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.
Das Buch führt in Grundprinzipien der Stichprobenziehung und der zugehörigen statistischen Auswertung ein. Dabei stehen Motivation und anschauliche Beschreibung der Verfahren im Vordergrund. Nach einer generellen Einführung werden sowohl modellbasierte als auch designbasierte Stichprobenverfahren wie Clusterstichprobe und geschichtete Stichprobe entwickelt. Jedes Kapitel wird mit der Umsetzung der Verfahren mit dem Programpaket R abgeschlossen. Hierdurch werden die Leserin und der Leser in die Lage versetzt, selbst komplexe Stichprobenverfahren direkt in R umzusetzen. Ein Ausblick auf weitere Verfahren und praktische Probleme schließt jedes Kapitel des Buches ab.
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, and managing nonresponse in household surveys Nonresponse and its impact on the sample selection mechanism of a survey is a common problem that often arises while collecting survey data. Handbook of Nonresponse in Household Surveys is a complete guide to handling the nonresponse problem, outlining statistical methods and techniques for improving response rates and correcting response data. The authors begin with an introduction to the nonresponse problem along with basic concepts and definitions. Subsequent chapters present current theories and methods that enable survey researchers to skillfully account for nonresponse in their research. Exploring the latest developments in the field, the book also features: An introduction to the R-indicator as an indicator of survey quality Discussion of the different causes of nonresponse Extensive treatment of the selection and use of auxiliary information 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 in a uniform fashion. Following an introduction, each chapter presents the key theories and formulas underlying the topic and then illustrates common applications. Discussion concludes with a summary of the main concepts as well as a glossary of key terms and a set of exercises that allows readers to test their comprehension of the presented material. Examples using real survey data are provided, 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 essential reference 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 for courses on survey methods at the upper-undergraduate and graduate levels.
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).
Psychology by Edith D. de Leeuw,Joop Hox,Don Dillman
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.
"Sampling of Populations, Fourth Edition continues to serve as an all-inclusive resource on the basic and most current practices in population sampling. Maintaining the clear and accessible style of the previous edition, this book outlines the essential statistical methods for survey design and analysis, while also exploring techniques that have developed over the past decade." "The Fourth Edition guides the reader through the basic concepts and procedures that accompany real-world sample surveys, such as sampling designs, problems of missing data, statistical analysis of multistage sampling data, and nonresponse and poststratification adjustment procedures. Rather than employ a heavily mathematical approach, the authors present illustrative examples that demonstrate the rationale behind common steps in the sampling process, from creating effective surveys to analyzing collected data."--BOOK JACKET.
Author: Michael Lewis-Beck,Alan E Bryman,Tim Futing Liao
Publisher: SAGE Publications, Incorporated
Category: Social Science
"This defining work will be valuable to readers and researchers in social sciences and humanities at all academic levels. As a teaching resource it will be useful to instructors and students alike and will become a standard reference source. Essential for general and academic collections." --CHOICE This Encyclopedia provides readers with authoritative essays on virtually all social science methods topics, quantitative and qualitative, by an international collection of experts. Organized alphabetically, the Encyclopedia of Social Science Research Methods covers research terms ranging from different methodological approaches to epistemological issues and specific statistical techniques. Written to be accessible to general readers, the Encyclopedia entries do not require advanced knowledge of mathematics or statistics to understand the purposes or basic principles of any of the methods. To accomplish this goal, there are two major types of entries: definitions consisting of a paragraph or two to provide a quick explanation of a methodological term; and topical treatments or essays that discuss the nature, history, applications, and implications of using a certain method, including suggested readings and references. Readers are directed to related topics via cross-referenced terms that appear in small capital letters. By assembling entries of varied origins and serving different research purposes, readers will be able to benefit from this immense source of methodological expertise in advancing their understanding of research. With three volumes and more than 900 signed entries, the Encyclopedia of Social Science Research Methods will be a critical addition to any social science library.
?konometrie; nicht nur der Begriff ist etwas sperrig, auch die Inhalte erschlie?en sich nicht jedem sofort. Wichtig und interessant ist sie aber trotzdem. Roberto Pedace erkl?rt Ihnen, worum es in der ?konometrie geht, wie Sie Test-Hypothesen aufstellen und vieles mehr. Er erl?utert, wie Sie mit Regressionsmodellen arbeiten und mit diskreten und abh?ngigen Variablen umgehen. Gegen Ende des Buches geht er ?ber die Basismodelle hinaus und f?hrt Sie in statische und dynamische Modelle sowie die Kunst der Vorhersagen ein.
In this eagerly-anticipated new text, a range of internationally-renowned experts draw on their extensive experience to provide a practical and accessible guide to the wide span of methods used in health research. Researching Health covers the background to conducting health research, qualitative and quantitative methods employed in researching health, contemporary issues such as research ethics, comparative research and the use of mixed methods, and how to disseminate health research. As such, it provides the perfect package to help readers carry out their own research projects and use the research of others more effectively in their work.
Wozu sollte man R lernen? Da gibt es viele Gründe: Weil man damit natürlich ganz andere Möglichkeiten hat als mit einer Tabellenkalkulation wie Excel, aber auch mehr Spielraum als mit gängiger Statistiksoftware wie SPSS und SAS. Anders als bei diesen Programmen hat man nämlich direkten Zugriff auf dieselbe, vollwertige Programmiersprache, mit der die fertigen Analyse- und Visualisierungsmethoden realisiert sind – so lassen sich nahtlos eigene Algorithmen integrieren und komplexe Arbeitsabläufe realisieren. Und nicht zuletzt, weil R offen gegenüber beliebigen Datenquellen ist, von der einfachen Textdatei über binäre Fremdformate bis hin zu den ganz großen relationalen Datenbanken. Zudem ist R Open Source und erobert momentan von der universitären Welt aus die professionelle Statistik. R kann viel. Und Sie können viel mit R machen – wenn Sie wissen, wie es geht. Willkommen in der R-Welt: Installieren Sie R und stöbern Sie in Ihrem gut bestückten Werkzeugkasten: Sie haben eine Konsole und eine grafische Benutzeroberfläche, unzählige vordefinierte Analyse- und Visualisierungsoperationen – und Pakete, Pakete, Pakete. Für quasi jeden statistischen Anwendungsbereich können Sie sich aus dem reichen Schatz der R-Community bedienen. Sprechen Sie R! Sie müssen Syntax und Grammatik von R nicht lernen – wie im Auslandsurlaub kommen Sie auch hier gut mit ein paar aufgeschnappten Brocken aus. Aber es lohnt sich: Wenn Sie wissen, was es mit R-Objekten auf sich hat, wie Sie eigene Funktionen schreiben und Ihre eigenen Pakete schnüren, sind Sie bei der Analyse Ihrer Daten noch flexibler und effektiver. Datenanalyse und Statistik in der Praxis: Anhand unzähliger Beispiele aus Medizin, Wirtschaft, Sport und Bioinformatik lernen Sie, wie Sie Daten aufbereiten, mithilfe der Grafikfunktionen des lattice-Pakets darstellen, statistische Tests durchführen und Modelle anpassen. Danach werden Ihnen Ihre Daten nichts mehr verheimlichen.