From jop-skills
Guides reproducible data analysis for JOP manuscripts: uncertainty reporting, robustness checks, and code that passes replication analyst review.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jop-skills:jop-data-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
At JOP, analysis and **reproducibility are the same task**: acceptance is **contingent on
At JOP, analysis and reproducibility are the same task: acceptance is contingent on replicability, and a JOP replication analyst re-runs your code at conditional acceptance. Write the analysis so that every number in the paper is regenerated by a script — and reported with honest uncertainty within the page budget.
The reviewer pool spans subfields, so an analysis only a specialist can audit reads as fragile. Map each demand to the move that satisfies it before the page count forces an ugly cut.
| Referee demand | Pass move | Fail signal |
|---|---|---|
| Usable magnitude | Marginal effect or predicted probability with CI | Coefficient stars, no magnitude in prose |
| Correct uncertainty | Cluster at assignment level; randomization inference | Default SEs on clustered or experimental data |
| Targeted robustness | Each check named to the threat it rebuts | A grid with no mapping to objections |
| Multiplicity honesty | Pre-specified families; adjusted p-values | One mined "significant" interaction |
| Reproducibility | Master script regenerates every number | "Available on request"; drifting numbers |
A hypothetical Short Article asks whether a state's adoption of automatic voter registration (AVR) raised turnout, using a staggered difference-in-differences across states. The first pass runs naive two-way fixed effects and reports a +3.1-point effect (illustrative). Because adoption is staggered, already-treated states act as forbidden controls and the estimate carries negative-weight comparisons. The JOP-credible re-analysis uses a heterogeneity-robust estimator (Callaway–Sant'Anna or Sun–Abraham), reports the group-time average as +1.8 points, 95% CI [0.4, 3.2] (illustrative), shows flat pre-trends, and clusters by state. The robustness grid goes to the Online Appendix, cited in one line of main text.
【Primary result】estimand + magnitude + uncertainty
【Robustness】each check ↔ the threat it answers (main vs appendix)
【Reproducible】master script + seeds + pinned versions + codebook? [Y/N]
【Numbers match】text == deposited output? [Y/N]
【Page discipline】main text lean, overflow in appendix? [Y/N]
【Next】jop-tables-figures
../../resources/external_tools.md — estimation packages and reproducibility tooling (renv, seeds, version pinning)../../resources/official-source-map.md — JOP replicability-contingent acceptance and replication-analyst checknpx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin jop-skillsGuides reproducible data analysis and reporting for AJPS manuscripts, covering uncertainty, robustness, heterogeneity, inference, and reproducibility norms.
Guides analysis and reporting for APSR manuscripts to survive expert double-anonymous review. Covers honest uncertainty, robustness, heterogeneity, and reproducibility.
Prepares replication and data-access packages for The Journal of Politics conditional acceptance review, including readme, datasets, codebook, and code.