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BigSurv18 Program


Wednesday 24th October Thursday 25th October Friday 26th October Saturday 27th October

Program at a glance    Floor plans

Download the BigSurv18 App





Saturday 27th October


08:00 - 18:00

Room: 30.SV01 HALL

Registration and Information Desk
(Registration closes at 12:00)

08:30 - 17:30

Room: 30.S02 S. Expo

Posters 2 (actively presented from 10.30 to 11.00 and 15.30 to 16.00)

These posters are the result of the Barcelona Dades Obertes Data Challenge organized by the city of Barcelona. For more information on the institutions and the data challenge please see:
Poster Title High School
Investigating Complaints in GraciaInstitut Vila de Gràcia
Social Cohesion and Type of NeighborhoodInstitut Ferran Tallada
Free WI-FI Points in BarcelonaInstitut Juan Manuel Zafra
Access to Housing in BarcelonaInstitut Joan Brossa
A Study of Traffic Accidents in BarcelonaInstitut J. Serrat i Bonastre
WI-FI PointsInstitut Josep Comas i Solà


Chair: Antje Kirchner (RTI)

08:00 - 20:30

Room: 40.033

Nursing Room available

09:00 - 10:30

Room: 40.004

Combining General Population Surveys With Big Data From Activity Trackers or Smartphone Apps

The goal of this session is to showcase 5 papers that have each tried to use smartphones or activity trackers to enrich large-scale general-population surveys with Big Data. Many of the current examples in the literature have used small samples of volunteers (e.g. students) to test the potential of trackers and smartphones. There are unique challenges to scaling up to general population surveys. The session centers around the following topics:
- Implementation issues: nonresponse, loss in field, technical problems, use of devices

- Issues in collecting, accessing and storing the data for large samples, across the general population.

- Additional value of Big dData in combination with survey data. How do combined data show a better picture of core variables of interest?

Chair: Peter Lugtig (Utrecht University)

09:00 - 10:30

Room: 40.006

Socializing with Surveys: Combining Big Data and Survey Data to Measure Public Opinion

Chair: Pascal Siegers (GESIS)

09:00 - 10:30

Room: 40.010

Translations Across Nations: Exploring Natural Language Processing in Multicultural Applications

Chair: Diana Zavala-Rojas (Universitat Pompeu Fabra)

09:00 - 10:30

Room: 40.012

The Bigger the Better? Exploring Opportunities and Challenges of Using Big Data for Rapid Ethnography

Chair: Frances Barlas (GfK Custom Research)

09:00 - 10:30

Room: 40.063

Crowdsourcing, Causality, and the Issue of Social Trust

Chair: Rene Bekkers (Vrije Universiteit Amsterdam)

10:30 - 11:00

Room: 30.S02 S. Expo

Coffee Break

11:00 - 12:30

Room: 40.002

Smartphone Sensor Measurement and Other Tasks in Mobile Web Surveys I

Smartphones allow researchers to collect data through sensors such as GPS and accelerometers to study movement, and passively collect data such as browsing history and smartphone and app usage in addition to self-reports. Passive mobile data collection potentially decreases measurement errors and reduces respondent burden. However, respondents have to be willing to provide access to sensor data or perform additional tasks (e.g., download apps, take pictures). If willing respondents differ from nonwilling respondents, results might be biased. This session brings together empirical evidence on the state-of-the-art use of sensor measurement and other additional tasks on smartphones. It combines presentations of results from (large-scale) studies with diverse sensors and tasks from multiple countries and research settings. Presentations discuss current practice in collecting these new types of data focusing on the willingness to allow sensor measurement and perform additional tasks and its implications for nonparticipation bias.

Chair: Bella Struminskaya (Utrecht University)

11:00 - 12:30

Room: 40.004

Big Data Enhancements to Surveys: Social Issues

Chair: Nicholas Biddle (Australian National University)

11:00 - 12:30

Room: 40.006

Fake News! Information Exposure in Complex Online Environments

Chair: Colleen McClain (University of Michigan)

11:00 - 12:30

Room: 40.008

Refining Big Data Methods Using Survey Data

Chair: Georgiy Bobashev (RTI International)

11:00 - 12:30

Room: 40.010

Enhancing Survey Quality With Big Data

Chair: Daniel Oberski (Utrecht University)

11:00 - 12:30

Room: 40.012

Leveraging Big Data for Improving Health Research… the Follow-up Visit

Chair: Naja Rod (Section of Epidemiology, University of Copenhagen)

11:00 - 12:30

Room: 40.063

Big Data Applications to Enterprise Statistics: Businesses, Employers, and Consumers

Chair: Mark Trappmann (IAB, University of Bamberg)

12:30 - 14:00

Room: 30.S02 S. Expo

Lunch

14:00 - 15:30

Room: 40.002

Smartphone Sensor Measurement and Other Tasks in Mobile Web Surveys II

Smartphones allow researchers to collect data through sensors such as GPS and accelerometers to study movement, and passively collect data such as browsing history and smartphone and app usage in addition to self-reports. Passive mobile data collection potentially decreases measurement errors and reduces respondent burden. However, respondents have to be willing to provide access to sensor data or perform additional tasks (e.g., download apps, take pictures). If willing respondents differ from nonwilling respondents, results might be biased. This session brings together empirical evidence on the state-of-the-art use of sensor measurement and other additional tasks on smartphones. It combines presentations of results from (large-scale) studies with diverse sensors and tasks from multiple countries and research settings. Presentations discuss current practice in collecting these new types of data focusing on the willingness to allow sensor measurement and perform additional tasks and its implications for nonparticipation bias.

Chair: Florian Keusch (University of Mannheim)

14:00 - 15:30

Room: 40.004

Leveraging Big and Nontraditional Datasets to Reduce Burden, Increase Response, and Assess Survey Quality

This session highlights research findings from a broad spectrum of Big Data applications across both household and establishment surveys. Within the context of establishment surveys, the presenters will share results from tests designed to reduce respondent burden and improve point estimates and geographic granularity when substituting survey reports with directly extracted data and point-of-sale transactions. Additionally, the use of machine learning to reduce burden for complicated classification systems reporting will be discussed. Within the context of household surveys, the presenters will share success stories of blending marketing data with more traditional variables to improve response propensity models for the purposes of adaptive survey designs and tailored and targeted nonresponse interventions. Finally, a two-way assessment of Big Data and survey data quality will be reported between Zillow.com data linked to household survey data to better understand the quality of each.

Chair: Nancy Bates (US Census Bureau)

14:00 - 15:30

Room: 40.006

How Do You Like Those Likes? Exploring the Validity of Measures Derived from Social Media Data

Chair: Lars Lyberg (Inizio)

14:00 - 15:30

Room: 40.008

New Approaches to Augment Sampling Frames I: Does Bigger Data mean Better Sampling Frames?

Chair: Trent Buskirk (Center for Survey Research, UMass Boston)

14:00 - 15:30

Room: 40.010

Applying Machine Learning and Automation to Improve Imputation - Replicate I

Chair: Steven Cohen (RTI International)

14:00 - 15:30

Room: 40.012

Using Big Data for Electoral Research I: What's the Sentiment for Using Sentiment in Electoral Research?

Chair: Susan Banducci (University of Exeter)

14:00 - 15:30

Room: 40.063

New Digital Data Sources and Official Statistics

New digital data sources and, in particular, the combination with survey and administrative data is becoming more and more an important Official Statistics production. However, still many challenges have to be solved including data privacy, sample selection, and integration into the statistical production process. The present session will provide four different approaches of using new digital data sources in combination with traditional data showing challenges and opportunities of its use. The data sources cover satellite data, mobile data, as well as twitter and related data.

Chair: Ralf Münnich (Trier University)

15:30 - 16:00

Room: 30.S02 S. Expo

Coffee Break

16:00 - 17:30

Room: 40.002

Social Science Infrastructure for Big Data

Survey research enjoys support from various infrastructures in the social sciences. These organizations carry out surveys (e.g. ESS or SHARE), they offer services for sampling, pretesting, data management and data archiving (e.g. CESSDA and its members) and they offer a plethora of training programs (e.g. Summer Schools from Essex, GESIS or ICPSR). With the spread of “Big Data”, e.g. data originating from digitization of everyday life (digital data from mobile devices, from online searches, from the Internet of things, from registers) social science infrastructures face new challenges. What kind of support should they provide for "Big Data"? What do their clients/users expect from them?
In this session we discuss with representatives from social science infrastructures and their users future needs relating to these new data types.
We also welcome presentations from users doing research with "Big Data" and discussing their needs for services.

Chair: Christof Wolf (GESIS - Leibniz-Institute for the Social Sciences)

16:00 - 17:30

Room: 40.006

Using Big Data for Electoral Research II: Likes, Tweets, and Votes?

Chair: Josh Pasek (University of Michigan)

16:00 - 17:30

Room: 40.008

New Approaches to Augment Sampling Frames II: Leveraging Data Science Methods for Sample Frame Construction

Chair: David Dutwin (SSRS)

16:00 - 17:30

Room: 40.010

Applying Machine Learning and Automation to Improve Imputation - Replicate II

Chair: Mansour Fahimi (GfK)

16:00 - 17:30

Room: 40.012

Big Data = Big Applications: From Data Linkage to Education

Chair: Ralph Meijers (Statistics Netherlands)

16:00 - 17:30

Room: 40.063

Can We Mix It? Big Data Tools, Social Network Analysis, and Causal Inference

Chair: Thomas Emery (NIDI)