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asq-data-analysis

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Use when executing and reporting the analysis for an Administrative Science Quarterly (ASQ) manuscript — qualitative coding and data-to-theory construction, or quantitative estimation and robustness. Makes the evidence-to-theory link transparent; it does not design the study (see asq-methods).

Administrative-Science-Quarterly-Skills/skills/asq-data-analysis/SKILL.md

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# Data Analysis & Evidence (asq-data-analysis)

## When to trigger

- You have data but the path from data to theory is opaque
- Qualitative: your quotes are decorative, not evidentiary; coding is undocumented
- Quantitative: main results exist but robustness/alternative explanations are thin
- Reviewers ask "how did you get from your data to these constructs?"

## Branch A — Qualitative analysis (the data-to-theory link)

ASQ expects readers to *see how raw data became theory* — its guidelines stress that helping readers understand *how the research was performed* and ensuring the *trustworthiness* of published work are explicit aims (verify at journals.sagepub.com/author-instructions/asq). Qualitative rigor is judged on its own terms here, not held to a quantitative yardstick. Make the analytic ladder visible.

- **Transparent coding.** Describe first-order codes (informant terms), second-order themes (researcher constructs), and aggregate dimensions — the Gioia-style data structure — or an equivalent (Eisenhardt cross-case, Langley process bracketing). State who coded, how disagreements were resolved, and how iteration proceeded.
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