Public Health Survey Sampling Frame and Calibration Weighting Verification Checklist
Audit complex sampling frames, stratifications, non-response adjustments, and survey calibration weights for public agencies.
Use this checklist when validating probability sample designs and weighting workflows for regional or national public health surveys. It ensures survey statisticians thoroughly check frame coverage, strata variances, rake weighting convergence, and design effect inflation prior to public data release.
Role: Principal Survey Statistician for Municipal and Public Health Agencies
Context
- Population Universe: {{target_population_definition}}
- Registry Frame: {{sampling_frame_source}}
- Strata & PSU Definitions: {{stratification_variables}}
- Non-Response Profile: {{expected_non_response_rate}}
- Calibration Targets: {{post_stratification_benchmarks}}
- Variance Engine: {{variance_estimation_method}}
Task
Deliver an end-to-end technical audit checklist to verify sample design integrity, non-response bias adjustments, and post-stratification weighting before producing official public health statistics.
Method
- Inspect coverage errors and under-coverage bias between {{sampling_frame_source}} and {{target_population_definition}}.
- Verify selection probabilities and base design weights across {{stratification_variables}}.
- Audit unit non-response adjustments against expected patterns in {{expected_non_response_rate}}.
- Check convergence tolerances and extreme weight trimming rules during raking against {{post_stratification_benchmarks}}.
- Evaluate Design Effect (DEFF) and Effective Sample Size (n_eff) to detect excessive weight variance.
- Validate replicate weights or linearization formulas configured for {{variance_estimation_method}}.
- Establish cell suppression and coefficient of variation (CV) flagging thresholds for reporting.
- Verify compliance with public sector statistical standards for data privacy and disclosure control.
Constraints
- Checklist MUST contain specific threshold guidance (e.g., max weight ratios, CV cutoff limits).
- MUST NOT approve final survey weights without checking trimming thresholds and raking convergence.
- All items MUST reflect complex survey design principles rather than simple random sampling assumptions.
- Use unambiguous pass/warning/fail criteria for each verification step.
Output format
- Markdown checklist categorized into distinct audit stages.
- Part 1: Frame Coverage & PSU Verification (4-5 checklist items)
- Part 2: Non-Response & Attrition Adjustments (4-5 checklist items)
- Part 3: Calibration, Raking & Weight Trimming (5-6 checklist items)
- Part 4: Variance Estimation & Reporting Safety (4-5 checklist items)
- Part 5: Sign-Off Governance Table (Task, Metric Checked, Passing Criteria, Analyst Sign-off)
Self-review
- Does the checklist specifically reference {{variance_estimation_method}} in the standard error validation steps?
- Are post-stratification adjustments against {{post_stratification_benchmarks}} explicit?
- Are design effect (DEFF) formulas and weight dispersion checks included?
Explicit role, a named task, and discrete steps the model can follow.
Background, inputs and variables the model needs before it starts.
Hard boundaries — what the model must and must not do.
A named, field-level shape for the response.
Ordered work items that force analysis before an answer.
Length and structure that travel across frontier models.
Signal density — instruction weight without padding.
Documented variables so the scaffold adapts to new inputs.
Quality bar, assumptions and behaviour when inputs are thin.
How much real usage the template has behind it.