sf-data-analysis

Data Analysis (sf-data-analysis)

Social Forces built its standing on methodological rigor, and its reviewers are sophisticated. The analysis must report uncertainty honestly, probe robustness that could actually break the result, and stay reproducible. This skill covers execution and reporting norms; design decisions live in sf-research-design. Keep an eye on the exhibit budget — results must fit within 10 tables and figure panels (see sf-tables-figures).

When to trigger

• Running main and supporting analyses; building the results section
• A reviewer asked for robustness, heterogeneity, or alternative specifications
• Reconciling confirmatory vs. exploratory analyses
• Making the analysis reproducible before drafting the data availability statement

Analysis norms SF expects

1. Report uncertainty honestly. Confidence/credible intervals, not just stars; the magnitude and substantive meaning of the estimate, not just its significance.
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; correct for multiple comparisons; do not mine for a significant interaction and theorize it post hoc.
4. Right inference. Cluster at the assignment/sampling level; use survey weights and design-based inference for complex samples; small-cluster corrections (wild-cluster bootstrap) when clusters are few.
5. Measurement. Validate constructs; report reliability; show the result is not an artifact of a coding/scaling choice.
6. Missing data. Be explicit — multiple imputation or principled handling, not silent listwise deletion that changes the sample.

Demographic / computational / network specifics

• Demographic: report standardization/decomposition choices; handle censoring and competing risks.
• Computational/text: document model/version, hyperparameters, seeds; validate against human labels.
• Network: state the null/baseline; report sensitivity to boundary and tie-definition choices.

Reproducibility while you work (not at the end)

• One master script regenerates every table and figure from the (raw or constructed) data.
• Set and report seeds for bootstrap, simulation, imputation, and any stochastic step.
• Pin software/package versions (renv.lock, requirements.txt, recorded ssc/net installs).
• Keep table/figure numbers matched to script outputs — and feed the data availability statement (see sf-data-and-transparency).

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
• Ignoring survey weights/design, or clustering at the wrong level
• A results section whose numbers the code cannot reproduce

Evidence pass for Social Forces

Treat this skill as an executable review pass, not a prose hint. First lock the social mechanism, data scope, identification or interpretation, and contribution to a wider literature; then judge whether the current manuscript answers the venue's real reader: social-science reviewers who want generalizable social-process evidence across sociology, demography, and policy-adjacent topics.

• 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/AJS for top sociology theory stakes, Demography for population process, JMF for family-specific claims; 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

【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?
【Inference】weights/clustering/few-cluster handled correctly? [Y/N]
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】sf-tables-figures

Supplementary resources

• ../../resources/external_tools.md — estimation, inference, demography, network, and text-as-data packages
• ../../resources/official-source-map.md — data availability statement requirement