apsr-data-analysis
GitHub指导APSR稿件的数据分析与报告规范，确保通过双盲评审。涵盖诚实报告不确定性、稳健性检验、异质性分析、正确推断及预注册纪律。强调代码可复现性、版本控制及计算文本数据的验证，避免结果造假，满足编辑重跑代码的要求。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill apsr-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "apsr-data-analysis",
"description": "Use when executing and reporting the analysis for an American Political Science Review (APSR) manuscript so it survives expert, double-anonymous review — honest uncertainty, robustness, and triangulation appropriate to quantitative, experimental, or computational work. Guides analysis norms; it does not fabricate results."
}
Data Analysis (apsr-data-analysis)
APSR reviewers are methodologically sophisticated and the editorial office will later re-run your
code against the manuscript's tables and figures (see apsr-transparency-and-data-policy). Analyze
as if both are true — because they are. This skill covers execution and reporting norms; design
decisions live in apsr-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 APSR 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.
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.
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 editors will check.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. APSR is general-interest political science — observational causal designs (DiD/IV/RDD) and survey/field experiments alike; cluster by the right unit and foreground identification.
- 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
- A results section whose numbers the 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?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】apsr-tables-figures
What APSR reviewers probe, by analytic tradition
APSR is the flagship of the American Political Science Association, published by Cambridge University Press, and its reviewers are drawn from across the discipline — so the same results section can be read by a formal theorist, a survey methodologist, and a comparativist at once. Calibrate the analysis to whichever lens is decisive, but expect all three to be in the room.
| Analytic tradition | The check an APSR referee runs first | The fix that earns the benefit of the doubt |
|---|---|---|
| Survey / lab experiment | Is inference randomization-based and pre-registered? | Randomization inference, pre-registered estimand, MDE reported |
| Observational causal | Is the "causal" word doing more than the design licenses? | State estimand + assumption; sensitivity to an unobserved confounder |
| Text-as-data / computational | Was the model validated against human labels? | Held-out validation set, stability across seeds, version pinned |
| Formal-empirical | Do the tests follow comparative statics, or a loose analogy? | Map each prediction to a parameter the model moves |
| Multi-method | Do quant and qual estimates actually corroborate? | Show where they agree, and own where they diverge |
Worked micro-example (illustrative numbers)
A hypothetical APSR survey experiment tests whether co-partisan endorsements raise support for a redistricting reform. The pre-registered ATE is +6.2 points (95% CI 3.1 to 9.3) on a 0–100 support scale, randomization-inference p = 0.004. The exploratory subgroup "low political-knowledge respondents" shows +11.8 points, but it was not pre-registered and the interaction p = 0.04 before any multiplicity correction — after a Bonferroni adjustment across the six exploratory subgroups it crosses 0.20. The disciplined write-up reports the +6.2 confirmatory effect with its interval and substantive meaning, flags the +11.8 figure as exploratory and not multiplicity-robust, and frames it as a hypothesis for future work rather than a finding. (All numbers illustrative.)
Referee-pushback patterns and the APSR-specific repair
- "This reads as a subfield result, not a general one." → Re-anchor the estimate to a discipline-wide stake (representation, accountability, institutional design) 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 did not.
- "Theory and empirics are loosely coupled." → Tie each estimate back to an observable implication the argument named in advance, not to a pattern noticed afterward.
- "I cannot tell registered from exploratory analyses." → Segregate them explicitly; the editorial office will later re-run the deposited code, so the split must survive verification.
Calibration anchors (hedged)
- The bar is general-political-science significance, not within-subfield novelty: an effect that only a specialist would value rarely clears APSR review.
- APSR practices methodological pluralism — a rigorous qualitative or formal analysis is not second-class to a regression. Match the inference standard to the design.
- Transparency expectations follow DA-RT / APSR Dataverse norms: write the analysis so a conditionally-accepted package reproduces every printed number. Exact deposit mechanics can change — confirm against the journal's current submission and transparency guidelines.
Supplementary resources
../../resources/external_tools.md— estimation, inference, and text-as-data packages../../resources/official-source-map.md— reproducibility-verification policy
Version History
- 1839142 Current 2026-07-05 12:21
