Latent structureMultivariate analysis

稳健的验证性因子分析

稳健的验证性因子分析在拟合预设因子结构到观测数据时，能够纠正多元正态分布假设不满足时产生的标准误和拟合优度统计量。当李克特量表、偏态或峰度过高的指标使得经典正态理论估计量不可靠时，它是CFA的首选变体。

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稳健的验证性因子分析

验证性因子分析（CFA）探索性因子分析（EFA）多层验证性因子分析 (MCFA)稳健探索性因子分析稳健结构方程模型结构方程模型稳健麦克唐纳欧米伽稳健路径分析稳健拉斯模型

来源

Satorra, A. & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage. link ↗
Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37(1), 62–83. DOI: 10.1111/j.2044-8317.1984.tb00789.x ↗

如何引用本页

ScholarGate. (2026, June 3). Robust Confirmatory Factor Analysis. ScholarGate. https://scholargate.app/zh/statistics/robust-confirmatory-factor-analysis

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被引用于

稳健探索性因子分析稳健麦克唐纳欧米伽稳健路径分析稳健拉斯模型稳健结构方程模型

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