| 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 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
