方法对比
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| 贝叶斯探索性因子分析 (Bayesian Exploratory Factor Analysis, BEFA)× | 验证性因子分析（CFA）× | |
|---|---|---|
| 领域 | 心理测量学 | 心理测量学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 2004 (Bayesian formulation); factor analysis roots: 1904 | 1969 |
| 提出者≠ | Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904) | Karl Gustav Jöreskog |
| 类型≠ | Probabilistic latent variable model | Hypothesis-testing latent variable model |
| 开创性文献≠ | Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67. link ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| 别名 | Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis | CFA, confirmatory FA, measurement model, restricted 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. | Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing. |
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