Individual skill
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.
European-Sociological-Review-Skills/skills/eursr-data-analysis/SKILL.md
Source commit 932eb23331721b16e967c59f093781be1928b498
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,402
- Repository last updated
- Sep 27, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- academic-writing Repository topic
- agent-skills Repository topic
- journal Repository topic
- academic-research Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- awesome-list Repository topic
- claude Repository topic
- claude-code Repository topic
- economics Repository topic
- data Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 8,045 bytes
Read excerpt
# 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
