Measuring public support for European integration using a Bayesian IRT model

Abstract

This study proposes the use of Bayesian item response theory models to measure aggregate public support for European integration. This approach addresses the limitations of other indicators and produces valid estimates of public attitudes over long time periods, even when available indicators change over time or present interruptions. I compare Bayesian item response theory models with alternative approaches used in the study of support for European integration, and demonstrate that they produce more accurate estimates of latent public opinion. The estimates are validated by showing their association both to alternative public opinion measures and to the vote share of Eurosceptic parties across Europe. I show that Bayesian models solve unaddressed issues like ensuring cross-country comparability of the estimates and modelling responses with multiple answer options.