cps-data-analysis

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)

1. 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.

1. 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.

1. 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.

1. 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.

1. Heterogeneity by theory. Subgroups/scope conditions pre-specified by the mechanism

(cps-theory-building), not data-mined; adjust for multiple comparisons.

1. 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](../../../shared-resources/empirical-methods/execution-with-mcp.md). 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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).

Checklist

• [ ] Headline specification = the one the design justifies
• [ ] SEs clustered at the assignment level; few-cluster correction where needed
• [ ] Every comparative variable's source and coding named; key construct robust to alt codings
• [ ] Robustness probes the design's named threats; not a kitchen sink
• [ ] Heterogeneity pre-specified by theory; multiple testing addressed
• [ ] Main text vs. appendix split is deliberate; every appendix result is referenced
• [ ] All results reproduce from the script destined for the CPS Dataverse

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

• [../../resources/code/](../../resources/code/) — clean → estimate → robustness → tables skeleton (Stata + Python)
• [../../resources/external_tools.md](../../resources/external_tools.md) — estimation and inference packages (R / Stata / Python)