Bjps Data Analysis

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

1. Report uncertainty honestly. Confidence/credible intervals, not just stars; the magnitude and substantive meaning of the estimate, not just its significance.
2. Robustness that probes, not decorates. Show specifications that could break the result (alternative measures, samples, estimators, fixed effects), and say what you learn.
3. Heterogeneity with discipline. Pre-specify subgroups where possible; correct for multiple comparisons; do not mine for a significant interaction and theorize it post hoc.
4. Right inference. Cluster at the assignment/sampling level; randomization inference for experiments; small-cluster corrections (wild-cluster bootstrap) when clusters are few.
5. Preregistration discipline. Clearly separate registered analyses from exploratory ones; reconcile deviations from the plan and justify them.
6. 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

• ../../resources/external_tools.md — estimation, inference, and text-as-data packages
• ../../resources/code/ — reproducible Stata + Python skeleton to adapt
• ../../resources/official-source-map.md — DA-RT / replication-data policy

Source: brycewang-stanford/Awesome-Journal-Skills → British-Journal-of-Political-Science-Skills/skills/bjps-data-analysis/SKILL.md