orgsci-data-analysis
Use when analyzing and reporting results for an Organization Science manuscript across its eclectic methods — establishing trustworthiness for qualitative work, the right estimator for quantitative/multilevel data, transparency for simulation/formal models, and mechanism-supported inference where causal identification is impossible. Executes and reports the analysis; it does not design the study (orgsci-methods) or frame the contribution (orgsci-contribution-framing).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill orgsci-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 & Transparency (orgsci-data-analysis) ## When to trigger - Data are collected (or a model built) and it is time to analyze and report - A reviewer says "the analysis does not support the inference" - You are unsure how to demonstrate rigor for qualitative, computational, or formal work - Your estimator may not match your design (nested, time-to-event, latent constructs) ## Rigor is method-specific — report what your design demands Because Organization Science is methodologically eclectic, "rigor" looks different by method. Report the standard that fits, transparently. - **Qualitative / inductive.** Validity becomes **trustworthiness**: a data structure (first-order codes → second-order themes → aggregate dimensions), an audit trail, and representative quotations so the path from raw data to constructs is traceable; state case-selection logic, saturation, and how disconfirming evidence was handled. - **Quantitative / multilevel.** Match the estimator to the design: multilevel/HLM for nested data (justify aggregation with ICC(1), ICC(2), r_wg), SEM for latent constructs and mediation, fixed/random effects and event-history for panel/founding-failure data; cluster SEs
- When to trigger
- Rigor is method-specific — report what your design demands
- Inference without identification
- Transparency and replicability
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Evidence pass for Organization Science
- Output format
What does the orgsci-data-analysis skill do?
Use when analyzing and reporting results for an Organization Science manuscript across its eclectic methods — establishing trustworthiness for qualitative work, the right estimator for quantitative/multilevel data, transparency for simulation/formal models, and mechanism-supported inference where causal identification is impossible. Executes and reports the analysis; it does not design the study (orgsci-methods) or frame the contribution (orgsci-contribution-framing).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill orgsci-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.