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.

| Claim type | Design burden | Practice-relevance check |

|---|---|---|

| Reform or mandate effect | Assignment/timing logic, counterfactual trend, spillover check, and clustering at assignment level | The finding changes how agencies time, target, or evaluate reforms |

| Managerial behavior | Sample frame tied to real public managers or frontline staff, realistic decision task, and measured behavioral outcome | The recommendation is feasible inside public organizations |

| Citizen response / public trust | Treatment realism, representativeness limits, manipulation checks, and ethical framing | The takeaway does not overgeneralize from survey preference to administrative behavior |

| Case/process account | Case-selection logic, process-tracing tests, chronology, and rival-account evidence | The lesson transfers to a defined class of agencies, programs, or jurisdictions |

| Mixed-method mechanism | Quantitative association/effect plus qualitative implementation or mechanism evidence | The qualitative strand explains what managers can act on, not just why results are interesting |

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

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