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	Analiza czynnikowa potwierdzająca	Alfa Cronbacha (Analiza Rzetelności)	Analiza Głównych Składowych
Dziedzina	Psychometria	Statystyka	Uczenie maszynowe
Rodzina	Latent structure	Latent structure	Machine learning
Rok powstania	1969	1951	2002
Twórca	Karl Jöreskog	Lee J. Cronbach	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Typ	Measurement model / latent variable analysis	Reliability / internal consistency coefficient	Unsupervised dimensionality reduction
Źródło pierwotne	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.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Inne nazwy	Doğrulayıcı Faktör Analizi — Ölçek Doğrulama (CFA), confirmatory factor analysis, measurement model testing	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Pokrewne	6	4	3
Podsumowanie	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.	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.