विधियों की तुलना करें

	क्रोनबैक का अल्फा (विश्वसनीयता विश्लेषण)	पुष्टिकारीय कारक विश्लेषण (CFA)	अन्वेषणात्मक कारक विश्लेषण (EFA)
क्षेत्र	सांख्यिकी	मनोमिति	सांख्यिकी
परिवार	Latent structure	Latent structure	Latent structure
उद्भव वर्ष	1951	1969	—
प्रवर्तक	Lee J. Cronbach	Karl Gustav Jöreskog	—
प्रकार	Reliability / internal consistency coefficient	Hypothesis-testing latent variable model	Latent variable / dimension reduction
मौलिक स्रोत	Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.	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.
उपनाम	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	CFA, confirmatory FA, measurement model, restricted factor analysis	common factor analysis, açımlayıcı faktör analizi, factor analysis
संबंधित	4	4	4
सारांश	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.	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.