Vertaile menetelmiä

	Cronbachin alfa (Reliability Analysis)	Vahvistava faktorianalyysi (CFA)	Pääkomponenttianalyysi
Tieteenala	Tilastotiede	Psykometriikka	Koneoppiminen
Menetelmäperhe	Latent structure	Latent structure	Machine learning
Syntyvuosi	1951	1969	2002
Kehittäjä	Lee J. Cronbach	Karl Gustav Jöreskog	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tyyppi	Reliability / internal consistency coefficient	Hypothesis-testing latent variable model	Unsupervised dimensionality reduction
Alkuperäislähde	Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Rinnakkaisnimet	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	CFA, confirmatory FA, measurement model, restricted factor analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Liittyvät	4	4	3
Tiivistelmä	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.	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.	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.