jcr-data-analysis
Use when running and reporting the evidence for a Journal of Consumer Research (JCR) manuscript — process evidence (mediation/moderation) for behavioral experiments, or trustworthy interpretation for Consumer Culture Theory (CCT) work — plus robustness and the transparency reporting JCR requires. Executes and reports; it does not design the studies (jcr-methods).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jcr-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 & Evidence (jcr-data-analysis) ## When to trigger - Studies are run and it is time to analyze and report - Reviewers will probe whether the data actually support the **process** claim - You need bootstrapped indirect effects, simple-slopes, or spotlight/floodlight analyses - You are reporting interpretive (CCT) evidence and need to defend trustworthiness ## Process evidence is the JCR currency (experiments) For the dominant experimental tradition, JCR reviewers ask whether the data support the **psychological process**, not just the effect: - **Mediation:** report indirect effects with **bias-corrected bootstrap confidence intervals** (e.g., 5,000 resamples). Treat measured-mediator mediation as suggestive; **moderation-of-process** and **manipulated-mediator** designs are stronger and expected for a clean process claim. - **Moderation:** report the interaction term, then **plot simple slopes** with regions of significance; for continuous moderators use **spotlight/floodlight** analysis rather than median splits. - **Effect sizes:** report standardized effects (Cohen's d, eta-squared, or equivalents) and discuss practical magnitude, not just p-values. - **Across stu
- When to trigger
- Process evidence is the JCR currency (experiments)
- Reporting standards and clean inference
- Interpretive (CCT) evidence: trustworthiness, not p-values
- Transparency reporting (JCR-specific)
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Output format
What does the jcr-data-analysis skill do?
Use when running and reporting the evidence for a Journal of Consumer Research (JCR) manuscript — process evidence (mediation/moderation) for behavioral experiments, or trustworthy interpretation for Consumer Culture Theory (CCT) work — plus robustness and the transparency reporting JCR requires. Executes and reports; it does not design the studies (jcr-methods).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jcr-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.