Introduction to Categorical Data Analysis 2nd Edition by Alan Agresti ISBN 0471226181 9780471226185

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ISBN 10: 0471226181 
ISBN 13: 9780471226185
Author: Alan Agresti

Praise for the First Edition “This is a superb text from which to teach categorical data analysis, at a variety of levels. . . [t]his book can be very highly recommended.”
&; Short Book Reviews “Of great interest to potential readers is the variety of fields that are represented in the examples: health care, financial, government, product marketing, and sports, to name a few.”
&; Journal of Quality Technology “Alan Agresti has written another brilliant account of the analysis of categorical data.”
&;The Statistician The use of statistical methods for categorical data is ever increasing in today’s world. An Introduction to Categorical Data Analysis, Second Edition provides an applied introduction to the most important methods for analyzing categorical data. This new edition summarizes methods that have long played a prominent role in data analysis, such as chi-squared tests, and also places special emphasis on logistic regression and other modeling techniques for univariate and correlated multivariate categorical responses. This Second Edition features: Written in an applied, nontechnical style, the book illustrates methods using a wide variety of real data, including medical clinical trials, drug use by teenagers, basketball shooting, horseshoe crab mating, environmental opinions, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Second Edition is an invaluable tool for social, behavioral, and biomedical scientists, as well as researchers in public health, marketing, education, biological and agricultural sciences, and industrial quality control.

Introduction to Categorical Data Analysis 2nd Table of contents:

• Introduction to Categorical Data Analysis

  • 1.1 Overview of Categorical Data
  • 1.2 Importance of Categorical Data in Research and Analysis
  • 1.3 Types of Categorical Variables: Nominal, Ordinal, and Binary
  • 1.4 Key Concepts and Terminology in Categorical Data Analysis
  • 1.5 The Role of Categorical Data in Statistical Inference

• Fundamentals of Categorical Data

  • 2.1 Nature of Categorical Variables
  • 2.2 Frequency Tables and Cross-tabulations
  • 2.3 Visualizing Categorical Data: Bar Charts, Pie Charts, and Mosaic Plots
  • 2.4 Measures of Association for Categorical Data

• Contingency Tables and Chi-Square Tests

  • 3.1 Introduction to Contingency Tables
  • 3.2 Chi-Square Test for Independence
  • 3.3 Chi-Square Goodness-of-Fit Test
  • 3.4 Assumptions and Limitations of Chi-Square Tests
  • 3.5 Adjustments for Small Sample Sizes

• Logistic Regression

  • 4.1 Introduction to Logistic Regression
  • 4.2 Binary Logistic Regression: Model Building and Interpretation
  • 4.3 Multinomial Logistic Regression: When There Are More Than Two Categories
  • 4.4 Ordinal Logistic Regression: Modeling Ordinal Outcomes
  • 4.5 Model Diagnostics and Goodness-of-Fit

• Multivariate Categorical Data Analysis

  • 5.1 Multivariate Contingency Tables and Associations
  • 5.2 Measures of Association for Multivariate Categorical Data
  • 5.3 Multivariate Logistic Regression Models
  • 5.4 Methods for Handling Interactions in Multivariate Models

• Generalized Linear Models (GLM) for Categorical Data

  • 6.1 Introduction to Generalized Linear Models
  • 6.2 GLM Framework for Categorical Data Analysis
  • 6.3 Poisson Regression and Log-Linear Models
  • 6.4 Negative Binomial Regression
  • 6.5 Model Interpretation and Diagnostics

• Model Selection and Evaluation

  • 7.1 Comparing Models for Categorical Data
  • 7.2 AIC, BIC, and Other Model Selection Criteria
  • 7.3 Cross-Validation and Model Performance Metrics
  • 7.4 Overfitting and Model Complexity

• Residuals and Diagnostics

  • 8.1 Residuals in Categorical Data Models
  • 8.2 Outliers and Influential Points
  • 8.3 Diagnostics for Logistic Regression Models
  • 8.4 Goodness-of-Fit Tests and Model Validation

• Bayesian Methods for Categorical Data Analysis

  • 9.1 Introduction to Bayesian Inference
  • 9.2 Bayesian Logistic Regression
  • 9.3 Markov Chain Monte Carlo (MCMC) Methods
  • 9.4 Bayesian Model Checking and Diagnostics

• Multilevel and Mixed-Effects Models

  • 10.1 Introduction to Multilevel Models
  • 10.2 Fixed and Random Effects
  • 10.3 Generalized Linear Mixed Models (GLMM) for Categorical Data
  • 10.4 Model Interpretation and Software Implementation

• Applications in Social Sciences and Medicine

  • 11.1 Categorical Data Analysis in Social Sciences
  • 11.2 Applications in Medical Research: Risk Factors and Disease Outcomes
  • 11.3 Applications in Market Research and Consumer Behavior
  • 11.4 Case Studies of Categorical Data Analysis in Real-World Scenarios

• Advanced Topics in Categorical Data Analysis

  • 12.1 Causal Inference and Categorical Data
  • 12.2 Nonparametric Methods for Categorical Data
  • 12.3 Multivariate Proportions and Association Tests
  • 12.4 Handling Missing Data in Categorical Variables

• Software for Categorical Data Analysis

  • 13.1 Using R for Categorical Data Analysis
  • 13.2 SAS and SPSS for Categorical Data Modeling
  • 13.3 Implementing Models in Python and Other Tools
  • 13.4 Choosing the Right Software for Your Analysis

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