jmis-data-analysis
Use when the estimation, identification, construct validity, or modeling is the bottleneck for a Journal of Management Information Systems (JMIS) manuscript — econometrics on firm/platform data, SEM/PLS for behavioral constructs, analytical-model derivations, or ML/analytics evaluation. Executes and stress-tests the analysis the design (jmis-methods) chose; it does not redesign the study.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmis-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 (jmis-data-analysis) ## When to trigger - A regression "works" but the identifying variation and threats are not pinned down - A survey model is run but reliability, validity, and common-method bias are not established - An ML/analytics artifact reports accuracy but no credible-baseline comparison or robustness - A reviewer says results are "not robust," "not identified," or "could be reverse causality" ## Run the analysis to the JMIS standard for your evidence type JMIS reviewers are method-literate across econometrics, psychometrics, analytical modeling, and data science. The bar is that the analysis credibly supports the verb in your claim. ### Empirical IT-value / platform econometrics - **Identification, made visible.** Show the source of variation (shock/rollout/breach), not just the regression. With staggered timing, use Callaway–Sant'Anna / Sun–Abraham / de Chaisemartin–D'Haultfœuille and report a clean event study with leads; for naive TWFE, run a Goodman-Bacon decomposition to expose contamination. - **Endogeneity head-on.** For IT investment and platform participation, address reverse causality and selection: IV with a defended exclusion restriction and a
- When to trigger
- Run the analysis to the JMIS standard for your evidence type
- Empirical IT-value / platform econometrics
- Behavioral IS (survey / experiment)
- Analytical / economic models
- Design-science / ML artifacts
- Report effects so a manager can read them
- Worked vignette: making robustness answer a threat (illustrative)
- Translate the headline into a managerial number
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Pre-empt the reverse-causality reflex
- Anti-patterns
- Keep the analysis reproducible and self-contained
What does the jmis-data-analysis skill do?
Use when the estimation, identification, construct validity, or modeling is the bottleneck for a Journal of Management Information Systems (JMIS) manuscript — econometrics on firm/platform data, SEM/PLS for behavioral constructs, analytical-model derivations, or ML/analytics evaluation. Executes and stress-tests the analysis the design (jmis-methods) chose; it does not redesign the study.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmis-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.