Methoden vergleichen

	Bestätigende Faktorenanalyse (CFA)	Cronbachs Alpha (Reliabilitätsanalyse)	Exploratorische Faktorenanalyse (EFA)	Hauptkomponentenanalyse
Fachgebiet	Psychometrie	Statistik	Statistik	Maschinelles Lernen
Familie	Latent structure	Latent structure	Latent structure	Machine learning
Entstehungsjahr	1969	1951	—	2002
Urheber	Karl Gustav Jöreskog	Lee J. Cronbach	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Typ	Hypothesis-testing latent variable model	Reliability / internal consistency coefficient	Latent variable / dimension reduction	Unsupervised dimensionality reduction
Wegweisende Quelle	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.	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	CFA, confirmatory FA, measurement model, restricted factor analysis	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	common factor analysis, açımlayıcı faktör analizi, factor analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Verwandt	4	4	4	3
Zusammenfassung	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.	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.