Design a Sampling Strategy
Determine how to select your study sample : method, size, stratification, and practical considerations.
Your Prompt
- What You Need
- Provide target population, question, frame, proposed design, precision or power target, expected rate, resources, and access constraints.
- Choose This Prompt When
- Choose this for the sampling component; choose baseline study for the full study design.
Describe your goal, audience, available information and constraints. Your words are added to the prompt as supplied. You can also use the guided fields below.
Kept in this tab while you work. Copy and Start includes this description.
Copy this prompt and paste it into your AI chat to start. If it needs source material, the AI will ask for it.
Evidence and applicability: use only supplied or verifiable evidence for historical facts, values, quotations, completed activities and approvals. Preserve conflicting sources with their provenance; do not choose a preferred value without adjudication. Mark missing evidence as unknown or not evidenced, not as proof an activity never occurred. Keep proposed plans and illustrative examples separate from findings. Never invent a baseline, quote, pilot, validation or sign-off to complete a template. Match methods to the purpose and population; a suitable single-method design is valid. Distinguish descriptive change from causal attribution. Check obligations in the applicable funding agreement rather than assuming a universal donor rule.
Design a sampling strategy for the study purpose and target population I provide. Ask for the population, intended estimate or comparison, and available frame if missing.
Define the target population, unit, frame, eligibility, estimand or qualitative information need, and scope of inference. Compare feasible probability or purposive approaches and choose one that fits the question, access, ethics, and resources. Do not present a nonprobability sample as statistically representative. For a quantitative precision or power calculation, state the formula and all required parameters, including confidence level, expected proportion or variance, target difference if comparing groups, design effect, finite population correction if relevant, and expected nonresponse. Calculate only when those values are supplied or explicitly proposed as planning assumptions; otherwise provide a parameter checklist and worked formula without a fake sample size. Distinguish stratification from later disaggregation and include only useful strata. Describe selection steps, cluster or household rules, replacements or nonresponse handling, weighting, logistics, consent, and likely bias. Give an implementation sequence and tentative timeline, clearly marking unknown inputs and decisions for the team. If stakeholder feedback is supplied, explain how it affects access, selection, consent, and participation without treating it as an approved design.