A complete guide to carrying out complex survey analysis usingR As survey analysis continues to serve as a core component ofsociological research, researchers are increasingly relying upondata gathered from complex surveys to carry out traditionalanalyses. Complex Surveys is a practical guide to theanalysis of this kind of data using R, the freely available anddownloadable statistical programming language. As creator of thespecific survey package for R, the author provides the ultimatepresentation of how to successfully use the software for analyzingdata from complex surveys while also utilizing the most currentdata from health and social sciences studies to demonstrate theapplication of survey research methods in these fields. The book begins with coverage of basic tools and topics withinsurvey analysis such as simple and stratified sampling, clustersampling, linear regression, and categorical data regression.Subsequent chapters delve into more technical aspects of complexsurvey analysis, including post-stratification, two-phase sampling,missing data, and causal inference. Throughout the book, anemphasis is placed on graphics, regression modeling, and two-phasedesigns. In addition, the author supplies a unique discussion ofepidemiological two-phase designs as well as probability-weightingfor causal inference. All of the book's examples and figures aregenerated using R, and a related Web site provides the R code thatallows readers to reproduce the presented content. Each chapterconcludes with exercises that vary in level of complexity, anddetailed appendices outline additional mathematical andcomputational descriptions to assist readers with comparing resultsfrom various software systems. Complex Surveys is an excellent book for courses onsampling and complex surveys at the upper-undergraduate andgraduate levels. It is also a practical reference guide for appliedstatisticians and practitioners in the social and health scienceswho use statistics in their everyday work.
A comprehensive guidebook to the current methodologies and practices used in health surveys A unique and self-contained resource, Handbook of Health Survey Methods presents techniques necessary for confronting challenges that are specific to health survey research. The handbook guides readers through the development of sample designs, data collection procedures, and analytic methods for studies aimed at gathering health information on general and targeted populations. The book is organized into five well-defined sections: Design and Sampling Issues, Measurement Issues, Field Issues, Health Surveys of Special Populations, and Data Management and Analysis. Maintaining an easy-to-follow format, each chapter begins with an introduction, followed by an overview of the main concepts, theories, and applications associated with each topic. Finally, each chapter provides connections to relevant online resources for additional study and reference. The Handbook of Health Survey Methods features: 29 methodological chapters written by highly qualified experts in academia, research, and industry A treatment of the best statistical practices and specific methodologies for collecting data from special populations such as sexual minorities, persons with disabilities, patients, and practitioners Discussions on issues specific to health research including developing physical health and mental health measures, collecting information on sensitive topics, sampling for clinical trials, collecting biospecimens, working with proxy respondents, and linking health data to administrative and other external data sources Numerous real-world examples from the latest research in the fields of public health, biomedicine, and health psychology Handbook of Health Survey Methods is an ideal reference for academics, researchers, and practitioners who apply survey methods and analyze data in the fields of biomedicine, public health, epidemiology, and biostatistics. The handbook is also a useful supplement for upper-undergraduate and graduate-level courses on survey methodology. Timothy P. Johnson, PhD, is Director of the Survey Research Laboratory and Professor of Public Administration at the University of Illinois at Chicago (UIC), where he is also Research Professor of Epidemiology and Deputy Director for Evaluation and Tracking of the UIC Center for Clinical and Translational Research. The author of numerous journal articles, Dr. Johnson is Coeditor of Survey Methods in Multicultural, Multinational, and Multiregional Contexts, also published by Wiley.
This book focuses on quantitative survey methodology, data collection and cleaning methods. Providing starting tools for using and analyzing a file once a survey has been conducted, it addresses fields as diverse as advanced weighting, editing, and imputation, which are not well-covered in corresponding survey books. Moreover, it presents numerous empirical examples from the author's extensive research experience, particularly real data sets from multinational surveys.
A comprehensive, one-stop guide to identifying, reducing, andmanaging nonresponse in household surveys Nonresponse and its impact on the sample selection mechanism ofa survey is a common problem that often arises while collectingsurvey data. Handbook of Nonresponse in Household Surveys is acomplete guide to handling the nonresponse problem, outliningstatistical methods and techniques for improving response rates andcorrecting response data. The authors begin with an introduction to the nonresponseproblem along with basic concepts and definitions. Subsequentchapters present current theories and methods that enable surveyresearchers to skillfully account for nonresponse in theirresearch. Exploring the latest developments in the field, the bookalso features: An introduction to the R-indicator as an indicator of surveyquality Discussion of the different causes of nonresponse Extensive treatment of the selection and use of auxiliaryinformation Best practices for re-approaching nonrespondents An overview of advanced nonresponse correction techniques Coverage of adaptive survey design Throughout the book, the treatment of each topic is presented ina uniform fashion. Following an introduction, each chapter presentsthe key theories and formulas underlying the topic and thenillustrates common applications. Discussion concludes with asummary of the main concepts as well as a glossary of key terms anda set of exercises that allows readers to test their comprehensionof the presented material. Examples using real survey data areprovided, and a related website features additional data sets,which can be easily analyzed using Stata® or SPSS®software. Handbook of Nonresponse in Household Surveys is an essentialreference for survey researchers working in the fields of business,economics, government, and the social sciences who gather, analyze,and draw results from data. It is also a suitable supplement forcourses on survey methods at the upper-undergraduate and graduatelevels.
Survey Methodology is becoming a more structured field of research, deserving of more and more academic attention. The SAGE Handbook of Survey Methodology explores both the increasingly scientific endeavour of surveys and their growing complexity, as different data collection modes and information sources are combined. The handbook takes a global approach, with a team of international experts looking at local and national specificities, as well as problems of cross-national, comparative survey research. The chapters are organized into seven major sections, each of which represents a stage in the survey life-cycle: Surveys and Societies Planning a Survey Measurement Sampling Data Collection Preparing Data for Use Assessing and Improving Data Quality The SAGE Handbook of Survey Methodology is a landmark and essential tool for any scholar within the social sciences.
This book describes a novel approach to the theory of sampling from finite populations. The new unifying approach is based on the sampling autocorrelation coefficient. Step by step, the author derives a general set of sampling equations that describe the estimators, their variances as well as the corresponding variance estimators. These equations are applicable for a whole family of different sampling designs, varying from simple surveys to complex surveys based on multistage sampling without replacement and unequal probabilities. This volume will be useful for survey practitioners faced with complex surveys. The book also considers constrained estimation problems that may occur in practice when linear or nonlinear economic restrictions are imposed on the population parameters to be estimated and the observations stem from different surveys. For example, regression estimators and consistent estimation of contingency tables are special cases within this rather broad class of constrained estimators. This volume also offers a guide to little-known connections between design-based survey sampling and other areas of statistics and related disciplines. The common underlying principles in the distinct fields are explained by an extensive use of the geometry of the ancient Pythagorean theorem. Apart from its applied importance, the book may also serve as a textbook in advanced courses and as a reference for researchers in statistics and empirical economics. In order to make the text as self-contained as possible, the treatise includes one chapter with the main results from statistics, including regression analysis. Some familiarity with calculus and matrix algebra is a sufficient prerequisite. Paul Knottnerus received his PhD in economics in 1989 from the University of Amsterdam. In 1995 he joined Statistics Netherlands, Department of Methods and Informatics. Previously he spent several years with Dutch Telecom. He is author of the book Linear Models with Correlated Disturbances (1991).
Taking into account both traditional and emerging modes, this comprehensive new Handbook covers all major methodological and statistical issues in designing and analyzing surveys. With contributions from the world's leading survey methodologists and statisticians, this invaluable new resource provides guidance on collecting survey data and creating meaningful results. Featuring examples from a variety of countries, the book reviews such things as how to deal with sample designs, write survey questions, and collect data on the Internet. A thorough review of the procedures associated with multiple modes of collecting sample survey information and applying that combination of methods that fit the situation best is included. The International Handbook of Survey Methodology opens with the foundations of survey design, ranging from sources of error, to ethical issues. This is followed by a section on design that reviews sampling challenges and tips on writing and testing questions for multiple methods. Part three focuses on data collection, from face-to-face interviews, to Internet and interactive voice response, to special challenges involved in mixing these modes within one survey. Analyzing data from both simple and complex surveys is then explored, as well as procedures for adjusting data. The book concludes with a discussion of maintaining quality. Intended for advanced students and researchers in the behavioral, social, and health sciences, this "must have" resource will appeal to those interested in conducting or using survey data from anywhere in the world, especially those interested in comparing results across countries. The book also serves as a state-of-the-art text for graduate level courses and seminars on survey methodology. A companion website contains additional readings and examples.
Combining theoretical, methodological, and practical aspects,Latent Class Analysis of Survey Error successfully guides readersthrough the accurate interpretation of survey results for qualityevaluation and improvement. This book is a comprehensive resourceon the key statistical tools and techniques employed during themodeling and estimation of classification errors, featuring aspecial focus on both latent class analysis (LCA) techniques andmodels for categorical data from complex sample surveys. Drawing from his extensive experience in the field of surveymethodology, the author examines early models for surveymeasurement error and identifies their similarities and differencesas well as their strengths and weaknesses. Subsequent chapterstreat topics related to modeling, estimating, and reducing errorsin surveys, including: Measurement error modeling forcategorical data The Hui-Walter model and othermethods for two indicators The EM algorithm and its role in latentclass model parameterestimation Latent class models for three ormore indicators Techniques for interpretation of modelparameter estimates Advanced topics in LCA, including sparse data, boundary values,unidentifiability, and local maxima Special considerations for analyzing datafrom clustered andunequal probability samples with nonresponse The current state of LCA and MLCA (multilevel latent classanalysis), and an insightful discussion on areas for furtherresearch Throughout the book, more than 100 real-world examples describethe presented methods in detail, and readers are guided through theuse of lEM software to replicate the presented analyses. Appendicessupply a primer on categorical data analysis, and a related Website houses the lEM software. Extensively class-tested to ensure an accessible presentation,Latent Class Analysis of Survey Error is an excellent book forcourses on measurement error and survey methodology at the graduatelevel. The book also serves as a valuable reference for researchersand practitioners working in business, government, and the socialsciences who develop, implement, or evaluate surveys.