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This book explores official statistics and their social function in modern societies. Digitisation and globalisation are creating completely new opportunities and risks, a context in which facts (can) play an enormously important part if they are produced with a quality that makes them credible and purpose-specific. In order for this to actually happen, official statistics must continue to actively pursue the modernisation of their working methods. This book is not about the technical and methodological challenges associated with digitisation and globalisation; rather, it focuses on statistical sociology, which scientifically deals with the peculiarities and pitfalls of governing-by-numbers, and assigns statistics a suitable position in the future informational ecosystem. Further, the book provides a comprehensive overview of modern issues in official statistics, embodied in a historical and conceptual framework that endows it with different and innovative perspectives. Central to this work is the quality of statistical information provided by official statistics. The implementation of the UN Sustainable Development Goals in the form of indicators is another driving force in the search for answers, and is addressed here. This book will be of interest to a broad readership. The topics of sociology, epistemology, statistical history and the management of production processes, which are important for official statistics and their role in social decision-making processes, are generally not dealt with in statistics books. The book is primary intended for official statisticians, but researchers and advanced students in statistics, economics, sociology and the political sciences will find the book equally stimulating. Last but not least, it offers a valuable source of reflection for policymakers and stakeholders.
Federal government statistics provide critical information to the country and serve a key role in a democracy. For decades, sample surveys with instruments carefully designed for particular data needs have been one of the primary methods for collecting data for federal statistics. However, the costs of conducting such surveys have been increasing while response rates have been declining, and many surveys are not able to fulfill growing demands for more timely information and for more detailed information at state and local levels. Innovations in Federal Statistics examines the opportunities and risks of using government administrative and private sector data sources to foster a paradigm shift in federal statistical programs that would combine diverse data sources in a secure manner to enhance federal statistics. This first publication of a two-part series discusses the challenges faced by the federal statistical system and the foundational elements needed for a new paradigm.
The world is witnessing the growth of a global movement facilitated by technology and social media. Fueled by information, this movement contains enormous potential to create more accountable, efficient, responsive, and effective governments and businesses, as well as spurring economic growth. Big Data Governance and Perspectives in Knowledge Management is a collection of innovative research on the methods and applications of applying robust processes around data, and aligning organizations and skillsets around those processes. Highlighting a range of topics including data analytics, prediction analysis, and software development, this book is ideally designed for academicians, researchers, information science professionals, software developers, computer engineers, graduate-level computer science students, policymakers, and managers seeking current research on the convergence of big data and information governance as two major trends in information management.
This book considers statistical innovation, 1900-45, in the Weimar Republic and the Third Reich.
Census workers need to capture and analyze information at the finest geographic level with mobile and geospatial-based technology. GIS and the 2020 Census: Modernizing Official Statistics provides statistical organizations with the most recent GIS methodologies and technological tools to support census workers' needs at all the stages of a census. Learn how to plan and carry out census work with GIS using new technologies for field data collection and operations management. After planning and collecting data, apply innovative solutions for performing statistical analysis, data integration and dissemination. Additional topics cover cloud computing, big data, Location as a Service (LaaS), and emerging data sources. While GIS and the 2020 Census focuses on using GIS and other geospatial technology in support of census planning and operations, it also offers guidelines for building a statistical-geospatial information infrastructure in support of the 2020 Round of Censuses, evidence-based decision making, and sustainable development. Case studies illustrate concepts in practice.
This open access book examines the question of collecting and disseminating data on ethnicity and race in order to describe characteristics of ethnic and racial groups, identify factors of social and economic integration and implement policies to redress discrimination. It offers a global perspective on the issue by looking at race and ethnicity in a wide variety of historical, country-specific contexts, including Asia, Latin America, Europe, Oceania and North America. In addition, the book also includes analysis on the indigenous populations of the Americas. The book first offers comparative accounts of ethnic statistics. It compares and empirically tests two perspectives for understanding national ethnic enumeration practices in a global context based on national census questionnaires and population registration forms for over 200 countries between 1990 to 2006. Next, the book explores enumeration and identity politics with chapters that cover the debate on ethnic and racial statistics in France, ethnic and linguistic categories in Québec, Brazilian ethnoracial classification and affirmative action policies and the Hispanic/Latino identity and the United States census. The third, and final, part of the book examines measurement issues and competing claims. It explores such issues as the complexity of measuring diversity using Malaysia as an example, social inequalities and indigenous populations in Mexico and the demographic explosion of aboriginal populations in Canada from 1986 to 2006. Overall, the book sheds light on four main questions: should ethnic groups be counted, how should they be counted, who is and who is not counted and what are the political and economic incentives for counting. It will be of interest to all students of race, ethnicity, identity, and immigration. In addition, researchers as well as policymakers will find useful discussions and insights for a better understanding of the complexity of categorization and related political and policy challenges.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Presents an interview-based study of how political and professional agendas affect government statistical agencies in five liberal democracies.
Interpreting Official Statistics examines the official statistics produced about the current state of British society. It documents some of the ways in which information has been suppressed, manipulated and misinterpreted since 1979. This invaluable guide is designed to help students know what figures are available, and to discover when and how politicians are misusing statistics. Data sets covered include: * Households below average income * Administrative and survey methods of unemployment and crime * Population census data on ethnicity * Data sources on women and work * Data on the relationship between class and health, and safety at work * New data sources on disability * Labour Force Survey.