isr-data-analysis
Use when executing and reporting the analysis for an Information Systems Research (ISR) manuscript — identification and validity for empirical work, proof discipline and comparative statics for analytical work, and rigorous evaluation for design-science work, with overflow routed to the electronic companion. Runs and reports the analysis; it does not design the study (isr-methods) or frame the contribution (isr-contribution-framing).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill isr-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.
# Analysis, Identification & Proof (isr-data-analysis) ## When to trigger - Data are collected, or the model is built, and it is time to estimate, derive, or evaluate - You are unsure whether your estimator matches the design, or whether a proof is complete - Reviewers will probe identification, measurement validity, or assumption sensitivity - A reviewer says "the analysis does not support the inference" ## Empirical genre — identification and validity first ISR empirical reviewers expect causal claims to rest on a credible **identification strategy**, not on a fitted regression: | Design / claim | Estimator / strategy | |-----------------------------------------------|---------------------------------------------------------------| | Manipulated IT design/policy | Experiment: randomization checks, manipulation/attention checks | | Quasi-experiment, staggered adoption | DiD (modern estimators), event study, parallel-trends evidence | | Endogenous IT investment/adoption (archival) | IV/2SLS, RDD, matching, panel FE with cluster-robust SE | | Latent behavioral constructs | SEM/CFA (fit: CFI/TLI/RMSEA/SRMR), AVE, discriminant validity; PLS-SEM where appropriate | | Nested data (users
- When to trigger
- Empirical genre — identification and validity first
- Analytical genre — proof discipline
- Design-science genre — rigorous evaluation
- Claim-to-evidence ledger
- Reproducibility and the electronic companion
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Output format
What does the isr-data-analysis skill do?
Use when executing and reporting the analysis for an Information Systems Research (ISR) manuscript — identification and validity for empirical work, proof discipline and comparative statics for analytical work, and rigorous evaluation for design-science work, with overflow routed to the electronic companion. Runs and reports the analysis; it does not design the study (isr-methods) or frame the contribution (isr-contribution-framing).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill isr-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.