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Practical Tools for Designing and Weighting Survey Samples

  • Richard Valliant
  • Jill A. Dever
  • Frauke Kreuter

Part of the Statistics for Social and Behavioral Sciences book series (SSBS)

Table of contents

  1. Front Matter
    Pages i-xxvi
  2. Richard Valliant, Jill A. Dever, Frauke Kreuter
    Pages 1-11
  3. Designing Single-Stage Sample Surveys

    1. Front Matter
      Pages 13-13
    2. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 15-23
    3. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 25-90
    4. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 91-128
    5. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 129-168
    6. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 169-190
    7. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 191-201
  4. Multistage Designs

    1. Front Matter
      Pages 203-203
    2. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 205-207
    3. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 209-264
    4. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 265-306
    5. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 307-314
  5. Survey Weights and Analyses

    1. Front Matter
      Pages 315-315
    2. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 317-320
    3. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 321-367
    4. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 369-420
    5. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 421-480
    6. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 481-504
  6. Other Topics

    1. Front Matter
      Pages 505-505
    2. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 507-563
    3. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 565-603
    4. Richard Valliant, Jill A. Dever, Frauke Kreuter
      Pages 605-628
  7. Back Matter
    Pages 629-776

About this book

Introduction

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 AssociationJournal 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 StatisticsSociological Methods and ResearchSurvey Research MethodsPublic Opinion QuarterlyAmerican 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.



Keywords

address-based sampling area probability sampling balanced repeated replication bootstrap calibration estimation general regression estimation jackknife variance estimation machine learning mathematical programming multiphase designs multistage sampling non-probability samples nonresponse adjustment poststratification power calculations process control raking sample size calculation survey weights

Authors and affiliations

  • Richard Valliant
    • 1
  • Jill A. Dever
    • 2
  • Frauke Kreuter
    • 3
  1. 1.University of MichiganAnn ArborUSA
  2. 2.RTI InternationalWashington, DCUSA
  3. 3.University of MarylandCollege ParkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-93632-1
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-93631-4
  • Online ISBN 978-3-319-93632-1
  • Series Print ISSN 2199-7357
  • Series Online ISSN 2199-7365
  • Buy this book on publisher's site