1 PRELIMINARIES 1
1.1 Introduction 1
1.2 A Brief History of Survey Sampling 1
1.3 Sampling Designs and an Overview of Sampling 3
1.4 Ingredients of a Survey 3
1.5 Probability Sampling 4
1.6 Precision and Confidence Intervals 5
1.7 Biased Estimators 5
1.8 The Mean-Squared Error 6
1.9 Unbiased Estimation 7
PROBLEMS 8
REFERENCES 9
2 VARYING-PROBABILITY SAMPLING 11
2.1 Introduction 11
2.2 Obtaining Varying-Probability Samples 11
2.3 Sampling Designs(Ordered and Unordered) 15
2.4 Sufficiency in Sampling from Finite Populations 19
2.5 Sampling with Varying Probabilities and Without Replacement 23
PROBLEMS 26
REFERENCES 27
3 SIMPLE RANDOM SAMPLING 28
3.1 Introduction 28
3.2 Notation 29
3.3 Properties of Estimates 30
3.4 Variances of Estimators 31
3.5 Confidence Intervals 33
3.6 Alternate Method for Evaluating var(？) 34
3.7 Random Sampling with Replacement 35
3.8 Estimates for Ratios 36
3.9 Estimates of Means or Totals over Subpopulations 38
3.10 Justification of the Normal Approximation 38
3.11 Asymptotic Normality of Estimates Arising from Simple Random Sampling 38
3.12 Best Unbiased Estimators 40
3.13 Distinct Units 42
3.14 The Distribution of W 44
3.15 Comparison of Simple Random Sampling with and without Replacement 49
3.16 Use of Balanced Incomplete Block Designs in Simple Random Sampling 51
3.17 Estimating Proportions and Percentages 52
3.18 Binomial and Hypergeometric Distributions and Their Use in Sampling 54
3.19 Confidence Limits for M 55
3.20 Confidence Intervals for Unknown Discrete Population Parameter 55
3.21 Use of the Finite Population Correction for Binomial Confidence Limits 57
3.22 Cluster Sampling:Estimation of Proportions 57
PROBLEMS 59
REFERENCES 63
4 ESTIMATION OFTHE SAMPLE SIZE 64
4.1 Introduction 64
4.2 Sample Size in Estimating Proportions 64
4.3 Inverse Sampling for Rare Attributes 66
4.4 Estimating Sample Size with Continuous Data 68
4.5 Estimation of S2 68
4.6 Estimation by Double Sampling 69
4.7 Estimation with Given Variance:Single Unknown Parameter 69
4.8 Sampling Procedure 69
4.9 Estimation of P with Specified Variance V 70
4.10 Estimation of P with Specified CV=C1/2 71
4.11 Estimation of ？with Specified CV=C1/2 71
4.12 Estimation of ？ with Specified Variance V 71
4.13 Computing Sample Size:Decision-Theoretic Approach 72
PROBLEMS 73
REFERENCES 74
5 STRATIFIED SAMPLING 75
5.1 Introduction 75
5.2 Estimators of Mean and Total and Their Properties 76
5.3 Confidence Limits(CI's) 78
5.4 Optimum Allocation of a Random Sample 79
5.5 Merits of Stratified Sampling(SS)Relative to Simple Random Sampling(SRS) 82
5.6 Modification of Optimal Allocation 84
5.7 Estimation of Sample Sizes in Stratified Sampling:Continuous Response Data 85
5.8 Estimation of the Population Mean ？ 85
5.9 Estimation of the Population Total 86
5.10 Application to Stratified Sampling for Proportions 86
5.11 Minimum Variance for Fxed n(Total Sample Size) 87
5.12 Gain by Stratified Sampling for Proportions 87
5.13 Sample Size for Proportions 88
5.14 Poststratification 89
5.15 How Should the Strata be Formed?2 92
5.16 Optimal Choice of L and n 100
5.17 Optimal Choice of L and n Via a Regression Variable 101
5.18 Controlled Sampling 103
5.19 Multiple Stratification 104
5.20 Interpenetrating Subsampling 105
PROBLEMS 108
REFERENCES 114
6 RATIO ESTIMATORS 116
6.1 Introduction 116
6.2 Variance of the Ratio Estimate 117
6.3 Estimates for var(？R) 117
6.4 Confidence Intervals for R 118
6.5 Efficiency Comparisons 118
6.6 An Optimum Property of the Ratio Estimators 120
6.7 Bias in the Ratio Estimate 123
6.8 An Exact Expression for the Bias of the Ratio Estimate 124
6.9 Ratio Estimates in Stratified Random Sampling 125
6.10 Comparison of ？Rs and ？Rc 126
6.11 Optimum Allocation with a Ratio Estimator 127
6.12 Unbiased Ratio Estimates 128
6.13 Jackknife Method for Obtaining a Ratio Estimate with Bias O(n-2) 128
6.14 Multivariate Ratio Estimators 130
6.15 A Dual Ratio Estimator 131
6.16 Comparison of Various Estimators 132
6.17 Unbiased Ratio Estimator 134
PROBLEMS 134
REFERENCES 142
7 REGRESSION ESTIMATORS 143
7.1 Introduction 143
7.2 Properties of Regression Estimators 144
7.3 Sample Estimate of Variance 147
7.4 Comparison of Regression,Ratio Estimates,and the Sample Mean 147
7.5 Properties of the Regression Estimator under a Super Population Model 149
7.6 Regression Estimates in Stratiffed Sampling 150
7.7 Sample Estimates 151
7.8 Unbiased Regression Estimation 154
PROBLEMS 156
REFERENCES 161
8 SYSTEMATIC SAMPLING 162
8.1 Circular Systematic Sampling 162
8.2 Relation to Cluster Sampling 163
8.3 Mean of the Systematic Sample 163
8.4 Variance of the Systematic Mean 164
8.5 An Alternate Form for the Variance of ？sy 164
8.6 Estimation of Sampling Variance 166
8.7 Populations in Random Order 169
8.8 Populations having Linear Trend 170
8.9 Further Developments in Systematic Sampling 171
8.10 Other Super Population Models 173
PROBLEMS 174
REFERENCES 176
9 CLUSTER SAMPLING 177
9.1 Necessity of Cluster Sampling 177
9.2 Notation 178
9.3 Precision of Survey Data 179
9.4 Relation between Variance and Intracluster Correlation 180
9.5 Estimation of M 182
9.6 Cost Analysis 182
9.7 Custer Sampling for Proportions 185
9.8 Case of Unequal Cluster Sizes 186
9.9 Probability Sampling Proportional to Size 188
9.10 Comparison of the Three Methods 192
PROBLEMS 193
REFERENCES 194
10 VARYING PROBABILITY SAMPLING:WITHOUT REPLACEMENT 196
10.1 Introduction and Preliminaries 196
10.2 Expected Values of Sums and Product-Sums 199
10.3 Estimation of the Population Total 200
10.4 Application of the Theory 204
10.5 Systematic Sampling:Unequal Probabilities 215
10.6 A New Systematic Sampling with an Unbiased Estimate of the Variance 220
10.7 Computing Inclusion Probabilities and Estimation Procedures 222
PROBLEMS 227
REFERENCES 228
11 TWO-PHASE AND REPETITIVE SAMPLING 229
11.1 Introduction 229
11.2 Difference Estimation 229
11.3 Unbiased Ratio Estimation 232
11.4 Biased Ratio Estimation 232
11.5 Regression Estimation 233
11.6 Estimation by Stratification 237
11.7 Repetitive Surveys 239
PROBLEMS 242
REFERENCES 245
12 TWO-STAGE SAMPLING 246
12.1 Introduction 246
12.2 Notation 247
12.3 Estimation of Population Totals 247
12.4 Two-Stage Scheme with Simple Random Sampling 248
12.5 Comparison with Single-Stage and Custer Sampling 252
12.6 Probability Sampling for a Two-Stage Design 255
PROBLEMS 259
REFERENCES 262
13 NONSAMPLING ERRORS 263
13.1 Introduction 263
13.2 Effect of Nonresponse on Sample Mean and Proportion 264
13.3 Required Sample Size When Nonresponse Is Present 265
13.4 Conditional Inference When Nonresponse Exists 269
13.5 Call-Backs 269
13.6 A Probabilistic Model for Nonresponse 276
13.7 Randomized Responses to Sensitive Questions 280
13.8 Measurement Errors 284
PROBLEMS 286
REFERENCES 288
14 BAYESIAN APPROACH FOR INFERENCE IN FINITE POPULATIONS 289
14.1 Introduction 289
14.2 Notation and the Model 289
14.3 Some Basic Results 291
14.4 Simple Random Sampling 292
14.5 Hypergeometric-Binomial Model 294
14.6 Stratified Sampling 298
14.7 Two-Stage Sampling 300
14.8 Response Error and Bias 304
PROBLEMS 306
REFERENCES 308
15 THE JACKKNIFE METHOD 309
15.1 Introduction 309
15.2 The General Method 309
15.3 Main Applications 316
15.4 Interval Estimation 317
15.5 Transformations 317
15.6 The Bias in the Jackknife Estimate of the Variance 318
PROBLEMS 322
REFERENCES 322
16 THE BOOTSTRAP METHOD 324
16.1 Introduction 324
16.2 The Bootstrap Method 324
16.3 Bootstrap Methods for General Problems 326
16.4 The Bootstrap Estimate of Bias 327
16.5 Case of Finite Sample Space 327
16.6 Regression Problems 329
16.7 Bootstrap Confidence Intervals 332
16.8 Application of Bootstrap Methods in Finance and Management Cases 333
PROBLEMS 333
REFERENCES 334
17 SMALL-AREA ESTIMATION 335
17.1 Introduction 335
17.2 Demographic Methods 336
17.3 Multiple Regression Methods 338
17.4 Synthetic Estimators 340
17.5 Composite Estimators 341
PROBLEMS 344
REFERENCES 345
18 IMPUTATIONS IN SURVEYS 347
18.1 Introduction 347
18.2 General Rules for Imputing 348
18.3 Methods of Imputation 349
18.4 Evaluation of Imputation Procedures 351
18.5 Secondary Data Analysis with Missing Observations 353
18.6 A Procedure for Assessing the Quality of Inferences 354
18.7 Bayesian Method 356
18.8 Comparison of the Various Imputation Methods 361
18.9 Multiple Imputation for Interval Estimation 362
18.10 Normal-Based Analysis of a Multiple Imputed Data Set 363
18.11 Confidence Interval for Population Mean Following Multiple Imputation 366
PROBLEMS 371
REFERENCES 372
Answers to Selected Problems 375
List of Cumulative References 401
Author Index 408
Subject Index 411