Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis Factorial Confirmatorio (AFC)× | Análisis Factorial Exploratorio (AFE)× | Análisis de Componentes Principales× | |
|---|---|---|---|
| Campo≠ | Estadística | Estadística | Aprendizaje automático |
| Familia≠ | Latent structure | Latent structure | Machine learning |
| Año de origen≠ | 1969 | — | 2002 |
| Autor original≠ | Karl Jöreskog | — | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Tipo≠ | Confirmatory latent variable model | Latent variable / dimension reduction | Unsupervised dimensionality reduction |
| Fuente seminal≠ | Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363 | 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. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Alias≠ | Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model | common factor analysis, açımlayıcı faktör analizi, factor analysis | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Relacionados≠ | 4 | 4 | 3 |
| Resumen≠ | Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships. | 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. |
| ScholarGateConjunto de datos ↗ |
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