{"product_id":"categorical-data-analysis-isbn-9780470463635","title":"Categorical Data Analysis","description":"\u003cp\u003e\u003cb\u003ePraise for the Second Edition\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"A must-have book for anyone expecting to do research and\/or applications in categorical data analysis.\"\u003cbr\u003e —\u003ci\u003eStatistics in Medicine\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\"It is a total delight reading this book.\"\u003cbr\u003e —\u003ci\u003ePharmaceutical Research\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\"If you do any analysis of categorical data, this is an essential desktop reference.\"\u003cbr\u003e —\u003ci\u003eTechnometrics\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eThe use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eCategorical Data Analysis, Third Edition\u003c\/i\u003e summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eAn emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models\u003c\/li\u003e \u003cli\u003eTwo new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis\u003c\/li\u003e \u003cli\u003eNew sections introducing the Bayesian approach for methods in that chapter\u003c\/li\u003e \u003cli\u003eMore than 100 analyses of data sets and over 600 exercises\u003c\/li\u003e \u003cli\u003eNotes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources\u003c\/li\u003e \u003cli\u003eA supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eCategorical Data Analysis, Third Edition\u003c\/i\u003e is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePreface xiii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction: Distributions and Inference for Categorical Data 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Categorical Response Data, 1\u003c\/p\u003e \u003cp\u003e1.2 Distributions for Categorical Data, 5\u003c\/p\u003e \u003cp\u003e1.3 Statistical Inference for Categorical Data, 8\u003c\/p\u003e \u003cp\u003e1.4 Statistical Inference for Binomial Parameters, 13\u003c\/p\u003e \u003cp\u003e1.5 Statistical Inference for Multinomial Parameters, 17\u003c\/p\u003e \u003cp\u003e1.6 Bayesian Inference for Binomial and Multinomial Parameters, 22\u003c\/p\u003e \u003cp\u003eNotes, 27\u003c\/p\u003e \u003cp\u003eExercises, 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Describing Contingency Tables 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Probability Structure for Contingency Tables, 37\u003c\/p\u003e \u003cp\u003e2.2 Comparing Two Proportions, 43\u003c\/p\u003e \u003cp\u003e2.3 Conditional Association in Stratified 2 × 2 Tables, 47\u003c\/p\u003e \u003cp\u003e2.4 Measuring Association in \u003ci\u003eI\u003c\/i\u003e × \u003ci\u003eJ\u003c\/i\u003e Tables, 54\u003c\/p\u003e \u003cp\u003eNotes, 60\u003c\/p\u003e \u003cp\u003eExercises, 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Inference for Two-Way Contingency Tables 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Confidence Intervals for Association Parameters, 69\u003c\/p\u003e \u003cp\u003e3.2 Testing Independence in Two-way Contingency Tables, 75\u003c\/p\u003e \u003cp\u003e3.3 Following-up Chi-Squared Tests, 80\u003c\/p\u003e \u003cp\u003e3.4 Two-Way Tables with Ordered Classifications, 86\u003c\/p\u003e \u003cp\u003e3.5 Small-Sample Inference for Contingency Tables, 90\u003c\/p\u003e \u003cp\u003e3.6 Bayesian Inference for Two-way Contingency Tables, 96\u003c\/p\u003e \u003cp\u003e3.7 Extensions for Multiway Tables and Nontabulated Responses, 100\u003c\/p\u003e \u003cp\u003eNotes, 101\u003c\/p\u003e \u003cp\u003eExercises, 103\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Introduction to Generalized Linear Models 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 The Generalized Linear Model, 113\u003c\/p\u003e \u003cp\u003e4.2 Generalized Linear Models for Binary Data, 117\u003c\/p\u003e \u003cp\u003e4.3 Generalized Linear Models for Counts and Rates, 122\u003c\/p\u003e \u003cp\u003e4.4 Moments and Likelihood for Generalized Linear Models, 130\u003c\/p\u003e \u003cp\u003e4.5 Inference and Model Checking for Generalized Linear Models, 136\u003c\/p\u003e \u003cp\u003e4.6 Fitting Generalized Linear Models, 143\u003c\/p\u003e \u003cp\u003e4.7 Quasi-Likelihood and Generalized Linear Models, 149\u003c\/p\u003e \u003cp\u003eNotes, 152\u003c\/p\u003e \u003cp\u003eExercises, 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Logistic Regression 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Interpreting Parameters in Logistic Regression, 163\u003c\/p\u003e \u003cp\u003e5.2 Inference for Logistic Regression, 169\u003c\/p\u003e \u003cp\u003e5.3 Logistic Models with Categorical Predictors, 175\u003c\/p\u003e \u003cp\u003e5.4 Multiple Logistic Regression, 182\u003c\/p\u003e \u003cp\u003e5.5 Fitting Logistic Regression Models, 192\u003c\/p\u003e \u003cp\u003eNotes, 195\u003c\/p\u003e \u003cp\u003eExercises, 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Building, Checking, and Applying Logistic Regression Models 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Strategies in Model Selection, 207\u003c\/p\u003e \u003cp\u003e6.2 Logistic Regression Diagnostics, 215\u003c\/p\u003e \u003cp\u003e6.3 Summarizing the Predictive Power of a Model, 221\u003c\/p\u003e \u003cp\u003e6.4 Mantel–Haenszel and Related Methods for Multiple 2 × 2 Tables, 225\u003c\/p\u003e \u003cp\u003e6.5 Detecting and Dealing with Infinite Estimates, 233\u003c\/p\u003e \u003cp\u003e6.6 Sample Size and Power Considerations, 237\u003c\/p\u003e \u003cp\u003eNotes, 241\u003c\/p\u003e \u003cp\u003eExercises, 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Alternative Modeling of Binary Response Data 251\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Probit and Complementary Log–log Models, 251\u003c\/p\u003e \u003cp\u003e7.2 Bayesian Inference for Binary Regression, 257\u003c\/p\u003e \u003cp\u003e7.3 Conditional Logistic Regression, 265\u003c\/p\u003e \u003cp\u003e7.4 Smoothing: Kernels, Penalized Likelihood, Generalized Additive Models, 270\u003c\/p\u003e \u003cp\u003e7.5 Issues in Analyzing High-Dimensional Categorical Data, 278\u003c\/p\u003e \u003cp\u003eNotes, 285\u003c\/p\u003e \u003cp\u003eExercises, 287\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Models for Multinomial Responses 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Nominal Responses: Baseline-Category Logit Models, 293\u003c\/p\u003e \u003cp\u003e8.2 Ordinal Responses: Cumulative Logit Models, 301\u003c\/p\u003e \u003cp\u003e8.3 Ordinal Responses: Alternative Models, 308\u003c\/p\u003e \u003cp\u003e8.4 Testing Conditional Independence in \u003ci\u003eI\u003c\/i\u003e × \u003ci\u003eJ\u003c\/i\u003e × \u003ci\u003eK\u003c\/i\u003e Tables, 314\u003c\/p\u003e \u003cp\u003e8.5 Discrete-Choice Models, 320\u003c\/p\u003e \u003cp\u003e8.6 Bayesian Modeling of Multinomial Responses, 323\u003c\/p\u003e \u003cp\u003eNotes, 326\u003c\/p\u003e \u003cp\u003eExercises, 329\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Loglinear Models for Contingency Tables 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Loglinear Models for Two-way Tables, 339\u003c\/p\u003e \u003cp\u003e9.2 Loglinear Models for Independence and Interaction in Three-way Tables, 342\u003c\/p\u003e \u003cp\u003e9.3 Inference for Loglinear Models, 348\u003c\/p\u003e \u003cp\u003e9.4 Loglinear Models for Higher Dimensions, 350\u003c\/p\u003e \u003cp\u003e9.5 Loglinear—Logistic Model Connection, 353\u003c\/p\u003e \u003cp\u003e9.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions, 356\u003c\/p\u003e \u003cp\u003e9.7 Loglinear Model Fitting: Iterative Methods and Their Application, 364\u003c\/p\u003e \u003cp\u003eNotes, 368\u003c\/p\u003e \u003cp\u003eExercises, 369\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Building and Extending Loglinear Models 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Conditional Independence Graphs and Collapsibility, 377\u003c\/p\u003e \u003cp\u003e10.2 Model Selection and Comparison, 380\u003c\/p\u003e \u003cp\u003e10.3 Residuals for Detecting Cell-Specific Lack of Fit, 385\u003c\/p\u003e \u003cp\u003e10.4 Modeling Ordinal Associations, 386\u003c\/p\u003e \u003cp\u003e10.5 Generalized Loglinear and Association Models, Correlation Models, and Correspondence Analysis, 393\u003c\/p\u003e \u003cp\u003e10.6 Empty Cells and Sparseness in Modeling Contingency Tables, 398\u003c\/p\u003e \u003cp\u003e10.7 Bayesian Loglinear Modeling, 401\u003c\/p\u003e \u003cp\u003eNotes, 404\u003c\/p\u003e \u003cp\u003eExercises, 407\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Models for Matched Pairs 413\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Comparing Dependent Proportions, 414\u003c\/p\u003e \u003cp\u003e11.2 Conditional Logistic Regression for Binary Matched Pairs, 418\u003c\/p\u003e \u003cp\u003e11.3 Marginal Models for Square Contingency Tables, 424\u003c\/p\u003e \u003cp\u003e11.4 Symmetry, Quasi-Symmetry, and Quasi-Independence, 426\u003c\/p\u003e \u003cp\u003e11.5 Measuring Agreement Between Observers, 432\u003c\/p\u003e \u003cp\u003e11.6 Bradley–Terry Model for Paired Preferences, 436\u003c\/p\u003e \u003cp\u003e11.7 Marginal Models and Quasi-Symmetry Models for Matched Sets, 439\u003c\/p\u003e \u003cp\u003eNotes, 443\u003c\/p\u003e \u003cp\u003eExercises, 445\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Clustered Categorical Data: Marginal and Transitional Models 455\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Marginal Modeling: Maximum Likelihood Approach, 456\u003c\/p\u003e \u003cp\u003e12.2 Marginal Modeling: Generalized Estimating Equations (GEEs) Approach, 462\u003c\/p\u003e \u003cp\u003e12.3 Quasi-Likelihood and Its GEE Multivariate Extension: Details, 465\u003c\/p\u003e \u003cp\u003e12.4 Transitional Models: Markov Chain and Time Series Models, 473\u003c\/p\u003e \u003cp\u003eNotes, 478\u003c\/p\u003e \u003cp\u003eExercises, 479\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Clustered Categorical Data: Random Effects Models 489\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Random Effects Modeling of Clustered Categorical Data, 489\u003c\/p\u003e \u003cp\u003e13.2 Binary Responses: Logistic-Normal Model, 494\u003c\/p\u003e \u003cp\u003e13.3 Examples of Random Effects Models for Binary Data, 498\u003c\/p\u003e \u003cp\u003e13.4 Random Effects Models for Multinomial Data, 511\u003c\/p\u003e \u003cp\u003e13.5 Multilevel Modeling, 515\u003c\/p\u003e \u003cp\u003e13.6 GLMM Fitting, Inference, and Prediction, 519\u003c\/p\u003e \u003cp\u003e13.7 Bayesian Multivariate Categorical Modeling, 523\u003c\/p\u003e \u003cp\u003eNotes, 525\u003c\/p\u003e \u003cp\u003eExercises, 527\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Other Mixture Models for Discrete Data 535\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Latent Class Models, 535\u003c\/p\u003e \u003cp\u003e14.2 Nonparametric Random Effects Models, 542\u003c\/p\u003e \u003cp\u003e14.3 Beta-Binomial Models, 548\u003c\/p\u003e \u003cp\u003e14.4 Negative Binomial Regression, 552\u003c\/p\u003e \u003cp\u003e14.5 Poisson Regression with Random Effects, 555\u003c\/p\u003e \u003cp\u003eNotes, 557\u003c\/p\u003e \u003cp\u003eExercises, 558\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Non-Model-Based Classification and Clustering 565\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Classification: Linear Discriminant Analysis, 565\u003c\/p\u003e \u003cp\u003e15.2 Classification: Tree-Structured Prediction, 570\u003c\/p\u003e \u003cp\u003e15.3 Cluster Analysis for Categorical Data, 576\u003c\/p\u003e \u003cp\u003eNotes, 581\u003c\/p\u003e \u003cp\u003eExercises, 582\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Large- and Small-Sample Theory for Multinomial Models 587\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Delta Method, 587\u003c\/p\u003e \u003cp\u003e16.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities, 592\u003c\/p\u003e \u003cp\u003e16.3 Asymptotic Distributions of Residuals and Goodness-of-fit Statistics, 594\u003c\/p\u003e \u003cp\u003e16.4 Asymptotic Distributions for Logit\/Loglinear Models, 599\u003c\/p\u003e \u003cp\u003e16.5 Small-Sample Significance Tests for Contingency Tables, 601\u003c\/p\u003e \u003cp\u003e16.6 Small-Sample Confidence Intervals for Categorical Data, 603\u003c\/p\u003e \u003cp\u003e16.7 Alternative Estimation Theory for Parametric Models, 610\u003c\/p\u003e \u003cp\u003eNotes, 615\u003c\/p\u003e \u003cp\u003eExercises, 616\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Historical Tour of Categorical Data Analysis 623\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Pearson–Yule Association Controversy, 623\u003c\/p\u003e \u003cp\u003e17.2 R. A. Fisher’s Contributions, 625\u003c\/p\u003e \u003cp\u003e17.3 Logistic Regression, 627\u003c\/p\u003e \u003cp\u003e17.4 Multiway Contingency Tables and Loglinear Models, 629\u003c\/p\u003e \u003cp\u003e17.5 Bayesian Methods for Categorical Data, 633\u003c\/p\u003e \u003cp\u003e17.6 A Look Forward, and Backward, 634\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Statistical Software for Categorical Data Analysis 637\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Chi-Squared Distribution Values 641\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences 643\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAuthor Index 689\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eExample Index 701\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSubject Index 705\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Software Details for Text Examples (text website)\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eALAN AGRESTI \u003c\/b\u003eis Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He has presented short courses on categorical data methods in thirty countries. He is the author of five other books, including \u003ci\u003eAn Introduction to Categorical Data Analysis, Second Edition\u003c\/i\u003e and \u003ci\u003eAnalysis of Ordinal Categorical Data, Second Edition,\u003c\/i\u003e both published by Wiley.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePraise for the Second Edition\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"A must-have book for anyone expecting to do research and\/or applications in categorical data analysis.\"\u003cbr\u003e —\u003ci\u003eStatistics in Medicine\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\"It is a total delight reading this book.\"\u003cbr\u003e —\u003ci\u003ePharmaceutical Research\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\"If you do any analysis of categorical data, this is an essential desktop reference.\"\u003cbr\u003e —\u003ci\u003eTechnometrics\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eThe use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eCategorical Data Analysis, Third Edition\u003c\/i\u003e summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eAn emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models\u003c\/li\u003e \u003cli\u003eTwo new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis\u003c\/li\u003e \u003cli\u003eNew sections introducing the Bayesian approach for methods in that chapter\u003c\/li\u003e \u003cli\u003eMore than 100 analyses of data sets and over 600 exercises\u003c\/li\u003e \u003cli\u003eNotes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources\u003c\/li\u003e \u003cli\u003eA supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eCategorical Data Analysis, Third Edition\u003c\/i\u003e is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988893024485,"sku":"NP9780470463635","price":128.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470463635.jpg?v=1761781949","url":"https:\/\/k12savings.com\/es\/products\/categorical-data-analysis-isbn-9780470463635","provider":"K12savings","version":"1.0","type":"link"}