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| Anàlisi de Fiabilitat (Alpha de Cronbach)× | Anàlisi Factorial Confirmatòria (CFA)× | Anàlisi de Components Principals× | |
|---|---|---|---|
| Camp≠ | Estadística | Psicometria | Aprenentatge automàtic |
| Família≠ | Latent structure | Latent structure | Machine learning |
| Any d'origen≠ | 1951 | 1969 | 2002 |
| Autor original≠ | Lee J. Cronbach | Karl Gustav Jöreskog | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Tipus≠ | Reliability / internal consistency coefficient | Hypothesis-testing latent variable model | Unsupervised dimensionality reduction |
| Font seminal≠ | Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. DOI ↗ | 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 ↗ |
| Àlies | coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha) | CFA, confirmatory FA, measurement model, restricted factor analysis | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Relacionats≠ | 4 | 4 | 3 |
| Resum≠ | Cronbach's alpha is a coefficient of internal consistency that quantifies the degree to which a set of items on a scale measures the same underlying construct. Introduced by Lee J. Cronbach in 1951, it remains the most widely reported reliability index in social-science, health, and educational research. | 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. |
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