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Apsr research design

Skill brycewang-stanford/Awesome-Journal-Skills/American-Political-Science-Review-Skills/skills/apsr-research-design

Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的 Claude Code/Codex 期刊技能包，从选题、识别策略到表格规范与审稿回复全流程，助你快速发论文。

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Use when defending the research design of an American Political Science Review (APSR) manuscript — causal identification for quantitative work, case selection and process tracing for qualitative work, experimental and survey-experimental design, or formal-empirical linkage. APSR judges each tradition on its own terms. Strengthens the design; it does not write code.

SKILL.md

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Research Design (apsr-research-design)

APSR accepts many methodologies but is demanding about each. The design must credibly connect the argument (apsr-theory-building) to evidence. This skill is mode-aware: pick the section that matches your work and defend it against the strongest alternative explanation.

When to trigger

  • Specifying identification, case selection, or experimental design
  • A reviewer questioned causal claims, case choice, external validity, or a confound
  • Preparing a pre-analysis plan or a Registered Report Stage 1 design
  • Justifying why your design adjudicates the rival account from apsr-literature-positioning

Quantitative / causal inference

  • Identification first. State the estimand and the assumptions that license a causal reading (ignorability, parallel trends, exclusion, continuity). Defend them, don't assert them.
  • Designs: experiments (incl. survey/conjoint), DID/event study (use modern staggered-adoption estimators, not naive TWFE), IV (first-stage strength, exclusion, weak-IV-robust inference), RDD (density/manipulation tests, bandwidth robustness), matching/weighting with balance + sensitivity.
  • Inference: cluster at the level of treatment assignment; randomization inference for experiments; multiple-comparison adjustment when testing many implications.
  • Sensitivity: how strong must an unobserved confounder be to overturn the result?

Qualitative / case-based

  • Case selection justified by design logic (typical, deviant, most/least-likely, paired comparison) — not convenience. Say what the case is a case of.
  • Process tracing with explicit tests (hoop, smoking-gun, straw-in-the-wind); state what evidence would have disconfirmed the argument.
  • Source transparency: archives, interviews, fieldnotes — plan how they will be documented and cited (see apsr-transparency-and-data-policy).

Experiments (lab / survey / field)

  • Preregister the design and primary analyses; report power/MDE; pre-specify subgroups.
  • Address attention/manipulation checks, attrition, and ethics/IRB and consent.
  • For survey experiments: sampling frame, treatment realism, and generalization claims.

Formal-empirical linkage

  • Make the empirical test follow from the model's comparative statics, not a loose analogy.
  • Distinguish predictions that are unique to your model from those shared with rivals.

The adjudication test (APSR-specific)

For the single strongest rival explanation, write one sentence: "If the rival were true rather than my argument, the data would look like ___; instead they look like ___." If you cannot, the design does not yet identify the contribution.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe 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.

  • detect_design → recommend → fit with as_handle=true → audit_result.
  • Observational causal claims: staggered DiD (callaway_santanna / sun_abraham + bacon_decomposition + honest_did_from_result); IV (effective_f_test + anderson_rubin_ci); RDD (rdrobust + mccrary_test).
  • Experiments: randomization-based inference, romano_wolf for many-outcome family-wise control, and mediate for mediation (not naive controlling-away).
  • Sensitivity: oster_delta / sensemakr for observational claims.

Report the effect size in interpretable units; route the full battery to the appendix/supplement. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.

Anti-patterns

  • Naive TWFE on staggered treatment; clustering at the wrong level
  • "Causal" language on a design that only supports association
  • Convenience case selection dressed up as theory-driven
  • Conjoint/survey experiments over-generalized to real-world behavior with no caveat
  • A design that cannot distinguish your argument from the leading alternative

Output format

【Mode】quant-causal / qualitative / experiment / formal-empirical
【Estimand or claim】what is being identified/shown
【Key assumption(s)】and how each is defended
【Rival ruled out】the adjudication sentence
【Robustness/sensitivity】planned checks
【Next】apsr-data-analysis

Supplementary resources

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