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:

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.

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