विधियों की तुलना करें

	अन्वेषणात्मक कारक विश्लेषण (EFA)	क्रोनबैक का अल्फा (विश्वसनीयता विश्लेषण)	प्रिंसिपल कंपोनेंट एनालिसिस
क्षेत्र	सांख्यिकी	सांख्यिकी	मशीन अधिगम
परिवार	Latent structure	Latent structure	Machine learning
उद्भव वर्ष	—	1951	2002
प्रवर्तक	—	Lee J. Cronbach	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
प्रकार	Latent variable / dimension reduction	Reliability / internal consistency coefficient	Unsupervised dimensionality reduction
मौलिक स्रोत	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.	Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
उपनाम	common factor analysis, açımlayıcı faktör analizi, factor analysis	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
संबंधित	4	4	3
सारांश	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.	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.	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.