Salīdzināt metodes

	Makdonalda hierarhiskais omega (ωh)	Bifaktora modelis (vispārējie un specifiskie faktori)
Nozare	Psihometrija	Psihometrija
Saime	Latent structure	Latent structure
Izcelsmes gads	1999	1937
Autors	Roderick P. McDonald	Holzinger & Swineford (1937); modern revival by Reise (2012)
Tips	Reliability / composite score validity coefficient	Confirmatory latent variable model
Pirmavots	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.	Reise, S. P. (2012). The Rediscovery of Bifactor Measurement Models. Multivariate Behavioral Research, 47(5), 667–696.
Citi nosaukumi	omega hierarchical, omega-h, bifactor omega, composite score validity coefficient	Bifaktör Modeli — Genel ve Spesifik Faktörler, hierarchical factor model, general-specific factor model, Schmid-Leiman model
Saistītās	5	6
Kopsavilkums	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.	The bifactor measurement model specifies that every indicator loads simultaneously on a single general factor and on one of several specific (group) factors. Formally introduced by Holzinger and Swineford in 1937 and brought into mainstream psychometrics by Reise (2012), it is now the standard tool for evaluating whether a multidimensional scale can legitimately yield a single composite score.