Analysis of Categorical Data with R

Analysis of Categorical Data with R

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ISBN 9781439855676
Cat# K12597

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Features

  • Provides descriptions and motivations of the analysis methods as well as worked examples with R code
  • Highlights applications in a wide range of disciplines, including medicine, psychology, sports, and ecology
  • Uses R not only as a data analysis method but also as a learning tool
  • Discusses solutions to problems frequently mishandled in practice, such as how to incorporate diagnostic testing error into an analysis and how to analyze data from a complex survey sampling design
  • Includes an introduction to R for inexperienced users
  • Presents an extensive set of exercises at the end of each chapter
  • Offers data sets, R programs, and videos on the book’s website

Solutions manual available upon qualifying course adoption

Summary

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.

Web Resource
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.

Table of Contents

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
Zero inflation

Model Selection and Evaluation
Variable selection
Tools to assess model fit
Overdispersion
Examples

Additional Topics
Binary responses and testing error
Exact inference
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

Bibliography

Index

Exercises appear at the end of each chapter.

Author Bio(s)

Editorial Reviews

"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