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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.

Public-Opinion-Quarterly-Skills/skills/poq-data-analysis/SKILL.md

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# 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
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