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| Teoría de Generalizabilidad Longitudinal× | Análisis Factorial Confirmatorio (AFC)× | |
|---|---|---|
| Campo | Psicometría | Psicometría |
| Familia | Latent structure | Latent structure |
| Año de origen≠ | 1990s–2000s | 1969 |
| Autor original≠ | Webb, Shavelson, and colleagues, building on Cronbach et al. (1963) G-theory foundations | Karl Gustav Jöreskog |
| Tipo≠ | Variance components / reliability estimation | Hypothesis-testing latent variable model |
| Fuente seminal≠ | Webb, N. M., Shavelson, R. J., & Harrigan, E. H. (2007). Generalizability theory: Overview. In C. R. Rao & S. Sinharay (Eds.), Handbook of Statistics, Vol. 26: Psychometrics (pp. 1–43). Elsevier. link ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Alias | longitudinal G-theory, longitudinal GT, repeated-measures generalizability theory, G-theory for longitudinal designs | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Relacionados | 4 | 4 |
| Resumen≠ | Longitudinal generalizability theory extends classical G-theory to repeated-measures and longitudinal designs, decomposing score variance across persons, measurement occasions, raters, and items simultaneously. It quantifies how reliably scores can be generalized across time points, evaluators, and conditions — information that is invisible to cross-sectional reliability indices. | 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. |
| ScholarGateConjunto de datos ↗ |
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