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)

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

• ../../resources/external_tools.md — design/identification packages (R/Stata/Python) and CAQDAS for qualitative work
• ../../resources/official-source-map.md — human-subjects / IRB requirements and submission policy