Big Data Meets Survey Science: A Collection of Innovative Methods

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.

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