The effect of clustering on lot quality assurance sampling: a probabilistic model to calculate sample sizes for quality assessmentsReport as inadecuate




The effect of clustering on lot quality assurance sampling: a probabilistic model to calculate sample sizes for quality assessments - Download this document for free, or read online. Document in PDF available to download.

Emerging Themes in Epidemiology

, 10:11

First Online: 26 October 2013Received: 30 December 2012Accepted: 17 September 2013

Abstract

BackgroundTraditional Lot Quality Assurance Sampling LQAS designs assume observations are collected using simple random sampling. Alternatively, randomly sampling clusters of observations and then individuals within clusters reduces costs but decreases the precision of the classifications. In this paper, we develop a general framework for designing the clusterC-LQAS system and illustrate the method with the design of data quality assessments for the community health worker program in Rwanda.

ResultsTo determine sample size and decision rules for C-LQAS, we use the beta-binomial distribution to account for inflated risk of errors introduced by sampling clusters at the first stage. We present general theory and code for sample size calculations.

The C-LQAS sample sizes provided in this paper constrain misclassification risks below user-specified limits. Multiple C-LQAS systems meet the specified risk requirements, but numerous considerations, including per-cluster versus per-individual sampling costs, help identify optimal systems for distinct applications.

ConclusionsWe show the utility of C-LQAS for data quality assessments, but the method generalizes to numerous applications. This paper provides the necessary technical detail and supplemental code to support the design of C-LQAS for specific programs.

KeywordsCluster-LQAS Lot quality assurance sampling Program evaluation Survey Community health workers Electronic supplementary materialThe online version of this article doi:10.1186-1742-7622-10-11 contains supplementary material, which is available to authorized users.

Download fulltext PDF



Author: Bethany L Hedt-Gauthier - Tisha Mitsunaga - Lauren Hund - Casey Olives - Marcello Pagano

Source: https://link.springer.com/







Related documents