Ordinal regression
Predict an ordered category, such as a 1-to-5 rating.
When to use it
Use ordinal regression when the outcome has ordered categories and the gaps between them are not equal scores. A 1-to-4 rating is the usual case. The model has one slope per predictor and a threshold between each pair of adjacent categories.
Assumptions
The outcome is ordered. The proportional-odds idea is that one slope works at every threshold. Tensr does not test that assumption.
Running it in Tensr
Options
Prop
Type
Reading the output
A 1–4 satisfaction rating predicted from study hours. Higher hours were built to push people up the scale, with a lot of overlap between adjacent ratings.
| Term | Estimate | p-value |
|---|---|---|
| hours | 0.551 | < .001 |
| 0.0/1.0 | 0.744 | .351 |
| 1.0/2.0 | 0.599 | < .001 |
| 2.0/3.0 | 0.498 | .002 |
Ordinal regression for satisfaction, n = 96. Ordinal regression for satisfaction, n = 96. Metrics: AIC = 250.7; Log-likelihood = -121.35.
Reporting (APA 7)
Ordinal regression for satisfaction, n = 96. Report the estimate in the table. This procedure is not summarised by one p-value.
Coming from SPSS
Analyze → Regression → Ordinal.
SPSS PLUM prints thresholds and a slope, with standard errors and Wald tests. Tensr’s table uses Term, Estimate, and p-value.
Related
A numeric score uses linear regression. Two categories use logistic regression.