| name | pubar-data-analysis |
| description | Use when executing and reporting the analysis for a Public Administration Review (PAR) manuscript so it survives expert, double-blind review and supports honest Evidence for Practice — uncertainty, robustness, and triangulation appropriate to quantitative, experimental, or mixed work. Guides analysis norms; it does not fabricate results. |
Data Analysis (pubar-data-analysis)
PAR reviewers are methodologically capable public-management scholars, and the journal endorses the
TOP transparency guidelines — so analyses should be reproducible and documented (see
pubar-transparency-and-data). Because PAR articles carry Evidence for Practice, every estimate
that drives a managerial takeaway must be analyzed honestly enough to bear that weight. This skill
covers execution and reporting; design decisions live in pubar-research-design.
When to trigger
- Running main and supporting analyses; building the results section
- A reviewer asked for robustness, heterogeneity, or alternative specifications
- Reconciling preregistered vs. exploratory analyses
- Making the analysis reproducible before deposit
Analysis norms PAR expects
- Report uncertainty honestly. Confidence/credible intervals, not just stars; the magnitude and
substantive/managerial meaning of the estimate, not just its significance. A practitioner needs
effect size, not a p-value.
- Robustness that probes, not decorates. Show specifications that could break the result
(alternative measures, samples, estimators, fixed effects), and say what you learn.
- Heterogeneity with discipline. Pre-specify subgroups where possible (agency type, jurisdiction
size, sector); correct for multiple comparisons; don't mine an interaction and theorize it post hoc.
- Right inference. Cluster at the assignment/sampling level (agency, district); wild-cluster
bootstrap when clusters are few — a common public-management data situation.
- Preregistration discipline. Clearly separate registered from exploratory analyses;
reconcile and justify deviations.
- Measurement. Validate constructs (red tape, PSM, performance); report reliability; show results
are not an artifact of a coding/scaling choice — measurement debates are central in PA.
Mixed-methods integration
- State explicitly where the qualitative evidence corroborates, refines, or contradicts the
quantitative estimate; do not present them in parallel silos with no integration.
Reproducibility while you work (not at the end)
- One master script regenerates every table and figure from the (raw or constructed) data.
- Set and report seeds for bootstrap, randomization inference, simulation, any stochastic step.
- Pin software/package versions (
renv.lock, requirements.txt, recorded ssc/net installs).
- Keep table/figure numbers matched to script outputs; document design/prep decisions in the
supplementary document PAR recommends.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. PAR is public administration — survey/observational and some experimental work; identification + clustered/multilevel inference, magnitude for practice.
- Many outcomes / specifications:
romano_wolf (step-down FWER) or benjamini_hochberg.
- OVB sensitivity:
oster_delta / sensemakr.
- Inference:
wild_cluster_bootstrap (few clusters), twoway_cluster / conley.
- Re-fit off one handle:
audit_result(result_id) lists missing checks + the exact
suggest_function for each.
- Exhibits:
etable / did_summary_to_latex from the handle — no retyped numbers.
Decisive checks in the body, exhaustive battery in the appendix.
JF execution walkthrough.
Anti-patterns
- Stars-only tables with no effect sizes or intervals (a practitioner can't act on stars)
- "Robustness" that only reruns near-identical specs to manufacture stability
- p-hacking / fishing for a significant interaction; HARKing exploratory results into hypotheses
- Clustering at the wrong level or ignoring few-cluster problems
- An Evidence-for-Practice point that the analysis does not actually support
Output format
【Main estimate】magnitude + interval + managerial meaning
【Identification check】(per research-design) result
【Robustness】specs that could break it → what held
【Heterogeneity】pre-specified? MHT-adjusted?
【Registered vs exploratory】clearly separated?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】pubar-tables-figures
What PAR reviewers probe, by analytic tradition
| Analytic tradition | The check a PAR referee runs first | The fix that earns the benefit of the doubt |
|---|
| Survey / managerial experiment | Is inference randomization-based and pre-registered? | Randomization inference, pre-registered estimand, MDE reported |
| Observational causal (reform) | Is the "causal" word (and the policy advice) doing more than the design licenses? | State estimand + assumption; sensitivity to an unobserved confounder |
| Performance / administrative data | Are measures validated, and is gaming/selection ruled out? | Construct validation, reliability, selection checks |
| Mixed methods | Do quant and qual estimates actually corroborate? | Show where they agree, and own where they diverge |
Worked micro-example (illustrative numbers)
A hypothetical PAR survey experiment tests whether a performance-feedback framing raises frontline
managers' willingness to adopt a new reporting tool. The pre-registered ATE is +7.4 points (95% CI
3.0 to 11.8) on a 0–100 willingness scale, randomization-inference p = 0.006. An exploratory
subgroup ("low-tenure managers") shows +13 points, but it was not pre-registered and after a
Bonferroni adjustment across five exploratory subgroups its interval crosses zero. The disciplined
write-up reports the +7.4 confirmatory effect with its interval and a managerial interpretation, flags
the +13 figure as exploratory and not multiplicity-robust, and frames it as a hypothesis — so the
Evidence-for-Practice point rests on the confirmatory estimate only. (All numbers illustrative.)
Calibration anchors (hedged)
- The bar is field-wide PA significance plus honest practice relevance; an effect only a specialist
values, or a takeaway the data can't support, rarely clears PAR review.
- PAR practices methodological breadth — a rigorous mixed-methods or case analysis is not second-class
to a regression. Match the inference standard to the design.
- TOP transparency expectations evolve; confirm the current data-policy wording on the journal's page
(检索于 2026-06；以官网为准).
Supplementary resources
