jm-data-analysis
Use when running and reporting the statistical analysis for a Journal of Marketing (JM) manuscript — the right estimator for a big-tent design, JM's exact p-value / standard-error / effect-size reporting mandate, identification and robustness, and the JM Dataverse replication packet. Executes and reports the analysis; it does not design the study (jm-methods) or frame the contribution (jm-contribution-framing).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jm-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 (jm-data-analysis) ## When to trigger - Data are collected and it is time to estimate and report - You are unsure your estimator matches a big-tent design (experiment, panel, choice, qualitative) - A reviewer will probe identification, robustness, or whether the effect is *managerially* meaningful - You reached conditional acceptance and must assemble the JM Dataverse replication packet ## JM's hard reporting mandate (non-negotiable) JM's submission rules bake in statistical transparency. Empirical papers **must report**: - **Actual p-values** — not thresholds such as "p < .05" or stars-only tables. - **Standard errors** for estimates. - **Effect sizes** — the *magnitude* of the effect, because JM judges substantive and managerial importance, not mere significance. Report these throughout the main text and tables. A results section that shows significance without magnitude fails JM's substantive bar: an effect that is "significant" but trivially small rarely changes a managerial decision. ## Choose the estimator that matches the design | Design / claim | Estimator | |---------------------------------------------------|------------------------------------
- When to trigger
- JM's hard reporting mandate (non-negotiable)
- Choose the estimator that matches the design
- Identification, mechanism, and robustness
- Effect size in managerial units
- JM Dataverse replication packet (at conditional acceptance)
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Output format
What does the jm-data-analysis skill do?
Use when running and reporting the statistical analysis for a Journal of Marketing (JM) manuscript — the right estimator for a big-tent design, JM's exact p-value / standard-error / effect-size reporting mandate, identification and robustness, and the JM Dataverse replication packet. Executes and reports the analysis; it does not design the study (jm-methods) or frame the contribution (jm-contribution-framing).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jm-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.