| name | ajps-research-design |
| description | Use when defending the research design of an American Journal of Political Science (AJPS) manuscript — causal identification for observational work, experimental and survey-experimental design, formal-empirical linkage, or case-based inference. AJPS reviewers are quantitatively demanding, so identification must license the claim being made. Strengthens the design; it does not write code. |
Research Design (ajps-research-design)
AJPS publishes many methods but holds identification and inference to a high standard. The design must
credibly connect the argument (ajps-theory-building) to evidence and rule out the strongest rival.
This skill is mode-aware: pick the section that matches your work and defend it on its own terms.
When to trigger
- Specifying identification, sampling, case selection, or experimental design
- A reviewer questioned causal claims, a confound, external validity, or inference
- Preparing a pre-analysis plan before collecting/analyzing data
- Justifying why your design adjudicates the rival from
ajps-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; do not 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; small-cluster corrections (wild-cluster bootstrap) when clusters are few.
- Sensitivity: how strong must an unobserved confounder be to overturn the result?
Experiments (lab / survey / field)
- Preregister the design and primary analyses; report power / MDE; pre-specify subgroups.
- Address attention/manipulation checks, attrition, balance, and ethics/IRB and consent (the AJPS
submission portal asks for human-subjects documentation — see
ajps-submission).
- For survey experiments: sampling frame, treatment realism, and the limits on generalization.
Formal-empirical linkage
- Make the empirical test follow from the model's comparative statics, not a loose analogy.
- Distinguish predictions unique to your model from those shared with rivals, and test the unique ones.
Case-based / qualitative & multi-method
- Case selection justified by design logic (typical, deviant, most/least-likely, paired) — 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; plan source documentation (see
ajps-replication-and-verification, qualitative path).
The adjudication test (AJPS-specific)
For the single strongest rival explanation, write: "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.
Design-credibility table (the bar AJPS referees apply by design)
| Design | What the referee demands | Common desk-reject / reject trigger |
|---|
| RDD | Density/manipulation test, bandwidth robustness, no sorting at cutoff | Treating a non-discontinuous threshold as sharp |
| DID / event study | Modern staggered-adoption estimator, pre-trend evidence | Naive TWFE with heterogeneous timing |
| IV | First-stage strength, defended exclusion, weak-IV-robust CIs | "Plausibly exogenous" instrument with no defense of exclusion |
| Matching/weighting | Balance + unobserved-confounder sensitivity bound | Selection-on-observables read as clean causation |
Worked micro-example (illustrative numbers)
A close-election RD on incumbency states the estimand (local effect of barely winning on next-cycle vote
share at the threshold) and the continuity assumption that licenses it. The density test shows no sorting
(illustrative p = 0.62); the estimate is stable across bandwidths h = 0.08-0.16; a donut-hole spec holds.
The adjudication sentence: if incumbency advantage were candidate-quality persistence rather than an
officeholding effect, the jump at the bare-win threshold would vanish — instead it is +6 points
(illustrative). That sentence converts a quantitatively demanding AJPS referee.
Referee-pushback patterns and the venue-specific fix
- "Identification leans on selection-on-observables." -> Add an Oster-style or sensitivity-bound analysis
and report how strong an unobserved confounder must be to overturn the result.
- "Theory and empirics are not tightly linked." -> Make the test follow from the model's comparative
statics and target a prediction unique to your argument, not one shared with the rival.
- "The DID uses naive TWFE under staggered adoption." -> Re-estimate with a heterogeneity-robust
estimator and show the event-study leads are flat.
Calibration anchor: AJPS spans American, comparative, IR, theory, and methods, but applies a hard premium
on credible identification across all of them; confirm any human-subjects/IRB specifics against the
journal's current submission guidelines.
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. AJPS prizes credible identification across American / comparative / IR subfields; DiD/IV/RDD for observational claims, randomization inference for experiments.
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 supports only association
- Convenience case selection dressed up as theory-driven
- Survey/conjoint 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 / experiment / formal-empirical / case-based
【Estimand or claim】what is being identified/shown
【Key assumption(s)】and how each is defended
【Rival ruled out】the adjudication sentence
【Inference】clustering / RI / small-cluster correction
【Robustness/sensitivity】planned checks
【Next】ajps-data-analysis
Supplementary resources
