विधियों की तुलना करें

	पुष्टिकारीय कारक विश्लेषण (CFA)	क्रोनबैक का अल्फा (विश्वसनीयता विश्लेषण)	अन्वेषणात्मक कारक विश्लेषण (EFA)
क्षेत्र	मनोमिति	सांख्यिकी	सांख्यिकी
परिवार	Latent structure	Latent structure	Latent structure
उद्भव वर्ष	1969	1951	—
प्रवर्तक	Karl Gustav Jöreskog	Lee J. Cronbach	—
प्रकार	Hypothesis-testing latent variable model	Reliability / internal consistency coefficient	Latent variable / dimension reduction
मौलिक स्रोत	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.	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.
उपनाम	CFA, confirmatory FA, measurement model, restricted factor analysis	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	common factor analysis, açımlayıcı faktör analizi, factor analysis
संबंधित	4	4	4
सारांश	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.	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.