Probit regression
Predict a yes-or-no outcome with a probit link instead of odds.
When to use it
Use probit regression for the same binary outcome as logistic regression when you want the slope on the probit scale. A positive slope still means the predictor makes a “yes” more likely. There is no odds ratio on this table.
Assumptions
The outcome is binary. The underlying probit model is a fair summary, and the rows are independent. Perfect separation makes the slopes unstable, the same way it does for logistic regression.
Running it in Tensr
Options
Prop
Type
Reading the output
The same pass/fail outcome and the same hours predictor, fit as a probit instead of a logit.
| Term | Estimate | p-value |
|---|---|---|
| const | -0.96 | .085 |
| hours | 0.187 | .085 |
| Predictor | Marginal effect (dP/dX at mean) |
|---|---|
| hours | 0.075 |
Probit regression predicting passed from 1 predictor(s), n = 96. The primary result is not significant (p = .085). This is not large enough to treat the comparison this page is about as a reliable association. The result is non-significant: the data are still compatible with no effect. Metrics: Pseudo R² = 0.023; Model χ² = 3.044; Model p = .081; AIC = 133.999; Observations = 96.
Reporting (APA 7)
Probit regression predicting passed from 1 predictor(s), n = 96. This result is not significant (p = .085). Report the estimate with that p, and do not describe the pattern as a reliable effect.
Coming from SPSS
Analyze → Regression → Probit.
SPSS Probit is often aimed at dose-response tables. Tensr fits a binary probit from row-level 0/1 data and a numeric predictor.
Related
Odds ratios for the same kind of outcome are on logistic regression.