| name | bjps-data-analysis |
| description | Use when executing and reporting the analysis for a British Journal of Political Science (BJPS) manuscript so it survives expert, double-blind review — honest uncertainty, robustness, and triangulation appropriate to quantitative, experimental, or computational work. Guides analysis norms; it does not fabricate results. |
Data Analysis (bjps-data-analysis)
BJPS reviewers are methodologically sophisticated, and the journal — a DA-RT signatory — expects the
replication data and code behind every reported result to be deposited at acceptance (see
bjps-transparency-and-data). Analyze as if a referee will re-run your code, because the materials
will be public. This skill covers execution and reporting norms; design decisions live in
bjps-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 BJPS expects
- Report uncertainty honestly. Confidence/credible intervals, not just stars; the magnitude and
substantive meaning of the estimate, not just its significance.
- 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; correct for multiple
comparisons; do not mine for a significant interaction and theorize it post hoc.
- Right inference. Cluster at the assignment/sampling level; randomization inference for
experiments; small-cluster corrections (wild-cluster bootstrap) when clusters are few.
- Preregistration discipline. Clearly separate registered analyses from exploratory ones;
reconcile deviations from the plan and justify them.
- Measurement. Validate constructs; report reliability; show that results are not an artifact of a
coding/scaling choice — especially for cross-national measures that must travel across contexts.
Computational / text-as-data specifics
- Document model/version, hyperparameters, seeds, and validation against human-labeled samples.
- For topic models/embeddings/LLM pipelines: report stability and a validation step; don't treat
outputs as ground truth.
Cross-national / comparative specifics
- Check measurement equivalence across countries/waves before pooling; report whether constructs
mean the same thing across contexts.
- Be explicit about what is identified within vs. between units, and where the variation comes from.
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, and any stochastic step.
- Pin software/package versions (
renv.lock, requirements.txt, recorded ssc/net installs).
- Keep table/figure numbers in the manuscript matched to script outputs — the package must reproduce them.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. BJPS is comparative/IR-heavy — cross-country panels with confounded institutions; emphasize fixed effects, clustering, and weak-IV-robust inference.
- Many outcomes / specifications:
romano_wolf (step-down FWER) or
benjamini_hochberg — report the adjusted threshold.
- OVB sensitivity:
oster_delta / sensemakr.
- Inference:
wild_cluster_bootstrap (few clusters), twoway_cluster / conley;
multilevel data → cluster at the right level.
- Re-fit off one handle:
audit_result(result_id) lists the missing checks and the
exact suggest_function for each.
- Exhibits:
etable / did_summary_to_latex from the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the supplement. See
the executed chain in the JF execution walkthrough.
Anti-patterns
- Stars-only tables with no effect sizes or intervals
- "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
- Pooling across countries without checking measurement equivalence
- A results section whose numbers the deposited code cannot reproduce
Output format
【Main estimate】magnitude + interval + substantive 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?
【Measurement equivalence】(if cross-national) checked?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】bjps-tables-figures
Referee-pushback patterns and the BJPS-specific repair
- "This reads as a single-case result, not a general one." → Re-anchor the estimate to the general
mechanism and show what it implies beyond the studied country before the numbers.
- "The robustness table only reruns near-identical specs." → Replace decorative checks with
specifications that could break the result, and say what you learned when they held.
- "You pooled countries without checking the measure travels." → Report measurement equivalence; show
the construct means the same thing across contexts before pooling.
- "I cannot tell registered from exploratory analyses." → Segregate them explicitly; the deposited code
is public via the BJPolS Dataverse, so the split must survive independent re-running.
Calibration anchors (hedged)
- The bar is wide political-science interest, not within-niche novelty: an effect only a country or
subfield specialist would value rarely clears BJPS review on its own.
- Transparency follows DA-RT / BJPolS Dataverse norms — write the analysis so the deposited package
reproduces every printed number; deposit mechanics can change, so confirm the current policy.
Supplementary resources
