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.
Scanned 6/5/2026
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---
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.
## 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)
## 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
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