Comparer des méthodes

	Oméga hiérarchique de McDonald (ωh)	Analyse factorielle exploratoire (AFE)
Domaine	Psychométrie	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1999	—
Auteur d'origine	Roderick P. McDonald	—
Type	Reliability / composite score validity coefficient	Latent variable / dimension reduction
Source fondatrice	Reise, S. P., Scheines, R., Widaman, K. F. & Haviland, M. G. (2013). Multidimensionality and structural coefficient bias in structural equation modeling: A bifactor perspective. Educational and Psychological Measurement, 73(1), 5–26.	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.
Alias	omega hierarchical, omega-h, bifactor omega, composite score validity coefficient	common factor analysis, açımlayıcı faktör analizi, factor analysis
Apparentées	5	4
Résumé	McDonald's hierarchical omega (ωh) is a coefficient derived from a bifactor confirmatory factor model that quantifies what proportion of total-score variance is attributable to a single general factor rather than to group-specific factors or item-level error. Introduced by Roderick P. McDonald (1999) and elaborated for bifactor applications by Reise and colleagues (2013) and Rodriguez and colleagues (2016), it is the primary index used in psychometrics to evaluate whether a composite total score is a defensible summary of a multidimensional scale.	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.