Big Data Meets Survey Science

Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources.

Survey Data Harmonization in the Social Sciences An expansive and incisive overview of the practical uses of harmonization and its implications for data quality and costs In Survey Data Harmonization in the Social Sciences, a team of distinguished social science researchers delivers a comprehensive collection of ex-ante and ex-post harmonization methodologies in the context of specific longitudinal and cross-national survey projects. The book examines how ex-ante and ex-post harmonization work individually and in relation to one another, offering practical guidance on harmonization decisions in the preparation of new data infrastructure for comparative research. Contributions from experts in sociology, political science, demography, economics, health, and medicine are included, all of which give voice to discipline-specific and interdisciplinary views on methodological challenges inherent in harmonization. The authors offer perspectives from Europe and the United States, as well as Africa, the latter of which provides insights rarely featured in survey research methodology handbooks. Readers will also find: A thorough introduction to approaches and concepts for survey data harmonization, as well as the effects of data harmonization on the overall survey research process Comprehensive explorations of ex-ante harmonization of survey instruments and non-survey data Practical discussions of ex-post harmonization of national social surveys, census and time use data, including explorations of survey data recycling A detailed overview of statistical issues linked to the use of harmonized survey data Perfect for upper undergraduate and graduate researchers who specialize in survey methodology, Survey Data Harmonization in the Social Sciences will also earn a place in the libraries of survey practitioners who engage in international research.

"Big Data Analytics is a field that dissects, efficiently extricates data from, or in any case manages informational indexes that are excessively huge or complex to be managed by customary information preparing application programming. Information with numerous cases (lines) offers more noteworthy factual force, while information with higher multifaceted nature may prompt a higher bogus disclosure rate. Enormous information challenges incorporate catching information, information stockpiling, information investigation, search, sharing, move, representation, and questioning, refreshing, data security and data source. Large information was initially connected with three key ideas: volume, variety and velocity. Consequently, huge information regularly incorporates information with sizes that surpass the limit of conventional programming to measure inside a satisfactory time and worth. Current utilization of the term enormous information will in general allude to the utilization of predictive analytics, user behavior analytics, or certain other progressed information investigation techniques that concentrate an incentive from information, and sometimes to a specific size of informational index. There is little uncertainty that the amounts of information now accessible are undoubtedly enormous, however that is not the most important quality of this new information biological system. Investigation of informational indexes can discover new relationships to spot business patterns or models. Researchers, business persons, clinical specialists, promoting and governments consistently meet challenges with huge informational collections in territories including Internet look, fintech, metropolitan informatics, and business informatics. Researchers experience constraints in e-Science work, including meteorology, genomics, connectomics, complex material science reproductions, science and ecological exploration. The main objective of this book is to write about issues, challenges, opportunities, and solutions in novel research projects about big data in various domains. The topics of interest include, but are not limited to: efficient storage, management and sharing large scale of data; novel approaches for analyzing data using big data technologies; implementation of high performance and/or scalable and/or real-time computation algorithms for analyzing big data; usage of various data sources like historical data, social networking media, machine data and crowd-sourcing data; using machine learning, visual analytics, data mining, spatio-temporal data analysis and statistical inference in different domains (with large scale datasets); Legal and ethical issues and solutions for using, sharing and publishing large datasets; and the results of data analytics, security and privacy issues"--

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 SAGE Handbook of Survey Development and Application provides a practical resource that researchers can go to for cutting-edge tools to ensure they are employing the best survey research techniques. This handbook not only covers the classic and innovational skills and approaches involved at every step of the survey research process, but also centres itself around applied, how-to guidance to aid readers in best practice. Chapters engage with a broad range of topics including sampling issues, approaches to establishment of measurement equivalence, and the use of online labour pools in survey development. With contributions from a global community of leading and emerging scholars across a wide variety of disciplines, this Handbook is focused on being applicable and accessible across the social sciences. Containing over 120 tables and figures, checklists and tutorial guides, The SAGE Handbook of Survey Development and Application will serve as a one stop resource for survey research. This handbook serves as a touchstone for a variety of fields such as Organizational Behavior, Industrial & Organizational Psychology, Management, Psychology, Educational Research, Marketing, Public Policy, and others. PART 1: Conceptual Issues and Operational Definition PART 2: Research Design Considerations PART 3: Item Development PART 4: Scale Improvement Methods PART 5: Data Collection PART 6: Data Management and Analysis PART 7: Research Production and Dissemination PART 8: Applications

The papers in this volume analyze the deployment of Big Data to solve both existing and novel challenges in economic measurement. The existing infrastructure for the production of key economic statistics relies heavily on data collected through sample surveys and periodic censuses, together with administrative records generated in connection with tax administration. The increasing difficulty of obtaining survey and census responses threatens the viability of existing data collection approaches. The growing availability of new sources of Big Data—such as scanner data on purchases, credit card transaction records, payroll information, and prices of various goods scraped from the websites of online sellers—has changed the data landscape. These new sources of data hold the promise of allowing the statistical agencies to produce more accurate, more disaggregated, and more timely economic data to meet the needs of policymakers and other data users. This volume documents progress made toward that goal and the challenges to be overcome to realize the full potential of Big Data in the production of economic statistics. It describes the deployment of Big Data to solve both existing and novel challenges in economic measurement, and it will be of interest to statistical agency staff, academic researchers, and serious users of economic statistics.

Big data raises more questions than it answers, particularly for those organizations struggling to deal with what has become an overwhelming deluge of data. It can offer marketers more than simple tactical predictive analytics, but organizations need a bigger picture, one that generates some real insight into human behaviour, to drive consumer strategy rather than just better targeting techniques. Humanizing Big Data guides marketing managers, brand managers, strategists and senior executives on how to use big data strategically to redefine customer relationships for better customer engagement and an improved bottom line. Humanizing Big Data provides a detailed understanding of the way to approach and think about the challenges and opportunities of big data, enabling any brand to realize the value of their current and future data assets. First it explores the 'nuts and bolts' of data analytics and the way in which the current big data agenda is in danger of losing credibility by paying insufficient attention to what are often fundamental tenets in any form of analysis. Next it sets out a manifesto for a smart data approach, drawing on an intelligent and big picture view of data analytics that addresses the strategic business challenges that businesses face. Finally it explores the way in which datafication is changing the nature of the relationship between brands and consumers and why this calls for new forms of analytics to support rapidly emerging new business models. After reading this book, any brand should be in a position to make a step change in the value they derive from their data assets.