G. David Garson – författare

"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."

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.

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.

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.

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 comprehensive…There is a need for a text like this…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."

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 codeExplicit comprehensive coverage of data assumptionsAdaptation of factor methods to binary, ordinal, and categorical dataResidual and outlier analysisVisualization of factor resultsFinal chapters that treat integration of factor analysis with neural network and time series methodsPresented 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.

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 codeExplicit comprehensive coverage of data assumptionsAdaptation of factor methods to binary, ordinal, and categorical dataResidual and outlier analysisVisualization of factor resultsFinal chapters that treat integration of factor analysis with neural network and time series methodsPresented 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.

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 governanceCross organizational integration and operabilityEnterprise architecture design, development, and managementThe impact of information technology on government service deliveryWith 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.

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.