How to Use LQAS to Classify Programme Coverage

LQAS classifies whether a lot meets or falls below a coverage threshold, rather than estimating a precise rate. This guide explains the underlying method first, then shows how to apply it using AnalyZ Solutions.

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Lot Quality Assurance Sampling classifies whether a coverage indicator meets or falls below a threshold, rather than estimating a precise coverage rate. It is widely used in humanitarian and health programme monitoring, where a fast pass or fail classification across many districts or lots matters more than a precise percentage in any one of them. This guide first explains how the classification method works, then walks through how to apply it in AnalyZ Solutions.

01How LQAS is different from a precision-based estimate

Every other tool in Sample Size Estimation answers a version of how many people do I need to estimate a value precisely. LQAS answers a different question: how many people do I need to sample from a lot, and what decision rule should I apply, to reliably classify that lot as meeting or not meeting a threshold, while keeping the chance of misclassification acceptably low.

Two error rates govern the design.

Alpha error. The chance of classifying a lot as failing when it actually meets the threshold, a false alarm.
Beta error. The chance of classifying a lot as passing when it actually falls below the threshold, a missed problem.

Standard LQAS designs keep both error rates at or below 10%. Given a coverage threshold and the upper and lower bounds that define what counts as clearly acceptable and clearly unacceptable, AnalyZ Solutions searches for the smallest lot size and decision rule that satisfies both constraints, using binomial probabilities.

Choosing your error rate tolerance

The 10% and 10% convention is a widely used default, not a fixed requirement. A programme making a high stakes decision, such as whether to divert emergency resources to a district, may prefer to tighten one or both error rates below 10%, at the cost of a larger lot size. A programme running frequent, lower stakes monitoring rounds across many districts may find the standard 10% and 10% convention gives an efficient balance between accuracy and the practical burden of repeated data collection.

Loosening either error rate increases the chance of a wrong classification, in a specific direction. A higher alpha error means more lots that actually meet the threshold get classified as failing, potentially triggering unnecessary corrective action. A higher beta error means more lots that actually fall short get classified as passing, allowing a genuine coverage gap to go unaddressed. Because the two errors trade off against the same lot size, tightening one without increasing sample size typically loosens the other.

A standard lot size of 19 appears throughout humanitarian and health monitoring guidance, including from UNICEF and WHO, because it is the smallest sample that reliably satisfies the 10% and 10% error criteria across the coverage ranges these programmes commonly work with.

How to Calculate This in AnalyZ Solutions

Set your coverage threshold. The value you are classifying against, for example 80% immunisation coverage.
Set the upper bound. The coverage level that should clearly be classified as acceptable.
Set the lower bound. The coverage level that should clearly be classified as unacceptable.
Calculate. The tool searches for the minimum lot size and decision rule that keeps both error rates at or below 10%, and reports both.

VIDEO WALKTHROUGH PLACEHOLDER, 90 SECONDS

A short screen recording showing these steps in the AnalyZ Solutions interface can be embedded here.

02Worked example

A programme wants to classify districts by whether vaccination coverage meets 80%. Coverage of 90% or above should clearly be classified as acceptable, and coverage of 70% or below should clearly be classified as unacceptable.

Inputs

Coverage threshold80%

Upper bound, acceptable90%

Lower bound, unacceptable70%

people per lot, with a decision rule reported alongside

Interpret the result as pass or fail for each district sampled, not as a precise coverage percentage. A district can be classified as passing without knowing its coverage is exactly 84% or 87%, only that it is reliably above the threshold given the sample drawn.

Frequently Asked Questions

Can I get a precise coverage estimate from LQAS instead of a pass or fail result?

No, not from the same sample. LQAS is built for classification, not estimation. If a precise coverage estimate with a confidence interval is needed, use the Cross-sectional tool instead.

Why does the standard lot size of 19 come up so often?

It is the smallest sample size that reliably satisfies the conventional 10% alpha and 10% beta error criteria across the coverage thresholds most commonly used in health and humanitarian monitoring, which is why it appears throughout published guidance rather than being calculated fresh for every study.

Can I use LQAS across many districts at once?

Yes. LQAS is typically applied identically across many lots, such as districts or health facility catchment areas, allowing a programme to classify each one independently using the same lot size and decision rule.

Ready to classify your programme coverage?

Open the LQAS tool in AnalyZ Solutions. Free, browser based.

Try it out

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