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| Phân tích Lớp Ẩn Mạnh mẽ× | Phân tích nhân tố khám phá mạnh mẽ× | |
|---|---|---|
| Lĩnh vực≠ | Thống kê | Trắc lượng tâm lý |
| Họ | Latent structure | Latent structure |
| Năm ra đời≠ | 2000s | 2000–2003 |
| Người khởi xướng≠ | Building on Hennig (2004) and Vermunt & Magidson (2004) | Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams) |
| Loại≠ | Robust latent variable / mixture model | Latent variable / dimension reduction (robust) |
| Công trình gốc≠ | Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340. DOI ↗ | Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58. DOI ↗ |
| Tên gọi khác≠ | robust LCA, outlier-resistant latent class analysis, trimmed-likelihood latent class analysis | robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation |
| Liên quan≠ | 6 | 4 |
| Tóm tắt≠ | Robust latent class analysis (robust LCA) extends the standard latent class model by incorporating outlier-resistant estimation techniques — such as trimmed likelihood, M-estimation, or downweighting — so that atypical response patterns do not distort the recovered class structure or class membership probabilities. | 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. |
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