A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.

An Introduction to Categorical Data Analysis, 3rd Edition (Wiley Series in Probability and Statistics)

Wiley & Sons, Incorporated, John; Wiley

American Geophysical Union

Wiley-Blackwell

Wiley series in probability and statistics, Third edition, Hoboken, NJ, 2019

John Wiley & Sons, Inc. Textbook Subscription, Hoboken, NJ, 2019

United States, United States of America

3, 2018-11-05

Bookmarks: p1 (p1): 1 Introduction
p1-1 (p1): 1.1 Categorical Response Data
p1-2 (p3): 1.2 Probability Distributions for Categorical Data
p1-3 (p5): 1.3 Statistical Inference for a Proportion
p1-4 (p10): 1.4 Statistical Inference for Discrete Data
p1-5 (p13): 1.5 Bayesian Inference for Proportions
p1-6 (p17): 1.6 Using R Software for Statistical Inference about Proportions
p1-7 (p21): Exercises
p2 (p25): 2 Analyzing Contingency Tables
p2-1 (p26): 2.1 Probability Structure for Contingency Tables
p2-2 (p29): 2.2 Comparing Proportions in 2×2 Contingency Tables
p2-3 (p31): 2.3 The Odds Ratio
p2-4 (p36): 2.4 Chi-Squared Tests of Independence
p2-5 (p42): 2.5 Testing Independence for Ordinal Variables
p2-6 (p46): 2.6 Exact Frequentist and Bayesian Inference
p2-7 (p52): 2.7 Association in Three-Way Tables
p2-8 (p56): Exercises
p3 (p65): 3 Generallzed Linear Models
p3-1 (p66): 3.1 Components of a Generalized Linear Model
p3-2 (p68): 3.2 Generalized Linear Models for Binary Data
p3-3 (p72): 3.3 Generalized Linear Models for Counts and Rates
p3-4 (p76): 3.4 Statistical Inference and Model Checking
p3-5 (p82): 3.5 Fitting Generalized Linear Models
p3-6 (p84): Exercises
p4 (p89): 4 Logistic Regression
p4-1 (p89): 4.1 The Logistic Regression Model
p4-2 (p94): 4.2 Statistical Inference for Logistic Regression
p4-3 (p98): 4.3 Logistic Regression with Categorical Predictors
p4-4 (p102): 4.4 Multiple Logistic Regression
p4-5 (p107): 4.5 Summarizing Effects in Logistic Regression
p4-6 (p110): 4.6 Summarizing Predictive Power：Classification Tables，ROC Curves，and Multiple Correlation
p4-7 (p113): Exercises
p5 (p123): 5 Buliding and Applying Logistlc Regression Models
p5-1 (p123): 5.1 Strategies in Model Selection
p5-2 (p130): 5.2 Model Checking
p5-3 (p136): 5.3 Infinite Estimates in Logistic Regression
p5-4 (p140): 5.4 Bayesian Inference，Penalized Likelihood，and Conditional Likelihood for Logistic Regression
p5-5 (p145): 5.5 Alternative Link Functions：Linear Probability and Probit Models
p5-6 (p150): 5.6 Sample Size and Power for Logistic Regression
p5-7 (p151): Exercises
p6 (p159): 6 Multicategory Logit Models
p6-1 (p159): 6.1 Baseline-Category Logit Models for Nominal Responses
p6-2 (p167): 6.2 Cumulative Logit Models for Ordinal Responses
p6-3 (p176): 6.3 Cumulative Link Models：Model Checking and Extensions
p6-4 (p184): 6.4 Paired-Category Logit Modeling of Ordinal Responses
p6-5 (p187): Exercises
p7 (p193): 7 Loglinear Models for Contingency Tables and Counts
p7-1 (p194): 7.1 Loglinear Models for Counts in Contingency Tables
p7-2 (p200): 7.2 Statistical Inference for Loglinear Models
p7-3 (p207): 7.3 The Loglinear - Logistic Model Connection
p7-4 (p210): 7.4 Independence Graphs and Collapsibility
p7-5 (p214): 7.5 Modeling Ordinal Associations in Contingency Tables
p7-6 (p217): 7.6 Loglinear Modeling of Count Response Variables
p7-7 (p221): Exercises
p8 (p227): 8 Models for Matched Pairs
p8-1 (p228): 8.1 Comparing Dependent Proportions for Binary Matched Pairs
p8-2 (p230): 8.2 Marginal Models and Subject-Specific Models for Matched Pairs
p8-3 (p235): 8.3 Comparing Proportions for Nominal Matched-Pairs Responses
p8-4 (p239): 8.4 Comparing Proportions for Ordinal Matched-Pairs Responses
p8-5 (p243): 8.5 Analyzing Rater Agreement
p8-6 (p247): 8.6 Bradley-Terry Model for Paired Preferences
p8-7 (p249): Exercises
p9 (p253): 9 Marginal Modeling of Correlated，Clustered Responses
p9-1 (p254): 9.1 Marginal Models Versus Subject-Specific Models
p9-2 (p255): 9.2 Marginal Modeling：The Generalized Estimating Equations （GEE） Approach
p9-3 (p260): 9.3 Marginal Modeling for Clustered Multinomial Responses
p9-4 (p263): 9.4 Transitional Modeling，Given the Past
p9-5 (p266): 9.5 Dealing with Missing Data
p9-6 (p268): Exercises
p10 (p273): 10 Random Effects：Generalized Linear Mixed Models
p10-1 (p273): 10.1 Random Effects Modeling of Clustered Categorical Data
p10-2 (p278): 10.2 Examples：Random Effects Models for Binary Data
p10-3 (p284): 10.3 Extensions to Multinomial Responses and Multiple Random Effect Terms
p10-4 (p288): 10.4 Multilevel （Hierarchical） Models
p10-5 (p291): 10.5 Latent Class Models
p10-6 (p295): Exercises
p11 (p299): 11 Classification and Smoothing
p11-1 (p300): 11.1 Classification：Linear Discriminant Analysis
p11-2 (p302): 11.2 Classification：Tree-Based Prediction
p11-3 (p306): 11.3 Cluster Analysis for Categorical Responses
p11-4 (p310): 11.4 Smoothing：Generalized Additive Models
p11-5 (p313): 11.5 Regularization for High-Dimensional Categorical Data （Large p）
p11-6 (p321): Exercises
p12 (p325): 12 A Historical Tour of Categorical Data Analysis
p13 (p331): Appendix：Software for Categorical Data Analysis
p13-1 (p331): A.1 R for Categorical Data Analysis
p13-2 (p332): A.2 SAS for Categorical Data Analysis
p13-3 (p342): A.3 Stata for Categorical Data Analysis
p13-4 (p346): A.4 spss for Categorical Data Analysis
p14 (p349): Brief Solutions to Odd-Numbered Exercises
p15 (p363): Bibliography
p16 (p365): Examples Index
p17 (p369): Subject Index

2023-01-31