Individual skill
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).
Organization-Science-Skills/skills/orgsci-data-analysis/SKILL.md
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- Last checked
- Nov 9, 2026
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- Repository last updated
- Sep 27, 2026
- License
- MIT
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SKILL.mdcore · 6,207 bytes
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# 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.