| name | cps-research-design |
| description | Use when defending the research design of a Comparative Political Studies (CPS) manuscript — cross-national/panel identification, case-based comparison and process tracing, experiments, or multi-method designs. CPS prizes comparative leverage. Strengthens the design; it does not write code. |
Research Design (cps-research-design)
CPS is methodologically pluralist but demanding about each tradition. The design must credibly connect
the comparative argument (cps-theory-building) to evidence and rule out the leading rival
(cps-literature-positioning). This skill is mode-aware: pick the section that matches your work and
defend the comparative leverage — the variation across cases or time that identifies the claim.
When to trigger
- Specifying the identification strategy, case selection, or experimental design
- A reviewer questioned causal claims, case choice, external validity, comparability, or a confound
- Designing a cross-national panel, a subnational comparison, or a natural experiment across borders
- Justifying why the design adjudicates the rival account, not just shows an association
Comparative-causal toolkit (cross-national / panel)
- Identification first. State the estimand and the assumptions that license a causal reading
(parallel trends, exclusion, continuity, ignorability). Defend them; don't assert them.
- Designs: cross-national panels with unit and period fixed effects; DiD/event study around reforms
(use modern staggered-adoption estimators, not naive TWFE); RD around institutional thresholds; IV
(first-stage strength, exclusion, weak-IV-robust inference); survey experiments fielded comparatively.
- Comparability. Defend that the units are measured the same way across countries (V-Dem vs. Polity
coding, harmonized surveys); address country-level confounding and cross-national measurement error.
- Inference: cluster at the level of treatment assignment (often country or country-year); few-cluster
corrections (wild bootstrap) when the number of countries is small; multiple-comparison adjustment.
- Sensitivity: how strong must an unobserved country-level confounder be to overturn the result?
Case-based / qualitative comparison
- Comparison logic: most-similar (control on shared traits, vary the cause) or most-different
(shared outcome despite different contexts) — justified by design, not convenience.
- Case selection: typical, deviant, most/least-likely, paired comparison. Say what each case is a
case of and avoid selecting on the outcome.
- Process tracing with explicit tests (hoop, smoking-gun, straw-in-the-wind); state what evidence
would have disconfirmed the mechanism.
- Source transparency: archives, interviews, fieldnotes — plan documentation (see
cps-transparency-and-data).
Experiments (survey / field, fielded comparatively)
- Preregister design and primary analyses; report power/MDE; pre-specify subgroups and the comparison.
- For cross-country survey/conjoint experiments: equivalence of instruments and treatment realism across
contexts; sampling frames; what the comparative contrast licenses about generalization.
Multi-method linkage
- Use the quantitative estimate for the average comparative effect and the case evidence for the
mechanism; state how each method covers the other's blind spot, not as decoration.
The adjudication test (CPS-specific)
For the single strongest rival, write one sentence: "If the rival were true rather than my
argument, the cross-case/over-time pattern would look like ___; instead it looks like ___." If you
cannot, the design does not yet identify the comparative contribution.
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. CPS is comparative politics — cross-national and sub-national designs; emphasize identification and clustered / multiway inference.
detect_design → recommend → fit with as_handle=true → audit_result.- Observational causal claims: staggered DiD (
callaway_santanna / sun_abraham +
bacon_decomposition + honest_did_from_result); IV (effective_f_test +
anderson_rubin_ci); RDD (rdrobust + mccrary_test).
- Experiments: randomization-based inference,
romano_wolf for many-outcome
family-wise control, and mediate for mediation (not naive controlling-away).
- Sensitivity:
oster_delta / sensemakr for observational claims.
Report the effect size in interpretable units; route the full battery to the
appendix/supplement. A run end-to-end (synthetic data, real returns) is in the
JF execution walkthrough.
Anti-patterns
- Naive TWFE on staggered reforms; clustering at the wrong level; ignoring the small-number-of-countries problem
- "Causal" language on a design that only supports cross-national correlation
- Convenience or selecting-on-the-outcome case selection dressed up as most-similar design
- Cross-national survey experiment with non-equivalent instruments across countries
- A design with no comparative leverage — one snapshot that cannot distinguish your argument from the rival
Output format
【Mode】comparative-causal / case-based / experiment / multi-method
【Comparative leverage】the across-case / over-time variation that identifies the claim
【Estimand or claim】what is being identified/shown
【Key assumption(s)】and how each is defended (incl. comparability)
【Rival ruled out】the adjudication sentence
【Robustness/sensitivity】planned checks
【Next】cps-data-analysis
Supplementary resources
