Learn How to Properly Analyze Categorical Data
Analysis of Categorical Data with R presents a modern account of categorical data analysis using the popular R software. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them.
The Use of R as Both a Data Analysis Method and a Learning Tool
Requiring no prior experience with R, the text offers an introduction to the essential features and functions of R. It incorporates numerous examples from medicine, psychology, sports, ecology, and other areas, along with extensive R code and output. The authors use data simulation in R to help readers understand the underlying assumptions of a procedure and then to evaluate the procedure’s performance. They also present many graphical demonstrations of the features and properties of various analysis methods.
The data sets and R programs from each example are available at www.chrisbilder.com/categorical. The programs include code used to create every plot and piece of output. Many of these programs contain code to demonstrate additional features or to perform more detailed analyses than what is in the text. Designed to be used in tandem with the book, the website also uniquely provides videos of the authors teaching a course on the subject. These videos include live, in-class recordings, which instructors may find useful in a blended or flipped classroom setting. The videos are also suitable as a substitute for a short course.
"… I really enjoyed reading it due to its unique examples and extensive R code…The book would be a great textbook for advanced undergraduate or postgraduate courses, especially if training in R programming is also a learning objective. A self-learner with basic knowledge of categorical data analysis would find the book easy to follow…Given the series of in-class videos provided on the associated website and all the R code available online at http://chrisbilder.com/categorical/, there is no doubt that this book would be a great textbook. Personally I would like to congratulate Bilder and Loughin on the writing of this valuable book. Even before I had finished reading the book, I had already recommended it to my students. Now, I highly recommend this book to all readers."
— Australian & New Zealand Journal of Statistics, April 2016
"This book presents an extensive introduction to analysis of categorical data with R. The context is relevant for a multitude of application areas such as biology, ecology, medicine and sports, just to name a few. Recent model-building techniques are covered…Throughout the book, R is used not only as a data analysis tool but also as a learning tool…The book takes an easy-to-understand approach by partnering practical explanations with numerous illustrative examples…To help students apply their knowledge, the book has also provided an extensive number of exercises…The textbook can also be a very useful reference."
— International Statistical Review, April 2016
"In summary, I think this book is well organized and nicely written. I really enjoyed reading it, though I did not have time to run all of the R codes by myself. I want to use this book as a textbook in a graduate course for CDA. This book has many advantages. Compared to other standard textbooks, its complete coverage of examples from many different research areas and the R codes would let the students (and other readers) become experts in CDA in all fields. Use of the same examples throughout different chapters consistently provides excellent process of data analysis. Furthermore, an extensive set of exercises at the end of each chapter (over 65 pages in all) that differ in scope and subject manner would be good supporting materials for enhancing practical experiences of real data analysis."
— Biometrics, 71,December 2015
"… a valuable asset to any person who wants to analyze categorical data. Bilder and Loughin demystify categorical data analysis using a simple approach, with enough statistical theory to allow the reader to understand the underlying assumptions of the analyses involved, but with minimal, unintimidating mathematical symbols, and equations. The authors have managed to explain the statistics involved in categorical data analysis in unadorned semantics and accompanied them with corresponding R codes … . This is a major plus for this book.
Overall, the book is well written: It contains easy-to-follow R codes, footnote explanations of material that could not be explained within the text, and plenty of exercises at the end of each chapter. … Excellent videos of Bilder teaching the material in class, full R codes, and corresponding data, each arranged by chapter, are available on a website. These resources make it easy for readers to acquire a deeper understanding of categorical data analysis. …
This book is a must-have tool for any biostatistician analyzing categorical data in R. It could very well be used as a text in intermediate-to-advanced applied courses in practical analysis of categorical data."
— Biometrical Journal, 57, 2015
"Bilder and Loughin have worked as a dynamic duo for a number of years, and they clearly are blending their knowledge, talents, experience, and teamwork to create this valuable book. Analyzing categorical data correctly and in-depth is not as simple as it appears in many courses and textbooks. As a result, many people can get the wrong idea about what could and should be done with categorical data, and hence their results can be inconclusive or incorrect. This book gives users the full scoop when it comes to analyzing categorical data of all types, and it does so in an easy-to-understand way, giving confidence to the reader to go ahead and apply the ideas in practice. The use of R for analyzing data is becoming a worldwide phenomenon and a staple for data analysts on every level. As its popularity grows, it becomes critical for beginners to become comfortable with understanding and using R to analyze their data. Through the special attention paid to teaching the basics of R, as well as providing step-by-step particulars in using R in each separate analysis, Bilder and Loughin help establish and promote a group of confident, comfortable users of this software that can seem a mystery to many. I highly and happily recommend this book to anyone who plans to analyze categorical data in their careers—which includes most all of us!"
— Deborah J. Rumsey, PhD, Auxiliary Professor and Statistics Education Specialist, Department of Statistics, The Ohio State University
Analyzing a Binary Response, Part 1: Introduction
One binary variable
Two binary variables
Analyzing a Binary Response, Part 2: Regression Models
Linear regression models
Logistic regression models
Generalized linear models
Analyzing a Multicategory Response
Multinomial probability distribution
I x J contingency tables and inference procedures
Nominal response regression models
Ordinal response regression models
Additional regression models
Analyzing a Count Response
Poisson model for count data
Poisson regression models for count responses
Poisson rate regression
Model Selection and Evaluation
Tools to assess model fit
Binary responses and testing error
Categorical data analysis in complex survey designs
"Choose all that apply" data
Mixed models and estimating equations for correlated data
Bayesian methods for categorical data
Appendix A: An Introduction to R
Appendix B: Likelihood Methods
Exercises appear at the end of each chapter.