方法对比
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| 贝叶斯潜在类别分析 (Bayesian Latent Class Analysis, BLCA)× | 贝叶斯验证性因子分析 (BCFA)× | |
|---|---|---|
| 领域≠ | 统计学 | 心理测量学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 1990s–2000s | 2007–2012 |
| 提出者≠ | Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009) | Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov |
| 类型≠ | Bayesian latent variable / finite mixture model | Bayesian latent variable model |
| 开创性文献≠ | Dunson, D. B. & Xing, C. (2009). Nonparametric Bayes modeling of multivariate categorical data. Journal of the American Statistical Association, 104(487), 1042–1051. DOI ↗ | Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232 |
| 别名 | Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model | BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA |
| 相关≠ | 6 | 4 |
| 摘要≠ | Bayesian latent class analysis extends classical LCA by placing prior distributions on all model parameters and using posterior inference — typically via MCMC — to classify individuals into unobserved categorical groups, quantify uncertainty around class membership, and select the number of classes in a principled, probabilistic way. | Bayesian confirmatory factor analysis tests a pre-specified factor structure using Bayesian inference. Instead of point estimates with p-values, it produces full posterior distributions for loadings, factor correlations, and residual variances, allowing the researcher to incorporate prior knowledge and propagate parameter uncertainty naturally. |
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