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  • Part 1. Sampling and Survey Design; Introduction to Part 1 3; Sharon L. Lohr; 1. Importance of survey design 3; 2. Framework and approaches to design and inference 4; 3. Challenges in survey design 6; Ch. 1. Introduction to Survey Sampling 9; Ken Brewer and Timothy G. Gregoire; 1. Two alternative approaches to survey sampling inference 9; 2. Historical approaches to survey sampling inference 13; 3. Some comman sampling strategies 21; 4. Condusion 37; Ch. 2. Sampling with Unequal Probabilities 39; Yves G. Berger and Yves Tille; 1. Introduction 39; 2. Same methods of unequal probability sampling 40; 3. Point estimation in unequal probability sampling without replacement 47; 4. Variance estimators free of joint indusion probabilities 48; 5. Variance estimation of a function of means 50; 6. Balanced sampling 51; Ch.3. Two-Phase Sampling 55; Jason C. Legg and Wayne A. Fuller; 1. Introduction 55; 2. Using auxiliary infonnation in estimation 60; 3. Three-phase sampling 65; 4. Two-phase estimation illustration 66; Ch.4. Multiple-Frame Surveys 71; Sharon L. Lohr; I. What are multiple-frame surveys, and why are they used? 71; 2. Point estimation in multiple-frame surveys 76; 3. Variance estimation in multiple-frame surveys 83; 4. Designing multiple-frame surveys 85; 5. New applications and challenges for multiple-frame surveys 85; Acknowledgments 88; Ch. 5. Designs for Surveys over Time 89; Graham Kalton; 1. Introduction 89; 2. Repeated surveys 91; 3. Rotating panel surveys 94; 4. Panel surveys 97; 5. Conc1usions 108; Ch. 6. Sampling of Rare Populations 109; Mary C. Christman; 1. Introduction 109; 2. Modifications to classical design-based sampling strategies 110; 3. Adaptive sampling designs 115; 4. Experimental design 123; 5. Confidence interval estimation 123; 6. Summary 124; Ch. 7. Design, Conduct, and Analysis of Random-Digit Dialing Surveys 125; Kirk Wolter, Sadeq Chowdhury and Jenny Kelly; 1. lntroduction 125; 2. Design of RDD surveys 126; 3. Conduct of RDD surveys 134; 4. Analysis of RDD surveys 146; Part 2. Survey Processing 155; Introduction to Part 2 157; Paul Biemer; I. Overview of data processing steps 157; 2. Data quality and data processing 162; Ch. 8. Nonresponse and Weighting 163; J. Michael Brick and Jill M. Montaquila; I. Nonresponse in surveys 163; 2. Response rates 167; 3. The relationship between response rates and nonresponse bias 170; 4. Weighting for nonresponse 174; 5. Variance and confidence interval estimation 181; 6. Discussion 183; Ch. 9. Statistical Data Editing 187; Ton De Waal; 1. Introduction 187; 2. The use of edit mies 188; 3. Interactive editing 189; 4. Editing during the data collection phase 191; 5. Selective editing 192; 6. Automatic editing 198; 7. Macro-editing 207; 8. A strategy for statistical data editing 211; 9. Discussion 213; Ch. 10. Imputation and Inference in the Presence of Missing Data 215; David Haziza; 1. Introduction 215; 2. Context and defnitions 216; 3. Bias of the imputed estimator 224; 4. Variance of the imputed estimator 230; 5. Imputation classes 231; 6. Variance estimation 235; 7. Multiple imputation 243; 8. Conclusions 246; Ch. 11. Dealing with Outliers in Survey Data 247; Jean-Francois Beaumont and Louis-Paul Rivest; I. Introduction 247; 2. Estimation of the mean of an asymmetric distribution in an infinite population 250; 3. The estimation oftotals in finite populations containing outliers 256; 4. The estimation of totals using auxiliary information in finite populations containing outliers 259; 5. Dealing with stratum jumpers 270; 6. Practical issues and future work 278; Ch. 12. Measurement Errors in SampIe Surveys 281; Paul Biemer; I. Introduction 281; 2. Modeling survey measurement error 282; 3. The truth as a latent variable: Latent class models 289; 4. Latent dass models for three or more polytomous indicators 294; 5. Some advanced topics 303; 6. Measurement error evaluation with continuous variables 309; 7. Discussion 315; Ch. 13. Computer Software für SampIe Surveys 317; Jelke Bethlehem; I. Survey process 317; 2. Data collection 318; 3. Statistical data editing 325; 4. Imputation 332; 5. Weighting adjustment 336; 6. Analysis 343; 7. Disdosure control 348; Ch. 14. Record Linkage 351; William E. Winkler; I. Introduction 351; 2. Overview of methods 353; 3. Data preparation 366; 4. More advanced methods 370; 5. Concluding remarks 380; Ch. 15. Statistical Disclosure Control for Survey Data 381; Chris Skinner; I. Introduction 381; 2. Tabular outputs 384; 3. Microdata 388; 4. Conclusion 396; Acknowledgments 396; Part 3. Survey Applications 397; Introduction to Part 3 399; Jack G. Gambino; I. Frames and designs 399; 2. Stratification, allocation and sampling 401; 3. Estimation 402; Table ofContents; 4. Auxiliary information 403; 5. Challenges 404; Ch. 16. Sampling and Estimation in Household Surveys 407; Jack G. Gambino and Pedro Luis do Nascimento Silva; I. Introduction 407; 2. Survey designs 408; 3. Repeated household surveys 415; 4. Data collection 425; 5. Weighting and estimation 427; 6. Nonsampling errors in household surveys 435; 7. Integration of household surveys 436; 8. Survey redesign 438; 9. Conclusions 438; Acknowledgments 439; Ch. 17. Sampling and Estimation in Business Surveys 441; Michael A. Hidiroglou and Pierre Lavaltee; 1. Introduction 441; 2. Sampling trames for business surveys 442; 3. Administrative data 446; 4. SampIe size determination and allocation 450; 5. SampIe selection and rotation 457; 6. Data editing and imputation 460; 7. Estimation 467; Ch. 18. Sampling, Data Collection. and Estimation in Agricultural Surveys 471; Sarah M. Nusser and Carol C. House; 1. Introduction 471; 2. Sampling 473; 3. Data collection 480; 4. Statistical estimation 482; 5. Contidentiality 485; 6. Concluding remarks 485; Acknowledgments 486; Ch. 19. Sampling and Inference in Environmental Surveys 487; David A. Marker and Don L. Stevens Jr.; 1. Introduction 487; 2. Sampling populations in space 489; 3. Defining sampIe frames tor environmental populations 490; 4. Designs for probability-based environmental sampIes 492; 5. Using ancillary information in design 500; 6. Inference for probability-based design 502; 7. Model-based optimal spatial designs 503; 8. Plot design issues 506; 9. Sources of error in environmental studies 508; 10. Conclusions 512; Acknowledgements 512; Ch. 20. Survey Sampling Methods in Marketing Research: A Review of Telephone,; Mall Intercept, Panel, and Web Surveys 513; Raja Velu and Gurramkonda M. Naidu; 1. Introduction 513; 2. Telephone surveys 517; 3. Fax surveys 527; 4. Shopping center sampling and interviewing 527; 5. Consumer panels 529; 6. Web surveys 534; 7. Conclusion 538; Ch. 21. Sampie Surveys and Censuses 539; Ronit Nirel and Hagit Glickman; 1. Introduction 539; 2. The use of sampIe surveys for estimating coverage errors 541; 3. The use of sampIe surveys to evaluate statistical adjustment of census counts 552; 4. The use of sampie surveys for carrying out a census 558; 5. SampIe surveys carried out in conjunction with a census 562; 6. Concluding remarks 564; Ch. 22. Opinion and Election Polis 567; Kathleen A. Frankovic. Costas Panagopoulos and Rohert Y. Shapiro; 1. Introduction: the reasons for public opinion and election polling 567; 2. General methodological issues in public opinion and election polls 575; 3. Preelection polling: methods, impact. and current issues 580; 4. Exit polling 584; 5. Postelection and between-election poils 588; 6. Other opinion measurements: focus groups. deliberative poils, and the effect; of political events 588; 7. Present and future challenges in polling 590; 8. Continued interest in public opinion and polling 594; Acknowledgments 595; References 597; Subject Index: Index of Vol. 29A 651; Subject Index: Index of Vol. 29B 661; Handbook of Statistics: Contents of Previous Volumes 671 (xsd:string)
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  • 2009 (xsd:gyear)
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  • Sample surveys : designs, methods and applications (xsd:string)
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  • Amsterdam u.a.: Elsevier, 2009.- XXIV, 698 S., graph. Darst. (xsd:string)
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