Analyzing Repeated Surveys

Repeated surveys, a technique for asking the same questions to different samples of people, allows researchers to analyse changes in society as a whole. Firebaugh shows how to separate cohort, period and age effects, and model aggregate trends.

Written by authors recognized in the area of repeated sampling, this book elaborates the relationship between survey design and analysis for repeated surveys, in particular the interpretation of estimates derived from sample surveys. Accompanied with a supplementary Web site that provides further information, the book provides the reader with a logical and user-friendly treatment of all the different processes involved in performing repeated surveys. Throughout the text exercises, questions and answers related to real-world issues from the authors? own practical experiences enhance the reader?s experience.

How to apply statistical methods to survey data--a guide toeffective analysis of health surveys. With large health surveys becoming increasingly available forpublic use, researchers with little experience in survey methodsare often faced with analyzing data from surveys to addressscientific and programmatic questions. This practical book providesstatistical techniques for use in survey analysis, making healthsurveys accessible to statisticians, biostatisticians,epidemiologists, and health researchers. The authors clearlyexplain the theory and methods of survey analysis along withreal-world applications. They draw on their work at the NationalInstitutes of Health as well as up-to-date information from acrossthe literature to present: * The sampling background necessary to understand health surveys. * The application of such techniques as t-tests, linear regression,logistic regression, and survival analysis to survey data. * The use of sample weights in survey data analysis. * Dealing with complications in variance estimation in large healthsurveys. * Applications involving cross-sectional, longitudinal, andmultiple cross-sectional surveys, and the use of surveys to performpopulation- based case-control analyses. * Guidance on the correct use of statistical methods found insoftware packages. * Extensive bibliography.

In this introduction to the different ways of analysing complex survey data, the authors consider new analytical approaches, review new software and introduce a model-based analysis that can be used for well-designed and relatively small-scale social surveys.

Highly recommended by the Journal of Official Statistics, The American Statistician, and other journals, Applied Survey Data Analysis, Second Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first edition, this second edition expands the topics covered and presents more step-by-step examples of modern approaches to the analysis of survey data using the newest statistical software. Designed for readers working in a wide array of disciplines who use survey data in their work, this book continues to provide a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. An example-driven guide to the applied statistical analysis and interpretation of survey data, the second edition contains many new examples and practical exercises based on recent versions of real-world survey data sets. Although the authors continue to use Stata for most examples in the text, they also continue to offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s updated website.

Complex Survey Data Analysis with SAS® is an invaluable resource for applied researchers analyzing data generated from a sample design involving any combination of stratification, clustering, unequal weights, or finite population correction factors. After clearly explaining how the presence of these features can invalidate the assumptions underlying most traditional statistical techniques, this book equips readers with the knowledge to confidently account for them during the estimation and inference process by employing the SURVEY family of SAS/STAT® procedures. The book offers comprehensive coverage of the most essential topics, including: Drawing random samples Descriptive statistics for continuous and categorical variables Fitting and interpreting linear and logistic regression models Survival analysis Domain estimation Replication variance estimation methods Weight adjustment and imputation methods for handling missing data The easy-to-follow examples are drawn from real-world survey data sets spanning multiple disciplines, all of which can be downloaded for free along with syntax files from the author’s website: http://mason.gmu.edu/~tlewis18/. While other books may touch on some of the same issues and nuances of complex survey data analysis, none features SAS exclusively and as exhaustively. Another unique aspect of this book is its abundance of handy workarounds for certain techniques not yet supported as of SAS Version 9.4, such as the ratio estimator for a total and the bootstrap for variance estimation. Taylor H. Lewis is a PhD graduate of the Joint Program in Survey Methodology at the University of Maryland, College Park, and an adjunct professor in the George Mason University Department of Statistics. An avid SAS user for 15 years, he is a SAS Certified Advanced programmer and a nationally recognized SAS educator who has produced dozens of papers and workshops illustrating how to efficiently and effectively conduct statistical analyses using SAS.

A complete guide to carrying out complex survey analysis using R As survey analysis continues to serve as a core component of sociological research, researchers are increasingly relying upon data gathered from complex surveys to carry out traditional analyses. Complex Surveys is a practical guide to the analysis of this kind of data using R, the freely available and downloadable statistical programming language. As creator of the specific survey package for R, the author provides the ultimate presentation of how to successfully use the software for analyzing data from complex surveys while also utilizing the most current data from health and social sciences studies to demonstrate the application of survey research methods in these fields. The book begins with coverage of basic tools and topics within survey analysis such as simple and stratified sampling, cluster sampling, linear regression, and categorical data regression. Subsequent chapters delve into more technical aspects of complex survey analysis, including post-stratification, two-phase sampling, missing data, and causal inference. Throughout the book, an emphasis is placed on graphics, regression modeling, and two-phase designs. In addition, the author supplies a unique discussion of epidemiological two-phase designs as well as probability-weighting for causal inference. All of the book's examples and figures are generated using R, and a related Web site provides the R code that allows readers to reproduce the presented content. Each chapter concludes with exercises that vary in level of complexity, and detailed appendices outline additional mathematical and computational descriptions to assist readers with comparing results from various software systems. Complex Surveys is an excellent book for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. It is also a practical reference guide for applied statisticians and practitioners in the social and health sciences who use statistics in their everyday work.