Call for Abstracts

Please please find below a list of relevant topics. Abstracts should be written in English, no longer than 500 words, and must include the substantive problem, the approach, data, and models employed as well as the main results of the analysis.

Submissions will be subject to a refereeing process. Notification of abstract acceptance for contributed abstracts will be issued by April 28th, 2025. The final version of all abstracts (keynote, invited and accepted contributed) can be submitted from April 28th to May 12th, 2025. Please find here a summary of the important dates

Papers presented at the ITACOSM2025-IASS conference will be given preferential consideration for the special issue of the journal Survey Methodology. For further details, please visit the dedicated session on our website.

 

 Click here to submit a contributed abstract.

 

 

List of Topics 

 

  • Adaptive sample designs
  • Administrative registers, big data and sample surveys
  • Artificial Intelligence and Survey Statistics
  • Calibration methods for data integration
  • Capture-recapture methods
  • Data disclosure strategies and privacy protection
  • Data fusion
  • Data production
  • Data quality
  • Estimation from different data sources
  • Estimation issues from informative designs
  • Graph sampling and learning
  • Innovation in data collection
  • Integration and weighting in probability and nonprobability-based sample surveys
  • Machine and statistical learning methods in survey estimation
  • Missing data and imputation methods
  • Mixed mode surveys
  • Multiple frame surveys
  • Network sampling
  • New methods for censuses
  • Opinion polls
  • Population-size estimation
  • Record linkage
  • Resampling methods
  • Sample designs for complex populations (networks) and for integrated data
  • Sample selectivity
  • Sample surveys for sensitive data and indirect questioning
  • Sampling and non-sampling errors
  • Sampling for hard-to-reach populations
  • Small area estimation
  • Smart surveys
  • Spatial sampling and use of remote sensing data
  • Statistical matching
  • Variance estimation