Comparar métodos

	Many-Facet Rasch Measurement	Teoría de la Generalizabilidad (G-Theory)
Campo	Education	Psicometría
Familia	Latent structure	Latent structure
Año de origen	1989	1963–1972
Autor original	John Michael Linacre	Lee J. Cronbach, Goldine Gleser, Harinder Nanda, Nageswari Rajaratnam
Tipo	Rasch model extension adding rater and other facets to person and item	Variance-components reliability model
Fuente seminal	Linacre, J. M. (1989). Many-Facet Rasch Measurement. MESA Press. ISBN: 9780941938020	Cronbach, L. J., Gleser, G. C., Nanda, H. & Rajaratnam, N. (1972). The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles. Wiley.
Alias	MFRM, Many-Faceted Rasch Model, Facets Model, Linacre Facets Model	G-theory, G-study / D-study framework, variance components reliability
Relacionados	4	4
Resumen	Many-facet Rasch measurement (MFRM) extends the basic Rasch model to assessments mediated by raters. Beyond examinee ability and item difficulty, it adds explicit parameters for rater severity and for any other facet of the rating situation — task, occasion, rating criterion — placing them all on one common logit scale. Developed by John Michael Linacre, MFRM lets analysts estimate and adjust for the fact that some raters are systematically harsh and others lenient, producing 'fair' ability estimates that do not penalize an examinee for happening to draw a severe judge.	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.