jop-research-design
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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jop-research-design --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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/we
- When to trigger
- Quantitative / causal inference
- Experiments (lab / survey / field)
- Formal-empirical linkage
- Qualitative / case-based
- The adjudication test
- Design for replicability (JOP-specific)
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Identification objections and the JOP-credible answer
- Worked micro-example (illustrative)
- Referee pushback patterns and the JOP fix
- Output format
- Supplementary resources
What does the jop-research-design skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jop-research-design --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.