Applied Categorical and Count Data Analysis: 1st Edition (Hardback) book cover

Applied Categorical and Count Data Analysis

1st Edition

By Wan Tang, Hua He, Xin M. Tu

Chapman and Hall/CRC

384 pages | 7 B/W Illus.

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pub: 2012-06-04
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Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors describe the basic ideas underlying each concept, model, and approach to give readers a good grasp of the fundamentals of the methodology without using rigorous mathematical arguments.

The text covers classic concepts and popular topics, such as contingency tables, logistic models, and Poisson regression models, along with modern areas that include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and methods for dealing with missing values. R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling readers to immediately experiment with the data in the examples and even adapt or extend the codes to fit data from their own studies.

Designed for a one-semester course for graduate and senior undergraduate students in biostatistics, this self-contained text is also suitable as a self-learning guide for biomedical and psychosocial researchers. It will help readers analyze data with discrete variables in a wide range of biomedical and psychosocial research fields.


"There is a lot to like about this book. The topics are well written and the issues are clearly explained. … It covers very well topics that are not traditionally discussed in CDA books and for this reason it certainly is a valuable addition to one’s bookshelf. For those who are looking for a book with a focus on applied data analysis (especially from a biostatistics perspective), this is a must-have book. For those who are interested in expanding their knowledge of recent advances in a broad range of CDA tools, [it] will serve you very well."

Australian & New Zealand Journal of Statistics, 2015

"… the book is well-written and for a mathematically oriented reader it should be quite easy to understand the methods introduced. Exercises, combined with practical data analyses, will certainly facilitate the adoption of the material."

—Tapio Nummi, International Statistical Review, 2014

"The combination of more advanced and mathematical explanations, newer topics, and sample code from all major software platforms makes this book a valuable addition to the literature on categorical data analysis."

—Russell L. Zaretzki, Journal of the American Statistical Association, September 2013

Table of Contents


Discrete Outcomes

Data Source

Outline of the Book

Review of Key Statistical Results


Contingency Tables

Inference for One-Way Frequency Table

Inference for 2 x 2 Table

Inference for 2 x r Tables

Inference for s x r Table

Measures of Association

Sets of Contingency Tables

Confounding Effects

Sets of 2 x 2 Tables

Sets of s x r Tables

Regression Models for Categorical Response

Logistic Regression for Binary Response

Inference about Model Parameters

Goodness of Fit

Generalized Linear Models

Regression Models for Polytomous Response

Regression Models for Count Response

Poisson Regression Model for Count Response

Goodness of Fit


Parametric Models for Clustered Count Response

Loglinear Models for Contingency Tables

Analysis of Loglinear Models

Two-Way Contingency Tables

Three-Way Contingency Tables

Irregular Tables

Model Selection

Analyses of Discrete Survival Time

Special Features of Survival Data

Life Table Methods

Regression Models

Longitudinal Data Analysis

Data Preparation and Exploration

Marginal Models

Generalized Linear Mixed-Effects Model

Model Diagnostics

Evaluation of Instruments


Criterion Validity

Internal Reliability

Test-Retest Reliability

Analysis of Incomplete Data

Incomplete Data and Associated Impact

Missing Data Mechanism

Methods for Incomplete Data




Exercises appear at the end of each chapter.

About the Authors

Wan Tang is a Clinical Associate Professor in the Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine. Dr. Tang’s research interests include longitudinal data analysis, missing data modeling, structural equation models, causal inference, and nonparametric smoothing methods.

Hua He, Ph.D. is an Associate Professor in Biostatistics at the Department of Epidemiology at Tulane University School of Public Health and Tropical Medicine. Dr. He is a highly experienced biostatistician with expertise in longitudinal data analysis, structural equation models, potential outcome based causal inference, distribution-free models, ROC analysis and their applications to observational studies, and randomized controlled trials across a range of disciplines, especially in the behavioral and social sciences. She has co-authored a series of publications in peer-reviewed journals, one textbook on categorical data analysis and co-edited a book on statistical causal inference and their applications in public health research.

Xin Tu (Ph.D.) is a Professor in the Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health, UCSD. Dr. Tu is well versed in statistical methods and their applications to a range of disciplines, particularly within the fields of biomedical, behavioral and social sciences. He has co-authored over 200 peer-reviewed publications, two textbooks on categorical data and applied U-statistics, and co-edited books on modern clinical trials and social network data analysis. He has done important work in the areas of longitudinal data analysis, U-statistics, survival analysis with interval censoring and truncation, and pooled testing, and has successfully applied his novel development to addressing important methodological problems in HIV/AIDS, mental health and psychosocial research.

About the Series

Chapman & Hall/CRC Texts in Statistical Science

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
MATHEMATICS / Probability & Statistics / Regression Analysis