Poq Data Analysis

Data Analysis (poq-data-analysis)

POQ reviewers are methodologically sophisticated, and the journal requires replication materials that reproduce exactly all published tables and figures (see poq-transparency-and-data-policy). Analyze as if both are true — because they are. The defining POQ demand is design-based inference: survey weights, strata, and clusters belong in the variance estimator, not just the point estimate. Design decisions live in poq-survey-design-and-measurement.

When to trigger

• Running main and supporting analyses; building the results section
• A reviewer asked for design-based SEs, robustness, or alternative weighting
• Reconciling preregistered vs. exploratory analyses
• Making the analysis reproducible before deposit

Analysis norms POQ expects

1. Design-based inference. Use complex-survey estimators (svy: / survey / samplics); declare weights, strata, and PSUs. Report the design effect (DEFF); do not present naive IID standard errors on a clustered, weighted sample.
2. Report uncertainty honestly. Confidence intervals, not just stars; the magnitude and substantive meaning of the estimate. For opinion shares, show the margin of error and the definition of precision.
3. Weighted vs. unweighted. Show both where they diverge and explain why; do not let weighting silently drive the headline result.
4. Robustness that probes, not decorates. Alternative weighting/calibration, alternative codings, sensitivity to nonresponse assumptions, mode controls — specs that could break the result.
5. Heterogeneity with discipline. Pre-specify subgroups; correct for multiple comparisons; do not mine for a significant interaction and theorize it post hoc.
6. Measurement carries through. Show the result is not an artifact of a coding/scaling choice; report reliability and, where relevant, measurement invariance across groups/modes.

Missing data, nonresponse & trends

• Distinguish item nonresponse handling (multiple imputation vs. listwise) and report the choice.
• Adjust for nonresponse explicitly; state assumptions (MAR vs. not) and probe sensitivity.
• For trend/Polls-in-Context analyses, hold question wording and mode constant or flag the break.

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, multiple imputation, simulation, and any stochastic step.
• Pin software/package versions (renv.lock, requirements.txt, recorded ssc/net installs).
• Keep table/figure numbers matched to script outputs — POQ re-runs the package against the exhibits.

POQ replication acceptance gate

Before writing results prose, run a local replication gate that mirrors the journal's expectations:

Only after this gate should the manuscript claim that the analysis is reproducible. POQ readers notice when design-based inference is described in text but not actually encoded in the scripts.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. Public Opinion Quarterly is survey methodology and public opinion; the chain serves causal/experimental claims, while survey-design and measurement contributions use their own standards (sampling, weighting, measurement error).

• 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

• Naive IID standard errors on a weighted, clustered survey (the most common POQ analysis flaw)
• Stars-only tables with no effect sizes, intervals, or margins of error
• Weighting the estimate but ignoring the weights/design in the variance
• p-hacking / HARKing exploratory subgroup results into hypotheses
• A results section whose numbers the deposited code cannot reproduce

Output format

【Estimator】complex-survey (weights/strata/PSUs declared)? [Y/N]
【Main estimate】magnitude + interval/MOE + substantive meaning
【Design effect】DEFF reported?
【Weighted vs unweighted】reconciled where they differ?
【Robustness】specs that could break it → what held
【Heterogeneity】pre-specified? MHT-adjusted?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】poq-tables-figures

Supplementary resources

• ../../resources/external_tools.md — complex-survey estimation, weighting, imputation packages
• ../../resources/official-source-map.md — reproducibility / replication-archive policy