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.

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. 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.

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

Source: brycewang-stanford/Awesome-Journal-Skills → Journal-of-Public-Administration-Research-and-Theory-Skills/skills/jpart-research-design/SKILL.md