Big Data Meets Survey Science
Big Data Meets Survey Science

Author: Craig A. Hill

Publisher: John Wiley & Sons

Published: 2020-09-29

Total Pages: 784

ISBN-13: 1118976320

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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.

Big Data for Twenty-First-Century Economic Statistics
Big Data for Twenty-First-Century Economic Statistics

Author: Katharine G. Abraham

Publisher: University of Chicago Press

Published: 2022-03-11

Total Pages: 502

ISBN-13: 022680125X

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Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.

Humanizing Big Data
Humanizing Big Data

Author: Colin Strong

Publisher: Kogan Page Publishers

Published: 2015-03-03

Total Pages: 226

ISBN-13: 074947212X

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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.

Big Data
Big Data

Author: Viktor Mayer-Schönberger

Publisher: Houghton Mifflin Harcourt

Published: 2013

Total Pages: 257

ISBN-13: 0544002695

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A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.

Survey Data Harmonization in the Social Sciences
Survey Data Harmonization in the Social Sciences

Author: Irina Tomescu-Dubrow

Publisher: John Wiley & Sons

Published: 2023-11-22

Total Pages: 420

ISBN-13: 1119712181

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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 and Social Science
Big Data and Social Science

Author: Ian Foster

Publisher: CRC Press

Published: 2016-08-10

Total Pages: 493

ISBN-13: 1498751431

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Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.

Migration Research in a Digitized World
Migration Research in a Digitized World

Author: Steffen Pötzschke

Publisher: Springer Nature

Published: 2022-07-11

Total Pages: 230

ISBN-13: 3031013190

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This open access book explores implications of the digital revolution for migration scholars’ methodological toolkit. New information and communication technologies hold considerable potential to improve the quality of migration research by originating previously non-viable solutions to a myriad of methodological challenges in this field of study. Combining cutting-edge migration scholarship and methodological expertise, the book addresses a range of crucial issues related to both researcher-designed data collections and the secondary use of “big data”, highlighting opportunities as well as challenges and limitations. A valuable source for students and scholars engaged in migration research, the book will also be of keen interest to policymakers.

Handbook of Computational Social Science, Volume 1
Handbook of Computational Social Science, Volume 1

Author: Uwe Engel

Publisher: Taylor & Francis

Published: 2021-11-10

Total Pages: 417

ISBN-13: 1000448584

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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.

Handbook of Research on Cloud Infrastructures for Big Data Analytics
Handbook of Research on Cloud Infrastructures for Big Data Analytics

Author: Raj, Pethuru

Publisher: IGI Global

Published: 2014-03-31

Total Pages: 592

ISBN-13: 1466658657

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Clouds are being positioned as the next-generation consolidated, centralized, yet federated IT infrastructure for hosting all kinds of IT platforms and for deploying, maintaining, and managing a wider variety of personal, as well as professional applications and services. Handbook of Research on Cloud Infrastructures for Big Data Analytics focuses exclusively on the topic of cloud-sponsored big data analytics for creating flexible and futuristic organizations. This book helps researchers and practitioners, as well as business entrepreneurs, to make informed decisions and consider appropriate action to simplify and streamline the arduous journey towards smarter enterprises.