{"product_id":"sampling-isbn-9780470402313","title":"Sampling","description":"\u003cb\u003ePraise for the Second Edition\u003c\/b\u003e  \u003cp\u003e\"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.\"\u003cbr\u003e —\u003ci\u003eTechnometrics\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\"Well-written . . . an excellent book on an important subject. Highly recommended.\"\u003cbr\u003e —\u003ci\u003eChoice\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\"An ideal reference for scientific researchers and other professionals who use sampling.\"\u003cbr\u003e —\u003ci\u003eZentralblatt Math\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eFeatures new developments in the field combined with all aspects of obtaining, interpreting, and using sample data\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eSampling\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eOrganized 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.\u003c\/p\u003e \u003cp\u003eFeaturing a broad range of topics, \u003ci\u003eSampling\u003c\/i\u003e, 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.\u003c\/p\u003e  \u003cb\u003ePreface xv\u003c\/b\u003e  \u003cp\u003e\u003cb\u003ePreface to the Second Edition xvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePreface to the First Edition xix\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Basic Ideas of Sampling and Estimation, 2\u003c\/p\u003e \u003cp\u003e1.2 Sampling Units, 4\u003c\/p\u003e \u003cp\u003e1.3 Sampling and Nonsampling Errors, 5\u003c\/p\u003e \u003cp\u003e1.4 Models in Sampling, 5\u003c\/p\u003e \u003cp\u003e1.5 Adaptive and Nonadaptive Designs, 6\u003c\/p\u003e \u003cp\u003e1.6 Some Sampling History, 7\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I BASIC SAMPLING 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Simple Random Sampling 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Selecting a Simple Random Sample, 11\u003c\/p\u003e \u003cp\u003e2.2 Estimating the Population Mean, 13\u003c\/p\u003e \u003cp\u003e2.3 Estimating the Population Total, 16\u003c\/p\u003e \u003cp\u003e2.4 Some Underlying Ideas, 17\u003c\/p\u003e \u003cp\u003e2.5 Random Sampling with Replacement, 19\u003c\/p\u003e \u003cp\u003e2.6 Derivations for Random Sampling, 20\u003c\/p\u003e \u003cp\u003e2.7 Model-Based Approach to Sampling, 22\u003c\/p\u003e \u003cp\u003e2.8 Computing Notes, 26\u003c\/p\u003e \u003cp\u003eEntering Data in R, 26\u003c\/p\u003e \u003cp\u003eSample Estimates, 27\u003c\/p\u003e \u003cp\u003eSimulation, 28\u003c\/p\u003e \u003cp\u003eFurther Comments on the Use of Simulation, 32\u003c\/p\u003e \u003cp\u003eExercises, 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Confidence Intervals 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Confidence Interval for the Population Mean or Total, 39\u003c\/p\u003e \u003cp\u003e3.2 Finite-Population Central Limit Theorem, 41\u003c\/p\u003e \u003cp\u003e3.3 Sampling Distributions, 43\u003c\/p\u003e \u003cp\u003e3.4 Computing Notes, 44\u003c\/p\u003e \u003cp\u003eConfidence Interval Computation, 44\u003c\/p\u003e \u003cp\u003eSimulations Illustrating the Approximate Normality of a Sampling Distribution with Small \u003ci\u003en\u003c\/i\u003e and \u003ci\u003eN\u003c\/i\u003e, 45\u003c\/p\u003e \u003cp\u003eDaily Precipitation Data, 46\u003c\/p\u003e \u003cp\u003eExercises, 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Sample Size 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Sample Size for Estimating a Population Mean, 54\u003c\/p\u003e \u003cp\u003e4.2 Sample Size for Estimating a Population Total, 54\u003c\/p\u003e \u003cp\u003e4.3 Sample Size for Relative Precision, 55\u003c\/p\u003e \u003cp\u003eExercises, 56\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Estimating Proportions, Ratios, and Subpopulation Means 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Estimating a Population Proportion, 58\u003c\/p\u003e \u003cp\u003e5.2 Confidence Interval for a Proportion, 58\u003c\/p\u003e \u003cp\u003e5.3 Sample Size for Estimating a Proportion, 59\u003c\/p\u003e \u003cp\u003e5.4 Sample Size for Estimating Several Proportions Simultaneously, 60\u003c\/p\u003e \u003cp\u003e5.5 Estimating a Ratio, 62\u003c\/p\u003e \u003cp\u003e5.6 Estimating a Mean, Total, or Proportion of a Subpopulation, 62\u003c\/p\u003e \u003cp\u003eEstimating a Subpopulation Mean, 63\u003c\/p\u003e \u003cp\u003eEstimating a Proportion for a Subpopulation, 64\u003c\/p\u003e \u003cp\u003eEstimating a Subpopulation Total, 64\u003c\/p\u003e \u003cp\u003eExercises, 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Unequal Probability Sampling 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Sampling with Replacement: The Hansen–Hurwitz Estimator, 67\u003c\/p\u003e \u003cp\u003e6.2 Any Design: The Horvitz–Thompson Estimator, 69\u003c\/p\u003e \u003cp\u003e6.3 Generalized Unequal-Probability Estimator, 72\u003c\/p\u003e \u003cp\u003e6.4 Small Population Example, 73\u003c\/p\u003e \u003cp\u003e6.5 Derivations and Comments, 75\u003c\/p\u003e \u003cp\u003e6.6 Computing Notes, 78\u003c\/p\u003e \u003cp\u003eWriting an R Function to Simulate a Sampling Strategy, 82\u003c\/p\u003e \u003cp\u003eComparing Sampling Strategies, 84\u003c\/p\u003e \u003cp\u003eExercises, 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II MAKING THE BEST USE OF SURVEY DATA 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Auxiliary Data and Ratio Estimation 93\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Ratio Estimator, 94\u003c\/p\u003e \u003cp\u003e7.2 Small Population Illustrating Bias, 97\u003c\/p\u003e \u003cp\u003e7.3 Derivations and Approximations for the Ratio Estimator, 99\u003c\/p\u003e \u003cp\u003e7.4 Finite-Population Central Limit Theorem for the Ratio Estimator, 101\u003c\/p\u003e \u003cp\u003e7.5 Ratio Estimation with Unequal Probability Designs, 102\u003c\/p\u003e \u003cp\u003e7.6 Models in Ratio Estimation, 105\u003c\/p\u003e \u003cp\u003eTypes of Estimators for a Ratio, 109\u003c\/p\u003e \u003cp\u003e7.7 Design Implications of Ratio Models, 109\u003c\/p\u003e \u003cp\u003e7.8 Computing Notes, 110\u003c\/p\u003e \u003cp\u003eExercises, 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Regression Estimation 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Linear Regression Estimator, 116\u003c\/p\u003e \u003cp\u003e8.2 Regression Estimation with Unequal Probability Designs, 118\u003c\/p\u003e \u003cp\u003e8.3 Regression Model, 119\u003c\/p\u003e \u003cp\u003e8.4 Multiple Regression Models, 120\u003c\/p\u003e \u003cp\u003e8.5 Design Implications of Regression Models, 123\u003c\/p\u003e \u003cp\u003eExercises, 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 The Sufficient Statistic in Sampling 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 The Set of Distinct, Labeled Observations, 125\u003c\/p\u003e \u003cp\u003e9.2 Estimation in Random Sampling with Replacement, 126\u003c\/p\u003e \u003cp\u003e9.3 Estimation in Probability-Proportional-to-Size Sampling, 127\u003c\/p\u003e \u003cp\u003e9.4 Comments on the Improved Estimates, 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Design and Model 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Uses of Design and Model in Sampling, 131\u003c\/p\u003e \u003cp\u003e10.2 Connections between the Design and Model Approaches, 132\u003c\/p\u003e \u003cp\u003e10.3 Some Comments, 134\u003c\/p\u003e \u003cp\u003e10.4 Likelihood Function in Sampling, 135\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III SOME USEFUL DESIGNS 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Stratified Sampling 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Estimating the Population Total, 142\u003c\/p\u003e \u003cp\u003eWith Any Stratified Design, 142\u003c\/p\u003e \u003cp\u003eWith Stratified Random Sampling, 143\u003c\/p\u003e \u003cp\u003e11.2 Estimating the Population Mean, 144\u003c\/p\u003e \u003cp\u003eWith Any Stratified Design, 144\u003c\/p\u003e \u003cp\u003eWith Stratified Random Sampling, 144\u003c\/p\u003e \u003cp\u003e11.3 Confidence Intervals, 145\u003c\/p\u003e \u003cp\u003e11.4 The Stratification Principle, 146\u003c\/p\u003e \u003cp\u003e11.5 Allocation in Stratified Random Sampling, 146\u003c\/p\u003e \u003cp\u003e11.6 Poststratification, 148\u003c\/p\u003e \u003cp\u003e11.7 Population Model for a Stratified Population, 149\u003c\/p\u003e \u003cp\u003e11.8 Derivations for Stratified Sampling, 149\u003c\/p\u003e \u003cp\u003eOptimum Allocation, 149\u003c\/p\u003e \u003cp\u003ePoststratification Variance, 150\u003c\/p\u003e \u003cp\u003e11.9 Computing Notes, 151\u003c\/p\u003e \u003cp\u003eExercises, 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Cluster and Systematic Sampling 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Primary Units Selected by Simple Random Sampling, 159\u003c\/p\u003e \u003cp\u003eUnbiased Estimator, 159\u003c\/p\u003e \u003cp\u003eRatio Estimator, 160\u003c\/p\u003e \u003cp\u003e12.2 Primary Units Selected with Probabilities Proportional to Size, 161\u003c\/p\u003e \u003cp\u003eHansen–Hurwitz (PPS) Estimator, 161\u003c\/p\u003e \u003cp\u003eHorvitz–Thompson Estimator, 161\u003c\/p\u003e \u003cp\u003e12.3 The Basic Principle, 162\u003c\/p\u003e \u003cp\u003e12.4 Single Systematic Sample, 162\u003c\/p\u003e \u003cp\u003e12.5 Variance and Cost in Cluster and Systematic Sampling, 163\u003c\/p\u003e \u003cp\u003e12.6 Computing Notes, 166\u003c\/p\u003e \u003cp\u003eExercises, 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Multistage Designs 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Simple Random Sampling at Each Stage, 173\u003c\/p\u003e \u003cp\u003eUnbiased Estimator, 173\u003c\/p\u003e \u003cp\u003eRatio Estimator, 175\u003c\/p\u003e \u003cp\u003e13.2 Primary Units Selected with Probability Proportional to Size, 176\u003c\/p\u003e \u003cp\u003e13.3 Any Multistage Design with Replacement, 177\u003c\/p\u003e \u003cp\u003e13.4 Cost and Sample Sizes, 177\u003c\/p\u003e \u003cp\u003e13.5 Derivations for Multistage Designs, 179\u003c\/p\u003e \u003cp\u003eUnbiased Estimator, 179\u003c\/p\u003e \u003cp\u003eRatio Estimator, 181\u003c\/p\u003e \u003cp\u003eProbability-Proportional-to-Size Sampling, 181\u003c\/p\u003e \u003cp\u003eMore Than Two Stages, 181\u003c\/p\u003e \u003cp\u003eExercises, 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Double or Two-Phase Sampling 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Ratio Estimation with Double Sampling, 184\u003c\/p\u003e \u003cp\u003e14.2 Allocation in Double Sampling for Ratio Estimation, 186\u003c\/p\u003e \u003cp\u003e14.3 Double Sampling for Stratification, 186\u003c\/p\u003e \u003cp\u003e14.4 Derivations for Double Sampling, 188\u003c\/p\u003e \u003cp\u003eApproximate Mean and Variance: Ratio Estimation, 188\u003c\/p\u003e \u003cp\u003eOptimum Allocation for Ratio Estimation, 189\u003c\/p\u003e \u003cp\u003eExpected Value and Variance: Stratification, 189\u003c\/p\u003e \u003cp\u003e14.5 Nonsampling Errors and Double Sampling, 190\u003c\/p\u003e \u003cp\u003eNonresponse, Selection Bias, or Volunteer Bias, 191\u003c\/p\u003e \u003cp\u003eDouble Sampling to Adjust for Nonresponse: Callbacks, 192\u003c\/p\u003e \u003cp\u003eResponse Modeling and Nonresponse Adjustments, 193\u003c\/p\u003e \u003cp\u003e14.6 Computing Notes, 195\u003c\/p\u003e \u003cp\u003eExercises, 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IV METHODS FOR ELUSIVE AND HARD-TO-DETECT POPULATIONS 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Network Sampling and Link-Tracing Designs 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Estimation of the Population Total or Mean, 202\u003c\/p\u003e \u003cp\u003eMultiplicity Estimator, 202\u003c\/p\u003e \u003cp\u003eHorvitz–Thompson Estimator, 204\u003c\/p\u003e \u003cp\u003e15.2 Derivations and Comments, 207\u003c\/p\u003e \u003cp\u003e15.3 Stratification in Network Sampling, 208\u003c\/p\u003e \u003cp\u003e15.4 Other Link-Tracing Designs, 210\u003c\/p\u003e \u003cp\u003e15.5 Computing Notes, 212\u003c\/p\u003e \u003cp\u003eExercises, 213\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Detectability and Sampling 215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Constant Detectability over a Region, 215\u003c\/p\u003e \u003cp\u003e16.2 Estimating Detectability, 217\u003c\/p\u003e \u003cp\u003e16.3 Effect of Estimated Detectability, 218\u003c\/p\u003e \u003cp\u003e16.4 Detectability with Simple Random Sampling, 219\u003c\/p\u003e \u003cp\u003e16.5 Estimated Detectability and Simple Random Sampling, 220\u003c\/p\u003e \u003cp\u003e16.6 Sampling with Replacement, 222\u003c\/p\u003e \u003cp\u003e16.7 Derivations, 222\u003c\/p\u003e \u003cp\u003e16.8 Unequal Probability Sampling of Groups with Unequal Detection Probabilities, 224\u003c\/p\u003e \u003cp\u003e16.9 Derivations, 225\u003c\/p\u003e \u003cp\u003eExercises, 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Line and Point Transects 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Density Estimation Methods for Line Transects, 230\u003c\/p\u003e \u003cp\u003e17.2 Narrow-Strip Method, 230\u003c\/p\u003e \u003cp\u003e17.3 Smooth-by-Eye Method, 233\u003c\/p\u003e \u003cp\u003e17.4 Parametric Methods, 234\u003c\/p\u003e \u003cp\u003e17.5 Nonparametric Methods, 237\u003c\/p\u003e \u003cp\u003eEstimating \u003ci\u003ef\u003c\/i\u003e (0) by the Kernel Method, 237\u003c\/p\u003e \u003cp\u003eFourier Series Method, 239\u003c\/p\u003e \u003cp\u003e17.6 Designs for Selecting Transects, 240\u003c\/p\u003e \u003cp\u003e17.7 Random Sample of Transects, 240\u003c\/p\u003e \u003cp\u003eUnbiased Estimator, 241\u003c\/p\u003e \u003cp\u003eRatio Estimator, 243\u003c\/p\u003e \u003cp\u003e17.8 Systematic Selection of Transects, 244\u003c\/p\u003e \u003cp\u003e17.9 Selection with Probability Proportional to Length, 244\u003c\/p\u003e \u003cp\u003e17.10 Note on Estimation of Variance for the Kernel Method, 246\u003c\/p\u003e \u003cp\u003e17.11 Some Underlying Ideas about Line Transects, 247\u003c\/p\u003e \u003cp\u003eLine Transects and Detectability Functions, 247\u003c\/p\u003e \u003cp\u003eSingle Transect, 249\u003c\/p\u003e \u003cp\u003eAverage Detectability, 249\u003c\/p\u003e \u003cp\u003eRandom Transect, 250\u003c\/p\u003e \u003cp\u003eAverage Detectability and Effective Area, 251\u003c\/p\u003e \u003cp\u003eEffect of Estimating Detectability, 252\u003c\/p\u003e \u003cp\u003eProbability Density Function of an Observed Distance, 253\u003c\/p\u003e \u003cp\u003e17.12 Detectability Imperfect on the Line or Dependent on Size, 255\u003c\/p\u003e \u003cp\u003e17.13 Estimation Using Individual Detectabilities, 255\u003c\/p\u003e \u003cp\u003eEstimation of Individual Detectabilities, 256\u003c\/p\u003e \u003cp\u003e17.14 Detectability Functions other than Line Transects, 257\u003c\/p\u003e \u003cp\u003e17.15 Variable Circular Plots or Point Transects, 259\u003c\/p\u003e \u003cp\u003eExercise, 260\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Capture–Recapture Sampling 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Single Recapture, 264\u003c\/p\u003e \u003cp\u003e18.2 Models for Simple Capture–Recapture, 266\u003c\/p\u003e \u003cp\u003e18.3 Sampling Design in Capture–Recapture: Ratio Variance Estimator, 267\u003c\/p\u003e \u003cp\u003eRandom Sampling with Replacement of Detectability Units, 269\u003c\/p\u003e \u003cp\u003eRandom Sampling without Replacement, 270\u003c\/p\u003e \u003cp\u003e18.4 Estimating Detectability with Capture–Recapture Methods, 271\u003c\/p\u003e \u003cp\u003e18.5 Multiple Releases, 272\u003c\/p\u003e \u003cp\u003e18.6 More Elaborate Models, 273\u003c\/p\u003e \u003cp\u003eExercise, 273\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Line-Intercept Sampling 275\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Random Sample of Lines: Fixed Direction, 275\u003c\/p\u003e \u003cp\u003e19.2 Lines of Random Position and Direction, 280\u003c\/p\u003e \u003cp\u003eExercises, 282\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART V SPATIAL SAMPLING 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Spatial Prediction or Kriging 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Spatial Covariance Function, 286\u003c\/p\u003e \u003cp\u003e20.2 Linear Prediction (Kriging), 286\u003c\/p\u003e \u003cp\u003e20.3 Variogram, 289\u003c\/p\u003e \u003cp\u003e20.4 Predicting the Value over a Region, 291\u003c\/p\u003e \u003cp\u003e20.5 Derivations and Comments, 292\u003c\/p\u003e \u003cp\u003e20.6 Computing Notes, 296\u003c\/p\u003e \u003cp\u003eExercise, 299\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Spatial Designs 301\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Design for Local Prediction, 302\u003c\/p\u003e \u003cp\u003e21.2 Design for Prediction of Mean of Region, 302\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Plot Shapes and Observational Methods 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Observations from Plots, 305\u003c\/p\u003e \u003cp\u003e22.2 Observations from Detectability Units, 307\u003c\/p\u003e \u003cp\u003e22.3 Comparisons of Plot Shapes and Detectability Methods, 308\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART VI ADAPTIVE SAMPLING 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Adaptive Sampling Designs 315\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Adaptive and Conventional Designs and Estimators, 315\u003c\/p\u003e \u003cp\u003e23.2 Brief Survey of Adaptive Sampling, 316\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Adaptive Cluster Sampling 319\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Designs, 321\u003c\/p\u003e \u003cp\u003eInitial Simple Random Sample without Replacement, 322\u003c\/p\u003e \u003cp\u003eInitial Random Sample with Replacement, 323\u003c\/p\u003e \u003cp\u003e24.2 Estimators, 323\u003c\/p\u003e \u003cp\u003eInitial Sample Mean, 323\u003c\/p\u003e \u003cp\u003eEstimation Using Draw-by-Draw Intersections, 323\u003c\/p\u003e \u003cp\u003eEstimation Using Initial Intersection Probabilities, 325\u003c\/p\u003e \u003cp\u003e24.3 When Adaptive Cluster Sampling Is Better than Simple Random Sampling, 327\u003c\/p\u003e \u003cp\u003e24.4 Expected Sample Size, Cost, and Yield, 328\u003c\/p\u003e \u003cp\u003e24.5 Comparative Efficiencies of Adaptive and Conventional\u003c\/p\u003e \u003cp\u003eSampling, 328\u003c\/p\u003e \u003cp\u003e24.6 Further Improvement of Estimators, 330\u003c\/p\u003e \u003cp\u003e24.7 Derivations, 333\u003c\/p\u003e \u003cp\u003e24.8 Data for Examples and Figures, 336\u003c\/p\u003e \u003cp\u003eExercises, 337\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Systematic and Strip Adaptive Cluster Sampling 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 Designs, 341\u003c\/p\u003e \u003cp\u003e25.2 Estimators, 343\u003c\/p\u003e \u003cp\u003eInitial Sample Mean, 343\u003c\/p\u003e \u003cp\u003eEstimator Based on Partial Selection Probabilities, 344\u003c\/p\u003e \u003cp\u003eEstimator Based on Partial Inclusion Probabilities, 345\u003c\/p\u003e \u003cp\u003e25.3 Calculations for Adaptive Cluster Sampling Strategies, 347\u003c\/p\u003e \u003cp\u003e25.4 Comparisons with Conventional Systematic and Cluster Sampling, 349\u003c\/p\u003e \u003cp\u003e25.5 Derivations, 350\u003c\/p\u003e \u003cp\u003e25.6 Example Data, 352\u003c\/p\u003e \u003cp\u003eExercises, 352\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Stratified Adaptive Cluster Sampling 353\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Designs, 353\u003c\/p\u003e \u003cp\u003e26.2 Estimators, 356\u003c\/p\u003e \u003cp\u003eEstimators Using Expected Numbers of Initial Intersections, 357\u003c\/p\u003e \u003cp\u003eEstimator Using Initial Intersection Probabilities, 359\u003c\/p\u003e \u003cp\u003e26.3 Comparisons with Conventional Stratified Sampling, 362\u003c\/p\u003e \u003cp\u003e26.4 Further Improvement of Estimators, 364\u003c\/p\u003e \u003cp\u003e26.5 Example Data, 367\u003c\/p\u003e \u003cp\u003eExercises, 367\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAnswers to Selected Exercises 369\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAuthor Index 395\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSubject Index 399\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSteven K. Thompson, PhD\u003c\/b\u003e, 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 \u003ci\u003eAdaptive Sampling\u003c\/i\u003e (Wiley).\u003c\/p\u003e  \u003cb\u003ePraise for the Second Edition\u003c\/b\u003e  \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\"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.\" —Technometrics\u003c\/p\u003e \u003cp\u003e\"Well-written . . . an excellent book on an important subject. Highly recommended.\" —Choice\u003c\/p\u003e \u003cp\u003e\"An ideal reference for scientific researchers and other professionals who use sampling.\" —Zentralblatt Math\u003c\/p\u003e \u003cp\u003eFeatures new developments in the field combined with all aspects of obtaining, interpreting, and using sample data\u003c\/p\u003e \u003cp\u003e\u003ci\u003eSampling\u003c\/i\u003e 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 \u003ci\u003eThird Edition\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eOrganized 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.\u003c\/p\u003e \u003cp\u003eFeaturing a broad range of topics, \u003ci\u003eSampling, Third Edition\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989986820325,"sku":"NP9780470402313","price":141.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470402313.jpg?v=1761786115","url":"https:\/\/k12savings.com\/es\/products\/sampling-isbn-9780470402313","provider":"K12savings","version":"1.0","type":"link"}