Research and collection

Sampling and weighting

Designs, sample size, weights, and the claims each supports.

Who this is for

Research and M&E officers, and analysts reading somebody else’s design.

No screenshot on this pageThe analysis workspace capture this guide carried was withdrawn from publication on 2026-09-22. Read by eye, it rendered a real administrator’s name and the real VoiceInsights Africa organisation rather than the demonstration tenant it had been published as. Re-capturing it on the demonstration tenant needs a staff sign-in, and Preview cannot currently deliver the email one-time code that sign-in requires, so no governed capture of this surface exists to publish. Putting a picture of an adjacent screen here instead would make a caption claim something the image does not show.

Designs and what they support

Eleven designs are available. What matters is which ones support a population inference: probability designs do, non-probability designs do not, and the platform enforces that rather than trusting the label on the study.

Representativeness is not a setting

A non-probability design cannot claim representativenessRegardless of what the study declares. If you set the flag, it is forced back to false. Such a design also cannot be post-stratified — producing weights would imply a sampling frame that never existed.

Sample size

Sample-size calculation runs Cochran, then the finite-population correction, then the design effect, then non-response — in that order — and returns every step, so the number can be checked rather than trusted.

Weights

Design weights, post-stratification and normalisation are available. Zero and negative weights are errors, not warnings. Every result states whether it is UNWEIGHTED, WEIGHTED or NOT_APPLICABLE.

Weighted variance uses the Kish effective sample size approximation. This is not design-based variance, and every weighted interval carries that as a stated assumption — it will understate uncertainty for a clustered design.

What this does not do

Stated here rather than discovered later

Weighted variance is an approximation, and the platform says so on every weighted interval rather than in a footnote. For a clustered design it will understate uncertainty.
No design is offered that would let a non-probability sample produce a population estimate. That is not a missing feature.

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