Módszerek összehasonlítása

	Konfirmatorikus faktoranalízis (KFA)	Feltáró Faktoranalízis (EFA)	Főkomponens-analízis	Strukturális egyenlet modellezés (SEM)
Tudományterület	Pszichometria	Statisztika	Gépi tanulás	Statisztika
Módszercsalád	Latent structure	Latent structure	Machine learning	Latent structure
Keletkezés éve	1969	—	2002	1970
Megalkotó	Karl Gustav Jöreskog	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Karl Jöreskog (LISREL framework, 1970s)
Típus	Hypothesis-testing latent variable model	Latent variable / dimension reduction	Unsupervised dimensionality reduction	Latent variable / causal modeling
Alapmű	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.	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
Alternatív nevek	CFA, confirmatory FA, measurement model, restricted factor analysis	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
Kapcsolódó	4	4	3	5
Összefoglaló	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.	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.