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Modern statistics consists of methods which help in drawing inferences about the population under consideration. These populations may actually exist, or could be generated by repeated· experimentation. The medium of drawing inferences about the population is the sample, which is a subset of measurements selected from the population. Each measurement in the sample is used for making inferences about the population. The populations and also the methods of sample selection differ from one field of science to the other. Social scientists use surveys tocollectthe sample information, whereas the physical scientists employ the method of experimentation for obtaining this information. This is because in social sciences the factors that cause variation in the measurements on the study variable for the population units can not be controlled, whereas in physical sciences these factors can be controlled, at least to some extent, through proper experimental design. Several excellent books on sampling theory are available in the market. These books discuss the theory of sample surveys in great depth and detail, and are suited to the postgraduate students majoring in statistics. Research workers in the field of sampling methodology can also make use of these books. However, not many suitable books are available, which can be used by the students and researchers in the fields of economics, social sciences, extension education, agriculture, medical sciences, business management, etc. These students and workers usually conduct sample surveys during their research projects.

Modern statistics consists of methods which help in drawing inferences about the population under consideration. These populations may actually exist, or could be generated by repeated· experimentation. The medium of drawing inferences about the population is the sample, which is a subset of measurements selected from the population. Each measurement in the sample is used for making inferences about the population. The populations and also the methods of sample selection differ from one field of science to the other. Social scientists use surveys tocollectthe sample information, whereas the physical scientists employ the method of experimentation for obtaining this information. This is because in social sciences the factors that cause variation in the measurements on the study variable for the population units can not be controlled, whereas in physical sciences these factors can be controlled, at least to some extent, through proper experimental design. Several excellent books on sampling theory are available in the market. These books discuss the theory of sample surveys in great depth and detail, and are suited to the postgraduate students majoring in statistics. Research workers in the field of sampling methodology can also make use of these books. However, not many suitable books are available, which can be used by the students and researchers in the fields of economics, social sciences, extension education, agriculture, medical sciences, business management, etc. These students and workers usually conduct sample surveys during their research projects.

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CONTENTS

PREFACE = xiii

1. COLLECTION OF SURVEY DATA

 1.1 Need for statistical data = 1

 1.2 Types of data = 1

 1.3 Methods collecting primary data = 2

 1.4 Framing of questionnaire ／ schedule = 3

 1.5 Some technical terms = 4

 1.6 Need for a sample = 5

 1.7 Sampling procedures = 6

 1.8 With and without replacement sampling = 7

 1.9 Planning and execution of sample surveys = 9

 Let us do = 11

2. ELEMENTARY CONCEPTS

 2.1 Introduction = 14

 2.2 Statistical preliminaries = 14

 2.3 Estimator and its sampling distribution = 16

 2.4 Unbiased estimator = 19

 2.5 Measures of error = 21

 2.6 Confidence intervals = 23

 2.7 Sample size determination = 26

 2.8 Sampling and nonsampling errors = 27

 Let us do = 28

3. SIMPLE RANDOM SAMPLING

 3.1 What is simple random sampling? = 30

 3.2 How to draw a simple random sample? = 30

 3.3 Estimation of population mean ／ total = 33

 3.4 Estimation of mean ／ total using distinct distinct units = 48

 3.5 Determining sample size for estimating population mean ／ total = 50

 3.6 Estimation of population proportion = 53

 3.7 Sample size for estimation of proportion = 55

 3.8 Estimation of proportion using inverse sampling = 56

 3.9 Estimation over subpopulations = 58

 3.10 Some further remarks Let us do = 62

 Let us do = 63

4. SAMPLING WITH VARYING PROBABILITES

 4.1 Introduction = 67

 4.2 Methods of selecting a PPS sample = 67

 4.3 Estimation in PPSWR sampling = 70

 4.4 Relative efficiency of PPSWR estimator = 72

 4.5 Determining sample size for estimating population mean ／ total = 76

 4.6 Sampling with PPS without replacement = 77

 4.7 Des Raj's ordered estimator = 78

 4.8 Murthy's unordered estimator = 82

 4.9 Horvitz-Thompson estimator = 84

 4.10 Sen-Midzuno method = 86

 4.11 Random group method = 91

 4.12 Relative efficiency of RHC estimator = 95

 4.13 Some further remarks = 97

 Let us do = 98

5. STRATIFIED SAMPLING

 5.1 Introduction = 102

 5.2 Notations = 104

 5.3 Estimation of mean and total using simple random sampling = 104

 5.4 Alloction of sample size = 108

 5.5 Relative efficiency of stratified estimator = 123

 5.6 Estimation of population proportion = 129

 5.7 Construction of strata = 132

 5.8 Poststratification = 136

 5.9 Some further remarks = 138

 Let us do = 140

6. SYSTEMATIC SAMPLING

 6.1 Linear systematic sampling = 145

 6.2 Circular systematic sampling = 148

 6.3 Estimating mean ／ total = 149

 6.4 Estimating eman ／ total throughinterpenetrating subsamples = 153

 6.5 Sample size determination for estimating mean ／ total = 156

 6.6 Estimation of proportion = 158

 6.7 Some further remarks = 160

 Let us do = 161

7. RATIO AND PRODUCT METHODS OF ESTIMATION

 7.1 Need for ratio estimation = 165

 7.2 Estimation of population ratio = 166

 7.3 Ratio estimator for population mean ／ total = 169

 7.4 Determining the sample size for estimation of ratio, mean, and total = 175

 7.5 Separate and combined ratio estimators = 178

 7.6 Some further remarks = 184

 7.7 Product method for estimating mean ／ total = 185

 7.8 Determination of sample size for product estimator = 189

 Let us do = 191

8. REGRESSION METHOD OF ESTIMATION

 8.1 Introduction = 197

 8.2 Estimation of mean ／ total using difference estimator = 197

 8.3 Estimation of mean ／ total using estimated regression coefficient = 201

 8.4 Sample size depermination for estimating mean ／ total = 204

 8.5 Separate and combined regression estimators = 206

 8.6 Some further remarks = 216

 Let us do = 217

9. TWO-PHASE SAMPLING

 9.1 Need for two-phase sampling = 221

 9.2 Two-phase sampling in ratio, product, and regression methods of estimation = 222

 9.3 Sample size determination for ratio, product, and regression estimators = 231

 9.4 Two-phase PPS sampling = 233

 9.5 Sampling on two occasions = 237

 9.6 Some further remarks = 242

 Let us do = 243

10. CLUSTER SAMPLING

 10.1 Introduction = 248

 10.2 Notations = 249

 10.3 Estimation of mean using simple random sampling = 250

 10.4 Estimation of total using simple random sampling = 258

 10.5 Relative efficiency of cluster sampling = 264

 10.6 Determining the sample size for estimating mean ／ total = 266

 10.7 Estimation of proportion = 269

 10.8 Sample size required for estimation of proportion = 273

 10.9 Selection of clusters with unequal probabilities = 275

 10.10 Some further remarks = 278

 Let us do = 278

11. MULTISTAGE SAMPLING

 11.1 Introduction = 283

 11.2 Notations = 284

 11.3 Estimation of mean ／ total in two-stage sampling using SRSWOR at both the stages = 284

 11.4 Estimation of proportion = 296

 11.5 Estimation of mean ／ total using PPSWOR and SRSWOR = 304

 11.6 Some further remarks = 307

 Let us do = 308

12. SAMPLING FROM MOBILE POPULATIONS

 12.1 Introduction = 283

 12.2 Estimation of population size using direct sampling = 315

 12.3 Estimation of population sizeusing inverse sampling = 320

 12.4 Determining the sample sizes = 324

 12.5 Some further remarks = 328

 Let us do = 329

13. NONRESPONSE ERRORS

 13.1 Introduction = 331

 13.2 Hansen and Hurwitz technique = 332

 13.3 Bias reduction without call-backs = 337

 13.4 Warner's randomized response model = 340

 13.5 Mangat and Singh's two-stage model = 342

 13.6 Urelated question model = 345

 13.7 Estimation of mean for quantitative characters = 350

 Let us do = 358

APPENDIXES

 A. Standard normal probaility distribution = 364

 B. Random numbers = 365

 C. Number of tractors, tube wells, and net irrigated area(in hectares) for 69 villages of Doraha development block of Punjab, India = 369

 D. Fifty WOR simple random samples = 371

 E. Explanation of certain local terms used = 372

REFERENCES = 373

AUTHOR INDEX = 383

SUBJECT INDEX = 385