评估在有序因子分析模型中与多重输入数据的接近匹配
Educational and psychological measurement
|January 22, 2024
概括
本研究介绍了使用多重归算 (MI) 评估顺序因子分析模型匹配的方法,使用SRMR和RMSEA等匹配指数. 提出的技术为缺少的数据分析提供了准确的估计.
科学领域:
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
- 数据分析 数据分析
背景情况:
- 缺少数据是统计建模中的一个常见挑战.
- 多重归算 (MI) 是处理缺失数据的推技术.
- 适合MI的顺序因子分析的适合性指数尚未得到充分确立.
研究的目的:
- 在顺序因子分析中引入计算基于MI的适应指数的方法.
- 评估使用多重归算数据的顺序因子分析模型的合适性.
- 为SRMR和RMSEA提供准确的点和间隔估计.
主要方法:
- 使用多重归算 (MI) 来处理缺失的数据.
- 开发了使用MI数据计算标准化根平均平方余值 (SRMR) 和根平均平方近似误差 (RMSEA) 的程序.
- 基于MI的适应指数的构建置信区间.
主要成果:
- 提出的方法为SRMR和RMSEA提供了准确的点和间隔估计.
- 随着更大的样本大小,更少的缺失数据,更多的响应类别和更高的不合适度,准确性得到了改善.
- 模拟结果支持开发的技术的有效性.
结论:
- 引入的方法有效地评估顺序因子分析模型与多重归算数据相匹配.
- 这些技术在缺少数据的情况下提供可靠的适合指数估计.
- 讨论了实际应用和未来研究的建议.
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