An Introduction to Categorical Data Analysis PDF
Preview An Introduction to Categorical Data Analysis
Preface Page: ix About the Companion Website Page: xiii Chapter 1 Introduction Page: 1 1.1 Categorical Response Data Page: 1 1.2 Probability Distributions for Categorical Data Page: 3 1.3 Statistical Inference for a Proportion Page: 5 1.4 Statistical Inference for Discrete Data Page: 9 1.5 Bayesian Inference for Proportions * Page: 13 1.6 Using R Software for Statistical Inference about Proportions * Page: 16 Exercises Page: 20 Notes Page: 24 Chapter 2 Analyzing Contingency Tables Page: 25 2.1 Probability Structure for Contingency Tables Page: 25 2.2 Comparing Proportions in 2 × 2 Contingency Tables Page: 29 2.3 The Odds Ratio Page: 31 2.4 Chi-Squared Tests of Independence Page: 36 2.5 Testing Independence for Ordinal Variables Page: 42 2.6 Exact Frequentist and Bayesian Inference * Page: 46 2.7 Association in Three-Way Tables Page: 52 Exercises Page: 56 Notes Page: 63 Chapter 3 Generalized Linear Models Page: 65 3.1 Components of a Generalized Linear Model Page: 66 3.2 Generalized Linear Models for Binary Data Page: 67 3.3 Generalized Linear Models for Counts and Rates Page: 72 3.4 Statistical Inference and Model Checking Page: 76 3.5 Fitting Generalized Linear Models Page: 82 Exercises Page: 83 Notes Page: 88 Chapter 4 Logistic Regression Page: 89 4.1 The Logistic Regression Model Page: 89 4.2 Statistical Inference for Logistic Regression Page: 94 4.3 Logistic Regression with Categorical Predictors Page: 98 4.4 Multiple Logistic Regression Page: 102 4.5 Summarizing Effects in Logistic Regression Page: 107 4.6 Summarizing Predictive Power: Classification Tables, ROC Curves, and Multiple Correlation Page: 109 Exercises Page: 113 Notes Page: 121 Chapter 5 Building and Applying Logistic Regression Models Page: 123 5.1 Strategies in Model Selection Page: 123 5.2 Model Checking Page: 130 5.3 Infinite Estimates in Logistic Regression Page: 136 5.4 Bayesian Inference, Penalized Likelihood, and Conditional Likelihood for Logistic Regression * Page: 140 5.5 Alternative Link Functions: Linear Probability and Probit Models * Page: 145 5.6 Sample Size and Power for Logistic Regression * Page: 150 Exercises Page: 151 Notes Page: 157 Chapter 6 Multicategory Logit Models Page: 159 6.1 Baseline-Category Logit Models for Nominal Responses Page: 159 6.2 Cumulative Logit Models for Ordinal Responses Page: 167 6.3 Cumulative Link Models: Model Checking and Extensions * Page: 176 6.4 Paired-Category Logit Modeling of Ordinal Responses* Page: 184 Exercises Page: 187 Notes Page: 192 Chapter 7 Loglinear Models for Contingency Tables and Counts Page: 193 7.1 Loglinear Models for Counts in Contingency Tables Page: 193 7.2 Statistical Inference for Loglinear Models Page: 199 7.3 The Loglinear – Logistic Model Connection Page: 207 7.4 Independence Graphs and Collapsibility Page: 210 7.5 Modeling Ordinal Associations in Contingency Tables Page: 213 7.6 Loglinear Modeling of Count Response Variables * Page: 217 Exercises Page: 221 Notes Page: 226 Chapter 8 Models for Matched Pairs Page: 227 8.1 Comparing Dependent Proportions for Binary Matched Pairs Page: 228 8.2 Marginal Models and Subject-Specific Models for Matched Pairs Page: 230 8.3 Comparing Proportions for Nominal Matched-Pairs Responses Page: 235 8.4 Comparing Proportions for Ordinal Matched-Pairs Responses Page: 239 8.5 Analyzing Rater Agreement * Page: 242 8.6 Bradley–Terry Model for Paired Preferences * Page: 246 Exercises Page: 248 Notes Page: 252 Chapter 9 Marginal Modeling of Correlated, Clustered Responses Page: 253 9.1 Marginal Models Versus Subject-Specific Models Page: 254 9.2 Marginal Modeling: The Generalized Estimating Equations (GEE) Approach Page: 255 9.3 Marginal Modeling for Clustered Multinomial Responses Page: 260 9.4 Transitional Modeling, Given the Past Page: 263 9.5 Dealing with Missing Data * Page: 266 Exercises Page: 268 Notes Page: 271 Chapter 10 Random Effects: Generalized Linear Mixed Models Page: 273 10.1 Random Effects Modeling of Clustered Categorical Data Page: 273 10.2 Examples: Random Effects Models for Binary Data Page: 278 10.3 Extensions to Multinomial Responses and Multiple Random Effect Terms Page: 284 10.4 Multilevel (Hierarchical) Models Page: 288 10.5 Latent Class Models * Page: 291 Exercises Page: 295 Notes Page: 298 Chapter 11 Classification and Smoothing * Page: 299 11.1 Classification: Linear Discriminant Analysis Page: 300 11.2 Classification: Tree-Based Prediction Page: 302 11.3 Cluster Analysis for Categorical Responses Page: 306 11.4 Smoothing: Generalized Additive Models Page: 310 11.5 Regularization for High-Dimensional Categorical Data (Large p) Page: 313 Exercises Page: 321 Notes Page: 323 Chapter 12 A Historical Tour of Categorical Data Analysis * Page: 325 Appendix: Software for Categorical Data Analysis Page: 331 A.1 R for Categorical Data Analysis Page: 331 A.2 SAS for Categorical Data Analysis Page: 331 A.3 STATA for Categorical Data Analysis Page: 342 A.4 SPSS for Categorical Data Analysis Page: 346 Brief Solutions to Odd-Numbered Exercises Page: 349 Bibliography Page: 363 Examples Index Page: 365 Subject Index Page: 369 End User License Agreement Page: 375
Description: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.
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