| name | poq-survey-design-and-measurement |
| description | Use when defending the survey design and measurement of a Public Opinion Quarterly (POQ) manuscript through the Total Survey Error framework — coverage, sampling, nonresponse, measurement (question wording/order/scales), mode effects, and weighting. POQ is the leading survey-methodology journal and expects AAPOR-standard rigor. Strengthens the design; it does not write code. |
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:
| TSE component | Design choice | Residual risk | Evidence or disclosure |
|---|
| Coverage | Frame and eligibility rule | Who is systematically absent? | Benchmark comparison or limitation |
| Sampling | Selection probabilities / panel recruitment | Selection into the sample | Weighting, calibration, or sensitivity |
| Nonresponse | Contact protocol and disposition codes | Nonresponse bias | AAPOR RR calculation plus bias check |
| Measurement | Wording, order, scale, translation | Construct mismatch or satisficing | Pretest/cognitive evidence and exact wording |
| Mode | Web/phone/mail/mixed mode | Mode-specific response pattern | Mode controls, split test, or caveat |
| Weighting | Design, nonresponse, calibration weights | Inflated variance / model dependence | Design effect and weighted/unweighted comparison |
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
