This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social science research. An explicit aim of the book is to integrate the transformational and the latent variable approach, two diverse but complementary traditions dealing with the analysis of categorical data. This is the first introductory text to cover models and methods for discrete dependent variables, cross-classifications, and longitudinal data in a rigorous, yet accessible, manner in a single volume.
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..." 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."
-Roberto Mare, University of California, Los Angeles, USA
"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, USA
, .." 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."
-Roberto Mare, University of California, Los Angeles, USA
"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, USA
.,." 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."
-Roberto Mare, University of California, Los Angeles, USA
"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, USA
"Statistical Methods for Categorical Data Analysis" by Daniel A. Powers and Yu Xie provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social science research. An explicit aim of the book is to integrate the transformational and the latent variable approach, two diverse but complementary traditions dealing with the analysis of categorical data. This is the first introductory text to cover models and methods for discrete dependent variables, cross-classifications, and longitudinal data in a rigorous, yet accessible, manner in a single volume.The second edition of this book includes new material on multilevel models for categorical data. Several chapters have undergone extensive revisions and extensions to include new applications and examples. Highlights of the 2nd edition include a detailed discussion of classical and Bayesian estimation techniques for hierarchical/multilevel models, extensive coverage of discrete-time hazard models and Cox regression models, and methods for evaluating and accommodating departures from model assumptions. The accompanying website contains programming scripts to replicate each example using various statistical packages, which has proven to be an invaluable resource for instructors, students, and researchers.This book presents the essential methods and models that form the core of contemporary social statistics. The book covers a remarkable range of models that have applications in sociology, demography, psychometrics, econometrics, political science, biostatistics, and other fields. It will be especially useful as a graduate textbook for students in advanced social statistics courses and as a reference book for applied researchers.
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