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| Analisi Fattoriale Confermativa Bayesiana (BCFA)× | Teoria della Generalizzabilità (G-Theory)× | |
|---|---|---|
| Campo | Psicometria | Psicometria |
| Famiglia | Latent structure | Latent structure |
| Anno di origine≠ | 2007–2012 | 1963–1972 |
| Ideatore≠ | Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov | Lee J. Cronbach, Goldine Gleser, Harinder Nanda, Nageswari Rajaratnam |
| Tipo≠ | Bayesian latent variable model | Variance-components reliability model |
| Fonte seminale≠ | Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232 | Cronbach, L. J., Gleser, G. C., Nanda, H. & Rajaratnam, N. (1972). The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles. Wiley. link ↗ |
| Alias≠ | BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA | G-theory, G-study / D-study framework, variance components reliability |
| Correlati | 4 | 4 |
| Sintesi≠ | 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. | Generalizability Theory is a psychometric framework that decomposes observed score variance into multiple sources — persons, items, raters, occasions, and their interactions — using analysis of variance. It replaces the single reliability coefficient of classical test theory with a family of coefficients that tell researchers how well scores generalize across different measurement conditions. |
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