Reading Intervention: Pretest–Posttest Across Two Arms
This program-evaluation study compares a reading-intervention arm against a control arm across 50 students (25 per arm), each measured at pretest and posttest — a mixed ANOVA design testing whether the intervention accelerates reading gains beyond what happens in the control group.
- To describe reading_score across the pretest and posttest phases within each arm.
- To determine whether reading_score differs across arm and phase, with subject_id identifying the repeated measurements per student.
| Column | Kind | Missing | Distinct |
|---|---|---|---|
| subject_id | categorical | 0 | 50 |
| arm | categorical | 0 | 2 |
| phase | categorical | 0 | 2 |
| reading_score | numeric | 0 | 86 |
Detected a comparative design with 3 analyzable variable(s) and 2 analytical question(s). Recommended 3 statistical test(s). Estimated cost: 34 credits.
| n | max | min | std | mean | mode | range | median | variable | variance |
|---|---|---|---|---|---|---|---|---|---|
| 100 | 85.500 | 39.100 | 9.479 | 59.686 | 55.800 | 46.400 | 58.150 | reading_score | 89.851 |
| F | df | kind | effect | p value | df error | significant | partial eta sq |
|---|---|---|---|---|---|---|---|
| 22.283 | 1 | between | phase | 0.000 | 48 | true | 0.317 |
| 6.930 | 1 | within | arm | 0.011 | 48 | true | 0.126 |
| 12.484 | 1 | interaction | arm × phase | 0.001 | 48 | true | 0.206 |
The result is statistically significant (p = 0.0000 < 0.05).
Publication-ready report
Tests used · Results · Discussion — generated from the computed statistics.
Download the exact Jupyter notebook or Python script that reproduces these results with widely-used libraries (scipy, statsmodels, lifelines).
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