Sampling Theory and Practice

The three parts of this book on survey methodology combine an introduction to basic sampling theory, engaging presentation of topics that reflect current research trends, and informed discussion of the problems commonly encountered in survey practice. These related aspects of survey methodology rarely appear together under a single connected roof, making this book a unique combination of materials for teaching, research and practice in survey sampling. Basic knowledge of probability theory and statistical inference is assumed, but no prior exposure to survey sampling is required. The first part focuses on the design-based approach to finite population sampling. It contains a rigorous coverage of basic sampling designs, related estimation theory, model-based prediction approach, and model-assisted estimation methods. The second part stems from original research conducted by the authors as well as important methodological advances in the field during the past threedecades. Topics include calibration weighting methods, regression analysis and survey weighted estimating equation (EE) theory, longitudinal surveys and generalized estimating equations (GEE) analysis, variance estimation and resampling techniques, empirical likelihood methods for complex surveys, handling missing data and non-response, and Bayesian inference for survey data. The third part provides guidance and tools on practical aspects of large-scale surveys, such as training and quality control, frame construction, choices of survey designs, strategies for reducing non-response, and weight calculation. These procedures are illustrated through real-world surveys. Several specialized topics are also discussed in detail, including household surveys, telephone and web surveys, natural resource inventory surveys, adaptive and network surveys, dual-frame and multiple frame surveys, and analysis of non-probability survey samples. This book is a self-contained introduction to survey sampling that provides a strong theoretical base with coverage of current research trends and pragmatic guidance and tools for conducting surveys.

Provides rich materials for teaching, research and practice in survey sampling Includes ample exercise problems to facilitate teaching and learning Integrates R packages that cover the book's topics for easy implementation Bridges the gap between survey data analysis and statistical methods in other fields Serves as a monograph for researchers and practitioners, and a textbook for upper-level undergraduates or graduate students

Auteur
Changbao Wu is Professor of Statistics at the University of Waterloo. He has done extensive research on the design and analysis of complex surveys. He received the CRM-­SSC Prize in Statistics in 2012 and was named Fellow of ASA and Elected Member of ISI in 2014. He has served as an Associate Editor for several statistical journals including Biometrika, Journal of the American Statistical Association and The Canadian Journal of Statistics.

Mary E. Thompson is Distinguished Professor Emerita in Statistics at the University of Waterloo. She is a leading expert on survey methodology and the author of the book Theory of Sample Surveys (1997). She has received numerous awards and honors including the Gold Medal of Statistical Society of Canada and Fellow of the Royal Society of Canada.

Texte du rabat

The three parts of this book on survey methodology combine an introduction to basic sampling theory, engaging presentation of topics that reflect current research trends, and informed discussion of the problems commonly encountered in survey practice. These related aspects of survey methodology rarely appear together under a single connected roof, making this book a unique combination of materials for teaching, research and practice in survey sampling. Basic knowledge of probability theory and statistical inference is assumed, but no prior exposure to survey sampling is required. The first part focuses on the design-based approach to finite population sampling. It contains a rigorous coverage of basic sampling designs, related estimation theory, model-based prediction approach, and model-assisted estimation methods. The second part stems from original research conducted by the authors as well as important methodological advances in the field during the past threedecades. Topics include calibration weighting methods, regression analysis and survey weighted estimating equation (EE) theory, longitudinal surveys and generalized estimating equations (GEE) analysis, variance estimation and resampling techniques, empirical likelihood methods for complex surveys, handling missing data and non-response, and Bayesian inference for survey data. The third part provides guidance and tools on practical aspects of large-scale surveys, such as training and quality control, frame construction, choices of survey designs, strategies for reducing non-response, and weight calculation. These procedures are illustrated through real-world surveys. Several specialized topics are also discussed in detail, including household surveys, telephone and web surveys, natural resource inventory surveys, adaptive and network surveys, dual-frame and multiple frame surveys, and analysis of non-probability survey samples. This book is a self-contained introduction to survey sampling that provides a strong theoretical base with coverage of current research trends and pragmatic guidance and tools for conducting surveys.

Résumé
"This is a comprehensive textbook on survey sampling, taking the reader from the foundations of the theory to well-established extensions ... . Each chapter is followed by a set of exercises. ... The text is richly complemented with citations to the relevant literature, including citations of historical importance. ... the monograph is suitable for undergraduate and graduate courses in survey sampling ... ." (Nicholas T. Longford, Mathematical Reviews, February, 2022)

Contenu
Part I Basic Concepts and Methods in Survey Sampling.- Chapter 1 Basic Concepts in Survey Sampling.- Chapter 2 Simple Single-Stage Sampling Methods.- Chapter 3 Stratified Sampling and Cluster Sampling.- Chapter 4 General Theory and Methods of Unequal Probability Sampling.- Chapter 5 Model-Based Prediction and Model-Assisted Estimation.- Part II Advanced Topics on Analysis of Probability Survey Samples.- Chapter 6 Calibration Weighting and Estimation.- Chapter 7 Regression Analysis and Estimating Equations.- Chapter 8 Empirical Likelihood Methods.- Chapter 9 Methods for Handling Missing Data.- Chapter 10 Resampling and Replication Methods.- Chapter 11 Bayesian Empirical Likelihood Methods.- Part III Practical Issues and Special Topics in Survey Sampling.- Chapter 12 Area Frame Household Surveys.- Chapter 13 Telephone and Web Surveys.- Chapter 14 Natural Resource Inventory Surveys.- Chapter 15 Adaptive and Network Surveys.- Chapter 16 Dual Frame and Multiple Frame Surveys.- Chapter 17 Non-Probability Survey Samples.- References.- Appendix.- Index.