joap-data-analysis
Use when analyzing and reporting results for a Journal of Applied Psychology (JAP) manuscript using SEM, multilevel (HLM) models, mediation/moderation, or meta-analysis. JAP requires effect sizes with confidence intervals, model-based indirect effects with bootstrap CIs, fit indices, full disclosure, and a clean confirmatory/exploratory split. Guides analysis norms; it does not fabricate results.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-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 (joap-data-analysis) JAP analyses must be **model-appropriate, fully reported, and reproducible**. The house toolkit is **SEM/CFA**, **multilevel (HLM) models**, **mediation and moderation** with proper inference, and **meta-analysis**. The journal expects **effect sizes with confidence intervals**, **fit indices**, **bootstrap CIs for indirect effects**, full disclosure of how data were handled, and a clean **confirmatory vs. exploratory** separation, with data and code shareable under TOP. ## When to trigger - Specifying and reporting the main and supporting analyses - A reviewer asked for fit indices, indirect-effect CIs, robustness, or disclosure - Reconciling preregistered analyses with exploratory follow-ups - Preparing analysis scripts and a data/codebook for deposit ## Reporting norms JAP expects 1. **Measurement before structure.** Report the measurement model (CFA fit: χ²/df, CFI, TLI, RMSEA, SRMR) and reliability/AVE before interpreting the structural model; report measurement invariance when comparing groups or waves. 2. **Effect sizes + uncertainty.** Give standardized and/or unstandardized estimates **with confidence intervals** for key paths — not jus
- When to trigger
- Reporting norms JAP expects
- Worked micro-example (illustrative numbers)
- Analysis-stage reviewer pushback and the venue fix
- Calibration anchors
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Output format
- Supplementary resources
What does the joap-data-analysis skill do?
Use when analyzing and reporting results for a Journal of Applied Psychology (JAP) manuscript using SEM, multilevel (HLM) models, mediation/moderation, or meta-analysis. JAP requires effect sizes with confidence intervals, model-based indirect effects with bootstrap CIs, fit indices, full disclosure, and a clean confirmatory/exploratory split. Guides analysis norms; it does not fabricate results.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-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.