Statistical Methods for Categorical Data Analysis

Statistical Methods for Categorical Data Analysis

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Description

"Statistical Methods for Categorical Data Analysis" is designed as an accessible reference work and textbook about categorical data (that is, data arising from counts instead of measurement. Examples include data about birth, death, marriage, and so forth). It integrates statistical and econometric approaches to the analysis of limited and categorical dependent variables, thereby offering a practical, mathematically uncomplicated approach to the topics of modern data analysis. The volume offers a comprehensive presentation of many different models in a one-volume format (with website). Two features distinguish this book from other analyses of categorical data. First, the authors present both the transformational and latent variable approaches and so synthesize similar methods in statistical and econometric literatures. Second, the book has an applied orientation and features actual examples from social science research. The authors keep discussions of theory to a minimum.
Key features include: exercises and examples utilize popular data already familiar to many social scientists; examples of the use of various popular software packages; non-standard applications of existing software for estimating models which cannot be handled directly using existing pre-programmed software.
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Product details

  • Hardback | 324 pages
  • 157.48 x 228.6 x 20.32mm | 566.99g
  • Academic Press Inc
  • San Diego, CA, United Kingdom
  • English
  • New.
  • 0125637365
  • 9780125637367
  • 1,793,330

Review quote

"Powers and Xie present the methods that form the core of contemporary social statistics. It is the first introductory text to cover, in a single volume, models and methods for discrete dependent variables, cross-classifications, and longitudinal data. A great strength of the text is the authors' informal yet sophisticated approach, which combines the discussion of general principles with illuminating and realistic empirical examples." --ROBERT D. MARE, University of California, Los Angeles "Teaching this book will be almost too easy. The prose is clear, the examples are well-chosen, and the Web site provides practical details." --MICHAEL HOUT, University of California, Berkeley "The Powers and Xie volume is a well-written and up-to-date treatment of many topics in categorical data analysis that should be taught to graduate students in the social sciences. It also will be useful as a reference work for social scientists who use these models in their work. The sample programs, data sets, and outputs available at the associated website should add to the utility of the volume." --KENNETH C. LAND, Duke University, Durham, North Carolina "This book makes an excellent text for courses aimed at second-year (and higher) graduate students in the social sciences and the corresponding website materials will be a boon for instructors and students. It will repay careful study. It will also be an invaluable reference for academicians and other research workers in the social sciences, including those of us who supposedly know this content already. Books are the lifeblood of the academic and scientific enterprise. This is a very good one." --HERBERT L. SMITH, University of Pennsylvania, Philadelphia "This is an accessible, but thorough and wide-ranging exposition of the statistical topics beyond linear regression most needed by social researchers: logistic regression, loglinear models and event-history analysis. There are many relevant examples from contemporary sociological datasets, and the book's Web site, featuring examples, data and software, is an exceptionally nice touch and will be very useful to students." --ADRIAN RAFTERY, University of Washington, Seattle
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Table of contents

Introduction. Review of Linear Regression Models. Logit and Probit Models for Binary Data. Loglinear Models for Contingency Tables. Statistical Models for Rates. Models for Ordinal Dependent Variables. Models for Unordered Dependent Variables. Appendix A: The Matrix Approach to Regression. Appendix B: Maximum Likelihood Estimation. Subject Index.
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About Daniel Powers

Yu Xie is John Stephenson Perrin Professor of Sociology and Associate Director of the Population Studies Center at the University of Michigan. He received his Ph.D. from the University of Wisconsin, Madison, and has received numerous awards for his scholarship and teaching.
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Rating details

6 ratings
3.66 out of 5 stars
5 33% (2)
4 17% (1)
3 33% (2)
2 17% (1)
1 0% (0)
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