Data Dictionary: student_engagement_ordinal.csv
Ordinal outcome dataset for optional Bayesian cumulative models.
Use this dataset for ordinal-outcome teaching. It supports the shift from coefficient reporting to predicted category probabilities.
Research Scenario
Students report engagement on a five-point Likert scale. A researcher asks whether an intervention is associated with higher engagement after accounting for baseline motivation and teacher feedback.
Variables
| Variable | Type | Description |
|---|---|---|
respondent_id |
string | Unique respondent identifier. |
intervention |
binary | 1 if student received the intervention, 0 otherwise. |
baseline_motivation |
integer | Baseline motivation rating from 1 to 5. |
teacher_feedback |
integer | Perceived teacher feedback rating from 1 to 5. |
engagement |
ordinal integer | Engagement response from 1 to 5. |
Main Questions
- How can Bayesian models handle ordinal outcomes without treating Likert responses as continuous by default?
- How can category probabilities be interpreted?
- How can posterior predictions communicate expected shifts in response categories?