Comparar métodos

	Análise de Cluster	Análise Fatorial Exploratória (AFE)	Modelagem de Equações Estruturais (MEE)
Área	Estatística	Estatística	Estatística
Família	Latent structure	Latent structure	Latent structure
Ano de origem	1939–1967	—	1970
Autor original	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means	—	Karl Jöreskog (LISREL framework, 1970s)
Tipo	Unsupervised classification / grouping	Latent variable / dimension reduction	Latent variable / causal modeling
Fonte seminal	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913	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.	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	clustering, unsupervised classification, data clustering, numerical taxonomy	common factor analysis, açımlayıcı faktör analizi, factor analysis	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Relacionados	5	4	5
Resumo	Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data.	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.	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.