方法对比
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| 贝叶斯探索性因子分析 (Bayesian Exploratory Factor Analysis, BEFA)× | 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≠ | 2004 (Bayesian formulation); factor analysis roots: 1904 | — |
| 提出者≠ | Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904) | — |
| 类型≠ | Probabilistic latent variable model | Latent variable / dimension reduction |
| 开创性文献≠ | Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67. 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≠ | Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| 相关 | 4 | 4 |
| 摘要≠ | Bayesian exploratory factor analysis applies a full probabilistic framework to the common factor model. By placing prior distributions over factor loadings and unique variances, it yields posterior distributions rather than point estimates, quantifies uncertainty around every loading, and can treat the number of factors as an unknown to be inferred from 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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