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Anàlisi Factorial Exploratòria (EFA)×Modelització d'equacions estructurals (SEM)×
CampEstadísticaEstadística
FamíliaLatent structureLatent structure
Any d'origen1970
Autor originalKarl Jöreskog (LISREL framework, 1970s)
TipusLatent variable / dimension reductionLatent variable / causal modeling
Font seminalFabrigar, 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. DOI ↗Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Àliescommon factor analysis, açımlayıcı faktör analizi, factor analysisYapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Relacionats45
ResumExploratory 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.
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ScholarGateCompara mètodes: EFA · SEM. Recuperat el 2026-06-15 de https://scholargate.app/ca/compare