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	Coefficiente di affidabilità Omega (ω) di McDonald	Confirmatory factor analysis	Analisi Fattoriale Esplorativa (AFE)
Campo	Psicometria	Psicometria	Statistica
Famiglia	Latent structure	Latent structure	Latent structure
Anno di origine	1999	1969	—
Ideatore	Roderick P. McDonald	Karl Gustav Jöreskog	—
Tipo	Reliability coefficient / latent variable model	Hypothesis-testing latent variable model	Latent variable / dimension reduction
Fonte seminale	McDonald, R. P. (1999). Test Theory: A Unified Treatment. Lawrence Erlbaum Associates. ISBN: 978-0805830750	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.	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 reliability, ω coefficient, omega total, omega hierarchical	CFA, confirmatory FA, measurement model, restricted factor analysis	common factor analysis, açımlayıcı faktör analizi, factor analysis
Correlati	6	4	4
Sintesi	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 a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.	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.