SOEP Research: Survey Methodology and Data Science

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, ...

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 ...

Dynamic Networks of Social Contact, Social Desire, and Affect Across Time Scales

Social relationships are central to well-being because they fulfill social affiliation needs. To explain how social needs are regulated, theories describe daily-life processes among social desire, social contact, and affect. Still, these processes remain empirically underexplored because of their complexity. In this study, we estimated multivariate associations of social desire and affect with social ...

Who stays? Tracking ultra-long-time participants in the SOEP

The relationship between municipal investments and satisfaction with democracy and populist voting

Presentation of the work on the project

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...

Methodological Challenges in Cross-Cohort Comparisons of Educational Achievement

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...

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 ...

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...