缺失的不是随机密集的纵向数据与动态结构方程模型
1Department of Psychology, Arizona State University.
Psychological methods
|February 10, 2025
概括
密集的纵向研究往往有缺失的数据. 一种新的动态结构方程模型方法有效地处理缺失的非随机 (MNAR) 数据,改进敏感健康行为的分析.
科学领域:
- 心理学研究方法 心理学研究方法
- 纵向数据分析的数据分析.
- 统计建模 统计建模
背景情况:
- 强烈的纵向设计能够捕捉到快速变化的情绪,影响和行为.
- 在这些研究中,高频率的数据收集导致不可避免的缺失数据.
- 在动态结构方程模型 (DSEM) 中缺失数据的现有研究是有限的,通常假设数据是随机缺失的 (MAR).
研究的目的:
- 解决在动态结构方程模型 (DSEM) 中缺少非随机 (MNAR) 数据的挑战.
- 提出和评估一种新的方法来处理密集的纵向研究中的MNAR数据,特别是对于敏感的结果.
- 为使用复杂缺失数据模式的DSEM的研究人员提供实用方法.
主要方法:
- 在DSEM框架中嵌入Diggle-Kenward类型的MNAR模型.
- 将拟议的方法应用于一个充满动机的暴饮暴食障碍示例,其中包含自我报告的过度饮食数据.
- 进行模拟研究,以评估拟议的MNAR模型对连续和二进制结果的性能.
主要成果:
- 拟议的DSEM方法有效地处理MNAR数据,在相关场景中表现优于标准MAR模型.
- 该方法被证明是适用的,并且相对容易在像Mplus.com这样的统计软件中实现.
- 模拟结果证明了模型对连续和二进制结果变量的实用性.
结论:
- 带有嵌入式MNAR组件的拟议的DSEM提供了一个强大的解决方案,用于分析具有缺失的密集纵向数据.
- 这种方法对于精确建模可能存在MNAR的敏感行为至关重要.
- 研究人员可以通过采用这种方法来处理DSEM中复杂的缺失数据来提高他们的发现的有效性.
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