Methoden vergleichen

	Exploratorische Faktorenanalyse (EFA)	Hauptkomponentenanalyse
Fachgebiet	Statistik	Maschinelles Lernen
Familie	Latent structure	Machine learning
Entstehungsjahr	—	2002
Urheber	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Typ	Latent variable / dimension reduction	Unsupervised dimensionality reduction
Wegweisende Quelle	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.
Aliasnamen	common factor analysis, açımlayıcı faktör analizi, factor analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Verwandt	4	3
Zusammenfassung	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.