Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› jpart-research-design
jpart-research-design
GitHub用于辩护JPART投稿的研究设计，涵盖实验、因果推断及混合方法。针对公共管理领域的理论主张，提供识别策略、有效性检验及替代解释的防御框架，强调因果识别与预注册规范，不生成代码。
Trigger Scenarios
指定识别策略、实验或抽样方案
回应审稿人对因果性、共同方法偏差或外部效度的质疑
准备预分析计划或预注册报告
论证研究设计如何排除来自文献定位技能的竞争性假设
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jpart-research-design -g -y
SKILL.md
Frontmatter
{
"name": "jpart-research-design",
"description": "Use when defending the research design of a Journal of Public Administration Research and Theory (JPART) manuscript — survey\/lab\/field experiments on public employees and citizens, causal observational designs, multilevel structures, or mixed methods. JPART has moved strongly toward experimental and causal identification. Strengthens the design; it does not write code."
}
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. JPART is public management — observational and experimental designs on public organizations; identification + clustered/multilevel inference.
detect_design→recommend→ fit withas_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_wolffor many-outcome control. - Sensitivity:
oster_delta/sensemakrfor 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
Version History
- 1839142 Current 2026-07-05 13:55
