• Step-by-step instructions for calculating survey weights
• Extensive real-world examples and applications throughout
• Demonstrates how to use existing software to solve survey problems

• The most up-to-date textbook and professional reference for statistical survey sampling

  Autorentext

  Richard Valliant, PhD, is Research Professor Emeritus at the Institute for Social Research at the University of Michigan and at the Joint Program in Survey Methodology at the University of Maryland. He is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and has been an Associate Editor of the Journal of the American Statistical Association, Journal of Official Statistics, and Survey Methodology.

Jill A. Dever, PhD, is Senior Research Statistician at RTI International in Washington, DC. She is a Fellow of the American Statistical Association, Associate Editor for Survey Methodology and the Journal of Official Statistics, and an Assistant Research Professor in the Joint Program in Survey Methodology at the University of Maryland. She has served on several panels for the National Academy of Sciences and as a task force member for the American Association of Public Opinion Research's report on nonprobability sampling.

Frauke Kreuter, PhD, is Professor and Director of the Joint Program in Survey Methodology at the University of Maryland, Professor of Statistics and Methodology at the University of Mannheim, and Head of the Statistical Methods Research Department at the Institute for Employment Research (IAB) in Nürnberg, Germany. She is a Fellow of the American Statistical Association and has been Associate Editor of the Journal of the Royal Statistical Society, Journal of Official Statistics, Sociological Methods and Research, Survey Research Methods, Public Opinion Quarterly, American Sociological Review, and the Stata Journal. She is founder of the International Program for Survey and Data Science and co-founder of the Coleridge Initiative.

Klappentext

The goal of this book is to put an array of tools at the fingertips of students, practitioners, and researchers by explaining approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed. This volume serves at least three audiences: (1) students of applied sampling techniques; 2) practicing survey statisticians applying concepts learned in theoretical or applied sampling courses; and (3) social scientists and other survey practitioners who design, select, and weight survey samples.

The text thoroughly covers fundamental aspects of survey sampling, such as sample size calculation (with examples for both single- and multi-stage sample design) and weight computation, accompanied by software examples to facilitate implementation. Features include step-by-step instructions for calculating survey weights, extensive real-world examples and applications, and representative programming code in R, SAS, and other packages.

Since the publication of the first edition in 2013, there have been important developments in making inferences from nonprobability samples, in address-based sampling (ABS), and in the application of machine learning techniques for survey estimation. New to this revised and expanded edition:

. Details on new functions in the PracTools package

. Additional machine learning methods to form weighting classes

. New coverage of nonlinear optimization algorithms for sample allocation

. Reflecting effects of multiple weighting steps (nonresponse and calibration) on standard errors

. A new chapter on nonprobability sampling

. Additional examples, exercises, and updated references throughout

Richard Valliant, PhD, is Research Professor Emeritus at the Institute for Social Research at the University of Michigan and at the Joint Program in Survey Methodology at the University of Maryland. He is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and has been an Associate Editor of the Journal of the American Statistical Association, Journal of Official Statistics, and Survey Methodology.

Jill A. Dever, PhD, is Senior Research Statistician at RTI International in Washington, DC. She is a Fellow of the American Statistical Association, Associate Editor for Survey Methodology and the Journal of Official Statistics, and an Assistant Research Professor in the Joint Program in Survey Methodology at the University of Maryland. She has served on several panels for the National Academy of Sciences and as a task force member for the American Association of Public Opinion Research's report on nonprobability sampling.

Frauke Kreuter, PhD, is Professor and Director of the Joint Program in Survey Methodology at the University of Maryland, Professor of Statistics and Methodology at the University of Mannheim, and Head of the Statistical Methods Research Department at the Institute for Employment Research (IAB) in Nürnberg, Germany. She is a Fellow of the American Statistical Association and has been Associate Editor of the Journal of the Royal Statistical Society, Journal of Official Statistics, Sociological Methods and Research, Survey Research Methods, Public Opinion Quarterly, American Sociological Review, and the Stata Journal. She is founder of the International Program for Survey and Data Science and co-founder of the Coleridge Initiative.

Inhalt

PrefaceAcknowledgements 1 An Overview of Sample Design and Weighting1.1 Background and Terminology1.2 Chapter Guide Part I Designing Single-Stage Sample Surveys2 Project 1: Design a Single-Stage Personnel Survey2.1 Specifications for the Study2.2 Questions Posed by the Design Team2.3 Preliminary Analyses2.4 Documentation2.5 Next Steps 3 Sample Design and Sample Size for Single-Stage Surveys 3.1 Determining a Sample Size for a Single-Stage Design 3.1.1 Simple Random Sampling3.1.2 Stratified Simple Random Sampling3.2 Finding Sample Sizes When Sampling with Varying Probabilities 3.2.1 Probability Proportional to Size Sampling3.2.2 Regression Estimates of Totals3.3 Other Methods of Sampling3.4 Estimating Population Parameters from a Sample3.5 Special Topics3.5.1 Rare Characteristics3.5.2 Domain Estimates3.6 More Discussion of Design Effects3.7 Software for Sample Selection3.7.1 R Packages3.7.2 SAS PROC SURVEYSELECTExercises 4 Power Calculations and Sample Size Determination 4.1 Terminology and One-Sample Tests4.2 Power in a One-Sample Test4.3 Two-Sample Tests4.3.1 Differences in Means4.3.2 Differences in Proportions4.3.3 Special Case: Relative Risk4.3.4 Special Case: Effect Sizes4.4 R Power Functions4.5 Power and Sample Size Calculations in SAS. Exercises 5 Mathematical Programming5.1 Multicriteria Optimization5.2 Microsoft Excel Solver5.3 SAS PROC NLP5.4 SAS PROC OPTMODEL5.5 R Alabama Package 6 Outcome Rates and Effect on Sample Size6.1 Disposition Codes6.2 Definitions of Outcome Rates6.3 Sample Units with Unknown AAPOR Classification6.4 Weighted Versus Unweighted Rates6.5 Accounting for Sample Losses in Determining Initial Sample Size6.5.1 Sample Size Inflation Rates at Work6.5.2 ReplicatesExercises 7 The Personnel Survey Design Project: One Solution 7.1 Overview of the Project 7.2 Formulate the Optimization Problem7.2.1 Objective Function 7.2.2 Decision Variables 7.2.3 Optimization Parameters7.2.4 Specified Survey Constraints 7.3 One Solution 7.3.1 Power Analyses7.3.2 Optimization Results7.4 Additional Sensitivity Analysis7.5 Conclusion Part II Multistage Designs 8 Project 2: Designing an Area Sample 9 Designing Multistage Samples 9.1 Types of PSUs 9.2 Basic Variance Results 9.2.1 Two-Stage Sampling 9.2.2 Nonlinear Estimators in Two-Stage Sampling 9.2.3 More General Two-Stage Designs 9.2.4 Three-Stage Sampling 9.3 Cost Functions and Optimal Allocations for Multistage Sampli…