Sample Design for Survey's Quality Evaluation

Sample designers and data collectors have always beenworking for assuring high quality standards of surveydata. Although much has been written aboutstatistical quality assurance methods, notably in theindustry setting, few resources in the literatureaddress the problem of assessing survey's quality.Sample Design for Survey's Quality Evaluationapproaches such a relevant topic focusing on thedesign of probabilistic samples for data collectionquality monitoring and measurement error evaluationof longitudinal surveys. In both cases, thetheoretical developments are motivated by potentialapplications to the U.S. National ResourcesInventory. Nevertheless, the employed process ofinvestigation as well as the results presented in thebook can be applied to several other surveyscenarios. The properties of several classes ofsample designs for data quality monitoring areinvestigated and the problem of designing asubsample to estimate the measurement errorcontribution to the variance of the Horvitz-Thompsonestimator is studied. The covered subjects make thisbook an essential source for any professionalinvolved in quality evaluation studies of samplesurveys.

Autorentext

received his PhD in Statistics from Iowa State University, Ames. He is currently the coordinator for undergraduate studies in Statistics and a faculty member of the graduate program in Statistics at the Federal University of Pernambuco, Brazil. Dr Ferraz's areas of work are design and analysis of sample surveys and experiments.

Klappentext
Sample designers and data collectors have always been working for assuring high quality standards of survey data. Although much has been written about statistical quality assurance methods, notably in the industry setting, few resources in the literature address the problem of assessing survey's quality. Sample Design for Survey's Quality Evaluation approaches such a relevant topic focusing on the design of probabilistic samples for data collection quality monitoring and measurement error evaluation of longitudinal surveys. In both cases, the theoretical developments are motivated by potential applications to the U.S. National Resources Inventory. Nevertheless, the employed process of investigation as well as the results presented in the book can be applied to several other survey scenarios. The properties of several classes of sample designs for data quality monitoring are investigated and the problem of designing a subsample to estimate the measurement error contribution to the variance of the Horvitz-Thompson estimator is studied. The covered subjects make this book an essential source for any professional involved in quality evaluation studies of sample surveys.