Quantitative/ Analysis
Probit regression
Use a normal-link model for discrete outcomes
Probit regression maps a linear predictor to a probability through the normal cumulative distribution. Binary probit concerns two outcomes; ordered probit uses thresholds for ordered categories. Multivariate probit permits dependence between discrete responses. Coefficients are on a latent scale and are usually interpreted through predicted probabilities or marginal effects.
WHEN IT FITS
Use when a discrete outcome is naturally modelled by a latent threshold process or a probit formulation fits the substantive framework. Specify ordering, dependence and the meaning of marginal effects.
Strengths
- Natural latent-threshold interpretation
- Extensions can model correlated discrete outcomes
Limitations
- Coefficients do not directly express probability changes
- Identification and distribution assumptions matter in multivariate extensions
Know the boundary
Logit and probit coefficients are not directly comparable; neither link alone supplies causal identification.
USED ACROSS
Banking & financeBusiness & MBAPsychologyComputer scienceEducation