jop-research-design

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](../../../shared-resources/empirical-methods/execution-with-mcp.md). 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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).

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

• [../../resources/external_tools.md](../../resources/external_tools.md) — design/identification packages (R/Stata/Python) and CAQDAS for qualitative work
• [../../resources/official-source-map.md](../../resources/official-source-map.md) — JOP methodological diversity and replicability-contingent acceptance