jbv-data-analysis
Use when running and reporting the analysis for a Journal of Business Venturing (JBV) manuscript — choosing estimators that fit entrepreneurial data (survival/event-history, selection models, panels, experiments, qualitative trustworthiness), handling attrition and endogenous founding, and reporting robustness. Executes and reports the analysis; it does not design the study (jbv-methods) or frame the contribution (jbv-contribution-framing).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jbv-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 (jbv-data-analysis) ## When to trigger - Data are collected and it is time to estimate and report - You are unsure the estimator matches an entrepreneurial-data structure (venture survival, founding choice, nested funding events) - Reviewers will probe survivorship, selection into founding, or endogeneity - A handling editor says "the analysis does not support the inference about entrepreneurship" ## Match the estimator to the entrepreneurial data structure JBV is methodologically pluralistic, so the right tool depends on the claim. Common patterns in new-venture data: | Data structure / claim | Estimator | |---------------------------------------------------------|------------------------------------------------------------------| | Time-to-exit / IPO / failure | Survival / event-history (Cox, parametric AFT, competing risks) | | Choice to found / endogenous selection | Heckman / Roy selection; control function | | Venture panel with unit heterogeneity | Fixed/random effects; cluster-robust SE (`reghdfe`, `fixest`) | | Policy / ecosystem / financing shock | DiD / event study / staggered-adoption estimators | | Counts (patents, funding rounds, ventures) |
- When to trigger
- Match the estimator to the entrepreneurial data structure
- Handle entrepreneurship-specific threats
- Robustness expected by JBV reviewers
- Reporting
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Referee-pushback patterns and the JBV-specific fix
- Worked micro-example (illustrative numbers)
- Calibration anchors (hedged)
- Anti-patterns
- Output format
What does the jbv-data-analysis skill do?
Use when running and reporting the analysis for a Journal of Business Venturing (JBV) manuscript — choosing estimators that fit entrepreneurial data (survival/event-history, selection models, panels, experiments, qualitative trustworthiness), handling attrition and endogenous founding, and reporting robustness. Executes and reports the analysis; it does not design the study (jbv-methods) or frame the contribution (jbv-contribution-framing).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jbv-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.