- 296 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
Statistical Methods for Categorical Data Analysis
About this book
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/
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Yes, you can access Statistical Methods for Categorical Data Analysis by Daniel Powers,Yu Xie in PDF and/or ePUB format, as well as other popular books in Psychology & Probability & Statistics. We have over one million books available in our catalogue for you to explore.
Information
general
concepts
behind
regression
models
for
continuous
dependent
variables.
This
is
a
natural
starting
point
since
many
of
the
familiar
ideas
and
principles
used
in
the
analysis
of
covariance
and
regression
for
continuous
variables
will
carry
over
to
the
analysis
of
categorical
dependent
variables.
These
concepts
are
described
in
Chapter
2,
along
with
a
general
orientation
to
regression
models.
Chapter
3
discusses
models
for
binary
data
and
issues
pertaining
to
estimation,
model
building,
and
the
interpretation
of
results.
Chapter
4
provides
an
overview
of
measures
of
association
and
models
for
contingency
tables.
Chapter
5
builds
on
material
in
Chapter
3
to
introduce
multilevel
(or
hierarchical)
models
for
binary
data.
Chapter
6
presents
methods
for
event
occurrences
in
time.
Chapters
7
and
8
outline
various
methods
for
the
analysis
of
polytomous
(or
multinomial)
response
variables
that
assume
ordinal
or
nominal
measures.
10
Statistical
Methods
for
Categorical
Data
Analysis
Table of contents
- Front cover
- Statistical Methods for Categorical Data Analysis: 2nd Edition
- Copyright page
- Dedication
- Contents
- List of Figures
- List of Tables
- Preface
- Chapter 1. Introduction
- Chapter 2. Review of Linear Regression Models
- Chapter 3. Models for Binary Data
- Chapter 4. Loglinear Models for Contingency Tables
- Chapter 5. Multilevel Models for Binary Data
- Chapter 6. Statistical Models for Event Occurrence
- Chapter 7. Models for Ordinal Dependent Variables
- Chapter 8. Models for Nominal Dependent Variables
- Appendix A: The Matrix Approach to Regression
- Appendix B: Maximum Likelihood Estimation
- References
- Subject Index