“Big Data Meets Survey Science”

TY - JOUR

T1 - “Big Data Meets Survey Science”

AU - Eck, Adam

AU - Cazar, Ana Lucía Córdova

AU - Callegaro, Mario

AU - Biemer, Paul

N1 - Funding Information: Adam Eck is an assistant professor of computer science and leads the Social Intelligence Lab at Oberlin College. He received his PhD and MS in computer science from the University of Nebraska–Lincoln, where he received a National Science Foundation Graduate Research Fellowship. His research interests include interdisciplinary applications of artificial intelligence and machine learning to solve real-world problems, such as data science and machine learning for improving data collection and analysis in the computational social sciences (e.g., Survey Informatics), as well as decision-making for intelligent agents in complex environments. E-mail: [email protected] Ana Lucía Córdova Cazar is an assistant professor of quantitative methods at Universidad San Francisco de Quito (Ecuador) and an adjunct professor of psychology at the University of Nebraska–Lincoln. She received her PhD and MS in survey research and methodology from the University of Nebraska–Lincoln. She also holds an MA in political economy from the University of Essex (United Kingdom), where she was a UK Chevening Scholar. Her research interests include the use of calendar and time diary data collection methods, the use of survey methods to improve health-care research, and the statistical analysis of complex survey data through multilevel modeling and structural equation modeling. E-mail: [email protected] Mario Callegaro is a senior UX Survey research scientist in the Google Cloud Platform UX team. He focuses on helping the team in collecting high-quality surveys about our cloud platform products. He consults on numerous survey, market research, and user experience projects in terms of survey design, questionnaire design, sampling, and reporting. He holds a MS and a PhD in survey research and methodology from the University of Nebraska-Lincoln. Mario has published over 40 peer-reviewed papers and book chapters and made over 150 conference presentations nationally and internationally in the areas of web surveys, online panels, paradata, telephone, and cell phone surveys; question wording, polling, and exit polls; event history calendar; longitudinal surveys; and survey quality. His 2015 book titled Web Survey Methodology written with Katja Lozar Manfreda and Vasja Vehovar is considered a classic textbook for web surveys. Finally, his book chapter (in collaboration with Yongwei Yang), The Role of Surveys in the Era of “Big Data , ” is the most downloaded chapter of the Palgrave Handbook of Survey Research (2018). E-mail: [email protected] Paul Biemer is a Distinguished Fellow in Statistics at RTI International and an Associate Director of Survey Research and Development for the Odum Institute at the University of North Carolina in Chapel Hill. He has examined the relationships between survey design and survey error and has developed statistical methods for assessing survey errors, particularly measurement errors and methods for analyzing survey data. He is a co-developer of the Adaptive Total Design system of total survey error and costs minimization, A System for Product Improvement, Review, and Evaluation (ASPIRE) for survey monitoring and quality improvement for official statistics, and audio computer-assisted self-interviewing and computer audio-recorded interviewing. He also pioneered the field of latent class analysis and mixture modeling for survey error evaluation. His book, Introduction to Survey Quality (with Lars Lyberg), is a widely used course text. His book, Latent Class Analysis of Survey Error , describes how latent class analysis can be applied to complex surveys, including panel surveys, to evaluate survey error. He edited a number of book volumes including Measurement Errors in Surveys , Survey Measurement and Process Quality , Telephone Survey Methodology , and most recently, Total Error in Practice , all published by John Wiley & Sons. In 2005, he founded the annual workshop series—the International Total Survey Error Workshops—which continues today. He is a Fellow of the ASA and the AAAS and an Elected Member of the ISI, he holds a number of awards for his contributions to the fields of statistics and survey methodology, including the W. S. Connor Award, H. O. Hartley Award, the Morris Hansen Award, and the Roger Herriot Award. E-mail: [email protected] 1 Oberlin College, Oberlin, OH, USA 2 Universidad San Francisco de Quito, Ecuador 3 Google, London, United Kingdom 4 RTI International, Raleigh, NC, USA 5 University of North Carolina, Chapel Hill, NC, USA Adam Eck, Oberlin College, 223D King Building, 10 N. Professor Street, Oberlin, OH 44074, USA. Email: [email protected] 2019 0894439319883393 © The Author(s) 2019 2019 SAGE Publications This article is part of the SSCR special issue on Big Data and Survey Science, guest edited by Adam Eck (Oberlin College), Ana Lucía Córdova Cazar (Universidad San Francisco de Quito), Mario Callegaro (Google Ltd.), and Paul Biemer (RTI International & UNC-CH). edited-state corrected-proof Authors’ Note Additional information about the BigSurv18 Conference can be found in two complementary publications. First, Hill et al. (2019) organized a conference report that details both the history of the origins of BigSurv18 as well as an extensive outline of the exciting research discussed at the conference. Second, an edited volume of additional invited conference papers comprised of 26 book chapters broadly exploring the theory and combination of big data and survey research—not necessarily focused on empirical studies as in this special issue—is forthcoming ( Hill et al., in press ). Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The authors received no financial support for the research, authorship, and/or publication of this article.

PY - 2021/8

Y1 - 2021/8

UR - https://www.scopus.com/pages/publications/85074987497

U2 - 10.1177/0894439319883393

DO - 10.1177/0894439319883393

M3 - Artículo

AN - SCOPUS:85074987497

SN - 0894-4393

VL - 39

SP - 484

EP - 488

JO - Social Science Computer Review

JF - Social Science Computer Review

IS - 4

ER -