处理多信息器研究中缺少的数据:方法的比较
Po-Yi Chen1, Fan Jia2, Wei Wu3
1Department of Educational Psychology and Counseling, National Taiwan Normal University, Taipei, Taiwan, 106308. poyichen@ntnu.edu.tw.
Behavior research methods
|February 28, 2024
概括
分析不完整的多信息器数据是具有挑战性的. 计划缺失数据的两种方法测量模型 (2MM-PMD) 显示了社会和行为科学研究的卓越性能,即使缺失数据.
科学领域:
- 社会和行为科学 社会和行为科学
- 心理测量 心理测量 心理测量
- 量化心理学 量化心理学
背景情况:
- 多信息人研究很普遍,但由于共享/独一无二的信息人数据和不完整性,存在分析挑战.
- 处理来自多个来源的不完整数据需要强大的统计方法.
研究的目的:
- 为了比较分析不完整的多信息元数据的三种方法的性能.
- 评估考虑参考和非参考信息的方法.
- 确定最有效的方法来处理缺失的数据在多信息人研究.
主要方法:
- 使用蒙特卡洛模拟来比较分析方法.
- 研究了三种方法:计划缺失数据的两种方法测量模型 (2MM-PMD),辅助变量方法 (FIML/MI) 和按列表删除.
- 该研究模拟了不完整的多信息器数据集,具有不同的失踪模式.
主要成果:
- 计划缺失数据的两种方法测量模型 (2MM-PMD) 显示出最佳性能.
- 2MM-PMD在点估计,I型错误率和数据随机缺失时的统计能力方面表现出卓越的准确性.
- 当数据不随机丢失时,这种方法也表现出更大的稳定性.
结论:
- 建议使用计划缺失数据的双方法测量模型 (2MM-PMD) 来分析不完整的多信息器数据.
- 与辅助变量方法和列表删除相比,这种方法提供了更好的准确性和稳定性.
- 对2MM-PMD进行适当的规范对于社会和行为研究的最佳结果至关重要.
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