jpart-research-design

Research Design (jpart-research-design)

JPART has moved toward experimental and causal identification, and reviewers expect the design to

connect the theory (jpart-theory-building) to evidence credibly. This skill is mode-aware: pick the

section that matches your work and defend it against the strongest alternative explanation a

public-management reviewer will raise.

When to trigger

• Specifying identification, an experiment, sampling, or measurement
• A reviewer questioned causal claims, common-method bias, endogeneity, or external validity
• Preparing a pre-analysis plan / preregistration (JPART accepts blinded pre-reg reports)
• Justifying why the design adjudicates the rival account from jpart-literature-positioning

Design-choice gate

Start by matching the theoretical claim to the minimum credible design. Do not

choose the design by data availability alone.

| Claim type | Minimum design burden | Common downgrade |

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

| "X causes Y in public organizations" | Identification strategy with an estimand, assignment/variation story, and falsification or sensitivity evidence | Reframe as association or theory-building descriptive evidence |

| "Mechanism M explains the effect" | Mediating evidence that is temporally and conceptually downstream of treatment/exposure, plus rival-mechanism checks | Reframe as a plausible mechanism to be tested, not demonstrated |

| "Public employees/citizens respond differently by condition C" | Pre-specified heterogeneity, adequate power, and measurement invariance across groups | Treat as exploratory moderation |

| "Policy/intervention improves performance" | Implementation fidelity, baseline comparability, outcome validity, and spillover/contamination checks | Reframe as pilot evidence |

| "Case evidence revises theory" | Case selection logic, process-tracing observations, rival explanations, and explicit scope conditions | Reframe as illustrative theory elaboration |

Experiments (the modern JPART workhorse)

• Population matters. Public-management theory often requires public employees or citizens as

subjects — defend the sample (e.g., real managers, frontline staff) over a generic MTurk pool.

• Design. Preregister the design and primary analyses; report power/MDE; pre-specify subgroups;

use vignette/conjoint/factorial designs where the theory is about trade-offs.

• Validity. Attention/manipulation checks, attrition, realism of treatment, and consent/IRB.
• Replication awareness. PA has an active experimental-replication norm — design so the experiment

could be re-run and pre-register to make exploratory vs. confirmatory analyses explicit.

Observational / causal

• 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: DID/event study (modern staggered-adoption estimators, not naive TWFE), IV (first-stage

strength, exclusion, weak-IV-robust inference), RDD (density/manipulation tests, bandwidth), matching

/weighting with balance + sensitivity.

• PA-specific confounds: self-selection into public service, common-source/common-method bias when

X and Y come from the same survey, endogenous sorting of managers to organizations.

Multilevel / organizational

• Employees nested in agencies nested in jurisdictions — use multilevel models; cluster SEs at the level

of treatment/assignment; report ICCs; do not ignore the nesting that PA data almost always has.

Mixed methods

• Make the qualitative and quantitative components answer the same theoretical question; say what each

buys and where they corroborate or diverge.

The adjudication test (JPART-specific)

For the single strongest rival explanation (often selection or common-method bias), write one

sentence: *"If the rival were true rather than my mechanism, the data would look like ___; instead they

look like ___."* If you cannot, the design does not yet identify the contribution.

Reviewer stress tests

Run these before the manuscript claims JPART-level causal or theoretical leverage:

• Theory-design alignment: the unit of theory, treatment/exposure, outcome, and

inference level match. A theory about managers is not proven by citizen vignettes

unless the bridge is explicit.

• Measurement separation: key independent/dependent variables are not merely

two self-reports from the same respondent at the same time; if they are, build a

common-method defense or narrow the claim.

• Assignment credibility: the reader can say why some units received more/less

treatment and why that variation is not just latent performance, resources, or

managerial quality.

• Organizational nesting: the standard errors, random effects, or design account

for agencies, offices, jurisdictions, schools, or teams where treatment and outcomes cluster.

• Generalization boundary: state whether the result generalizes to public

employees, citizens, organizations, jurisdictions, or one institutional setting.

• Transparency path: preregistration, data/code release, and any restricted-data

exception can be anonymized and reconciled with jpart-transparency-and-data.

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). JPART is public management — observational and experimental designs on public organizations; identification + clustered/multilevel 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 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

• A "behavioral PA" experiment on a generic online panel when the theory is about public managers
• Common-source bias: X and Y from the same self-report survey, called a causal effect
• Naive TWFE on staggered adoption; clustering at the wrong level; ignoring agency-level nesting
• Treating self-selection into public service as ignorable
• A design that cannot distinguish your mechanism from selection or the leading alternative

Output format

【Mode】experiment / observational-causal / multilevel / mixed
【Population】public employees / citizens / orgs — defended? [Y/N]
【Estimand or claim】what is being identified/shown
【Key assumption(s)】and how each is defended
【Design-choice gate】causal / mechanism / heterogeneity / policy / case-theory burden met?
【Rival ruled out】the adjudication sentence (often selection / common-method)
【Stress-test gaps】theory-design / measurement / assignment / nesting / generalization / transparency
【Preregistered?】confirmatory vs exploratory split
【Next】jpart-data-analysis

Supplementary resources

• [../../resources/external_tools.md](../../resources/external_tools.md) — experiment/causal packages (R/Stata/Python) and survey platforms
• [../../resources/code/](../../resources/code/) — reproducible DiD/IV/RDD/DML skeleton to adapt
• [../../resources/official-source-map.md](../../resources/official-source-map.md) — preregistration / blinded pre-reg report policy