Porovnat metody

	Konfirmační faktorová analýza (CFA)	Cronbachovo alfa (analýza spolehlivosti)	Analýza hlavních komponent
Obor	Psychometrika	Statistika	Strojové učení
Rodina	Latent structure	Latent structure	Machine learning
Rok vzniku	1969	1951	2002
Tvůrce	Karl Gustav Jöreskog	Lee J. Cronbach	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Typ	Hypothesis-testing latent variable model	Reliability / internal consistency coefficient	Unsupervised dimensionality reduction
Původní zdroj	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.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Další názvy	CFA, confirmatory FA, measurement model, restricted factor analysis	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Příbuzné	4	4	3
Shrnutí	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.	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.