eursr-data-analysis
Use when executing and reporting the analysis for a European Sociological Review (ESR) manuscript so it survives expert double-blind review — correct multilevel and longitudinal modeling, honest uncertainty, robustness, and small-macro-N inference on comparative survey or register data. Guides analysis norms; it does not fabricate results.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eursr-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 (eursr-data-analysis) ESR reviewers are quantitatively demanding and comparative by instinct. Whether your evidence is multilevel coefficients, hazard ratios, growth trajectories, or decompositions, the analysis must be transparent, correctly specified for the data structure, and reproducible. Design decisions live in `eursr-research-design`; the replication package lives in `eursr-transparency-and-data`. ## When to trigger - Running main and supporting analyses; building the results section - A reviewer asked for robustness, the right clustering/level, heterogeneity, or alternative specifications - Reporting cross-level interactions or country-level effects from few clusters - Making the analysis reproducible before depositing materials ## Analysis norms ESR expects 1. **Report uncertainty and magnitude**, not just significance — confidence intervals and substantive effect sizes (predicted probabilities, marginal effects), respecting survey design (weights, clustering, strata; design-based SEs). 2. **Model the data structure correctly.** Multilevel data → random effects or cluster-robust / country fixed effects with the *right* level; panel data → within estimators
- When to trigger
- Analysis norms ESR expects
- Reproducibility while you work
- What an ESR analyst-reviewer is checking
- Worked micro-example (illustrative numbers)
- Referee pushback → ESR-specific fix
- Calibration anchors
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Output format
- Supplementary resources
What does the eursr-data-analysis skill do?
Use when executing and reporting the analysis for a European Sociological Review (ESR) manuscript so it survives expert double-blind review — correct multilevel and longitudinal modeling, honest uncertainty, robustness, and small-macro-N inference on comparative survey or register data. Guides analysis norms; it does not fabricate results.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eursr-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.