Around the world a multitude of surveys are conducted every day, on a variety of subjects, and consequently surveys have become an accepted part of modern life. However, in recent years survey estimates have been increasingly affected by rising trends in nonresponse, with loss of accuracy as an undesirable result. Whilst it is possible to reduce nonresponse to some degree, it cannot be completely eliminated. Estimation techniques that account systematically for nonresponse and at the same time succeed in delivering acceptable accuracy are much needed. Estimation in Surveys with Nonresponse provides an overview of these techniques, presenting the view of nonresponse as a normal (albeit undesirable) feature of a sample survey, one whose potentially harmful effects are to be minimised. Builds in the nonresponse feature of survey data collection as an integral part of the theory, both for point estimation and for variance estimation. Promotes weighting through calibration as a new and powerful technique for surveys with nonresponse. Highlights the analysis of nonresponse bias in estimates and methods to minimize this bias. Includes computational tools to help identify the best variables for calibration. Discusses the use of imputation as a complement to weighting by calibration. Contains guidelines for dealing with frame imperfections and coverage errors. Features worked examples throughout the text, using real data. The accessible style of Estimation in Surveys with Nonresponse will make this an invaluable tool for survey methodologists in national statistics agencies and private survey agencies. Researchers, teachers, and students of statistics, social sciences and economics will benefit from the clear presentation and numerous examples.

For many household surveys in the United States, responses rates have been steadily declining for at least the past two decades. A similar decline in survey response can be observed in all wealthy countries. Efforts to raise response rates have used such strategies as monetary incentives or repeated attempts to contact sample members and obtain completed interviews, but these strategies increase the costs of surveys. This review addresses the core issues regarding survey nonresponse. It considers why response rates are declining and what that means for the accuracy of survey results. These trends are of particular concern for the social science community, which is heavily invested in obtaining information from household surveys. The evidence to date makes it apparent that current trends in nonresponse, if not arrested, threaten to undermine the potential of household surveys to elicit information that assists in understanding social and economic issues. The trends also threaten to weaken the validity of inferences drawn from estimates based on those surveys. High nonresponse rates create the potential or risk for bias in estimates and affect survey design, data collection, estimation, and analysis. The survey community is painfully aware of these trends and has responded aggressively to these threats. The interview modes employed by surveys in the public and private sectors have proliferated as new technologies and methods have emerged and matured. To the traditional trio of mail, telephone, and face-to-face surveys have been added interactive voice response (IVR), audio computer-assisted self-interviewing (ACASI), web surveys, and a number of hybrid methods. Similarly, a growing research agenda has emerged in the past decade or so focused on seeking solutions to various aspects of the problem of survey nonresponse; the potential solutions that have been considered range from better training and deployment of interviewers to more use of incentives, better use of the information collected in the data collection, and increased use of auxiliary information from other sources in survey design and data collection. Nonresponse in Social Science Surveys: A Research Agenda also documents the increased use of information collected in the survey process in nonresponse adjustment.

We elaborate a general workflow of weighting-based survey inference, decomposing it into two main tasks. The first is the estimation of population targets from one or more sources of auxiliary information. The second is the construction of weights that calibrate the survey sample to the population targets. We emphasize that these tasks are predicated on models of the measurement, sampling, and nonresponse process whose assumptions cannot be fully tested. After describing this workflow in abstract terms, we then describe in detail how it can be applied to the analysis of historical and contemporary opinion polls. We also discuss extensions of the basic workflow, particularly inference for causal quantities and multilevel regression and poststratification.

A comprehensive framework for both reduction of nonresponse andpostsurvey adjustment for nonresponse This book provides guidance and support for survey statisticianswho need to develop models for postsurvey adjustment fornonresponse, and for survey designers and practitioners attemptingto reduce unit nonresponse in household interview surveys. Itpresents the results of an eight-year research program that hasassembled an unprecedented data set on respondents andnonrespondents from several major household surveys in the UnitedStates. Within a comprehensive conceptual framework of influences onnonresponse, the authors investigate every aspect of surveycooperation, from the influences of household characteristics andsocial and environmental factors to the interaction betweeninterviewers and householders and the design of the surveyitself. Nonresponse in Household Interview Surveys: * Provides a theoretical framework for understanding and studyinghousehold survey nonresponse * Empirically explores the individual and combined influences ofseveral factors on nonresponse * Presents chapter introductions, summaries, and discussions onpractical implications to clarify concepts and theories * Supplies extensive references for further study and inquiry Nonresponse in Household Interview Surveys is an important resourcefor professionals and students in survey methodology/researchmethods as well as those who use survey methods or data inbusiness, government, and academia. It addresses issues critical todealing with nonresponse in surveys, reducing nonresponse duringsurvey data collection, and constructing statistical compensationsfor the effects of nonresponse on key survey estimates.

Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the advantages of modern computing to such handle surveys, and demonstrates the benefit of this statistical technique for researchers who must analyze them. Also presents the background for Bayesian and frequentist theory. After establishing that only standard complete-data methods are needed to analyze a multiply-imputed set, the text evaluates procedures in general circumstances, outlining specific procedures for creating imputations in both the ignorable and nonignorable cases. Examples and exercises reinforce ideas, and the interplay of Bayesian and frequentist ideas presents a unified picture of modern statistics.

This volume offers coverage of research in the field of survey nonresponse, the primary threat to the statistical integrity of surveys. This book was written in conjunction with the International Conference on Survey Nonresponse, October 1999.