Sampling

From the reviews of the First Edition:
"A well-written book that deserves to be widely used, both by statistics teachers and by researchers."
-Biometrics
"The explanations are clear and concise, the presentation is extremely pleasing, the references are up to date, and there is an abundance of examples."
-Short Book Reviews
"This is a highly recommended acquisition for any statistician concerned with the collection of sample information."
-Technometrics
All aspects of obtaining, interpreting, and using sample data in a new revised volume
Sampling provides an up-to-date treatment of both classical and modern sampling design and estimation methods, along with sampling methods for rare, clustered, and hard-to-detect populations. This Second Edition's extensive new material includes descriptions of new developments, a wider range of approaches to common problems, historical notes, and increased coverage of topics such as methods that combine design and model-based approaches, adjusting for nonsampling errors, and the use of link-tracing designs. Updated chapters show how relevant sampling methods function within such fields as the biological, environmental, and geological sciences; social and health sciences; and sampling the Internet.
Organized into six sections, this edition covers basic sampling, from simple random to unequal probability sampling; the use of auxiliary data with ratio estimators and regression estimation; sufficient data, model, and design in practical sampling; useful designs such as stratified, cluster and systematic, multistage, double and network sampling; detectability methods for elusive populations; spatial sampling; and adaptive sampling designs. Additional derivations, notes on underlying ideas, exercises, and examples foster a greater mastery of the presented techniques. New numerical examples, small-population examples, and exercises have been added.
Featuring a wider range of topics than other sampling books, Sampling, Second Edition is the ideal reference for scientific researchers and other professionals who use sampling, as well as students in sampling courses.

STEVEN K. THOMPSON, PhD, is Professor of Statistics at the Pennsylvania State University. During his career he has served on the faculties of the University of Auckland and the University of Alaska. He is the author of Adaptive Sampling (with George Seber), also published by Wiley.