Porovnat metody

	Konfirmační faktorová analýza	Cronbachovo alfa (analýza spolehlivosti)	Exploratorní faktorová analýza (EFA)
Obor	Psychometrika	Statistika	Statistika
Rodina	Latent structure	Latent structure	Latent structure
Rok vzniku	1969	1951	—
Tvůrce	Karl Jöreskog	Lee J. Cronbach	—
Typ	Measurement model / latent variable analysis	Reliability / internal consistency coefficient	Latent variable / dimension reduction
Původní zdroj	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). Guilford Press. ISBN: 978-1462515363	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.
Další názvy	Doğrulayıcı Faktör Analizi — Ölçek Doğrulama (CFA), confirmatory factor analysis, measurement model testing	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	common factor analysis, açımlayıcı faktör analizi, factor analysis
Příbuzné	6	4	4
Shrnutí	Confirmatory factor analysis is a measurement modelling technique that tests whether a hypothesised factor structure — typically derived from theory or an earlier exploratory analysis — fits observed data from a new sample. Developed by Karl Jöreskog in 1969, it became the dominant tool for validating psychological scales because it requires the researcher to specify in advance which items belong to which latent factor and then assesses the adequacy of that specification against explicit statistical fit criteria.	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.