Ajs Data Analysis

Data Analysis (ajs-data-analysis)

At AJS the analysis exists to make the theoretical claim credible — not to display technique. A generalist, double-blind reviewer will ask whether the evidence actually warrants the claim and whether candor about uncertainty is present. This skill stress-tests the analysis chain in the idiom of your work.

When to trigger

• Planning the analysis, or auditing it before writing up
• A reader doubts robustness, the evidence-to-claim link, or the handling of uncertainty
• Reconciling multiple methods or data sources into one coherent argument
• Deciding which analyses are confirmatory vs. exploratory

Analysis norms (by tradition)

Quantitative

• Report uncertainty honestly (intervals, not just stars); avoid implying causality the design cannot support.
• Show that results are not artifacts: principled robustness (alternative specifications, samples, measures), not a fishing expedition; keep seeds and pinned versions.
• Distinguish preregistered/confirmatory from exploratory analyses where applicable.

Comparative-historical

• Make the inferential logic explicit (necessary/sufficient conditions, sequence, conjuncture); show how disconfirming evidence was sought and weighed.
• Cite primary sources so a reader could follow the trail.

Ethnographic / interview

• Show the analytic procedure: how codes/themes were built, how negative cases were handled, how representativeness within the case is judged.
• Quote enough to let the reader assess the inference from data to claim.

Triangulation (an AJS strength)

AJS often rewards convergent evidence — a mechanism shown through more than one window (e.g., statistics + cases, or interviews + administrative data). When methods disagree, say so and theorize the discrepancy rather than hiding it.

Referee-pushback patterns on the evidence chain (AJS fixes)

At a theory-forward generalist journal the analysis is judged by whether it makes the claim credible, not by technical novelty:

Calibration (AJS appetite, hedged)

Orienting heuristics; confirm against the journal's current submission guidelines. AJS rewards convergent evidence and candor over a dense methods display, judging each tradition by its own standard; where a parsimony-first sibling prizes one clean estimate, AJS often prizes a mechanism shown through more than one window. Illustrative: a paper claims a mentoring program narrows a promotion gap "by building cross-rank ties" (an illustrative 6-point reduction, 95% CI ~2–10). A referee writes "the mechanism is asserted, not shown." The fix maps it to an observable implication (mentees gain cross-rank ties), triangulates with an illustrative 24 interviews, reports two units where the gap did not close, and softens causal phrasing to "consistent with."

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. AJS is general sociology with a strong theory tradition; apply the chain below to its quantitative-empirical lane.

• 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 reporting; implying causation from association
• Robustness theater (a wall of tables that never tests the load-bearing assumption)
• Cherry-picked quotes or cases that ignore negative evidence
• Presenting exploratory results as if confirmatory
• Technique foregrounded over the theoretical question it serves
• A single-window analysis where triangulation was feasible and would have settled the mechanism

Evidence pass for American Journal of Sociology

Treat this skill as an executable review pass, not a prose hint. First lock the social process, data leverage, causal or interpretive warrant, and theoretical payoff; then judge whether the current manuscript answers the venue's real reader: sociology reviewers who value deep theory, durable empirical leverage, and careful social-mechanism claims.

• Do the pass: Audit the research design before polishing prose: unit of analysis, comparison set, uncertainty, sensitivity, missingness, and reproducibility must be visible.
• Return a ledger: give claim / evidence / risk / manuscript location rows, so the next agent can edit rather than rediscover the issue.
• Sibling guard: compare against ASR for broader empirical sociology, Social Forces for wider substantive range, Demography for population mechanisms; if a sibling owns the contribution, recommend re-routing before polishing format.
• Stop condition: do not give submission-ready advice until the pack's resources/official-source-map.md has been checked for volatile rules and the manuscript has one concrete fix for the largest venue-specific risk.

Output format

【Claim under test】from theory-building
【Primary evidence】the analysis that carries the claim
【Uncertainty】how it is reported and bounded
【Robustness / negative cases】load-bearing checks done? [Y/N]
【Triangulation】convergent evidence across windows? [Y/N/NA]
【Confirmatory vs. exploratory】labeled where relevant? [Y/N]
【Next】ajs-tables-figures

Supplementary resources

• ../../resources/external_tools.md — analysis packages (R / Stata / Python / CAQDAS / QCA)
• ../../resources/official-source-map.md — AJS evidence expectations and live-check boundary for data policy

Source: brycewang-stanford/Awesome-Journal-Skills → American-Journal-of-Sociology-Skills/skills/ajs-data-analysis/SKILL.md