قارن الطرق

	معامل موثوقية أوميغا (ω) لماكدونالد	تحليل العوامل التأكيدي (CFA)	التحليل العاملي الاستكشافي (EFA)
المجال	القياس النفسي	القياس النفسي	الإحصاء
العائلة	Latent structure	Latent structure	Latent structure
سنة النشأة	1999	1969	—
صاحب الطريقة	Roderick P. McDonald	Karl Gustav Jöreskog	—
النوع	Reliability coefficient / latent variable model	Hypothesis-testing latent variable model	Latent variable / dimension reduction
المصدر التأسيسي	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.
الأسماء البديلة	omega reliability, ω coefficient, omega total, omega hierarchical	CFA, confirmatory FA, measurement model, restricted factor analysis	common factor analysis, açımlayıcı faktör analizi, factor analysis
ذات صلة	6	4	4
الملخص	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.