Uwe Engel – författare

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth in new directions.With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientifi c and engineering sectors.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth in new directions.With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientifi c and engineering sectors.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions.With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

This state-of-the-art volume provides insight into the recent developments in survey research. It covers topics like: survey modes and response effects, bio indicators and paradata, interviewer and survey error, mixed-mode panels, sensitive questions, conducting web surveys and access panels, coping with non-response, and handling missing data. The authors are leading scientists in the field, and discuss the latest methods and challenges with respect to these topics.Each of the book’s eight parts starts with a brief chapter that provides an historical context along with an overview of today’s most critical survey methods. Chapters in the sections focus on research applications in practice and discuss results from field studies. As such, the book will help researchers design surveys according to today’s best practices. The book’s website www.survey-methodology.de provides additional information, statistical analyses, tables and figures. An indispensable reference for practicing researchers and methodologists or any professional who uses surveys in their work, this book also serves as a supplement for graduate or upper level-undergraduate courses on survey methods taught in psychology, sociology, education, economics, and business. Although the book focuses on European findings, all of the research is discussed with reference to the entire survey-methodology area, including the US. As such, the insights in this book will apply to surveys conducted around the world.

This state-of-the-art volume provides insight into the recent developments in survey research. It covers topics like: survey modes and response effects, bio indicators and paradata, interviewer and survey error, mixed-mode panels, sensitive questions, conducting web surveys and access panels, coping with non-response, and handling missing data. The authors are leading scientists in the field, and discuss the latest methods and challenges with respect to these topics.Each of the book’s eight parts starts with a brief chapter that provides an historical context along with an overview of today’s most critical survey methods. Chapters in the sections focus on research applications in practice and discuss results from field studies. As such, the book will help researchers design surveys according to today’s best practices. The book’s website www.survey-methodology.de provides additional information, statistical analyses, tables and figures. An indispensable reference for practicing researchers and methodologists or any professional who uses surveys in their work, this book also serves as a supplement for graduate or upper level-undergraduate courses on survey methods taught in psychology, sociology, education, economics, and business. Although the book focuses on European findings, all of the research is discussed with reference to the entire survey-methodology area, including the US. As such, the insights in this book will apply to surveys conducted around the world.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions.With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. The first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. The second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital-trace and textual data, as well as probability-, non-probability-, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. The first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. The second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital-trace and textual data, as well as probability-, non-probability-, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

This open access book presents detailed findings about the ethical, legal, and social acceptance of robots in the German and European context. The book raises the core issue of how governments can gain the needed social, ethical, and user acceptance of AI and robots in everyday life.

No detailed description available for "Analysis of Change".

Society is changing – it is becoming more diverse and digital. Social media today plays a central role in human communication. With a changing society, the way social scientists analyze it is also evolving. In recent years, it has become significantly more difficult to encourage people to participate in surveys. Furthermore, digitization opens up data options that go beyond the survey method. Information published online represents valuable digital behavioral traces and provides social researchers with another important source of data alongside their scientific surveys, which in recent years have increasingly been conducted online.Computational methods have played a central role in social research from the very beginning. This applies today more than ever to data analysis, but now also to data collection. The increasing attention paid to machine learning methods in the statistical analysis of social science data represents a further remarkable development in the analysis of social science data.This textbook addresses these developments and familiarizes readers with both elementary and more advanced methods of data analysis. Fundamentals and techniques of data management, programming with R, statistical data analysis, descriptive and causal inference, as well as predictive modeling are covered in depth. All methods are exemplified using real data either from survey research, the social media platform Bluesky or a large digital newspaper archive. Thematically, these data are focused on current sociological topics, particularly those related to human happiness, energy transition and climate policy, AI, political attitudes and the rise in right-wing voting.Data science encompasses more than the algorithms required for data analysis and statistical learning. No less relevant are the rules by which social research and data analysis are conducted, data quality is ensured, and the results are validated. This textbook aims to provide this overview.