方法对比
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| 稳健的验证性因子分析× | 探索性因子分析（EFA）× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 1984–1994 | — |
| 提出者≠ | Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator) | — |
| 类型≠ | Confirmatory latent variable model with robust estimation | Latent variable / dimension reduction |
| 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 ↗ | Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. DOI ↗ |
| 别名≠ | Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| 相关≠ | 6 | 4 |
| 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. | Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance. |
| ScholarGate数据集 ↗ |
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