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| Analisi Canonica delle Correlazioni Bayesiana (Bayesian CCA)× | Confirmatory factor analysis× | |
|---|---|---|
| Campo≠ | Statistica | Psicometria |
| Famiglia | Latent structure | Latent structure |
| Anno di origine≠ | 2005-2013 | 1969 |
| Ideatore≠ | Francis Bach & Michael Jordan (probabilistic formulation, 2005); Klami, Virtanen & Kaski (fully Bayesian treatment, 2013) | Karl Gustav Jöreskog |
| Tipo≠ | Latent variable model / dimensionality reduction | Hypothesis-testing latent variable model |
| Fonte seminale≠ | Bach, F. R. & Jordan, M. I. (2005). A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Department of Statistics, University of California, Berkeley. link ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Alias≠ | Bayesian CCA, probabilistic CCA, BCCA | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | Bayesian canonical correlation analysis is a probabilistic generative model that identifies shared latent structure between two or more sets of observed variables. It extends classical CCA by placing priors on model parameters, enabling principled uncertainty quantification, automatic determination of the number of shared dimensions, and robustness when sample sizes are small relative to dimensionality. | 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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