Comparar métodos

	Teoría de la Generalizabilidad Multigrupo	Análisis de fiabilidad multigrupo
Campo	Psicometría	Psicometría
Familia	Latent structure	Latent structure
Año de origen	1963–2001	1990s–2000s
Autor original	Lee J. Cronbach and colleagues (Cronbach, Gleser, Nanda, Rajaratnam), extended to multi-group contexts by Brennan and others	Classical test theory traditions; synthesized in modern practice by Vandenberg & Lance (2000) and Sijtsma (2009)
Tipo	Variance component / reliability generalization	Reliability estimation and comparison
Fuente seminal	Brennan, R. L. (2001). Generalizability Theory. Springer. ISBN: 978-0387952826	Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.
Alias	MG G-theory, multi-group G-theory, generalizability theory across groups, cross-group G-study	reliability comparison across groups, group-specific reliability estimation, multi-sample reliability analysis, cross-group internal consistency
Relacionados	6	4
Resumen	Multi-group generalizability theory (MG G-theory) extends classical generalizability theory to estimate and compare variance components — attributable to persons, items, raters, occasions, and their interactions — simultaneously across two or more defined groups. It reveals whether a measurement procedure is equally reliable and generalizable for every group studied, supporting fair and equitable score interpretation.	Multi-group reliability analysis estimates internal consistency or stability coefficients separately within each group and then formally compares them to determine whether a scale functions with equal precision across populations. It is a foundational step in cross-group measurement research, typically carried out alongside or prior to measurement invariance testing.