Jmf Data Analysis

Data Analysis (jmf-data-analysis)

JMF reviewers are methodologically sophisticated and family data are tricky: complex-survey weights, panel attrition, missingness, and the non-independence of partners and family members all bite. This skill covers execution and reporting norms; design decisions live in jmf-research-design.

When to trigger

• Running main and supporting analyses; building the results section
• A reviewer asked for robustness, heterogeneity, selection checks, or alternative specifications
• Reconciling preregistered vs. exploratory analyses
• Making the analysis reproducible before deposit

Analysis norms JMF expects

1. Report uncertainty and magnitude honestly. Confidence intervals and effect sizes (not stars alone); state the substantive size in family terms (months to divorce, percentage-point change in a transition, points on a relationship-quality scale).
2. Address selection explicitly. In observational family research, show what survives within-person/ couple fixed effects, sibling comparisons, propensity adjustment + sensitivity, or honest scoping.
3. Respect the data structure. Apply complex-survey weights/clusters/strata for PSID, NSFG, Add Health, etc.; cluster or model non-independence for dyads and families; use survival models for time-to-event outcomes.
4. Handle missingness principled-ly. Multiple imputation or FIML rather than listwise deletion; report the missing-data mechanism assumption and the share imputed; probe panel attrition.
5. Robustness that probes, not decorates. Alternative measures, samples, estimators, and specifications that could break the result — and what you learn.
6. Heterogeneity with discipline. Pre-specify subgroups (by gender, family structure, race/ ethnicity, cohort) where possible; correct for multiple comparisons; do not mine interactions.

Family-data specifics

• Report the analytic sample and how it was derived from the full dataset (eligibility, attrition, missingness) — reviewers will check the funnel.
• For dyadic models, report actor and partner effects and whether dyads are distinguishable.
• For growth/event-history models, report the time metric, censoring, and competing risks.

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 imputation, bootstrap, simulation, and any stochastic step.
• Pin software/package versions (renv.lock, requirements.txt, recorded ssc/net installs).
• Keep table/figure numbers in the manuscript matched to script outputs.

What JMF referees check in a results section

For the flagship journal of the National Council on Family Relations, the fastest desk-reject-adjacent outcome is an analysis that ignores the survey design or the dependence built into couple and family data.

Worked micro-example (illustrative)

A divorce-timing study uses discrete-time event-history on national-panel marriages. Numbers illustrative.

• Naive (flagged): a logit pooling person-years with no clustering reports OR = 1.40 for job loss on divorce, stars only.
• JMF-grade: a discrete-time hazard with survey weights and SEs clustered on the couple; job loss raises the divorce hazard ~28% (HR 1.28, 95% CI 1.09–1.50), shifting modeled median time to divorce earlier by ~1.6 years. Selection probe: within-couple fixed effects leave a smaller HR ≈ 1.18, so it is not purely sorting. Competing risks are modeled and the sample funnel reported.

Referee-pushback patterns and the venue-specific fix

• "Dyadic dependence ignored." Re-estimate with members nested in couples; for distinguishable dyads report actor and partner effects separately and test equality.
• "Selection into family transitions." Add a within-unit (couple/sibling) comparison or a sensitivity bound, and soften causal language to match.
• "Survey design ignored." Apply the dataset's weight/strata/PSU variables and report design-based SEs.

Calibration (hedged): for family panels (PSID, Fragile Families, Add Health, NSFG), design-based inference and principled missing-data handling are defaults; confirm dataset-specific guidance with provider documentation.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. JMF is quantitative family demography/sociology; emphasize identification, selection, and decomposition methods.

• 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, intervals, or substantive interpretation
• Ignoring survey weights/design or treating dyad members as independent
• Listwise deletion that silently changes the sample; ignoring attrition
• p-hacking / fishing for a significant interaction; HARKing exploratory results into hypotheses
• A results section whose numbers the code cannot reproduce

Output format

【Main estimate】magnitude + interval + substantive (family) meaning
【Selection check】(per research-design) result
【Data structure】weights/clusters/dyad-family non-independence handled? [Y/N]
【Missing data】MI/FIML + attrition probed? [Y/N]
【Robustness】specs that could break it → what held
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】jmf-tables-figures

Supplementary resources

• ../../resources/external_tools.md — estimation, survey, dyadic, survival, and imputation packages
• ../../resources/official-source-map.md — replication-detail and data expectations