{"product_id":"analysis-of-ordinal-categorical-data-isbn-9780470082898","title":"Analysis of Ordinal Categorical Data","description":"Statistical science’s first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy. \u003ci\u003eAnalysis of Ordinal Categorical Data, Second Edition\u003c\/i\u003e provides an introduction to basic descriptive and inferential methods for categorical data, giving thorough coverage of new developments and recent methods. Special emphasis is placed on interpretation and application of methods including an integrated comparison of the available strategies for analyzing ordinal data. Practitioners of statistics in government, industry (particularly pharmaceutical), and academia will want this new edition.  \u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003e1. Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1. Ordinal Categorical Scales.\u003c\/p\u003e \u003cp\u003e1.2. Advantages of Using Ordinal Methods.\u003c\/p\u003e \u003cp\u003e1.3. Ordinal Modeling Versus Ordinary Regession Analysis.\u003c\/p\u003e \u003cp\u003e1.4. Organization of This Book.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Ordinal Probabilities, Scores, and Odds Ratios.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1. Probabilities and Scores for an Ordered Categorical Scale.\u003c\/p\u003e \u003cp\u003e2.2. Ordinal Odds Ratios for Contingency Tables.\u003c\/p\u003e \u003cp\u003e2.3. Confidence Intervals for Ordinal Association Measures.\u003c\/p\u003e \u003cp\u003e2.4. Conditional Association in Three-Way Tables.\u003c\/p\u003e \u003cp\u003e2.5. Category Choice for Ordinal Variables.\u003c\/p\u003e \u003cp\u003eChapter Notes.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Logistic Regression Models Using Cumulative Logits.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1. Types of Logits for An Ordinal Response.\u003c\/p\u003e \u003cp\u003e3.2. Cumulative Logit Models.\u003c\/p\u003e \u003cp\u003e3.3. Proportional Odds Models: Properties and Interpretations.\u003c\/p\u003e \u003cp\u003e3.4. Fitting and Inference for Cumulative Logit Models.\u003c\/p\u003e \u003cp\u003e3.5. Checking Cumulative Logit Models.\u003c\/p\u003e \u003cp\u003e3.6. Cumulative Logit Models Without Proportional Odds.\u003c\/p\u003e \u003cp\u003e3.7. Connections with Nonparametric Rank Methods.\u003c\/p\u003e \u003cp\u003eChapter Notes.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Other Ordinal Logistic Regression Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1. Adjacent-Categories Logit Models.\u003c\/p\u003e \u003cp\u003e4.2. Continuation-Ratio Logit Models.\u003c\/p\u003e \u003cp\u003e4.3. Stereotype Model: Multiplicative Paired-Category Logits.\u003c\/p\u003e \u003cp\u003eChapter Notes.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Other Ordinal Multinomial Response Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1. Cumulative Link Models.\u003c\/p\u003e \u003cp\u003e5.2. Cumulative Probit Models.\u003c\/p\u003e \u003cp\u003e5.3. Cumulative Log-Log Links: Proportional Hazards Modeling.\u003c\/p\u003e \u003cp\u003e5.4. Modeling Location and Dispersion Effects.\u003c\/p\u003e \u003cp\u003e5.5. Ordinal ROC Curve Estimation.\u003c\/p\u003e \u003cp\u003e5.6. Mean Response Models.\u003c\/p\u003e \u003cp\u003eChapter Notes.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Modeling Ordinal Association Structure.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1. Ordinary Loglinear Modeling.\u003c\/p\u003e \u003cp\u003e6.2. Loglinear Model of Linear-by-Linear Association.\u003c\/p\u003e \u003cp\u003e6.3. Row or Column Effects Association Models.\u003c\/p\u003e \u003cp\u003e6.4. Association Models for Multiway Tables.\u003c\/p\u003e \u003cp\u003e6.5. Multiplicative Association and Correlation Models.\u003c\/p\u003e \u003cp\u003e6.6. Modeling Global Odds Ratios and Other Associations.\u003c\/p\u003e \u003cp\u003eChapter Notes.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Non-Model-Based Analysis of Ordinal Association.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1. Concordance and Discordance Measures of Association.\u003c\/p\u003e \u003cp\u003e7.2. Correlation Measures for Contingency Tables.\u003c\/p\u003e \u003cp\u003e7.3. Non-Model-Based Inference for Ordinal Association Measures.\u003c\/p\u003e \u003cp\u003e7.4. Comparing Singly Ordered Multinomials.\u003c\/p\u003e \u003cp\u003e7.5. Order-Restricted Inference with Inequality Constraints.\u003c\/p\u003e \u003cp\u003e7.6. Small-Sample Ordinal Tests of Independence.\u003c\/p\u003e \u003cp\u003e7.7. Other Rank-Based Statistical Methods for Ordered Categories.\u003c\/p\u003e \u003cp\u003eAppendix: Standard Errors for Ordinal Measures.\u003c\/p\u003e \u003cp\u003eChapter Notes.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Matched-Pairs Data with Ordered Categories.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1. Comparing Marginal Distributions for Matched Pairs.\u003c\/p\u003e \u003cp\u003e8.2. Models Comparing Matched Marginal Distributions.\u003c\/p\u003e \u003cp\u003e8.3. Models for The Joint Distribution in A Square Table.\u003c\/p\u003e \u003cp\u003e8.4. Comparing Marginal Distributions for Matched Sets.\u003c\/p\u003e \u003cp\u003e8.5. Analyzing Rater Agreement on an Ordinal Scale.\u003c\/p\u003e \u003cp\u003e8.6. Modeling Ordinal Paired Preferences.\u003c\/p\u003e \u003cp\u003eChapter Notes.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Clustered Ordinal Responses: Marginal Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1. Marginal Ordinal Modeling with Explanatory Variables.\u003c\/p\u003e \u003cp\u003e9.2. Marginal Ordinal Modeling: GEE Methods.\u003c\/p\u003e \u003cp\u003e9.3. Transitional Ordinal Modeling, Given the Past.\u003c\/p\u003e \u003cp\u003eChapter Notes.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Clustered Ordinal Responses: Random Effects Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1. Ordinal Generalized Linear Mixed Models.\u003c\/p\u003e \u003cp\u003e10.2. Examples of Ordinal Random Intercept Models.\u003c\/p\u003e \u003cp\u003e10.3. Models with Multiple Random Effects.\u003c\/p\u003e \u003cp\u003e10.4. Multilevel (Hierarchical) Ordinal Models.\u003c\/p\u003e \u003cp\u003e10.5. Comparing Random Effects Models and Marginal Models.\u003c\/p\u003e \u003cp\u003eChapter Notes.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Bayesian Inference for Ordinal Response Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1. Bayesian Approach to Statistical Inference.\u003c\/p\u003e \u003cp\u003e11.2. Estimating Multinomial Parameters.\u003c\/p\u003e \u003cp\u003e11.3. Bayesian Ordinal Regression Modeling.\u003c\/p\u003e \u003cp\u003e11.4. Bayesian Ordinal Association Modeling.\u003c\/p\u003e \u003cp\u003e11.5. Bayesian Ordinal Multivariate Regression Modeling.\u003c\/p\u003e \u003cp\u003e11.6. Bayesian Versus Frequentist Approaches to Analyzing Ordinal Data.\u003c\/p\u003e \u003cp\u003eChapter Notes.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix Software for Analyzing Ordinal Categorical Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eExample Index.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSubject Index.\u003c\/b\u003e\u003c\/p\u003e \u003cb\u003eALAN AGRESTI\u003c\/b\u003e, PhD, is Distinguished Professor Emeritus in the Department of Statistics at the University of Florida and Visiting Professor in the Department of Statistics at Harvard University. A Fellow of the American Statistical Association and the Institute of Mathematical Statistics, Dr. Agresti has published extensively on the topic of categorical data analysis and has presented lectures and short courses on the subject in more than thirty countries. He is the author of \u003ci\u003eCategorical Data Analysis\u003c\/i\u003e, Second Edition and \u003ci\u003eAn Introduction to Categorical Data Analysis\u003c\/i\u003e, Second Edition, both published by Wiley.  \u003cb\u003ePraise for the First Edition:\u003c\/b\u003e  \u003cp\u003e\"The author has a fluent, easy-to-read style, and well-chosen interesting material to present and illustrate the ideas to the newcomer and the old hand alike.\"\u003cbr\u003e —\u003ci\u003e\u003cb\u003eJournal of the Royal Statistical Society\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eCategorical data having ordered categories are common in practice, especially in applications throughout the biomedical and social sciences. Thoroughly updated to reflect developments since the publication of its predecessor, \u003ci\u003eAnalysis of Ordinal Categorical Data\u003c\/i\u003e, Second Edition presents a comprehensive survey of methods for analyzing ordinal categorical data, complete with coverage of the most recent research.\u003c\/p\u003e \u003cp\u003eThe author highlights various modeling techniques, including cumulative logit models with and without proportional odds structure, adjacent-categories logit and continuation-ratio logit models, stereotype models, association models for ordinal odds ratios, and models for clustered ordinal data. Additional features of this Second Edition include:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eA new chapter on marginal models for multivariate ordinal responses, using maximum likelihood and generalized estimating equations for model fitting\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eA new chapter on random effects models for clustered ordinal data\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eA new chapter on Bayesian approaches for analyzing ordinal data\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eModels and order-restricted inference methods for various types of ordinal odds ratios, including local odds ratios, cumulative odds ratios, and global odds ratios\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003ePresentation of non-model-based methods, such as nonparametric rank methods that also apply to ordered categorical data\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eEach chapter concludes with notes on relevant research literature as well as exercises that allow readers to test their comprehension of the presented concepts. A detailed appendix discusses the use of the latest software such as SAS, R, SPSS, and Stata, and the book's related Web site provides further instructions for the use of these software packages along with complete data sets.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAnalysis of Ordinal Categorical Data\u003c\/i\u003e, Second Edition is an excellent book for courses on categorical data analysis at the upper-undergraduate and graduate levels. It is also an invaluable resource for researchers and practitioners who conduct data analysis in the areas of public health, business, medicine, and the social and behavioral sciences.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988734361829,"sku":"NP9780470082898","price":161.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470082898.jpg?v=1761781379","url":"https:\/\/k12savings.com\/es\/products\/analysis-of-ordinal-categorical-data-isbn-9780470082898","provider":"K12savings","version":"1.0","type":"link"}