[美]丹尼爾•A.鮑威斯（Daniel A. Powers 美國得剋薩斯大學奧斯汀分校社會學係副教授和人口研究中心研究員。其研究領域包括：應用統計學和研究方法、社會人口學、社會分層、生育和死亡研究，最近的研究主要是嬰兒死亡的種族差異和非綫性模型的分解技術。主要著作有《分類數據分析的統計方法》。

謝宇（Xie Yu），美國密歇根大學的Otis Dudley Duncan傑齣教授，同時擔任密歇根大學社會學係、統計係和中國研究中心的教授，社會研究院（ISR）人口研究中心和調查研究中心的研究員，調查研究中心量化方法組主任。2004年當選美國藝術與科學院院士和“颱灣中央研究院”院士。其研究領域包括：社會分層、統計方法、人口學、科學社會學和中國研究。主要著作有《分類數據分析的統計方法》、《科學界的女性》、《美國亞裔的人口統計描述》、《社會學方法與定量研究》、《婚姻與同居》等。

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.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. Companion website also available, at https://webspace.utexas.edu/dpowers/www/