Comparar métodos

	Análise de Componentes Principais	Modelagem de Equações Estruturais (MEE)
Área	Aprendizado de máquina	Estatística
Família	Machine learning	Latent structure
Ano de origem	2002	1970
Autor original	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Karl Jöreskog (LISREL framework, 1970s)
Tipo	Unsupervised dimensionality reduction	Latent variable / causal modeling
Fonte seminal	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
Outros nomes	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Relacionados	3	5
Resumo	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.