方法对比
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| 多维尺度分析 (MDS)× | 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≠ | 1952–1964 | — |
| 提出者≠ | Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964) | — |
| 类型≠ | Dimensionality reduction / visualization | Latent variable / dimension reduction |
| 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 ↗ | 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≠ | MDS, metric MDS, non-metric MDS, proximity scaling | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| 相关≠ | 5 | 4 |
| 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. | 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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