Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise Fatorial Exploratória Bayesiana (BEFA)× | Análise Fatorial Confirmatória Bayesiana (AFCB)× | |
|---|---|---|
| Área | Psicometria | Psicometria |
| Família | Latent structure | Latent structure |
| Ano de origem≠ | 2004 (Bayesian formulation); factor analysis roots: 1904 | 2007–2012 |
| Autor original≠ | Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904) | Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov |
| Tipo≠ | Probabilistic latent variable model | Bayesian latent variable model |
| Fonte seminal≠ | 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 |
| Outros nomes | Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis | BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA |
| Relacionados | 4 | 4 |
| Resumo≠ | 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. |
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