方法对比
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| 稳健探索性因子分析× | 探索性因子分析（EFA）× | |
|---|---|---|
| 领域≠ | 心理测量学 | 统计学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 2000–2003 | — |
| 提出者≠ | Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams) | — |
| 类型≠ | Latent variable / dimension reduction (robust) | Latent variable / dimension reduction |
| 开创性文献≠ | Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58. DOI ↗ | 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 EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| 相关 | 4 | 4 |
| 摘要≠ | Robust exploratory factor analysis discovers the latent factor structure of a set of items using estimation methods that are resistant to outliers and violations of multivariate normality. It applies the same measurement model as standard EFA but replaces classical covariance estimation with robust counterparts — such as minimum covariance determinant or iteratively reweighted least squares — so that a small fraction of atypical cases cannot distort the recovered factor loadings. | 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. |
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