Features new developments in the field combined with all aspects of obtaining, interpreting, and using sample data
"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 Third Edition retains the general organization of the two previous editions, but incorporates extensive new material-sections, exercises, and examples-throughout. Inside, readers will find all-new approaches to explain the various techniques in the book; new figures to assist in better visualizing and comprehending underlying concepts such as the different sampling strategies; computing notes for sample selection, calculation of estimates, and simulations; and more.
Organized into six sections, the book covers basic sampling, from simple random to unequal probability sampling; the use of auxiliary data with ratio 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.
Featuring a broad range of topics, "Sampling", Third Edition serves as a valuable reference on useful sampling and estimation methods for researchers in various fields of study, including biostatistics, ecology, and the health sciences. The book is also ideal for courses on statistical sampling at the upper-undergraduate and graduate levels.
Praise for the Second Edition:
"This book has never had a competitor. It is the only book that takes a broad approach to sampling [...] any good personal statistics library should include a copy of this book."
"Well-written [...] an excellent book on an important subject. Highly recommended."
"An ideal reference for scientific researchers and other professionals who use sampling."
– Zentralblatt Math
Preface to the Second Edition xvii
Preface to the First Edition xix
1 Introduction 1
PART I BASIC SAMPLING 9
2 Simple Random Sampling 11
3 Confidence Intervals 39
4 Sample Size 53
5 Estimating Proportions, Ratios, and Subpopulation Means 57
6 Unequal Probability Sampling 67
7 Auxiliary Data and Ratio Estimation 93
8 Regression Estimation 115
9 The Sufficient Statistic in Sampling 125
10 Design and Model 131
PART III SOME USEFUL DESIGNS 139
11 Stratified Sampling 141
12 Cluster and Systematic Sampling 157
13 Multistage Designs 171
14 Double or Two-Phase Sampling 183
PART IV METHODS FOR ELUSIVE AND HARD-TO-DETECT POPULATIONS 199
15 Network Sampling and Link-Tracing Designs 201
16 Detectability and Sampling 215
17 Line and Point Transects 229
18 Capture-Recapture Sampling 263
19 Line-Intercept Sampling 275
PART V SPATIAL SAMPLING 283
20 Spatial Prediction or Kriging 285
21 Spatial Designs 301
22 Plot Shapes and Observational Methods 305
PART VI ADAPTIVE SAMPLING 313
23 Adaptive Sampling Designs 315
24 Adaptive Cluster Sampling 319
25 Systematic and Strip Adaptive Cluster Sampling 339
26 Stratified Adaptive Cluster Sampling 353
Author Index 395
Subject Index 399
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Steven K. Thompson, PhD, is Shrum Chair in Science and Professor of Statistics at the Simon Fraser University. During his career, he has served on the faculties of the Pennsylvania State University, the University of Auckland, and the University of Alaska. He is also the coauthor of Adaptive Sampling (Wiley).