Catalogue


Categorical data analysis /
Alan Agresti.
imprint
New York : Wiley, c1990.
description
xv, 558 p. : ill. ; 24 cm. --
ISBN
0471853011
format(s)
Book
Holdings
Subjects
More Details
imprint
New York : Wiley, c1990.
isbn
0471853011
general note
"A Wiley-Interscience publication."
catalogue key
1819668
 
Includes bibliographical references (p. 508-540) and indexes.
A Look Inside
About the Author
Author Affiliation
Alan Agresti: University of Florida, Gainesville
Excerpts
Flap Copy
The past quarter-century has seen an explosion in the development of methods for analyzing categorical data. These methods have influenced-and been influenced by-the increasing availability of multivariate data sets with categorical responses in the social, behavioral, and biomedical sciences, as well as in public health, ecology, education, marketing, food science, and industrial quality control. Categorical Data Analysis describes the most important new methods, offering a unified presentation of modeling using generalized linear models and emphasizing loglinear and logit modeling techniques. Contributions of noted statisticians (Pearson, Yule, Fisher, Neyman, Cochran), whose pioneering efforts set the pace for the evolution of modern methods, are examined as well. Special features of the book include: Coverage of methods for repeated measurement data, which have become increasingly important in biomedical applications Prescriptions for how ordinal variables should be treated differently than nominal variables Derivations of basic asymptotic and fixed-sample-size inferential methods Discussion of exact small sample procedures More than 40 examples of analyses of "real" data sets, including: aspirin use and heart disease; job satisfaction and income; seat belt use and injuries in auto accidents; and predicting outcomes of baseball games More than 400 exercises to facilitate interpretation and application of methods Categorical Data Analysis also contains an appendix that describes the use of computer software currently available for performing the analyses presented in the book. A comprehensive bibliography and notes at the end of each chapter round out the work, making it a complete, invaluable reference for statisticians, biostatisticians, and professional researchers.
Flap Copy
The past quarter-century has seen an explosion in the development of methods for analyzing categorical data. These methods have influenced-and been influenced by-the increasing availability of multivariate data sets with categorical responses in the social, behavioral, and biomedical sciences, as well as in public health, ecology, education, marketing, food science, and industrial quality control. Categorical Data Analysis describes the most important new methods, offering a unified presentation of modeling using generalized linear models and emphasizing loglinear and logit modeling techniques. Contributions of noted statisticians (Pearson, Yule, Fisher, Neyman, Cochran), whose pioneering efforts set the pace for the evolution of modern methods, are examined as well. Special features of the book include:Coverage of methods for repeated measurement data, which have become increasingly important in biomedical applicationsPrescriptions for how ordinal variables should be treated differently than nominal variablesDerivations of basic asymptotic and fixed-sample-size inferential methodsDiscussion of exact small sample proceduresMore than 40 examples of analyses of 'real' data sets, including: aspirin use and heart disease; job satisfaction and income; seat belt use and injuries in auto accidents; and predicting outcomes of baseball gamesMore than 400 exercises to facilitate interpretation and application of methodsCategorical Data Analysis also contains an appendix that describes the use of computer software currently available for performing the analyses presented in the book. A comprehensive bibliography and notes at the end of each chapter round out the work, making it a complete, invaluable reference for statisticians, biostatisticians, and professional researchers.
Full Text Reviews
Appeared in Choice on 1991-02:
Agresti does an exceptional job of summarizing the methods that are used for analyzing categorical data. Over the past 25 years many methods have been developed and influenced by the increasing availability of multivariate data sets with categorical responses. This book describes the most important among these new methods, which provide a framework for descriptive and inferential analyses of categorical response variables. Logit models (used with binomial or multinomial responses) and loglinear models (used with Poisson responses) are presented. The book has four main parts: descriptive and inferential methods for bivariate categorical data, the basics of model building, applications and generalizations of these models, and categorical data model theory. Derivations of basic asymptotic and fixed-sample-size inferential methods are also covered. Agresti has included 40 examples of analyses of real data sets and more than 400 exercises. Highly recommended for undergraduate and graduate libraries. -D. J. Gougeon, University of Scranton
Reviews
This item was reviewed in:
Choice, February 1991
To find out how to look for other reviews, please see our guides to finding book reviews in the Sciences or Social Sciences and Humanities.
Summaries
Long Description
Categorical Data Analysis describes the most important methods, offering a unified presentation of modeling using generalized linear models and emphasizing loglinear and logit modeling techniques. Contributions of noted statisticians (Pearson, Yule, Fisher, Neyman, Cochran), whose pioneering efforts set the pace for the evolution of modern methods, are examined as well.Special features of the book include:Coverage of methods for repeated measurement data, which have become increasingly important in biomedical applicationsPrescriptions for how ordinal variables should be treated differently than nominal variablesDerivations of basic asymptotic and fixed-sample-size inferential methodsDiscussion of exact small sample proceduresMore than 40 examples of analyses of "real" data sets, including: aspirin use and heart disease; job satisfaction and income; seat belt use and injuries in auto accidents; and predicting outcomes of baseball gamesMore than 400 exercises to facilitate interpretation and application of methodsCategorical Data Analysis also contains an appendix that describes the use of computer software currently available for performing the analyses presented in the book. A comprehensive bibliography and notes and the end of each chapter round out the work, making it a complete, invaluable reference for statisticians, biostatisticians and professional researchers.
Main Description
Categorical Data Analysis describes the most important methods, offering a unified presentation of modeling using generalized linear models and emphasizing loglinear and logit modeling techniques. Contributions of noted statisticians (Pearson, Yule, Fisher, Neyman, Cochran), whose pioneering efforts set the pace for the evolution of modern methods, are examined as well. Special features of the book include: Coverage of methods for repeated measurement data, which have become increasingly important in biomedical applications Prescriptions for how ordinal variables should be treated differently than nominal variables Derivations of basic asymptotic and fixed-sample-size inferential methods Discussion of exact small sample procedures More than 40 examples of analyses of "real" data sets, including: aspirin use and heart disease; job satisfaction and income; seat belt use and injuries in auto accidents; and predicting outcomes of baseball games More than 400 exercises to facilitate interpretation and application of methods Categorical Data Analysis also contains an appendix that describes the use of computer software currently available for performing the analyses presented in the book. A comprehensive bibliography and notes and the end of each chapter round out the work, making it a complete, invaluable reference for statisticians, biostatisticians and professional researchers.
Table of Contents
Prefacep. xiii
Introduction: Distributions and Inference for Categorical Datap. 1
Categorical Response Datap. 1
Distributions for Categorical Datap. 5
Statistical Inference for Categorical Datap. 9
Statistical Inference for Binomial Parametersp. 14
Statistical Inference for Multinomial Parametersp. 21
Notesp. 26
Problemsp. 28
Describing Contingency Tablesp. 36
Probability Structure for Contingency Tablesp. 36
Comparing Two Proportionsp. 43
Partial Association in Stratified 2 X 2 Tablesp. 47
Extensions for I X J Tablesp. 54
Notesp. 59
Problemsp. 60
Inference for Contingency Tablesp. 70
Confidence Intervals for Association Parametersp. 70
Testing Independence in Two-Way Contingency Tablesp. 78
Following-Up Chi-Squared Testsp. 80
Two-Way Tables with Ordered Classificationsp. 86
Small-Sample Tests of Independencep. 91
Small-Sample Confidence Intervals for 2 x 2 Tablesp. 98
Extensions for Multiway Tables and Nontabulated Responsesp. 101
Notesp. 102
Problemsp. 104
Introduction to Generalized Linear Modelsp. 115
Generalized Linear Modelp. 116
Generalized Linear Models for Binary Datap. 120
Generalized Linear Models for Countsp. 125
Moments and Likelihood for Generalized Linear Modelsp. 132
Inference for Generalized Linear Modelsp. 139
Fitting Generalized Linear Modelsp. 143
Quasi-likelihood and Generalized Linear Modelsp. 149
Generalized Additive Modelsp. 153
Notesp. 155
Problemsp. 156
Logistic Regressionp. 165
Interpreting Parameters in Logistic Regressionp. 166
Inference for Logistic Regressionp. 172
Logit Models with Categorical Predictorsp. 177
Multiple Logistic Regressionp. 182
Fitting Logistic Regression Modelsp. 192
Notesp. 196
Problemsp. 197
Building and Applying Logistic Regression Modelsp. 211
Strategies in Model Selectionp. 211
Logistic Regression Diagnosticsp. 219
Inference About Conditional Associations in 2 x 2 x K Tablesp. 230
Using Models to Improve Inferential Powerp. 236
Sample Size and Power Considerationsp. 240
Probit and Complementary Log-Log Modelsp. 245
Conditional Logistic Regression and Exact Distributionsp. 250
Notesp. 257
Problemsp. 259
Logit Models for Multinomial Responsesp. 267
Nominal Responses: Baseline-Category Logit Modelsp. 267
Ordinal Responses: Cumulative Logit Modelsp. 274
Ordinal Responses: Cumulative Link Modelsp. 282
Alternative Models for Ordinal Responsesp. 286
Testing Conditional Independence in I x J x K Tablesp. 293
Discrete-Choice Multinomial Logit Modelsp. 298
Notesp. 302
Problemsp. 302
Loglinear Models for Contingency Tablesp. 314
Loglinear Models for Two-Way Tablesp. 314
Loglinear Models for Independence and Interaction in Three-Way Tablesp. 318
Inference for Loglinear Modelsp. 324
Loglinear Models for Higher Dimensionsp. 326
The Loglinear-Logit Model Connectionp. 330
Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributionsp. 333
Loglinear Model Fitting: Iterative Methods and their Applicationp. 342
Notesp. 346
Problemsp. 347
Building and Extending Loglinear/Logit Modelsp. 357
Association Graphs and Collapsibilityp. 357
Model Selection and Comparisonp. 360
Diagnostics for Checking Modelsp. 366
Modeling Ordinal Associationsp. 367
Association Modelsp. 373
Association Models, Correlation Models, and Correspondence Analysisp. 379
Poisson Regression for Ratesp. 385
Empty Cells and Sparseness in Modeling Contingency Tablesp. 391
Notesp. 398
Problemsp. 400
Models for Matched Pairsp. 409
Comparing Dependent Proportionsp. 410
Conditional Logistic Regression for Binary Matched Pairsp. 414
Marginal Models for Square Contingency Tablesp. 420
Symmetry, Quasi-symmetry, and Quasi-independencep. 423
Measuring Agreement Between Observersp. 431
Bradley-Terry Model for Paired Preferencesp. 436
Marginal Models and Quasi-symmetry Models for Matched Setsp. 439
Notesp. 442
Problemsp. 444
Analyzing Repeated Categorical Response Datap. 455
Comparing Marginal Distributions: Multiple Responsesp. 456
Marginal Modeling: Maximum Likelihood Approachp. 459
Marginal Modeling: Generalized Estimating Equations Approachp. 466
Quasi-likelihood and Its GEE Multivariate Extension: Detailsp. 470
Markov Chains: Transitional Modelingp. 476
Notesp. 481
Problemsp. 482
Random Effects: Generalized Linear Mixed Models for Categorical Responsesp. 491
Random Effects Modeling of Clustered Categorical Datap. 492
Binary Responses: Logistic-Normal Modelp. 496
Examples of Random Effects Models for Binary Datap. 502
Random Effects Models for Multinomial Datap. 513
Multivariate Random Effects Models for Binary Datap. 516
GLMM Fitting, Inference, and Predictionp. 520
Notesp. 526
Problemsp. 527
Other Mixture Models for Categorical Datap. 538
Latent Class Modelsp. 538
Nonparametric Random Effects Modelsp. 545
Beta-Binomial Modelsp. 553
Negative Binomial Regressionp. 559
Poisson Regression with Random Effectsp. 563
Notesp. 565
Problemsp. 566
Asymptotic Theory for Parametric Modelsp. 576
Delta Methodp. 577
Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilitiesp. 582
Asymptotic Distributions of Residuals and Goodness-of-Fit Statisticsp. 587
Asymptotic Distributions for Logit/Loglinear Modelsp. 592
Notesp. 594
Problemsp. 595
Alternative Estimation Theory for Parametric Modelsp. 600
Weighted Least Squares for Categorical Datap. 600
Bayesian Inference for Categorical Datap. 604
Other Methods of Estimationp. 611
Notesp. 615
Problemsp. 616
Historical Tour of Categorical Data Analysisp. 619
Pearson-Yule Association Controversyp. 619
R. A. Fisher's Contributionsp. 622
Logistic Regressionp. 624
Multiway Contingency Tables and Loglinear Modelsp. 625
Recent (and Future?) Developmentsp. 629
Using Computer Software to Analyze Categorical Datap. 632
Software for Categorical Data Analysisp. 632
Examples of SAS Code by Chapterp. 634
Chi-Squared Distribution Valuesp. 654
Referencesp. 655
Examples Indexp. 689
Author Indexp. 693
Subject Indexp. 701
Table of Contents provided by Syndetics. All Rights Reserved.

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