Ajps Data Analysis

Data Analysis (ajps-data-analysis)

AJPS reviewers are methodologically sophisticated, and after acceptance an independent third-party verifier re-runs your code against the numbers in the main text before the article is published (see ajps-replication-and-verification). Analyze as if both facts are true — because they are. This skill covers execution and reporting; design choices live in ajps-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 so the verifier's re-run will match

Analysis norms AJPS expects

1. Report uncertainty and magnitude. Confidence/credible intervals and substantive effect sizes, not just significance stars — say what the estimate means.
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; adjust 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; wild-cluster bootstrap when clusters are few.
5. Preregistration discipline. Separate registered from exploratory analyses; justify any deviation from the plan.
6. Measurement. Validate constructs; report reliability; show results are not an artifact of a single coding/scaling choice.

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; do not treat raw outputs as ground truth.

Reproducibility while you work (so the verifier's re-run matches)

• One master script regenerates every table and figure from the (raw or constructed) data, in order, setting the working directory once.
• Set and report seeds for every stochastic step (bootstrap, randomization inference, simulation, jittered plots) — the verifier needs identical draws.
• Record exact software versions (e.g., "R 4.3.2", "Stata/MP 18.0") and pin packages (renv.lock / requirements.txt / logged ssc/net installs).
• Keep the manuscript's table/figure numbers matched to script outputs — the verifier checks the numerical results in the main text line by line.

Analysis-decision checklist a quantitative AJPS referee runs

Worked micro-example (illustrative numbers)

A survey experiment tests whether a co-partisan endorsement raises policy support. The pre-analysis plan names the estimand (ITT on a 0-100 scale), the primary contrast, and one moderator (political knowledge). Result: +7.4 points (95% CI 3.1-11.7), randomization-inference p = 0.004 (illustrative). A knowledge interaction that was not pre-specified as confirmatory goes to an exploratory subsection, flagged, with a multiple-comparison note. Every number is emitted by one seeded master script, so the AJPS verifier's re-run reproduces the main-text figures exactly.

Referee-pushback patterns and the venue-specific fix

• "Identification leans on selection-on-observables." -> Report an unobserved-confounder sensitivity bound (how strong a confounder must be to overturn the estimate); soften causal language if fragile.
• "This interaction looks mined." -> Show it was pre-registered, or relabel it exploratory and adjust for multiple comparisons; never HARK it into a hypothesis.
• "I cannot reproduce Table 2 from your code." -> Fatal at AJPS verification; rebuild the master script so every number regenerates with fixed seeds and pinned versions.

Calibration anchor: AJPS's independent verifier re-runs deposited code against the main-text numbers before publication, so "it works on my machine" is not enough — confirm the live verification wording against the journal's current guidelines.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. AJPS prizes credible identification across American / comparative / IR subfields; DiD/IV/RDD for observational claims, randomization inference for experiments.

• 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
• Hand-edited numbers in the manuscript that the deposited code cannot regenerate (verification fails)

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?
【Reproducible】master script + seeds + pinned versions, numbers match? [Y/N]
【Next】ajps-tables-figures

Supplementary resources

• ../../resources/external_tools.md — estimation, inference, and text-as-data packages
• ../../resources/official-source-map.md — third-party verification of numerical results

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