Sammenlign metoder

	Cronbachs Alpha (Reliabilitetsanalyse)	Eksplorerende faktoranalyse (EFA)	Hovedkomponentanalyse
Fagfelt	Statistikk	Statistikk	Maskinlæring
Familie	Latent structure	Latent structure	Machine learning
Opprinnelsesår	1951	—	2002
Opphavsperson	Lee J. Cronbach	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Type	Reliability / internal consistency coefficient	Latent variable / dimension reduction	Unsupervised dimensionality reduction
Opprinnelig kilde	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.
Alias	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
Relaterte	4	4	3
Sammendrag	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.