BigSurv18 program
Wednesday 24th October Thursday 25th October Friday 26th October Saturday 27th October
Saturday 27th October
| 08:00 - 18:00 Room: 30.SV01 HALL | Registration and Information Desk | ||||||||||||||
| 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:
Chair: Antje Kirchner (RTI) Indirect Sampling Applied to Dual Frames Estimation of Selection Error and Bias in Internet Data Sources by Linking With Register Data Old Problems, New Approaches: The Appearance of Suicide and Depression in the Online Social Media - A Study of Instagram Merits and Limits of Measuring the Total Acceleration of Smartphones in Mobile Web Surveys Using SurveyMotion Supplementing Probability-Based Surveys With Nonprobability Surveys to Reduce Survey Errors and Survey Costs Exploring Random Respondent Matching With Simulated Multi-Wave Survey Data Reimagining Survey Research: Transforming a Traditional Survey Program Through Advanced Analytics Calibrating Key Performance Indicators for an Eye Tracking Attention Panel The Classification of Comments About Mobile Phones in the Online Shops The Generations & Gender Survey: The Future of a Cross-National Survey Online Predicting Political Behavior and Attitudes Using Digital Trace Data | ||||||||||||||
| 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 AppsThe 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) Measuring Young People's Physical Activity Using Accelerometers in the UK Millennium Cohort Study Testing the Logistics of the Accelerometer Project in SHARE Using GPS Data as Auxiliary Data to Review the Data Quality of a Time Use Survey Quality of Spending Data Collected With a Receipt Scanning App in a Probability Household Panel WHO IS WHO. An Algorithm to Attribute the Device's Navigation to Users Sharing the Same Device | ||||||||||||||
| 09:00 - 10:30 Room: 40.006 | Socializing with Surveys: Combining Big Data and Survey Data to Measure Public OpinionChair: Pascal Siegers (GESIS) Social Media as an Alternative to Surveys of Opinions About the Economy Measuring the Strength of Attitudes in Social Media Data Protest Within an Authoritarian Context: Perception of Opportunities and Support for Protest Among Citizens in the Arab World | ||||||||||||||
| 09:00 - 10:30 Room: 40.010 | Translations Across Nations: Exploring Natural Language Processing in Multicultural ApplicationsChair: Diana Zavala-Rojas (Universitat Pompeu Fabra) Creating Synergies Between Survey Research and Machine Learning: A Road Map for Applying Tools From Computational Linguistics in the Translation of Survey Questionnaires Country Comparative Surveys Using Word Embeddings The Meaning of Democracy: Using a Distributional Semantic Lexicon to Collect Co-Occurrence Information From Online Data Across Languages Lost in Translation - How Differences in Word Intensity Affect Citizens' Satisfaction With the Working of Democracy | ||||||||||||||
| 09:00 - 10:30 Room: 40.012 | The Bigger the Better? Exploring Opportunities and Challenges of Using Big Data for Rapid EthnographyChair: Frances Barlas (GfK Custom Research) A Sample Survey on the Current Level of Awareness Regarding Big Data Among Academics and Practitioners of Statistics in Pakistan Download presentation Run Silent, Run Deep: Passive Online Monitoring and Survey Data Fusion Marketing Research in the Digital Era: A Comparison Between Adaptive Conjoint Analysis Methods | ||||||||||||||
| 09:00 - 10:30 Room: 40.063 | Crowdsourcing, Causality, and the Issue of Social TrustChair: Rene Bekkers (Vrije Universiteit Amsterdam) The Gift of Trust Problems in Identifying Causality in Observational Data Crowdsourced Small Area Estimation. Crowdsourcing and Estimating Safety Perceptions at Neighbourhood Level in London | ||||||||||||||
| 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 ISmartphones 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) Emergent Issues in the Combined Collection of Self-Reports and Passive Data Using Smartphones Combining Active and Passive Mobile Data Collection: A Survey of Concerns Collecting Smartphone Sensor Measurements in the General Population: Willingness and Nonparticipation Bias Data Collection Using Mobile Technologies: Changes Over Time in the Barriers to Participation | ||||||||||||||
| 11:00 - 12:30 Room: 40.004 | Big Data Enhancements to Surveys: Social IssuesChair: Nicholas Biddle (Australian National University) How Does Research Productivity Relate to Gender? Analyzing Gender Differences for Multiple Publication Dimensions Social Diffusion of Xenophobic Attacks in Germany - An Application of Web Crawling Potentials of Linking Administrative Data and Survey Data for Inequality Research The Four Faces of Political Participation in Comparative Perspective | ||||||||||||||
| 11:00 - 12:30 Room: 40.006 | Fake News! Information Exposure in Complex Online EnvironmentsChair: Colleen McClain (University of Michigan) When Does the Campaign Matter? Attention to Campaign Events in News, Twitter, and Public Opinion Is Informal Flagging for Propaganda in User Comments Helpful to Identify Anti-Western Narratives? The Benefits and Risks of Relying on User-Based Labeling Echo Chambers: Twitter Versus Online News Exposure Boys on the Tweet Bus: Identifying Information Flows Between Political Journalists During the 2016 U.S. Presidential Campaign | ||||||||||||||
| 11:00 - 12:30 Room: 40.008 | Refining Big Data Methods Using Survey DataChair: Georgiy Bobashev (RTI International) The Effect of Survey Measurement Error on Clustering Algorithms Efficiency of Classification Algorithms as an Alternative to Logistic Regression in Propensity Score Adjustment for Survey Weighting Accessing the Opinions of a Billion People: Mobile Surveys in the Age of Big Data How YouTube Uses Survey Data to Improve Video Recommendations | ||||||||||||||
| 11:00 - 12:30 Room: 40.010 | Enhancing Survey Quality With Big DataChair: Daniel Oberski (Utrecht University) Applying the Multi-Level/Multi-Source (MLMS) Approach to the 2016 General Social Survey Using Multiple Imputation of Latent Classes (MILC) to Construct Consistent Population Census Tables Using Data From Multiple Sources Using Machine Learning Models to Predict Follow-Up Survey Participation in a Panel Study Designing Surveys to Account for Endogenous Nonresponse Sunday Assemblies: From "Believing Without Belonging" to "Belonging Without Believing"? When Survey and Big Data Combine to Study an Under-Theorized Phenomenon | ||||||||||||||
| 11:00 - 12:30 Room: 40.012 | Leveraging Big Data for Improving Health Research… the Follow-up VisitChair: Naja Rod (Section of Epidemiology, University of Copenhagen) Smartphone Interactions and Mental Well-Being in Young Adults - A Longitudinal Study Based on High-Resolution Smartphone Data How to Operationalize Adaptive Sampling Along With Various Big Data Phenotypic-Neurologic-Ecological-Genotypic Elements Across a Multi-Site Trauma-Based Prospective Data Collection, the AURORA Cooperative Agreement Applying a Geospatial Big Data Approach to Survey Data: The Next Stage in Population Health Studies Classifying Health Insurance Type From Survey Responses Using Enrollment Data | ||||||||||||||
| 11:00 - 12:30 Room: 40.063 | Big Data Applications to Enterprise Statistics: Businesses, Employers, and ConsumersChair: Mark Trappmann (IAB, University of Bamberg) Synthesising Big Data and Business Survey Data Consumer Expenditure Statistics From Retail Transaction Data Fuzzy Identification, from Raw Survey Data to a Structured Register: An Example from Official Statistics Finding the Employer Declared During the Census in the Companies Register Identifying Innovative Companies From Their Website Requirements in Job Advertisements: Automated Detection and Classification Into a Hierarchical Taxonomy of Work Equipment (Tools) | ||||||||||||||
| 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 IISmartphones 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) Framing Consent Questions in Mobile Surveys: Experiments on Question Wording What Do Researchers Have to Invest to Collect Smartphone Data? Willingness to Participate in a Metered Online Panel The Impact of Motion Instructions on the Acceleration of Smartphones and Completion Times in Web Surveys | ||||||||||||||
| 14:00 - 15:30 Room: 40.004 | Leveraging Big and Nontraditional Datasets to Reduce Burden, Increase Response, and Assess Survey QualityThis 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) Alternative Approaches for Measuring the Movement of Goods in the United States Using Alternative Data Sources to Reduce Respondent Burden in United States Census Bureau Economic Data Products Using Linked Survey and Administrative Data to Assess the Quality of Each Contributing Data Source Leveraging Nontraditional Data to Improve Response Propensity Models and Design Tailored and Targeted Geographical Nonresponse Interventions Discussant for Organized Session Titled: Leveraging Big and Nontraditional Datasets to Reduce Burden, Increase Response, and Assess Survey Quality | ||||||||||||||
| 14:00 - 15:30 Room: 40.006 | How Do You Like Those Likes? Exploring the Validity of Measures Derived from Social Media DataChair: Lars Lyberg (Inizio) Can Facebook "Likes" Measure Human Values? On the Validity of Statistical Inference Using Social Media Data: Two Interpretations of an Existing Study Improving the Measurement of Political Behavior by Integrating Survey Data and Digital Trace Data External and Internal Quality of Big Data | ||||||||||||||
| 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) Investigating the Value of Appending New Types of Big Data to Address-Based Survey Frames and Samples Is More Data Better Data? Assessing the Quality of Commercial Data Appended to an Address-Based Sampling Survey Frame Feedback Loop: Using Surveys to Build and Assess RBS Religious Flags Research on Combination of Probability and Nonprobability Samples | ||||||||||||||
| 14:00 - 15:30 Room: 40.010 | Applying Machine Learning and Automation to Improve Imputation - Replicate IChair: Steven Cohen (RTI International) The Enigma of Survey Research in the Digital Age - A Paradigm Mass Imputation Combining Information From Big Data An Imputation Solution for Differentiating Between Unreported Attitudes and Genuine Nonattitudes in Survey Data Can Missing Patterns in Covariates Improve Imputation for Missing Data? | ||||||||||||||
| 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) Political Sentiment and Election Forecasting A Full Spectrum Approach to Election Polling and Forecasting I Get It! Using Qualitative and Quantitative Data to Investigate Comprehension Difficulties in Political Attitude Questions Voter Information and Learning in the U.S. 2016 Presidential Election: Evidence From a Panel Survey Combined With Direct Observation of Social Media Activity | ||||||||||||||
| 14:00 - 15:30 Room: 40.063 | New Digital Data Sources and Official StatisticsNew 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) The Use of Big Data to Improve Small Area Estimates of Multidimensional Poverty Indicators City Data From LFS and Big Data From Experimental to Official Statistics: The Case of Solar Energy Satellite Data for Developing Social and Economic Indicators | ||||||||||||||
| 15:30 - 16:00 Room: 30.S02 S. Expo | Coffee Break | ||||||||||||||
| 16:00 - 17:30 Room: 40.002 | Social Science Infrastructure for Big DataSurvey 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) Big Data at FORS Linking Social Survey and Twitter Data - Consent, Operationalisation, Archiving, and Sharing SOMAR: ICPSR's New Social Media Archive Infrastructure for Digital Behavioral Data in the Social Sciences: The GESIS Perspective | ||||||||||||||
| 16:00 - 17:30 Room: 40.006 | Using Big Data for Electoral Research II: Likes, Tweets, and Votes?Chair: Josh Pasek (University of Michigan) Gauging the Horserace Buzz: How the Public Engages With Election Polls on Twitter Social Media and Political Participation Digital 'Cold Wars' in the U.S. Mid-Term Election Scaling the Civility Wall: Examining Social Media Discourse on Immigration During the 2016 U.S. Presidential Election Analyzing Right-Wing Discourse on Twitter: A Case Study of the 2017 German Federal Election Download presentation 2 | ||||||||||||||
| 16:00 - 17:30 Room: 40.008 | New Approaches to Augment Sampling Frames II: Leveraging Data Science Methods for Sample Frame ConstructionChair: David Dutwin (SSRS) Using Big Data to Improve Sampling Efficiency Machine Made Sampling Designs: Applying Machine Learning Methods for Generating Stratified Sampling Designs Machine Made Sampling Frames: Creating Sampling Frames of Windmills and Other Non-Traditional Sampling Units Using Machine Learning with Neural Networks The View From Above - Virtual Listing Using GIS | ||||||||||||||
| 16:00 - 17:30 Room: 40.010 | Applying Machine Learning and Automation to Improve Imputation - Replicate IIChair: Mansour Fahimi (GfK) AI and Machine Learning Derived Efficiencies for Large Scale Survey Estimation Efforts Sequential Imputation of Missing Data in High-Dimensional Data Sets Approximate Nearest Neighbour Imputation | ||||||||||||||
| 16:00 - 17:30 Room: 40.012 | Big Data = Big Applications: From Data Linkage to EducationChair: Ralph Meijers (Statistics Netherlands) 5324 Euros Per Hour: Outlier or Football Player? Unsupervised Learning Methods for Anomaly Detection: Application to the Individual Declaration of Social Data The Best of Two Worlds: Combining Longitudinal Health and Learning to Learn Surveys With National Registry Data Enriching Education Survey Data With Knowledge Graphs | ||||||||||||||
| 16:00 - 17:30 Room: 40.063 | Can We Mix It? Big Data Tools, Social Network Analysis, and Causal InferenceChair: Thomas Emery (NIDI) Learning on Survey Data to Qualify Big Data in a Web Environment Surveys and Big Data for Estimating Brand Lift Finding Friends - A Network Approach to Geo-Locating Twitter Users Analyzing Big and Small Collections of Books With Network Coincidence Analysis Climatic Visual Art for Farmers Insights |