| name | cps-data-analysis |
| description | Use when running and reporting the analyses for a Comparative Political Studies (CPS) manuscript — estimation, uncertainty, robustness, and multi-method triangulation on comparative data. Sets analysis norms; it does not choose the identification strategy (see cps-research-design). |
Data Analysis (cps-data-analysis)
Once the design is fixed (cps-research-design), this skill governs how the analyses are run and
reported so a CPS reviewer trusts them. Comparative data bring distinctive hazards: few clusters
(countries), cross-national measurement error, missing data that differ by regime, and the temptation to
over-read a panel correlation as causal. The standard is modern, transparent, and replication-ready.
When to trigger
- Estimating the main results, robustness, and heterogeneity
- A reviewer questioned standard errors, specification, measurement, or fragility of the result
- Deciding what goes in the main text vs. the supplementary/online appendix
- Triangulating quantitative estimates with case evidence
Analysis priorities (in order)
- Main estimate that matches the design. The headline specification should be the one the
identification argument justifies — not the one with the biggest coefficient or most stars.
- Honest uncertainty. Cluster at the assignment level (usually country / country-year); with few
countries use wild-cluster bootstrap or randomization inference. Report CIs, not just stars.
- Measurement transparency. Name the source and coding of each comparative variable (e.g., V-Dem,
Polity, CSES, Manifesto Project); show robustness to alternative codings of the key construct.
- Robustness as a coherent story. Alternative specifications, samples, codings, and estimators that
probe the threats named in the design — not a scattershot table of every variant.
- Heterogeneity by theory. Subgroups/scope conditions pre-specified by the mechanism
(
cps-theory-building), not data-mined; adjust for multiple comparisons.
- Mechanism evidence. Tie the quantitative result to the mechanism — mediation cautiously, or case
evidence in a multi-method design.
Comparative-data hazards to address explicitly
| Hazard | Symptom | Fix |
|---|
| Few clusters (countries) | over-rejection, tiny SEs | wild-cluster bootstrap / randomization inference |
| Cross-national measurement error | results flip across codings | show robustness to V-Dem/Polity/alt scales |
| Differential missingness | sample changes by regime type | report attrition; multiple imputation with caution |
| Time-series confounding | spurious trend correlations | unit + period FE; over-time placebo |
Failure-mode audit
Run this audit before interpreting the main coefficient:
- Concept equivalence: Does the key variable mean the same thing across regimes, languages, regions, or
institutions? If not, report measurement-invariance checks, alternative codings, or scope limits.
- Selection into observation: Are only more democratic, richer, more peaceful, or better-measured
cases observed? Report the observation process and show how estimates change under credible sample
restrictions.
- Temporal dependence: Are observations mechanically persistent across years? Use lag structure,
unit trends, event-time plots, or placebo leads to avoid re-labeling persistence as effect.
- Cluster leverage: Does one country, region, election, conflict, or reform episode drive the result?
Show leave-one-cluster-out or influence diagnostics for claims that hinge on few cases.
- Subgroup multiplicity: If theory predicts heterogeneity, pre-specify the dimensions and report how
many comparisons were examined.
The output should connect each failure mode to a design threat. Do not add a robustness table unless it
answers a named threat in cps-research-design.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. CPS is comparative politics — cross-national and sub-national designs; emphasize identification and clustered / multiway inference.
- Many outcomes / specifications:
romano_wolf (step-down FWER) or
benjamini_hochberg — report the adjusted threshold.
- OVB sensitivity:
oster_delta / sensemakr.
- Inference:
wild_cluster_bootstrap (few clusters), twoway_cluster / conley;
multilevel data → cluster at the right level.
- Re-fit off one handle:
audit_result(result_id) lists the missing checks and the
exact suggest_function for each.
- Exhibits:
etable / did_summary_to_latex from the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the supplement. See
the executed chain in the JF execution walkthrough.
Checklist
Anti-patterns
- Treating a cross-national panel correlation as causal without the design to back it
- Default OLS SEs with 20 countries (massively over-rejects)
- Cherry-picking the coding of the key variable that gives significance
- Robustness theater — many variants that never test the actual threat
- Data-mined subgroups reported as confirmed heterogeneity
- Results in the paper that the deposited code does not reproduce
Output format
【Headline result】estimate + CI, with the design it rests on
【Inference】clustering level + few-cluster correction if any
【Measurement】sources/codings + alt-coding robustness
【Failure-mode audit】concept equivalence / observation selection / temporal dependence / cluster leverage / multiplicity
【Robustness】the design-threats probed
【Heterogeneity】theory-driven subgroups + multiple-testing fix
【Reproducible?】script regenerates every exhibit [Y/N]
【Next】cps-tables-figures
Supplementary resources
