विधियों की तुलना करें

	पुष्टिकारीय कारक विश्लेषण (CFA)	क्रोनबैक का अल्फा (विश्वसनीयता विश्लेषण)	अन्वेषणात्मक कारक विश्लेषण (EFA)	प्रिंसिपल कंपोनेंट एनालिसिस
क्षेत्र	मनोमिति	सांख्यिकी	सांख्यिकी	मशीन अधिगम
परिवार	Latent structure	Latent structure	Latent structure	Machine learning
उद्भव वर्ष	1969	1951	—	2002
प्रवर्तक	Karl Gustav Jöreskog	Lee J. Cronbach	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
प्रकार	Hypothesis-testing latent variable model	Reliability / internal consistency coefficient	Latent variable / dimension reduction	Unsupervised dimensionality 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.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
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	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
संबंधित	4	4	4	3
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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.