**Statistical Methods for Categorical Data Analysis**

**2 ^{nd} Edition**

Daniel A. Powers and Yu Xie

*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 2^{nd} 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.

Computing examples

http://la.utexas.edu/users/dpowers/Powers%26Xie%202nd%20Ed