השוואת שיטות

	ניתוח גורמים מאשר (CFA)	אלפא של קרונבך (ניתוח מהימנות)	אומגה היררכית של מקדונלד (ωh)
תחום	פסיכומטריה	סטטיסטיקה	פסיכומטריה
משפחה	Latent structure	Latent structure	Latent structure
שנת המקור	1969	1951	1999
הוגה השיטה	Karl Gustav Jöreskog	Lee J. Cronbach	Roderick P. McDonald
סוג	Hypothesis-testing latent variable model	Reliability / internal consistency coefficient	Reliability / composite score validity coefficient
מקור מכונן	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.	Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.	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.
כינויים	CFA, confirmatory FA, measurement model, restricted factor analysis	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	omega hierarchical, omega-h, bifactor omega, composite score validity coefficient
קשורות	4	4	5
תקציר	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.	Cronbach's alpha is a coefficient of internal consistency that quantifies the degree to which a set of items on a scale measures the same underlying construct. Introduced by Lee J. Cronbach in 1951, it remains the most widely reported reliability index in social-science, health, and educational research.	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.