| name | asr-data-analysis |
| description | Use when executing and reporting the analysis for an American Sociological Review (ASR) manuscript so it survives expert masked review — honest uncertainty, robustness, and evidence handling appropriate to quantitative, demographic, comparative-historical, or computational sociology. Guides analysis norms; it does not fabricate results. |
Data Analysis (asr-data-analysis)
ASR reviewers are methodologically demanding across very different traditions. Whether your evidence
is regression coefficients, life tables, archival sequences, or coded fieldnotes, the analysis must be
transparent, well-documented, and reproducible to the extent your data allow. Design decisions live in
asr-research-design.
When to trigger
- Running main and supporting analyses; building the results/findings section
- A reviewer asked for robustness, heterogeneity, alternative specifications, or more evidence
- Documenting how qualitative claims are grounded in the data
- Making the analysis reproducible before sharing materials
Analysis norms ASR expects
Quantitative / demographic
- Report uncertainty and magnitude, not just significance — intervals and substantive effect
sizes; respect survey design (weights, clustering).
- Robustness that probes, not decorates — alternative measures, samples, estimators, and
specifications that could break the result; say what you learn.
- Heterogeneity with discipline — pre-specify or justify subgroups; adjust for multiple
comparisons; don't mine an interaction and theorize it post hoc.
- Measurement — validate constructs, report reliability, show results aren't an artifact of a
coding/scaling choice (especially for inequality and well-being measures).
Comparative-historical / ethnographic
- Make the chain of evidence explicit: link each claim to specific sources, observations, or
cases; present negative/disconfirming evidence.
- Use evidence tables, timelines, or coded excerpts so reviewers can trace claims to data.
Computational / text-as-data
- Document model/version, hyperparameters, seeds, preprocessing; validate against human-labeled
samples; report stability. Don't treat model outputs as ground truth.
Reproducibility while you work
- One master script regenerates every table/figure from raw/constructed data (quantitative).
- Set and report seeds for any stochastic step.
- Pin software/package versions (
renv.lock, requirements.txt, recorded ssc/net installs).
- For qualitative work, keep a documented codebook and analytic memos.
What an ASR analyst-reviewer is checking
As the ASA's flagship, ASR draws referees who police analysis on each tradition's terms while asking
one disciplinary question — does the evidence warrant a claim that speaks to general sociological
theory? Use this table to pre-empt the masked reviewer.
| Reviewer probe | Clears the ASR bar | Triggers a revision flag |
|---|
| "Just a significant coefficient?" | magnitude + interval tied to a mechanism | stars-only, no interpretation |
| "Survives a reasonable confounder?" | sensitivity bound reported | one preferred spec, no probing |
| "Weighted and clustered right?" | design-respecting SEs | default SEs on a complex sample |
| "Where is disconfirming evidence?" | negative cases / null subgroups | only confirming evidence |
| "Heterogeneity real or mined?" | pre-specified or MHT-adjusted | one fished interaction theorized post hoc |
Worked micro-example (illustrative numbers)
A hypothetical ASR study links employer credit-checking to a Black-white callback gap using
administrative hiring records across 1,200 firms.
Main effect: callback gap 8.0 pp (95% CI 5.1–10.9) under firm + occupation FE
Mechanism: gap concentrated in customer-facing roles (11.2 pp) vs back-office (2.3 pp)
Sensitivity: a confounder must be ~1.7× the strongest covariate to nullify
Negative case: no gap where state law bans the practice (0.4 pp, CI −2.0–2.8) → boundary evidence
Reproducible: one master script, seed=2026, renv.lock pinned
The intervals carry the claim, the role contrast names a portable mechanism (statistical
discrimination via screening signals), and the law-ban null is reported as evidence, not buried.
Referee pushback → ASR-specific fix
- "Significant but does it matter?" → Give a scenario magnitude and name what changes for inequality
theory.
- "Robustness agrees by construction." → Add a spec that could break it (placebo period,
falsification subgroup) and report what you learned.
- "This reads as a within-subfield exercise." → State which general sociological debate the estimate
adjudicates before the table, not after.
Calibration anchors
- Theory-forward, not table-forward. ASR rewards an analysis that reads as a test of a mechanism;
a results dump that defers the "why" under-performs.
- The "so what for sociology" bar. Each headline number should map to a sentence a generalist could
repeat about how social processes work.
- Breadth of admissible evidence. Coefficients, life tables, coded fieldnotes, and validated model
outputs all qualify — the standard is the claim-to-evidence link.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. ASR is general sociology where observational designs dominate; foreground identification (DiD/IV/RDD), decomposition, and clustered 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; ignoring survey weights
- "Robustness" that only reruns near-identical specs to manufacture stability
- p-hacking / HARKing exploratory results into hypotheses
- Qualitative claims with no traceable evidence or negative cases
- Computational outputs reported without validation
Output format
【Main result】magnitude + interval (quant) OR evidence chain (qual)
【Identification/grounding check】(per research-design) result
【Robustness / negative cases】what held
【Heterogeneity】pre-specified? MHT-adjusted? (quant)
【Reproducible】master script + seeds + pinned versions OR documented codebook? [Y/N]
【Next】asr-tables-figures
Supplementary resources
