jmgmt-data-analysis
Use when estimation and results are the bottleneck for a Journal of Management (JOM) manuscript — SEM/CFA, HLM/multilevel, regression and interactions, mediation/moderation, and meta-analytic estimation with artifact corrections. Runs and validates the analysis; it does not design the study (jmgmt-methods) or frame the contribution (jmgmt-contribution-framing).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmgmt-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 (jmgmt-data-analysis) ## When to trigger - The model is fit but a reviewer questions the measurement model or fit indices - Mediation/moderation is tested in a way a JOM methods reviewer would challenge - Nested data are being analyzed without modeling the nesting - A meta-analysis needs the right estimator, corrections, and heterogeneity diagnostics - Results are reported with significance asterisks and no effect sizes ## The JOM analysis bar JOM houses some of the field's leading **research-methods** scholars and runs methods reviews, so analysis is read by an unusually demanding audience. The expectation is a **transparent measurement model before the structural model**, **effect sizes and confidence intervals** alongside tests (not p-stars alone), and analysis choices that match the level and design set in `jmgmt-methods`. Report enough that the analysis is reconstructable from the paper and the (anonymized) data transparency table. ## Branch paths ### Branch A: SEM / CFA (latent-variable micro models) - Report the **measurement model first**: standardized loadings, reliability (composite reliability/ω, not only α), AVE, and a **discriminant-validity** check (AV
- When to trigger
- The JOM analysis bar
- Branch paths
- Branch A: SEM / CFA (latent-variable micro models)
- Branch B: Multilevel / HLM (nested data)
- Branch C: Regression / interactions (archival or single-level)
- Branch D: Meta-analysis
- Robustness the JOM audience expects
- Worked vignette (illustrative)
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Reproducibility under masked review
- Anti-patterns
- Output format
What does the jmgmt-data-analysis skill do?
Use when estimation and results are the bottleneck for a Journal of Management (JOM) manuscript — SEM/CFA, HLM/multilevel, regression and interactions, mediation/moderation, and meta-analytic estimation with artifact corrections. Runs and validates the analysis; it does not design the study (jmgmt-methods) or frame the contribution (jmgmt-contribution-framing).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmgmt-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.