Agent Skills
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popdevr-data-analysis
GitHub用于执行和报告人口与发展评论（PDR）稿件的数据分析，确保方法严谨、结果可复现且发展含义清晰。涵盖率构建、不确定性报告、分解及APC分析等规范，指导符合双盲同行评审标准的统计实践。
Trigger Scenarios
构建率和生命表或撰写结果部分
运行分解、事件历史、年龄-时期-队列或投影分析
回应审稿人关于稳健性或敏感性检验的要求
在数据存档前确保分析可复现并明确发展意义
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill popdevr-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "popdevr-data-analysis",
"description": "Use when executing and reporting the analysis for a Population and Development Review (PDR, Wiley \/ Population Council) manuscript so it survives expert, double-anonymized review — correct rate construction, honest uncertainty, and demographic methods done right, with the development\/policy meaning of each quantity made clear. Guides analysis and reporting norms; it does not fabricate results."
}
Data Analysis (popdevr-data-analysis)
PDR reviewers are expert demographers and development scholars, and the journal expects analyses that
are reproducible and interpretable to a broad readership. Analyze as if a methodologist will re-derive
your rates and an economist will ask what each number means for development — because both may. This
skill covers execution and reporting norms; method choice lives in popdevr-research-design.
When to trigger
- Constructing rates and life tables; building the results section
- Running a decomposition, event-history, APC, or projection analysis
- A reviewer asked for robustness, sensitivity, or alternative specifications
- Making the analysis reproducible and its development meaning explicit before deposit
Analysis norms PDR expects
- Get the denominators right. Exposure (person-years), the correct base population, and age/period alignment are where demographic analyses live or die. Document how rates were built.
- Report uncertainty honestly. Confidence/credible intervals for rates, life-expectancy contributions, projection scenarios, and derived quantities — not just point estimates or stars. Bootstrap or delta-method intervals for decomposition components and life-table functions.
- Decomposition with clear components. State precisely what each component (rate vs. composition, age contribution, factor) represents and which maps to a development channel; ensure components sum to the total being explained.
- APC discipline. Be explicit about the identification problem; report under the stated constraint and show sensitivity to plausible alternatives — never imply a unique decomposition.
- Survival/event-history rigor. Check proportional hazards; handle censoring, truncation, and competing risks correctly; report on the right time scale (age, duration, period).
- Right inference for the data. Survey/design weights and complex-design variance where applicable; cluster at the appropriate level; small-sample corrections when groups (e.g., countries) are few.
- Make the development meaning explicit. For each headline quantity, say what it implies for the social, economic, or environmental outcome — the PDR bar is not a clean estimate alone.
Demographic and comparative computation specifics
- Document data version/vintage (e.g., HMD/HFD/WPP release, DHS round), harmonization steps, and any smoothing/graduation applied to rates.
- For projections: report the scenarios, base population, transition-rate assumptions, and sensitivity; tie scenarios to development or policy futures where that is the contribution.
- For cross-country work: be explicit about comparability (definitions, coverage, data quality) before reading a cross-national contrast as a development effect.
Reproducibility while you work (not at the end)
- One master script regenerates every table, figure, life table, decomposition, and projection from the (raw or constructed) data.
- Set and report seeds for bootstrap and simulation.
- Pin software/package versions (
renv.lock,requirements.txt, recordedssc/netinstalls). - Keep table/figure numbers in the manuscript matched to script outputs (see
popdevr-transparency-and-data).
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. PDR is population studies blending quantitative and policy work; apply the chain to its empirical-causal papers.
- 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
- Mismatched numerator/denominator or wrong exposure (the classic demographic error)
- Point estimates of life expectancy, decomposition components, or projections with no uncertainty
- An APC model presented as the uniquely correct partition
- Reading a cross-country correlation as a development effect without addressing comparability
- A results section whose rates and decompositions the code cannot reproduce
Evidence pass for PDR
Run this as a concrete capability pass. First lock the population process, the development/policy linkage, the data and time scale, the selection/measurement issue, and the uncertainty; then test whether the manuscript addresses PDR's broad audience who inspect both the population evidence and its development meaning.
- Primary move: Audit unit, comparison, uncertainty, missingness, sensitivity, comparability, and reproducibility before making any prose or submission recommendation.
- Decision ledger: return
claim / evidence / blocker / next editrows so the next pass can patch the manuscript directly. - Sibling comparison: compare against Demography and Population Studies (methods-forward), Population Research and Policy Review (applied policy), and Studies in Family Planning (programs); if a neighbor has the stronger audience claim, recommend re-routing before polishing.
- Verification floor: before submission-ready advice, re-open
resources/official-source-map.mdfor volatile rules and name the one unresolved fact that could change the recommendation.
Output format
【Main quantity】rate / e0 / decomposition / hazard / projection + magnitude + interval
【Development meaning】what it implies for the social/economic/environmental outcome
【Exposure / denominator check】correctly constructed? [Y/N]
【Decomposition】components defined + sum to total? [Y/N/NA]
【APC / comparability】constraint stated / cross-country comparability addressed? [Y/N/NA]
【Inference】weights/clustering/competing risks handled? [Y/N]
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】popdevr-tables-figures
Supplementary resources
../../resources/external_tools.md— life-table, decomposition, survival, APC, and projection packages../../resources/official-source-map.md— data-availability and reproducibility expectations
Version History
- 1839142 Current 2026-07-05 14:12
