| name | vera-stat-repeated-testing |
| description | Trajectory diagnostics and primary hypothesis tests for repeated measures / longitudinal designs with a continuous outcome. Produces spaghetti plots with group mean ribbons, ICC, descriptives per time point, attrition check, and one interpreted test: paired t-test for 2 time points, repeated measures ANOVA for 3+ time points in one group, or mixed ANOVA (time x group) for 3+ time points with 2+ groups, including Mauchly sphericity and Greenhouse-Geisser correction. Ends with a recommendation block. Outputs .R and .py scripts with publication-quality plots. Trigger when user has a continuous outcome measured on the same subjects over time and says "repeated measures," "longitudinal," "within-subjects," "pre-post," "paired," "panel data," "growth curve," "multilevel," "mixed model," or "correlated observations." Does not handle binary, count, survival, ordinal, or cross-sectional outcomes. |
| user-invocable | true |
| allowed-tools | Read, Bash, Write, Edit |
Repeated Measures Outcome — Trajectory Diagnostics & Hypothesis Testing
Open-source skill.
Workflow
Read each step file in workflow/ before executing that step.
| Step | File | Executor | Output |
|---|
| Collect | workflow/01-collect-inputs.md | Main Agent | Structured input summary |
| Diagnose | workflow/02-check-distribution.md | Main Agent | PART 1 code block |
| Test | workflow/03-run-primary-test.md | Main Agent | PART 2-3 code blocks |
Decision Tree
1. DATA FORMAT CHECK
├── Wide → reshape to long before proceeding
└── Long → continue
2. TIME POINTS
├── Exactly 2 → paired comparisons path
└── 3+ → repeated measures ANOVA / mixed ANOVA path
3. GROUP STRUCTURE
├── No between-subjects factor → one-way RM-ANOVA (within-subjects only)
└── Between-subjects factor → mixed ANOVA (time × group)
4. SPHERICITY (if 3+ time points)
├── Mauchly p ≥ .05 → uncorrected F
└── Mauchly p < .05 → Greenhouse-Geisser / Huynh-Feldt correction
Required Inputs
| Role | What to collect |
|---|
| Outcome (Y) | Variable name, units, what it measures (continuous) |
| Time variable | Measurement occasions, how many, equally spaced? |
| Subject/ID variable | What identifies the same individual across time |
| Group variable | Between-subjects factor (treatment, condition, etc.) |
| Covariates | Time-varying? Time-invariant? |
| Data format | Long (one row per obs) or wide (one row per subject) |
Code Structure
PART 0: Setup & Data Loading (+ reshape if wide)
PART 1: Trajectory Diagnostics → plot_01_trajectories.png
PART 2: Primary Hypothesis Test → plot_02_interaction.png
PART 3: Recommendation Block → text listing additional analyses available
Reporting Standards
- p-values: "< .001" not "0.000"; exact to 3 decimals otherwise
- Effect sizes: Cohen's d (paired t-test), partial eta-squared (RM-ANOVA/mixed ANOVA)
- 95% CIs: always for mean differences
- Degrees of freedom: always with t and F statistics
- Sphericity: Mauchly's W, p; if violated, GG epsilon and corrected p
- ICC: report value with interpretation
- Sample size: final analytic N (subjects) and total observations
- Decimal places: 2 for M/SD, 3 for p and effect sizes
- Non-significance: "not statistically significant at alpha = .05" — never "no effect"
Hypothesis Tests
| Scenario | Normal | Non-Normal |
|---|
| 2 time points (paired) | Paired t-test | Wilcoxon signed-rank |
| 3+ time points, 1 group | One-way RM-ANOVA + sphericity | Friedman test |
| 3+ time points, 2+ groups | Mixed ANOVA (time x group) | — |
Cross-sectional group comparisons → vera-stat-continuous-testing.
Example Dataset
R built-in ChickWeight: outcome = weight, time = Time (0, 2, 4, ..., 21 days),
subject = Chick, group = Diet (1, 2, 3, 4).
Python: sm.datasets.get_rdataset("ChickWeight").data (with offline fallback to bundled examples/chickweight.csv).
Cross-Skill Interface
Output:
├── code_r → .R script
├── code_python → .py script
├── figures/ → 2 PNGs (trajectories + interaction)
└── recommendations → text block (additional analyses available)
