纵向参与者-合作伙伴相互依赖模型在大量缺失值的情况下的表现:挑战和可能的替代方案
Yuanyuan Ji1, Jordan Revol1, Anna Schouten2
1Research Group of Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, https://ror.org/05f950310KU Leuven, Leuven, Belgium.
Psychometrika
|June 13, 2025
概括
使用纵向行为者-合作伙伴相互依赖模型 (L-APIM) 进行密集的纵向数据 (ILD) 分析,由于缺少数据,往往会失败. 替代模型可以帮助克服二极研究中的融合问题.
科学领域:
- 心理学 心理学 心理学
- 量化心理学 量化心理学
- 统计建模 统计建模
背景情况:
- 密集的纵向数据 (ILD) 越来越多地被用于二次研究.
- 纵向行为者-合作伙伴相互依赖模型 (L-APIM) 是分析ILD的常用方法.
- 缺失的数据在ILD中常见,原因是不合规和有条件的问题,这对L-APIM估计构成了重大挑战,特别是在二次性研究中.
研究的目的:
- 系统地检查L-APIM在二代性ILD中缺少数据的性能.
- 确定可以在L-APIM失败时克服融合问题的替代模型.
主要方法:
- 进行了一项模拟研究,以评估在各种缺失数据条件下L-APIM的性能.
- 31种替代统计模型在模拟和实证二度数据上进行了调查和评估.
主要成果:
- 经常观察到L-APIM的非趋同,特别是在小样本大小的情况下.
- 当分析趋同时,固定的个人内部参与者和合作伙伴效应得到了良好估计.
- 几种替代模型表明,它们有潜力缓解与L-APIM相关的趋同问题.
结论:
- 缺少的数据显著影响了L-APIM在二极性ILD中的趋同.
- 当L-APIM无法收时,研究人员应该考虑适应多个替代模型.
- 建议评估替代模型,以确保二纵向研究结果的可靠性.
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