Applied Categorical and Count Data Analysis

Wan Tang, Hua He, Xin M. Tu

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June 4, 2012 by Chapman and Hall/CRC
Textbook - 384 Pages - 7 B/W Illustrations
ISBN 9781439806241 - CAT# K10311
Series: Chapman & Hall/CRC Texts in Statistical Science

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Features

  • Shows how statistical models for noncontinuous responses are applied to real studies, emphasizing difficult and overlooked issues along the pathway from models to data
  • Reviews classic concepts and models for categorical data analysis, such as log-linear models for contingency tables and exact inference for logistic regression
  • Offers an easy-to-follow presentation of modern concepts and approaches for count data, such as structural zeros and population mixtures
  • Covers useful topics in modern-day clinical trials and observation studies, including longitudinal data analysis, measure scales, and counterfactual outcomes
  • Presents a systematic treatment of instrumentation and measurement models for latent constructs, including measures of agreement and internal consistency
  • Compares popular models for clustered data, such as GLMM and GEE/WGEE
  • Gives an in-depth study of missing values and their impact on parametric and semiparametric (distribution-free) models
  • Includes exercises at the end of each chapter, many real data examples, and sample programming codes in SAS, SPSS, and STATA for model implementations
  • Provides the codes and their updates on a supporting website

Summary

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.

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