Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовский исследовательский факторный анализ (BEFA)× | Байесовский конфирматорный факторный анализ (BCFA)× | |
|---|---|---|
| Область | Психометрия | Психометрия |
| Семейство | Latent structure | Latent structure |
| Год появления≠ | 2004 (Bayesian formulation); factor analysis roots: 1904 | 2007–2012 |
| Автор метода≠ | Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904) | Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov |
| Тип≠ | Probabilistic latent variable model | Bayesian latent variable model |
| Основополагающий источник≠ | Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67. link ↗ | Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232 |
| Другие названия | Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis | BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA |
| Связанные | 4 | 4 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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