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Wp Data Analysis

ASecurity

Use when executing and reporting the analysis for a World Politics manuscript so it survives expert triple-blind review and the Dataverse replication requirement — honest uncertainty, robustness, and triangulation appropriate to comparative cross-national, qualitative, or formal-empirical work. Guides analysis norms; it does not fabricate results.

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Added 6/5/2026
ai-agents

Security Analysis

A100/100

Scanned 6/5/2026

Install to Claude Code

$npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill wp-data-analysis --agent claude-code

Installs into .claude/skills of the current project.

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SKILL.md
---
name: wp-data-analysis
description: Use when executing and reporting the analysis for a World Politics manuscript so it survives expert triple-blind review and the Dataverse replication requirement — honest uncertainty, robustness, and triangulation appropriate to comparative cross-national, qualitative, or formal-empirical work. Guides analysis norms; it does not fabricate results.
---

# Data Analysis (wp-data-analysis)

World Politics reviewers are methodologically demanding, and authors who **rely on quantitative data
must deposit replication materials in the World Politics Dataverse** that let others reproduce the
**exact numerical results** (see `wp-transparency-and-data-policy`). Analyze as if both are true —
because they are. This skill covers execution and reporting norms; design decisions live in
`wp-research-design`.

## When to trigger

- Running main and supporting analyses; building the results/findings section
- A reviewer asked for robustness, heterogeneity, or alternative specifications
- Reconciling the cross-case pattern with within-case evidence (mixed methods)
- Making the analysis reproducible before deposit

## Analysis norms World Politics expects

1. **Report uncertainty honestly.** Confidence/credible intervals, not just stars; the magnitude and
   substantive meaning of the estimate across cases, not just its significance.
2. **Robustness that probes, not decorates.** Show specifications that could *break* the result
   (alternative measures of regime/institution/conflict, alternative samples of cases, estimators,
   fixed effects), and say what you learn.
3. **Cross-national inference.** Cluster at the appropriate level (often country); address serial
   correlation and cross-sectional dependence in TSCS; small-N panels need honest few-cluster
   corrections (e.g., wild-cluster bootstrap).
4. **Measurement that travels.** Validate constructs across cases; report reliability; show results are
   not an artifact of one coding/scaling choice or one source (V-Dem vs. Polity, COW vs. UCDP).
5. **Heterogeneity with discipline.** Pre-specify subgroups/regions where possible; correct for
   multiple comparisons; do not mine for a significant interaction and theorize it post hoc.
6. **Triangulation.** Where the design is mixed-method, show the within-case process evidence and the
   cross-case statistics point the same way, and reconcile where they don't.

## Qualitative / comparative-historical specifics
- Make the **evidentiary basis explicit** — which sources support which inferential step; link claims
  to documents/interviews via evidence tables (see `wp-tables-figures`).
- For process tracing, report the tests passed/failed and what would have disconfirmed the argument.

## Reproducibility while you work (not at the end)
- One **master script** regenerates every table and figure from the (raw or constructed) data.
- **Set and report seeds** for bootstrap, randomization, and any stochastic step.
- Pin software/package versions (`renv.lock`, `requirements.txt`, recorded `ssc`/`net` installs).
- Keep table/figure numbers matched to script outputs — the Dataverse package must reproduce them.

## Anti-patterns

- Stars-only tables with no effect sizes or intervals
- "Robustness" that only reruns near-identical specs to manufacture stability
- Results that hinge on one data source or one coding choice without showing alternatives
- Clustering at the wrong level; ignoring TSCS serial correlation / cross-sectional dependence
- A findings section whose numbers the deposited code cannot reproduce

## Output format

```
【Main estimate】magnitude + interval + substantive meaning across cases
【Identification check】(per research-design) result
【Robustness】specs / alternative sources that could break it → what held
【Measurement】construct validated across cases? source sensitivity shown?
【Triangulation】within-case + cross-case agree? reconciled?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】wp-tables-figures
```

## Supplementary resources

- [`../../resources/external_tools.md`](../../resources/external_tools.md) — estimation, TSCS/panel, survival, and text-as-data packages
- [`../../resources/official-source-map.md`](../../resources/official-source-map.md) — Dataverse replication requirement

Attribution

brycewang-stanfordbrycewang-stanford
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3 founder slots · $299/mo · GSC-verified traffic · sponsors can never buy grades.

See placements

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