jpart-data-analysis
GitHub面向JPART期刊投稿的数据分析技能，指导执行可复现的实证分析。涵盖报告不确定性、稳健性检验、应对公共管理特有偏差及预注册纪律，确保数据代码公开透明，满足双盲评审要求。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jpart-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "jpart-data-analysis",
"description": "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."
}
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
- 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.
- 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.
- Confront the PA-specific threats. Common-method/common-source bias, social desirability, and self-selection into public service are the objections raised first — address them, don't ignore them.
- Heterogeneity with discipline. Pre-specify subgroups where possible; correct for multiple comparisons; do not mine for a significant interaction and theorize it post hoc.
- Right inference. Cluster at the assignment/agency level; randomization inference for experiments; small-cluster corrections (wild-cluster bootstrap) when agencies are few.
- Preregistration discipline. Separate confirmatory from exploratory analyses; reconcile any deviation from the plan and justify it.
Measurement (a perennial JPART referee focus)
- Validate constructs (PSM, red tape, goal ambiguity); report reliability; show the result is not an
artifact of a single scale or coding choice. Concept defined in
jpart-theory-buildingmust match the measure used here.
Reproducibility while you work (not at the end)
- One master script regenerates every table and figure from raw/constructed data.
- Set and report seeds for bootstrap, randomization inference, simulation, any stochastic step.
- Pin software/package versions (
renv.lock,requirements.txt, recordedssc/netinstalls). - Keep table/figure numbers matched to script outputs — the materials are public and will be checked.
What JPART reviewers probe, by design
| Design | The check a JPART referee runs first | The fix that earns benefit of the doubt |
|---|---|---|
| Survey of public employees | Are X and Y from the same self-report (common-method)? | separate sources / objective Y / marker variable + Harman caution |
| Survey/field experiment | Is it pre-registered, powered, on the right population? | preregistered estimand, MDE reported, public-employee sample |
| Observational causal | Is "effect" really selection into public service? | state estimand + assumption; sensitivity to an unobserved confounder |
| Multilevel | Is the agency-level nesting modeled? | random effects / clustered SEs, ICC reported |
| Mixed methods | Do quant and qual actually corroborate? | show agreement and own divergence |
Worked micro-example (illustrative numbers)
A hypothetical JPART field experiment tests whether a goal-clarity intervention raises frontline performance among real caseworkers. The pre-registered ITT is +0.18 SD (95% CI 0.06 to 0.30), randomization-inference p = 0.006. An exploratory split by tenure shows +0.41 SD for new hires, but it was not pre-registered and the interaction p = 0.03 before correction; after a Bonferroni adjustment across five exploratory subgroups it crosses 0.20. The disciplined write-up reports the confirmatory +0.18 SD effect with its interval and substantive meaning, flags the +0.41 figure as exploratory and not multiplicity-robust, and frames it as a hypothesis for future work. (All numbers illustrative.)
Referee-pushback patterns and the JPART repair
- "This is common-method bias, not an effect." → Use a separate/objective outcome or a marker variable; report the sensitivity, don't wave it away with a single Harman test.
- "The robustness table only reruns near-identical specs." → Replace decorative checks with specs that could break the result (alternative PSM/red-tape measures, samples), and say what held.
- "This is selection into public service." → State the estimand and assumption; report how strong an unobserved confounder must be to overturn it.
- "I cannot tell confirmatory from exploratory." → Segregate them explicitly; the deposited code is public, so the split must survive a re-run.
Calibration anchors (hedged)
- The bar is a public-management theory payoff carried by credible numbers — an estimate with no mechanism rarely clears JPART review.
- JPART increasingly rewards experimental and causal designs, but a rigorous multilevel or mixed study is judged on its own terms.
- The data-and-code release is mandatory (where ethically possible) — write the analysis so the public package reproduces every printed number. Confirm exact wording on the live policy page.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JPART is public management — observational and experimental designs on public organizations; identification + clustered/multilevel inference.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley. - Re-fit off one handle:
audit_result(result_id)lists missing checks + the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.
Output format
【Main estimate】magnitude + interval + substantive meaning
【PA threat handled】common-method / selection — how?
【Robustness】specs that could break it → what held
【Heterogeneity】pre-specified? MHT-adjusted?
【Confirmatory vs exploratory】clearly separated?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】jpart-tables-figures
Supplementary resources
../../resources/code/— Stata + Python estimation/inference skeleton../../resources/external_tools.md— estimation, inference, and experiment packages../../resources/official-source-map.md— data-and-code release policy
Version History
- 1839142 Current 2026-07-05 13:54
