Sampling : design and analysis

Table of Contents 1. Introduction 2. Simple Probability Samples 3. Stratified Sampling 4. Ratio and Regression Estimation 5. Cluster Sampling with Equal Probabilities 6. Sampling with Unequal Probabilities 7. Complex Surveys 8. Nonresponse 9. Variance Estimation in Complex Surveys 10. Categorical Data Analysis in Complex Surveys 11. Regression in Complex Surveys 12. Two-Phase Sampling 13. Estimating the Size of a Population 14. Rare Populations and Small Area Estimation 15. Nonprobability Samples 16. Survey Quality

What is the unemployment rate? How many adults have high blood pressure? What is the total area of land planted with soybeans? Sampling: Design and Analysis tell you how to design and analyze surveys to answer these and other questions. This authoritative text, used as a standard reference by numerous survey organizations, teaches sampling using real data sets from social sciences, public opinion research, medicine, public health, economics, agriculture, ecology, and other fields. The book is accessible to students from a wide range of statistical backgrounds. By appropriate choice of sections, it can be used for a graduate class for statistics students or for a class with students from business, sociology, psychology, or biology. Readers should be familiar with concepts from an introductory statistics class including linear regression; optional sections contain the statistical theory, for readers who have studied mathematical statistics. Distinctive features include More than 450 exercises. In each chapter, Introductory Exercises develop skills, Working with Data Exercises give practice with data from surveys, Working with Theory Exercises allow students to investigate statistical properties of estimators, and Projects and Activities Exercises integrate concepts. A solutions manual is available. An emphasis on survey design. Coverage of simple random, stratified, and cluster sampling; ratio estimation; constructing survey weights; jackknife and bootstrap; nonresponse; chi-squared tests and regression analysis. Graphing data from surveys. Computer code using SAS® software. Online supplements containing data sets, computer programs, and additional material.