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使用动态结构方程建模研究个体变异:耳的教程.

Thomas L Rodebaugh1, Marilyn L Piccirillo1, Madelyn R Frumkin1

  • 1Department of Psychological and Brain Sciences, Washington University in St Louis.

Clinical psychological science : a journal of the Association for Psychological Science
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PubMed
概括
此摘要是机器生成的。

标准群体模型可能不反映个人的耳声经历. 动态结构方程建模 (DSEM) 显示,小组发现并未对大多数个体进行概括,突出了在耳研究中需要个性化分析的需要.

关键词:
声 (Tinnitus) 是一种听力障碍.异形图形模型的特征模型.强烈的纵向数据密集.进行多层次分析.

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科学领域:

  • 心理学 心理学 心理学
  • 医学研究 医学研究
  • 数据科学数据科学数据科学

背景情况:

  • 基于群体的标准研究模型可能会掩盖个体差异.
  • 烦人的耳是一个异质的情况,需要细致的调查.

研究的目的:

  • 为了比较基于小组的与烦性耳的个人预测因素.
  • 为了证明动态结构方程建模 (DSEM) 对密集的纵向数据的应用.
  • 评估小组发现对个案的概括性.

主要方法:

  • 使用了动态结构方程建模 (DSEM) 与密集的纵向数据.
  • 收集了每位参与者最多200份调查答复,来自43名听的个人.
  • 采用多层次的DSEM和完全的语言模型来分析 tinnitus 烦,认知症状和焦虑.

主要成果:

  • 在多层次的DSEM模型中,在耳烦和焦虑之间发现了相互关系.
  • 完全的特征模型对一些个体来说不太适合.
  • 多层次模型没有对大多数个人进行概括,可能是由于统计能力有限.

结论:

  • 对于 tinnitus 麻烦的团体级发现可能不适用于个人.
  • 动态结构方程建模 (DSEM) 提供了一种有价值的方法来研究复杂条件下的个体变化.
  • 未来对耳等异质疾病的研究应该考虑个性化分析方法.