| name | wp-data-analysis |
| description | Use when executing and reporting the analysis for a World Politics manuscript so it survives expert triple-blind review and the Dataverse replication requirement — honest uncertainty, robustness, and triangulation appropriate to comparative cross-national, qualitative, or formal-empirical work. Guides analysis norms; it does not fabricate results. |
Data Analysis (wp-data-analysis)
World Politics reviewers are methodologically demanding, and authors who rely on quantitative data
must deposit replication materials in the World Politics Dataverse that let others reproduce the
exact numerical results (see wp-transparency-and-data-policy). Analyze as if both are true —
because they are. This skill covers execution and reporting norms; design decisions live in
wp-research-design.
When to trigger
- Running main and supporting analyses; building the results/findings section
- A reviewer asked for robustness, heterogeneity, or alternative specifications
- Reconciling the cross-case pattern with within-case evidence (mixed methods)
- Making the analysis reproducible before deposit
Analysis norms World Politics expects
- Report uncertainty honestly. Confidence/credible intervals, not just stars; the magnitude and
substantive meaning of the estimate across cases, not just its significance.
- Robustness that probes, not decorates. Show specifications that could break the result
(alternative measures of regime/institution/conflict, alternative samples of cases, estimators,
fixed effects), and say what you learn.
- Cross-national inference. Cluster at the appropriate level (often country); address serial
correlation and cross-sectional dependence in TSCS; small-N panels need honest few-cluster
corrections (e.g., wild-cluster bootstrap).
- Measurement that travels. Validate constructs across cases; report reliability; show results are
not an artifact of one coding/scaling choice or one source (V-Dem vs. Polity, COW vs. UCDP).
- Heterogeneity with discipline. Pre-specify subgroups/regions where possible; correct for
multiple comparisons; do not mine for a significant interaction and theorize it post hoc.
- Triangulation. Where the design is mixed-method, show the within-case process evidence and the
cross-case statistics point the same way, and reconcile where they don't.
Qualitative / comparative-historical specifics
- Make the evidentiary basis explicit — which sources support which inferential step; link claims
to documents/interviews via evidence tables (see
wp-tables-figures).
- For process tracing, report the tests passed/failed and what would have disconfirmed the argument.
Reproducibility while you work (not at the end)
- One master script regenerates every table and figure from the (raw or constructed) data.
- Set and report seeds for bootstrap, randomization, and any stochastic step.
- Pin software/package versions (
renv.lock, requirements.txt, recorded ssc/net installs).
- Keep table/figure numbers matched to script outputs — the Dataverse package must reproduce them.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. World Politics is comparative/IR with a strong qualitative tradition; apply the chain below to its quantitative-causal lane and say so when work is case-based.
- 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.
Anti-patterns
- Stars-only tables with no effect sizes or intervals
- "Robustness" that only reruns near-identical specs to manufacture stability
- Results that hinge on one data source or one coding choice without showing alternatives
- Clustering at the wrong level; ignoring TSCS serial correlation / cross-sectional dependence
- A findings section whose numbers the deposited code cannot reproduce
Referee-pushback patterns and the World Politics fix
World Politics referees span a methodologically plural community, so each tradition is judged on its
own terms. The recurring objections, and the answering move, are stable.
| Referee objection | The fix this skill drives |
|---|
| "Robustness only reruns near-identical specs" | Show specs that could break it: rival measures (V-Dem vs. Polity, COW vs. UCDP), alternative case samples; report what each did |
| "Stars but no magnitudes" | Lead with effect size + interval + substantive meaning across cases |
| "Quant and case evidence diverge" | State it, adjudicate with within-case evidence, narrow the scope condition |
A frequent risk is the fishing concern: an interaction found post hoc and theorized as if
predicted. Pre-specify subgroups, correct for multiple comparisons, report the unconditional result
too. (Confirm expectations against the journal's reviewer guidelines.)
Worked micro-example (illustrative numbers)
A hypothetical mixed-method study asks whether fiscal decentralization dampens ethnic conflict
onset across ~120 countries, paired with two within-case process-tracing narratives.
Main estimate (illustrative): 1-SD rise in decentralization → onset HR 0.72, CI [0.58, 0.90]
reading: ~28% lower onset risk at the mean, not just "p < 0.05"
Robustness: swap V-Dem for OECD measure → HR 0.79 [0.61, 1.02] (weaker, crosses 1)
Few clusters (41) → wild-cluster bootstrap p = 0.04 (vs naive 0.01)
Triangulation: HR and both case narratives agree on a budgetary-bargain mechanism; the lone case
where decentralization did NOT dampen conflict had centrally appointed governors → scope condition.
The honest reading: the effect is real but scope-conditioned on genuine fiscal autonomy, with
the weaker interval reported, not suppressed. Figures illustrative only.
Output format
【Main estimate】magnitude + interval + substantive meaning across cases
【Identification check】(per research-design) result
【Robustness】specs / alternative sources that could break it → what held
【Measurement】construct validated across cases? source sensitivity shown?
【Triangulation】within-case + cross-case agree? reconciled?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】wp-tables-figures
Supplementary resources
