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| Regressione Logistica Ordinale× | Modello Probit di Regressione× | |
|---|---|---|
| Campo≠ | Statistica | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1980 | 2018 |
| Ideatore≠ | Peter McCullagh | Greene (textbook treatment); classical discrete-choice modelling |
| Tipo≠ | Ordinal regression / GLM | Binary discrete-choice model |
| Fonte seminale≠ | McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142. DOI ↗ | Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366 |
| Alias≠ | proportional-odds model, cumulative link model, ordered logit, OLR | probit regression, normit model, Probit Modeli |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | Ordinal logistic regression — most commonly the proportional-odds model — estimates the relationship between one or more predictors and an ordered categorical outcome (e.g., Likert scales, disease severity grades, educational attainment levels). It models cumulative log-odds across the ordered categories while assuming a single shared effect of each predictor at all thresholds. | The probit model is a regression method for a binary (0/1) outcome that maps a linear index of the predictors through the standard normal cumulative distribution function to produce a probability. It is a classical discrete-choice alternative to logistic regression, developed in standard econometrics treatments such as Greene's Econometric Analysis (2018). |
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