Unlike some other reproductions of classic texts (1) We have not used OCR(Optical Character Recognition), as this leads to bad quality books with introduced typos. (2) In books where there are images such as portraits, maps, sketches etc We have endeavoured to keep the quality of these images, so they represent accurately the original artefact. Although occasionally there may be certain imperfections with these old texts, we feel they deserve to be made available for future generations to enjoy.

Higher education has changed significantly over the past 50 years, and the individuals who provide leadership for these institutions has similarly changed. The pathway to the college presidency, once the domain of academic administration, has diversified as an increasing number of development officers, student affairs and enrollment management professionals, and even politicians have become common in the role. It is important to understand who the presidents are in the current environment and the challenges they face. Challenges such as dealing with the COVID-19 pandemic, enrollment shortfalls, Title IX, and athletic scandals have risen to the forefront and have contributed to the issues and role of college and university leadership. The Handbook of Research on the Changing Role of College and University Leadership provides important research on the topic of college and university leadership, especially focusing on the changing role of the college president. The chapters discuss college leadership as it is now and how it will evolve into the future. Topics included are the role of the president at various types of universities, their involvement within university functions and activities, and the duties they must carry out and challenges they face. This book is ideal for professionals and researchers working in higher education, including faculty members who specialize in education, public administration, the social sciences, and management, along with teachers, administrators, teacher educators, practitioners, researchers, academicians, and students who are interested in college and university leadership and how this role is transforming.

Database technology can be used for various ends, ranging from promotion of democracy to strengthening of nationalism to shoring up authoritarian regimes through misinformation. Its use affects every layer of society: from individuals to households to local governments, and is a consuming issue in the United States Governments stance on privacy, security, and technology.

This anthology brings together multiple viewpoints on the social dimensions of the revolution in information technology. The chapters cover social, political, educational, personal, and international dimensions of information technology impacts. Each chapter focuses on different aspects of the effects of computing and the new information technologies that have accelerated every area of human life. Social Dimensions of Information Technology: Issues for the New Millennium raises important issues with profound implications for public policy and societal development.

With the alarming rate of information technology changes over the past two decades, it is not unexpected that there is an evolution of the human side of IT that has forced many organizations to rethink their strategies in dealing with the human side of IT. People, just like computers, are main components of any information systems. And just as successful organizations must be willing to upgrade their equipment and facilities, they must also be alert to changing their viewpoints on various aspects of human behavior. New and emerging technologies result in human behavior responses which must be addressed with a view toward developing better theories about people and IT. IT Solutions Series: Humanizing Information Technology: Advice from Experts brings out a variety of views expressed by practitioners from corporate and public settings offer their experiences in dealing with the human byproduct of IT.

"The nature of governance is rapidly changing, due to new technologies which expand public sector capabilities. Modern Public Information Technology Systems: Issues and Challenges examines the most important dimensions of managing information technology in the public sector. It explores the impact of information technology on governmental accountability and distribution of power, the implications of privatization as an IT business model, and the global governance of information technology. Modern Public Information Technology Systems: Issues and Challenges provides a fresh look at the evolution of federal technology and political accountability in governmental information systems. Descriptions of general policy and technical applications, as well as practical implementation guidelines make this book a must-have for professors, students, and practitioners."

Multiple myeloma is the second most common hematologic malignancy and c- rently affects approximately 50,000 people in the United States. Each year about 20,000 people are diagnosed with myeloma. Although new treatments have been developed, which signi?cantly prolong the survival of patients, myeloma bone d- ease still remains a major cause of severe morbidity and increased mortality in patients with myeloma. Myeloma bone disease is characterized by "punched out" lytic lesions caused by increased osteoclastic bone destruction accompanied by suppressed or even absent osteoblast activity. Advances in our understanding of both the pathophysiology of myeloma bone disease and the development of novel agents that target speci?c pathways involved in both the increased osteoclast f- mation and the suppressed osteoblast activity in myeloma provide new hope for these patients. The treatment of myeloma bone disease was revolutionized by cl- ical trials that demonstrated the signi?cant bene?t of intravenous bisphosphonate therapy in patients with myeloma bone disease. With the identi?cation of many of the cytokines and chemokines involved in myeloma bone disease, novel th- apies such as denosumab that blocks RANKL activity, anti-DKK1, which targets the inhibition of osteoblast activity by blocking Wnt signaling inhibition, and the potential anabolic effects of agents such as bortezomib and activin have greatly improved our potential to block the progression or reverse myeloma bone disease.

E-government has emerged not merely as a specialization in public administration but as a transformative force affecting all leaves and functions in government. Digital Government: Principles and Best Practices, written by a collection of practitioners and researchers, provides an overview of the management challenges and issues involved in seeking a new form of governance - digital government.

"Describes the quantitative research process--framing analytical questions, developing a comprehensive outline, providing a roadmap for the reader, and accessing indispensable computer and program tools. Supplies end-of-chapter checklists, extensive examples, and biobliographies."

G. David Howard combines two passionate interests-humor and politics-in his first collection of personal essays that creatively examine current events in America and the actions of our government.

Howard, a seasoned stand-up comedian, addresses a variety of controversial topics with both funny and frank tones intended to provoke laughter and thought about our leaders and the direction our country is headed. As he explores how groupless people have no representation, why gasoline prices have doubled, and how socialists want to destroy freedom and increase taxes, Howard blends facts with his own spin on potential solutions. Included is a script from an imaginary and imaginative golf game between Barack Obama, John Boehner, Joe Biden and Chuck Schumer, as well as a montage of Howard's opinions on a wide range of subjects, such as climate change, driver distractions, and gun control.

Half of America Is Nuts, and They Were Allowed to Vote shares one man's take on our world as he presents an unforgettable roller-coaster ride through America and its political system.

Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers. The book starts with an ethics chapter on the uses and potential abuses of data analytics. Chapters 2 and 3 show how to implement a broad range of statistical procedures in R. Chapters 4 and 5 deal with regression and classification trees and with random forests. Chapter 6 deals with machine learning models and the "caret" package, which makes available to the researcher hundreds of models. Chapter 7 deals with neural network analysis, and Chapter 8 deals with network analysis and visualization of network data. A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. All empirical chapters have two "Quick Start" exercises designed to allow quick immersion in chapter topics, followed by "In Depth" coverage. Data are available for all examples and runnable R code is provided in a "Command Summary". An appendix provides an extended tutorial on R and RStudio. Almost 30 online supplements provide information for the complete book, "books within the book" on a variety of topics, such as agent-based modeling. Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.

Recently, the public sector has given an increasing amount of national and international attention to electronic government systems. Therefore, it is inevitable that the theoretical implications and intersections between information technology and governmental matters are more widely discussed. Public Information Management and E-Government: Policy and Issues offers a fresh, comprehensive dialogue on issues that occur between the public management and information technology domains. With its focus on political issues and their effects on the larger public sector, this book is valuable for administrators, researchers, students, and educators who wish to gain foundational and theoretical knowledge on e-government policies.

Factor Analysis and Dimension Reduction in R provides coverage, with worked examples, of a large number of dimension reduction procedures along with model performance metrics to compare them. Factor analysis in the form of principal components analysis (PCA) or principal factor analysis (PFA) is familiar to most social scientists. However, what is less familiar is understanding that factor analysis is a subset of the more general statistical family of dimension reduction methods. The social scientist's toolkit for factor analysis problems can be expanded to include the range of solutions this book presents. In addition to covering FA and PCA with orthogonal and oblique rotation, this book's coverage includes higher-order factor models, bifactor models, models based on binary and ordinal data, models based on mixed data, generalized low-rank models, cluster analysis with GLRM, models involving supplemental variables or observations, Bayesian factor analysis, regularized factor analysis, testing for unidimensionality, and prediction with factor scores. The second half of the book deals with other procedures for dimension reduction. These include coverage of kernel PCA, factor analysis with multidimensional scaling, locally linear embedding models, Laplacian eigenmaps, diffusion maps, force directed methods, t-distributed stochastic neighbor embedding, independent component analysis (ICA), dimensionality reduction via regression (DRR), non-negative matrix factorization (NNMF), Isomap, Autoencoder, uniform manifold approximation and projection (UMAP) models, neural network models, and longitudinal factor analysis models. In addition, a special chapter covers metrics for comparing model performance. Features of this book include: Numerous worked examples with replicable R code Explicit comprehensive coverage of data assumptions Adaptation of factor methods to binary, ordinal, and categorical data Residual and outlier analysis Visualization of factor results Final chapters that treat integration of factor analysis with neural network and time series methods Presented in color with R code and introduction to R and RStudio, this book will be suitable for graduate-level and optional module courses for social scientists, and on quantitative methods and multivariate statistics courses.

This annual publication deals with how microcomputers and other computers can be applied to improving the explanatory and evaluative roles of modern social science. Each volume contains chapters by experts in political science, psychology, sociology, economics and computer science.

Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers. The book starts with an ethics chapter on the uses and potential abuses of data analytics. Chapters 2 and 3 show how to implement a broad range of statistical procedures in R. Chapters 4 and 5 deal with regression and classification trees and with random forests. Chapter 6 deals with machine learning models and the "caret" package, which makes available to the researcher hundreds of models. Chapter 7 deals with neural network analysis, and Chapter 8 deals with network analysis and visualization of network data. A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. All empirical chapters have two "Quick Start" exercises designed to allow quick immersion in chapter topics, followed by "In Depth" coverage. Data are available for all examples and runnable R code is provided in a "Command Summary". An appendix provides an extended tutorial on R and RStudio. Almost 30 online supplements provide information for the complete book, "books within the book" on a variety of topics, such as agent-based modeling. Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.

Organizational Behavior and Public Management reveals how organizational behavior enables managers to direct resources that advance the programs and policies of public and government. This edition offers a public sector perspective of core topics, such as communication, decision-making, leadership, management ethics, motivation, organizational change, participation and performance appraisal. Contemporary Psychology called this book "skillful and comprehensivea ]There is a need for a text like thisa ]the device of juxtaposing theory and application is a sound one." The authors discuss such topics as communication, decision making, worker participation and total quality management, organizational change, management systems, information, computers and organization theory in public management.

Factor Analysis and Dimension Reduction in R provides coverage, with worked examples, of a large number of dimension reduction procedures along with model performance metrics to compare them. Factor analysis in the form of principal components analysis (PCA) or principal factor analysis (PFA) is familiar to most social scientists. However, what is less familiar is understanding that factor analysis is a subset of the more general statistical family of dimension reduction methods. The social scientist's toolkit for factor analysis problems can be expanded to include the range of solutions this book presents. In addition to covering FA and PCA with orthogonal and oblique rotation, this book's coverage includes higher-order factor models, bifactor models, models based on binary and ordinal data, models based on mixed data, generalized low-rank models, cluster analysis with GLRM, models involving supplemental variables or observations, Bayesian factor analysis, regularized factor analysis, testing for unidimensionality, and prediction with factor scores. The second half of the book deals with other procedures for dimension reduction. These include coverage of kernel PCA, factor analysis with multidimensional scaling, locally linear embedding models, Laplacian eigenmaps, diffusion maps, force directed methods, t-distributed stochastic neighbor embedding, independent component analysis (ICA), dimensionality reduction via regression (DRR), non-negative matrix factorization (NNMF), Isomap, Autoencoder, uniform manifold approximation and projection (UMAP) models, neural network models, and longitudinal factor analysis models. In addition, a special chapter covers metrics for comparing model performance. Features of this book include: Numerous worked examples with replicable R code Explicit comprehensive coverage of data assumptions Adaptation of factor methods to binary, ordinal, and categorical data Residual and outlier analysis Visualization of factor results Final chapters that treat integration of factor analysis with neural network and time series methods Presented in color with R code and introduction to R and RStudio, this book will be suitable for graduate-level and optional module courses for social scientists, and on quantitative methods and multivariate statistics courses.

One of the most exciting developments in fighting crime at the turn of the twenty-first century has been the integration of Geographic Information Systems (GIS) into law enforcement, and includes crime analysis. This book provides an overview of the implementation and integration of GIS technology into various aspects of law enforcement, including important mapping concepts and their use in crime analysis. Crime mapping basics are discussed, including pin mapping, mapping « hot spots, mapping crime density, and creating briefing maps. Other topics include the integration of crime mapping with police decision-making, the use of various forms of spatial modeling in law enforcement, and integrating inter-agency data as part of a regional approach to crime. As a way of better understanding the practical applications, the authors include a list of police agencies providing real crime data and analysis tools on the World Wide Web.

Both algorithms and the software . and hardware of automatic computers have gone through a rapid development in the past 35 years. The dominant factor in this development was the advance in computer technology. Computer parameters were systematically improved through electron tubes, transistors and integrated circuits of ever-increasing integration density, which also influenced the development of new algorithms and programming methods. Some years ago the situation in computers development was that no additional enhancement of their performance could be achieved by increasing the speed of their logical elements, due to the physical barrier of the maximum transfer speed of electric signals. Another enhancement of computer performance has been achieved by parallelism, which makes it possible by a suitable organization of n processors to obtain a perform ance increase of up to n times. Research into parallel computations has been carried out for several years in many countries and many results of fundamental importance have been obtained. Many parallel computers have been designed and their algorithmic and program ming systems built. Such computers include ILLIAC IV, DAP, STARAN, OMEN, STAR-100, TEXAS INSTRUMENTS ASC, CRAY-1, C mmp, CM*, CLIP-3, PEPE. This trend is supported by the fact that: a) many algorithms and programs are highly parallel in their structure, b) the new LSI and VLSI technologies have allowed processors to be combined into large parallel structures, c) greater and greater demands for speed and reliability of computers are made."

This book was first published in 2008. Multiple pregnancies are the most frequent and serious complication of assisted reproduction. Both high-order multiple and twin pregnancies entail a number of medical and economic outcomes that affect the children, the mother, the parents, the families, and society as a whole. Limiting the number of embryos to transfer is the only method available to decrease the incidence of multiple pregnancies. Single Embryo Transfer reviews the advantages and limitations of this approach to assisted reproduction. The crucial issue of selecting the best embryo will be reviewed in detail. All clinical issues involved in setting up and running an SET programme will be covered, including important topics such as cryopreservation of embryos, embryo donation, and patient counselling. The final chapters on future SET trends in Europe and North America are written by leading figures in the IVF world. The book is of interest to physicians, embryologists, nurses, insurers, politicians, ethicists and patients.