Pubar Research Design

Research Design (pubar-research-design)

PAR accepts many methodologies but is demanding about each. The design must credibly connect the argument (pubar-theory-building) to evidence drawn from public organizations, bureaucrats, citizens, or jurisdictions. This skill is mode-aware: pick the section that matches your work and defend it against the strongest alternative explanation.

When to trigger

• Specifying identification, case selection, or experimental design
• A reviewer questioned causal claims, case choice, external validity, or a confound
• Preparing a pre-analysis plan or a pre-registration (PAR offers pre-registration badges)
• Justifying why your design adjudicates the rival account from pubar-literature-positioning

PAR design-fit gate

PAR is a generalist flagship, so the design must support both an academic claim and a usable public- management implication. Start with this gate before polishing methods language.

Quantitative / causal inference (public-management settings)

• Identification first. State the estimand and the assumptions that license a causal reading (ignorability, parallel trends, exclusion, continuity). Defend them, don't assert them.
• Designs common in PA: DiD/event study around a reform or mandate (use modern staggered-adoption estimators — Callaway–Sant'Anna, Sun–Abraham, BJS — not naive TWFE); IV (first-stage strength, exclusion, weak-IV-robust inference); RD around eligibility/funding thresholds; matching/weighting with balance + sensitivity.
• Inference: cluster at the level of treatment assignment (often agency, district, or jurisdiction); wild-cluster bootstrap when clusters are few (a recurring PA problem with state- or agency-level treatments).
• Sensitivity: how strong must an unobserved confounder be to overturn the result (Oster / E-value)?

Experiments on bureaucrats and citizens

• Preregister the design and primary analyses; report power/MDE; pre-specify subgroups.
• Bureaucrat/managerial experiments: realism of the decision task, sample frame (which public managers), and generalization to real administrative behavior.
• Citizen survey/conjoint experiments: treatment realism, attention/manipulation checks, attrition, and ethics/IRB and consent.

Qualitative / case-based & mixed methods

• Case selection justified by design logic (typical, deviant, most/least-likely, paired comparison) — not convenience. Say what the case is a case of (a reform, a governance form).
• Process tracing with explicit tests (hoop, smoking-gun, straw-in-the-wind); state what evidence would have disconfirmed the argument.
• Mixed methods: say what the qualitative strand adds that the quantitative cannot (mechanism, context, implementation), and where the two corroborate or diverge.

The adjudication test (PAR-specific)

For the single strongest rival explanation, write one sentence: "If the rival were true rather than my argument, the agencies/managers/citizens would look like ___; instead they look like ___." If you cannot, the design does not yet identify the contribution — and the practitioner takeaway is unsafe.

Practice-safe inference rules

• Separate evidence from recommendation. A credible association may justify a diagnostic warning; a causal design may justify a stronger managerial recommendation; neither automatically justifies a universal policy prescription.
• Name the implementation margin. If the intervention is staffing, training, targeting, rule design, citizen communication, or interagency coordination, say which margin the design actually tests.
• Check administrative feasibility. A design can be internally valid but still imply an action no manager can implement. Flag cost, authority, data availability, and equity constraints.
• Bound external validity. Identify the agency type, policy domain, country/state/local context, and population to which the evidence should and should not travel.
• Route transparency early. If the result relies on confidential administrative data, plan the restricted-data path with pubar-transparency-and-data before claims harden.

Reviewer stress tests

• Would the result survive if the strongest agency-level selection story were true?
• Is the treatment/exposure measured before the outcome and at the right organizational level?
• Are standard errors clustered at the assignment or sampling level, not merely the observation level?
• For qualitative work, what observation would have disconfirmed the mechanism?
• For mixed methods, do both strands answer the same claim, or are they two parallel papers?
• Can the Evidence for Practice box be written without making a claim the design cannot support?

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe it. Full map: execution-with-mcp. PAR is public administration — survey/observational and some experimental work; identification + clustered/multilevel inference, magnitude for practice.

• 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 control.
• Sensitivity: oster_delta / sensemakr for observational claims.

Report the magnitude in interpretable units; route the full battery to the appendix. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.

Anti-patterns

• Naive TWFE on a staggered reform rollout; clustering below the assignment level
• "Causal" language (and a managerial recommendation) on a design that only supports association
• Convenience case selection dressed up as theory-driven
• Bureaucrat/citizen experiments over-generalized to real administrative behavior with no caveat
• A design that cannot distinguish your argument from the leading alternative

Output format

【Mode】quant-causal / experiment / qualitative / mixed
【Estimand or claim】what is being identified/shown
【Design-fit gate】academic claim + practice relevance supported? [Y/N]
【Key assumption(s)】and how each is defended
【Rival ruled out】the adjudication sentence
【Robustness/sensitivity】planned checks (clustering, few-cluster, Oster/E-value)
【Practice-safe inference】recommendation strength + implementation margin + external-validity boundary
【Transparency handoff】public / restricted / qualitative-controlled-access path
【Next】pubar-data-analysis

Supplementary resources

• ../../resources/external_tools.md — design/identification packages (R/Stata/Python) and CAQDAS for qualitative work
• ../../resources/official-source-map.md — pre-registration badges and TOP notes