Σύγκριση μεθόδων

	Διερευνητική Ανάλυση Παραγόντων (EFA)	Ανάλυση Κύριων Συνιστωσών	Μοντελοποίηση Δομικών Εξισώσεων (SEM)
Πεδίο	Στατιστική	Μηχανική Μάθηση	Στατιστική
Οικογένεια	Latent structure	Machine learning	Latent structure
Έτος προέλευσης	—	2002	1970
Δημιουργός	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Karl Jöreskog (LISREL framework, 1970s)
Τύπος	Latent variable / dimension reduction	Unsupervised dimensionality reduction	Latent variable / causal modeling
Θεμελιώδης πηγή	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.	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Εναλλακτικές ονομασίες	common factor analysis, açımlayıcı faktör analizi, factor analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Συναφείς	4	3	5
Σύνοψη	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.	Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences.