---
name: ajps-data-analysis
description: Use when executing and reporting the analysis for an American Journal of Political Science (AJPS) manuscript. AJPS will have a third-party verifier re-run your exact code against the numerical results in the main text before publication, so analyze reproducibly from the first line. Guides analysis and reporting norms; it does not fabricate results.
---

# 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.

## 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`](../../resources/external_tools.md) — estimation, inference, and text-as-data packages
- [`../../resources/official-source-map.md`](../../resources/official-source-map.md) — third-party verification of numerical results