Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
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bjps-data-analysis
GitHub指导BJPS稿件的定量、实验或计算分析执行与报告，确保结果经得起双盲同行评审。强调诚实报告不确定性、稳健性检验、异质性分析及可复现性规范，涵盖预注册纪律、测量等价性及计算细节。
Trigger Scenarios
运行主分析和支持性分析并构建结果部分
回应审稿人关于稳健性、异质性或替代设定的要求
协调预注册分析与探索性分析
在数据公开前确保分析过程可复现
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill bjps-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "bjps-data-analysis",
"description": "Use when executing and reporting the analysis for a British Journal of Political Science (BJPS) manuscript so it survives expert, double-blind review — honest uncertainty, robustness, and triangulation appropriate to quantitative, experimental, or computational work. Guides analysis norms; it does not fabricate results."
}
Data Analysis (bjps-data-analysis)
BJPS reviewers are methodologically sophisticated, and the journal — a DA-RT signatory — expects the
replication data and code behind every reported result to be deposited at acceptance (see
bjps-transparency-and-data). Analyze as if a referee will re-run your code, because the materials
will be public. This skill covers execution and reporting norms; design decisions live in
bjps-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 BJPS expects
- Report uncertainty honestly. Confidence/credible intervals, not just stars; the magnitude and substantive meaning of the estimate, not just its significance.
- Robustness that probes, not decorates. Show specifications that could break the result (alternative measures, samples, estimators, fixed effects), and say what you learn.
- 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/sampling level; randomization inference for experiments; small-cluster corrections (wild-cluster bootstrap) when clusters are few.
- Preregistration discipline. Clearly separate registered analyses from exploratory ones; reconcile deviations from the plan and justify them.
- Measurement. Validate constructs; report reliability; show that results are not an artifact of a coding/scaling choice — especially for cross-national measures that must travel across contexts.
Computational / text-as-data specifics
- Document model/version, hyperparameters, seeds, and validation against human-labeled samples.
- For topic models/embeddings/LLM pipelines: report stability and a validation step; don't treat outputs as ground truth.
Cross-national / comparative specifics
- Check measurement equivalence across countries/waves before pooling; report whether constructs mean the same thing across contexts.
- Be explicit about what is identified within vs. between units, and where the variation comes from.
Reproducibility while you work (not at the end)
- One master script regenerates every table and figure from the (raw or constructed) data.
- Set and report seeds for bootstrap, randomization inference, simulation, and any stochastic step.
- Pin software/package versions (
renv.lock,requirements.txt, recordedssc/netinstalls). - Keep table/figure numbers in the manuscript matched to script outputs — the package must reproduce them.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. BJPS is comparative/IR-heavy — cross-country panels with confounded institutions; emphasize fixed effects, clustering, and weak-IV-robust inference.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley; multilevel data → cluster at the right level. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the supplement. See the executed chain in the JF execution walkthrough.
Anti-patterns
- Stars-only tables with no effect sizes or intervals
- "Robustness" that only reruns near-identical specs to manufacture stability
- p-hacking / fishing for a significant interaction; HARKing exploratory results into hypotheses
- Clustering at the wrong level or ignoring few-cluster problems
- Pooling across countries without checking measurement equivalence
- A results section whose numbers the deposited code cannot reproduce
Output format
【Main estimate】magnitude + interval + substantive meaning
【Identification check】(per research-design) result
【Robustness】specs that could break it → what held
【Heterogeneity】pre-specified? MHT-adjusted?
【Registered vs exploratory】clearly separated?
【Measurement equivalence】(if cross-national) checked?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】bjps-tables-figures
Referee-pushback patterns and the BJPS-specific repair
- "This reads as a single-case result, not a general one." → Re-anchor the estimate to the general mechanism and show what it implies beyond the studied country before the numbers.
- "The robustness table only reruns near-identical specs." → Replace decorative checks with specifications that could break the result, and say what you learned when they held.
- "You pooled countries without checking the measure travels." → Report measurement equivalence; show the construct means the same thing across contexts before pooling.
- "I cannot tell registered from exploratory analyses." → Segregate them explicitly; the deposited code is public via the BJPolS Dataverse, so the split must survive independent re-running.
Calibration anchors (hedged)
- The bar is wide political-science interest, not within-niche novelty: an effect only a country or subfield specialist would value rarely clears BJPS review on its own.
- Transparency follows DA-RT / BJPolS Dataverse norms — write the analysis so the deposited package reproduces every printed number; deposit mechanics can change, so confirm the current policy.
Supplementary resources
../../resources/external_tools.md— estimation, inference, and text-as-data packages../../resources/code/— reproducible Stata + Python skeleton to adapt../../resources/official-source-map.md— DA-RT / replication-data policy
Version History
- 1839142 Current 2026-07-05 12:25
