Sabine Zinn

Abrupt social and political transformations in twentieth-century Germany may have shifted how polygenic indices relate to educational outcomes across birth cohorts. Following a prespecified analysis plan, we harmonized genomic, educational, and regional data from four German studies, comprising 13,049 individuals born between 1918 and 1995. Polygenic index associations with educational attainment were robust and strengthened modestly across more recent birth cohorts, with no detectable difference between East and West Germany. Associations with intergenerational educational mobility showed no evidence of such strengthening but were stronger in the East than in the West. Polygenic indices were less predictive of educational attainment and educational mobility for women than for men, with no evidence of regional variation. We discuss the methodological limitations of polygenic indices for examining gene– environment interactions, and emphasize that evaluating whether these differences represent progress towards equal educational opportunity requires a normative framework that social science genetics alone cannot supply.

Respondent-driven sampling (RDS), a network-based method of sampling has become increasingly popular as a means of developing subgroup samples, particularly in web-first or web-only design (webRDS). WebRDS can reduce participant burden as well as increase the potential size and geographic scope of the sample, allowing for development of national samples with greater generalizability. However, webRDS can also introduce technical challenges as it requires all RDS processes to be mediated through internet-communication technology. In this paper, we focus on the challenges of engaging participants and reducing burden following a request to participate in webRDS. We present the results of two studies in which we tested several design features for their effects on webRDS recruitment success. We use social exchange theory as a lens through which to improve webRDS recruitment by maximizing benefits of participation and reducing burden. Based on our findings, we present recommendations for webRDS implementation.

Respondent-driven sampling (RDS) has been shown to be a promising network sampling strategy. However, it has also been shown to fail in some applications. Basing an RDS procedure on an existing panel study may, therefore, contain the risk of failure and might even alienate non-compliant seeds. This study therefore investigates the hypothetical willingness of general population panel participants to recruit their social network members using RDS. The study reveals that nearly half of the online participants of the panel survey selected for the study are willing to recruit. Reasons for unwillingness include discomfort in inviting others and the belief that they are already contributing sufficiently. Proportional odds ordinal logistic regression analyses reveal that most of the anticipated predictor variables for willingness have no effect. However, an increased willingness to recruit is observed among respondents of younger age, with an immigration background, and with politically extreme opinions. The findings suggest a substantial potential for RDS in online surveys but highlight the need for further empirical studies to evaluate actual recruitment behaviors and refine RDS strategies.

Longitudinal surveys play a central role in social science research and policy advice but increasing demands for timely data pose substantial challenges for managing time, resources, and data quality across survey waves. While the Survey Life Cycle (SLC) provides a well established framework for organizing survey production, it is primarily geared toward single wave studies and offers limited guidance for coordinating interdependent longitudinal processes. This article introduces the Longitudinal Survey Life Spirals (LSLS), a conceptual planning and reflection framework that reimagines the SLC in a spiral structure to explicitly account for cross wave interdependencies, overlapping project phases, and learning processes over time. By integrating considerations of timing, resource allocation, and feedback loops across waves, LSLS support more coordinated longitudinal survey management. The framework is illustrated using the Socio Economic Panel (SOEP), a large scale longitudinal survey infrastructure characterized by simultaneous work on multiple survey waves. The article concludes by outlining key implications for survey practice, highlighting how LSLS can help survey teams to improve planning, quality assurance, and the timely dissemination of high quality longitudinal data.

Volume 55 - Article 8 | Pages 275–306

This paper examines the gaps in Germany’s data infrastructure for responding to crises, as revealed during COVID-19, and argues that linking the Socio-Economic Panel (SOEP) with administrative health records would strengthen the country’s ability to generate equity-sensitive evidence during future health-related crises. Drawing on evidence from the RKI-SOEP study, we illustrate the added value of combining household survey data with administrative health measures to identify unequal risks and burdens across social groups. We discuss why such linkages remain limited in Germany and outline practical possibilities for implementation, highlighting benefits for crisis preparedness and broader health and social research.

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, and education domains. The vignette-based items describe different AI applications that are administered in a planned missingness design to meet the brevity requirements of large-scale studies. A qualitative interview study ( N = 30) confirmed the comprehensibility of the items, while a quantitative web-based study ( N = 1,084) demonstrated satisfactory measurement structure and precision. These findings attest to good psychometric properties of the AIEAS that allow for a multidimensional measurement of AI experiences and evaluations. Integrating the instrument into panel studies will support examining societal trends over time to inform the scientific community and policies in different domains.

Since its first round, the European Social Survey (ESS) has included a 21-item measure of ten basic human values shared across cultures, known as the Human Values Scale (HVS), developed by Shalom H. Schwartz. Recently, the scale has been revised to a new 20-item HVS with shorter, simpler items (usually only one sentence instead of two sentences per item in the past version) and introducing a single gender-neutral version. This latter change is crucial as the ESS transitions to a self-completion mode using online and paper questionnaires. To ensure consistent measurement across countries and languages, the ESS included the new scale in its ESS Round 12 Advance Translation, creating a detailed list of annotations to clarify the source items’ meanings. The new HVS has also been fielded in the German Socio-Economic Panel (SOEP) in 2025, including a refugee sample. A multilingual cognitive interview pretest assessed the consistency of interpretations among respondents with diverse migration backgrounds. Findings from the ESS translation efforts and the SOEP cognitive interview pretest were instrumental in refining the final version of the new HVS. This note provides an overview of the revised HVS and addresses the challenges of cross-cultural adaptation.