| name | jop-research-design |
| description | Use when defending the research design of a The Journal of Politics (JOP) manuscript — causal identification for quantitative work, experimental and survey-experimental design, formal-empirical linkage, or case selection and process tracing for qualitative work. JOP is methodologically diverse and makes acceptance contingent on replicability, so design with reproducibility in mind. Strengthens the design; it does not write code. |
Research Design (jop-research-design)
JOP is methodologically diverse and demanding about each tradition. The design must credibly connect
the argument (jop-theory-building) to evidence — and, because acceptance is contingent on
replicability, it must be one a JOP replication analyst can re-run. This skill is mode-aware: pick
the section matching your work and defend it against the strongest alternative.
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
- Justifying why your design adjudicates the rival account from
jop-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?
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 unique to your model from those shared with rivals.
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: plan how archives, interviews, and fieldnotes will be documented and cited
(see
jop-replication-and-data-policy).
The adjudication test
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.
Design for replicability (JOP-specific)
- Choose estimators and software you can fully script — the analyst re-runs your code.
- Fix the analysis plan so results are not a moving target between drafts.
- Keep the design within the page budget: defend it crisply in the main text, push diagnostics to
the Online Appendix.
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. Journal of Politics spans observational and experimental political science; report the identifying assumption and the magnitude, not just stars.
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
- A design whose results cannot be regenerated by a clean script (fails the replication check)
Identification objections and the JOP-credible answer
A JOP referee asks the same question of every empirical design: would the result survive if the cleanest
rival story were true? Match the objection to the design move that closes it; keep diagnostics in the
Online Appendix.
| Referee objection | The design answer |
|---|
| "Leans on selection-on-observables" | Move to a counterfactual design (DID, IV, RDD) or show E-value/Oster bounds |
| "Parallel trends is asserted, not shown" | Event-study pre-trends plot; heterogeneity-robust estimator |
| "Exclusion restriction is doubtful" | Argue exclusion substantively; report first-stage F and weak-IV CIs |
| "Case selection is convenience" | Justify by design logic; say what the case is a case of |
Worked micro-example (illustrative)
A hypothetical paper claims a new transparency law cut local corruption, identifying off staggered
adoption across municipalities. A referee objects that early adopters were already cleaning up — a
selection story. The author answers structurally: an event study shows flat pre-trends, a
Callaway–Sant'Anna estimate gives a −0.6 SD drop, 95% CI [−1.0, −0.2] (illustrative), and an Oster bound
shows an unobserved confounder would need to be 1.7× the observed covariates to null the effect
(illustrative).
Referee pushback patterns and the JOP fix
- "Your causal claim outruns the design." Either downgrade the language to association or add the design
feature (counterfactual, instrument, discontinuity) that licenses the causal reading.
- "This is not reproducible." Choose estimators you can fully script; the analyst re-runs the code, so a
bespoke hand-tuned procedure is a liability.
Output format
【Mode】quant-causal / experiment / formal-empirical / qualitative
【Estimand or claim】what is being identified/shown
【Key assumption(s)】and how each is defended
【Rival ruled out】the adjudication sentence
【Replicable?】fully scriptable for the JOP analyst? [Y/N]
【Next】jop-data-analysis
Supplementary resources
