Complex Surveys: A Guide to Analysis Using R
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About this ebook
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.
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Titles in the series (27)
Advances in Telephone Survey Methodology Rating: 0 out of 5 stars0 ratingsIntroduction to Survey Quality Rating: 0 out of 5 stars0 ratingsAnalysis of Health Surveys Rating: 0 out of 5 stars0 ratingsComplex Surveys: A Guide to Analysis Using R Rating: 0 out of 5 stars0 ratingsMethods for Testing and Evaluating Survey Questionnaires Rating: 0 out of 5 stars0 ratingsEnvisioning the Survey Interview of the Future Rating: 0 out of 5 stars0 ratingsApplied Survey Methods: A Statistical Perspective Rating: 0 out of 5 stars0 ratingsLatent Class Analysis of Survey Error Rating: 0 out of 5 stars0 ratingsStatistical Disclosure Control Rating: 0 out of 5 stars0 ratingsDesigning and Conducting Business Surveys Rating: 0 out of 5 stars0 ratingsQuestion Evaluation Methods: Contributing to the Science of Data Quality Rating: 0 out of 5 stars0 ratingsNonresponse in Household Interview Surveys Rating: 0 out of 5 stars0 ratingsImproving Survey Response: Lessons Learned from the European Social Survey Rating: 0 out of 5 stars0 ratingsMethodology of Longitudinal Surveys Rating: 0 out of 5 stars0 ratingsEstimation in Surveys with Nonresponse Rating: 0 out of 5 stars0 ratingsOnline Panel Research: A Data Quality Perspective Rating: 0 out of 5 stars0 ratingsStatistical Matching: Theory and Practice Rating: 0 out of 5 stars0 ratingsImplementation of Large-Scale Education Assessments Rating: 0 out of 5 stars0 ratingsCognitive Interviewing Methodology Rating: 0 out of 5 stars0 ratingsTotal Survey Error in Practice Rating: 0 out of 5 stars0 ratingsSmall Area Estimation Rating: 0 out of 5 stars0 ratingsRegister-based Statistics: Statistical Methods for Administrative Data Rating: 0 out of 5 stars0 ratingsAdvances in Comparative Survey Methods: Multinational, Multiregional, and Multicultural Contexts (3MC) Rating: 0 out of 5 stars0 ratingsAnalysis of Poverty Data by Small Area Estimation Rating: 0 out of 5 stars0 ratings
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