A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
π e t ) n for the binomial distribution, (b)m(t) = exp{μ[exp(t) – 1]} for the Poisson distribution. ... Construct a largesample test statistic for H 0 :μ=μ0 using (i) the Wald method, (ii) thescore method, and (iii) the likelihoodratio ...
The confidence interval of the form presented above is called a Wald confidence interval. ... Let θ denote a particular ordinal odds ratio, such as a global odds ratio for a 2 × 2 table of counts {m ij } that results from a particular ...
This book deals with the analysis of categorical data.
Khamboonruang, C., Thongcharoen, P., Robb, M., Michael, N., Kunasol, P., and Kim, J. (2009). Vaccination with ALVAC and AIDSVAX to ... Richert, B., Tokach, M., Goodband, R., and Nelssen, J. (1995). Assessing producer awareness of the ...
This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social science research. Companion website also available, at https://webspace.utexas.edu/dpowers/www/
This is followed by an in-depth focus and explanation of the relationship between the initial research question, the use of software to perform the analyses, and how to interpret the output substantively.
EXAMPLE 3.8: Weldon's dice Going a bit further, we can compare Weldon's data with the theoretical binomial distribution as shown below. Because the WeldonDice data collapsed the frequencies for 10–12 successes as 10+, we do the same ...
Data sets and computer code are available at a web site devoted to the text. Adopters of this book may request a solutions manual from: [email protected]. Jeffrey S. Simonoff is Professor of Statistics at New York University.
A companion website for this book contains datasets and R commands used in the book for students, and solutions for the end-of-chapter exercises on the instructor site.
After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.