Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› apsr-research-design
apsr-research-design
GitHub用于辩护APSR稿件的研究设计，涵盖定量因果推断、定性案例选择与过程追踪、实验设计及形式-经验关联。针对审稿人质疑提供方法论辩护，强调识别策略、敏感性分析及对最强替代解释的证伪测试。
触发场景
指定识别策略、案例选择或实验设计
审稿人质疑因果主张、案例选择、外部有效性或混淆因素
准备预分析计划或注册报告第一阶段设计
论证设计如何裁决来自文献定位技能的竞争理论
安装
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill apsr-research-design -g -y
SKILL.md
Frontmatter
{
"name": "apsr-research-design",
"description": "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."
}
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 withas_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_wolffor many-outcome family-wise control, andmediatefor mediation (not naive controlling-away). - Sensitivity:
oster_delta/sensemakrfor 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
../../resources/external_tools.md— design/identification packages (R/Stata/Python) and CAQDAS for qualitative work../../resources/official-source-map.md— preregistration and Registered Reports notes
版本历史
- 1839142 当前 2026-07-05 12:21
