jpart-data-analysis
Use when executing and reporting the analysis for a Journal of Public Administration Research and Theory (JPART) manuscript so it survives expert, double-blind review and the journal's mandatory data-and-code release. Covers honest uncertainty, robustness, and the PA-specific traps (common-method bias, selection). Guides analysis norms; it does not fabricate results.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jpart-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 (jpart-data-analysis) JPART reviewers are methodologically sophisticated public-management scholars, and the journal **requires authors to release the data and software code** underlying the paper as a condition of publication (see `jpart-transparency-and-data`). Analyze as if a referee will re-run the code — because the materials are public. This skill covers execution and reporting; design lives in `jpart-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 the mandatory data/code deposit ## Analysis norms JPART expects 1. **Report uncertainty and magnitude.** Confidence/credible intervals and the *substantive* size of the effect (e.g., a fraction of an SD of PSM), not stars alone. 2. **Robustness that probes, not decorates.** Show specifications that could *break* the result (alternative measures of red tape/PSM, samples, estimators, fixed effects), and say what you learned. 3. **Confront the PA-specific threats.** Common-method/common-sou
- When to trigger
- Analysis norms JPART expects
- Measurement (a perennial JPART referee focus)
- Reproducibility while you work (not at the end)
- What JPART reviewers probe, by design
- Worked micro-example (illustrative numbers)
- Referee-pushback patterns and the JPART repair
- Calibration anchors (hedged)
- Execution bridge (StatsPAI / Stata MCP)
- Output format
- Supplementary resources
What does the jpart-data-analysis skill do?
Use when executing and reporting the analysis for a Journal of Public Administration Research and Theory (JPART) manuscript so it survives expert, double-blind review and the journal's mandatory data-and-code release. Covers honest uncertainty, robustness, and the PA-specific traps (common-method bias, selection). Guides analysis norms; it does not fabricate results.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jpart-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.