方法对比
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| 多维尺度分析 (MDS)× | 潜在类别分析 (Latent Class Analysis, LCA)× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 1952–1964 | 1950s–1968 |
| 提出者≠ | Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964) | Paul F. Lazarsfeld |
| 类型≠ | Dimensionality reduction / visualization | Latent variable / person-centered classification |
| 开创性文献≠ | Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27. DOI ↗ | Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231. DOI ↗ |
| 别名 | MDS, metric MDS, non-metric MDS, proximity scaling | LCA, latent class model, latent categorical analysis, finite mixture of multinomials |
| 相关≠ | 5 | 6 |
| 摘要≠ | Multidimensional scaling maps objects described only by pairwise similarities or dissimilarities into a low-dimensional geometric space so that distances in that space reflect the original proximity structure as faithfully as possible. It is widely used to visualize the hidden structure of psychological, social, and behavioral data. | Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data. |
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