在结构方程建模中使用顺序数据评估基于计算的合适统计:MI2S方法
Suppanut Sriutaisuk1, Yu Liu2, Seungwon Chung3
1Faculty of Psychology, Chulalongkorn University, Bangkok, Thailand.
Educational and psychological measurement
|November 20, 2024
概括
多重归算两阶段 (MI2S) 方法改善了结构方程模型对顺序数据的合适性评估. 基于MI2S的调整平均值的测试统计数据显示,在各种条件下表现优异,提高了模型评估的准确性.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 量化心理学 量化心理学
背景情况:
- 评估结构方程模型 (SEM) 与顺序变量相匹配,并乘以归算数据提出了挑战.
- 之前的研究重点是基于残留的测试统计数据在多重归算两阶段 (MI2S) 框架内.
- 在这种情况下,对替代测试统计表现的理解有限.
研究的目的:
- 扩展对具有顺序变量的结构方程模型的多重归算双阶段 (MI2S) 方法的评估.
- 在MI2S框架内检查平均值调整 (T_adj) 和平均值和方差调整 (T_adj,av) 测试统计数据的性能.
- 将这些替代统计数据与以前研究的基于MI2S的剩余统计数据进行比较.
主要方法:
- 在各种条件下进行模拟研究,以评估测试统计性能.
- 将MI2S方法应用于具有顺序变量的结构方程模型.
- 使用Mplus和R的实现,为可重现性提供代码.
主要成果:
- 基于MI2S的平均值调整的测试统计数据 (T_adj) 在广泛的条件下普遍优于其他检查的测试统计数据.
- 基于MI2S的根平均平方误差近似 (RMSEA) 也表现良好.
- 该研究提供经验验证和实际实施指导.
结论:
- MI2S方法,特别是对平均值调整的测试统计数据,提供了一种可靠的方法来评估结构方程模型,适用于具有多重归算数据集的顺序数据.
- 这些发现支持MI2S框架在复杂的统计分析中的更广泛的适用性和可靠性.
- 提供了实际指导和代码,以促进采用这种改进的方法.
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