The social sciences study phenomena that are inherently uncertain, context-dependent, and multi-layered. Bayesian methods are well suited to these challenges, offering a coherent framework for incorporating prior knowledge, modeling hierarchical structures, and quantifying the uncertainty that pervades social data. The ISBA Section on Bayesian Social Sciences brings together researchers who develop and apply Bayesian methods across the social science disciplines.
Why Bayesian Methods in the Social Sciences?
Social science data often feature small samples, complex survey designs, missing values, and nested structures (e.g., students within schools, voters within districts). Bayesian hierarchical models provide a natural framework for these situations, enabling partial pooling of information across groups, principled handling of missing data through data augmentation, and the incorporation of substantive prior knowledge. Moreover, the Bayesian posterior distribution provides direct probabilistic statements about parameters of interest, which are often more interpretable to social scientists than frequentist confidence intervals and p-values.
Political science has been a particularly active area for Bayesian applications. Bayesian item-response models are used to estimate the ideological positions of legislators, judges, and voters. Bayesian approaches to causal inference, including Bayesian structural equation models and Bayesian synthetic control methods, are increasingly used to evaluate policy interventions. Election forecasting models, such as those used by major media outlets, frequently employ Bayesian hierarchical frameworks.
Application Areas
The section's scope encompasses a diverse range of social science applications. In psychology, Bayesian methods support cognitive modeling, psychometrics, and the analysis of experimental data. In economics, Bayesian approaches are fundamental to structural econometric modeling, forecasting, and decision analysis. In sociology, Bayesian methods are used for multilevel modeling, network analysis, and the analysis of longitudinal survey data. Education research, demography, and public health are additional areas where section members are active.
Activities
The section organizes sessions at ISBA World Meetings, JSM, and social science conferences, creating opportunities for cross-disciplinary exchange. Workshops and tutorials introduce social scientists to Bayesian methods and tools, while methodological sessions showcase new developments driven by social science problems.
"Social scientists need methods that can handle complexity and uncertainty with honesty. The Bayesian framework provides exactly that—a principled way to learn from messy, imperfect data about the most important questions we face as a society."— Andrew Gelman
Community
The section serves as a bridge between the statistics and social science communities, ensuring that methodological advances in Bayesian statistics are communicated to social scientists and that the unique challenges of social science data drive new methodological research. This two-way exchange has enriched both fields and continues to grow as Bayesian methods become standard tools in the social science toolbox.