Survey Statistics

In the field of survey statistics, we investigate different aspects of the quality of surveys. Sample design, in particular sample size and sample composition, have a direct impact on the representativeness of survey results. Unit nonresponse and item nonresponse pose further challenges to data quality. The research activities in the field of survey statistics therefore focus on problems and solutions associated with random sampling. These are also investigated in several third-party funded projects.

Our research on Survey Statistics

  • Drawing procedures for complex sample designs
  • Nonresponse (bias) analysis
  • Weighting procedures for survey designs
  • Imputation of missing values
  • Variance estimation under complex sample designs and imputation of missing values
  • Application of machine learning methods in survey statistics
  • Daikeler, Jessica, Leon Fröhling, Indira Sen, Lukas Birkenmaier, Tobias Gummer, Jan Schwalbach, Henning Silber, Bernd Weiß, Katrin Weller, and Clemens Lechner. 2024. "Assessing Data Quality in the Age of Digital Social Research: A Systematic Review." Social Science Computer Review. doi:
  • Felderer, Barbara, Ludwig Bothmann, Lydia Repke, Jonas Schweisthal, and Wiebke Weber. 2023. "Predicting survey quality in SQP 3.0." European Survey Research Association (ESRA) 2023 Conference, Università degli Studi di Milano-Bicocca, Milano, 2023-07-17.
  • Bach, Ruben, Carina Cornesse, and Jessica Daikeler. 2023. "Equipping the offline population with internet access in an online panel: Does it make a difference? ." Journal of Survey Statistics and Methodology online first. doi:
  • Felderer, Barbara, and Jessica M. E. Herzing. 2023. "What about the Less IT Literate? A Comparison of Different Postal Recruitment Strategies to an Online Panel of the General Population." Field Methods 35 (3): 219–235. doi:
  • Felderer, Barbara, Jannis Kück, and Martin Spindler. 2023. "Using Double Machine Learning to Understand Nonresponse in the Recruitment of a Mixed-Mode Online Panel." Social Science Computer Review 41 (2): 461-481. doi:
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