Forschung SOEP: Survey-Methodologie und Data Science

Aktivitäten und Ergebnisse der SOEP-Forschung

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1170 Ergebnisse, ab 1
  • SOEP Brown Bag Seminar

    Measurement error models on anonymized georeferenced data

    Georeferenced data are often anonymized for data protection reasons. This is done either by aggregating the data into larger spatial units (e.g., higher-level administrative units or grids with larger cell sizes) or by using stochastic methods to deliberately overlay the original coordinates. These methods significantly distort the data and associated variables, making further modeling steps...

    29.04.2026| Lorena Gril, Freie Universität Berlin
  • SOEP Brown Bag Seminar

    Messfehlermodelle für anonymisierte, georeferenzierte Daten

    Der englische Originaltitel des Seminars lautet: “Measurement error models on anonymized georeferenced data”. Die Präsentation findet auf Englisch statt. Eine kurze Zusammenfassung zum Vortrag ist nur auf der englischen Veranstaltungsseite verfügbar!

    29.04.2026| Lorena Gril, Freie Universität Berlin
  • SOEP Brown Bag Seminar

    Methodische Herausforderungen bei kohortenübergreifendem Vergleich von Bildungsleistungen

    Der englische Originaltitel des Seminars lautet: “Methodological Challenges in Cross-Cohort Comparisons of Educational Achievement”. Die Präsentation findet auf Englisch statt. Eine kurze Zusammenfassung zum Vortrag ist nur auf der englischen Veranstaltungsseite verfügbar!

    27.05.2026| Timo Gnambs, Leibniz-Institut für Bildungsverläufe(LIfBi)
  • SOEP Brown Bag Seminar

    Methodological Challenges in Cross-Cohort Comparisons of Educational Achievement

    Comparisons of educational achievement across cohorts are frequently used to examine changes in educational performance and to evaluate the impact of societal or educational events, such as the COVID-19 pandemic. However, drawing causal inferences from observational data without strong experimental control remains challenging, as often no single clear methodological approach is universally...

    27.05.2026| Timo Gnambs, Leibniz Institute for Educational Trajectories (LIfBi)
  • Seminar

    Filling in the Blanks: Augmenting Survey Data Imputation with an External Prior

    Multiple imputation of missing values in survey data analysis is a state-of-the-art technique. Typically, methods like multivariate imputation by chained equations (mice, van Buuren 2018) are employed, replacing missing values on a variable-by-variable basis. Generally, the information used for imputation comes from the survey dataset being analysed. Valid analysis results are achieved when the...

    23.04.2026| Char Hilgers
  • Seminar

    Lücken füllen: Ergänzung der Imputation von Umfragedaten durch eine externe Prior-Verteilung

    Der englische Originaltitel des Seminars lautet: “Filling in the Blanks: Augmenting Survey Data Imputation with an External Prior”. Die Präsentation findet auf Englisch statt. Eine kurze Zusammenfassung zum Vortrag ist nur auf der englischen Veranstaltungsseite verfügbar!

    23.04.2026| Char Hilgers
  • Weitere referierte Aufsätze

    Publishing Fine-Grained Standardized Metadata: Lessons Learned from Three Research Data Centers

    FAIRness of research data, meaning that data are managed according to the principles of being Findable, Accessible, Interoperable, and Reusable, has become a ubiquitous requirement in research data policies as well as in general guidelines for research data management. Meeting this requirement largely depends on the availability of rich and standardized DDI metadata—based on the Data Documentation ...

    In: Data Science Journal 25 (2026), 13, S. 1-13 | Knut Wenzig, Andreas Daniel, Dominique Hansen, Tobias Koberg, Mihaela Tudose
  • Weitere referierte Aufsätze

    The German Artificial Intelligence Experience and Attitude Survey (AIEAS): A Brief Measure for Socio-Economic Panel Studies

    Despite the profound impact of artificial intelligence (AI) in diverse contexts, large-scale socio-economic panel studies have rarely addressed the use and evaluation of AI for individual respondents. Therefore, the Artificial Intelligence Experience and Attitude Survey (AIEAS) is introduced to measure awareness, experience, attitude valence, and usage intention regarding AI in the work, healthcare, ...

    In: Psychological Test Adaptation and Development 7 (2026), S. 27–41 | Timo Gnambs, Florian Griese, Sabine Zinn
  • Referierte Aufsätze Web of Science

    Heterogeneity in Gender Differences in Self-Reported Political Preferences, Trust, and Well-Being across 39 European Countries

    Previous research suggests that women tend to self-report higher life satisfaction and happiness, lower health status and trust, and more left-leaning political preferences than men. We revisit the gender gap in these outcome variables using random-effects meta-analysis, aggregating data across 39 countries surveyed in the European Social Survey (n ≈ 500,000). Measured in Cohen’s d units, women, on ...

    In: Scientific Reports 16 (2026), 3406, 12 S. | Yifan Yang, Magnus Johannesson, Frank Fossen, Levent Neyse, Felix Holzmeister
  • SOEP Brown Bag Seminar

    Measuring Ambiguity Attitudes Reliably in Surveys

    This seminar introduces ambiguity, explains why it matters in economics, and discusses how ambiguity attitudes are typically measured in empirical research. Ambiguity plays an important role in decision-making, as most situations in life involve unknown outcomes or probabilities. However, people’s attitudes toward ambiguity are hard to measure precisely, as standard measures based on incentivized...

    06.05.2026| Roy Kouwenberg, Mahidol University
1170 Ergebnisse, ab 1
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