Model Assisted Survey Sampling

Now available in paperback. This book provides a comprehensive account of survey sampling theory and methodology which will be suitable for students and researchers across a variety of disciplines. A central theme is to show how statistical modeling is a vital component of the sampling process and in the choice of estimation technique. Statistical modeling has strongly influenced sampling theory in recent years and has clarified many issues related to the uses of auxiliary information in surveys. This is the first textbook that systematically extends traditional sampling theory with the aid of a modern model assisted outlook. The central ideas of sampling theory are developed from the unifying perspective of unequal probability sampling. The book covers classical topics as well as areas where significant new developments have taken place notably domain estimation, variance estimation, methods for handling nonresponse, models for measurement error, and the analysis of survey data. The authors have taken care to presuppose nothing more on the part of the reader than a first course in statistical inference and regression analysis. Throughout, the emphasis is on statistical ideas rather than advanced mathematics. Each chapter concludes with a range of exercises incorporating the analysis of data from actual finite populations. As a result, all those concerned with survey methodology or engaged in survey sampling will find this an invaluable and up-to-date coverage of the subject.

Suitable for graduate students and researchers in many fields, this book has become the classic reference for a model-assisted approach to sampling theory. It provides a comprehensive account of survey sampling theory and methodology. A central theme is to show how statistical modeling is a vital component of the sampling process and in the choice of estimation technique. The book covers classical topics as well as areas where significant new developments have taken place notably domain estimation, variance estimation, methods for handling nonresponse, models for measurement error, and the analysis of survey data.

I Principles of Estimation for Finite Populations and Important Sampling Designs.- 1 Survey Sampling in Theory and Practice.- 2 Basic Ideas in Estimation from Probability Samples.- 3 Unbiased Estimation for Element Sampling Designs.- 4 Unbiased Estimation for Cluster Sampling and Sampling in Two or More Stages.- 5 Introduction to More Complex Estimation Problems.- II Estimation through Linear Modeling, Using Auxiliary Variables.- 6 The Regression Estimator.- 7 Regression Estimators for Element Sampling Designs.- 8 Regression Estimators for Cluster Sampling and Two-Stage Sampling.- III Further Questions in Design and Analysis of Surveys.- 9 Two-Phase Sampling.- 10 Estimation for Domains.- 11 Variance Estimation.- 12 Searching for Optimal Sampling Designs.- 13 Further Statistical Techniques for Survey Data.- IV A Broader View of Errors in Surveys.- 14 Nonsampling Errors and Extensions of Probability Sampling Theory.- 15 Nonresponse.- 16 Measurement Errors.- 17 Quality Declarations for Survey Data.- Appendix A Principles of Notation.- Appendix B The MU284 Population.- Appendix C The Clustered MU284 Population.- Appendix D The CO124 Population.- References.- Answers to Selected Exercises.- Author Index.