jom-data-analysis
Use when running and reporting the statistical analysis for a Journal of Operations Management (JOM) empirical manuscript — measurement validity for survey constructs, identification and endogeneity for archival operations data, manipulation checks for behavioral-OM experiments, and robustness. Executes and reports the analysis; it does not design the study (jom-methods) or frame the contribution (jom-contribution-framing).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jom-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 & Validity for Empirical OM (jom-data-analysis) ## When to trigger - Operations data are collected and it is time to estimate and report - You are unsure whether your estimator matches your design (survey constructs, archival panel, experiment, multilevel/nested plants) - Reviewers (and the Empirical Research Methods Department) will probe measurement, common-method bias, or endogeneity - A decision letter says "the analysis does not support the operational inference" ## Establish measurement before estimation (survey/behavioral) For survey-based OM constructs, defend the measurement model first: - **Reliability:** Cronbach's alpha and/or composite reliability for each multi-item operations scale. - **CFA:** report fit (CFI, TLI, RMSEA, SRMR) and show the hypothesized factor structure beats plausible alternatives (one-factor, combined-factor). - **Convergent & discriminant validity:** AVE per construct; AVE > inter-construct squared correlations (or HTMT). Report the correlation matrix with reliabilities on the diagonal. - **Aggregation** (plant/team level): justify with ICC(1), ICC(2), r_wg(j) before aggregating respondents. - **Qualitative/IBR:** establish trustwo
- When to trigger
- Establish measurement before estimation (survey/behavioral)
- Choose the estimator that matches the design
- Common-method bias (survey OM)
- Endogeneity (archival OM)
- Robustness
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Reporting thresholds the Empirical Research Methods reviewers probe
- Desk-reject and method-check failure patterns
- Worked vignette: endogeneity in an operational-practice regression
- Analysis objections reviewers raise, with the fix
- Output format
What does the jom-data-analysis skill do?
Use when running and reporting the statistical analysis for a Journal of Operations Management (JOM) empirical manuscript — measurement validity for survey constructs, identification and endogeneity for archival operations data, manipulation checks for behavioral-OM experiments, and robustness. Executes and reports the analysis; it does not design the study (jom-methods) or frame the contribution (jom-contribution-framing).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jom-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.