orgstud-data-analysis
Use when executing and reporting the analysis for an Organization Studies (OS) manuscript — qualitative coding and the data-to-theory ladder, process analysis, or quantitative estimation and robustness. Makes the evidence-to-theory link transparent; it does not design the study (see orgstud-methods).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill orgstud-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 (orgstud-data-analysis) ## When to trigger - You have data but the path from raw material to theory is opaque - Qualitative: your quotes are decorative, not evidentiary; the coding is undocumented - Process: you have events but no visible analytic structure turning them into a model - Quantitative: main results exist but robustness and alternative explanations are thin - A reviewer asks "how did you get from your data to these constructs?" ## OS expects readers to *see* how data became theory OS's interpretive, European tradition makes **analytic transparency** a first-class criterion — qualitative rigor is judged on its own terms, not against a quantitative yardstick. The reader must be able to *audit the inference* from raw data to theoretical claim. Make the analytic ladder visible. ## Branch A — Qualitative analysis (the data-to-theory ladder) - **Transparent coding.** Show first-order codes (informant terms), second-order themes (researcher constructs), and aggregate dimensions — the **Gioia data structure** — or an equivalent (Eisenhardt cross-case tables, Langley process bracketing). State who coded, how disagreements were resolved, and how iterati
- When to trigger
- OS expects readers to see how data became theory
- Branch A — Qualitative analysis (the data-to-theory ladder)
- Branch B — Process analysis (when the contribution is a process model)
- Branch C — Quantitative analysis
- Either branch — the "so what" of the evidence
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Output format
What does the orgstud-data-analysis skill do?
Use when executing and reporting the analysis for an Organization Studies (OS) manuscript — qualitative coding and the data-to-theory ladder, process analysis, or quantitative estimation and robustness. Makes the evidence-to-theory link transparent; it does not design the study (see orgstud-methods).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill orgstud-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.