1 PRELIMINARIES

1.1 Introduction

1.2 A Brief History of Survey Sampling

1.3 Sampling Designs and an Overview of Sampling

1.4 Ingredients of a Survey

1.5 Probability Sampling

1.6 Precision and Confidence Intervals

1.7 Biased Estimators

1.8 The Mean-Squared Error

1.9 Unbiased Estimation

PROBLEMS

REFERENCES

2 VARYING-PROBABILITY SAMPLING

2.1 Introduction

2.2 Obtaining Varying-Probability Samples

2.3 Sampling Designs(Ordered and Unordered)

2.4 Sufficiency in Sampling from Finite Populations

2.5 Sampling with Varying Probabilities and Without Replacement

PROBLEMS

REFERENCES

3 SIMPLE RANDOM SAMPLING

3.1 Introduction

3.2 Notation

3.3 Properties of Estimates

3.4 Variances of Estimators

3.5 Confidence Intervals

3.6 Alternate Method for Evaluating var(？)

3.7 Random Sampling with Replacement

3.8 Estimates for Ratios

3.9 Estimates of Means or Totals over Subpopulations

3.10 Justification of the Normal Approximation

3.11 Asymptotic Normality of Estimates Arising from Simple Random Sampling

3.12 Best Unbiased Estimators

3.13 Distinct Units

3.14 The Distribution of W

3.15 Comparison of Simple Random Sampling with and without Replacement

3.16 Use of Balanced Incomplete Block Designs in Simple Random Sampling

3.17 Estimating Proportions and Percentages

3.18 Binomial and Hypergeometric Distributions and Their Use in Sampling

3.19 Confidence Limits for M

3.20 Confidence Intervals for Unknown Discrete Population Parameter

3.21 Use of the Finite Population Correction for Binomial Confidence Limits

3.22 Cluster Sampling:Estimation of Proportions

PROBLEMS

REFERENCES

4 ESTIMATION OFTHE SAMPLE SIZE

4.1 Introduction

4.2 Sample Size in Estimating Proportions

4.3 Inverse Sampling for Rare Attributes

4.4 Estimating Sample Size with Continuous Data

4.5 Estimation of S2

4.6 Estimation by Double Sampling

4.7 Estimation with Given Variance:Single Unknown Parameter

4.8 Sampling Procedure

4.9 Estimation of P with Specified Variance V

4.10 Estimation of P with Specified CV=C1/2

4.11 Estimation of ？with Specified CV=C1/2

4.12 Estimation of ？ with Specified Variance V

4.13 Computing Sample Size:Decision-Theoretic Approach

PROBLEMS

REFERENCES

5 STRATIFIED SAMPLING

5.1 Introduction

5.2 Estimators of Mean and Total and Their Properties

5.3 Confidence Limits(CI's)

5.4 Optimum Allocation of a Random Sample

5.5 Merits of Stratified Sampling(SS)Relative to Simple Random Sampling(SRS)

5.6 Modification of Optimal Allocation

5.7 Estimation of Sample Sizes in Stratified Sampling:Continuous Response Data

5.8 Estimation of the Population Mean ？

5.9 Estimation of the Population Total

5.10 Application to Stratified Sampling for Proportions

5.11 Minimum Variance for Fxed n(Total Sample Size)

5.12 Gain by Stratified Sampling for Proportions

5.13 Sample Size for Proportions

5.14 Poststratification

5.15 How Should the Strata be Formed?2

5.16 Optimal Choice of L and n

5.17 Optimal Choice of L and n Via a Regression Variable

5.18 Controlled Sampling

5.19 Multiple Stratification

5.20 Interpenetrating Subsampling

PROBLEMS

REFERENCES

6 RATIO ESTIMATORS

6.1 Introduction

6.2 Variance of the Ratio Estimate

6.3 Estimates for var(？R)

6.4 Confidence Intervals for R

6.5 Efficiency Comparisons

6.6 An Optimum Property of the Ratio Estimators

6.7 Bias in the Ratio Estimate

6.8 An Exact Expression for the Bias of the Ratio Estimate

6.9 Ratio Estimates in Stratified Random Sampling

6.10 Comparison of ？Rs and ？Rc

6.11 Optimum Allocation with a Ratio Estimator

6.12 Unbiased Ratio Estimates

6.13 Jackknife Method for Obtaining a Ratio Estimate with Bias O(n-2)

6.14 Multivariate Ratio Estimators

6.15 A Dual Ratio Estimator

6.16 Comparison of Various Estimators

6.17 Unbiased Ratio Estimator

PROBLEMS

REFERENCES

7 REGRESSION ESTIMATORS

7.1 Introduction

7.2 Properties of Regression Estimators

7.3 Sample Estimate of Variance

7.4 Comparison of Regression,Ratio Estimates,and the Sample Mean

7.5 Properties of the Regression Estimator under a Super Population Model

7.6 Regression Estimates in Stratiffed Sampling

7.7 Sample Estimates

7.8 Unbiased Regression Estimation

PROBLEMS

REFERENCES

8 SYSTEMATIC SAMPLING

8.1 Circular Systematic Sampling

8.2 Relation to Cluster Sampling

8.3 Mean of the Systematic Sample

8.4 Variance of the Systematic Mean

8.5 An Alternate Form for the Variance of ？sy

8.6 Estimation of Sampling Variance

8.7 Populations in Random Order

8.8 Populations having Linear Trend

8.9 Further Developments in Systematic Sampling

8.10 Other Super Population Models

PROBLEMS

REFERENCES

9 CLUSTER SAMPLING

9.1 Necessity of Cluster Sampling

9.2 Notation

9.3 Precision of Survey Data

9.4 Relation between Variance and Intracluster Correlation

9.5 Estimation of M

9.6 Cost Analysis

9.7 Custer Sampling for Proportions

9.8 Case of Unequal Cluster Sizes

9.9 Probability Sampling Proportional to Size

9.10 Comparison of the Three Methods

PROBLEMS

REFERENCES

10 VARYING PROBABILITY SAMPLING:WITHOUT REPLACEMENT

10.1 Introduction and Preliminaries

10.2 Expected Values of Sums and Product-Sums

10.3 Estimation of the Population Total

10.4 Application of the Theory

10.5 Systematic Sampling:Unequal Probabilities

10.6 A New Systematic Sampling with an Unbiased Estimate of the Variance

10.7 Computing Inclusion Probabilities and Estimation Procedures

PROBLEMS

REFERENCES

11 TWO-PHASE AND REPETITIVE SAMPLING

11.1 Introduction

11.2 Difference Estimation

11.3 Unbiased Ratio Estimation

11.4 Biased Ratio Estimation

11.5 Regression Estimation

11.6 Estimation by Stratification

11.7 Repetitive Surveys

PROBLEMS

REFERENCES

12 TWO-STAGE SAMPLING

12.1 Introduction

12.2 Notation

12.3 Estimation of Population Totals

12.4 Two-Stage Scheme with Simple Random Sampling

12.5 Comparison with Single-Stage and Custer Sampling

12.6 Probability Sampling for a Two-Stage Design

PROBLEMS

REFERENCES

13 NONSAMPLING ERRORS

13.1 Introduction

13.2 Effect of Nonresponse on Sample Mean and Proportion

13.3 Required Sample Size When Nonresponse Is Present

13.4 Conditional Inference When Nonresponse Exists

13.5 Call-Backs

13.6 A Probabilistic Model for Nonresponse

13.7 Randomized Responses to Sensitive Questions

13.8 Measurement Errors

PROBLEMS

REFERENCES

14 BAYESIAN APPROACH FOR INFERENCE IN FINITE POPULATIONS

14.1 Introduction

14.2 Notation and the Model

14.3 Some Basic Results

14.4 Simple Random Sampling

14.5 Hypergeometric-Binomial Model

14.6 Stratified Sampling

14.7 Two-Stage Sampling

14.8 Response Error and Bias

PROBLEMS

REFERENCES

15 THE JACKKNIFE METHOD

15.1 Introduction

15.2 The General Method

15.3 Main Applications

15.4 Interval Estimation

15.5 Transformations

15.6 The Bias in the Jackknife Estimate of the Variance

PROBLEMS

REFERENCES

16 THE BOOTSTRAP METHOD

16.1 Introduction

16.2 The Bootstrap Method

16.3 Bootstrap Methods for General Problems

16.4 The Bootstrap Estimate of Bias

16.5 Case of Finite Sample Space

16.6 Regression Problems

16.7 Bootstrap Confidence Intervals

16.8 Application of Bootstrap Methods in Finance and Management Cases

PROBLEMS

REFERENCES

17 SMALL-AREA ESTIMATION

17.1 Introduction

17.2 Demographic Methods

17.3 Multiple Regression Methods

17.4 Synthetic Estimators

17.5 Composite Estimators

PROBLEMS

REFERENCES

18 IMPUTATIONS IN SURVEYS

18.1 Introduction

18.2 General Rules for Imputing

18.3 Methods of Imputation

18.4 Evaluation of Imputation Procedures

18.5 Secondary Data Analysis with Missing Observations

18.6 A Procedure for Assessing the Quality of Inferences

18.7 Bayesian Method

18.8 Comparison of the Various Imputation Methods

18.9 Multiple Imputation for Interval Estimation

18.10 Normal-Based Analysis of a Multiple Imputed Data Set

18.11 Confidence Interval for Population Mean Following Multiple Imputation

PROBLEMS

REFERENCES

Answers to Selected Problems

List of Cumulative References

Author Index

Subject Index