aerj-data-analysis
Use when planning or reporting the analysis for an American Educational Research Journal (AERJ) manuscript — multilevel/HLM and growth models, IRT/measurement, quasi-experimental estimation, or qualitative coding and thematic analysis. Analysis must meet the AERA reporting standards (warrant + transparency). Strengthens analysis reporting; it does not run models for you.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aerj-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 (aerj-data-analysis) AERJ analyses must be **warranted** (adequate evidence for the claim) and **transparent** (explicit logic of inquiry), per the AERA reporting standards. Whatever the method, report enough that a reader can judge — and a replicator could reproduce — the result. ## When to trigger - Specifying the analytic strategy or writing the results section - A reviewer questioned model specification, uncertainty, or coding rigor - Reporting effect sizes, fit, robustness, or qualitative warrant - Reconciling quantitative and qualitative results in a mixed-methods paper ## Quantitative analysis norms - **Respect nesting.** Multilevel/HLM (or cluster-robust) inference for students-in-schools data; report ICC, level-specific predictors, and random effects. Center predictors deliberately (group- vs grand-mean) and say which. - **Report effect sizes and uncertainty**, not just p-values: standardized effects, confidence intervals, and practical significance for education stakes. - **Measurement.** Report reliability and validity evidence; for scales, factor/IRT results; handle measurement error rather than ignoring it. - **Missing data.** State the mechanism assump
- When to trigger
- Quantitative analysis norms
- Qualitative analysis norms
- Mixed-methods integration
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Warrant-and-transparency checklist by method (AERJ referees)
- Worked analysis vignette (illustrative)
- Referee pushback and the AERA-standard fix
- Output format
- Supplementary resources
What does the aerj-data-analysis skill do?
Use when planning or reporting the analysis for an American Educational Research Journal (AERJ) manuscript — multilevel/HLM and growth models, IRT/measurement, quasi-experimental estimation, or qualitative coding and thematic analysis. Analysis must meet the AERA reporting standards (warrant + transparency). Strengthens analysis reporting; it does not run models for you.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aerj-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.