Võrdle meetodeid

	Kinnitav faktorianalüüs (CFA)	Eksploratiivne faktorianalüüs (EFA)	Pricipaalanalüüs
Valdkond	Statistika	Statistika	Masinõpe
Perekond	Latent structure	Latent structure	Machine learning
Tekkeaasta	1969	—	2002
Looja	Karl Jöreskog	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tüüp	Confirmatory latent variable model	Latent variable / dimension reduction	Unsupervised dimensionality reduction
Algallikas	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363	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.
Rööpnimetused	Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model	common factor analysis, açımlayıcı faktör analizi, factor analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Seotud	4	4	3
Kokkuvõte	Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships.	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.