Módszerek összehasonlítása

	A McDonald-féle Omega (ω) reliabilitási együttható	Konfirmatív Faktoranalízis (CFA)	Feltáró Faktoranalízis (EFA)
Tudományterület	Pszichometria	Statisztika	Statisztika
Módszercsalád	Latent structure	Latent structure	Latent structure
Keletkezés éve	1999	1969	—
Megalkotó	Roderick P. McDonald	Karl Jöreskog	—
Típus	Reliability coefficient / latent variable model	Confirmatory latent variable model	Latent variable / dimension reduction
Alapmű	McDonald, R. P. (1999). Test Theory: A Unified Treatment. Lawrence Erlbaum Associates. ISBN: 978-0805830750	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363	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.
Alternatív nevek	omega reliability, ω coefficient, omega total, omega hierarchical	Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model	common factor analysis, açımlayıcı faktör analizi, factor analysis
Kapcsolódó	6	4	4
Összefoglaló	McDonald's omega is a factor-analysis-based reliability coefficient introduced by Roderick P. McDonald (1999) that quantifies the internal consistency of a composite score without requiring the restrictive assumption that all items contribute equally to the latent factor. It yields two complementary indices: ω_total, which captures overall reliability of the sum score, and ω_hierarchical (ωh), which reports how much of the composite's variance is explained specifically by a single general factor.	Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships.	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.