poq-survey-design-and-measurement

Survey Design & Measurement (poq-survey-design-and-measurement)

This is the POQ core. Reviewers are survey scientists who will probe every link in the Total Survey Error (TSE) chain. The design must credibly connect the hypotheses (poq-theory-and-hypotheses) to data that measure what you claim, and you must be able to disclose every methodological element to AAPOR standards. Defend each error source against the strongest alternative.

When to trigger

• Specifying the sampling frame, questionnaire, mode, or weighting scheme
• A reviewer questioned coverage, nonresponse, question wording, mode, or representativeness
• Building the Appendix A: Disclosure Elements (do this here, not at the end)
• Designing a survey experiment (wording/order split-ballot, conjoint, list experiment)

Walk the Total Survey Error chain

• Coverage. Frame vs. target population; who is missed (online/RDD/ABS undercoverage). State the target population precisely.
• Sampling. Probability vs. nonprobability; stratification, clustering, PSUs; selection probabilities. Nonprobability/online panels need an explicit representativeness argument.
• Nonresponse. Report unit and item nonresponse; the response rate computed per AAPOR Standard Definitions (say RR1–RR6 and show the disposition-code calculation); assess nonresponse bias, not just the rate.
• Measurement. Question wording, order, response scales, reference periods, social desirability, acquiescence. Validate constructs; pretest (cognitive interviews, behavior coding).
• Mode. Single vs. mixed mode; quantify and adjust for mode effects; do not conflate a mode artifact with a substantive change.
• Adjustment / weighting. Design weights, nonresponse adjustment, calibration/raking, post-stratification (incl. MRP). Report what the weights correct for and the resulting design effect.

AAPOR disclosure (build Appendix A now)

POQ requires you to disclose — for all data reported — or link to public documentation: funding; exact question wording; population under study; sample design; method and dates of collection; response rate and how it was calculated (AAPOR definitions); sample sizes and precision of findings; and any design effect due to clustering and weighting. Assemble these in "Appendix A: Disclosure Elements" as you design — see poq-transparency-and-data-policy.

Survey experiments

• Split-ballot wording/order experiments: randomize, report the manipulation, isolate the measurement effect.
• Conjoint / list experiments / vignettes: pre-specify estimands; address attention checks and attrition.
• Preregister design and primary analyses; report power/MDE.

The artifact-vs-effect test (POQ-specific)

For the headline result, write one sentence: "If this were a survey artifact (coverage / nonresponse / wording / order / mode / weighting) rather than a real opinion signal, the data would look like ___; instead they look like ___." If you cannot, the design does not yet isolate the contribution.

Design audit table

Build a one-page audit before submission:

The final article should not merely say these issues were considered; it should point readers to the appendix row, table, or supplement where each was handled.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe 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).

• detect_design → recommend → fit with as_handle=true → audit_result.
• Observational causal claims: staggered DiD (callaway_santanna / sun_abraham + bacon_decomposition + honest_did_from_result); IV (effective_f_test + anderson_rubin_ci); RDD (rdrobust + mccrary_test).
• Experiments: randomization-based inference, romano_wolf for many-outcome family-wise control, and mediate for mediation (not naive controlling-away).
• Sensitivity: oster_delta / sensemakr for observational claims.

Report the effect size in interpretable units; route the full battery to the appendix/supplement. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.

Anti-patterns

• Reporting a response rate with no AAPOR definition or calculation shown
• Treating a nonprobability online sample as representative with no argument or benchmark
• Ignoring mode effects in a mixed-mode design; conflating mode artifacts with opinion change
• Weighting the point estimate but ignoring weights/clusters in the variance (see poq-data-analysis)
• Leaving Appendix A disclosure until submission
• Naming the target population broadly while the frame covers only reachable or panelized respondents

Output format

【Target population & frame】coverage gaps named
【Sample design】probability/nonprobability; strata/clusters/PSUs
【Nonresponse】RR definition + value + bias assessment
【Measurement】wording/order/scale + validation + pretest
【Mode】single/mixed; mode effect handled?
【Weighting】what it corrects + design effect
【Artifact ruled out】the artifact-vs-effect sentence
【Design audit】TSE table complete; residual risks disclosed
【Appendix A started?】[Y/N]
【Next】poq-data-analysis

Supplementary resources

• ../../resources/external_tools.md — complex-survey, weighting, measurement, and pretesting tools
• ../../resources/official-source-map.md — AAPOR disclosure elements and Standard Definitions