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≠ | Psicometría | Aprendizaje automático |
| Familia≠ | Latent structure | Machine learning |
| Año de origen≠ | 1969 | 2002 |
| Autor original≠ | Karl Gustav Jöreskog | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Tipo≠ | Hypothesis-testing latent variable model | Unsupervised dimensionality reduction |
| Fuente seminal≠ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Alias | CFA, confirmatory FA, measurement model, restricted factor analysis | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Relacionados≠ | 4 | 3 |
| Resumen≠ | Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing. | 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 ↗ |
|
|