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| Analisi Fattoriale× | Regressione Logistica× | |
|---|---|---|
| Campo | Statistica per la ricerca | Statistica per la ricerca |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1931 | 1958 |
| Ideatore≠ | Louis Leon Thurstone | David Roxbee Cox |
| Tipo | Method | Method |
| Fonte seminale≠ | Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Alias | EFA, CFA, latent variable modeling | logit model, binomial logistic regression, LR |
| Correlati | 3 | 3 |
| Sintesi≠ | Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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