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.
Research topics are in particular:
- 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
Learn more about our consulting and services:
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Data harmonization
Tools and topic-specific harmonized data for your research project
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Harmonisation
Consulting on the harmonization of content elements and social-structural characteristics
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Harmonizing and synthesizing partnership histories
A tool for harmonization and synthesis of couple biographical data.
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Harmonizing substantive instruments
Consultation on harmonization of latent construct measurement instruments.
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Imputation of Missing Values
We perform imputation of missing values for your complete data set or even selected variables
- 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.
- 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: https://doi.org/10.1177/1525822X221132940.
- 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: https://doi.org/10.1177/08944393221095194.
- Friedel, Sabine, Barbara Felderer, Ulrich Krieger, Carina Cornesse, and Annelies Blom. 2023. "The Early Bird Catches the Worm! Setting a Deadline for Online Panel Recruitment Incentives." Social Science Computer Review 41 (2): 370-389. doi: https://doi.org/10.1177/08944393221096970.
- Bruch, Christian, and Barbara Felderer. 2022. "Prior Choice for the Variance Parameter in the Multilevel and Poststratification Approach for Highly Selective Data: A Monte Carlo Simulation Study." Austrian Journal of Statistics 51 (4): 76-95. doi: https://doi.org/10.17713/ajs.v51i4.1361.