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."

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.

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."

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.

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.

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.

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.

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.

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.

"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."

Delivering IT projects on time and within budget, while maintaining privacy, security, and accountability, remains one of the major public challenges of our time. In the four short years since the publication of the second edition of the Handbook of Public Information Systems, the field of public information systems has continued to evolve. This evolution has elucidated many issues that public sector managers face as they wrestle with the information age. Completely revised and updated, the third edition addresses all aspects of public IT projects while emphasizing a common theme: Information technology initiatives are neither simple nor routine. These initiatives carry substantial implications for democratic values, policy development, strategic planning, and the mobilization of human capital.

The third edition provides further insight into significant issues such as:

• Network governance
• Cross organizational integration and operability
• Enterprise architecture design, development, and management
• The impact of information technology on government service delivery

With articles contributed by experts in the field, the coverage includes 21st century public information systems, modern IT needs, and the development of e-governments. The book examines the growth and use of information technology within and among government agencies and organizations. It examines current policy issues, offers case studies, and demonstrates successful public sector applications. Each section leads to a holistic approach that emphasizes communication, understanding, and participation from top management, technology teams, and end users.

The more we learn about e-government and e-governance issues, the more it becomes apparent that the interrelationships between political environments, organizational environments, and technological capabilities are often difficult to summarize and predict. As the field advances, our understanding of the complexity of the relevant issues increases, and more guidance becomes available to administrators. This book puts you on the path to a better understanding of the issues and successful implementation of IT projects.

Written By A Leading Scholar Of Public Information Systems, Public Information Technology And E-Governance Is A Comprehensive, Well-Balanced And Up-To-Date Resource On Public Information Technology And E-Government. Based On Thousands Of Academic And Practitioner Studies And Reports, This Book Provides Policy Information On E-Democracy, Access Issues, Privacy, Security, Regulatory, Enforcement And Taxation Issues, As Well As Management Information On Business Plans, Public-Private Partnerships, Strategic Planning, Project Management, Implementation Factors, And Evaluation. An Excellent Text Or Reference, This Book Features Several Chapter Case Studies, A Glossary, Discussion Questions, And Chapter Summaries To Maximize Comprehension Of The Subject.

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.