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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

1. Report uncertainty and magnitude, not just significance — intervals and substantive effect sizes; respect survey design (weights, clustering).
2. Robustness that probes, not decorates — alternative measures, samples, estimators, and specifications that could break the result; say what you learn.
3. Heterogeneity with discipline — pre-specify or justify subgroups; adjust for multiple comparisons; don't mine an interaction and theorize it post hoc.
4. 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

• ../../resources/external_tools.md — estimation, demography, networks, and text-as-data packages
• ../../resources/official-source-map.md — ASA data-sharing norms