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/

Alarmists argue that the United States urgently needs more and better-trained scientists to compete with the rest of the world. Their critics counter that, far from facing a shortage, we are producing a glut of young scientists with poor employment prospects. Both camps have issued reports in recent years that predict the looming decline of American science. Drawing on their extensive analysis of national data sets, Yu Xie and Alexandra Killewald have welcome news to share: American science is in good health. Is American Science in Decline? does reveal areas of concern, namely scientists' low earnings, the increasing competition they face from Asia, and the declining number of doctorates who secure academic positions. But the authors argue that the values inherent in American culture make the country highly conducive to science for the foreseeable future. They do not see globalization as a threat but rather a potential benefit, since it promotes efficiency in science through knowledge-sharing. In an age when other countries are catching up, American science will inevitably become less dominant, even though it is not in decline relative to its own past. As technology continues to change the American economy, better-educated workers with a range of skills will be in demand. So as a matter of policy, the authors urge that science education not be detached from general education.

Why do so few women choose a career in science--even as they move into medicine and law in ever-greater numbers? In one of the most comprehensive studies of gender differences in science careers ever conducted, "Women in Science" provides a systematic account of how U.S. youth are selected into and out of science education in early life, and how social forces affect career outcomes later in the science labor market.

Studying the science career trajectory in its entirety, the authors attend to the causal influences of prior experiences on career outcomes as well as the interactions of multiple life domains such as career and family. While attesting to the progress of women in science, the book also reveals continuing gender differences in mathematics and science education and in the progress and outcomes of scientists' careers. The authors explore the extent and causes of gender differences in undergraduate and graduate science education, in scientists' geographic mobility, in research productivity, in promotion rates and earnings, and in the experience of immigrant scientists. They conclude that the gender gap in parenting responsibilities is a critical barrier to the further advancement of women in science.

Boundary Methods: Elements, Contours, and Nodes presents the results of cutting-edge research in boundary-based mesh-free methods. These methods combine the dimensionality advantage of the boundary element method with the ease of discretization of mesh-free methods, both of which, for some problems, hold distinct advantages over the finite element method. After introducing some novel topics related to the boundary element method (BEM), the authors focus on the boundary contour method (BCM)-a variant of the BEM that further reduces the dimensionality of a problem. The final section of the book explores the boundary node method, which combines the BEM with moving least-squares approximants to produce a mesh-free, boundary-only method. The authors, who are also the primary developers of these methods, clearly introduce and develop each topic. In addition to numerical solutions of boundary value problems in potential theory and linear elasticity, they also discuss topics such as shape sensitivities, shape optimization, and adaptive meshing. Numerical results for selected problems appear throughout the book, as do extensive references.