及其在计数回归中的相互作用.
  • 基于GLM的传统方法,LV-CRM为数量数据分析提供了更准确,更可靠的替代方案.
  • 拟议的框架对于处理复杂计数数据结构的心理学和社会科学研究人员来说是有价值的.