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<p>Introduction (Hill, Biemer, Buskirk, Japec, Kirchner, Kolenikov, Lyberg)</p> <p>Section 1:  The New Survey Landscape</p> <p>1.            Why Machines Matter for Survey and Social Science Researchers: Exploring Applications of Machine Learning Methods for Design, Data Collection, and Analysis</p> <p>Trent D. Buskirk and Antje Kirchner</p> <p>2.            The Future Is Now: How Surveys Can Harness Social Media To Address 21st Century Challenges</p> <p>Amelia Burke-Garcia, Brad Edwards, and Ting Yan</p> <p>3.            Linking Survey Data with Commercial or Administrative Data for Data Quality Assessment</p> <p>A.            Rupa Datta, Gabriel Ugarte, and Dean Resnick</p> <p>Section 2:  Total Error and Data Quality</p> <p>4.            Total Error Frameworks for Hybrid Estimation and Their Applications</p> <p>Paul P. Biemer and Ashley Amaya</p> <p>5.            Measuring the Strength of Attitudes in Social Media Dataa</p> <p>Ashley Amaya, Ruben a, Frauke Kreuter, and Florian Keusch</p> <p>6.            Attention to Campaign Events: Do Twitter and Self-Report Metrics Tell the Same Story?</p> <p>Josh Pasek, Lisa O. Singh, Yifang Wei, Stuart N. Soroka, Jonathan M. Ladd, Michael W. Traugott, Ceren Budak, Leticia Bode, and Frank Newport</p> <p>7.            Improving Quality of Administrative Data: A Case Study with FBI’s National Incident-Based Reporting System Data</p> <p>Dan Liao, Marcus Berzofsky, Lance Couzens, Ian Thomas, and Alexia Cooper</p> <p>8.            Performance and Sensitivities of Home Detection on Mobile Phone Data</p> <p>Maarten Vanhoof, Clement Lee, and Zbigniew Smoreda</p> <p>Section 3:  Big Data in Official Statistics</p> <p>9.            Big Data Initiatives in Official Statistics</p> <p>Lilli Japec and Lars Lyberg</p> <p>10.          Big Data in Official Statistics: A Perspective from Statistics Netherlands</p> <p>Barteld Braaksma, Kees Zeelenberg, and Sofie De Broe</p> <p>11.          Mining the New Oil for Official Statistics</p> <p>Siu-Ming Tam, J. K. Kim, Lyndon Ang, and Han Pham</p> <p>12.          Investigating Alternative Data Sources to Reduce Respondent Burden in United States Census Bureau Retail Economic Data Products</p> <p>Rebecca J. Hutchinson</p> <p>Section 4:  Combining Big Data with Survey Statistics: Methods and Applications</p> <p>13.          Effects of Incentives in Smartphone Data Collection</p> <p>Georg-Christoph Haas, Frauke Kreuter, Florian Keusch, Mark Trappmann, and Sebastian Bähr</p> <p>14.          Using Machine Learning Models to Predict Attrition in a Survey Panel</p> <p>Mingnan Liu</p> <p>15.          Assessing Community Well-being using Google Street-View and Satellite Imagery</p> <p>Dr. Pablo Diego-Rosell, Stafford Nicols, Dr. Rajesh Srinivasan, and Dr. Ben Dilday</p> <p>16.          Nonparametric Bootstrap and Small Area Estimation to Mitigate Bias in Crowdsourced Data: Simulation Study and Application to Perceived Safety</p> <p>David Buil-Gil, Reka Solymosi, and Angelo Moretti</p> <p>17.          Using Big Data to Improve Sample Efficiency</p> <p>Jamie Ridenhour, Joe McMichael, Karol Krotki, and Howard Speizer</p> <p>Section 5:  Combining Big Data with Survey Statistics: Tools</p> <p>18.          Feedback Loop: Using Surveys to Build and Assess Registration-Based Sample Religious Flags for Survey Research</p> <p>David Dutwin</p> <p>19.          Artificial Intelligence and Machine Learning Derived Efficiencies for Large-Scale Survey Estimation Efforts</p> <p>Steven B. Cohen, PhD and Jamie Shorey, PhD</p> <p>20.          Worldwide Population Estimates for Small Geographic Areas: Can We Do a Better Job?</p> <p>Safaa Amer, Dana Thomson, Rob Chew, and Amy Rose</p> <p>Section 6:  The Fourth Paradigm, Regulations, Ethics, Privacy</p> <p>21.          Reproducibility in the Era of Big Data: Lessons for Developing Robust Data Management and Data Analysis Procedures</p> <p>D.B. McCoach, J. Necci Dineen, Sandra M. Chafouleas, and Amy Briesch</p> <p>22.          Combining Active and Passive Mobile Data Collection: A Survey of Concerns</p> <p>Florian Keusch, Bella Struminskaya, Frauke Kreuter, and Martin Weichbold</p> <p>23.          Attitudes Toward Data Linkage: Privacy, Ethics, and the Potential for Harm</p> <p>Aleia Clark Fobia, Jennifer Hunter Childs, and Casey Eggleston</p> <p>24.          Moving Social Science into the Fourth Paradigm: The Data Life Cycle</p> <p>Craig A. Hill</p>

<p><b>Craig A. Hill, PhD,</b> is Senior Vice President at RTI International and focuses on application of new technology to quantitative social science research. He is also the lead editor of <i>Social Media, Sociality, and Survey Research</i> (Wiley, 2013).</p> <p><b>Paul P. Biemer, PhD,</b> is Distinguished Fellow, Statistics at RTI International. He is an author, co-author, and co-editor of 6 other books published by Wiley.</p> <p><b>Trent D. Buskirk, PhD,</b> is the Novak Family Distinguished Professor of Data Science and the Chair of the Applied Statistics and Operations Research Department in the College of Business at Bowling Green State University.</p> <p><b>Lilli Japec, PhD,</b> former Director of Research and Development Department at Statistics Sweden. She co-chaired AAPOR's Task Force on Big Data.</p> <p><b>Antje Kirchner, PhD,</b> is a Survey Methodologist at RTI International. She is the Chair of the Scientific Committee of the Big Data Meets Survey Science (BigSurv20) conference.</p> <p><b>Stanislav (Stas) Kolenikov, PhD</b>, is Principal Scientist at Abt Associates. His work focuses on survey statistics, including issues in sampling, weighting, variance estimation, multiple imputation, and small area estimation.</p> <p><b>Lars E. Lyberg</b><b>,</b> <b>PhD,</b> is former Head of the Research and Development Department at Statistics Sweden. He is the founder of the <i>Journal of Official Statistics (JOS)</i> and served as its Chief Editor for 25 years.</p>

<p><b>Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem</b> <p>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. <p><i>Big Data Meets Survey Science: A Collection of Innovative Methods</i> 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. <ul> <li>Presents groundbreaking survey methods being utilized today in the field of Big Data</li> <li>Explores how machine learning methods can be applied to the design, collection, and analysis of social science data</li> <li>Filled with examples and illustrations that show how survey data benefits Big Data evaluation</li> <li>Covers methods and applications used in combining Big Data with survey statistics</li> <li>Examines regulations as well as ethical and privacy issues</li> </ul> <p><i>Big Data Meets Survey Science: A Collection of Innovative Methods</i> 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.

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