Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis Factorial Confirmatorio (AFC)× | Análisis de Componentes Principales× | |
|---|---|---|
| Campo≠ | Estadística | Aprendizaje automático |
| Familia≠ | 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 | Unsupervised dimensionality reduction |
| Fuente seminal≠ | Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363 | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Alias≠ | Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Relacionados≠ | 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. | 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 ↗ |
|
|