Multilevel Modeling of Public Opinion in American States

Multilevel modelling of public opinion in American states integrates individual survey responses with contextual covariates at the state level, allowing researchers to capture the nested structure of voters within states and regions. By combining individual-level predictors (such as age, race and income) with state-level factors (including demographic composition, policy environment and geographic indicators), these models disentangle variation in opinions attributable to personal attributes from that driven by broader social and political contexts. Core to this approach is the use of hierarchical regression frameworks, which permit partial pooling of information across units to stabilise estimates in jurisdictions with limited data. When paired with post-stratification—adjusting sample estimates to known population margins—the methodology yields fine-grained, subnational estimates of attitudes on electoral preferences, policy mood and issue salience. Recent advances include the incorporation of complex interactions, non-linear effects via splines, and scalable estimation techniques such as variational inference, enhancing both accuracy and computational efficiency. This body of work has deepened understanding of regional heterogeneity in policy preferences and electoral behaviour, informing debates on racial polarisation, the diffusion of national political trends and the validity of state-level opinion measures.

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Multilevel Modeling of Public Opinion in American States publication trend

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Technical terms

Multilevel regression and post-stratification (MRP): A two-stage method combining hierarchical modelling with adjustment to known population margins to estimate subnational opinion.

Hierarchical model: A statistical framework that accounts for data nested across multiple levels (for example, individuals within states), enabling partial pooling of information.

Cluster sampling: A survey design that selects groups of units (clusters) rather than individuals, often requiring specialised modelling to correct for intra-cluster correlation.

Policy mood: An aggregate measure of public preferences along ideological or policy dimensions at state or national levels.

Variational approximation: A computational technique that accelerates estimation of complex models by approximating posterior distributions.

References

1. The Geography of Racially Polarized Voting: Calibrating Surveys at the District Level. American Political Science Review (2023).
2. Re-Evaluating Machine Learning for MRP Given the Comparable Performance of (Deep) Hierarchical Models. American Political Science Review (2023).
3. Improving Subnational Opinion Estimation from Cluster-Sampled Polls. State Politics & Policy Quarterly (2024).