Research Design (govern-research-design)
Governance welcomes any rigorous approach but is demanding about each. The design must credibly
connect the argument (govern-theory-building) to comparative/institutional evidence and rule out the
strongest rival institutional explanation. This skill is mode-aware: pick the section that matches your
work. (For the conceptual contribution, this is the empirical-design variant.)
When to trigger
- Specifying identification, case selection, or comparative design
- A reviewer questioned causal claims, case choice, country selection, or an institutional confound
- Choosing governance/institutions measures (V-Dem, QoG, WGI, etc.) and defending them
- Justifying why the design adjudicates the rival account from
govern-literature-positioning
(a) Comparative / causal designs (governance & institutions)
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Identification first. State the estimand and the assumptions that license a causal reading
(parallel trends, exclusion, continuity, ignorability). Defend them; don't assert them.
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Reform DiD / event study. When a reform rolls out across units/countries over time, use modern
staggered-adoption estimators (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille,
Borusyak et al.) — not naive TWFE, which is biased under heterogeneous/dynamic effects. Show
pre-trends and event-study leads/lags.
-
Cross-national panels. Justify fixed effects (country, year), the level of clustering, and what is
identified off within-country vs. between-country variation.
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IV / RDD where applicable. IV: first-stage strength, exclusion, weak-IV-robust inference. RDD
(e.g., electoral or threshold-based reform rules): density/manipulation tests, bandwidth robustness.
(b) Qualitative / comparative-historical
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Case selection by design logic (most/least likely, typical, deviant, paired comparison) — not
convenience. Say what each case is a case of, and how the selection adjudicates the argument.
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Process tracing with explicit tests (hoop, smoking-gun, straw-in-the-wind, doubly-decisive); state
what evidence would have disconfirmed the argument in each case.
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QCA where used. Justify calibration of set membership, the truth table, and consistency/coverage
thresholds; report and interpret limited diversity, not just the solution formula.
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Source transparency. Archives, interviews, fieldnotes — plan how they will be documented and cited
(see
govern-transparency-and-data).
(c) Mixed methods
- State the integration logic up front: does the qualitative work generate, test, or explain the
quantitative result (or vice versa)? Sequencing and the role of each strand must be deliberate.
- Show where the strands converge and own where they diverge — divergence is informative, not a flaw
to bury.
(d) Measuring governance & institutions (caveats)
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V-Dem, QoG, WGI, Bertelsmann, ICRG, etc. are estimates, not facts. Report the version, the
construct each index actually captures, and the measurement model's uncertainty (e.g., V-Dem posterior
credible intervals). Do not treat composite indices as ground truth.
-
WGI in particular aggregates perceptions and is endogenous to outcomes — flag this when it sits
near the dependent or treatment variable.
- Show results are not an artifact of one index: triangulate across measures where the concept allows.
The rival-institutional adjudication move (Governance-specific)
For the strongest rival institutional explanation, write one sentence: "If the rival were true
rather than my argument, the cases/data would look like ___; instead they look like ___." A design that
cannot distinguish your account of governing from the leading institutional alternative has not yet
identified the contribution.
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. Governance is public administration and institutions research — comparative and causal designs on governance reforms; the chain serves its quantitative-causal lane, while comparative-historical / qualitative work uses its own standards.
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detect_design → recommend → fit with as_handle=true → audit_result.
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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 a staggered reform; clustering below the level of treatment assignment
- "Causal" language on a cross-national correlation the design only supports as association
- Convenience country selection dressed up as theory-driven case logic
- Treating V-Dem/WGI/QoG scores as exact, ignoring index uncertainty and construct mismatch
- A design that cannot rule out the leading rival institutional account
Output format
【Mode】comparative-causal / qualitative / comparative-historical / mixed
【Estimand or claim】what is being identified/shown
【Key assumption(s)】and how each is defended
【Governance measures】index + version + uncertainty/construct caveat
【Rival ruled out】the rival-institutional adjudication sentence
【Robustness/sensitivity】planned checks
【Next】govern-data-analysis
Supplementary resources
