Statistical tools to analyze correlated binary data are spread out in the existing literature. This book makes these tools accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages that are tailored to analyzing correlated binary data. The authors showcase both traditional and new methods for application to health-related research. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book. The inclusion of code for R, SAS, and SPSS allows for easy implementation by readers. For readers interested in learning more about the languages, though, there are short tutorials in the appendix. Accompanying data sets are available for download through the book s website. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects. Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful.
Reviews / Votes
"The monograph is devoted to logistic regression modeling and its extensions useful for complex survey sampling data. . this book will be useful for students and practitioners in various fields needed binary outcome modeling for analysis and predictions in applied research." (Stan Lipovetsky, Technometrics, Vol. 58 (4), April, 2016)
Series
Edition
Softcover Reprint of the Original 1st 2015 ed.
Language
Place of publication
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
26 s/w Abbildungen
XXIII, 264 p. 26 illus.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 16 mm
Weight
ISBN-13
978-3-319-37361-4 (9783319373614)
DOI
10.1007/978-3-319-23805-0
Schweitzer Classification
Jeffrey Wilson is Professor of Statistics and Biostatistics, and Associate Dean of Research in W. P. Carey School of Business, Arizona State University, Tempe. He is the former Statistics Associate Editor for The Journal of Minimally Invasive Gynecology and the Faculty Athletics Representative for Arizona State University. He has published more than 90 articles in leading journals such as Statistics in Medicine, American Journal of Public Health, Journal of Royal Statistics Series C, Management Science, Journal of Business and Economic Statistics, Computational Statistics, and Australian Journal of Statistics, among others.
Kent A. Lorenz is Associate Professor of Physical Education and Physical Activity in the Department of Kinesiology at San Francisco State University. He teaches courses in physical fitness, and elementary and secondary curriculum and instruction in the Integrated Teacher Education Program in Physical Education, and the introduction to statistics course for the Masters of Science in Kinesiology degree program. His research interests center on youth physical activity and physical fitness, with a particular emphasis on Comprehensive School Physical Activity Programs. Dr. Lorenz has published 25 peer-reviewed journal articles, contributed to various book chapters and the first edition of the Modeling Correlated Binary Data using SAS, SPSS and R.
Lori P. Selby is a PhD candidate in the School of Mathematics and Statistics. She was a lecturer in Mathematics and Biostatistics at the University of Trinidad and Tobago. She is a member of the American
Statistical Association (ASA) and the Arizona Chapter of ASA. She is also a member of the American Public Health Association.
Introduction to Binary logistic Regression.- Growth of the Logistic Regression Model.- Standard Binary Logistic Regression Model.- Overdispersed Logistic Regression Model.- Weighted Logistic Regression Model.- Generalized Estimating Equations Logistic Regression.- Generalized Method of Moments logistic regression Model.- Exact Logistic Regression Model.- Two-Level Nested Logistic Regression Model.- Hierarchical Logistic Regression models.- Fixed Effects Logistic Regression Model.- Heteroscedastic Logistic Regression Model.
