NTTS 2019 list of topics

Track A. Data collection and integration
• Mixed-mode and web data collection
• Crowdsourcing
• Collection and use of paradata
• Adaptive and responsive survey designs
• Designed data collection by (mobile) devices or qualitative approaches
• Non-response, response propensity, respondent behavior and response burden
• Behavioral economics applied to surveys
• Measurement of longitudinal phenomena
• Data linking and statistical matching with different sources
• Multinational repositories and exchange of micro-data
• Integrated data collection systems
Track B. Estimation and analysis
• Big data nowcasting
• Time series analysis (outlier detection, seasonal adjustment, revisions, etc.)
• Modelling
• Variance estimation
• Small area estimation
Track C. Dissemination and visualisation
• Communicating uncertainty of official statistics
• (Linked) open data dissemination
• Visualizations, GIS, spatial statistics
• Storytelling
• Statistical disclosure control, output checking
• Secure data access, GDPR
• Data validation
Track D. Statistics and Society – stakeholder relations
• Digital economy, digitalisation (data from financial sector, health, environment, etc.)
• Migration and new techniques of data collection
• Sustainable development goals – opportunities for collaboration between statistical and social sciences research communities
• Methods for capturing user input, assessing user needs and user satisfaction
• (Statistical) literacy in the data age
• Ethics, digital skills, cybersecurity
• New challenges in the measurement of economic insecurity, inequality and poverty
• Statistical systems in developing countries – opportunities and risk from alternative methods
Track E. New methods for new (and existing) data sources
• Data analytics, data as a service, data architecture
• Sharing data and statistical services
• Experimental statistics
• Web scraping
• Blockchain
• Artificial intelligence in statistics (challenges and opportunities)
• New metadata concepts, structures and formats (semantic web, RDF, etc.)
Track F. Enablers of modernisation
• From data4policy to policy4data
• Enabling conditions for the use of big data in statistical production
• Skills for tomorrow’s official statisticians
• Social data mining
• Machine learning
• Big data analytics
• Enterprise architecture and use of standards (e.g. GSBPM, GSIM, GAMSO, CSPA)
Track G. Environments, softwares and tools
• Use of R in official statistics
• New Statistical tools and software (Python, Julia, Shiny, etc.)
• Opening sources and sharing codes (git, Github, etc.)