jais-data-analysis
Use when running and reporting the empirical core of a Journal of the Association for Information Systems (JAIS) manuscript — SEM measurement and structural models for behavioral IS, identification and robustness for economics-of-IS, artifact evaluation for design science, or trustworthiness for qualitative work — and assembling the data/matrix materials JAIS requires. Executes and reports the analysis; it does not design the study (jais-methods) or frame the contribution (jais-contribution-framing).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jais-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 & Evidence (jais-data-analysis) ## When to trigger - Data are collected (or the artifact built) and it is time to estimate, evaluate, and report - A reviewer probes measurement validity, identification, artifact utility, or replicability - You must prepare the **correlation/covariance matrix plus descriptives** JAIS requires for quantitative studies - Effects are reported as stars with no magnitude or theoretical meaning ## Analyze in the currency of your tradition JAIS's pluralism means there is no single mandated estimator; the standard is the rigor norm of your tradition, reported transparently enough for a developmental Senior Editor to interrogate. Pick the row. | Tradition | What to report | |-----------|----------------| | **Behavioral** | reliability (alpha/CR), CFA or PLS measurement model, AVE, discriminant validity (Fornell-Larcker / HTMT); structural paths with effect sizes; mediation via bootstrap CIs; moderation via simple slopes | | **Economics of IS** | the identifying variation, parallel-trends/exogeneity evidence, clustered SEs, and a robustness battery (alternative specs, placebo/event-time tests, sensitivity to the key assumption) | | **Design sc
- When to trigger
- Analyze in the currency of your tradition
- Behavioral IS: defend measurement, then satisfy the JAIS matrix rule
- Economics of IS: make the causal claim earn its keep
- Report robustness as a defense of the theory, not a ritual
- Design science: evaluate the artifact, not just the math
- Qualitative: make the path from data to theory traceable
- Tie every result back to the theory
- Prepare the JAIS data materials
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Referee pushback mapped to the analysis fix
- Worked vignette: the SEM submission that stalls on transparency (illustrative)
- Anti-patterns
What does the jais-data-analysis skill do?
Use when running and reporting the empirical core of a Journal of the Association for Information Systems (JAIS) manuscript — SEM measurement and structural models for behavioral IS, identification and robustness for economics-of-IS, artifact evaluation for design science, or trustworthiness for qualitative work — and assembling the data/matrix materials JAIS requires. Executes and reports the analysis; it does not design the study (jais-methods) or frame the contribution (jais-contribution-framing).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jais-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.