方法对比
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| 稳健的验证性因子分析× | 多层验证性因子分析 (MCFA)× | |
|---|---|---|
| 领域≠ | 统计学 | 心理测量学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 1984–1994 | 1994 |
| 提出者≠ | Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator) | Bengt O. Muthen |
| 类型≠ | Confirmatory latent variable model with robust estimation | Latent variable model / measurement model |
| 开创性文献≠ | 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 ↗ | Muthen, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398. DOI ↗ |
| 别名 | Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA | MCFA, multilevel measurement model, two-level CFA, hierarchical CFA |
| 相关 | 6 | 6 |
| 摘要≠ | Robust confirmatory factor analysis fits a pre-specified factor structure to observed data while correcting standard errors and goodness-of-fit statistics for violations of multivariate normality. It is the preferred variant of CFA whenever Likert-type, skewed, or kurtotic indicators make the classical normal-theory estimator unreliable. | Multilevel confirmatory factor analysis tests a pre-specified factor structure while simultaneously accounting for the non-independence of observations caused by clustered data. It decomposes item variance into within-group and between-group components, fitting a separate measurement model at each level, making it the standard tool for validating psychometric scales administered within natural groups such as classrooms, clinics, or organisations. |
| ScholarGate数据集 ↗ |
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