joap-study-design
Use when designing studies and measurement for a Journal of Applied Psychology (JAP) manuscript so they meet the journal's high bar on construct validity, causal inference, common-method variance, nested/multilevel data, and sample-size justification. Strengthens the design and measurement plan; it does not write code.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-study-design --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.
# Study Design & Measurement (joap-study-design) JAP holds measurement and design to an exacting standard. The recurring killers are **common-method variance (CMV)**, **weak causal warrants** (cross-sectional single-source data), **unmodeled nesting**, and **construct validity** gaps. This skill hardens the design *before* data collection, where most of these problems can actually be solved. ## When to trigger - Planning a study, a multi-study package, or a measurement strategy - Writing a preregistration / pre-analysis plan - A reviewer questioned CMV, causal inference, measurement, nesting, or power - Justifying sample size at the relevant level of analysis ## Design standards 1. **Construct validity first.** Use validated measures; report reliability and, where the construct is new or contested, provide validity evidence (CFA, convergent/discriminant, measurement invariance across groups/time). A weak measure dooms an otherwise good design. 2. **Earn the causal claim.** Cross-sectional single-source correlation rarely suffices. Strengthen with **temporal separation** (multi-wave), **multiple sources** (self + supervisor + objective), **experimental or quasi-experimental** legs,
- When to trigger
- Design standards
- Common-method variance — the JAP design playbook
- Sample-size justification — worked example (illustrative)
- Pre-data lockdown checklist
- Design-stage reviewer pushback and the venue fix
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Output format
- Supplementary resources
What does the joap-study-design skill do?
Use when designing studies and measurement for a Journal of Applied Psychology (JAP) manuscript so they meet the journal's high bar on construct validity, causal inference, common-method variance, nested/multilevel data, and sample-size justification. Strengthens the design and measurement plan; it does not write code.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-study-design --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.