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respol-data-analysis

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Use when executing and stress-testing the empirical analysis for a Research Policy (RP) manuscript — building bibliometric/patent variables, running estimation or qualitative coding, and assembling robustness that an innovation-studies referee will accept. Executes the analysis; it does not choose the design (respol-methods) or present exhibits (respol-tables-figures).

Research-Policy-Skills/skills/respol-data-analysis/SKILL.md

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# Data Analysis (respol-data-analysis)

## When to trigger

- Patent/bibliometric variables are built but the construction steps are not documented or reproducible
- Headline results exist but robustness to alternative measures and specifications is thin
- A count outcome (patents, citations) is run with OLS instead of an appropriate count model
- Qualitative coding lacks a transparent coding scheme or inter-coder reliability
- A referee says results are "not robust," "driven by outliers/one sector," or "the data are a black box"

## The Research Policy analysis bar

RP referees know innovation data intimately and distrust opaque pipelines. The two things they probe hardest are **how the variables were built** (especially patent/bibliometric ones) and **whether the finding survives the obvious alternatives**. Counts and skewed distributions are the norm in innovation data, so estimators must respect that; and because most RP indicators are noisy proxies, robustness is not optional decoration — it is how you show the innovation claim, not the measure's artifacts, drives the result.
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