poq-data-analysis
Use when executing and reporting the analysis for a Public Opinion Quarterly (POQ) manuscript so it survives expert, double-blind review — design-based inference that respects survey weights, strata, and clusters, honest uncertainty, robustness, and reproducibility. POQ verifies that code reproduces every table and figure. Guides analysis norms; it does not fabricate results.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill poq-data-analysis --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.
# Data Analysis (poq-data-analysis) POQ reviewers are methodologically sophisticated, and the journal requires replication materials that **reproduce exactly all published tables and figures** (see `poq-transparency-and-data-policy`). Analyze as if both are true — because they are. The defining POQ demand is **design-based inference**: survey weights, strata, and clusters belong in the variance estimator, not just the point estimate. Design decisions live in `poq-survey-design-and-measurement`. ## When to trigger - Running main and supporting analyses; building the results section - A reviewer asked for design-based SEs, robustness, or alternative weighting - Reconciling preregistered vs. exploratory analyses - Making the analysis reproducible before deposit ## Analysis norms POQ expects 1. **Design-based inference.** Use complex-survey estimators (`svy:` / `survey` / `samplics`); declare weights, strata, and PSUs. Report the **design effect (DEFF)**; do not present naive IID standard errors on a clustered, weighted sample. 2. **Report uncertainty honestly.** Confidence intervals, not just stars; the magnitude and substantive meaning of the estimate. For opinion shares, show the ma
- When to trigger
- Analysis norms POQ expects
- Missing data, nonresponse & trends
- Reproducibility while you work (not at the end)
- POQ replication acceptance gate
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Output format
- Supplementary resources
What does the poq-data-analysis skill do?
Use when executing and reporting the analysis for a Public Opinion Quarterly (POQ) manuscript so it survives expert, double-blind review — design-based inference that respects survey weights, strata, and clusters, honest uncertainty, robustness, and reproducibility. POQ verifies that code reproduces every table and figure. Guides analysis norms; it does not fabricate results.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill poq-data-analysis --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.