pubar-data-analysis
Use when executing and reporting the analysis for a Public Administration Review (PAR) manuscript so it survives expert, double-blind review and supports honest Evidence for Practice — uncertainty, robustness, and triangulation appropriate to quantitative, experimental, or mixed work. Guides analysis norms; it does not fabricate results.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pubar-data-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Analysis (pubar-data-analysis) PAR reviewers are methodologically capable public-management scholars, and the journal endorses the **TOP transparency guidelines** — so analyses should be reproducible and documented (see `pubar-transparency-and-data`). Because PAR articles carry **Evidence for Practice**, every estimate that drives a managerial takeaway must be analyzed honestly enough to bear that weight. This skill covers execution and reporting; design decisions live in `pubar-research-design`. ## When to trigger - Running main and supporting analyses; building the results section - A reviewer asked for robustness, heterogeneity, or alternative specifications - Reconciling preregistered vs. exploratory analyses - Making the analysis reproducible before deposit ## Analysis norms PAR expects 1. **Report uncertainty honestly.** Confidence/credible intervals, not just stars; the **magnitude and substantive/managerial meaning** of the estimate, not just its significance. A practitioner needs effect size, not a p-value. 2. **Robustness that probes, not decorates.** Show specifications that could *break* the result (alternative measures, samples, estimators, fixed effects), and s
- When to trigger
- Analysis norms PAR expects
- Mixed-methods integration
- Reproducibility while you work (not at the end)
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Output format
- What PAR reviewers probe, by analytic tradition
- Worked micro-example (illustrative numbers)
- Calibration anchors (hedged)
- Supplementary resources
What does the pubar-data-analysis skill do?
Use when executing and reporting the analysis for a Public Administration Review (PAR) manuscript so it survives expert, double-blind review and supports honest Evidence for Practice — uncertainty, robustness, and triangulation appropriate to quantitative, experimental, or mixed work. Guides analysis norms; it does not fabricate results.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pubar-data-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.