Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Desarrollo de escalas bayesianas× | Análisis Factorial Confirmatorio Bayesiano (BCFA)× | |
|---|---|---|
| Campo | Psicometría | Psicometría |
| Familia | Latent structure | Latent structure |
| Año de origen≠ | 1990s–2000s | 2007–2012 |
| Autor original≠ | Harold Jeffreys, expanded into psychometrics by Mislevy and colleagues | Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov |
| Tipo≠ | Bayesian probabilistic scale construction | Bayesian latent variable model |
| Fuente seminal≠ | De Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. Guilford Press. ISBN: 978-1593858698 | Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232 |
| Alias | Bayesian psychometric scale construction, Bayesian measurement modeling, Bayesian item development, BSD | BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | Bayesian scale development applies Bayesian statistical inference to the construction and evaluation of psychometric scales. Rather than relying on single point estimates of item and person parameters, it produces full posterior distributions that quantify uncertainty, incorporate prior knowledge, and support principled decisions about item retention, reliability, and validity in small or complex samples. | 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. |
| ScholarGateConjunto de datos ↗ |
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