jmr-data-analysis
Use when running and reporting the analysis for a Journal of Marketing Research (JMR) manuscript — selecting the estimator that matches the design, and meeting JMR's hard journal-level reporting mandate of exact p-values, standard errors, and effect sizes, plus replication-ready disclosure. Executes and reports; jmr-methods designs the study and jmr-contribution-framing states the payoff.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmr-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 & Reporting (jmr-data-analysis) ## When to trigger - Data are collected (experimental or observational) and it is time to estimate and report - You are unsure whether your estimator matches your design - You must conform to JMR's exact-statistics reporting rules - A reviewer says "the analysis does not support the inference" or "report effect sizes" ## JMR's hard reporting mandate (journal-level) JMR enforces statistics reporting more explicitly than generic top journals. Empirical papers must report: - **Actual p-values to three digits** — *not* thresholds (no "p < .05"), *not* asterisks. - **Standard errors** of parameter estimates in tables. - **Effect sizes** — and a discussion of practical magnitude, not just significance. AMA results-reporting style: **no leading zero** before the decimal (write `.97`, `p = .032`), and **no more than three decimal places**. Apply this to every table and in-text statistic. ## Choose the estimator that matches the design | Design / claim | Estimator | |-------------------------------------------------|-----------------------------------------------------------------| | Experiment (factorial, between/within) | ANOVA / regression;
- When to trigger
- JMR's hard reporting mandate (journal-level)
- Choose the estimator that matches the design
- Behavioral analysis specifics
- Modeling / econometric specifics
- Result-to-claim ledger
- Replication & robustness (AMA transparency policy)
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Output format
- Resources
What does the jmr-data-analysis skill do?
Use when running and reporting the analysis for a Journal of Marketing Research (JMR) manuscript — selecting the estimator that matches the design, and meeting JMR's hard journal-level reporting mandate of exact p-values, standard errors, and effect sizes, plus replication-ready disclosure. Executes and reports; jmr-methods designs the study and jmr-contribution-framing states the payoff.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmr-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.