Sampling theory facilitates development of economical, effective and rapid measurement of a population. While national policy makers value survey results measuring indicators representative of a large area (a country, state or province), measurement in smaller areas produces information useful for managers at the local level.
It is often not possible to disaggregate a national survey to obtain local information if that was not the intent of the original survey design. Cluster sampling is typically used for national or large area surveys because sampling in clusters lowers the cost of a survey.
Lot Quality Assurance Sampling (LQAS) is used to measure results at a local level, since it requires small random samples and produces results useful to local managers. However, current LQAS methodology requires all local areas (strata) be included in the survey in order to be aggregated to produce point estimates for the nation or state. In large countries it is not feasible to sample all strata for logistical and financial reasons.
This paper resolves this problem by presenting Large Country-Lot Quality Assurance Sampling (LC-LQAS), a method with two concurrent objectives: (1) provide local managers with accurate local information to enable data driven decisions, and (2) provide central policy makers with the aggregate information they require. These are achieved by integrating cluster sampling with LQAS methodologies.
Two examples of the implementation of LC-LQAS are provided, in an HIV/AIDS program in Kenya and a Malaria Booster Project in Nigeria. Classifications of local health units into performance categories and aggregate estimates of coverage, with associated confidence intervals, are provided for select indicators in order to demonstrate its use, analysis, and costs. This paper is written as a manual to support the use of LC-LQAS by others.