Large Country-Lot Quality Assurance Sampling : A New Method for Rapid Monitoring and Evaluation of Health, Nutrition and Population Programs at Sub-National Levels
Sampling theory facilitates development of economical, effective and rapid measurement of a population. While national policy maker value survey results measuring indicators representative of a large area (a country, state or province), measurement...
| Main Authors: | , , , |
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| Format: | Working Paper |
| Language: | English en_US |
| Published: |
World Bank, Washington, DC
2014
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/2008/05/18673016/large-country-lot-quality-assurance-sampling-new-method-rapid-monitoring-evaluation-health-nutrition-population-programs-sub-national-levels http://hdl.handle.net/10986/16962 |
| Summary: | Sampling theory facilitates development
of economical, effective and rapid measurement of a
population. While national policy maker 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
(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. |
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